A National Laboratory Market 

and Technology Assessment of 

the 30x30 Scenario 

NREL/TP-510-40942 

March 2007 

NREL is operated by Midwest Research Institute • Battelle Contract No. DE-AC36-99-GO10337
 
A National Laboratory Market 

and Technology Assessment 

of the 30x30 Scenario 

Thomas D. Foust, Robert Wooley, John Sheehan, 

Robert Wallace, Kelly Ibsen, David Dayton, 

Mike Himmel, John Ashworth, Robert McCormick, 

and Margo Melendez 

National Renewable Energy Laboratory (NREL) 

J. Richard Hess, Kevin Kenney, 

Christopher Wright, and Corey Radtke 

Idaho National Laboratory (INL) 

Robert Perlack and Jonathan Mielenz 

Oak Ridge National Laboratory (ORNL) 

Michael Wang and Seth Synder 

Argonne National Laboratory (ANL) 

Todd Werpy 

Pacific Northwest National Laboratory (PNNL) 

Reviewers: 

John Jechura, Andy Aden, and Wendy Clark, NREL 

Robert Perlack, ORNL 

Kevin Craig, DOE-GO 

Amy Miranda, Zia Haq, and John Ferrell, DOE-OBP 

Rick Orth, PNNL 

Prepared under Task No(s). BB07.6110 

Technical Report 

NREL/TP-510-40942 

March 2007 

 
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A National Laboratory Market and Technology Assessment of the 30x30 Scenario

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Acknowledgements 

The authors would like to acknowledge the support and guidance of the Office of the Biomass 

Program (OBP). In particular, the authors would like to thank Douglas Kaempf, Jacques 

Beaudry-Losique, John Ferrell, Larry Russo, Amy Miranda, Valerie Sarisky-Reed, Paul 

Grabowski, Sam Tagore, Mark Decot, and Melissa Klembara. 

Many people lent their time and energies to helping this project along, providing input on 

issues large and small including coordination and assembly of the final document, reviewing 

drafts and preparation and distribution of the final print copy of the document. The authors 

would especially like to thank Billie Christen who contributed significantly towards this 

document. 

Foreword 

To achieve the goals of the President’s Advanced Energy Initiative DOE-OBP set a goal of 

30x30 (Supply 30% of 2004 motor gasoline demand with biofuels by 2030). To set a research 

agenda towards accomplishing the 30x30 goal three efforts were initiated. Firstly, a workshop 

was held on August 1 and 2, 2006 where industrial, academia, and governmental biomass 

experts were assembled to discuss and develop a plan for achieving the 30x30 goal (OBP 

2006). Secondly, the National Laboratories were charged with performing a market drivers and 

technology needs assessment of the 30x30 scenario, which is this document. Finally a posture 

plan was developed by OBP in conjunction with the other appropriate agencies and 

departments to coordinate an R&D plan for achieving the 30x30 goal. Together these three 

documents set the plan for achieving the 30x30 goal. 

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Executive Summary 

Ethanol production from both corn and cellulosic feedstocks shows considerable promise to 

help lessen demand for imported oil as well as increase domestic energy security. However, we 

need to address the question: “What is an appropriate volume goal for ethanol production and 

in what timeframe?” Then given this goal, what do we need to accomplish in terms of in R&D, 

deployment, and market investment to achieve this goal? 

After much discussion and analysis, the U.S. Department of Energy (DOE) selected the goal of 

supplying 30% of the 2004 motor gasoline demand with ethanol by the year 2030. This roughly 

translates to producing 60 billion gallons of ethanol per year on a Btu-adjusted basis by 2030—

the 30x30 goal. This goal was selected because it represents an aggressive target that translates 

into a significant positive national impact but is well within the maximum potential for ethanol. 

A timeframe of 24 years from the present is considered aggressive but not short enough to be 

overly disruptive to market change dynamics. 

This scenario report describes the market drivers and technology requirements and presents a 

plan for achieving the 30x30 goal with ethanol produced from both starch and cellulosic 

feedstocks, and outlines a scenario that is realistically achievable with sustained, integrated 

effort by government, academia, and the private sector. Since the 30x30 goal is in essence a 

market goal, this report describes a market driven approach for achieving the 30x30 goal. 

Meeting the 30x30 goal calls for strategies that reduce biomass feedstock cost, technology risk, 

and ethanol price during the early years of industry adoption of lignocellulosic biomass 

technology as corn ethanol production is reaching its peak. Investor attitudes toward new 

technology raise the risk of building the first full-scale commercial plant to a higher level. 

Investors wait until market value for fuel raises enough to overcome their risk-aversion. 

Design studies and the associated production costs are tied to specific years to account for 

inflation effects on operating and capital costs. The studies from the national labs are typically 

aligned with petroleum cost studies from the Energy Information Administration (EIA). These 

costs can be updated using inflation factors if the original cost calculations are available. For 

example, the Aden et al. (2002) biochemical design report calculations were originally done for 

2000 dollars, and subsequently updated to 2002 dollars. The Phillips et al. (2007) 

thermochemical design report calculations are done in 2002 dollar. Hence for consistency 

purposes all dollar targets in this report are presented in 2002 dollars. 

Ethanol can have major effects on the fuel supply for transportation in the United States—

especially by 2050. However, scenarios for meeting the 2030 goal associated with the 30x30 

initiative call for aggressive government policy and the ability to build on past decades of 

research on cellulosic ethanol technology. The scenario that achieves the 30x30 scenario is one 

in which prices prevail, initial incentive payments of $20/dry ton are made to farmers, and a 

relatively brief extension of the federal incentive for ethanol is enacted. 

The technology targets of the 30x30 initiative for 2012 and 2030 are not, by themselves, 

sufficient to create a 60-billion-gallon/year ethanol industry by 2030. Meeting the 30x30 

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goal—even under a high oil price forecast—calls for a combination of strategies to reduce 

biomass feedstock cost, conversion cost, technology risk, and ethanol price during the early 

years of industry adoption of lignocellulosic biomass technology, once corn ethanol production 

has reached its peak. 

An important strategy for speeding up technology adoption is to rely on past and ongoing DOE 

industrial partnerships to deploy first-generation technology that may be ready before the 

program meets its 2012 target of $1.07/gallon ethanol. To look at the effect of such a strategy 

in the BSM, we assumed that some form of first-generation technology is 80% complete in 

terms of R&D up to the pilot scale and that this technology would lead to ethanol at a nominal 

production price of $1.30/gallon. With this jump-start on technology development and the 

assumption of high oil prices, investors begin demonstration work much earlier. 

Supplementing this strategy with two other policy levers leads to achievement of 60 billion 

gallons/year. These policies include extending the existing ethanol subsidy by five years to 

2015 and providing payments to lignocellulosic biomass suppliers for residues and energy 

crops. A payment of $20/dry ton of biomass is equivalent to paying producers $40/ton of 

carbon dioxide recycled in the ethanol fuel life cycle. 

The modeled scenario for achieving the 30x30 goal builds as follows: 

• Prior to the President’s Advanced Energy Initiative (Pre-Initiative) which had the 

$1.07/gallon cellulosic ethanol production target set for 2020, only corn ethanol is 

deployed to the maximum potential of ~16 billion gallons. Cellulosic ethanol does not 

achieve deployment under this scenario due to the fact that it never overcomes the risk 

hurdle for investors. 

• In the first step, achieving the $1.07/gallon target is accelerated to 2012. Research 

indicates that this action has essentially no effect on ethanol deployment since cellulosic 

ethanol fails to clear the risk hurdle. Therefore under this scenario only starch-based 

ethanol is deployed up to the maximum potential of ~ 16 billion gallons. 

• In the second step, adding the 1st generation technology incentive provides for a 

government cost share of 50% for initial demonstration-scale facilities and 40% for 

initial full-scale facilities. In this case ~25 billion gallons of cellulosic ethanol capacity 

are deployed by 2030. Combined with a starch-based ethanol capacity of ~13 billion 

gallons, this equals a total ethanol deployment of ~ 38 billion gallons of ethanol by 

2030. Although this amount is significant, it fails to achieve the 30x30 goal. 

• In the third step, adding a policy incentive of a payment for biomass to the grower of 

$20/dry ton through 2020 increases cellulosic ethanol deployed capacity to over 44 

billion gallons by 2030, and when combined with the 2030 starch-based ethanol 

deployed capacity of 11.2 billion gallons, total ethanol capacity reaches ~55 billion 

gallons. Although this is close the 30x30 goal, the goal is still not achieved. 

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• Finally, in the fourth step, by extending the current ethanol subsidy of a fixed 

$0.51/gallon of ethanol by 5 years (to 2015), ethanol production and use reaches ~ 60 

billion gallons/year. 

Adding the additional policy incentives essentially accelerates cellulosic ethanol deployment in 

the marketplace by three to four years. This acceleration of cellulosic ethanol capacity has a 

significant impact in the total amount of deployed ethanol capacity by 2030. Although this data 

is not presented in this report, when our model was run out to 2050 all scenarios essentially 

predicted the same amount of deployed ethanol capacity, hence the model predicts that the 

primary affects of the grower payment and ethanol subsidy policy incentives is to accelerate 

cellulosic ethanol capacity into the marketplace and will have minimal impact on total ethanol 

capacity in the long-term. 

To fully address energy and environmental effects of the 30x30 scenario, we also conducted a life 

cycle analysis for several ethanol production options. The analyses included all major activities 

associated with the production and distribution of biomass feedstocks and ethanol and the use of 

ethanol in motor vehicles. 

To compare ethanol’s relative energy and emission merits, life-cycle analysis of petroleum 

gasoline was also included. Thus, we analyzed five life-cycle cases: 

1. Petroleum gasoline (the baseline fuel) 

2. Corn to ethanol 

3. Corn stover (representative of agricultural residues) to ethanol through the biochemical 

conversion process 

4. Switchgrass to ethanol through the biochemical conversion process 

5. Forest residues to ethanol through the thermochemical conversion process 

Central to the success of the 30x30 goal is the availability of biomass resources and the 

efficiency with which these resources can be converted into transportation fuels. Aligning these 

components to replace 30% of 2004 motor gasoline demand by 2030 will require, among all 

else, process and technology flexibility. 

The feedstock resource assessment in the “Billion Ton” study (Perlack et al. 2005) validates the 

feasibility of achieving the 30x30 goal, provided R&D efforts overcome technical barriers to 

the development of economically viable biomass conversion technologies. The feedstock 

resource base, however, includes a variety of regionally specific biomass materials with a range 

of chemical and physical properties. 

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Contents 

Acknowledgements ....................................................................................................................... i 

Foreword ........................................................................................................................................ i 

Executive Summary ...................................................................................................................... ii 

Contents ........................................................................................................................................ v 

List of Figures ............................................................................................................................. vii 

List of Tables ............................................................................................................................. viii 

1. Introduction ...................................................................................................................... 1 

1.1 President’s Advanced Energy Initiative – Changing the Way We Fuel our Vehicles ..... 1

1.2 Basis for Ethanol .............................................................................................................. 2

1.2.1 Ethanol Caveat ...................................................................................................... 3 

1.3 Basis for Choosing Ethanol Production Technology Routes ........................................... 3

1.4 Technology Targets as They Relate to Production Cost Targets ..................................... 4

1.4.1 The 2012 Technical Target ................................................................................... 4 

1.4.2 The 2030 Technical Target ................................................................................... 6 

1.5 Achieving The 30x30 Goal: A Market Driven Approach ................................................ 7

1.5.1 Deployment .......................................................................................................... 9 

1.5.2 Fuel Distribution Infrastructure and the End User ............................................. 10 

1.5.3 Investors and Risk .............................................................................................. 10 

1.5.4 DOE’s Role in Risk Mitigation .......................................................................... 10 

1.6 Achieving the 30x30 Goal .............................................................................................. 13

2.0 The Biofuels Industry ..................................................................................................... 14

2.1 The Biomass-to-Ethanol Supply Chain .......................................................................... 14

2.1.1 The Supply Chain ............................................................................................... 14 

2.1.2 Key Actors .......................................................................................................... 16 

2.2 Possible Futures .............................................................................................................. 18

2.2.1 Oil Price Scenarios ............................................................................................. 19 

2.2.2 The Role of Risk ................................................................................................. 23 

2.2.3 Ethanol in the Long Run ..................................................................................... 24 

2.2.4 U.S. Fleet Capacity for Ethanol .......................................................................... 24 

2.2.5 Biomass Feedstock Production ........................................................................... 25 

2.2.6 Ethanol and Fuel Market Price Dynamics .......................................................... 25 

2.3 Summary ......................................................................................................................... 27

3. Energy and Environmental Assessment ................................................................................. 28

3.1 Wells-to-Wheels Analysis of Energy and Greenhouse Gas Emissions .......................... 28

3.2 Annual Reductions in Oil Consumption and Greenhouse Gas Emissions Caused by 

Ethanol Use ................................................................................................................................ 32

3.3 Other Potential Environmental Issues ............................................................................ 34

3.3.1 Land .................................................................................................................... 34 

3.3.2 Water .................................................................................................................. 35 

3.3.3 Emissions ............................................................................................................ 36 

3.4 Benefits of the Integrated Biorefinery ............................................................................ 36

4. Cellulosic Ethanol Production ........................................................................................ 37

4.1 Feedstocks ...................................................................................................................... 39

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4.1.1 Feedstock Accessibility ...................................................................................... 39 

4.1.2 Feedstock R&D Pathway ................................................................................... 41 

4.2 Conversion Technologies ............................................................................................... 50

4.2.1 Biochemical Process Technology Target for 2012 ............................................ 50 

4.2.2 Thermochemical Process Technology Target for 2012 ...................................... 60 

4.2.3 Long-Term Technology Targets for the 30x30 Market Goal ............................. 68 

5. Ethanol Storage, Distribution, Blending, and Vehicle Infrastructure Needs ................. 78

5.1 Existing Ethanol Distribution Infrastructure .................................................................. 78

5.2 Infrastructure Changes Required To Meet the 30x30 Goal ............................................ 79

5.2.1 Storage ................................................................................................................ 79 

5.2.2 Distribution (Plant to Pump) .............................................................................. 80 

5.2.3 Blending ............................................................................................................. 85 

5.2.4 Refueling Stations .............................................................................................. 85 

5.3 Total Infrastructure Investments Required to Meet the 30x30 Goal .............................. 86

5.4 Vehicle Fleet ................................................................................................................... 86

5.4.1 Vehicle Requirements for E10 ........................................................................... 86 

5.4.2 Flexible Fueled Vehicles (FFVs) and Ethanol ................................................... 87 

5.4.3 Advanced Vehicle Technologies ........................................................................ 88 

6.0 Conclusions .......................................................................................................................... 90 

References .................................................................................................................................. 91 

 Appendices 

Appendix A: Starch Ethanol Deployment 

Appendix B: Market Penetration Modeling: The Biomass Scenario Model (BSM) 

Appendix C: Assessment of Uncertainty and Risk for Biomass Conversion Technologies 

Appendix D: Feedstock Technology R&D 

Appendix E: Biochemical Conversion Technology R&D Needs 

Appendix F: Thermochemical Conversion 

Appendix G: Advanced Conversion Technology R&D for Integrated Biorefineries 

Appendix H: Life Cycle Environmental Analysis of Biofuels: GREET and VISION (Vehicle 

Needs) Simulation Results 

Appendix I: Storage, Distribution, Transport, Blending and Refueling Infrastructure Needs
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List of Figures 

Figure 1-1. Resource-based Conversion Pathways ...................................................................... 4 

Figure 1-2. U.S. List Prices for Ethanol ....................................................................................... 5 

Figure 1-3. Ethanol and Gasoline Price Comparison ................................................................... 6 

Figure 1-4. Ethanol Production Profile for the 30x30 Market Target .......................................... 9 

Figure 1-5. Risk Framework ....................................................................................................... 11 

Figure 2-1. The Biomass-to-Ethanol Supply Chain ................................................................... 14 

Figure 2-2. World Oil Price Scenarios ....................................................................................... 15 

Figure 2-3. Dynamic Interactions among Investors, Farmers, and Policymakers in the Biomass 

Transition Model ........................................................................................................................ 16 

Figure 2-4. Applying Technology and Policy Levers to Achieve 60 Billion Gallons/year of 

Ethanol from All Sources in 2030 in a High Oil Price Future ................................................... 20 

Figure 2-5: Ethanol Growth Rates under Different BSM Scenarios for AEO 2006 High Oil ... 22 

Figure 2-6. Applying Technology and Policy Levers to Achieve 60 Billion Gallons/year in 

2030 in a Reference Oil Price Future ......................................................................................... 23 

Figure 2-7. The Effect of Investor Risk Premiums .................................................................... 24 

Figure 2-8. Long-term Effects of the 30x30 Initiative ............................................................... 25 

Figure 2-9. Price Dynamics for Ethanol and the Fuel Market ................................................... 26 

Figure 3-1. Life-cycle Fossil Energy Use of Ethanol versus Gasoline ...................................... 30 

Figure 3-2. Life-cycle Petroleum Use of Ethanol versus Gasoline ............................................ 31 

Figure 3-3. Life-cycle Greenhouse Gas Emissions of Ethanol versus Gasoline (carbon dioxideequivalent emissions of carbon dioxide, methane, and nitrous oxide) ....................................... 31 

Figure 3-4. Life-cycle Oil Use under the Three Cases ............................................................... 33 

Figure 3-5. Life-cycle Greenhouse Gas Emissions under the Three Cases ............................... 34 

Figure 4-1. Distribution of Feedstock Resources ....................................................................... 37 

Figure 4-2. Primary Conversion Routes for Cellulosic Biomass ............................................... 38 

Figure 4-3. Agricultural and Forest Lands Resource Potential .................................................. 39 

Figure 4-4. Accessible Agricultural and Forest Resources with Biorefinery Capacity Potential 

in 2012 ........................................................................................................................................ 40 

Figure 4-5. Accessible Agricultural and Forest Resources with Biorefinery Capacity Potential 

to Produce 60 Billion Gallons of Ethanol in 2030 ..................................................................... 41 

Figure 4-6. Supply Schematic for $35/Dry Ton Feedstock ........................................................ 41 

Figure 4-7. Sensitivity Analysis of Unit Operations in the Feedstock Assembly and Associated 

Variable Costs for All Feedstock Types ..................................................................................... 50 

Figure 4-8. Research State of Technology for Biochemical Conversion (Foust et al. 2006) ..... 52 

Figure 4-9. Process Schematic for Biochemical Conversion ..................................................... 52 

Figure 4-10. Process Flow Diagram Highlighting Major Research Barriers ............................. 54 

Figure 4-11. Research Outcomes and Variables with Potentially Large Effects on the 2012 

Technical Target ......................................................................................................................... 60 

Figure 4-12. Research State-of-Technology Assessments for Thermochemical Ethanol 

Production to Reach the $1.07/Gallon Market Target (Foust et al. 2006) ................................. 62 

Figure 4-13. Process Flow Diagram with Research Barriers for Thermochemical Ethanol 

Production at $1.07/Gallon ......................................................................................................... 63 

Figure 4-14. Thermochemical Process Sensitivity Analysis ...................................................... 68 

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Figure 4-15. Advanced Feedstock Supply System Technologies: Value-Add Feedstock PreProcessing ................................................................................................................................... 70 

Figure 4-16. Process Simplification through Systems Biology Research .................................. 71 

Figure 4-17. Selective Thermochemical Processing .................................................................. 75 

Figure 5-1. Existing Ethanol Distribution System ..................................................................... 78 

Figure 5-2. Ethanol Pipeline Costs ............................................................................................. 84 

List of Tables 

Table 1-1. Economic Assumptions Used in all Calculations ....................................................... 6 

Table 1-2. Advanced Technology Conversion Targets ................................................................ 7 

Table 1-3. Deployment Assistance Recommendations .............................................................. 12 

Table 2-1. Achieving the 30x30 Goal ........................................................................................ 20 

Table 4-1. Timeline of Key Feedstock R&D Activities to Accomplish the 2012 Technical 

Target .......................................................................................................................................... 44 

Table 4-2. Technical Research Milestones and Validation Targets for Feedstock Cost 

Reductions and Increased Tonnage ............................................................................................ 46 

Table 4-3. Timeline of Key Activities to Accomplish the 2012 Technical Target .................... 55 

Table 4-4. Thermochemical Ethanol (Gasification/Mixed Alcohols) R&D Targets ................. 65 

Table 4-5. Timeline of Key Activities to Accomplish the 2030 Biochemical Technical Target 73 

Table 4-6. Timeline of Key Activities to Accomplish the 2030 Thermochemical Technical 

Target .......................................................................................................................................... 76 

Table 5-1. Infrastructure Requirements to Reach 60 Billion Gallons by 2030 .......................... 81 

Table 5-2. Estimated Plant Gate to Pump Costsa

 ....................................................................... 81 

Table 5-3. Distribution Costs Using Pipeline ............................................................................. 84 

Table 5-4. Total Infrastructure Costs .......................................................................................... 86 
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1. Introduction 

Ethanol production from both corn and cellulosic feedstocks shows considerable promise to 

help lessen demand for imported oil as well as increase domestic energy security. However, we 

need to address the question: “What is an appropriate volume goal for ethanol production and 

in what timeframe?” Then given this goal, what do we need to accomplish in terms of in 

research and development (R&D), deployment, and market investment to achieve this goal? 

After much discussion and analysis, the U.S. Department of Energy (DOE) selected the goal of 

supplying 30% of the 2004 motor gasoline demand with ethanol by the year 2030. This roughly 

translates to producing 60 billion gallons of ethanol per year on a Btu-adjusted basis by 2030, 

herein after referred to as the 30x30 goal. This goal was selected because it represents an 

aggressive target that translates into a significant positive national impact but is well within the 

maximum potential for ethanol. A timeframe of 24 years from the present is considered 

aggressive but not short enough to be overly disruptive to market change dynamics. 

 

This scenario report describes the market drivers and technology requirements and presents a 

plan for achieving the 30x30 goal with ethanol produced from both starch and cellulosic 

feedstocks, and outlines a scenario that is realistically achievable with sustained, integrated 

effort by government, academia, and the private sector. Since the 30x30 goal is in essence a 

market goal, this report describes technology needs and a market driven approach for achieving 

the 30x30 goal. 

1.1 President’s Advanced Energy Initiative – Changing the Way We 

Fuel our Vehicles 

President Bush succinctly stated in his 2006 State of the Union Address: “America is addicted 

to oil.” In 2004, the United States used almost 21 million barrels of crude oil per day; 

approximately 58% of this total was supplied by imports. The transportation sector, which 

relies almost entirely on petroleum products, accounts for two-thirds of U.S. petroleum use. 

Gasoline is the dominant transportation fuel in the United States, with a 2004 consumption of 

approximately 140 billion gallons. The Energy Information Administration (EIA) predicts that 

expected increases in total miles traveled will outweigh all vehicle efficiency improvements 

and lead to a one-third increase in gasoline consumption that can only be met by imported 

crude oil. In this scenario, EIA predicts that imports will account for 62.5% of total domestic 

oil use by 2030. 

In order to reduce the Nation’s future demand for oil, President Bush proposed the Advanced 

Energy Initiative (White House, January 2006) which outlines significant new investments and 

policies to (1) change the way we fuel our vehicles and, (2) change the way we power our 

homes and businesses. Together, these efforts will help the United States reach the President’s 

long-term goal of reducing our oil imports from the Middle East by 75% by 2025 (Bush, State 

of the Union Address, January 31, 2006). 

Currently, most ethanol is produced from grain; however cellulosic (biomass) ethanol has long 

been recognized as an important component of a sustainable ethanol marketplace. The 

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President's specific goal for biomass, as stated in the Advanced Energy Initiative, is “to foster 

the breakthrough technologies needed make cellulosic ethanol cost-competitive with cornbased ethanol by 2012” (White House, 2006). Reducing the cost of cellulosic ethanol will be a 

major step in enabling greater use of this alternative fuel to help reduce future U.S. oil 

consumption and is a critical component of “changing the way we fuel our vehicles.” 

Although biomass has long been recognized as the only domestic, sustainable, and renewable 

primary energy resource that can provide liquid transportation fuels, long-standing questions 

regarding the domestic biomass supply have been: 

• What is the production potential of cellulosic ethanol, and 

• Can it have a significant impact on imported oil displacement and long-term energy 

security? 

In response to these questions, an in-depth study, “Biomass as Feedstock for a Bioenergy and 

Bioproduct Industry: the Technical Feasibility of a Billion-Ton Annual Supply” (Perlack et al., 

2005) was performed, herein after referred to as the Billion Ton Study. This study estimated 

that the United States has the potential to produce up to 1.3 billion tons of biomass annually on 

a sustainable basis without impacting food, feed, or fiber uses. To put the ethanol production 

potential in perspective, almost 60% of 2004 motor gasoline demands on a Btu-adjusted basis 

could be met with ethanol from grain and biomass, or approximately 125 billion gallons of 

ethanol. 

1.2 Basis for Ethanol 

This scenario is for biomass only, with the primary driver being a policy for reduction in 

demand for imported oil. This scenario does not include linkages with other scenarios for 

imported oil displacement such as coal to liquids. 

Ethanol is the biofuel selected for this scenario both because of its near-term availability and to 

simplify the analysis of the distribution and vehicle needs. Other key justifications for choosing 

ethanol include: 

• There is a well-developed body of work addressing the use of ethanol as a 

transportation fuel; both in the near term as low volume percentage (E10) blending 

stock with gasoline (an octane enhancer and to meet the oxygen requirements for 

RFG-mandated areas) and at higher blends (E85) as the primary constituent of 

transportation fuels. 

• Ethanol is recognized as a commodity fuel and has market acceptance. 

• Grain-based ethanol has already created market acceptance with well documented 

fuel properties and emission profiles. 

• Flexible fuel vehicle (FFV) technology exists, is in the marketplace, and is growing. 

• Many life cycle analysis (LCA) studies and energy balance studies have been 

performed on both starch and cellulosic ethanol so the environmental and energy 

profiles are well known. 

• Wet and dry mill grain-based ethanol production is a mature commercial technology 

with well proven economics. 

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• Cellulosic ethanol conversion technology, although not yet a commercially viable 

technology, has received considerable development in the laboratory and R&D 

pathways exist to develop the technology to the market target production cost of 

$1.07 gallon (Foust et al., 2006). 

However, a major barrier towards achieving the 30x30 goal is developing and deploying the 

necessary distribution and end use infrastructure (Section 5). Although there is an ethanol 

distribution and use infrastructure in place to distribute existing grain ethanol production (~ 5 

billion gallons/year), it is highly unlikely that this infrastructure can be simply extended to 

accommodate the 60 billion gallons/year required to meet the 30x30 goal. Therefore 

distribution infrastructure is included as a critical component of realizing the 30x30 goal. 

 

1.2.1 Ethanol Caveat 

An important caveat is that this analysis presents a scenario to meet the 30x30 goal with 

ethanol. Clearly ethanol is not the only biofuel that can be produced; Fischer-Tropsch liquids, 

di-methyl ether (DME), methanol from biomass gasification, and butanol from alternative 

fermentation processes are just a few examples of other biofuels that could be produced. As in 

any scenario, forecasting is not absolute and exclusive in that other equally credible scenarios 

could be developed using other biofuels choices, feedstocks, and conversion technologies. 

Ultimately, market factors will dictate how the biofuels industry develops. Readers should not 

misinterpret this scenario as stating nor claiming that ethanol is the only viable biofuel option, 

but simply that a credible argument can be made for ethanol deployment at 30% of motor 

gasoline given favorable market drivers and reasonable assumptions. 

1.3 Basis for Choosing Ethanol Production Technology Routes 

Because of the wide diversity of biomass feedstocks, conversion technologies, integration 

scenarios, and potential products, a multitude of biorefinery options are possible. Even limiting 

the 30x30 scenario to the production of ethanol gives us many options to consider regarding the 

selection of specific feedstocks and conversion technologies. To help sort out all the 

possibilities, guide the research efforts, and identify the key interfaces that will enable the 

establishment of commercially-viable integrated biorefineries, the Office of the Biomass 

Program (OBP) has identified seven primary technology pathways (OBP Multi-Year Program 

Plan [MYPP], 2006). These pathways are linked to the resource base identified in the Billion 

Ton Study, the existing segments of today’s bio-industry where possible, and future bioindustry market segments where envisioned. The pathways are focused on feedstock 

production, feedstock logistics, and ethanol production elements of the supply chain. The 

details of each pathway are described in the OBP MYPP. Each pathway represents a generic set 

of potential biorefinery scenarios for a specific biomass resource base, as shown in Figure 1-1.

The oil seed pathway is not considered in this 30x30 scenario because of the focus on ethanol. 

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Feedstock Production 

and Logistics

Existing Corn Wet Mills

Ethanol Production 

(Pathways)

Ethanol Distribution 

and End-Use

Existing Oil Seed Mills 

Agricultural Residue Processing

Perennial Energy Crops Processing

Existing Forest Products/Pulp and Paper Mills

Forest Residues Processing 

Infrastructure

and

Vehicles

Existing Corn Dry Mills

Forest Residues Processing 

Existing Forest Products/Pulp and Paper Mills

Perennial Energy Crops Processing

Agricultural Residue Processing

Existing Oil Seed Mills 

Existing Corn Wet Mills

Ethanol Production 

(Pathways)

Existing Corn Dry Mills

Corn Grain

Corn Fiber Residue

Soybeans

Corn Stover

Wheat Straw

Rice Straw

Switchgrass

Hybrid Poplar

Bark

Hog Fuel

Black Liquor

Logging Residues

Forest Thinnings

Figure 1-1. Resource-based Conversion Pathways 

From the diverse range of feedstocks outlined in the Billion Ton Study, we have selected four 

feedstock into ethanol conversion technology options to analyze in this scenario. 

1. Existing corn wet and dry mill technology for continued deployment of corn grain 

based ethanol. Since this is already a vibrant commercial technology, the 30x30 

scenario does not require additional development of this technology. 

2. Biochemical conversion (Section 4.2.1) of cellulosic feedstocks to ethanol. 

3. Thermochemical conversion (Section 4.2.2) of cellulosic feedstocks to ethanol. 

4. Advanced state of technology conversion (Section 4.2.3) incorporating both advanced 

biochemical and thermochemical aspects at larger scales. 

1.4 Technology Targets as They Relate to Production Cost Targets 

1.4.1 The 2012 Technical Target 

As stated earlier, the Advanced Energy Initiative defines the technology goal for cellulosic 

ethanol as “to foster the breakthrough technologies needed to make cellulosic ethanol costcompetitive with corn-based ethanol by 2012.” Although this is a quantifiable technology target 

in the context of the 30x30 goal, it needs to be considered in the larger sense to determine if it 

is sufficient to enable large-scale market penetration of cellulosic ethanol at a level where it can 

directly compete with gasoline as a motor fuel. 

The $1.07 per gallon production cost for cellulosic ethanol has been defined as competitive 

with corn-ethanol. At this cost, cellulosic ethanol production would be low enough for entry 

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into the existing ethanol market. The value can be put in context with the historic ethanol price 

data as shown in Figure 1-2. The $1.07 per gallon value represents the low side of the historical 

fuel ethanol prices and hence given historical price data, cellulosic ethanol would be 

commercially viable at this cost of production. 

0

50

100

150

200

250

300

350

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

¢ per gallon

Fuel Alcohol

Ethyl Alchohol

Specially Denatured Alcohol

Figure 1-2. U.S. List Prices for Ethanol 

In addition, the $1.07 per gallon of ethanol price target is also in-line with current gasoline rack 

or pre-tax, prices. To compare the target ethanol price with the price of gasoline on an “apples 

to apples” basis at the pump, the ethanol price must be adjusted as follows: 

1) Adjust the ethanol price from dollars per gallon of ethanol to dollars per gallon of 

gasoline equivalent by correcting for the two-thirds lower energy content of ethanol 

compared to gasoline. This increases the $1.07 to $1.62 per gallon of gasoline 

equivalent. 

2) Adjust ethanol price from plant gate to retail price. The price of gasoline includes, on 

average, $0.40 per gallon for taxes and $0.23 for distribution. Assuming the same costs 

for ethanol gives it a retail price slightly higher than that of gasoline when oil is at $55 

per barrel, as shown in Figure 1-3. This price at pump analysis does not assume any 

subsidy for ethanol. 

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Price Per Gallon ($2003)

$1.07

$2.18

0

0.5

1

1.5

2

2.5

Nominal Ethanol

Price Target in 2012

Retail Gasoline Price

based on $55/bbl Oil

Price Per Gallon of gasoline Equivalent 

($2003)

$1.62

$2.18

$0.40

$0.23

0

0.5

1

1.5

2

2.5

Retail Ethanol Retail Gasoline Price

based on $55/bbl Oil

Figure 1-3. Ethanol and Gasoline Price Comparison 

In 2002, the National Renewable Energy Laboratory (NREL) published a detailed technology 

assessment (Aden et al., 2002) that outlines the technology basis for the $1.07 market target 

case via a biochemical conversion pathway. The $1.07 market target includes feedstock 

production, feedstock logistics, and conversion cost components. Imbedded in this market 

target are the assumptions discussed in Table 1-1. 

Table 1-1. Economic Assumptions Used in all Calculations 

Nth plant” cost and performance based on pilot plant scale (2 to 5 tons per day of biomass) 

data 

A minimum selling price based on a 10% rate of return on investment (ROI) 

A minimum selling price that is not based on availability of tax incentive 

A biomass feedstock cost of $35 per dry ton at the throat of the pretreatment reactor 

A minimum selling price at the plant gate (exclusive of fuel distribution costs, marketing costs, 

and taxes). 

Sections 4.2.1 and 4.2.2 outline the research pathways towards achieving the $1.07 market 

target via biochemical and thermochemical conversion routes. However, the goal of 

$1.07/gallon in 2012 is not sufficient in of itself to achieve the 30x30 goal even given DOE’s 

EIA crude oil high price projections out to 2030. Market analysis (Section 2) shows that, if the 

goal of a production price of $1.07/gallon is the ultimate endpoint for the technology, the 

30x30 goal will not be achieved. Hence a second technology target is used to meet the goal. 

1.4.2 The 2030 Technical Target 

An advanced state of technology target with a 70% reduction in conversion cost from the $1.07 

case by 2030 is used to meet the 2030 goal. This target is needed because: 

1. Feedstock costs will probably not remain constant at $35/ton. As the biorefinery 

industry matures, feedstock prices will likely increase (as is the case for most 

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commodity feedstocks). The price increase will be driven by the use of higher-cost 

biomass and natural market dynamics. Analysis indicates that grower payments could 

reach up to $50/1

ton (Section 4.2.3) to capture all the cellulosic feedstocks necessary to 

achieve the 30x30 goal. 

2. To drive the necessary growth of the ethanol industry, the delta between production 

price and selling price must be sufficient to drive maximum capital investment. Section 

4.2.3 outlines the advanced state of technology research pathways that will be required 

to achieve this target. 

The advanced technology case attempts to reduce the conversion cost component of the ethanol 

production cost to accommodate these concerns as well as take advantage of economies of 

scale advancements that naturally come with a more mature industry. Table 1-2 outlines aspects 

of the advanced conversion target. 

Table 1-2. Advanced Technology Conversion Targets 

Case Year Plant Size Operating Costs 

Feedstock 

Contribution 

Minimum 

Ethanol 

Selling Price 

(MESP) 

 (tonnes/day) ($/gallon) (S/gal) & ($/bdt) (S/gallon) 

$1.07/Gallon 

Market Target 2012 

2,000 

2,185 dry tons/day $0.68 

$0.39/gal -- 

$35/bdt $1.07 

Advanced 

Technology 2030 2,000 $0.49 

$0.761/gal -- 

$71/bdt $1.25 

Advanced 

Technology 2030 

10,0002

11,025 dry tons/day $0.21 

$0.761

/gal -- 

$71/bdt $0.97 

 

Cost Reduction in Conversion Technology Because of 

Advanced Technology $0.19 

Cost Reduction in Conversion Technology Because of 

Economies of Scale $0.28 

1

Estimated 2030 feedstock cost based on larger grower payments to capture all cellulosic feedstocks (see Section 

4.2.3.1). 

2

 This plant size is specified here purely to show relative cost impacts of economies of scale versus advanced 

technologies. Realistically feedstock logistics would limit plant size below this level. 

However, as the market assessment will show in Section 2, the proposed targets for ethanol 

technology improvement are not, by themselves, sufficient to push the ethanol industry to the 

30x30 goal—even with higher oil prices. Hence, in addition to technology cost targets, market 

and policy incentives will be required to achieve the 30x30 goal. 

1.5 Achieving The 30x30 Goal: A Market Driven Approach 

Meeting the 30x30 goal calls for strategies that reduce biomass feedstock cost, technology risk, 

and ethanol price during the early years of industry adoption of lignocellulosic biomass 

technology as corn ethanol production is reaching its peak. Investor attitudes toward new 

 

1

 All “ton” references in this report are “dry” 
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technology raise the risk of building the first full-scale commercial plant to a higher level. 

Investors wait until market value for fuel raises enough to overcome their risk-aversion. 

The market analysis presented in this scenario makes use of a newly developed “system 

dynamics” model that can be used to understand the timing (“dynamic”) implications of 

hypotheses about how the players in the marketplace behave and how they will respond to new 

technology developments. That model—the Biomass Scenario Model (BSM) (Appendix B) —

has served as a useful prototype for the modeling that will be needed to strategically assess 

R&D and deployment strategies. Given assumptions about the behavior of farmers and 

investors and about the future state of agricultural, energy and crude oil, and vehicle markets, 

we used the BSM to identify scenarios under which the 30x30 goal can be met. Thus, these 

results are hypotheses and not forecasts. Furthermore, the scenarios sketched out in this report 

do not represent the only possible strategies. 

There are two sets of drivers in the BSM that can be used to determine the success of the 

scenario technology goals as they relate to production cost targets, and policy and market 

incentives to make ethanol more competitive in the marketplace. The scenario presented here 

uses the best mix of these tools to achieve the 30x30 goal. The scenario for achieving the 30x30 

goal is based on achieving the cost and technology targets goals stated below. 

1. Continue the successful deployment of corn-based ethanol up to its maximum 

potential (9.3 – 17.2 billion gallons/year). 

2. Develop and demonstrate at the pilot scale both biochemical and 

thermochemical cellulosic ethanol conversion technology at the $1.072

 market 

target by 2012. 

3. Develop and demonstrate at the pilot scale advanced cellulosic ethanol 

conversion technology that significantly reduces the conversion cost component 

from 64% of the total ethanol production cost in the $1.07 case to 22% of the 

total ethanol production cost by 2030. 

4. Provide two market incentives to accelerate the deployment of ethanol. 

a. Continue the current $0.51/gallon ethanol tax incentive till 2020. 

b. Provide a $20/ton subsidy for cellulosic feedstocks until 2025. 

5. Develop the necessary distribution and vehicle infrastructure to support the 

30x30 goal. 

6. Compare with EIA Annual Energy Outlook (AEO) high oil price projections3

 

Applying the assumptions listed above to the BSM produces the ethanol production profile 

shown in Figure 1-4. 

 

2

 The $1.07 per gallon of ethanol market target price is calculated using year 2002 dollars. 

3

 (http://www.eia.doe.gov/oiaf/analysispaper/biomass.html 7/2002) 
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Ethanol Output

0

10,000

20,000

30,000

40,000

50,000

60,000

2000 2005 2010 2015 2020 2025 2030

Year

Million Gallons 

Corn Ethanol Cellulosic Ethanol

Figure 1-4. Ethanol Production Profile for the 30x30 Market Target 

The market analysis shows that extending the ethanol subsidy beyond 2020 has no added 

benefit if progress in R&D continues and is successful in reducing the conversion cost of 

ethanol to the targets shown in Table 1-2. The same holds true for the biomass feedstock 

payments. Hence, achieving the Table 1-2 targets by 2030 is critical to allowing these market 

incentives to sunset. The analysis also shows if research does not progress towards achieving 

the 2030 target, extending the ethanol subsidy until 2030 or until the advanced state of 

technology target is achieved provides additional robustness towards achieving the 30x30 goal, 

but at a far greater cost. 

1.5.1 Deployment 

A critical aspect of achieving the 30x30 goal is deployment of all the aspects of the ethanol 

supply chain from feedstock production to end use vehicle technology. As outlined in Section 

2, a market driven approach will be used to develop this scenario. We assume that market 

factors will drive the deployment of ethanol and the types of ethanol production technologies 

deployed. 

This scenario relies on the development of two cellulosic ethanol conversion technologies, a 

biochemical and a thermochemical option. This two-tiered approach to cellulosic ethanol 

technology development provides robustness and near term options; with technology 

opportunities for improved yields and improved economics in the long-term by integrating the 

two options. 

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Attempts are not made in this scenario to crosswalk feedstocks with production technologies 

and industrial sectors. Geographic and market factors will dictate feedstock and conversion 

technology options and the assumption of this scenario is that industry will choose the 

combinations that provide the best economics for their particular set of conditions. 

1.5.2 Fuel Distribution Infrastructure and the End User 

The amount of ethanol required to meet the 30x30 goal far exceeds current production levels. 

Current infrastructure pathways are already close to capacity and researchers need to address 

the factors that will drive distribution infrastructure development. Currently, the BSM does not 

have a robust fuel distribution infrastructure market dynamics module; the BSM assumes that 

there will always be ample distribution pathways to accommodate the volume of fuel 

production. Section 5 provides additional analysis to determine the factors that will be 

necessary to drive the development of the distribution infrastructure to achieve these volumes 

and beyond. The BSM does have a vehicle end-use demand function that is based on future 

fleet assumptions. 

1.5.3 Investors and Risk 

Meeting the 30x30 goal will require that the financial and industrial communities invest close 

to $100 billion to construct new ethanol-production facilities. If, as detailed in this report, 

technology is developed such that production cost targets are met and the cost of competing 

petroleum products goes as projected in the EIA AEO (2006), there will be sufficient monetary 

return to support this level of investment. The challenge will not be in the long term, but will be 

in building the first or “pioneer” plants that prove the technology by taking higher risks and 

solving the real problems of the new technology. 

Merrow studied cost growth in the synfuels industry and found that it was not uncommon to 

drastically underestimate the costs associated with building production facilities based on new 

technology (Merrow et al., 1981). The synfuels industry was very similar to the now emerging 

biomass industry, where new process designs consisted of many new operations, including 

solids handling, and the product was a low margin, commodity fuel. To overcome these 

apparently inherent technology development hurdles, close attention to detail in the research, 

development, and demonstration phases is a must. Care must be taken to collect the appropriate 

data and scale-up a fully integrated process while making sure that all components are 

accurately accounted for in products, by-products, wastes, and recycles. 

1.5.4 DOE’s Role in Risk Mitigation 

A. Non Technical Areas of Risk 

In an effort to identify non-technical barriers to cellulosic ethanol deployment, DOE held a 

forum (DOE 2004) of representatives from the private and public sectors. Participants included 

members of finance, policy, industry, and engineering organizations. DOE found that 

regulatory, policy, and market barriers are important, but the primary barrier is financial. 

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Figure 1-5 illustrates the magnitude of investment that must be made by industry and 

government to successfully commercialize a new technology. As shown in the figure, most 

investment is from industry. It should be noted that although construction costs can be 

predicted, unexpected costs incurred after the plant is built (e.g., process modifications to 

achieve design yields, rates, and operating costs) can be considerable and must be borne by 

operators and investors. 

Source: DOE 2004. 

Figure 1-5. Risk Framework 

The DOE forum recommended five areas for continued federal assistance to overcome these 

non-technical barriers. The recommendations were: 

1. Create a development primer. 

2. Form a working group within government to help developers. 

3. Help with off-take agreements (where possible). 

4. Co-fund research and development. 

5. Help with risk mitigation. 

Table 1-3 summarizes how DOE will help in these areas. 

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Table 1-3. Deployment Assistance Recommendations 

Recommendation Reason Approach 

1. Project 

Development 

Primer 

To overcome major logistical 

barriers of the deployment 

process 

Outline a deployment primer 

on market analysis, 

permitting, applying for 

financing, and developing 

strategic partnerships. 

2. Working Group To expedite the permitting/ 

regulatory process 

Coordinate with EPA and 

DOE to advise developers 

on how to streamline the 

process and minimize the 

effects of regional variation. 

3. Off-Take 

Agreements 

To increase financial 

backing (Banks prefer to 

finance projects that have 

secured a buyer of the final 

product.) 

Identify and secure off-take 

agreements with local 

consumers as well as state, 

regional, and federal 

sources to secure markets 

for new technology. 

4A. Current R&D 

Funding (20-80) 

To help industry develop 

technology 

Provide a 20-80 cost-share 

with industry for R&D 

efforts. 

4B. Bench-/Pilot-Scale 

Testing (50-50) 

To help developers prove 

technology functionality 

Collaborate with industry to 

provide a 50/50 cost-share 

of small-scale testing of 

technology. 

4C. Commercially 

Viable 

Demonstrations 

(80-20) 

To help developers 

demonstrate technical and 

economic feasibility 

(Investors are unwilling or 

unable to provide high-risk 

capital for commercial 

plants.) 

Provide 20%–30% costshare for a large-scale 

demonstration plant to 

prove technical and 

economic feasibility. 

5. Risk Mitigation To allow corrective actions 

(when other options are 

exhausted) to ensure a 

successful commercial 

launch (Most projects fail 

because they have 

exhausted their capital prior 

to meeting performance 

guarantees.) 

Establish a type of 

insurance program for 

commissioning and 

performance acceptance 

through the first year of 

operation, with review by 

independent engineers. 

Source: DOE 2004 

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B. Technical Areas of Risk 

Important activities for successful deployment include scale-up and demonstration of new 

technologies, followed by investment in full-scale facilities to offset the risks of cost overruns 

in pioneer plants. 

DOE has sponsored research at several national laboratories and universities since the early 

1980s to improve cellulosic biomass-to-ethanol conversion technology. Considerable progress 

has been made (Sheehan and Riley 2001a; Foust et al. 2006), and further cost reductions are 

being pursued. 

In the past six years, DOE has increased its support of industrial projects. In 2004, two critical 

projects were completed that resulted in the world’s largest enzyme producers reducing the cost 

of cellulose enzymes (which are critical to the conversion of cellulose to fermentable sugar) by 

more than 20 times. Two other ongoing cost-shared projects are with Abengoa Bioenergy and 

DuPont. These companies plan to conduct experiments with or leading to scaled-up integrated 

pilot plants. Abengoa’s process will integrate cellulosic pretreatment with newly developed 

enzymes and a robust biocatalyst for the fermentation of biomass-derived sugars to ethanol 

(Abengoa 2003). Abengoa’s pilot-plant phase will be completed in 2007. DuPont will conduct 

the laboratory work and engineering modeling and design necessary to scale-up to a pilot plant 

(DuPont 2003). This work will also be completed in 2007. 

Activities that DOE is planning with industrial partners include a cost-shared, one-tenth-scale 

demonstration facility and a cost-shared commercial demonstration facility. These scale-up 

activities are critical to the successful long-term deployment of cellulosic ethanol conversion 

technology. 

It assumed that DOE-led industrial projects will lead to scale-up technology that will satisfy the 

inflated return on investment required for pioneer plants prior to 2012. The initial plants will be 

augmented with cost-reduction technology developed by DOE through the pilot-plant scale. 

Once a sufficient number of plants have been established and optimized, the investment 

community will be comfortable with investments at lower, more conventional returns on 

investment. In a second scenario, DOE continues to reduce costs through 2012 at the pilot-plant 

scale. This will enable follow-on industrial cost-shared scale-up activities to ultimately build 

the pioneer plants. Following the same path as the first scenario, once a sufficient number of 

pioneer plants have been built and optimized, the investment community will consider this a 

mature technology with standard investment returns. 

1.6 Achieving the 30x30 Goal 

The significant increase in crude oil prices over the past two years and resulting high gasoline 

prices have renewed interest in biofuels and their potential. This report presents a detailed 

assessment of what is required to supply 30% of 2004 motor gasoline demand with ethanol by 

2030. 

A credible scenario for achieving the 30x30 goal with ethanol can be made, given AEO oil 

price projections (EIA, 2006). However, the scenario is extremely aggressive and can be 

achieved only with a focused and sustained government and private sector effort. To identify 

critical areas and the probability of success of meeting the goal, this document will be 

rigorously analyzed with a comprehensive risk assessment to validate the preferred approach to 

the 30x30 goal. 
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2.0 The Biofuels Industry 

Most analyses performed to characterize the potential of biofuels technology are static. They 

are snapshots of the market and technology. But understanding deployment requires another 

dimension: time. Only in the context of time can we understand the sequence of events behind 

the deployment of biofuels technology. 

Many software tools and techniques, with varying degrees of complexity and ease of use, are 

available for assessing and predicting dynamic behavior. The BSM (Appendix B) was created 

using a software tool known as STELLA, which provides a simple visual language for 

representing dynamic behavior in systems. Its relatively intuitive nature is well suited to the 

kind of general “what if” scenario building needed. The software allows users to evaluate 

interactions among supply chain components as they unfold over time, without the need for 

fluency in ordinary and partial differential equations. 

2.1 The Biomass-to-Ethanol Supply Chain 

2.1.1 The Supply Chain 

The supply chain for biofuels production and use—from the farms to the vehicles—is shown in 

Figure 2-1. 

Figure 2-1. The Biomass-to-Ethanol Supply Chain 

Analytically characterizing all aspects of the supply chain in the scenario model would make it 

unwieldy. Instead, the model takes advantage of the spectrum of analytic descriptions offered 

by various (and individually more detailed) modeling activities. Indeed, the BSM has proved to 

be a valuable strategic modeling tool that pulls together results from other analytical efforts to 

construct a coherent “story” for the build-up of each component of the biofuels supply chain. 

The model allows ethanol (from corn or lignocellulosic biomass) to compete with petroleum 

fuels in two markets: as a fuel additive (as an oxygenate, and/or an octane enhancer) and as 

bulk fuel supply (gasoline). The backdrop for these markets is the world oil market. The DOE 

EIA’s AEO (Energy Information Administration 2006) provides projections for oil prices to 

2030. The BSM incorporates a static picture of oil prices based on AEO 2006, which is 

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extended to 2050 using linear extrapolation. The results presented here focus on the AEO 2006 

“reference case” and the “high oil price case” (see Figure 2-2). 

Figure 2-2. World Oil Price Scenarios 

Oil prices are translated into gasoline prices using a linear regression of historical prices of oil 

and refinery gate gasoline prices (EIA 2005). If ethanol and gasoline compete solely on the 

basis of delivering energy to a vehicle, then gasoline prices can serve as a direct measure of the 

market competition that ethanol must beat. Analyses by ORNL were used to establish the 

added value of ethanol relative to gasoline in the fuel blend market (Hadder, 2000). 

However, the BSM ignores the dynamics of building a fuel distribution system for E10 and 

E85. It is likely federal or state policies will be needed to ensure the timely introduction of an 

ethanol fuel infrastructure. This issue is addressed further in Section 5. The dynamics of 

building a fuel distribution system are dwarfed by the relative dynamics of developing, 

investing in, and building biomass conversion facilities. 

Another possible barrier to high-volume market penetration of ethanol is the availability of 

ethanol-capable vehicles. The BSM includes a fleet vehicle turnover model that considers 

timing constraints on the introduction of E85 FFVs. The model contains two scenarios: (1) the 

business-as-usual introduction of FFVs combined with business-as-usual fleet average fuel 

economy and (2) an aggressive policy/technology-driven scenario proposed by the Union of 

Concerned Scientists (UCS) in which hybrid vehicle technology is combined with FFV 

technology to allow greater fuel efficiency and higher fleet capacity for E85 use. 

The aggressive vehicle scenario is used in all results summarized in this section so as to not 

constrain the demand for ethanol. By not constraining the demand potential for ethanol, all of 

the areas in the biomass-to-ethanol supply chain can freely expand in the BSM. The businessas-usual vehicle scenario affects the ability to meet the 30x30 goal by retarding the demand. 

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However, since the UCS projections were chosen only to eliminate demand, we selected 

another model, VISION, to predict fleet growth for the 30x30 (Section 3). 

The BSM includes four scales of technology: pilot (1–5 tons/day), demonstration (50 tons/day), 

pioneer (500 tons/day), and full (5,000 tons/day). 

2.1.2 Key Actors 

The BSM models the behavior of three key actors: 

• Private sector investors 

• 4Farmers or growers 

• Policymakers 

Figure 2-3 shows schematically how these actors interact with one other and with the supply 

chain. The investor’s response to the conditions of the fuel market, the feedstock supply 

market, and the state of the industry are the primary determinants of whether investments in 

ethanol (corn or cellulosic ethanol) production capacity occur. Investment in ethanol production 

capacity is, in turn, the primary determinant of the dynamics of ethanol production. 

Figure 2-3. Dynamic Interactions among Investors, Farmers, and Policymakers in 

the Biomass Transition Model 

 

4

 The term farmer, grower, producer, and landowner are used interchangeably throughout this report 
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Although it is a popular notion that the build-up of the supply chain for a new fuel such as 

cellulosic ethanol is driven by “market pull,” Figure 2-3 illustrates that this is only partly 

correct. The investor must take into account market requirements for and constraints on the 

acceptance of ethanol as well as the requirements and constraints of the market controlling 

feedstock supply. Given these factors, the state of technology for converting biomass to ethanol 

is the remaining factor that can make or break investment in new ethanol production capacity. 

2.1.2.1 Investor 

The investor’s calculation is relatively simple. The investor must consider: 

• The cost of delivered (plant gate) biomass 

• The capital and operating costs associated with converting that biomass into ethanol 

• The prevailing market value of ethanol 

• The market capacity for using ethanol 

Working backward from the vehicle and fuel markets, the investor starts by seeing if the 

combination of E10 and E85 capacity in the vehicle fleet at a given point in time has room to 

absorb new ethanol production. If vehicle fleet demand is being met, the investor determines 

that the investment is not attractive, and no money is invested in new ethanol capacity. The 

investor also compares the minimum price of ethanol with the prevailing value for ethanol in 

the marketplace. As the ratio of prevailing market value to minimum ethanol selling price 

becomes greater, investment in new ethanol production capacity becomes more attractive—and 

money flows more rapidly into new ethanol projects. 

It is important to understand how the investor establishes a minimum selling price for ethanol 

for a full-scale commercial facility. It is calculated by establishing: 

• Total project investment (TPI) for the plant 

• Non-feedstock operating costs for the plant 

• Feedstock operating costs for the plant. 

TPI and operating costs are based on the best available technology at a given time. The investor 

inflates the TPI to reflect cost growth if the project is a first-of-a-kind plant (Merrow 1981). 

Lignocellulosic biomass feedstock costs are determined through a dynamic bidding process in 

which the investor in a new cellulosic ethanol plant looks at the available biomass inventory to 

determine if, at current prices, there is enough inventory to support a new processing facility. If 

not, the investor bids up the market price until the response from the biomass feedstock supply 

system is sufficient to meet demand. The BSM model shows that lags in the response of the 

feedstock supply market (e.g., because of the time required to establish a new stand of 

switchgrass) can lead to “commodity cycles” in which market price increases are followed by 

price collapse. 

The investor will assign a risk factor that affects the required return on investment (ROI); the 

higher the risk, the higher the minimum ROI. This risk factor depends on perceived industry 

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experience with the technology and the completed R&D and piloting. Thus, until cellulosic 

ethanol becomes established, investment in this technology is handicapped relative to corn 

ethanol technology because its risk-weighted cost of capital is higher. 

2.1.2.2 Producer 

The biomass producer’s calculations are also relatively simple. The farmer (or grower) needs to 

answer the question: “Where is my best opportunity for generating income from my investment 

in farmland?” The farmer compares the potential revenue per acre for switchgrass (or for some 

other perennial energy crop) with the current revenue generated. If the ratio of potential 

revenue from switchgrass to revenue from existing crops is greater than one, then the farmer 

moves a percentage of land into switchgrass production. As the ratio increases, the rate of land 

turnover into switchgrass increases. The decision to supply biomass is “automatic.” That is, the 

amount of biomass available is determined by the price offered by the biomass processor and is 

determined directly from fixed feedstock supply curves. 

2.1.2.3 Policymaker 

The BSM model makes no attempt to apply a logic or calculus to the decisions of the 

policymaker. Instead, it treats the response of policymakers as an adjustable input to the 

system. The policymaker has several levers available, depending on what “social good” is 

being considered. In the model, the policymaker can influence the economic calculi of the 

farmer and the investor in a number ways. For example, the policymaker can: 

• Pay the farmer a credit for collecting, producing, and selling lignocellulosic biomass for 

fuels production 

• Apply or modify fuel mandates (e.g., the Renewable Fuel Standard) 

• Charge a carbon tax on all vehicle fuels based on carbon dioxide emissions (this is 

where life cycle analysis data characterizing gasoline, corn ethanol, and lignocellulosic 

ethanol are used) 

• Extend the existing ethanol tax incentive 

• Provide capital funding for first-of-a-kind technology investments at the commercial 

and demonstration scale. 

Although all these options are available in the model, only options 1, 4, and 5 were necessary to 

achieve the 30x30 goal. 

2.2 Possible Futures 

All of the analyses presented here include the following assumptions: 

• Private investment in demonstration-scale (50-ton/day) facilities occurs when the 

nominal price of ethanol reported from R&D meets the prevailing market value and 

facility operation shows a minimal (2%) ROI. (The BSM model downplays the role of 

ROI as a decision-making tool for the demonstration because the investor understands 

that the demonstration facility itself will not be a source of profit or return on capital.) 

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• Government funds are available for initial demonstration-scale facilities to cover 50% 

of total project investment. 

• Government funds are available for the first full-scale demonstration facility to cover 

40% of total project investment. 

• Initial demonstration-scale cellulosic ethanol facilities will be built in existing corn 

ethanol facilities, which is predicted to reduce total project investment by 50% 

compared with a Greenfield project. 

• Investors are willing to jump from demonstration scale (50 tons/day) to full commercial 

scale (5,000 tons/day). 

• Financing is 40% debt and 60% equity, with debt financing available at 7.5%. 

• Equity financing of a first-of-a-kind commercial facility requires a 30% ROI to account 

for technology risk. The ROI then declines to 10% after about the first 200 million 

gallons of production. 

• Equity financing for corn ethanol facilities has a 10% ROI. 

• There is aggressive adoption of FFVs. 

2.2.1 Oil Price Scenarios 

2.2.1.1 Meeting the 30x30 Goal in a High Oil Price World 

The technology targets of the 30x30 initiative for 2012 and 2030 (see Section 1) are not, by 

themselves, sufficient to create a 60-billion-gallon/year ethanol industry by 2030. Meeting the 

30x30 goal—even under a high oil price forecast—calls for a combination of strategies to 

reduce biomass feedstock cost, conversion cost, technology risk, and ethanol price during the 

early years of industry adoption of lignocellulosic biomass technology. 

An important strategy for speeding up technology adoption is to rely on past and ongoing DOE 

industrial partnerships to deploy first-generation technology that may be ready before the 

program meets its 2012 target of $1.07/gallon ethanol (2002 dollars). To look at the effect of 

such a strategy in the BSM, we assumed that some form of first-generation technology is 80% 

complete in terms of R&D up to the pilot scale and that this technology would lead to ethanol 

at a nominal production price of $1.30/gallon. With this jump-start on technology development 

and the assumption of high oil prices, investors begin demonstration work much earlier. 

Supplementing this strategy with two other policy levers allows us to produce 60 billion 

gallons/year (see Table 2-1) of cellulosic and grain ethanol. These policies include extending 

the existing ethanol subsidy by 5 years to 2015 and providing payments to lignocellulosic 

biomass suppliers for residues and energy crops. A payment of $20/ton of biomass is 

equivalent to paying producers a carbon credit of $40/ton of carbon dioxide recycled in the 

ethanol fuel life cycle. 

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Table 2-1. Achieving the 30x30 Goal 

Factor Description

World Oil AEO 2006 high oil price case 

Technology Progress Accelerated targets under the 30x30 initiative 

First-Generation Cellulose Technology 80% complete; $1.30/gallon nominal production price 

Biomass Feedstock Subsidy $20/dry ton payment through 2020 

Ethanol Subsidy $0.51/gallon subsidy extended through 2015 

To understand how each of the factors in Table 2-1 affects market outcomes for ethanol, it is 

necessary to add the factors one at time. The results are summarized in Figure 2-4. 

Figure 2-4. Applying Technology and Policy Levers to achieve 60 Billion Gallons/year of 

Ethanol from All Sources in 2030 in a High Oil Price Future 

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As shown in Figure 2-4 the scenario modeled in the BSM for achieving the 30x30 goal builds 

as follows: 

BSM Pre-Initiative Case:

Prior to the President’s Advanced Energy Initiative (Pre-Initiative) which had the 

$1.07/gallon cellulosic ethanol production target set for 2020, only corn ethanol is deployed 

to the maximum potential of ~ 16 billion gallons. Cellulosic ethanol does not achieve 

deployment under this scenario due to the fact that it never overcomes the risk hurdle for 

investors. 

BSM First Initiative Case:

In the first initiative case, achieving the $1.07/gallon target is accelerated to 2012. As 

shown in Figure 2-4 this action has essentially no effect on ethanol deployment since 

cellulosic ethanol fails to clear the risk hurdle. Therefore under this scenario only starchbased ethanol is deployed up to the maximum potential of ~ 16 billion gallons. 

BSM Second Initiative Case:

In the second initiative case, adding the 1st generation technology incentive provides for a 

government cost share of 50% for initial demonstration-scale facilities and 40% for initial 

full-scale facilities. In this case ~25 billion gallons of cellulosic ethanol capacity are 

deployed by 2030. Combined with a starch-based ethanol capacity of ~13 billion gallons, 

this equals a total ethanol deployment of ~ 38 billion gallons of ethanol by 2030. Although 

this amount is significant, it fails to achieve the 30x30 goal. 

A particular point of interest is that under this scenario the BSM predicts a slightly lower 

volume of corn ethanol deployment by 2030 than in the earlier cases—12.5 billion gallons 

versus ~16 billion gallons in the earlier scenarios. This is due to the fact that under this 

scenario cellulosic ethanol becomes more attractive to investors than starch-based ethanol 

prior to starch-based ethanol achieving its maximum potential; hence investment capital is 

shifted to cellulosic ethanol. This is also true for the subsequent scenarios. 

BSM Third Initiative Case:

In the third initiative case, adding a policy incentive of a payment for biomass to the grower 

of $20/dry ton through 2020 increases cellulosic ethanol deployed capacity to over 44 

billion gallons by 2030, and when combined with the 2030 starch-based ethanol deployed 

capacity of 11.2 billion gallons, total ethanol capacity reaches ~55 billion gallons. Although 

this is close the 30x30 goal, the goal is still not achieved. 

BSM Fourth Initiative Case:

Finally, in the fourth initiative case, by extending the current ethanol subsidy of a fixed 

$0.51/gallon of ethanol by 5 years (to 2015), ethanol production and use reaches just less 

than 60 billion gallons/year. 

Figure 2-5 shows the ethanol deployment as a function of time for the four initiative scenarios. 

As shown in Figure 2-5, adding the additional policy incentives essentially accelerates 

cellulosic ethanol deployment in the marketplace by three to four years. This acceleration of 

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cellulosic ethanol capacity has a significant impact in the total amount of deployed ethanol 

capacity by 2030. Although this data is not presented here, when the BSM was run out to 2050 

all scenarios essentially predicted the same amount of deployed ethanol capacity, hence the 

BSM predicts the primary affects of the grower payment and ethanol subsidy policy incentives 

is to accelerate cellulosic ethanol capacity into the marketplace and will have minimal impact 

on total ethanol capacity in the long-term. 

Initiative

0

10,000

20,000

30,000

40,000

50,000

60,000

2000 2005 2010 2015 2020 2025 2030

Year

Million Gallons 

Corn Ethanol Cellulosic Ethanol

Initiative, 1st Generation Technology

0

10,000

20,000

30,000

40,000

50,000

60,000

2000 2005 2010 2015 2020 2025 2030

Year

Million Gallons 

Corn Ethanol Cellulosic Ethanol

Initiative, 1st Generation Technology, $20/ton 

Grower Payment through 2020

0

10,000

20,000

30,000

40,000

50,000

60,000

2000 2005 2010 2015 2020 2025 2030

Year

Million Gallons 

Corn Ethanol Cellulosic Ethanol

Initiative, 1st Generation Technology, $20/ton 

Grower Payment through 2020, EtOH subsidy 

to 2015

0

10,000

20,000

30,000

40,000

50,000

60,000

2000 2005 2010 2015 2020 2025 2030

Year

Million Gallons 

Corn Ethanol Cellulosic Ethanol

Figure 2-5: Ethanol Growth Rates under Different BSM Scenarios for AEO 2006 High Oil 

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2.2.1.2 Meeting the 30x30 Goal in a Reference Case Oil Price World 

Although the AEO 2006 reference oil price case is a substantial departure from prior-year 

forecasts (reflecting a significant jump in oil price projections), it is based on the assumption 

that oil production can catch up with demand over the next 10 years to offset the oil price 

growth seen over the past few years. This increases the challenge for alternative fuels such as 

ethanol. When the same strategies as described in the previous section are applied under the 

AEO 2006 reference oil price case, 46 billion gallons/year of ethanol make it into the 

marketplace by 2030 (see Figure 2-6). Although the 30x30 goal is not achieved for AEO 2006 

Reference Oil projections, we do achieve significant ethanol deployment at ~80% of what is 

achieved for AEO 2006 High Oil Price projections. Dynamic growth charts for ethanol 

production similar to the ones shown in Figure 2-6 are shown in Appendix B for the AEO 2006 

Reference Oil case. 

Figure 2-6. Applying Technology and Policy Levers to achieve 60 Billion Gallons/year in 

2030 in a Reference Oil Price Future 

2.2.2 The Role of Risk 

One of the unique aspects of the BSM is that it quantitatively introduces investor attitudes 

toward risk. To understand how critical this risk translation is, consider what happens when the 

risk premium is removed (see Figure 2-7). Eliminating investor risk premiums dramatically 

increases ethanol deployment by 2030. In fact, the combination of the more aggressive 

President’s Advanced Energy Initiative and an assumption of access to first-generation 

technology nearly meets the goal of 60 billion gallons in 2030, and adding the biomass 

payment easily exceeds 60 billion gallons —even under the reference case oil scenario. 

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AEO Reference Oil

Initiative and 1st Gen Technology

11,564 10,653

45,891

2,244

0

10,000

20,000

30,000

40,000

50,000

60,000

Risk No Risk

Million Gallons per year

Cellulosic Ethanol

Corn Ethanol

AEO Reference Oil

Initiative and 1st Gen Technology with 

Biomass Payment

10,687 8,450

58,072

32,434

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Risk No Risk

Million Gallons per year

Cellulosic Ethanol

Corn Ethanol

Figure 2-7. The Effect of Investor Risk Premiums 

2.2.3 Ethanol in the Long Run 

Meeting the 30x30 goal is not necessarily the ultimate outcome for ethanol. Once this 

aggressive goal has been met, ethanol production and use can continue to grow (see Figure 2-

8). The BSM shows that, even when only half of the 2030 target is met, total ethanol 

production could reach 125 billion gallons/year by 2050, compared with 150 billion 

gallons/year for the same policy scenario under the high oil price. 

2.2.4 U.S. Fleet Capacity for Ethanol 

The BSM includes a vehicle fleet turnover module. In all of the analyses in this section, an 

aggressive scenario of FFV introduction is used, based on work done by the Union of 

Concerned Scientists for the Natural Resources Defense Council (Greene et al., 2005). Ethanol 

capacity in the U.S. fleet includes conventional vehicles able to legally operate on blends of up 

to 10% by volume ethanol in gasoline and FFVs that can operate on up to 85% ethanol. The 

aggressive scenario assumes a linear rise in the percentage of light-duty trucks and cars sold 

annually that are flex-fuel capable, from around 2.5% today to just more than 50% by 2015. 

Under these conditions, growth in fleet capacity for using ethanol, as shown in Figure 2-8, far 

outstrips the ability to deploy ethanol production facilities. 

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Reference Oil, Initiative, 1st Generation 

Technology, $20/ton Grower Payment through 

2025, EtOH subsidy to 2015

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

200,000

225,000

250,000

2000 2010 2020 2030 2040 2050

Year

Million Gallons per year

E10 and E85 Capacity U.S. Fleet

Total Ethanol

Corn Ethanol

High Oil, Initiative, 1st Generation Technology, 

$20/ton Grower Payment through 2020, EtOH 

subsidy to 2015

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

200,000

225,000

250,000

2000 2010 2020 2030 2040 2050

Year

Million Gallons

E10 and E85 Capacity U.S. Fleet

Total Ethanol

Corn Ethanol

Figure 2-8. Long-term Effects of the 30x30 Initiative 

2.2.5 Biomass Feedstock Production 

Corn ethanol has an ultimate production potential of 9–17 billion gallons/year, depending on 

market and policy conditions (Appendix A). Beyond these levels, biomass for ethanol 

production becomes feasible. In the BSM model, feedstocks for cellulosic ethanol include 

agricultural residues, forest residues, and perennial energy crops (switchgrass or hybrid poplar). 

The BSM model assumes that production of cellulosic ethanol will begin by using agricultural 

and forest residues. Once consumption of residues reaches more than 100 million dry tons/year 

or approximately 10 billion gallons of cellulosic ethanol production, switchgrass production 

and use begins in the BSM. The predominant reason switchgrass/energy crop use lags 

agricultural residue use in the BSM is due to slower adoption rates of farmers, who must decide 

to dedicate land for 10–12 years to energy crop production (relative to the adoption rates of 

farmers who simply collect a portion of residues from corn and wheat already in the field). 

2.2.6 Ethanol and Fuel Market Price Dynamics 

The pricing of cellulosic ethanol is affected not only by total capital and operating costs but 

also by investors’ required ROI, which in the BSM is a function of the relative risk of the 

technology compared with that of mature processes. Thus, in the early years, before R&D is 

complete, ethanol pricing is risk-inflated. Such prices are not realistic estimates of what 

investors think they will get; rather, they serve as a proxy for decision-making about the risk of 

the technology relative to the price that the market will bear. High ROIs and concomitant high 

price requirements simply translate into a “no go” decision on the part of the investor when the 

price is compared with prevailing value in the market. As pilot-plant work is completed and 

demonstration facilities come on line, the risk-adjusted price drops dramatically (see Figure 2-

9). 

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In the BSM model, by 2012 risk-adjusted prices for cellulosic ethanol cross over market values, 

and investment in the first commercial-scale plants begins. Minimum selling price for 

cellulosic ethanol drops well below market value. This does not mean that ethanol prices will 

stay below fuel market value. The difference between market value and minimum price 

establishes the relative profitability of ethanol over gasoline. The higher these margins, the 

more rapidly investors build cellulosic ethanol facilities. Maintaining a reasonable margin is 

critical to rapid deployment of ethanol technology. This is why the target for ethanol pricing 

must be very aggressive, vis-a-vis gasoline. Furthermore, demand for feedstock eventually 

drives the minimum price for ethanol above market value, and industry growth slows (after 

2046 in Figure 2-9). For this reason, ethanol conversion costs must be as low as possible to 

support increased feedstock prices as the industry grows. 

AEO 2006 high Oil

1st Generation Technology 80% Complete

$0.51 Ethanol Subsidy through 2015

$20 per ton biomass supply payment through 2020

0

1

2

3

4

5

6

7

8

9

10

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

$2003/gallon gasoline equivalent

Prevailing Fuel Market Value Cellulosic EtOH Min Price

Corn EtOH Min Price

Figure 2-9. Price Dynamics for Ethanol and the Fuel Market 

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2.3 Summary 

Ethanol can have major effects on the U.S. transportation fuel supply—especially by 2050. 

However, scenarios for meeting the 2030 goal associated with the 30x30 initiative call for 

aggressive government policy and the ability to build on decades of past research on cellulosic 

ethanol technology. The scenario that achieves the 30x30 scenario is one in which initial 

incentive payments of $20/dry ton are made to farmers, and a relatively brief extension of the 

federal incentive for ethanol is enacted. Although this is the only scenario in which the 30x30 

goal is achieved, significant ethanol penetration at 80% of the 30x30 goal occurs for AEO 2006 

reference oil price projections, which still makes significant strides towards energy security and 

decreased reliance on imported oil. 

In the following sections of this scenario report the environmental and energy balance 

implications of this scenario will be analyzed, R&D pathways will be outlined for 

accomplishing the near-term $1.07 production target for competitive cellulosic ethanol by 2012 

from both a biochemical and thermochemical route as well as long-term R&D targets for the 

advanced state of technology conversion cost targets necessary for utilizing the higher cost 

feedstocks to achieve the 30x30 goal. Finally the distribution and vehicle needs to achieve the 

30x30 goal will be assessed. 
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3. Energy and Environmental Assessment 

3.1 Wells-to-Wheels Analysis of Energy and Greenhouse Gas 

Emissions 

To fully address the energy and environmental effects of the 30x30 scenario, we conducted lifecycle analysis for several ethanol production options. The analyses included all major activities 

associated with producing and distributing biomass feedstocks and ethanol and the use of ethanol 

in motor vehicles. Life-cycle analysis of vehicle/fuel systems is often called “wells to wheels” 

(WTW) analysis (or, more precisely, “farms to wheels” analysis for biofuels). 

The primary tool used in the ethanol life-cycle analysis is the Greenhouse Gases, Regulated 

Emissions, and Energy Use in Transportation (GREET) model developed by Argonne National 

Laboratory (ANL). Researchers at ANL began developing the GREET model in 1995, with 

support primarily from DOE’s Office of Energy Efficiency and Renewable Energy (EERE). 

Argonne released the first version of the GREET model 1.0, in June 1996. The Microsoft 

Excel-based, multidimensional spreadsheet model addresses the life cycle analytical challenges 

associated with alternative fuels (including ethanol) and vehicle technologies. 

For a given vehicle and fuel system, GREET calculates: 

• Consumption of total energy (energy in non-renewable and renewable sources), fossil 

fuels (i.e., petroleum, natural gas, and coal), and petroleum 

• Emissions of carbon dioxide-equivalent greenhouse gases, or GHGs (primarily carbon 

dioxide, methane, and nitrous oxide) 

• Emissions of five criteria pollutants: volatile organic compounds (VOCs), carbon 

monoxide, nitrogen oxide, particulate matter smaller than 10 microns, and sulfur oxides. 

These emissions are further separated into total and urban emissions. 

For the 30x30 analysis we used the latest version of the GREET model, GREET 1.7 to generate 

results for all the items listed above. This model is capable of analyzing more than 90 

transportation fuel pathways and 75 vehicle/fuel systems (Wang et al., 2005). However, only 

fossil energy consumption, petroleum consumption, and GHG emissions are presented in this 

section. Complete results are presented in Appendix H. 

Although the 30x30 effort addresses many ethanol production technologies, because of data and 

resource limitations, life-cycle simulations were conducted only for selected ethanol production 

options. In particular, we examined four feedstocks: corn, agricultural residues, switchgrass, 

and forest residues. 

Corn-based ethanol currently accounts for essentially all of U.S. ethanol, and it will continue to 

play a major role in the U.S. fuel ethanol market both in the near- and long-term hence corn was 

included as one of the life-cycle simulations. The cellulosic feedstocks for the life-cycle 

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simulations were chosen according to the DOE-USDA Billion Ton study (Perlack et al. 2005). 

We used the following assumptions from the Billion Ton study: 

• Approximately 550 million tons of crop residues could be available each year (about 

half of which is corn stover) 

• Nearly 400 million tons of perennial crops such as switchgrass could be available in 

the future 

• About 370 million tons of forest residues could be available. 

These cellulosic biomass feedstocks, in addition to corn grains, could support large-scale 

ethanol production. To compare ethanol’s relative energy and emission merits, life-cycle 

analysis of petroleum gasoline was also included. Thus, we analyzed five life-cycle cases: 

1. Petroleum gasoline (the baseline fuel) 

2. Corn to ethanol 

3. Corn stover (representative of agricultural residues) to ethanol through the biochemical 

conversion process 

4. Switchgrass to ethanol through the biochemical conversion process 

5. Forest residues to ethanol through the thermochemical conversion process. 

Ethanol production technology will advance over time. This is especially true for the three 

cellulosic ethanol production options above. To address technology advancement, life-cycle 

analysis was conducted for each of the cases in 2012 and 2030 separately. 

The life-cycle input parameters were collected from several sources: 

• The petroleum gasoline and corn ethanol cases for 2012 and 2030 used data 

accumulated from previous analyses. 

• The three cellulosic ethanol cases in 2012 used ethanol production process data for the 

2012 design case for both the biochemical and thermochemical case (Aden et al., 

2002) (Spath and Dayton, 2003). 

• The two biochemical conversion cellulosic ethanol cases (corn stover and switchgrass 

to ethanol) in 2030 simulated the case of ethanol production with lignin used in a 

combined-cycle gas turbine for electricity co-generation. (Although technologies such 

as steam boilers can be used for electricity co-generation, combined-cycle gas turbines 

with gasification of biomass residues represent advanced combustion technology. In 

fact, this technology is now being installed in corn-based ethanol plants to generate 

steam and electricity from gasified biomass and distillers dry grains.) 

• The two cellulosic ethanol cases adopted process data generated from the Role of 

Biomass in America’s Energy Future (RBAEF) project for a similar cellulosic ethanol 

case (Wu, Wu, and Wang 2006). 

• The thermochemical cellulosic ethanol case (forest residues to ethanol) in 2030 relied 

on preliminary process data consistent with the thermochemical advanced state of 

technology (Section 4.2.2). 

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GREET life-cycle simulations produced energy and emissions results for each million British 

thermal units (mmBtu) of fuel produced and used. Figure 3-1 shows life-cycle fossil energy use 

for the four ethanol options and gasoline. Fossil energy includes petroleum, coal, and natural gas. 

Relative to gasoline, corn-based ethanol achieves moderate reductions in fossil energy use, but 

the three cellulosic ethanol options achieve significant reductions. Figure 3-2 shows life-cycle 

petroleum use results, which are a subset of the fossil energy use results. As the figure shows, all 

four ethanol options result in huge reductions in petroleum use relative to the petroleum gasoline 

case. Figure 3-3 compares life-cycle GHG emissions of ethanol and gasoline. GHG emissions 

are in grams of carbon dioxide-equivalent emissions of carbon dioxide, methane, and nitrous 

oxide weighted with their global warming-potential factors. As the figure shows, although cornbased ethanol achieves moderate reductions in GHG emissions, cellulosic ethanol achieves very 

large ones. 

Figure 3-1. Life-cycle Fossil Energy Use of Ethanol versus Gasoline 

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Figure 3-2. Life-cycle Petroleum Use of Ethanol versus Gasoline 

Figure 3-3. Life-cycle Greenhouse Gas Emissions of Ethanol versus Gasoline (carbon 

dioxide-equivalent emissions of carbon dioxide, methane, and nitrous oxide) 

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Differences in energy use and GHG emissions exist among the three cellulosic ethanol options. 

For example, there is high fossil energy and petroleum use in forest residue-based ethanol. This 

is because of the use of natural gas and electricity in forest residue ethanol plants (especially in 

2012) and significant diesel fuel use for stumpage, collection, and transportation of forest 

residues. Further, forest residue-based ethanol has similar or reduced GHG emissions relative to 

other cellulosic ethanol options. This is because forest residue-based ethanol is not associated 

with soil nitrous oxide emissions whereas the other two options are. 

3.2 Annual Reductions in Oil Consumption and Greenhouse Gas 

Emissions Caused by Ethanol Use 

In this section, the estimated annual ethanol supplies under the EIA reference case (the low 

ethanol supply case) and the EIA high oil price case (the high ethanol supply case) are used to 

estimate annual reductions in oil use and GHG emissions caused by ethanol use. 

The VISION model, developed at ANL, was used to match ethanol demand by motor vehicles 

with ethanol supply under each case. The VISION model uses vehicle survival and agedependent usage procedures to track vintage-specific vehicle stock and usage. The model 

develops estimates of light- and heavy-duty vehicle stock composition, vehicle miles traveled, 

and energy use. The current version can simulate conventional vehicles and six advancedtechnology vehicles in car and light truck categories (Singh, Vyas, and Steiner 2004). The 

model was calibrated to match EIA’s 2006 Annual Energy Outlook projections, which are 

provided only to 2030. The projections were therefore extended to 2050 through a collaborative 

effort with DOE EERE Office of Planning and Budget Analysis. The VISION model has been 

used extensively by EERE to evaluate the effects of new technology. 

In VISION simulations, we assumed that ethanol will penetrate the gasoline market for the 

light-duty vehicle fleet, including passenger cars, sport utility vehicles, and light-duty trucks. 

The VISION simulations assume that ethanol will gradually penetrate the gasoline market, 

with low-level ethanol blends up to E10 reaching 100% of the gasoline market by 2020. We 

then assumed that the remaining ethanol supply (i.e., the amount that exceeds E10 demand) 

will be used in FFVs in ethanol blends up to E85. Light-duty vehicles have high survival 

probability through their first 10 years. Consequently, FFVs sold in earlier years may not have 

enough fuel ethanol available to operate at 85% volume. In this situation, we assumed FFVs 

would use ethanol blends of less than 85% ethanol or that some FFVs will operate on gasoline 

only, as most FFVs do now. The resulting ethanol consumption will match production 

estimates, but the average ethanol content of FFV fuel may be less than 85%. Furthermore, the 

same gasoline-equivalent fuel economy was assumed for E85 FFVs and gasoline vehicles 

accounting for the lower Btu content of E85. 

With these assumptions, the VISION model estimated that the number of FFVs on the road in 

2050 will reach 207 million under the low ethanol supply case and 244 million under the high 

ethanol supply case, respectively accounting for 52.6% and 62.1% of the total light-duty fleet of 

that year. Annual new FFV sales reach a 50% share of the new light-duty vehicles market in 

2038 under the low ethanol supply case and in 2032 under the high ethanol supply case. 

Historical data of new vehicle technology penetration show these penetration schemes can be 

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achieved without much difficulty. This implies that ethanol supply, not ethanol demand by the 

FFV market, will be the main determinant of ethanol’s share in the transportation fuel market. 

The simulated annual consumption estimates for gasoline and ethanol were used to compute oil 

demand and life-cycle GHG emissions. To accomplish this, we used the life-cycle output of 

petroleum use and GHG emissions from GREET (as presented in Section 3.1). For VISION 

simulations, the split between corn ethanol and cellulosic ethanol was based on supply 

estimates given in Section 2 for both reference oil and high oil (Figures 2-4 and 2-6). For the 

three types of cellulosic ethanol, we assumed that 30% is produced from forest residues, 35% 

from agricultural residues, and the remaining 35% from switchgrass. 

Figure 3-4 shows life-cycle oil use under three scenarios: (1) the base case, (2) the low ethanol 

supply case, and (3) the high ethanol supply case. As shown in the figure, by 2030, the low 

ethanol supply case helps reduce oil use by about 2 million barrels/day, and the high ethanol 

supply case achieves an additional reduction of 0.8 million barrels/day. By 2050, ethanol can 

help reduce oil use by 6.5 to 7.5 million barrels/day. 

The resultant GHG emissions, in million metric tons (MT) carbon dioxide-equivalent per year, 

are shown in Figure 3-5. By 2030, the use of ethanol could reduce GHG emissions by 220–340 

million MT/year. By 2050, the reduction is 960–1,110 million MT/year. For comparison, 

VISION estimated that total life-cycle GHG emissions of the U.S. light-duty vehicle fleet were 

1,530 million MT in 2005. 

Figure 3-4. Life-cycle Oil Use under the Three Cases 

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Figure 3-5. Life-cycle Greenhouse Gas Emissions under the Three Cases 

3.3 Other Potential Environmental Issues 

The successful implementation of this scenario will require sustainable processes that minimize 

impacts on the environment and avoid depleting resources needed in other sectors of society. 

Land use and availability, biodiversity, water resource management, and solid, liquid, and air 

emissions must be considered. In addition, environmental impact analyses must consider 

feedstock production, feedstock collection and storage, biofuels production, and product storage 

and distribution. 

3.3.1 Land 

The biggest land issues are associated with feedstock production. Using long-term, conservative 

production targets of 10 dry tons of biomass/acre with ethanol yields of 100 gallons of 

ethanol/dry ton, approximately 20 million acres of land will be needed to produce the 20 billion 

gallons of cellulosic ethanol from switchgrass using the feedstock allocations assumptions 

stated in Section 3.2. Although this amount of land is only slightly more than 5% of current 

cropland (excluding cropland that is inactive or idle and pasture) of about 340 million acres it is 

still a significant amount of land. If we assume that prime arable land will remain dedicated to 

food and feed, then biomass production will increasingly require sub-prime and marginal land 

of poorer soil quality and insufficient water resources. Lower-quality land will reduce biomass 

crop density and, therefore, increase the infrastructure and costs associated with collection 

(Tuskan et al. 2004). 

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Further, there are potential issues associated with each feedstock type. For example, if inactive 

land enrolled in the Conservation Reserve Program land and undisturbed forests are used for 

energy crops, feedstock production could affect native biodiversity. If agricultural and forest 

residues are primary feedstocks, then large increases in land use could be avoided, but the 

collection of corn stover could negatively affect soil quality. The amount of stover collected 

must be limited to avoid soil depletion and was taken into account in the life-cycle analysis 

presented earlier. On the other hand, perennial crops such as switchgrass reduce fertilizer inputs 

and reduce land and water impacts. 

Another land issue concerns storage and production facilities. Depending on the success of 

densification efforts, the land requirements for crop residue or switchgrass could be substantial. 

(Wood and grain are significantly more dense and therefore do not pose a storage problem. 

Moreover, woody resources can be stored “on the stump.”) Because feedstock collection will be 

conducted in a harvest window but ethanol production will be conducted most of the year, a 

distributed storage system of lower-density biomass will require regular shipments from a 

storage grid (Schlicher 2006). This will increase truck traffic in rural areas. Production 

facilities, in contrast, do not have large footprints. They should have land impact issues only in 

specific cases. Finally, depending on chemical requirements, biomass conversion facilities may 

generate wastes such as gypsum that have little market value and may require land filling. 

3.3.2 Water 

Water management includes the volume and quality of water required for, and released by, 

ethanol production activities. As with land, the biggest water management issues are associated 

with feedstock production. 

The most productive portion of the Corn Belt does not require irrigation. However, extending 

biomass production to marginal lands may require irrigation. For energy crops that cannot be 

diverted to the food and feed sectors, opportunities to use “impaired waters,” such as waters 

produced from electric power plants, could be a net benefit of biomass production. In addition, 

the use of perennial crops that retain their nutrients can reduce releases to water systems. In the 

future, energy crops with the capability to deep root and draw from underground aquifers could 

increase biomass production without requiring additional water resources. 

A related issue is the effect of agricultural fertilizer runoff on local and regional water systems. 

These effects have been well documented, but shifting to hybrid seeds that require reduced 

nitrogen will reduce impact on water quality (Illinois Corn Growers Association 2005). 

In production facilities, biochemical conversion uses significant water. Current grain-to-ethanol 

processes require about three gallons of water for every gallon of ethanol produced (Swain 

2006). But with treatment and recycle, water inputs can be minimized. However, these 

processes will still require some water release and “blowdown” to avoid a buildup of impurities. 

Thermochemical processes do not have significant water requirements and could be net 

producers of water during feedstock drying. In future integrated biorefineries, synergies 

between biochemical and thermochemical processes could greatly reduce water input 

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requirements. For example, thermochemical drying could supply most of the makeup water for 

plants. 

3.3.3 Emissions 

In the emissions arena, biofuel production facilities could have a larger impact than feedstock 

production activities. As ethanol plant size increases, some plants may fall under the category 

of “major pollutant emitter,” which requires increased permitting, monitoring, and other 

oversight. Potential emissions that should be considered include ash and inorganics from 

thermochemical processes, nitrogen oxides, sulfur oxides, and VOCs, and particulate air 

emissions, as presented in Appendix H. 

Emissions and their impact will be strongly dependent on the feedstock, conversion process, 

and facility location. For example, black liquor residues in pulp and paper mills have sulfur 

contents, which could potentially result in significant sulfur oxide emissions. 

3.4 Benefits of the Integrated Biorefinery 

Integrated biorefineries offer opportunities to reduce the environmental impact of biofuel 

production (Energy-Water Nexus 2006). Process integration and intensification will reduce 

energy and water inputs as well as net solid, liquid, and air emissions. In addition, 

improvements in process efficiency will reduce the land required to produce the required 

amounts of feedstock. 

In a sustainable process, biomass will be collected without degrading land, soil, water, or 

biodiversity. In the future, integrated biorefineries will produce ethanol and other products with 

potentially zero liquid discharge, limited air emissions, and the efficient use, recycle, or 

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4. Cellulosic Ethanol Production 

Central to the success of the 30x30 goal is the availability of biomass resources and the 

efficiency with which these resources can be converted into transportation fuels. Aligning 

these components to replace 30% of 2004 motor gasoline demand by 2030 will require, above 

all else, process and technology flexibility. 

The feedstock resource assessment in the “Billion Ton” study (Perlack et al. 2005) validates 

the feasibility of achieving the 30x30 goal, provided R&D efforts overcome technical barriers 

to the development of economically viable biomass conversion technologies. The feedstock 

resource base, however, includes a variety of regionally specific biomass materials with a 

range of chemical and physical properties. Figure 4-1 shows the distribution of feedstock 

resources described in the “Billion Ton” study. 

Figure 4-1. Distribution of Feedstock Resources 

This implies that the conversion technologies of future integrated biorefineries will be a 

function of the available feedstock resources. For this reason, cellulosic ethanol production 

technology must be sufficiently diverse to optimize the conversion of multiple biomass 

resources to fuel. 

In this section, two conversion technologies that can potentially process the projected 

feedstock resources are considered (see Figure 4-2). Both of these routes have several 

variations, but the main difference is in the primary catalysis system. 

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Figure 4-2. Primary Conversion Routes for Cellulosic Biomass 

Biochemical conversion uses biocatalysts (such as enzymes and microbial cells), heat, and 

chemicals to convert biomass first to an intermediate sugar stream and then to ethanol and coproducts such as heat, power, and chemicals. Thermochemical conversion reduces biomass to 

a fundamental chemical building block, syngas (carbon monoxide and hydrogen), that can be 

converted into ethanol and other products through fuel synthesis processes. 

 

For this discussion, forest residues—from small-wood forest thinnings or residues such as 

“hog fuel” from the forest products industry—are considered primarily for thermochemical 

conversion because of their anticipated higher lignin content. Agricultural residues, in 

contrast, are considered primarily for biochemical conversion technologies. These resources 

are expected to have a more uniform chemical composition because they are derived from 

cultivated crops that can be genetically engineered or selected for properties more amenable 

to biochemical conversion technologies (such as low recalcitrance or high cellulose or xylan 

content). This also holds for energy crops. Biomass grown specifically for transportation fuel 

production can be engineered or selected to have the most desirable chemical and physical 

properties for a conversion technology. In addition, increasing the biomass resource base that 

can be biochemically converted to fuels provides an additional resource: lignin-rich 

fermentation residues that can be used for combined heat and power production or converted 

to biofuel in advanced, integrated biochemical-thermochemical biorefineries. 

The 30x30 goal of developing 60 billion gallons of ethanol capacity by 2030 highlights the 

need for optimization of all aspects of biofuels production, including: 

• Conversion technology-specific feedstock resources 

• Feedstock production, storage, and distribution 

• Biochemical conversion technologies 

• Thermochemical conversion technologies. 

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As technology develops, existing conversion routes will mature and others will be developed, 

including some with the ability to reduce the energy expenditure of the overall biomass-toethanol process. Although specific scenarios are considered in this section, it is the 

responsibility of the developing bioenergy industry to use opportunistic resources with 

specific conversion technologies to produce products (such as fuels, chemicals, materials, and 

power) that meet strategic business goals. 

4.1 Feedstocks 

The emerging biorefining industry is dependent on a large and sustainable supply of biomass 

resources provided at an effective cost and quality. The Billion Ton study found that the 

biomass feedstock resource potential in the United States is more than sufficient to meet the 

30x30 goal. About 368 million dry tons of sustainably removable biomass could be produced 

on forest lands, and about 998 million dry tons (including agriculture residues and new 

perennial crops) could come from agricultural lands (see Figure 4-3). This resource potential 

could be available roughly around the mid-21st century. 

Figure 4-3. Agricultural and Forest Lands Resource Potential 

4.1.1 Feedstock Accessibility 

The ethanol price target of $1.07/gallon by 2012 is based on a total delivered biomass 

feedstock cost of $35/dry ton. This 2012 feedstock cost target can be subdivided into a 

$10/dry ton grower payment (or “stumpage fee” for forest resources) to cover the biomass 

value and a $25/dry ton payment for feedstock supply system costs, which include harvest and 

collection, storage, pre-processing, and transportation and handling. The $10/dry ton grower 

payment is much too low to cover production costs for perennial energy crops. It is estimated 

that, from the 1.3-billion-ton potential, as much as 130 million tons could be accessed for a 

grower payment of or less than or equal to $10/dry ton. Therefore, technology advancements 

that reduce feedstock supply system costs to $25/dry ton for each of the major agricultural and 

forestry feedstock resource types will result in a 2012 biomass resource sufficiently large to 

establish both biochemical and thermochemical biorefining capacity in every region of the 

United States (see Figure 4-4). 

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Figure 4-4. Accessible Agricultural and Forest Resources with Biorefinery Capacity 

Potential in 2012 

As the industry expands from grain ethanol to cellulosic ethanol, it is expected that 

agricultural crop residues and forest logging residues will be the first resources developed for 

biorefining. Energy crops will become integrated into the agricultural cropping system as the 

biorefining industry matures and creates demand for them. The increase in energy crop 

production will likely occur as land managers (e.g., farmers and plantation foresters) use the 

additional crop options provided by the biomass energy market to maximize the productive 

capacity and economic returns of the land they manage. Collaborations with USDA and 

regional partners will become critical in the development of sustainable biomass production 

and crop rotation strategies for existing and new biomass resources. 

The expanding use of lignocellulosic biomass resources will create a demand for them, which 

will result in biorefineries paying more to access larger tonnages of more expensive 

feedstocks (i.e., resources that require more than a $10/dry ton grower payment). However, 

feedstock demand will always be limited by the price the biorefining industry can pay while 

remaining competitive in the ethanol fuel market. Initially, government policies and programs 

may be the means to access higher-value feedstocks. Up to and beyond the 2030 time frame, 

technology advancements will reduce biomass processing costs, which will then provide 

increased purchasing power for biorefineries to access higher-value biomass feedstocks. This 

strategy of improving supply and conversion technologies to purchase higher-value 

feedstocks is well-established in other processing and refining industries (Stoppert 2005). 

This combination of policy and technology advancement will develop a U.S. biomass 

resource large enough to support the 30x30 goal (see Figure 4-5). 

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Figure 4-5. Accessible Agricultural and Forest Resources with Biorefinery Capacity 

Potential to Produce 60 Billion Gallons of Ethanol in 2030 

4.1.2 Feedstock R&D Pathway 

Feedstock R&D encompasses all the unit operations necessary to move biomass feedstocks 

from the land to the conversion process of the biorefinery (Cushman et al. 2003) (see 

Figure 4-6). General descriptions of the feedstock R&D pathway, the state of technology, 

and R&D and technology needed to achieve and validate the $35/ dry ton cost target are 

presented in this section. 

Figure 4-6. Supply Schematic for $35/Dry Ton Feedstock 

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Biomass production is the beginning of the feedstock supply chain. It involves producing 

biomass feedstocks to the point of harvest. Production addresses important factors such as 

selection of feedstock type, land use issues, policy issues, and agronomic practices that drive 

biomass yield rates and directly affect harvest and collection operations. 

Harvest and collection encompasses all operations associated with getting the biomass from 

its source to the storage or queuing location. In addition to obvious operations such as cutting 

(or combining, swathing, or logging) and hauling, this often includes some form of 

densification such as baling, bundling, or chipping to facilitate handling and storage. 

Storage and queuing are essential operations in the feedstock supply system. They are used to 

deal with seasonal harvest times, variable yields, and delivery schedules. The objective of a 

storage system is to provide the lowest-cost method (including cost incurred from losses) of 

holding the biomass material in a stable, unaltered form (i.e., neither quality improvements 

nor reductions) until it is called for by the biorefinery. 

Prior to conversion, the feedstock must be pre-processed to physically transform it into the 

format required by the biorefinery. Pre-processing can be as simple as grinding and formatting 

the biomass for increased bulk density or improved conversion efficiency, or it can be as 

complex as improving feedstock quality through fractionation, tissue separation, and blending. 

Transportation generally consists of moving the biomass from the storage location to the 

biorefinery via truck, rail, barge, or pipeline. The system used will directly affect how the 

feedstock is handled and fed into the conversion process. Transporting and handling methods 

are highly dependent on the format and bulk density of the material; this makes them tightly 

coupled to each other and all other operations in the feedstock supply chain. 

Significant advances have been made to transform the feedstock supply process from 

traditional technologies used in smaller distributed livestock, forage, and wood products 

industries to an assembly system specifically designed for the biorefinery industry. However, 

much work remains. 

Feedstock infrastructure development is difficult because equipment, methods, and logistics 

vary not only among resources (e.g., agricultural residues versus forest residues) but also 

among geographic regions (e.g., dry agricultural residues in the West versus wet agricultural 

residues in the Midwest and Northeast). Consequently, the feedstock supply infrastructure 

must be developed for each class of biomass resource. 

These classes, categorized according to feedstock type are: 

• Dry herbaceous (examples: stover, straw, and switchgrass that are harvested at 

<15% moisture dry basis by weight)

Dry herbaceous feedstocks have been the model feedstock for work done to date to 

develop the feedstock infrastructure. Consequently, the dry herbaceous feedstock 

supply infrastructure is well-developed, and significant progress has been made 

toward achieving the $35/ dry ton cost target. 

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• Wet herbaceous (examples: stover and switchgrass that are harvested at > 15% 

moisture dry basis by weight)

The use of wet herbaceous feedstocks is limited by a host of infrastructure barriers. As 

a result, wet feedstock costs are well beyond the $35/dry ton cost target. Because wet 

herbaceous feedstocks represent a significant portion of the overall feedstock resource, 

overcoming these barriers provides the greatest potential to achieve the projected 

tonnage targets. 

• Woody (example: logging residues)

Logging residues have been used for energy in Europe and the United States for nearly 

30 years. As a result, the logging residue supply system is quite mature, and systems 

and methods are already developed to support this industry (Food and Agricultural 

Organization 1976; Hakkila 2004). Because near-term woody feedstock will consist 

largely of logging residues, the infrastructure for this feedstock can be readily adapted 

and validated against resource environment, resource policy, and other regional 

factors. 

• Energy crops (example: switchgrass)

With the exception of production practices, energy crops can be accommodated by 

dry, wet, or woody feedstock supply systems, depending on the specific type and 

geographic region. Thus, the energy crop supply system needs to mirror those of the 

dry and wet herbaceous and woody systems. 

The R&D activity plan for developing and validating a feedstock supply infrastructure 

capable of achieving the $35/dry ton cost target for each feedstock type at the tonnages 

represented in Figure 4-4 above, is shown in Table 4-1. This plan outlines the necessary 

progress that must be supported by the feedstock R&D in order to meet the validation 

milestones shown for the dry (2009), woody (2011), and wet (2012) feedstock supply systems 

and achieve the 2012 technical target. Some of the specific intermediate feedstock R&D 

targets associated with this R&D plan are shown in Table 4-2. Each of these targets addresses 

one of three key factors—equipment capacity, equipment efficiency, or feedstock quality—

affecting feedstock supply system costs. The specific research plan that focuses on the 

application of these cost factors to each of the supply system elements for achieving the 

feedstock R&D targets is described below, with a more detailed description given in 

Appendix D. 

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Table 4-1. Timeline of Key Feedstock R&D Activities to Accomplish the 2012 Technical 

Target 

Completion Year

R&D Area 2006 2007 2008 2009 2010 2011 2012

Production 

(Grower 

Payment 

and Energy 

Crops) 

 Validate and 

characterize by 

region biomass 

value (i.e., 

grower 

payment) 

Establish the 

SE, NC, W, 

SC, and NE 

Regional 

Partnerships 

Identification, 

qualification 

and validation 

of feedstock 

for 1 and 5 B 

gal ethanol 

targets 

Energy crops 

established 

and integrated 

by region 

Site-specific 

residue 

removal 

parameters 

and constraints 

determined 

Regional GISbased biomass 

atlas for 1, 5, 

and 60 B gal 

ethanol 

Feedstock 

Supply 

(Dry 

Biomass) 

Dry biomass 

$35/dt 

analytical 

engineering 

design 

complete 

 Build, 

instrument, 

and test dry 

pilot scale 

assembly 

system 

equipment 

capable of 

validating 

established 

performance 

targets to meet 

$35/dt cost 

target 

Dry biomass 

design target 

validated by 

demonstrating 

the 

achievement of 

all 

performance 

targets for 

each unit 

operation (pilot 

scale 

operation)

 

Feedstock 

Supply 

(Wet 

Biomass) 

 Wet storage 

and 

preprocessing 

technoeconomic 

assessment 

complete 

Wet harvest, 

transportation, 

and handling 

technoeconomic 

assessment 

complete 

Wet biomass 

$35/dt 

analytical 

engineering 

design 

complete 

 Build, 

instrument, 

and test wet 

pilot scale 

assembly 

system 

equipment 

capable of 

validating 

established 

performance 

targets to meet 

$35/dt cost 

target 

Wet biomass 

design target 

validated by 

demonstrating 

the 

achievement of 

all 

performance 

targets for 

each unit 

operation (pilot 

scale 

operation)

Feedstock 

Supply 

(Woody 

Biomass) 

Woody 

biomass 

technoeconomic 

assessment 

complete 

Woody 

biomass $35/dt 

analytical 

engineering 

design 

complete 

Build, 

instrument, 

and test woody 

pilot scale 

assembly 

system 

equipment 

capable of 

validating 

established 

performance 

targets to meet 

$35/dt cost 

target 

Woody 

biomass 

design target 

validated by 

demonstrating 

the 

achievement of 

all 

performance 

targets for 

each unit 

operation (pilot 

scale 

operation)

 

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Production 

Production is a critical component of the feedstock supply system, and it is key to ensuring an 

adequate and sustainable feedstock supply. Through USDA and regional partnership 

collaborations, a number of regionally based, validated assessments of production 

parameters—such as feedstock resource types and potentials, agronomy, crop/resource 

alternatives, and rotations—will be accomplished. Key milestones in this area are shown in 

Table 4-1. 

Specific research needed to address production issues includes: 

• Assessing the cost and availability of the feedstock resource on a local basis to define 

production costs (e.g., grower payments) and identify regional tonnages available 

within each feedstock type or classification at or under the feedstock threshold costs 

• Identifying and validating sustainable agronomic and silvicutlure practices specific to 

feedstock types and regional variables to ensure sustainable production 

• Investigating crop production improvements (e.g., increased yields, decreased yield 

variability, and consistent quality) through genetic modification 

• Developing a perennial crop program that includes matching varieties to site 

conditions, establishing optimum agronomic and silviculture practices, and developing 

a seed production program. 

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Table 4-2. Technical Research Milestones and Validation Targets for Feedstock Cost 

Reductions and Increased Tonnage 

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Harvest and Collection 

The feedstock R&D plan depicted in Table 4-2 shows harvest and collection advances in three 

key areas: (1) selective harvest (including forest thinning operations), (2) single-pass or 

minimum-impact harvest, and (3) harvest and collection efficiencies. The primary drivers for 

improved harvest technologies are reduced costs and access to larger tonnages of biomass 

through increased producer participation. For example, improved harvest technologies that 

address soil quality concerns—such as carbon sequestration, nutrient/water retention, erosion, 

and compaction will become increasingly important for enticing grower participation and 

accessing biomass resources. 

Performance metrics for new harvest and collection systems include (1) efficiency, (2) 

equipment capacity (an element of efficiency that includes technologies that reduce capital 

and improve throughput of equipment), and (3) quality. Without these improvements, the 

accessible biomass tonnage remains restricted. 

Needed research in this area includes: 

• Developing innovative harvest and collection methods for all resource types to 

eliminate or reduce unit operation costs and agronomic silviculture operational impacts 

• Understanding, quantifying, and validating harvesting-specific quality related to 

compositional effects, pretreatment effects, contaminant reductions, and bulk handling 

improvements 

• Developing and testing innovative equipment specific to woody feedstocks for which 

existing equipment is too costly and inefficient. 

Pre-Processing 

Significant R&D advances in dry herbaceous pre-processing have been made that will enable 

the transition from the current state-of-technology bale-based system to a more cost effective 

bulk feedstock system for biorefineries. However, as indicated in the feedstock R&D plan in 

Table 4-2, additional advances are needed in three key areas: (1) pre-processing equipment 

capacity, (2) feedstock bulk density, and (3) feedstock quality. Equipment capacity and bulk 

density directly affect feedstock cost. Thus, they are important technical parameters to 

address, along with the interrelated effect on feedstock rheological properties. Furthermore, a 

key component of feedstock R&D is to extend pre-processing beyond size reduction to 

include value-added operations that improve feedstock quality for the biorefinery. These 

operations involve fractionation and separation of higher-value components. These operations 

are necessary to achieve the feedstock cost targets embedded in the 30x30 target, both in the 

near- term 2012 targets as well as the long-term 2030 volume targets. 

. 

Specific research needed in this area includes: 

• Developing pre-processing requirements for each feedstock type 

• Understanding the relationship between biomass structure and composition for 

assessing quality-upgrade potential and developing equipment and methods to 

achieve these upgrades 

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• Understanding and controlling biomass tissue deconstruction in pre-processing and the 

relationships among grinder configuration, tissue fractions, tissue moisture, and grinder 

capacity to optimize grinder configuration for fractionation, capacity, and efficiency 

• Increasing bulk densities by coupling the understanding of biomass deconstruction and 

rheological properties with innovative bulk compaction methods 

• Understanding and controlling feedstock rheological properties resulting from preprocessing operations to provide a product that minimizes problems in transportation, 

handling, and queuing operations. 

Storage and Queuing 

Feedstock shrinkage (or dry matter loss) and quality reductions are major considerations of 

feedstock storage. Shrinkage and quality reduction risks and mitigation strategies vary widely 

from region to region. 

The core R&D program of DOE’s Office of the Biomass Program (OBP) has demonstrated 

that annual dry matter loss can be as low as 0.85%, but in wetter regions, dry matter loss may 

exceed 25%. To achieve the 2012 cost and 30x30 supply targets, dry matter losses must be 

less than 5% for all feedstock types. 

Specific research needed in this area includes: 

• Assessing storage options and their effects on dry matter losses, compositional 

changes, and functional biomass changes specific to resource type and regional 

variables 

• Establishing baselines of the costs of storage systems at scales from 0.8 million 

tons/year to 10 million tons/year to identify key cost and infrastructure issues and 

develop paths to minimize industrial-scale storage costs 

• Understanding soluble sugar and carbohydrate loss and evaluating the feasibility of 

preventing or reclaiming those soluble sugars and carbohydrates from the feedstock 

during storage 

• Developing cost effective methods of large-scale bulk storage that reduce handling, 

eliminate bulk flowability problems, and minimize adverse physical changes that may 

affect plant processing. 

Transportation and Handling 

Transportation is a significant cost and it can be a barrier to the use of some feedstock 

resources. Transportation and handling operations can account for nearly 50% of the capital 

investment of a feedstock assembly system. Unlike the other unit operations in the feedstock 

supply system that can impart additional value to the feedstock, transportation costs simply 

move the feedstock to the biorefinery. Hence reducing these costs to the minimal practically 

achievable is paramount to achieving the 2012 cost target and the 30x30 supply targets. 

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Regardless of the method of transport (e.g., truck, rail or barge), bulk density is the key 

technical parameter that must be addressed to decrease transportation costs. As such, methods 

to increase bulk density are a focus of the transportation and handling R&D. In addition, bulk 

handling is affected by feedstock rheological properties. This, too, is an area of focus. 

Specific research needed to reduce transportation and handling costs includes: 

• Understanding feedstock physical and rheological properties (including bulk density) 

as they relate to handling systems to optimize handling and transportation efficiencies 

• Evaluating innovative transportation and handling methods. 

Validation and Demonstration 

As dry, wet, and woody supply system technologies are developed and the technical research 

targets (Table 4-2) are achieved, the supply systems will be validated to demonstrate that the 

resources can be supplied at the $35/dry ton cost target. This validation will be accomplished 

in an integrated pilot-scale facility that includes the supply system equipment and unit 

operations necessary to demonstrate the capacities, bulk densities, rheological properties, 

composition, and quality that contribute to the $35/dry ton feedstock cost for dry, wet, and 

woody feedstock systems. 

This validation and demonstration plan is supported by single-point sensitivity analysis to 

relate the cost effects of technical and market parameters on the $35/dry ton feedstock cost 

target. (See Appendix B for a risk assessment methodology description.) The sensitivity of 

each unit operation identified in the feedstock roadmap (Cushman et al. 2003) for all feedstock 

types is shown in Figure 4-7. 

This sensitivity analysis represents delivered feedstock cost ranges, where the mean value is 

based on the 2012 market target of $35/dry ton. The high-end cost ranges, shown in black, 

represent worst-case conditions and operational parameters based on the current state of 

technology. In the cases of storage, pre-processing, and transportation and handling, the highend costs are directly attributed to the moisture content of the biomass at the time of each unit 

operation. For example, the wet storage unit operation is shown to increase the delivered 

feedstock cost by 55% because of 30% dry matter and function yield losses during storage and 

transportation. Likewise, pre-processing costs increase by 45% because of reduced capacities 

in grinding wet biomass, and transportation and handling costs increase by 19% because of 

non-productive water weight. Further, the 17% increase in harvest and collection costs comes 

from reduced machine efficiencies as a result of additional biomass throughput. Finally, the 

increase in production costs (grower payment) stems from conditions not easily controlled or 

mitigated by technology (such as weather, grower business practices and harvesting 

preferences, competing biomass markets, and transportation laws and road limits). 

The cost ranges shown in blue identify potential reductions in the delivered cost of biomass 

feedstocks through advances in infrastructure technologies. Feedstock quality improvements 

account for the 15% and 17% cost reductions in storage and harvest and collection, 

respectively. Storage quality improvements involve capturing some of the lost sugar yield, 

which results from in-storage degradation. The potential cost decrease in the harvest and 

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collection operation is a result of harvest efficiency gains and selectively harvesting 

(separating) the higher-sugar-content component of biomass residues from the lower-sugarcontent components. These separated streams allow for a more uniform biomass feedstock, in 

terms of composition, which for the higher-sugar-content stream, produces more ethanol per 

ton of biomass and, for the lower sugar content stream, requires a less severe pretreatment. The 

cost reductions in pre-processing and transportation and handling, 22% and 4% respectively, 

are a result of improved equipment efficiencies and bulk material properties. The potential to 

achieve these improvements has been shown on reduced scales through laboratory and field 

testing results, vender specifications and equipment performance guidelines, and integrated 

feedstock assembly models. 

A more detailed sensitivity analysis of the technical parameters that affect individual unit 

operations is discussed in Appendix C. 

Figure 4-7. Sensitivity Analysis of Unit Operations in the Feedstock Assembly and 

Associated Variable Costs for All Feedstock Types 

4.2 Conversion Technologies 

4.2.1 Biochemical Process Technology Target for 2012 

4.2.1.1 Introduction 

Basically, biochemical conversion is the fermentation of sugars liberated from biomass 

feedstocks. The challenge is to efficiently convert the carbohydrate portion of the biomass to 

sugars, or “saccharify” it, and ferment the impure sugars to ethanol with a robust microorganism. In this process the lignin component of the biomass provides the heat and power 

needs of the process. This process shows great promise for the cost-effective production of 

ethanol at high yields and with minimal environmental impact. 

There are two primary routes for saccharification: (1) acid hydrolysis, either concentrated or 

multiple stages of dilute, and (2) pretreatment followed by enzymatic hydrolysis. In the 

1980s, DOE evaluated the long-term potential of each process (Wright 1987) and although at 

the time acid hydrolysis technology was further developed and appeared less expensive, a 

comparison of progress and future potential suggested that enzymes offered greater 

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opportunity for ethanol cost reduction in the long run (Sheehan and Riley 2001b). Acid 

hydrolysis technologies are certainly feasible, however, and in proper niche situations they are 

being pursued to commercialization. 

Enzyme hydrolysis requires a pretreatment to generate an intermediate material that can be 

effectively digested by enzymes. Dilute acid pretreatment of corn stover followed by 

enzymatic hydrolysis can achieve more than 90% conversion of cellulose to glucose (Jechura 

2005), compared with around 50% conversion for acid hydrolysis technologies (Zerbe and 

Baker 1987). Various pretreatment methods have been suggested; most use heat coupled with a 

chemical catalyst such as an acid, base, or other solvent. Recent advances (Decker and Selig 

2006) suggest that “accessory” enzyme systems such as hemicellulases could lead to lowseverity and low-cost pretreatment processes in the future. The biochemical conversion route 

using dilute acid as pretreatment was tentatively selected because it has strong potential and is 

one of the better studied options available. However, an assessment of pretreatment 

technologies for best applicability to meeting cost and performance goals will be performed in 

2008 so that possible advancements in alternative pretreatment methods can best be 

accommodated. 

To understand how much more development is required to meet the 2012 cost targets, DOE 

tracks the research state of technology by extrapolating current-year laboratory results to a 

conceptual process design and cost estimate. The design and cost estimates are based on 

engineering company consultations and ASPEN (ASPEN Plus, releases 10.1–12.1, Aspen 

Technology, Inc.) modeling (Aden et al. 2002). The state of technology includes only ethanol 

and excess electricity sales. It does not include any proprietary, company-specific 

enhancements over the baseline conversion technology being investigated by DOE’s core 

biomass research program. 

Figure 4-8 shows the research state of technology advances from 2001 to 2005 and reveals 

what is required to achieve the technical target in 2012. Most of the cost reductions from 2001 

through 2005 were because of DOE-industry partnerships to reduce the cost of enzymes 

(Harris et al. 2006; Mitchinson 2006). As shown in Figure 4-8, the 2012 target was 

accelerated by DOE to accommodate the 2012 $1.07/gallon of ethanol target component of 

the 30x30 goal. The research outlined here addresses the R&D needs to achieve this target. 

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Figure 4-8. Research State of Technology for Biochemical Conversion (Foust et al. 

2006) 

4.2.1.2 Process Description 

The biochemical conversion process selected for this scenario uses co-current dilute acid 

pretreatment, enzymatic saccharification, and fermentation to convert lignocellulosic 

feedstocks to ethanol. The process includes ancillary supporting operations such as feedstock 

interface handling and storage, product recovery, wastewater treatment, residue processing 

(lignin combustion), and product storage. See Figure 4-9. 

Figure 4-9. Process Schematic for Biochemical Conversion 

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The feedstock, initially corn stover and later other agricultural residues and energy crops, is 

delivered to the feed-handling area for size reduction and storage. From there, the biomass is 

conveyed to pretreatment and conditioning. In this area, the biomass is treated with a dilute 

sulfuric acid catalyst (current candidate pretreatment technology for this scenario – see 

Appendix D for a description of other pretreatment approaches) at a high temperature for a 

short time. This hydrolyzes the hemicellulose to a mixture of sugars (i.e., xylose, arabinose, 

galactose, mannose, and a small amount of glucose) and other compounds. In addition, the 

pretreatment step makes the remaining biomass more accessible for later enzyme 

saccharification. A conditioning process then removes byproducts from the pretreatment 

process that are toxic to the fermenting organism. 

In hybrid saccharification and co-fermentation (HSF), the pretreated solids (now primarily 

cellulose) are saccharified with cellulase enzymes to form monomeric glucose. This requires a 

couple of days, after which the mixture of sugars and any unreacted cellulose is transferred to 

a fermenter. An inoculum of fermenting microorganism is added, and all sugars are fermented 

to ethanol. Meanwhile, the enzymes are used for further glucose production from any 

remaining biomass, which is now at conditions optimal to fermentation. After a few days of 

fermentation and continued saccharification, nearly all sugars are converted to ethanol. The 

resulting beer (or low-concentration ethanol) is sent to product recovery. 

Product recovery involves distilling the beer to separate the ethanol from water and residual 

solids. An azeotrope of water and ethanol is purified to pure ethanol using vapor-phase 

molecular sieves. Solids from the distillation bottoms are separated and sent to the boiler 

(called residue processing). Distillation bottoms liquid is then concentrated by evaporation 

using waste heat. The evaporated condensate is returned to the process, and the concentrated 

syrup is sent to the burner. 

Part of the evaporator condensate, along with other wastewater, is treated by anaerobic and 

aerobic digestion. The biogas (which is high in methane) from anaerobic digestion is sent to 

the burner for energy recovery. The treated water is suitable for recycle and returned to the 

process. 

The solids from distillation, the concentrated syrup from the evaporator, and biogas from 

anaerobic digestion are combusted in a fluidized bed combustor to produce steam for process 

heat. The majority of the steam demand is in the pretreatment reactor and distillation areas. 

Generally, the process co-generates electricity for use in the plant and for sale to the grid. 

4.2.1.3 R&D Needs To Achieve the 2012 Technical Target 

The R&D required to meet the 2012 technical target is outlined in Table 4-2. It is important to 

note that technology advancement must be verified at the pilot scale as well as at the 

laboratory bench scale. By 2012, the technology, and subsequently the data for calculating 

costs, will be generated from integrated pilot plant runs. 

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Accomplishing the 2012 goal requires additional technology advancement in key areas of the 

dilute acid and enzymatic hydrolysis process (Foust et al. 2006). 

Figure 4-10 shows the technical barrier areas that must be addressed by R&D for the 

individual unit operations to meet the 2012 target. Table 4-3 provides quantitative targets that 

must be achieved at various stages of the development process to achieve the cost target of 

$1.07/gallon by - 2012 target. As stated earlier, this process scenario is specific to dilute acid 

pretreatment, for a description of possible contributions of other pretreatment technologies 

and contingency plans, see Appendix D. The following sections will describe the R&D 

approach to meeting the targets by the dates specified in Table 4-3 

Figure 4-10. Process Flow Diagram Highlighting Major Research Barriers 

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Table 4-3. Timeline of Key Activities to Accomplish the 2012 Technical Targ

Completion Year 

R&D Area Current 2007 2008 2009 2010 2011 Feedstock Interface 

Corn stover Determine which 

feedstock types will be used in pioneer plants and have the potential to provide significant volumes (>100 million tons/year) 

Develop/adapt dry corn stover analytical methods for diverse samples (>3) of another feedstock type (e.g. switchgrass) achieving mass balance closure of 100% ± 5% 

Define the relationships between variations in the feedstock composition and key processing parameters 

Develop/adapt dry corn stover analytical methods for diverse samples (>3) of another feedstock type (e.g. ensiled corn stover) achieving mass balance closure of 100% ± 5% 

Define the relationshipbetween variations in feedstock composition key processparameters 

Pretreatment 63% xylan yields 

and 13% sugar degradation in continuous reactor with > 30% solids from corn stover 

1) Achieve 75% xylose yield in laboratory scale high solids pretreatment reactor on corn stem internode 2) Define the relationships between pretreatment conditions and the chemical/ultrastructural changes in corn stover stems that result in biphasic xylan hydrolysis 

Validate > 75% xylan yield & < 8% degradation from corn stover using a continuous reactor 

1) Understand sugar degradation kinetics & how to reduce degradation to < 6% for corn stover 2) Test accessory enzymes' effect on reducing pretreatment costs for corn stover in lab equipment 

1) Achieve > 85% xylan yields and < 6% sugar degradation from corn stover using a continuous reactor with > 30% solids 2) Make final decision on pretreatment process to use in 2012 pilot operation 

Provide benscale pretreatmeon two othefeedstocks switchgrassensiled corstover) to dcost correlaagainst the stover base

Hydrolyzate Conditioning 

13% sugar losses in overliming conditioning step on corn stover 

Define the relationships between corn stover hydrolyzate conditioning and fermentation performance in lab equipment 

Reduce sugar losses in corn stover hydrolyzate conditioning step to < 7% in laboratory equipment (accessory enzymes is one option) 

Reduce sugar losses in corn stover hydrolyzate conditioning step to < 2% in laboratory equipment 

Provide benscale condidata on two feedstocks switchgrassensiled corstover) to dcost correlaagainst the stover base

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Enzyme Production 

$0.32/gallon of ethanol 

Develop the first generation computational model of CBH I capable of describing structure and function, and verify CBH I structure 

1) Baseline commercial cellulases' specific activity 2) Determine how cellulase enzymes move along cellulose chains 

Conduct targeted substitutions of cellobiohydrolase to increase specific activity by 2-fold relative to native 

1) Define cellulase interactions at the plant cell wall 2) Validate the cost contribution of purchased enzyme at $0.16/gal EtOH 

Validate the contribution purchased enzyme at $0.10/gal E

Enzymatic Saccharification and Fermentation 

> 85% Cellulose to EtOH, > 75% xylose to EtOH, 0% other sugars to EtOH in a total of 7 days in lab equipment with > 20% total solids from corn stover 

Demonstrate > 85% cellulose to EtOH, > 80% xylose to EtOH, > 40% other sugars to EtOH in a total of 7 days in lab equipment with > 20% total solids 

Define the relationships between lignin redeposition and enzyme kinetics 

Demonstrate > 85% cellulose to EtOH, > 80% non-glucose sugar to EtOH in a total of 5 days in lab equipment with > 20% total solids 

1) Demons85% celluloEtOH, > 85non-glucossugar to EtOa total of 3 lab equipmwith > 20% solids 2) Develop scale SSF don two othefeedstocks (switchgrasensiled corstover) to dcost correlaagainst the stover base

Integration/Modeling 

Research state-oftechnology utilizing current data and modeling shows a $2.26/gal ethanol selling price with total capital of $3.04/gal of annual installed capacity for corn stover 

Complete rapid analysis method to predict component concentrations in pretreated slurry stream to same accuracy as wet chemistry methods 

Completed biochemical pilot facility with 2 or more pretreatment trains and occupancy formally handed over to operating entity 

1) Biochemical pilot facility shakedown completed (For NREL, demonstrated by successful operation of new pretreatment train(s) on corn stover) 2) Process cost estimate updated with latest data and engineering consultations 

Provide equipment operating cfor the corn $1.07/gal bto develop correlations other feeds
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Feedstock/Process Interface R&D Needs 

The feedstock/process interface targets are to: 

• Determine the sensitivity of the overall process to differences in feedstock type and 

quality 

• Determine how to adjust and modify the process to accommodate changes in 

feedstock. 

The research goals are to: 

• Understand the range of feedstock types expected to be used in pioneer plants 

• Work with feedstock suppliers and researchers to improve the quality (physical and 

chemical characteristics) of the feedstocks 

• Determine the impacts of the feedstocks on downstream unit operations 

• Understand how to adjust the process to maintain optimal yields and productivities 

with varying feedstock quality or different feedstocks. 

Pretreatment and Hydrolyzate Conditioning R&D Needs 

The first pretreatment and hydrolyzate conditioning target is to increase the xylan-to-xylose 

conversion to 90% while reducing the xylan lost to degradation products to 5% and 

maintaining or increasing the solids loading of 30% in a continuous pilot-scale reactor. 

The research goals are to: 

• Determine the location of the xylan in the plant cell wall and optimize pretreatments 

that selectively remove and hydrolyze it to xylose 

• Reduce sugar degradation to minimal levels by understanding the kinetic 

mechanisms that lead to undesirable degradation products and then systematically 

blocking these pathways 

• Down-Select one primary pretreatment technology for further development. 

The second target is to reduce the capital cost of pretreatment through the use of ancillary 

enzymes. The research goal here is to determine if other enzymes, such as xylanases, can 

improve xylose yields, minimize the formation of degradation products, reduce costs 

associated with the pretreatment process by reducing severity, and reduce the need for 

conditioning. 

The final target is to eliminate or greatly reduce the need for conditioning. 

The research goals are to: 

• Understand the role of hydrolyzate conditioning to eliminate sugar losses 

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• Understand and control the degradation kinetics to minimize or eliminate the 

formation of inhibitory compounds. 

Enzyme Production R&D Needs 

The enzyme production target is to reduce purchased enzyme cost to $0.10/gallon of ethanol 

produced for a 90% conversion of cellulose to glucose within 3 days in an HSF system. 

The research goals are to: 

• Understand cellulase interactions at the plant cell wall ultrastructural level to optimize 

hydrolysis processes, enzyme kinetics, and, ultimately, cellulase use and cost 

• Determine how cellulase enzymes move along the cellulose chain and the roles of 

enzyme substructures 

• Conduct targeted substitutions of enzyme components to increase specific activity 

guided by molecular modeling of cellulase/substrate interactions 

• Identify enzyme production processes and logistics to minimize processing and 

transportation costs of enzyme products. 

Enzymatic Saccharification and Fermentation R&D Needs 

The first enzymatic saccharification and fermentation target is to develop a robust, 

commercially viable biocatalyst (or micro-organism) capable of fermenting 85% of 

hemicellulose sugars and 95% of glucose to a concentration of at least 6% ethanol in three 

days in combined hybrid saccharification and fermentation. 

The research goals are to: 

• Identify strain candidates that exhibit superior “wildtype” performance 

• Use metabolomics, proteomics, and other tools to understand metabolic bottlenecks in 

the carbon assimilation pathways that limit pentose sugar uptake and the ability to 

withstand fermentation inhibitors such as organic acids, low pH, and increased 

temperature 

• Extend “omics” studies to identify and understand secondary pathway limitations 

related to reaction cofactors and regulation of metabolism 

• Increase pentose uptake rates by applying protein and metabolic engineering to 

increase sugar transporter efficiency, pentose specificity, and expression 

• Improve strain robustness by manipulating cell membrane composition to reduce its 

permeability to organic acids and improve its temperature stability 

• Use a combination of metabolic engineering, mutagenesis, and long-term culture 

adaptation strains on actual pretreatment hydrolyzate to achieve targeted fermentation 

performance 

• Perform parametric analysis of such factors as lignin redeposition and the detrimental 

effects this can have on enzyme kinetics to minimize these effects. 

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The second target is to develop an HSF process capable of saccharifying 90% of cellulose to 

glucose and fermenting 85% of hemicellulose sugars and 95% of glucose to ethanol in three days 

while maintaining the solids concentration necessary for the ethanol concentration target above. 

The research goals are to: 

• Use information about the enzyme capabilities and fermenting strain’s performance to 

develop and test strategies for efficiently integrating enzymatic hydrolysis with 

biomass sugar fermentation to maximize cellulose hydrolysis and sugar fermentation 

rates and yields 

• Quantify the effects of enzyme loading, strain inoculation time, and inoculum charge 

on batch process performance 

• Use reactor designs and operational schemes to maximize the solids loading and 

conversion of cellulose and other sugars to ethanol. 

Integration/Process Engineering R&D Needs 

The integration/process engineering targets are to: 

• Optimize key unit operations, pretreatment, hybrid saccharification and fermentation, 

product recovery, and residue processing (separations only) in an integrated pilot plant 

with appropriate recycles 

• Obtain the data necessary to update the process design and cost estimate to validate the 

2012 technical target. 

The research goals are to: 

• Set up all unit operations in a safe, integrated system capable of continuous operation 

— 24 hours per day, 7 days per week — with data-gathering capabilities 

• Develop analytical methods and equipment to monitor the process and collect data 

• Test the integrated process and optimize conditions to maximize performance 

• Use data from the operating pilot plant to complete a conceptual full-scale process 

design and cost estimate to validate the 2012 technical target. 

As shown, reaching the 2012 technical target for biomass conversion requires a specific set of 

research targets. As outlined in Table 4-2 missing any one of these targets would mean 

missing the 2012 target. However, other combinations of research results could result in 

achieving the 2012 technical target of $1.07/gallon ethanol. Hence continuous rigorous R&D 

progress tracking with appropriate contingency plans coupled with risk assessment needs to 

be a critical component of implementing this research plan (Appendix E). 

 In addition, there is more sensitivity ( a larger benefit if exceeded or a bigger detriment if not 

met) to some research activities than others. A set of ethanol selling prices were calculated 

using variations of research targets to understand the effect of missing or exceeding targets. 

These variations are based on the judgment of researchers in the field. In some cases, the low 

value is what has been accomplished. Figure 4-11 illustrates the effect on calculated selling 

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price of a range of research results and parameters. The figure includes only the targets that 

exhibit a sensitivity range greater than $0.10/gallon. (See Appendix E for additional 

sensitivities.) The figure illustrates that: 

• Some targets will have significant effects on the final outcome 

• If some targets are exceeded and others are missed, the final target selling price of 

$1.07/gallon can still be achieved. 

Figure 4-11. Research Outcomes and Variables with Potentially Large Effects on the 

2012 Technical Target 

4.2.2 Thermochemical Process Technology Target for 2012 

4.2.2.1 Introduction 

Thermochemical conversion technology options include gasification and pyrolysis. Although 

both routes show considerable long-term promise, gasification is more suitable for alcohol 

fuels production and shows considerable near-term promise for economic competitiveness. 

Therefore, gasification is the chosen thermochemical conversion route to meet the 2012 

technology target. As stated in Section 4.2, the thermochemical process to alcohol fuels adds 

technology robustness to this 30x30 scenario in converting the required portion of the 

feedstock based to ethanol in that it can more easily convert low-carbohydrate, or “nonfermentable,” biomass materials such as forest and wood residues to alcohol fuels than the 

biochemical conversion process route. This section describes the R&D needed to achieve the 

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2012 market target production price for a standalone biomass gasification/ mixed alcohol 

process. 

In the long-term advanced technology scenarios rely on yield enhancements achieved by 

combining biochemical and thermochemical conversion technologies in an integrated 

biorefinery. Integrated biorefineries will thermochemically convert lignin-rich bioconversion 

residues into biofuel to maximize the liquid fuel yield per delivered ton of biomass. Once the 

technology has been proven in a standalone manner and the biochemical facilities become 

large enough to provide sufficient feed, integrated biochemical and thermochemical facilities 

will be possible. 

Biomass gasification converts heterogeneous feedstock supplies into a consistent gaseous 

intermediate that can be converted to liquid fuels. The product gas (called “synthesis gas” or 

“syngas”) has a low to medium energy content (depending on the gasifying agent) and 

consists mainly of carbon monoxide, hydrogen, carbon dioxide, water, nitrogen, and 

hydrocarbons. Minor components, also referred to as contaminants, include tars, sulfur and 

nitrogen oxides, alkali metals, and particulates. These contaminants threaten the success of 

downstream syngas to liquid fuels conversion and must either be reformed or removed. For 

this scenario an indirect gasification technology is used (Spath et al. 2005) predominantly 

because of the lower capital cost of indirect gasification compared to direct gasifier 

technology (Appendix F). 

Commercially available and near-commercial syngas conversion processes were evaluated on 

technological, environmental, and economic bases by Spath and Dayton (Spath and Dayton 

2003). Their report provides the basis for identifying promising, cost-effective fuel synthesis 

technologies that maximize the impact of biomass gasification. For the purpose of this scenario, 

a pre-commercial, mixed-alcohols synthesis process that implements an alkali-promoted 

molybdenum disulfide catalyst, a variant of Fischer-Tropsch synthesis, was selected as the 

technology of choice because high yields of ethanol are possible with targeted R&D 

advancements. The sulfided molybdenum catalyst is also tolerant of low levels of sulfur gases, 

which are common catalyst poisons. Appendix F contains a detailed evaluation of other mixedalcohol synthesis catalyst options. 

Conceptual designs and techno-economic models were developed for a standalone biomass 

gasification process with thermochemical ethanol production via mixed-alcohols synthesis. 

The models were developed to determine how overcoming technical barriers could contribute 

to reductions in finished ethanol costs (Aden and Spath 2005). For example, the proposed 

mixed-alcohol process does not produce ethanol with 100% selectivity. The production of 

higher normal alcohols (e.g., n-propanol, n-butanol, and n-pentanol) is unavoidable. 

Fortunately, the byproduct higher alcohols have value as commodity chemicals and fuel 

additives as well as mixed-alcohols fuels. Therefore, two thermochemical ethanol scenarios 

and their economic ramifications were considered. 

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1. Separating the higher alcohols from the mixed-alcohol product and selling them at a 

percentage of their chemical market value 

2. Selling the unseparated mixed-alcohol stream as fuel at a btu adjusted basis with 

ethanol. 

Both scenarios achieve the $1.07/gallon (2002 dollars) (Jechura, Thermochemical Design 

Report, 2007) market target. However, the case with the higher alcohols separated and sold 

into their respective markets provides the initial economic benefit to accelerate deployment of 

these thermochemical technologies by making the first plants economically more attractive. 

The conceptual process design and ethanol production cost estimate quantify the benefits of 

meeting R&D goals for tar reforming and improved mixed-alcohol catalyst performance, as 

shown in Figure 4-12. The current case design defines today’s R&D state of technology, 

particularly with regard to removal/conversion of tars and literature data for mixed-alcohol 

synthesis catalyst performance. The schedule for improved tar reforming and mixed-alcohol 

synthesis catalyst performance was accelerated by the 30x30 scenario to achieve $1.07/gallon 

thermochemical ethanol by 2012. 

Figure 4-12. Research State-of-Technology Assessments for Thermochemical Ethanol 

Production to Reach the $1.07/Gallon Market Target (Foust et al. 2006) 

4.2.2.2 Process Description 

Figure 4-13 shows a block process flow diagram of the thermochemical process necessary to 

reach the $1.07/gallon market target and major technical barriers to the target case. The 

feedstock interface addresses the main biomass properties that affect the long-term technical 

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and economic success of a thermochemical conversion process: moisture content, fixed 

carbon and volatiles content, impurity concentrations, and ash content. High moisture and ash 

content reduce the usable fraction of delivered biomass. Therefore, maximum system 

efficiencies are possible with dry, low-ash biomass. 

Figure 4-13. Process Flow Diagram with Research Barriers for Thermochemical 

Ethanol Production at $1.07/Gallon 

Biomass gasification is a complex thermochemical process that begins with the thermal 

decomposition of a lignocellulosic fuel. This is followed by partial oxidation of the fuel with a 

gasifying agent—usually air, oxygen, or steam—to yield raw syngas. The raw gas 

composition and quality are dependent on a range of factors, including feedstock composition, 

type of gasification reactor, gasification agents, stoichiometry, temperature, pressure, and the 

presence or lack of catalysts. 

Gas cleanup is the removal of contaminants from biomass gasification product gas. It 

generally involves an integrated, multi-step approach, which varies depending on the intended 

end use of the product gas. However, gas cleanup normally entails removing or reforming tars 

and acid gas, ammonia scrubbing, capturing alkali metal, and removing particulates. Gas 

conditioning is the final modification to gas composition to make it suitable for a fuel 

synthesis process. Typical gas conditioning steps include sulfur polishing (to reduce levels of 

hydrogen sulfide to acceptable amounts for fuel synthesis) and water-gas shift (to adjust the 

final hydrogen-carbon monoxide ratio for optimized fuel synthesis). 

Comprehensive cleanup and conditioning of the raw biomass gasification product gas yields a 

“clean” syngas composed of carbon monoxide and hydrogen, in a given ratio. This gas can be 

converted to a mixed-alcohol product. The separation of ethanol from this product yields a 

methanol-rich stream that can be recycled with unconverted syngas to improve process yield. 

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The higher-alcohol-rich stream yields byproduct chemical alcohols. The fuel synthesis step is 

exothermic, so heat recovery is essential to maximize process efficiency. 

4.2.2.3 R&D Needs To Achieve the 2012 Technical Target 

The 2007–2012 R&D activities essential to overcoming technical barriers and meeting the 

2012 technical target for thermochemical ethanol production are outlined in Table 4-4. These 

activities include fundamental kinetic measurements, micro-activity catalyst testing, benchscale thermochemical conversion studies, pilot-scale validation of tar-reforming catalyst 

performance, and pilot-scale demonstration of integrated biomass gasification mixed-alcohol 

synthesis. Process data collected in integrated pilot-scale testing will provide the basis for 

process optimization and cost estimates to guide technology deployment. 

Feedstock/Process Interface R&D Needs 

Because the 30x30 scenario envisions mixed-alcohol conversion of low-grade or “nonfermentable” feedstocks, refinements in dry biomass feeder systems will be required to meet 

cost targets. These refinements should reduce up front feed-processing requirements to yield 

biomass feedstocks at $35/ton at less than 15% moisture (dry basis) by weight delivered to the 

gasifier. Additional challenges that will be associated with feeding the biomass into 

pressurized biomass gasification systems are discussed in Appendix F. 

Gasification Studies R&D Needs 

The thermochemical mixed-alcohol synthesis conversion route is envisioned initially for 

forest resources and other low-carbohydrate feedstocks and residues. Hence, gasification 

studies will need to determine how feedstock composition affects syngas composition, 

quality, and efficiency. The gasifier technology chosen for this analysis is the Battelle 

Columbus Laboratory indirectly heated gasifier; however, other gasifier technologies could 

prove more promising (see Appendix F). These technologies must be tracked to ascertain their 

applicability to the mixed-alcohol synthesis process. 

Cleanup and Conditioning R&D Needs 

Techno-economic analysis (Aden and Spath 2005) has shown that removing chemical 

contaminants such as tar, ammonia, chlorine, sulfur, alkali metals, and particulates has the 

greatest effect on the cost reduction of mixed-alcohol synthesis. To date, gas cleanup and 

conditioning technologies are unproven in integrated biorefinery applications. The goal is to 

eliminate tar removal and disposal via water quench, which is problematic from efficiency 

and waste disposal perspectives, and develop a consolidated tar and light hydrocarbon 

reforming case. 

Current laboratory-scale demonstration results and target conversions for impurities in 

biomass-derived syngas are listed in Table 4-3. The goal conversions were selected to yield an 

economically viable, clean syngas suitable for a catalytic fuel synthesis process without 

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Table 4-4. Thermochemical Ethanol (Gasification/Mixed Alcohols) R&D Targ

R&D Area Current 2007 2008 2009 2010 20

Feedstock Interface 

$30/dry ton wood chips 50% moisture dried to 12% - 2000 tpd plant 

$30/dry ton bio

residues baseton corn stovemoisture dried 2000 tpd plant 

Thermochemical Conversion - Gasification 

Wood chips (model) - Indirect (atm) gasification – 78% syngas efficiency: H2/CO = 1.0-1.5 

CH4≤15vol% 

Tars ≤30 g/Nm3

benzene ≤ 1vol% 

H2S = 50-600 ppm 

NH3 and HCl to be 

determined 

Biorefinery residues - Indirect (atm) gasification : corn stover; switchgrass; wheat straw; lignin - 78% syngas efficiency: H2/CO = 1.0-1.5 

CH4≤15vol% 

Tars ≤30 g/Nm3; benzene 

≤ 1vol% H2S = 50-600 ppm 

NH3 and HCl to be 

determined 

Demonstrate biomass gasification for $6.88/MMBtu syngas cost based on 2007 

Indirect (atm) gasification –78% syngas efficiency: H2/CO = 1.0-1.5 CH4≤8vol% 

Tars ≤10 g/Nm3; 

benzene ≤ 0.1vol%; H2S 

≤ 20 ppm; NH3 and HCl to be 

determined 

Demonstrate biomass gasification for $5.25/MMBtu syngas cost 

Indirect (atm) gasification – efficiency: H2/CO = 1.0 

CH4 ≤ 5vol% 

Tars ≤ 1 g/Nm

0.04 vol%; H2S 

NH3 and HCl t

determined 

Cleanup and Conditioning 

Cyclone particulate removal H2S ≥ 50 ppm (based on 

feedstock) with no S removal Tar Reformer Efficiency CH4 ≥20% 

Benzene ≥70% heavy tars ≥95% (79% CH4 conversion in 

separate SMR) 

Sorbent injection to maintain H2S levels ≤ 50 

ppm for syngas from biomass to reduce sulfur deactivation of tar reforming catalysts. 

Tar Reformer Efficiency CH4 ≥50% 

Benzene ≥90% heavy tars ≥97% (79% CH4 conversion in 

separate SMR) 

Improve tar reforming catalyst performance - Regen/TOS ratio ≤ 600 

Tar Reformer Efficiency CH4 ≥80% 

Benzene ≥99% heavy tars ≥99.9% Eliminate SMR; highest activity re-gained by regenerating deactivated catalyst 

Improve tar recatalyst perforRegen/TOS ra

Catalytic Fuels Synthesis (Mixed Alcohols) 

H2/CO = 1.2 

Pressure ≤ 2000 psia Productivity = 100-400 gMA/kg(cat)/hr EtOH Selectivity ≥70% 

(CO2-free) 

H2/CO ≤ 1.2 

Pressure ≤ 2000 psia Productivity ≥ 150 gMA/kg(cat)/hr EtOH Selectivity ≥70% (CO2-free) 

Demonstrate 500 hours catalyst lifetime at 2007 performance with bottled syngas for mixed alcohol catalyst cost of ≤$0.50/gal EtOH 

H2/CO ≤ 1.0 

Pressure ≤ 1500 psia Productivity ≥ 300 gMA/kg(cat)/hr EtOH Selectivity ≥75% (CO2-free) 

Demonstrate 500 hours catalyst lifetime at 2009 performance. with biomass syngas for mixed alcohol catalyst cost of ≤ $0.22/gal EtOH 

H2/CO ≤ 1.0 

Pressure ≤ 10

Productivity ≥ gMA/kg(cat)/hEtOH Selectiv

(CO2-free) 

Integration and Modeling 

Research state-oftechnology - 56 gal/dry ton EtOH $2.02/gal minimum EtOH selling price (higher alcohols sold at 85% of market value) at $2.71/gal installed capital costs. 

Biomass 

Gasification/Mixed Alcohol Design Report - 

Establishes a cost and quality baseline for technology improvements for $1.07/gal thermochemical ethanol by 2012 from indirect biomass gasification through a clean syngas intermediate. 

Improved hydrocarbon conversion efficiency yields- 56 gal/dry ton EtOH $1.73/gal minimum EtOH selling price (higher alcohols priced as gasoline on an energy adjusted basis - $1.15/gal) at $2.69/gal installed capital costs. 

Validated $1.73/gal EtOH for integrated Cleanup & Conditioning + Mixed Alcohol synthesis 

Demonstrate feasibility of system (8000 hr on stream with ≤10% catalyst losses per year) based on regenerating fluidizable tar reforming catalyst to eliminate SMR 

Validated $1.3for integrated Condtioning + Alcohol synthe

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The research target will be met when tar and light hydrocarbons are sufficiently converted to 

additional syngas to technically validate the elimination of an additional steam methane-reforming 

unit operation. Specific research needed for the design and demonstration of a regenerating tarreforming reactor for long-term, reliable gas cleanup and conditioning includes: 

• Performing tar deactivation/regeneration cycle tests to determine activity profiles to 

maintain the required long-term tar-reforming catalyst activity 

• Performing catalyst studies to determine deactivation kinetics and mechanisms by 

probing catalyst surfaces to uncover molecular-level details 

• Determining optimized catalyst formulations and materials at the pilot scale to 

demonstrate catalyst performance and lifetime as a function of process conditions 

and feedstock. 

Although consolidated tar and light hydrocarbon reforming tests performed with nickel-based 

catalysts have demonstrated the technical feasibility of this strategy, alternative catalyst 

formulations can optimize reforming catalyst activity, lifetime, and functionality. Specific 

research needed to realize improvements in catalyst functionality include: 

• Designing catalysts with higher tolerances for sulfur and chlorine poisons to enable 

further process intensification 

• Lowering or eliminating the sulfur and chlorine removal cost prior to reforming to 

achieve further reductions in gas cleanup costs 

• Optimizing the water gas shift activity of reforming catalysts to reduce or eliminate the 

need for an additional downstream shift reactor. 

Catalytic Fuels (Mixed-Alcohol) Synthesis R&D Needs 

The commercial success of mixed-alcohol synthesis has been limited by poor selectivity and 

low product yields. Single-pass yields are on the order of 10% syngas conversion (38.5% 

carbon monoxide conversion) to alcohols, with methanol typically being the most abundant 

alcohol produced (Wender 1996; Herman 2000). For mixed-alcohol synthesis to become an 

economical commercial process, improved catalysts are needed (Fierro 1993). Improvements in 

mixed-alcohol synthesis catalysts could increase alcohol yields and the selectivity of ethanol 

production from clean syngas, as well as improve the overall economics of the process through 

better heat integration and control and fewer syngas recycling loops. 

Specific research needed to achieve the $1.07/gallon 2012 market target case includes: 

• Developing improved mixed-alcohol catalysts that increase the single-pass carbon 

monoxide conversion from 38.5% - 50% (and potentially higher) and improve the 

carbon monoxide selectivity to alcohols from 80% - 90% 

• Developing improved mixed-alcohol catalysts with higher activity that require a lower 

operating pressure (1,000 psia compared with 2,000 psia) to decrease process operating 

costs. (The combination of lower syngas pressure for alcohol synthesis and less 

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unconverted syngas to recompress and recycle has the added benefit of lowering the 

energy requirement for the improved synthesis loop.) 

• Exploring alternative mixed-alcohol synthesis reactors and catalysts. (Greatly improved 

temperature control of the exothermic synthesis reaction has been demonstrated to 

improve yields and product selectivity. Precise temperature control reactor designs need 

to be developed for the mixed-alcohol synthesis reaction to improve the yields and 

economics of the process.) 

Integration/Demonstration Needs 

For any sophisticated conversion process, combining individual unit operations into a 

complete, integrated, systematic process is a challenge. To demonstrate the $1.07/gallon 

technology, individual pilot-scale operations and complete, integrated pilot development runs 

will be required. A specific challenge is to continue to demonstrate process intensification and 

higher yields at pilot scale to reduce capital costs. 

Achieving the technical target for the accelerated path to thermochemical ethanol requires 

meeting the research targets outlined above. Missing or delaying any of these targets forfeits 

the 2012 target and jeopardizes technology deployment to meet the 30x30 goal. The cost 

implications of missing, hitting, or exceeding a target or set of targets are determined with 

process uncertainty analysis. Figure 4-14 shows the results of a single-point uncertainty 

analysis based on 2012 thermochemical ethanol technology. The uncertainties in the figure 

show the range of ethanol costs around the $1.07/gallon target. 

Figure 4-14 does not provide an exhaustive list of uncertainties but rather focuses on those 

thought a priori to have the greatest effects. The market and financial uncertainties examined 

here are essentially the same as those explored for the biochemical process. The analysis shows 

that the effect of certain process variables is less than expected (e.g., the relatively small effect 

of reforming catalyst lifetime). 

Combinations of sensitivity analyses can reveal several ways to achieve the same $1.07/gallon 

cost target, which reduces the overall risk of the process. Quantifying the relative cost savings 

for process improvements allows work to be directed to the most cost-effective R&D to achieve 

the 2012 technical target for thermochemical ethanol production. 

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68 

($0.50) ($0.25) $0.00 $0.25 $0.50 $0.75 $1.00 $1.25 $1.50

Co-Product Values (69% Chemical Market:baseline:Fuel Oil Value)

Plant Size (10,000:2000:600 dry tonnes/day).

Total Project Investment (-10%:baseline:+30%)

Average Installation Factors (-10%:baseline:+30%)

Contingency (0%:3%:15% of TIC)

Return on Investment (0%:10%:30%)

Loan vs. Equity Financing (100% debt @ 7.5%:100% Equity:100% Equity)

Feedstock Cost ($10:$35:$53 per dry ton)

Feedstocks (Lignin:wood:Corn Stover)

Feedstock Quality - Ash (1%:1%:12%)

Sulfur Content (Baseline to 4X)

Feed Moisture Content (15%:50%:70%)

Reduced CH4 to CO (baseline:baseline:25%)

Olivine cost (1/10:baseline:10X)

Tar Reformer Methane Conversion (95%:80%:50%)

Tar Reformer Benzene Conversion (99.9%:99%:90%)

Tar Reformer Tar Conversion (99.9%:99.9%:95%)

Combined Tar Reformer Conversions

Tar Reformer Equipment Costs (-10%:baseline:+100%)

Acid Gas Removal Costs (-10%:baseline:+100%)

Level of CO2 removal (10:5:0.1 mol%)

Single Pass CO conversion (80%:60%:30%)

CO Selectivity to Alcohols (95%:90%:70%)

Operating Pressure (800:1000:2,000 psia)

Total Alcohol Catalyst Productivity (1,000:600:200 g/kg-cat/hr) 

Catalyst Lifetime (10 yrs:5yrs: 1 yr)

Catalyst Poison Allowability (100:50:10 ppm)

Catalyst cost ($2.50:$5.25:$2,250 per lb)

Recycling Unconverted Syngas to Synthesis Reactor (25%:0%:0%)

Change to MESP ($ per gallon Ethanol)

Research

Financial / Market

Figure 4-14. Thermochemical Process Sensitivity Analysis 

4.2.3 Long-Term Technology Targets for the 30x30 Market Goal 

Achieving the intermediate technology target of $1.07/gallon ethanol in 2012 will enable a 

viable lignocellulosic ethanol industry. Ethanol from lignocellulosic feedstocks will then join 

ethanol from starch feedstocks to displace imported petroleum and provide a sustainable, 

renewable resource for the nation’s transportation needs. However, market analysis (see 

Section 2) indicates that displacing 30% of 2004 gasoline demand with ethanol by 2030 will 

require additional technology advancements and the continued reduction of feedstock supply 

system and processing costs. This is predominately driven by the need to capture higher cost 

feedstocks to meet the 30x30 goal. 

Future R&D efforts will focus on four complementary approaches. Independently, the 

approaches will not result in technology improvements that will meet the 30x30 goal. However, 

collectively, they combine revolutionary scientific breakthroughs with evolutionary process 

developments to meet the 30x30 goal. 

Some cost reductions will be achieved by continuous process improvement to technology 

developed to meet the 2012 target. For example, the construction and operation of full-scale 

biorefineries will highlight opportunities for unit operation optimization and provide 

operational experience for process optimization and cost reductions. The accumulation of 

operating experience and engineering data will enable the design of larger-scale biorefineries, 

which will further reduce biofuels production costs by leveraging economies of scale. 
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These are the evolutionary cost reductions. More dramatic cost reductions will be required from 

scientific breakthroughs to reach the reduced conversion cost target of 2030. (See Section 2 for 

an explanation of the effect of this cost reduction on market penetration.) 

Earlier sections of this report described technologies for feedstock supply systems, biochemical 

conversion, and thermochemical conversion to accomplish the 2012 technology target. In the 

future, advancements will be made in all three areas, and there will be opportunities for cost 

savings through the integration of the two conversion technologies and through larger facilities. 

See Section 1 for a delineation of the cost targets for the advanced state of technology research 

goals. 

The four areas of future technology advancement to accomplish the 2030 goal are: 

1. Advanced, large-tonnage feedstock supply systems 

2. Systems biology to improve biochemical processing 

3. Selective thermal transformation to improve thermochemical processing 

4. Technology integration, economies of scale, and evolutionary process optimization. 

4.2.3.1 Advanced, Large-Tonnage Feedstock Supply Systems 

By 2012, functional feedstock supply systems will be demonstrated for all major biomass 

resources. Feedstock R&D will then shift to increasing the accessible biomass tonnage to 

enable production of 60 billion gallons of ethanol/year. As the biorefining industry expands, 

process improvements will drive biorefinery capacities up. Therefore, the longer-term 

feedstock supply R&D challenge is to ensure supply systems do not limit biorefinery size or 

consume biorefinery profits that could be used to purchase higher-value feedstocks (see Figure 

4-15). By increasing the purchase price for feedstock up to about $50/ton, all of the feedstock 

required to produce 60 billion gallons of ethanol can be accessed (see Appendix D). Adding 

estimated feedstock supply system costs gives a final feedstock cost of about $70/ton. Notice 

that linear cost increases do not produce linear tonnage estimates. The $70/ton cost is a 

maximum feedstock cost for only the largest tonnage levels. 

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Figure 4-15. Advanced Feedstock Supply System Technologies: Value-Add Feedstock 

Pre-Processing 

An advanced feedstock supply system will be needed to collect the large tonnages of feedstock 

required for large-scale biorefineries. An efficient interface between producers and the 

commodity biomass system is important for large-scale feedstock supply technology 

development. Production, harvesting, and collection systems will be widely varied, based on 

biomass resources and local practices. Primary research needs include storage, pre-processing, 

and transportation systems suited to these varied systems. 

The development of value-add feedstock pre-processing and blending technologies will provide 

flexibility in the biomass feedstock supply system and allow suppliers to: 

1. Reformat/condition different feedstocks into a common format and quality 

2. Fractionate secondary co-products for local markets 

3. Produce blended, large-scale commodity biomass. 

Value-add pre-processing will help create a market specification for feedstocks (which will 

help in the transition of biomass to a large-scale commodity) and ensure that feedstocks from 

varied sources can supply a large-scale biorefinery without process upset. 

Advanced Feedstock Transportation and Handling Systems 

Advanced feedstock supply systems will also rely heavily on new transportation methods and 

technologies to take advantage of the value-add pre-processing and merchandising of raw 

feedstock material. Truck transportation may not be economically possible because of transport 

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distances, traffic congestion, and community opposition. Rail transport reduces load frequency, 

but it is often more expensive than truck transport because of infrastructure constraints. 

Advanced transportation systems will likely incorporate technologies that not only provide 

infrastructure and operational cost savings but also in-transit value-add processes. 

4.2.3.2 Systems Biology to Improve Biochemical Processing 

Systems biology research will result in improvements to feedstock to maximize the recoverable 

liquid fuel per acre of land and drastically simplify the conversion process. These 

improvements have the potential to reduce the cost of converting lignocellulosic biomass to 

ethanol by about 30% for a similar sized 2,000 ton/day facility as the 2012 $1.07 target. 

Additionally as the advanced state of technologies facilitate larger scale facilities, an additional 

cost of production benefit of about 40% could be realized for a 10,000 ton/day operation. These 

kinds of cost reductions are typical of conversion technologies as they mature. The oil industry, 

corn industry, and others have seen processing costs drop dramatically over time until 

feedstock is the predominate cost. 

It is envisioned that, through systems biology, the overall conversion process can be simplified 

and capital and operating costs can be reduced (see Figure 4-16). 

Figure 4-16. Process Simplification through Systems Biology Research 

The advanced technology will combine several unit operations and improve the pretreatment 

operation. Enzyme production and fermentation will be combined in a single organism. Thus, 

with enzyme produced during saccharification and fermentation processes, the three process 

operations are combined into one. In addition, more robust micro-organisms will eliminate the 

need for hydrolyzate conditioning. These technology improvements will lower the total capital 

cost (project cost) of a 2,000dry ton/day facility by about 22% (Jechura 2006a) from 

$2.66/installed annual gallon of ethanol capacity (for the 2012 technical target). Combining the 

technology improvement and the larger scale of a 10,000ton/day facility lowers the total capital 

cost to $1.44/installed annual gallon (Jechura 2006a). 

Translational science concepts will be adapted to pursue these advancements. This approach, 

familiar to the biomedical industry, integrates basic research (or fundamental biological 

science) with industrial application (such as bio-engineering). To meet the 2030 technical 

target, significant fundamental science must be completed. To accomplish this in the available 

time frame, the research activities of DOE’s EERE and Office of Science must be coordinated. 

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The Office of Science has developed an extensive roadmap for its systems biology research 

approach to this endeavor (Thomassen and Johnson 2006). 

The following section and Table 4-5 describe the research required to accomplish the 2030 

technical target for biochemical conversion. 

Fundamental Biological Science 

A full and detailed integration of science and engineering research will lead to the most efficient 

process development plan. Fundamental R&D in biomass conversion must be targeted to 

process improvements based on technical barriers. An integrated fundamental and applied 

research program in biochemical conversion must include advancements in three areas: 

Feedstock engineering 

Develop GTL and agronomic/silvicutlure strategies to maximize the yield and quality of 

developing energy crops. 

Design and manipulate plant cell wall composition and structure to maximize the yield of 

fermentable sugars. 

Cell wall saccharification 

Analyze glycosyl hydrolase structure/function as it applies to plant cell wall deconstruction. 

Develop improved (engineered) enzymes for advanced biochemical conversion technologies, 

and integrate them with pretreatment chemistries. 

Strain development 

Apply systems biology and biochemistry to strain improvement to increase the conversion of 

sugars released during biomass deconstruction to ethanol and products. 

Focus on strains that will produce saccharifying enzymes and ferment the resulting sugars to 

ethanol. 

The detailed R&D strategies of these research areas are presented in Appendix G. 

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Table 4-5. Timeline of Key Activities to Accomplish the 2030 Biochemical Technical 

Target 

Completion Year 

R&D Area Current 2015 2020 2030 

Feedstock 

Engineering 

E1 cellulase 

expressed at 2% 

in Arabidopsis 

Aspen 

demonstrated 

with 15% 

increase in 

cellulose 

Demonstrate 

cellulase 

expression in 

feedstocks at 

economically 

viable level 

Demonstrate 

energy crop 

cultivation with 

25% increase in 

carbohydrates 

Cell Wall 

Saccharification 

Cell walls have 

been studied 

from a synthesis 

perspective but 

not a 

deconstruction 

perspective 

Enzyme 

hydrolysis at 

$0.32/gallon 

ethanol produced 

Develop and apply 

systems biology 

methods (e.g., highthroughput and 

computational 

simulation) for 

enhanced 

understanding of 

the basic science 

questions in 

biomass conversion 

Demonstrate >5x 

improved cellulase 

activities based on 

a more complete 

understanding of 

cell wall 

deconstruction 

Demonstrate 

feedstocks with 

modified cell walls 

and new enzymes 

that easily digest 

and have high 

yields of 

fermentable sugars 

Strain 

Development 

Limited 

hydrolyzate 

sugar conversion 

Develop organism 

for single-step 

processing that 

compares with 

commercial 

fermentative 

organisms and 

enzymes in 

laboratory 

fermenters 

Develop 

commercially 

available organism 

for single-step 

processing that 

produces ethanol 

yields and 

productivities 

comparable with 

existing individual 

organisms 

Engineering 

Research 

Identify the best 

pretreatment 

technology for use 

with single-step 

biological 

processing in the 

laboratory 

Operate a pilotscale pretreatment 

for single-step 

biological 

processing with 

multiple feedstocks 

Combine best 

pretreatment and 

organism for 

single-step 

biological 

processing into an 

integrated pilot 

plant 

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Bio-Engineering Research 

The objective of bio-engineering research is to acquire new understanding in broad-based 

aspects of applied process engineering research. An example is the support of experimental 

consortia (e.g., the multi-university Biomass Refining Consortium for Applied Fundamentals 

and Innovation) that propose to develop improved biomass pretreatment processes and 

feedstock qualification work to build databases for those considering new feedstock options for 

process design. The application of commercial enzyme preparation components to various 

pretreated biomass samples, within and beyond the scope of established consortia, is also a 

function of 30x30-impacting engineering research. This work will extend the comparative 

pretreatment analysis to multiple feedstocks (e.g., corn stover, switchgrass, and hybrid poplars) 

and additional pretreatment process impacts (e.g., the identification of hydrolyzate conditioning 

requirements for different pretreatments). 

The applied research program required to meet 30x30 objectives will include advancements in 

process application knowledge at two levels. The first will address process-related engineering 

research that converts new understanding from fundamental research to the biorefinery context. 

The second will use process-related engineering information to develop recommendations for 

industry regarding process parameters, equipment, and operating conditions. 

Process Unit Operation Engineering 

This work targets the interface between fundamental science and process-scale integration 

engineering. Again, the objective is to acquire new understanding in broad-based aspects of 

applied process engineering research, potentially through experimental consortia, such as the 

Consortium for Applied Fundamentals and Innovation. 

4.2.3.3 Selective Thermal Transformation to Improve Thermochemical Processing 

Achieving the 2012 technology target for biomass gasification – mixed-alcohol synthesis – 

requires improvements in catalytic tar and light hydrocarbon reforming to increase conversion 

efficiencies and reduce the capital costs of syngas cleanup and conditioning. To further 

improve thermochemical conversion to meet the 2030 technical target, two complementary 

approaches will be adopted: 

• Pursue scientific achievements to improve yields and efficiencies and maximize process 

integration opportunities in existing thermochemical processes (engineering approach) 

• Use a rigorous research program to investigate fundamental biomass thermochemical 

conversion to enable alternative processes that will help erase the lines between 

gasification and pyrolysis as separate technology options (scientific approach). 

R&D efforts for thermochemical technology will focus on the front end of processes while the 

downstream unit operations continue to be optimized. Significant improvements in catalytic 

gasification will be made to increase carbon conversion efficiencies to syngas and decrease tar 

formation. Within the gasifier, this converts 50% of the methane produced during biomass 

gasification to carbon monoxide and hydrogen (the syngas components required for 

downstream conversion to ethanol). Throughput of the gasifier also increases 25%. This 

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improved technology will reduce thermochemical conversion cost by 38% over the 2012 

technology target (Spath 2006). 

Process consolidation will continue to lower capital and operating costs to meet the technology 

cost targets. The block flow diagram in Figure 4-17 illustrates the R&D required to advance 

thermochemical conversion technology and meet the 2030 technology target. 

Figure 4-17. Selective Thermochemical Processing 

The following sections and Table 4-6 describe the research needed to accomplish the 2030 

technical target for thermochemical conversion. 

Catalytic Gasification and Pyrolysis 

Since the beginning of coal gasification, catalysts have been sought to improve carbon 

conversion to products and increase gasification rates while minimizing temperature to increase 

process efficiency. Alkali metals have long demonstrated catalytic activity in steam gasification 

of solid fuels, and metal-based catalysts—particularly nickel-based materials—are active and 

effective for hydrocarbon reforming. 

Lignin Utilization 

The integration and use of lignin residues will be key to establishing the commercial viability 

of lignocellulosic biorefineries and maximizing biomass use for fuel production. Because lignin 

is a complex but lower-value biomass component, it is essential that new technologies increase 

its value to enhance the competitiveness of integrated biorefineries. 

Biomass Deconstruction/Pretreatment 

Technology advancements in these thermochemical processes highlight the need for 

breakthrough R&D to optimize the fractionation of biomass and provide separate feed streams 

with compositions suitable for specific conversion technologies. 

 

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Selective Thermal Transformation of Fractionated Biomass 

A range of alternative conversion options is envisaged through the fractionation of biomass into 

specific components. A narrower, more uniform biomass fraction opens the possibility of 

developing thermochemical conversion options with high yields and selectivities. 

Table 4-6. Timeline of Key Activities to Accomplish the 2030 Thermochemical Technical 

Target 

Completion Year

R&D Area Current 2015 2020 2030

Catalytic 

Gasification 

New Identify viable 

catalysts for use in 

the gasifier to 

modify the product 

gas in the 

laboratory 

Increase carbon 

efficiencies to syngas 

Reduce methane 

produced to 50% 

Use biochemical 

process residue 

from a combined 

process with 

previous carbon 

yields at 

integrated pilotplant scale 

Lignin Utilization Heat and power 

generation 

Identify the best 

process for 

thermochemical 

use of lignin 

Verify the best 

process for 

thermochemical use 

of lignin at the 

laboratory scale

Verify the best 

process for 

thermochemical 

use of lignin at an 

integrated pilotplant scale 

Biomass 

Deconstruction/

Pretreatment 

Validate 

integrated 

biochemicalthermochemical 

process that prefractionates 

biomass to 

optimize 

conversion 

efficiency of 

carbohydrate-rich 

streams and 

lignin-rich streams 

4.2.3.4 Technology Integration, Economies of Scale, and Evolutionary Process Optimization 

Biochemical and thermochemical conversion technologies can be integrated for additional 

efficiency and cost improvements. Biochemical conversion extracts the carbohydrate portion of 

the feedstock and then converts it to fermentable sugars and, ultimately, ethanol. The remaining 

residue, primarily lignin, cannot be fermented, but it is a valuable organic feedstock. By 

directing this byproduct to a thermochemical process, it can be converted to syngas and, 

ultimately, ethanol. The integration of these technologies will improve the energy efficiency of 

the process, lower costs, and produce more ethanol than a standalone biochemical or 

thermochemical process. 

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77 

Recent studies have examined other thermochemical conversion options coupled with 

biochemical processes. One study evaluated the economics of integrating advanced 

biochemical conversion (consolidated bio-processing) and gasification with single-pass 

Fischer-Tropsch synthesis (Lynd et al. 2005). The processes had substantial heat integration 

and byproduct electricity production. The analysis showed additional fuel could be produced, 

but the incremental capital cost (on a per-gallon basis) increased. An integrated biorefinery can 

increase liquid fuel yield beyond the maximum from carbohydrate-only conversion, but there is 

an incremental increase in capital cost to do so. 

Figure 4-18 depicts the advanced, integrated biochemical and thermochemical alcohol 

production scheme analyzed. Some of the lignin-rich residue is used to provide steam and 

electricity to the biochemical process, and the remainder is processed in the thermochemical 

process. The larger biochemical processes (8,000 -10, 000 metric tons/day) expected in the 

2020–2030 time frame will be needed to feed a reasonably sized gasification plant (1,500 -

2,000 metric tons/day) with only lignin-rich residues. The scale of the biochemical processing 

plant is five times larger than that targeted for 2012, but the scale of the thermochemical 

conversion plant is the same. 

This combined process can maximize feedstock handling efficiencies and heat and power 

integration. Integrated biorefineries can also process feedstocks with both high and low 

carbohydrate contents. A steady supply of low-carbohydrate feedstock could be fed directly 

into the thermochemical process, which allows increased size and some benefit to the capital 

cost. Integrated biochemical-thermochemical biorefineries also capitalize on the process 

improvements identified in the independent developments of the two technologies. 

Figure 4-18. Integrated Biorefinery Gasification Scenario with Excess Lignin Converted 

to Ethanol 

Ethanol

1,193,000 gpd 

417.7 MM 

Gal/Yr 

2,140 

Ethanol Yield = 108 Gal/Dry Ton 

Stover 

Ethanol 

159,000 gpd 
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5. Ethanol Storage, Distribution, Blending, and 

Vehicle Infrastructure Needs 

5.1 Existing Ethanol Distribution Infrastructure 

The existing corn grain ethanol industry transports approximately 5 billion gallons of ethanol 

each year. The process for delivering ethanol from the production plant to the consumer is 

illustrated in Figure 5-1. 

Figure 5-1. Existing Ethanol Distribution System 

Ethanol is denatured with gasoline to form a blend of 95% ethanol and 5% gasoline (called E95 

or fuel grade ethanol) and stored at the plant. The purity and properties of fuel grade ethanol are 

specified in ASTM D4806. Shipping is by rail or tanker truck to wholesale gasoline terminals, 

where the E95 is stored in specialized tanks until it is blended into gasoline. To blend, the 

terminal operator adds ethanol into a load of gasoline in a tanker truck or rail tanker car (called 

“splash” or “top-off blending”) and relies on the movement of the truck or tanker during transit 

to mix the blend. Alternatively, the operator can use an inline system to blend the ethanol into 

the gasoline in the terminal piping and then feed the blend into the tanker truck or rail tanker car. 

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E10 pumps are available at service stations throughout the Midwest, East, and West Coast and 

in many urban areas designated as non-attainment areas for air quality by the U.S. EPA. There 

is no census of how many of the 169,000 gasoline retail outlets in the United States carry E10, 

but market penetration is remarkably high in some parts of the Midwest (National Petroleum 

News 2005). In Iowa, for example, 65% of all gasoline sold in 2004 was blended with ethanol 

(Iowa, 2006). 

Approximately 600 service stations nationwide dispense E85 blends, which consist of 85% 

ethanol and 15% gasoline (Downstream, 1999). [In reality, E85 composition varies seasonally, 

particularly in colder climates. For example, in winter, E85 is in fact a minimum of E70 in 

Minnesota and a minimum of E79 in Texas (National Ethanol 2006).] The properties of E85 are 

specified in ASTM D5798. Four hundred thirty of these stations are located in the Upper 

Midwest states of Minnesota, Iowa, South Dakota, North Dakota, Wisconsin, and Illinois 

(American Lung, 2006). Some are public, and others service state or federal FFV fleets. 

E85 delivery and storage systems are virtually identical to those for E10. E85 can be splashblended at the terminal and delivered by dedicated tanker truck to dedicated underground tanks 

at service or fleet refueling stations. It can also be blended at the retail station in the E85 

underground tank. 

Although this distribution infrastructure is sufficient for existing ethanol production, significant 

enhancements will be required to meet the 30x30 goal. 

5.2 Infrastructure Changes Required To Meet the 30x30 Goal 

As explained previously, the ethanol infrastructure from the plant to the consumer has four 

distinct parts: 

• Storage 

• Distribution (transportation from the plant gate to refueling stations) 

• Blending 

• Refueling. 

To ascertain how the infrastructure will develop and determine associated costs, each part of 

the infrastructure was considered individually. Each section was investigated on the basis of 

cost per gallon of ethanol delivered, total capital cost, and logistical issues and technical 

challenges. Distinctions were not made among the types of fuel delivered (i.e., among E10, 

E20, E85, etc.). 

5.2.1 Storage 

Two major assumptions, based on the storage practices for petroleum products, were used for 

the storage of ethanol (Reynolds 2006): 

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1. The storage volume required is 10% of the cumulative production rate plus 30% (of the 

10%) for inventory receipts and extra working space. 

2. The installed cost for storage in conventional tanks is $22.40/barrel. 

Based on these assumptions, total capital investment for storage will be approximately $4.15 

billion. The fully loaded cost per gallon of ethanol is approximately $0.07/gallon. 

Developing the storage infrastructure is relatively straightforward because a robust fuel storage 

industry is already well developed in the United States. Hence, developing the necessary 

storage infrastructure should not be a hurdle to the 30x30 goal. 

5.2.2 Distribution (Plant to Pump) 

This is the most complicated infrastructure needed to meet the 30x30 goal, and it will likely 

require the largest effort. Essentially, three approaches could meet 30x30 needs: 

1. Continue development of the existing ethanol distribution infrastructure 

2. Leverage the existing gasoline distribution infrastructure to distribute ethanol 

3. Develop a new infrastructure that is optimized for the distribution of ethanol at the 

30x30 scale and beyond. 

5.2.2.1 Continue Development of the Existing Ethanol Distribution Infrastructure 

This likely will be the preferred option in the near term because it is the least risky technically 

and it can accommodate the near-term growth in ethanol production projected in Section 2. 

However, there are serious issues related to the suitability of this infrastructure for the ethanol 

production volumes envisioned in the 30x30 goal. 

This section analyzes three possible modes of ethanol transport from the plant gate to the 

distribution terminals: truck, rail, and barge. See Appendix I for a detailed description of the 

ethanol shipments required to meet the 30x30 goal. 

Trucks are used primarily for short-haul routes. Although trucks are less efficient than barges 

and railcars, they provide rapid transport to nearby gasoline blending terminals. Tanker trucks 

are also used in most markets to transport E10 to local retail outlets. 

Rail transport has a significant cost advantage over trucks when greater distances or volumes 

are involved. As ethanol-related demand for rail transport has grown, the system has evolved 

from single cars to dedicated trains (often referred to as “unit trains”). An increase in ethanol 

production to meet the 30x30 goal would require additional rail cars and lines. 

Barges can move large quantities of fuel cheaply along major navigable waterways. Each barge 

can handle 10,000–30,000 barrels of fuel. Ten thousand-barrel barges routinely move ethanol 

up and down the Mississippi and Missouri river systems and to Gulf Coast blending points. 

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River and coastal barges will continue to serve as a low-cost option to transport ethanol to Gulf 

Coast states, the East Coast, and pipeline terminals (when and if they begin to accept ethanol). 

Table 5-1 shows the distribution for ethanol shipping using these transportation modes 

(Reynolds, 2006). Reynolds estimated the distribution breakdown among barge, rail, and truck 

for 42 billion gallons of ethanol annually. The units, gallons shipped, and dollars invested were 

extrapolated to 60 billion gallons for the 30x30 scenario. In this scenario, trucks are used only 

for short distances: plant to distribution terminal and distribution terminal to service stations. 

For longer shipping distances, unit trains and barges are used because they are lower-cost 

options. The capital investment for each mode of transportation was depreciated using a 15-

year MACRS (Modified Accelerated Cost Recovery System) method. 

Table 5-1. Infrastructure Requirements to Reach 60 Billion Gallons by 2030 

Mode 

% of 

Ethanol 

Shipped Units 

Gallons 

(Billions) 

($ Billion 

Gallons 2004) 

Barges 20% 317 12 0.51 

Railcars 22% 10,536 13.2 0.68 

Trucks 54% 6,490 32.5 0.81 

Feasibility studies and business plans commonly use the ranges shown in Table 5-2 to estimate 

costs from the plant gate to the blending terminal. Assuming the same trucking rate and a 

radius for ethanol transport from the blending terminal to pumps, we can estimate the delivered 

ethanol cost from plant gate to pump. 

Table 5-2. Estimated Plant Gate to Pump Costsa

Plant Gate 

to Final Blending Terminal 

Range 

(Miles) Type 

Cost 

($/Gallon) 

Localb 0–150 Truck $0.03–$0.04 

Regionalc 150–450 Truck or rail $0.07 

Nationald 450+ Truck to rail or 

barge 

$0.11–$0.15 

Blending Terminal to Pump 75–100 Truck $0.02–$0.03 

Total Cost From Gate to Pump Local Regional National 

Cost/Gallon $0.05–$0.08 $0.09–$0.10 $0.13–$0.18 

a From a June 15, 2006, conversation with Mark Yancy from BBI International. b Fully loaded truck rate assumed $70/hr. c 450 miles is an average 8-hour trucking day. No appreciable benefits are seen for rail in this range. d Transport from Nebraska to the West coast is $0.12–0.15/gallon. It is slightly less expensive to the East Coast. 

Conventional distribution methods have a variety of logistical issues. One issue is the strain 

increased ethanol distribution would add to already overloaded infrastructures (e.g., roads, rail 

lines, and waterways). Equipment capacity has been tight for several years, and the recent 

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demand surge has exacerbated the difficulties (D’Amico, 2006). A substantial increase in costs 

for all types of carriers has been driven by increased demand and the cost of steel. 

Other issues are specific to the transportation mode. 

• Trucking issues include road traffic, filling and emptying delays, and fuel use. 

• Rail issues include the increased burden on existing rail lines and the cost and logistical 

issues associated with building new lines to accommodate demand 

• At issue with barge transportation is inter-coastal waterways traffic. Locks along the 

major rivers (i.e., the Mississippi, Missouri, and Ohio) are advanced in age and 

undersized for even current transportation load. This already causes long delays during 

peak transportation months. Another issue, especially in major Midwestern peak ethanol 

production areas, is that the upper Mississippi and Missouri rivers are closed for up to 

three months each winter because of ice blockage. 

Given these costs and logistical challenges, meeting the distribution needs of the 30x30 goal 

with this option is quite challenging. 

5.2.2.2 Leverage the Existing Gasoline Distribution Infrastructure to Distribute Ethanol 

Pipelines are a mature transportation technology, and they are considered the safest way to 

transport fuels in bulk. Pipelines are typically the least-inexpensive mode of shipping large 

quantities of liquid and gaseous fuels (e.g., crude oil, gasoline, diesel fuel, and natural gas). 

There are an estimated 72,000 miles of product pipeline in the United States. With predictions 

that the world is approaching peak oil production, it is possible there will be excess pipeline 

capacity in the future that could be used to transport ethanol or gasoline-ethanol blends. 

The ethanol industry could use the gasoline/product pipeline currently used by the petroleum 

industry if: 

• There is unused pipeline capacity, which would allow ethanol to be shipped (in 

agreement with current pipeline owners) 

• The technical barriers that keep ethanol from being mixed in pipelines can be overcome. 

One advantage of leveraging the existing petroleum pipeline infrastructure is the maturity of 

the petroleum industry. Blending, distribution, and logistical issues have already been solved. 

In addition, minimum capital would be needed to ship large quantities of fuel. 

 

Potential barriers include the ebb and flow of petroleum supply in the pipeline, which limits the 

ethanol that could be shipped on a spot basis. This could have a volatile impact on spot ethanol 

prices in the marketplace. However the main barrier is technical in nature. Currently, pipelines 

are not used with ethanol or gasoline blends that contain ethanol because of solvent and water 

issues (Reynolds, 2006). 

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Pipeline transport is problematic because: 

• Ethanol is a stronger solvent than the petroleum products moved via pipeline. 

Consequently, ethanol will remove water, rust, gums, and other contaminants in the 

system. This can result in contamination and discoloration of the ethanol or gasolineethanol blend. This, in turn, downgrades the value of the product and adds backend 

costs to bring the product back to specification. 

• Alcohol has a strong affinity for water. Ethanol would pick up water present in the 

pipeline system. Ethanol gasoline blends with excessive content can phase separate at 

low temperatures, depending upon the amount of water entrained. 

• Strategies have been proposed to solve these issues, and in some cases, small tests have 

been successful with gasoline-ethanol mixtures in dry pipelines. Gasoline-ethanol 

mixtures have also been shipped, successfully in pipelines, in other countries. Although 

it is unknown at this time whether U.S. pipeline operators would consider shipping 

ethanol blends, it is unlikely they would actually do so until ethanol production is large 

enough to justify the additional investments needed. At that time, they would likely also 

consider dedicated ethanol pipelines. 

Defining a delivered cost per gallon for this scenario is difficult. In a best-case scenario, 

minimal capital investment would be incurred. The cost of fuel delivery from the plant to the 

pump would consist only of the cost of transport from the plant to the blending terminal and 

from the blending terminal to the refueling station. For cost estimation purposes we assume that 

the owners of the pipeline charge a fee, agreed to by all parties, as they do for hydrocarbon 

fuels, to use the delivery system. The cost to transport liquid fuels via pipeline is estimated to 

be $0.09/gallon based on current averages. 

Clearly, this option is desirable. However, major logistical and technical issues, as stated above, 

must be addressed and overcome. In addition, this option is contingent on available capacity in 

the existing system to accommodate 60 billion gallons of ethanol. 

5.2.2.3 Develop a New Infrastructure Optimized for the Distribution of Ethanol at the Scale of 

30x30 and Beyond 

The timing of dedicated ethanol pipelines will be a function of industry growth, the capacity of 

the distribution infrastructure, and the extent to which the ethanol industry can leverage the 

current pipeline system. However, dedicated ethanol pipelines will logically be built when the 

quantity of ethanol is large enough to justify the costs. Another factor that will influence largescale pipeline transport is demand generated for fuel by vehicle fleet needs. 

A rough cost estimate for pipeline is $1 million per mile installed (Reynolds, 2006). As with 

conventional transportation costs, pipeline capital is depreciated using MACRS depreciation 

for a 15-year period. Figure 5-2 shows average pipeline costs on a cost per gallon delivered 

basis for the entire 60 Billion gallons of ethanol for the 30x30 scenario from refinery gate to 

fuel distribution terminal. 

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20 30 40 50 60 70

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Cents/gallon

Pipeline (Thousand Miles)

2050 2040 2030

These figures assume that all the ethanol from starch comes from PADD 2 (see above). 

Figure 5-2. Ethanol Pipeline Costs 

Even with a dedicated pipeline system, there will be a need for short-distance distribution from 

the plants to terminals and from terminals to refueling stations. Table 5-2 shows cost range 

estimates for ethanol delivered by dedicated pipelines. The later start dates are lower on a cost 

per gallon shipped basis because of the higher amount of ethanol that flows through the 

pipeline. 

Comparing costs of the three options clearly shows that using the existing petroleum pipeline 

systems would be the lowest cost option, followed by dedicated pipelines, and finally 

expanding the current ethanol distribution system to meet the needs of the 30x30 scenario 

would be the highest cost option. This analysis does not include the logistical issues that would 

be associated with building new dedicated ethanol pipelines. The issue would need to be 

balanced and need to be balanced against alleviating the strain on the U.S. road, rail, and inner 

coastal waterway infrastructures. 

Table 5-3. Distribution Costs Using Pipeline 

Plant Gate 

to Final Blending Terminal 

Range 

(Miles) Type 

Cost 

($/Gallon) 

Local 0–150 Truck $0.03–$0.04 

Regional or National Any Pipeline $0.003–$0.09 

Blending Terminal to Pump 75–100 Truck $0.02–$0.03 

Total Cost From Gate to Pump Local Regional or National 

Cost/Gallon $0.05–$0.08 $0.08–$0.16 

In the end, there will likely be a combination of current infrastructure, leveraged petroleum 

industry resources, and dedicated pipelines. To investigate the options for ethanol distribution 

at this scale, experts from the U.S. Department of Commerce and U.S. Department of 

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Transportation as well as stakeholders in the ethanol and petroleum industries must be 

involved. 

5.2.3 Blending 

Currently, ethanol is denatured before leaving the plant gate, per Bureau of Alcohol, Firearms 

and Tobacco (BATF) rules. Denatured ethanol is transported to gasoline terminals, where it is 

splash-blended with gasoline. The blended product is then transported to end-user markets. 

It is logical to assume that, as ethanol production increases and a geographical shift of ethanol 

distribution from the Midwest to the coastal United States takes shape, there will be changes in 

blending practices. As the light-vehicle fleet moves toward a dedicated E85 fleet, gasoline will 

be blended into ethanol. The estimated cost of installing terminal blending capabilities is 

approximately $200 million. However, blending costs are relatively insignificant when 

compared with storage and transportation costs. They add less than $0.005/gallon to final 

distribution costs. As is the case for storage, developing the necessary blending infrastructure 

should not be a major hurdle towards achieving the 30x30 goal. 

5.2.4 Refueling Stations 

Expanding ethanol consumption through the widespread use of E10 will have relatively few 

effects on the existing vehicle and refueling infrastructure. The gasoline refueling infrastructure 

is already compatible with E10, and many stations already dispense E10. The situation is 

similar for the existing vehicle fleet. Gasoline vehicles are already functionally compatible with 

ethanol blends up to E10. Therefore, preparing the refueling infrastructure and vehicle stock for 

E10 should require no capital investment. 

However, increasing use of E85 involves hurdles. These include the availability of appropriate 

vehicles, the need for specialized refueling equipment, and the ability of drivers to choose their 

fuel based on convenience, price, and attitudes. Drivers must have FFVs, there must be 

conveniently located refueling stations, the fuel must be competitively priced on a miles 

traveled basis, and the vehicle’s performance on E85 should at least match that of gasoline. 

 

At an approximate cost of $25,000/refueling station retrofit, an installed capital cost of nearly 

$3 billion will be required over the next 30 years for storage and dispensing modifications for 

1⁄2 the stations in the targeted PADDs (Appendix I). Using the same capital depreciation used 

previously, this equates to approximately $0.07/gallon. 

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5.3 Total Infrastructure Investments Required to Meet the 30x30 

Goal 

Table 5-4 shows the cumulative investments and the normalized cost per gallon of ethanol 

delivered from production plant to consumer. 

Table 5-4. Total Infrastructure Costs

Infrastructure 

Section

Total Capital Cost 

(Billion $)1

Cost/Gallon Delivered 

($/Gallon) 

Storage $4.15 $0.07/gallon 

Distribution 

Option 1 2 3 1 2 3 

$2 $0 $202 $0.15 $0.09 $0.12 

Blending $0.2 $0.003/gallon 

Refueling 

Infrastructure $4.2 $0.07/gallon 

1 2 3 1 2 3 

Total $10.5 $8.5 $28.5 $0.29/gallon $0.23/gallon $0.26/gallon 

1 Storage, blending, and refueling costs were calculated by dividing the estimated capital by 60 

billion gallons. All distribution costs were calculated using a 15-year MACRS depreciation 

period. 

2 Based on the assumed 20,000 miles of pipeline required. See Appendix I for details. 

3 Assumed fee charged by pipeline owner. This number is based on the average existing cost per 

gallon of gasoline shipped via pipeline. 

As shown in Table 5-4, the biggest variable is distribution. If dedicated pipelines are required, 

they would represent the largest capital investment. 

How and when the distribution infrastructure will be developed is the biggest unknown at this 

time. Achieving the 30x30 scenario will require that these critical issues be addressed. 

5.4 Vehicle Fleet 

Necessary vehicle fleet changes can be categorized into (1) vehicle requirements for E10, (2) 

vehicle requirements for E85, and (3) advanced vehicle technologies. 

5.4.1 Vehicle Requirements for E10 

As previously explained, the widespread use of E10 will have no effect on the existing vehicle 

fleet from an operability and durability perspective. There are, however, concerns about the 

potential for increased emissions caused by using ethanol. When ethanol is blended with 

gasoline, the fuel’s Reid vapor pressure (RVP) increases, which leads to increased ozoneforming evaporative emissions from the vehicle and during refueling. This can be controlled by 

reducing the RVP of the gasoline stock into which the ethanol is blended. This is already done 

in many EPA ozone non-attainment areas. In non-attainment areas for carbon monoxide, the 

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EPA can grant a 1-psi maximum increase waiver for RVP requirements to allow the use of 

ethanol as an oxygenate to reduce carbon monoxide emissions. Additionally, ethanol can cause 

an increase in the permeability of fuel system hoses and seals that can increase evaporative 

emissions by up to 65%, independent of the efforts to limit or control RVP. There is also 

uncertainty regarding the impact of ethanol on tail-pipe emissions of NOx and aldehydes. While 

some studies show little or no effect on these emissions other studies suggest that increases 

might be significant. Additional testing is warranted to alleviate the address air quality impacts 

of the 30x30 scenario. Appendix I details emissions concerns associated with E10. 

5.4.2 Flexible Fueled Vehicles (FFVs) and Ethanol 

Increasing the use of ethanol via E85 involves different hurdles. These include the availability 

of FFVs and the ability of drivers to select a preferred fuel based on convenience, price, and 

performance along with some slightly different environmental concerns. Ethanol capacity in 

the U.S. fleet includes conventional vehicles able to legally operate on blends of up to 10% by 

volume ethanol and FFVs that can operate on up to 85% ethanol. 

FFVs are considered alternative fueled vehicles. Through incentives, the National Highway and 

Traffic Safety Administration gives vehicle manufacturers corporate average fuel economy 

credits for them. Therefore, FFVs have a tangible economic benefit to manufacturers and are 

offered to consumers at no incremental cost. Vehicle manufacturers estimate that the cost of 

converting a conventional vehicle to an FFV in the original manufacturing process is less than 

$100. Today, Ford, General Motors, and Daimler-Chrysler all manufacture, certify, and sell 

FFVs. These vehicles have proved to be as durable and clean as their conventionally fueled 

counterparts on the road and in the laboratory. However, because E85 is not as widespread as 

conventional gasoline at the pump, manufacturers have not thoroughly optimized vehicles for 

E85 operation. 

A number of modifications are made to FFVs to enable E85 use. Material compatibility 

changes, such as using appropriately resistant and durable plastics and elastomers and 

corrosion-resistant metals, are necessary for acceptable performance. Fuel sensors, if used, 

must be able to interpret ethanol blend level. Other changes that are made so that the proper airto-fuel ratio for complete combustion requires higher fuel flow and injection rates. 

Additionally, because of the roughly 20% higher octane rating of E85 than regular gasoline, the 

engine spark timing can be adjusted to improve efficiency such that FFVs running on E85 

typically exhibit a 26% reduction in fuel economy, rather than the 29% expected based on fuel 

volumetric energy content (US EPA, 2005). 

Overall, today’s FFVs have been able to slightly take advantage of E85 properties using the 

methods described above to get a small improvement in engine efficiency. However, on a 

volumetric basis, consumers will notice a decrease in vehicle range when operating their FFVs 

on E85 instead of gasoline. This can be a deterrent for consumers when they are given a choice 

between E85 and conventional gasoline and will generally require that E85 cost less on 

normalized miles per gallon of fuel basis than gasoline, at least initially. 

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E85 has significantly lower vapor pressure than E10 or gasoline and in fact additional gasoline 

must be added to E85 in winter to achieve the minimum vapor pressure required for cold 

starting. Thus, evaporative emissions are not an issue, however in winter months E85 may 

actually need to be adjusted to have an ethanol content as low as 70%. We also have a poor 

understanding of the impact of intermediate (between E10 and E85) blends on evaporative and 

tailpipe emissions. These blends occur because consumers can commingle gasoline, E10 (or 

E5.7 Currently in California, and E85 in their vehicles. Although we expect E85 to reduce tailpipe emissions relative to gasoline, little data exist on recent model year vehicles. Finally since 

few FFVs have significant operating history on E85, researchers need to validate the long-term 

durability of FFVs operating on E85. 

In the future, manufacturers may convert the entire national fleet to FFVs. This would 

minimize vehicle manufacturing and certification complexities and increase national 

production and availability. Given additional supplies of competitively priced ethanol, the 

expansion of the FFV fleet would allow more drivers to refuel with E85. As ethanol production 

grows and the availability of FFV vehicles expands, it may even be possible to completely 

convert dispensers to E85. 

In this analysis of the 30x30 scenario we use an aggressive scenario of FFV introduction, which 

is based on work done by the Union of Concerned Scientists for the Natural Resources Defense 

Council (Greene et al., 2005). Ethanol capacity in the U.S. fleet includes conventional vehicles 

able to legally operate on blends of up to 10% by volume ethanol and FFVs that can operate on 

up to 85% ethanol. The aggressive scenario assumes a linear rise in the percentage of light-duty 

trucks and cars sold annually that are flex-fuel capable, from around 2.5% today to just more 

than 50% by 2015. Under these conditions, growth in fleet capacity for using ethanol, as shown 

in Fig. 2-8, far outstrips the ability to deploy ethanol production facilities. Hence availability of 

FFVs should not be an impediment to achieving the 30x30 goal. 

5.4.3 Advanced Vehicle Technologies 

Advanced vehicle technologies are dedicated E85 vehicles with engines fully optimized for 

E85. These dedicated vehicles will have equivalent or enhanced performance on E85 but will 

not be able to operate on gasoline or E10. Engines specifically designed for ethanol would take 

advantage of ethanol characteristics such as high octane number, flame speed, and heat of 

vaporization to provide an efficiency increase that would largely compensate for the lower 

volumetric energy content of ethanol. 

Engine designs to exploit the high octane rating of E85 have been developed. For example, 

Saab, using a 2.3-liter turbo-charged engine running on E85, showed a 20% improvement in 

engine performance (but did not calibrate for lowest tailpipe emissions) over baseline gasoline 

engines (Saab, http://www.saabo.com/index.php/category/enviromental 2006). The enhanced 

octane of E85 enables high levels of turbocharging and increased compression ratios boosting 

horsepower and efficiency. In principle, this should allow smaller engines to generate 

equivalent horsepower and, therefore, show fuel savings. In this country, however, some of the 

improvements would be sacrificed to control emissions to the extremely low levels required in 

the US. More advanced engine designs could also take advantage of the higher latent heat of 

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vaporization of ethanol which causes charge cooling, leading to a higher charge density and 

increased power output and efficiency. Finally, the higher laminar flame speed of ethanol 

allows leaner air-fuel mixtures increasing the volume of working fluid, providing an additional 

efficiency improvement. Results of an engine designed and run on E30 showed a 10%–12% 

gain in fuel economy, which more than compensates for the 8% reduction in fuel energy 

density. As ethanol becomes a bigger component of the fuel pool for light vehicles, automakers 

will continue to improve engine technology to better use E85 as a fuel. 

While ASTM quality standards currently exist for fuel grade ethanol (D4806) and for E85 

(D5798), development of advanced engines and vehicles for operation on E85 will be enabled 

by development of improved ASTM standards. 
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6.0 Conclusions 

This report presents a scenario for achieving the 30x30 goal of replacing 30% of the 2004 

motor gasoline demand with biofuels by 2030. Achieving the 30x30 goal as presented in this 

report, is based on Annual Energy Outlook high oil price projections with the key objectives as 

follows: 

• Continue to successfully deploy corn-based ethanol up to its maximum potential (9.3–

17.2 billion gallons) 

• Develop and demonstrate at the pilot scale biochemical and thermochemical cellulosic 

ethanol conversion technology at the $1.07/gallon market target by 2012 

• Develop and demonstrate at the pilot scale advanced cellulosic ethanol conversion 

technology that reduces conversion cost from 64% of the total ethanol production cost 

to 22% in the $1.07/gallon case by 2030 

• Provide two market incentives to accelerate the deployment of ethanol 

• Continue the $0.51/gallon ethanol tax incentive until 2020 

• Provide a $20/ton subsidy for cellulosic feedstocks until 2025 

• Develop the necessary distribution and vehicle infrastructure. 

As detailed in Section 2 only the scenario outlined above achieved the 30x30 goal, however for 

AEO reference oil price projections 80% of the 30x30 goal was achieved which would still 

significantly impact energy security and imported oil displacement. Section 3 analyzed the 

energy balance and environmental implications of the 30x30 scenario. It showed that achieving 

the 30x30 goal will be a significant step toward a more sustainable energy future for the United 

States. 

In this analysis of the 30x30 scenario, we use an aggressive scenario of FFV introduction, 

which is based on work done by the Union of Concerned Scientists for the Natural Resources 

Defense Council (Greene et al., 2005). The aggressive scenario assumes a linear rise in the 

percentage of light-duty trucks and cars sold annually that are flex-fuel capable, from around 

2.5% today to just more than 50% by 2015. Under these conditions, growth in fleet capacity for 

using ethanol far outstrips the ability to deploy ethanol production facilities. Hence availability 

of FFVs should not be an impediment to achieving the 30x30 goal. 

A market-driven assessment was used to develop this scenario. Hence, like any scenario, it 

reflects some of the authors’ personnel opinions about the best mix of technology targets and 

market incentives. Every attempt was made to substantiate targets chosen from the peerreviewed literature or, in the case of the market incentives, indicated from existing incentives or 

incentives consistent with the goals of the Energy Policy Act of 2005. 

The scenario presented here is not absolute, and it is not the only scenario for achieving high 

levels of biofuels use. However, it does provide a comprehensive, systematic analysis of what it 

will take to achieve the 30x30 goal. Ultimately, however, market factors will dictate how and 

when biofuels are deployed. 

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References 

Abengoa Bioenergy Corp. (2003). “Statement of Objectives – Advanced Biorefining of 

Distiller’s Grain and Corn Stover Blends: Pre-Commercialization of Biomass-Derived Process 

Technology.” DOE-Golden Field Office memo. 

Aden, A.; Ruth, M.; Ibsen, K.; Jechura, J.; Neeves, K.; Sheehan, J.; Wallace, B.; Montague, L.; 

Slayton, A.; Lukas, J. (2002). Lignocellulosic Biomass to Ethanol Process Design and 

Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn 

Stover. NREL/TP-510-32438. Golden, CO: National Renewable Energy Laboratory. 

Aden, A.; Spath, P.L. (2005). “The Potential of Thermochemical Ethanol Via Mixed Alcohols 

Production.” Milestone completion report. September 2005. 

American Coalition for Ethanol. (2005). Fuel Economy Study: Comparing Performance and 

Cost of Various Ethanol Blends and Standard Unleaded Gasoline. Sioux Falls, SD: American 

Coalition for Ethanol. 

American Lung Association of the Upper Midwest. (2006). “E85 Background.” Clean Air 

Choice. http://www.cleanairchoice.org/outdoor/E85background.asp. 

Anderson-Gunneras, S.; Mellerrowicz, E.J.; Love, J.; Segerman, B.; Ohmiya, Y.; Coutinho, 

P.M.; Nilsson, P.; Henrissat, B.; Moritz, T.; Sundberg. (2006). “Biosynthesis of CelluloseEnriched Tension Wood in Populus: Global Analysis of Transcripts and Metabolites Identifies 

Biochemical and Developmental Regulations in Secondary Wall Biosynthesis.” The Plant 

Journal (45:2), pp. 144–145. 

Antal, M.J.; Gronli, M. (2003). “The Art, Science, and Technology of Charcoal Production.” 

Industrial and Engineering Chemistry Research (42:8); pp. 1,619–1,640. 

Bain, R. (2005). “The Role of Thermochemical Processing in an Integrated 

Sugars/Thermochemical Biorefinery: Conceptual Process Techno-Economic Analysis.” NREL 

technical memo. 

Bizzari, S.N.; Gubler, R.; Kishi, A. (2002). “Oxo Chemicals.” Chemical Economics Handbook. 

Report 682.7000. Menlo Park, CA: SRI International. 

Boeckmann, B.; Bairoch, A.; Apweiler, R.; Blatter, M.C.; Estreicher, A.; Gasteiger, E.; Martin, 

M. J.; Michoud, K.; O’Donovan, C.; Phan, I.; Pilbout, S.; Schneider, M. (2003). “The SWISSPROT Protein Knowledgebase and Its Supplement TrEMBL in 2003.” Nucleic Acids Research 

(31:1); pp. 365–370. 

Bothast, R.J.; Saha, B.C.; Flosenzier, A.V.; Ingram, L.O. (1994). “Fermentation of LArabinose, D-Xylose, and D-Glucose by Ethanologenic Recombinant Klebsiella oxytoca Strain 

P2.” Biotechnology Letters (16:4); pp. 401–406. 

91 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Boucher, M.E.; Chaala, A.; Roy, C. (2000). “Bio-Oils Obtained by Vacuum Pyrolysis of 

Softwood Bark as a Liquid Fuel for Gas Turbines. Part I: Properties of Bio-Oil and Its Blends 

With Methanol and a Pyrolytic Aqueous Phase.” Biomass and Bioenergy (19:5); pp. 337–350. 

Bridgwater, A.V. (1999). “Principles and Practice of Biomass Fast Pyrolysis Processes for 

Liquids.” Journal of Analytical and Applied Pyrolysis (51:1–2); pp. 3–22. 

Bridgwater, A.V.; Cottam, M.L. (1992). “Opportunities for Biomass Pyrolysis Liquids 

Production and Upgrading.” Energy and Fuels (6:2); pp. 113–120. 

Bridgwater, A.V.; Peacocke, G.V.C. (2000). “Fast Pyrolysis Processes for Biomass.” 

Renewable and Sustainable Energy Reviews (4:1); pp. 1–73. 

Brinkman, N.; Wang, M.; Weber, T.; Darlington, T. (2005). Well-to-Wheels Analysis of 

Advanced Fuel/Vehicle Systems — A North American Study of Energy Use, Greenhouse Gas 

Emissions, and Criteria Pollutant. General Motors, Argonne National Laboratory, and Air 

Improvement Resource. 

Bush, G.W. (2006). “State of the Union Address,” Weekly Compilation of Presidential 

Documents 42, no. 5 (Feb. 3, 2006). 

Chiaramonti, D.; Bonini, M.; Fratini, E.; Tondi, G.; Gartner, K.; Bridgwater, A.V.; Grimm, 

H.P.; Soldaini, I.; Webster, A.; Baglioni, P. (2003). “Development of Emulsions From Biomass 

Pyrolysis Liquid and Diesel and Their Use in Engines – Part 2: Tests in Diesel Engines.” 

Biomass and Bioenergy (25:1); pp. 101–111. 

Cottam, M.L.; Bridgwater, A.V. (1994). “Technoeconomic Modeling of Biomass Flash 

Pyrolysis and Upgrading Systems.” Biomass and Bioenergy (7:1–6); pp. 267–273. 

Coutinho, P. M.; Henrissat, B. “CAZy – Carbohydrate Active enZYmes.” http://afmb.cnrsmrs.fr/CAZY/. 

Couture, J. (2006). “2006 Saab BioPower Hybrid Concept.” Automobile.com. http://carreviews.automobile.com/Saab/specs/2006-saab-biopower-hybrid-concept/1790/. 

Cushman, J.H.; Easterly, J.L.; Erbach, D.C.; Foust, T.D.; Graham, R.; Hess, J.R.; Hettenhaus, 

J.R.; Hoskinson, R.L.; Perlack, R.D.; Sheehan, J.J.; Sokhansanj, S.; Tagore, S.; Thompson, 

D.N.; Turhollow, A.; Wright, L.L. (2003). Roadmap for Agricultural Biomass Feedstock 

Supply in the United States. DOE/NE-ID-11129 Rev 1. Idaho Falls, ID: Idaho National 

Engineering and Environmental Laboratory. 

Czernik, S.; Bridgwater, A.V. (2004). “Overview of Applications of Biomass Fast Pyrolysis 

Oil.” Energy and Fuels (18:2); pp. 590–598. 

92 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Czernik, S.; Scahill, J.; Diebold, J. (1995). “The Production of Liquid Fuel by Fast Pyrolysis of 

Biomass.” Journal of Solar Energy Engineering – Transactions of the American Society of 

Mechanical Engineers (117:1); pp. 2–6. 

D’Amico, E. (2006). “Increased Ethanol Demand Heats Up Tank Car Use.” Chemical Week. 

May 24, 2006. 

Davis, G.W. (2006). Alcohol Fuels: Fuels for Today and Tomorrow. To be published by 

Kettering University. 

De Bari, I.; Barisano, D.; Cardinale, M.; Matera, D.; Nanna, F.; Viggiano, D. (2000). “Air 

Gasification of Biomass in a Downdraft Fixed Bed: A Comparative Study of the Inorganic and 

Organic Products Distribution.” Energy & Fuels (14:4); pp. 889–898. 

De La Torre Ugarte, D.G.; Ray, D.E. (2000). “Biomass and Bioenergy Applications of the 

POLYSYS Modeling Framework.” Biomass and Bioenergy (12:1). 

Dean, J.F.D. (2005). “Synthesis of Lignin in Transgenic and Mutant Plants.” Biotechnology of 

Biopolymers: From Synthesis to Patents. Steinbüchel, A; Doi, Y.; eds. DE; pp. 4–26. 

DOE (2006a) “Alternative Fueling Station Counts by State and Fuel Type.” Alternative Fuels 

Data Center. http://www.eere.energy.gov/afdc/infrastructure/station_counts.html. 

DOE. (2006b). Breaking the Biological Barriers to Cellulosic Ethanol: A 

Joint Research Agenda. DOE/SC-0095. U.S. DOE Office of 

Science and EERE. 

DOE Office of the Biomass Program (2004). “Overcoming Non-Technical Barriers to Biomass 

Technology Deployment.” White paper draft. Oct. 4, 2004. 

DOE, Office of the Biomass Program (2006) 30x30 Workshop, 

http://www.30x30workshop.biomass.govtools.us/, L'Enfant Plaza Hotel, Washington, D.C. 

August 1-2, 2006. 

Downstream Alternatives Inc. (1999). The Use of Ethanol in California Clean-Burning 

Gasoline: Ethanol Supply/Demand and Logistics. Bremen, IN: Downstream Alternatives Inc. 

Dupont Co. (2003). “Statement of Objectives – Integrated Corn-Based Bio-Refinery (ICBR).” 

DOE-Golden Field Office memo. 

EIA. (2006). Alternative-Fueled Vehicles Made Available: Table 14. Washington, DC: U.S. 

DOE. http://www.eia.doe.gov/ 

cneaf/alternate/page/datatables/atf14-20_04.html. 

EIA. (2005). Annual Energy Review 2004. DOE/EIA-0384(2004). Washington, DC: Energy 

Information Administration. 

93 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

EIA. (2006a). Annual Energy Outlook 2006: With Projections to 2030. DOE/EIA-0383(2006). 

Washington, DC: Energy Information Administration. 

EIA. (2006b). “Petroleum Navigator: Product Supplied.” Energy Information Administration. 

http://tonto.eia.doe.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_m.htm. 

Elliott, D.C.; Beckman, D.; Bridgwater, A.V.; Diebold, J.P.; Gevert, S.B.; Solantausta, Y. 

(1991). “Developments in Direct Thermochemical Liquefaction of Biomass – 1983–1990.” 

Energy and Fuels (5:3); pp. 399–410. 

Energy-Water Nexus. (2006). Energy-Water Nexus. http://www.sandia.gov/energy-water/. 

Ferris, J.; Joshi, S. (2004). “Evaluating the Impacts of an Increase in Fuel-Ethanol Demand on 

Agriculture and the Economy.” Prepared for the American Agricultural Economics Association 

Meeting, Aug. 1–4, 2004. 

Fierro, J.L.G. (1993). “Catalysis in C1 Chemistry: Future and Prospect.” Catalysis Letters

(22:1–2); pp. 67–91. 

Food and Agricultural Organization of the United Nations (1976). “Wood Chips Production, 

Handling, and Transport.” Rome, Italy. 

Foust, T.; Ibsen, K.; Himmel, M.; Ashworth, J.; Wallace, B.; Jechura, J. (Jan. 20, 2006) 

“Accelerated Path Towards Achieving $1.07 Cellulosic Ethanol.” Technical memo. National 

Renewable Energy Laboratory, Golden, CO. 

Ganesh, A.; Banerjee, R. (2001). “Biomass Pyrolysis for Power Generation – A Potential 

Technology.” Renewable Energy (22:1–3); pp. 9–14. 

Gayubo, A.G.; Aguayo, A.T.; Atutxa, A.; Aguado, R.; Olazar, M.; Bilbao, J. (2004a). 

“Transformation of Oxygenate Components of Biomass Pyrolysis Oil on a HZSM-5 Zeolite. I. 

Alcohols and Phenols.” Industrial and Engineering Chemistry Research (43:11); pp. 2,610–

2,618. 

Gayubo, A.G.; Aguayo, A.T.; Atutxa, A.; Aguado, R.; Olazar, M.; Bilbao, J. (2004b). 

“Transformation of Oxygenate Components of Biomass Pyrolysis Oil on a HZSM-5 Zeolite. II. 

Aldehydes, Ketones, and Acids.” Industrial and Engineering Chemistry Research (43:11); pp. 

2,619–2,626. 

Godbole, S.; Decker, S.R.; Nieves, R.A.; Adney, W.S.; Vinzant, T.B.; Baker, J.O.; Thomas, 

S.R.; Himmel, M.E. (1999). “Cloning and Expression of Trichoderma reesei CBH I in Pichia 

pastoris.” Biotechnology Progress (15:3); pp. 828–833. 

Grassi, G.; Bridgwater, A.V. (1993). “The Opportunities for Electricity Production From 

Biomass by Advanced Thermal-Conversion Technologies.” Biomass and Bioenergy (4:5); pp. 

339–345. 

94 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Greene, N. (2004). “Growing Energy – How Biofuels Can Help End America’s Oil 

Dependence.” National Resources Defense Council. 

Greene, N.; Celik, F.E.; Dale, B.; Jackson, M.; Jayawardhana, K.; Jin, H.; Larson, E.D.; Laser, 

M.; Lynd, L.; MacKenzie, D.; Mark, J.; McBride, J.; McLaughlin, S.; Saccardi, D. (2005). 

Growing Energy: How Biofuels Can Help End America’s Oil Dependence. New York, NY: 

Natural Resources Defense Council. 

Hadder, G. (2000). Ethanol Demand in United States: Regional Production of OxygenateLimited Gasoline. ORNL/TM-2000/165. Oak Ridge, TN: Oak Ridge National Laboratory. 

Hakkila, P. (2004). Developing Technology for Large-Scale Production of Forest Chips: Wood 

Energy Technology Programme 1999-2003. Helsinki, Finland: Tekes. 

Harris, P.; Teter, S.; Cherry, J. (2006). “The Path to Commercialization: Planting the Enzymes 

for Biomass Business.” Presented at the DOE Biomass Program Pretreatment Core R&D 

Interim Stage Gate Review, Golden, CO, June 9–10, 2006. 

Haynes, R. (2003). An Analysis of the Timber Situation in the United States: 1952-2050. PNWGTR-560. Portland, OR: USDA Forest Service, Pacific Northwest Research Station. 

Herman, R.G. (2000). “Advances in Catalytic Synthesis and Utilization of Higher Alcohols.” 

Catalysis Today (55:3). 

Hess, R.; Kelley, S. (2006). Personal communication. Idaho National Laboratory (Hess) and 

North Carolina State University (Kelley) to M. Wu, Argonne National Laboratory. 

Hess, R.; Perlack, R. (2006). Personal communication. Idaho National Laboratory (Hess) and 

Oak Ridge National Laboratory (Perlack) to M. Wu, Argonne National Laboratory. 

Himmel, M.E.; Adney, W.S.; Baker, J.O.; Elander, R.; McMillan, J.D.; Nieves, R.A.; Sheehan, 

J.J.; Thomas, S.R.; Vinzant, T.B.; Zhang, M. (1997). “Advanced Bioethanol Production 

Technologies: A Perspective.” Fuels and Chemicals From Biomass (666); pp. 2–45. 

Himmel, M.E.; Baker, J.O.; Overend, R.P. (1994). “Enzymatic Conversion of Biomass for 

Fuels Production.” American Chemical Society Symposium Series (566). Washington, DC: 

American Chemical Society. 

Himmel, M.E.; Baker, J.O.; Saddler, J.N. (2001). “Glycosyl Hydrolases for Biomass 

Conversion.” American Chemical Society Symposium Series (769). Washington, DC: American 

Chemical Society. 

Himmel, M.E.; Georgiou, G. eds. (1993). Biocatalyst Design for Stability and Specificity. 

American Chemical Society Series 516, Washington, DC: American Chemical Society. 

95 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Himmel, M.E.; Ruth, M.F.; Wyman, C.E. (1999). “Cellulase for Commodity Products From 

Cellulosic Biomass. Current Opinion in Biotechnology (10); pp. 358–364. 

Hood, E.E. (2004). “Bioindustrial and Biopharmaceutical Products From Plants.” New 

Directions for a Diverse Planet: Proceedings of the Fourth International Crop Science 

Congress; Sept. 26–Oct 1, 2004, Brisbane, Australia. ISBN 1 920842 20 9. 

Horne, P.A.; Williams P.T. (1996). “Upgrading of Biomass-Derived Pyrolytic Vapours Over 

Zeolite ZSM-5 Catalyst: Effect of Catalyst Dilution on Product Yields.” Fuel (75:9); pp. 1,043–

1,050. 

Illinois Corn Growers Association. (2005). “Corn and Soybean Supply: Demand to 2015 With 

Ethanol Expansion.” Presentation by the Illinois Corn Growers Association. 

Inside Fuels and Vehicles (2006). “Former Cal/EPA Chief Endorses E-85 over E-10 Ethanol 

Blends.” Inside Fuels and Vehicles Online. http://www.fuelsandvehicles.com/. 

Interagency Agricultural Projections Committee (February 2005). USDA Agricultural Baseline 

Projections to 2014. OCE-2005-1. Washington, DC: USDA Office of the Chief Economist. 

Intergovernmental Panel on Climate Change. (1996). Intergovernmental Panel on Climate 

Change Guidelines for National Greenhouse Gas Inventories. Paris, France: Intergovernmental 

Panel on Climate Change. 

Iowa Corn Promotion Board/Iowa Corn Growers Association. (2006). “Twenty-Five Years of 

Ethanol.” Iowa Corn. http://www.iowacorn.org/ethanol/ethanol_3e.html. 

Jechura, J. (2005). “Sugar Platform Post Enzyme-Subcontract Case.” NREL technical memo. 

Oct. 11, 2005. 

Jechura, J. (2006a). “Integrated Biochemical and Thermochemical Biorefinery Scenarios 

Involving Mixed Alcohol Production.” NREL technical memo. June 27, 2006. 

Jechura, J. (2006b). Personal communication, National Renewable Energy Laboratory, to May 

Wu, Argonne National Laboratory. 

Kim, S.; Dale, B. (2005). “Life-Cycle Assessment of Various Cropping Systems Utilized for 

Producing Biofuels: Bio-Ethanol and Bio-Diesel.” Biomass and Bioenergy (29). 

Kulp, K. (2000). Handbook of Cereal Science and Technology. 2nd edition. New York, NY: 

Marcel Dekker Inc. 

Laser, M.; Jin, H. (2004). Personal communication, Dartmouth College, to May Wu, Argonne 

National Laboratory. 

96 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Leer, S. (2005). “Economists: Ethanol’s Impact on Agriculture a Mixed Blessing.” Purdue 

University News. Published by Purdue University. Oct. 19, 2005. 

Littlewood, K. (1977). “Gasification: Theory and Application.” Progress in Energy and 

Combustion Science (3:1); pp. 35–71. 

Maschio, G.; Lucchesi, A.; Stoppato, G. (1994). “Production of Syngas From Biomass.” 

Bioresource Technology (48:2); pp. 119–126. 

Matthews, J.F.; Skopec, C.E.; Mason, P.E.; Zuccato, P.; Torget, R.W.; Sugiyama, J.; Himmel, 

M.E.; Brady, J.W. (2006). “Computer Simulations of Water Structuring Adjacent to 

Microcrystalline Cellulose 1beta Surfaces.” Carb. Res. (341), pp. 138–152. 

Merrow, E.W.; Phillips, K.E.; Myers, C.W. (1981). Understanding Cost Growth and 

Performance Shortfalls in Pioneer Process Plants. Prepared for the U.S. DOE by Rand Corp. 

Mitchinson, C. (2006). “Genencor’s Perspective.” Presented at the DOE Biomass Program 

Pretreatment Core R&D Interim Stage Gate Review, Golden, CO, June 9–10, 2006. 

National Corn Growers Association (Undated). “NCGA Corn-to-Ethanol Capacity Estimates 

for 10% Fuel Ethanol Inclusion Nationwide by 2015.” Washington, DC: National Corn 

Growers Association. 

National Economic Council (2006). Advanced Energy Initiative. Washington, DC: The White 

House. 

National Ethanol Vehicle Coalition. (2006). “E85 Blending and Tax Incentives.” 

http://www.e85fuel.com/forsuppliers/docs/e85_primer_rack_to_retail.doc. 

Nimlos, M.R.; Mathews, J.F.; Qian, X.; Crowley, M.F.; Chukkapalli, G.; Brady, J.W.; Adney, 

W.S.; Cleary, M.; Himmel, M.E. (2006). “Type A Carbohydrate Binding Module Aids 

Decrystallization of Cellulose.” In preparation. 

OBP. (2005). “Multi-Year Program Plan: 2007–2012.” DOE EERE OBP. 

http://www1.eere.energy.gov/biomass/pdfs/mypp.pdf. 

Olayan, H.B.M. (1987). “Selection of Technology for Synthesis Gas-Based Products in Saudi 

Arabia.” Energy Progress (7:1); pp. 9–17. 

Oasmaa, A.; Czernik, S. (1999). “Fuel Oil Quality of Biomass Pyrolysis Oils – State of the Art 

for the End User.” Energy and Fuels (13:4); pp. 914–921. 

Peacocke, G.V.C.; Bridgwater, A.V. (1994). “Ablative Plate Pyrolysis of Biomass for Liquids.” 

Biomass and Bioenergy (7:1–6); pp. 147–154. 

97 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Perlack, R. (2006). Personal communications. Oak Ridge National Laboratory to M. Wu, 

Argonne National Laboratory. 

Perlack, R.; Hess, R. (2006). “Biomass Resources for Liquid Fuels Production.” Presented at 

the 28th Symposium on Biotechnology for Fuels and Chemicals, Nashville, TN, April 30– May 

3, 2006. 

Perlack, R.D.; Wright, L.L.; Turhollow, A.F.; Graham, R.L.; Stokes, B.J.; Erbach; D.C. (2005). 

Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of 

a Billion-Ton Annual Supply. DOE/GO-102005-2135. Oak Ridge, TN: Oak Ridge National 

Laboratory. 

Persson, S.; Wei, H.; Milne, J.; Page, G.P.; Somerville, C.R. “Identification of Genes Required 

for Cellulose Synthesis by Regression Analysis of Public Microarray Data Sets.” Proceedings 

of the National Academy of Sciences (102:24); pp. 8,633–8,638. 

Radlein, D. (1999). “The Production of Chemicals From Fast Pyrolysis Bio-Oils.” Fast 

Pyrolysis of Biomass: A Handbook. Bridgwater, A., ed. Newbury, UK: CPL Press. 

Renewable Fuels Association (2006). “U.S. Fuel Ethanol Industry Plants and Production 

Capacity.” Renewable Fuels Association. http://www.ethanolrfa.org/industry/statistics/#C. 

Reynolds, R. (2006). Ethanol’s True Potential Contribution to U.S. Energy Security: Ethanol 

Demand Pull Study Analysis. South Bend, IN: Downstream Alternatives Inc. 

Rose, J.C.; Bennett, A.B. (1999). “Cooperative Disassembly of the Cellulose-Xyloglucan 

Network of Plant Cell Walls: Parallels Between Cell Expansion and Fruit Ripening.” Trends in 

Plant Science (4); pp. 176–183. 

Roussel, P.A. (1983) “From Uncertainty to Risk: Reducing the Guesswork in R&D.” Harvard 

Business Review. http://wbww.adl.com/insights/prism/pdf/1990_q2_01-06.pdf. 

Ruth, M.; Ibsen, K. (2005). “Scenarios Developed for OBP Budget and Benefits Analysis for 

FY05.” NREL technical memo. April 16, 2005. 

Saab, (2006). http://www.saabo.com/index.php/category/enviromental 2006 

Schlicher, M. (2006). Personal communication. National Corn to Ethanol Research Center 

Based on a calculation using corn stover density supplying a commercial-scale ethanol mill 

with feedstock storage meeting fire codes. 

Sheehan, J.; Aden, A.; Riley, C.; Paustian, K.; Killian, K.; Brenner, J.; Lightle, D.; Nelson, R.; 

Walsh, M.; Cushman, J. (2002). Is Ethanol From Corn Stover Sustainable? Adventures in 

Cyber-Farming. Draft report. Golden, CO: National Renewable Energy Laboratory. 

98 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Sheehan, J.; Riley, C. (2001a) Annual Ethanol Outlook – 2001. NREL milestone report. 

Golden, CO: National Renewable Energy Laboratory. June 20, 2001. 

Sheehan, J.; Riley, C. (2001b). Annual Bioethanol Outlook: FY 2001. Internal NREL-DOE 

report. Golden, CO: National Renewable Energy Laboratory. 

Shihadeh, A.; Hochgreb, S. (2000). “Diesel Engine Combustion of Biomass Pyrolysis Oils.” 

Energy and Fuels (14:2); pp. 260–274. 

Singh, M.; Vyas, A.; Steiner, E. (2004). VISION Model: Description of Model Used to Estimate 

the Impact of Highway Vehicle Technologies and Fuels of Energy Use and Carbon Emissions 

to 2050. ANL/ESD/04-1. Argonne, IL: Argonne National Laboratory. 

Solantausta, Y.; Beckman, D.; Bridgwater, A.V.; Diebold, J.P.; Elliott, D.C. (1992). 

“Assessment of Liquefaction and Pyrolysis Systems.” Biomass and Bioenergy (2:1–6); pp. 

279–297. 

Solantausta, Y.; Bridgwater, A.T.; Beckman, D. (1995). “Feasibility of Power Production With 

Pyrolysis and Gasification Systems.” Biomass and Bioenergy (9:1–5); pp. 257–269. 

Spatari, S.; Zhang, Y.; Maclean, H. (2005). “Life-Cycle Assessment of Switchgrass- and Corn 

Stover-Derived Ethanol-Fueled Automobiles.” Environmental Science and Technology (39:24). 

Spath, P.; Aden, A.; Eggeman, T.; Ringer, M.; Wallace, B.; Jechura, J. (2005). Biomass Syngas 

to Hydrogen Production Design Report. Golden, CO: National Renewable Energy Laboratory. 

Spath, P.L.; Dayton, D. C. (2003). Preliminary Screening -- Technical and Economic 

Assessment of Synthesis Gas to Fuels and Chemicals with Emphasis on the Potential for 

Biomass-Derived Syngas. NREL/TP-510-34929. Golden, CO: National Renewable Energy 

Laboratory. 

Sticklen, M. (2006) “Plant Genetic Engineering to Improve Biomass Characteristics for 

Biofuels.” Submitted to Current Opinion in Biotechnology. 

Stoppert, J. (2005). “Industrial Bio-Products: Where Performance Comes Naturally.” BIO 

World Congress Meeting, April 2005. Orlando, FL. 

Swain, B. (2006). “Water Conservation and Treatment Strategies for Ethanol Plants.” Presented 

at the Governor’s Ethanol Coalition, U.S. EPA Joint Meeting. Kansas City, MO, Feb. 10, 2006. 

Tabatabaieraissi, A.; Trezek, G.J. (1987). “Parameters Governing Biomass Gasification.” 

Industrial & Engineering Chemistry Research (26:2); pp. 221–228. 

Thomassen, D.; Johnson, G. (2005) DOE Genomics: GTL Roadmap – Systems Biology for 

Energy and Environment. DOE/SC-0090. U.S. DOE Office of Science. 

99 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Toon, S.T.; Philippidis, G.P.; Ho, N.W.Y.; Chen, Z.D.; Brainard, A.; Lumpkin, R.E.; Riley, 

C.J. (1997). “Enhanced Co-Fermentation of Glucose and Xylose by Recombinant 

Saccharomyces Yeast Strains in Batch and Continuous Operating Modes.” Applied 

Biochemistry and Biotechnology (63:5); pp. 243–255. 

Tuskan, G.A.; Wullschleger, S.D.; Cushman, J.H.; Graham, R.L.; Thomas, S.R. (2004). 

“Mitigation of Greenhouse Warming, Biomass-Based Energy Supply Systems, and Accelerated 

Domestication of Energy Crops,” Chapter 3. Applications of Biotechnology to Mitigation of 

Greenhouse Warming. Proceedings of the St. Michaels II Workshop, April 2003. Rosenberg, 

N.J.; Metting, F.B.; Izaurralde, R.C., eds. Columbus, OH: Battelle Press. 

U.S. Congress. Senate. Petroleum Pipeline Regulatory Reform Bill. HR 1301. 97th Cong., 2nd 

sess. (May 21, 1982) 

USDA National Agricultural Statistics Service (February 2006). “Statistics: Crops and Plants.” 

National Agricultural Statistics Service. http://www.nass.usda.gov/ 

Statistics_by_Subject/index.asp. 

U.S. EPA. (1995) Compilation of Air Pollutant Emission Factors. 5th edition. AP-42. Last 

updated March 22, 2006. http://www.epa.gov/ttn/chief/ap42/index.html. 

U.S. EPA. (1999). National Emissions Inventory. 

http://www.epa.gov/ttn/chief/net/1999inventory.html. 

U.S. EPA. (2005). “EPA 2006 Fuel Economy Data.” 

www.fueleconomy.gov/fed/compx2005f.jsp. 

VanRensburg, P.; Zyl, W.H.V.; Pretorius, I.S. (1998) “Engineering Yeast for Efficient 

Cellulose Degradation.” Yeast (14:1); pp. 67–76. 

VanRooyen, R.; Hahn-Hagerdal, B.; Grange, D.C.L.; VanZyl, W.H. (2003). “Comparative 

Expression of Novel Beta-Glucosidases in Saccharomyces cerevisiae.” Yeast (20); pp. S223–

S223. 

Vinzant, T.B.; Ehrman, C.I.; Himmel, M.E. (1997). “SSF of Pretreated Hardwoods: Effect of 

Native Lignin Content.” Appl. Biochem. Biotechnol. (62: 1), pp. 97–102. 

Vitolo, S.; Bresci, B.; Seggiani, M.; Gallo, M.G. (2001). “Catalytic Upgrading of Pyrolytic Oils 

Over HZSM-5 Zeolite: Behaviour of the Catalyst When Used in Repeated UpgradingRegenerating Cycles.” Fuel (80:1); pp. 17–26. 

Wallace, R. (2003) “Cost Estimation for a Lignocellulosic Biomass to Ethanol Pioneer Plant.” 

NREL technical memo. April 15, 2003. 

Wallace, R. (2006) “Vision for Biomass: 30% Displacement of Gasoline by 2030.” NREL 

technical memo. January 2006. 

100 
A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Wang, M.; Wu, Y.; Elgowainy, A. (2005). Operation Manual: GREET Version 1.7. 

ANL/ESD/05-03. Argonne, IL: Center for Transportation Research, Argonne National 

Laboratory. 

Wender, I. (1996). “Reactions of Synthesis Gas.” Fuel Processing Technology (48:3). 

White House, January (2006) Advanced Energy Initiative 

Wilcox, L. (2005). “Energy Outlook: Impacts on Biofuels and Implications for the Farm 

Sector.” Presentation at the 2005 Crop Management Conference; Dec 15, 2005. 

http://www.fapri.missouri.edu/outreach/presentations/2005/cropmang_dec05.pdf. 

Williams, P.T.; Horne, P.A. (1995a). “The Influence of Catalyst Type on the Composition of 

Upgraded Biomass Pyrolysis Oils.” Journal of Analytical and Applied Pyrolysis (31); pp. 39–

61. 

Williams, P.T.; Horne, P.A. (1995b). “The Influence of Catalyst Regeneration on the 

Composition of Zeolite-Upgraded Biomass Pyrolysis Oils.” Fuel (74:12); pp. 1,839–1,851. 

Wooley, R.; Ruth, M.; Sheehan, J.; Ibsen, K.; Majdeski, H.; Galvez, A. (1999). Lignocellulosic 

Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid 

Prehydrolysis and Enzymatic Hydrolysis Current and Futuristic Scenarios. NREL/TP-580-

26157. Golden, CO: National Renewable Energy Laboratory. 

Wornat, M.J.; Porter, B.G.; Yang, N.Y.C. (1994). “Single Droplet Combustion of Biomass 

Pyrolysis Oils.” Energy and Fuels (8:5); pp. 1,131–1,142 

Wright, J. (1987). “Fuel Ethanol Technology Evaluation” in Biofuels and Municipal Waste 

Technology Research Program Summary FY 1986. DOE/CH/1093-6, National Technical 

Information Service Report DE87001140. Golden, CO: Solar Energy Research Institute. 

Wu, M.; Wu, Y.; Wang, M. (2006). “Energy and Emission Benefits of Alternative 

Transportation Liquid Fuels Derived From Switchgrass: A Fuel Life Cycle Analysis.” 

Forthcoming in Journal of Biotechnology Progress. 

Wu, M.; Wu, Y.; Wang, M. (2005). Mobility Chains Analysis of Technologies for Passenger 

Cars and Light-Duty Vehicles Fueled with Biofuels: Application of the GREET Model to the 

Role of Biomass in America’s Energy Future (RBAEF) Project. Argonne, IL: Center for 

Transportation Research, Argonne National Laboratory. 

Xu, Q.; Adney, W.S.; Ding, S.Y.; Himmel, M.E. (2006) “Cellulases for Biomass Conversion.” 

Polaina, J., ed. Industrial Enzymes: Structure, Function, and Applications. London: SpringerVerlag. In press. 

Yaman, S. (2004). “Pyrolysis of Biomass to Produce Fuels and Chemical Feedstocks.” Energy 

Conversion and Management (45:5); pp. 651–671. 

101 
102 

A National Laboratory Market and Technology Assessment of the 30x30 Scenario

Version 1.0 

Zerbe, J.; Baker, A. (1987). “Investigation of Fundamentals of Two-Stage Dilute Sulfuric Acid 

Hydrolysis of Wood.” Klass, D., ed. “Energy From Biomass and Wastes X.” Chicago, IL: 

Institute of Gas Technology. 

Zhang, Y.H.P.; Himmel, M.E.; Mielenz, J.R. (2006). “Outlook for Cellulase Improvement: 

Screening and Selection Strategies.” Biotechnology Advances, in press. 

Zhang, Y.H.P.; Lynd, L.R. (2005) “Cellulose Utilization by Clostridium Thermocellum: 

Bioenergetics and Hydrolysis Product Assimilation.” Proceedings of the National Academy of 

Sciences (102:20); pp. 7,321–7,325. 
Appendix A: Starch Ethanol Deployment 

A.1 Near-Term Ethanol Production Expansion 

The U.S. wet and dry mill ethanol industries are adding production capacity at unprecedented 

rates to meet demand for gasoline blends and the mandates of the Renewable Fuels Standard 

of the 2005 Energy Policy Act. The Renewable Fuels Association estimates there are 4.8 

billion gallons of installed capacity in the United States, and 38 new ethanol plants or plant 

expansions—totaling approximately 2.2 billion gallons of new capacity—are under way 

(Renewable Fuels 2006). Therefore, installed capacity should reach 7 billion gallons in the 

near term, which is well on the way to meeting the terms of the Renewable Fuels Standard. It 

projects U.S. annual ethanol production will reach 7.5 billion gallons by 2012. 

For a 30x30 biofuels scenario, it is necessary to project the contribution of starch-based ethanol 

and the timing of production. Four critical questions must be answered to make this assessment: 

• How much will U.S. corn production expand in the next decade, and what portion of 

the crop will be available for ethanol production given other demands (e.g., exports, 

animal feed, and industrial uses)? 

• How much ethanol can be produced from each bushel of corn available? 

• What are the likely effects of the projected level of ethanol production on corn prices? 

• What is the potential for ethanol production from other grain crops and sugars? 

A.1.1 Corn Available for Ethanol Production in 2015 

Corn production is a function of the yield of corn per acre and the number of acres planted. 

Numerous studies have looked at the near-term supply of corn grain in the United States 

based on projections for these parameters, but two studies serve as benchmarks for low and 

high projections of domestic corn production over the next decade. 

• The USDA’s Baseline Agricultural Predictions, released in January 2005, provides 

projections for key crops to 2014 and serves as the low projection. It projects U.S. 

average corn yield in 2014 to be 161.8 bushels/acre, harvestable acreage to be 76.6 

million acres, and resulting production to be 12,395 million bushels (Interagency 2005). 

• In contrast, the National Corn Growers Association (NCGA) provides the high 

projection. In its scenario, it projects yield to be 193 bushels/acre, harvestable acreage 

to be 78 million acres, and resulting corn production to be 15,054 million bushels. 

The NCGA estimates that “whole corn” use in non-ethanol markets is 9.1 billion bushels in 

2005/2006 (USDA/PRX). For this projection, this demand is assumed to remain constant until 

2015. This assumption does not take into account the possible role that corn distillers dried 

grains with solubles might play in a ramped-up ethanol production scenario by reducing the 

corn needed for cattle feed. 

Table A-1 shows the high and low cases of corn available for ethanol production in 2015 

given these assumptions. 

A-1 
Table A-1. High and Low Corn Production Scenarios, 2014–2015 

Scenario 

Harvested 

Acreage 

(Millions) 

Yield/Acre 

(Bushels) 

Total Annual 

Corn Output 

(Million Bushels) 

Corn Output 

Available for 

Ethanol Production 

(Millions) 

High Case 

(NCGA) 

78 193 15,094 5,954

Low Case 

(USDA Baseline 

Projection) 

76.6 161.8 12,395 3,295

A.1.2 Ethanol Production for Each Bushel of Corn Available 

Low and high projections are also appropriate in this case. The current dry mill industry 

average ethanol yield is 2.6 gallons/bushel. However, improved yield is expected in plants 

under construction because of economies of scale, higher fermentation titers, and improved 

fermentation organisms. In its high case, the NCGA projects yield will reach 2.89 

gallons/bushel by 2015—close to the theoretical limit. However, because older, less-efficient 

plants will still be in operation in 2015, the low estimate is 2.81 gallons/bushel. Crosswalking the high and low projections of ethanol yield with total U.S. corn production 

projections gives the 2015 range of annual U.S. corn-based ethanol production of 9.2–17.2 

billion gallons. See Table A-2. 

Table A-2. High and Low Scenarios for 2014–2015 Corn-Based Ethanol 

Production 

Scenario 

Corn Output Available 

for Ethanol Production 

(Million Bushels) 

Ethanol Yield 

(Gallons/Bushel) 

Annual U.S. 

Ethanol Production 

(Billion Gallons) 

High Case 

(NCGA) 

5,954 2.89 17.2 

Low Case 

(USDA Baseline Projection) 

3,295 2.81 9.2 

A.1.3 Likely Effects of Ethanol Production on Future Corn Prices 

Under these scenarios, U.S. ethanol production capacity would increase from the current ~5 

billion gallons/year to 9.2–17.2 billion gallons/year. Assuming this level of ethanol 

production from corn is achieved in 2015, corn ethanol production will account for 27%–39% 

of total U.S. corn crop consumption, compared with the current 2006 NCGA estimate of 15%. 

A-2 
The USDA and land grant universities have made various estimates of the effects this level of 

ethanol production would have on the price of corn. The projections vary according to yield 

per acre and number of acres planted because rising yield and increased acreage tend to blunt 

price effects as more supply enters the market. On the low-impact side, some researchers 

project only local price increases of $0.10–$0.12/bushel within a 15-mile radius of a new 

ethanol plant (Leer 2005), with minimal impact on national corn prices. On the high-impact 

side, some researchers project a nationwide effect of $0.12/bushel by 2014 (Wilcox 2005). 

The USDA baseline projections analysis combined this impact with other market drivers to 

project a $0.55/bushel price increase in the 2004–2015 period, which would result in corn 

prices moving from $1.90/bushel to $2.45/bushel in that period (Interagency 2005). A 

complementary analysis by Ferris and Joshi of Michigan State University (2004) looked at the 

economic effects of implementing a renewable fuels standard and banning the use of methyl 

tertiary butyl ether as a gasoline additive. They found that corn prices, under various 

scenarios, could reach $2.70 by 2007–2010 from their projected base of $2.10/bushel in 2005. 

Notably, this projected increase is based on ethanol production of only 5 billion gallons by 

2012. The authors note that even though farmers are expected to react to the higher prices by 

planting more corn and less other food and forage crops, this price response is still expected. 

Using the range of a $0.55–$0.60/bushel increase in corn prices by 2015 and assuming a yield 

of 2.81–2.89 gallons/bushel, the production cost of corn grain-based ethanol (if all other costs 

remain stable) will increase by $0.19–$0.21/gallon. If the consensus 2004 dry mill production 

cost of $0.91/gallon of ethanol is used, then this rise in the cost of feedstock alone will raise 

the U.S. starch ethanol production cost to $1.10–$1.12/gallon. This does not factor in recent 

sharp rises in the cost of natural gas and other key utilities. Included in the production cost of 

ethanol is the production cost and market value of distillers dried grains with solubles. A large 

increase in ethanol production could have a negative effect on the selling price of distillers 

dried grains with solubles because of flooding of the feed market. Therefore, the $1.10–$1.12 

cost could rise even more. 

A.1.4 Other Crops for Starch- or Sugar-Based Ethanol Production 

Corn grain is the most abundant and economical source of starch in the United States. Corn is 

inexpensive, widely available, and easily processed. It traditionally is used as feedstock in wet 

and dry mill ethanol plants. If the U.S. dry mill and wet mill industries continue to expand 

rapidly, the increased use of corn is expected to have two effects on feedstock supply: 

1. It will put upward pressure on the price of corn for ethanol production. 

2. Other grains and sugars will become more attractive relative to corn for ethanol 

production if their prices remain stable. 

One ethanol production option is to supplement or substitute common grains and sugars for 

corn as feedstock. The feedstock sources most often mentioned are wheat, sugar beets, sugar 

cane, sorghum, barley, and oats. Figure A-1 depicts annual U.S. production of these crops in 

relation to annual U.S. corn production. Combined production of these crops is equal to 43% 

of the U.S. 2004 corn crop based on USDA data. 

 

A-3 
Figure A-1. 2004 U.S. Grain and Sugar Output 

USDA crop estimates for 2015 (Interagency 2005) linearly projected to 2020 show that only 

corn and wheat production is forecast to increase markedly in this time frame. Current USDA 

projections indicate secondary grain and sugar crops will become less of a factor in U.S. 

agriculture over the next 15 years. Hence if current cropping trends continue, secondary 

grains and US grown sugar crops only offer modest opportunity for ethanol production based 

on volume. 

Another key issue is how these crops compare with corn on a cost-normalized basis and 

whether market drivers can drive a dramatic increase in their production. Table A-3 (Statistics 

Service 2006, Kulp 2000)shows how cereal grain crops (i.e., wheat, oats, barley, and 

sorghum) compare with corn, and Table A-4 shows how U.S. sugar crops (i.e., sugar beets 

and sugar cane) compare with corn. 

Barley, wheat, oats, and the sugar crops have current feedstock costs at least 40% more than 

corn. Because previously outlined enhanced ethanol production effects are not expected to 

raise corn prices by 40% in the next 10–15 years, it is unlikely—barring unforeseen 

reductions below historical norms of crop prices—that the other crops can compete 

economically with corn and hence they will not likely have a significant effect on ethanol 

production. 

However, world sugar prices are considerably lower than U.S. prices. Hence, imported sugar 

could be economically viable for ethanol production. But because this analysis is for domestic 

ethanol production, this scenario is not evaluated. Therefore, market drivers do not exist to 

A-4 
drive the increased production of these crops for ethanol production, and ethanol production 

from these crops is not considered in this scenario. 

However, given the favorable economics of ethanol production from sorghum, which are 

actually lower on a cost-normalized basis than those of corn, enhanced ethanol production 

from sorghum merits investigation. To determine the potential for ethanol production from 

sorghum, a low bound is set at 27% of total annual average crop output and a high bound is 

set at 40%.The total acreage planted in sorghum is assumed to increase proportionately to 

meet non-ethanol demand. Table A-5 shows the ethanol production from this amount of 

sorghum use for ethanol production. 

Table A-5. Ethanol Production from Sorghum 

Scenario 

Sorghum Output Available 

for Ethanol Production 

(Million Bushels) 

Ethanol Yield 

(Gallons/Bushel) 

Annual U.S.

Ethanol Production 

(Million Gallons) 

High Case 

(40% of Existing 

Sorghum Crop) 

168 2.7 450 

Low Case 

(27% of Existing 

Sorghum Crop) 

113.4 2.7 310 

Table A-3 shows that, even at these aggressive levels of sorghum utilization for ethanol 

production, this amount of production would have little effect on the 30x30 goal. Hence, 

ethanol production from sorghum is unlikely to play a major role on the national scale unless 

proper incentives are provided to transition large acreages into sorghum production. 

A.2 The U.S. Potential for Grain/Starch Ethanol Production 

Combining the potentials for sorghum with the potentials for corn gives total projections for 

U.S. grain/starch-based ethanol production in the range of 9.5–17.6 billion gallons. This ethanol 

production will be critical to the establishment of a large-scale market for ethanol as a lowconcentration blend stock for octane enhancement (as E10) and as a commodity fuel (at the 

larger concentration of E85). Given the favorable economics and proven technology of grain 

ethanol compared with those of cellulosic ethanol, market factors will likely dictate that this 

level of grain ethanol production be deployed prior to market deployment of cellulosic ethanol. 

The exception will be if government incentives—such as cost-shared programs, loan guarantees 

and tax credits—are offered to make cellulosic ethanol more attractive. 

In any case—even the high projection—grain-based ethanol is not sufficient to achieve the 60 

billion gallons of ethanol production required to achieve the 30x30 goal. Cellulosic ethanol 

production in the range of 40–50 billion gallons by 2030 will be required. 

A-5 
Table A-3. Cereal Grain Comparison 

Crop 

U.S. Acres 

Harvested Crop Yield 

Bushel 

Weight

Moisture 

Content 

% 

Starch Crop Price 

(Million 

Acres) 

Trend (Bu/Acre) Avg (Lbs) (Dry Wt %) ($/ Bushel) Avg ($/Ton

Barley 3.3–

4.7 

Decreasing 55–70 60 48 14.5 55 $2.10–

$2.80 

$2.50 $104 

Oats (hulless) 

1.8–

2.3 

Variable 62–65 63 34 14 60 $1.10–

$1.80 

$1.60 $94 

Sorghum 5.5–

8.5 

Decreasing 51–70 60 56 70 $1.60–

$2.40 

$2.00 $71 

Wheat 46–53 Variable 35–44 40 60 13.5 72 $2.80–

$3.60 

$3.25 $108 

Corn 69–75 Increasing 130–160 145 56 15 72 $1.80–

$2.80 

$2.25 $80 

Table A-4. Sugar Crop Comparison 

Crops U.S. Acres Harvested Crop Yield % Sucrose Crop Price Average Ethanol Y

Million Acres Trend Ton/Acre Dry Basis $/Ton $/Ton Gallon/To

Sugar Beets 1.2–1.4 Decreasing 20–24 64–70 34–41 37.50 22.1 Sugar Cane 0.9–1.0 Decreasing 29–35 70–90 26–30 28.00 16 

A-6 
Appendix B: Market Penetration Modeling: The Biomass 

Scenario Model (BSM) 

Most analyses performed to characterize the potential of biofuels technology are static in 

nature in that they are snapshots of the current market and technology status. But 

understanding deployment requires another dimension: time. Only in the context of time can 

one understand the sequence of events behind the deployment of biofuels technology. A 

dynamic system model to investigate potential market penetration scenarios for cellulosic 

ethanol is an essential component to understanding the interplay between different areas of the 

supply chain and investor behavior. 

B.1 The Biomass Scenario Model (BSM)i

In 2005, the Biomass Program at NREL—working with Dartmouth College and the Natural 

Resources Defense Council—developed a dynamic systems model to understand the market 

dynamics associated with deployment of bioethanol technology. That model—the Biomass 

Scenario Model (BSTM)—has served as a useful prototype for the type of modeling work that 

will be needed to strategically assess R&D and deployment strategies of DOE’s Biomass 

Program. See Figure B-1 for a simplified schematic of the model. 

The model attempts to define the dynamics (i.e., the timing) of building each of the 

components of the supply chain for a future ethanol industry. The key components of the 

supply chain are shown along the top of Figure B-2. The model consists of multiple, 

interrelated “layers” as shown below. The key components of the dynamic framework are: 

• The supply chain represents the physical movement of materials starting at the farm 

and ending in the vehicle utilization of the fuel. 

• The infrastructure that supports movement through the supply chain. 

• R&D, piloting, technology risk reduction. 

• Conversion Plant investment decisions. 

• Policies effecting biomass-based fuel production & usage. 

• The “external economy,” infrastructure development, corn, EtOH and Fuel Markets. 

B-1 
Figure B-1. Schematic of BSM 

B-2 
Figure B-2. Supply Chain and BSM Framework 

B.2 BSM Model Platform (STELLATM) 

There are many software tools and techniques available for assessing and predicting dynamic 

behavior, with varying degrees of complexity and ease-of-use. After assessing the pros and 

cons of the various available options, STELLATM was chosen as the platform for the BSM. 

This general modeling tool provides a simple visual language for representing different types 

of dynamic behavior in systems. Its relatively intuitive nature is well suited to the kind of 

general “what if” scenario building we are interested in doing as we look ahead to understand 

B-3 
the possible paths for a new bioindustry. The software translates the elements of ordinary 

differential equations into relatively intuitive elements. 

B.3 The Language of Dynamic Systems

STELLATM requires modelers to learn the language of dynamic systems. This systems 

language is used to write a story. It comes with its own set of nouns, verbs and adjectives. 

Nouns, verbs and adjectives are combined to create descriptive “sentences” that make up parts 

of the systems story we are trying to tell. A simple sentence in STELLATM’s graphical 

interface looks like Figure B-3 and detailed in Table B-1. 

Stock

Converter

Flow

Figure B-3. The Language in STELLATM 

Table B-1. Elements of STELLATM Components 

Component Systems Language 

Analog Definition 

Noun Stock An accumulation of some “thing” 

Verb Flow A movement into or out of an accumulating stock 

Adjectives and 

Adverbs Converters Algebraic statements describing the behavior or logic 

associated with a flow (adverb) or a stock (adjective) 

The modeler develops sentences using this framework to describe and model the behavior of 

the system. A simple example of this modeling process is as follows: 

To describe agricultural land use to support the bioindustry, a sentence is developed as 

follows and modeled as shown in Figure B-4: 

“The amount of land dedicated to switchgrass production increases as farmers decide 

to plant switchgrass and decreases as farmers decide to replace switchgrass fields 

with other crops” 

B-4 
Figure B-4. Example of STELLATM Language for Biomass Production 

As Figure B-5 shows, the STELLATM modeling tool is used to integrate all aspects of the 

model. The STELLATM component is used to focus on the dynamic behavior, and other, more 

specialized models and sources of information are relayed on for much of the technical and 

market characterizations that is needed. 

Figure B-5. STELLATM Model as an Integrating Tool for Other Detailed Biomass 

Analysis 

B-5 
B.4 Detail of Supply Chain Components in the BSM 

B.4.1 Biomass Feedstock Supply 

The biomass feedstock sections of the BSM describe the relationships of biomass production 

and land use as they pertain to the bioindustry. Feedstock production modules calculate the 

potential of biomass that can be used from forest residues, agricultural residues and 

switchgrass sources. 

Agricultural and forest residue availability are calculated as a function of offer price. The 

annual forest residue availability is fixed and is a function of price only, while the agricultural 

residues have the ability to grow at the rate of 1% annually for a 25 year period. The 2005 

baseline availability for forest and Agricultural residues is shown in the figure below. 

 

Figure B-6. Agricultural and Forest Residue Availability 

0

20

40

60

80

100

120

140

$0 $20 $40 $60 $80

$/bdt

Million Tons per Year

Forest Residues Agricultural Residues

Switchgrass production is a function of the yield (ton per acre), driven by R&D in the 

agriculture sector, and the rate in which land is planted in switchgrass. The yield increase that 

results from R&D is a function of the investment into the various R&D programs. The BSM 

has a maximum effective investment ceiling of $10-million annually. This means that any 

amount spent over the ceiling will have no further affect on improving yield. The R&D yield 

growth rate defined in the model is analogous to historical corn yields. Corn yields grew 

exponentially for some time before slowing to linear growth over time. Switchgrass 

production volumes are a sum of the production from land in transition and land established in 

switchgrass. The land in transition is defined as the product of acreage converting to 

B-6 
switchgrass and the yield based on the state of the art agricultural R&D. Production from 

established land is the product of mature switchgrass and the yield based on actual production. 

Land allocated to switchgrass is based on the economic attractiveness to grow and harvest 

switchgrass for ethanol production. The decision for the farmer is simple; if there is more net 

profit to be made by planting switchgrass, they will. The BSM controls the growth rate of 

switchgrass with the following graphical function, where the switchgrass revenue per acre is 

compared to the revenue of land in other (non-switchgrass) uses. Depending on the ratio, 

switchgrass is either introduced or replaced at the rate shown on the y-axis in Figure B-7. 

-60%

-40%

-20%

0%

20%

40%

60%

80%

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3

SG revenue per acre relative to other uses

Net % Growth in SG land per 

year

Figure B-7. Rate of Land Movement into Switchgrass 

B.4.2 Biomass Transportation 

The biomass transportation section of the supply chain calculates the cost of delivering the 

feedstock from the farm gate to the plant gate. The feedstock price is calculated in two pieces; 

1) payment to grower and; 2) transportation cost. 

The grower payment in the BSM is not static. Instead, lignocellulosic biomass feedstock costs 

are determined through a dynamic bidding process in which the investor in a new cellulosic 

ethanol plant looks at the available biomass inventory to determine if, at current prices, there 

is enough to support a new processing facility. If not, the investor bids up the market price 

until the response from the biomass feedstock supply system is sufficient to meet demand. 

The cost of transport is usually broken down into two types of costs—fixed and variable (see 

Table B-2). Fixed costs—expressed in dollars per ton of biomass—are not affected by 

transport distances. Thus, loading and unloading operations are included here. For simplicity, 

we also lump annualized capital costs for all equipment in the fixed cost. Variable costs are 

B-7 
including all operating costs associated with transportation. In either case, operating costs 

include labor, maintenance, fuel and oil. 

Table B-2. Fixed and Variable Transportation Costs 

Type of Cost Component

Fixed Loading Operating Costs

Unloading Operating Costs

Annualized Capital Costs for Loading Equipment

Annualized Capital Costs for Transportation Equipment

Variable Costs Transportation Operating Costs

Cost of transport is expressed simply as: 

Costtransport = Cost fixed + Cost var iableDp

 Total cost of transportation is expressed in $ per wet ton of biomass. The variable costs are 

expressed in $ per wet ton-mile, while the fixed costs are expressed in $ per wet-ton. The 

larger the distance for transport (Dp), the higher the overall cost for delivering the feedstock 

to the plant. Thus, unlike the capital and operating costs for the conversion facility, feedstock 

delivery cost actually increases in proportion to the square root of the size of the plant from 

which biomass is collected. The area of collection is function of yield of biomass per unit area 

and the percent of surrounding land that has biomass available: 

 

2

onlinep p

collection onlinep

collection p

dC R

A dC

A R

ρπ

ρπ

=

=

= 2

Where; 

Acollection is the area of biomass collection 

surrounding the plant, which will meet 

the total annual biomass supply of the 

facility 

Rp is the radial distance of collection 

around the conversion facility in miles 

Cp is the daily biomass throughput of the 

conversion facility 

donline is the number of days per year 

online 

ρ is the density of available biomass in 

the surrounding area in ton per acre of 

surrounding land, and is calculated as: 

ρ = Yharvest fbiomass acre /mi2

[ ]

Figure B-8. Biomass Collection Radius Logic 

B-8 
Where; 

Yharvest is the yield of harvestable biomass in dry tons per acre of land which is providing 

biomass to the facility 

fbiomass is the fraction of the surrounding land that is actually providing biomass to the 

conversion facility, and 

[acre/mi2

] is a conversion factor of 640 acres per square mile 

Solving for the radial collection distance around the plant gives the following: 

Rp = Cp donline

πYharvest fbiomass acre /mi2

[ ] 

B.5 Biomass Conversion Plants 

The biomass conversion plants section is made up of several modules. In order for the 

biofuels industry to grow, there must be successful R&D programs to reduce the risk for 

conversion technology to enter the marketplace. Once the R&D has been proven at the pilot 

scale, plants may begin to be built at the demonstration, pioneer and finally the full size scale 

as long as the investors find the market for biofuels production attractive. 

B.6 R& D 

There are two areas of R&D that must be successful in order to enable the biofuels industry; 

the area of applied fundamentals and piloting. Analogous to agricultural R&D efforts 

described earlier, successful R&D in fundamental sciences is a function of the amount of 

dollars invested. As the amount of effort ($) is increased, the R&D yield (gal/ton) is increases 

until it reaches its maximum. Similarly, the capital cost growth is lowered as a result of 

increased effort. 

Piloting R&D builds on the R&D yield and capital cost growth reduction accomplished in 

fundamental R&D. R&D in piloting enables yields to approach those found through 

fundamental R&D efforts. Once again, the amount of success in piloting is directly related to 

the funding input. Without piloting, the maximum yield that can be obtained is only 50% of 

that obtained though fundamental R&D. 

B.7 Conversion Facilities 

Fundamental R&D and piloting efforts has a direct impact on plant performance. Plants are 

designed based on feedstock availability and design yield (gal/ton). Plant performance is also 

influenced by the overall experience of the industry, which goes through a learning curve until 

the yields reach their maximums. The BSM assumes that the cellulosic ethanol industry 

reaches maturity and maximum yields when the average plant capacity is at 600 million 

gallons annually. The demand for ethanol coupled with the increased plant yield due to 

industrial maturation dictate the growth of the industry by new plants being constructed. 

Demonstration, pioneer and full scale plants are constructed depending on performance. Each 

plant goes through a design and construction, start up and online phase where costs and yields 

are determined separately. Utilizing the number of plants coming on line and operating, 

average yield and utilization allows the BSM to calculate the industry output in gallons and 

B-9 
feedstock requirements. If the feedstock availability is less than the plant requirements, the 

industry will reduce its growth rate to match the available feedstock supply. 

B.8 Investment 

Investor attractiveness to build plants depends on the cost of cellulosic ethanol production, 

including rate of return on investment. Other factors, such as the demand and competitiveness 

of the marketplace are also crucial factors, as are any government subsidies for biomass or 

fuel production. 

Capital costs are calculated for each scale of plant in the BSM. Using experience at previous 

scales allows for capital cost growth reductions as the size of plants increase. Reductions in 

capital can also be realized through co-location options for the first facilities, before standalone Greenfield plants become economically viable. As the industry ramps up from 

demonstration to pioneer to full scale facilities, the capital growth factor at each stage is 

dependent on the accumulated experience at previous scales. 

Depending on the scale of the plant and the prior scale experience, investors will require a 

certain rate of return on their investment. The BSM models three types of investor behavior; 

conservative, moderate and aggressive, along with a mature industry rate of return and a new 

industry risk factor to determine the required rate of return on plants. To reduce risk, investors 

(depending on what type) require experience at prior scales before lowering the required rate 

of return on the next larger scale. For demonstration plants, the BSM only requires a 2% rate 

of return, assuming that demonstration plants are an investment in proving out technology and 

the investor is willing to accept a lower return as a trade off for return on full scale plants. 

Finally, total plant costs can be calculated by adding the cost of capital + taxes + loan. The 

BSM utilizes standard financial calculations to calculate the capital recovery factor, annual 

return before and after taxes and annual loan payments. 

B.8.1 Fuel market 

Penetration of ethanol from cellulosic feedstocks finally is a function of the marketplace. The 

price of corn and its effect on the corn ethanol market, along with the current gasoline market. 

Costs have a major influence on the rate in which cellulosic ethanol can penetrate into the 

fuels market. The price of corn is determined from a relationship of price to its supply demand 

ratio, which is a function of demand for other uses to the total production minus corn being 

used for ethanol. Corn production is calculated as the yield per acre times the number of 

planted acres. The BSM models the yield to increase linearly at a rate of 1% annually for 25 

years from the current 2006 level of 160bu/acre. Other traditional non-ethanol demand for 

whole corn increases annually at its historic rate of 0.8%. Finally, demand for corn for ethanol 

production is calculated from the corn ethanol plant capacity and plant yields. 

The price for corn ethanol is calculated based on specified capital, operating, and corn costs 

minus DDG revenues and any existing subsidies. The attractiveness to build corn ethanol 

plants is based on the competing prices of cellulosic ethanol and gasoline. The attractiveness 

is a ratio of the corn ethanol price to the lower of either the cellulosic ethanol or gasoline 

B-10 
price. The BSM uses an attractiveness factor along with a maximum growth rate to determine 

the corn ethanol growth and total corn ethanol industry capacity. 

The fuel marketplace for ethanol is based on various future oil price scenarios. At low 

volumes, the value of ethanol is enhanced due to its value as an oxygenate and octane 

enhancer. However, that advantage disappears at an ethanol production rate of around 8-

billion gallons per year, when ethanol begins to enter the market more as a primary fuel. 

B.8.2 Vehicle Market 

The vehicle market in the BSM is designed to calculate the potential demand for ethanol. In 

the BSM, there are two future vehicle scenarios; business as usual which has a very slow 

growth rate of E-85 vehicles and in essence inhibits ethanol growth due to lack of demand, 

and the Union of Concerned Scientists (UCS) estimates, where the influx of E-85 vehicle 

takes place at an accelerated rate and thereby allows for unconstrained ethanol industry 

growth from a demand perspective. The UCS estimates were used for all the runs in the 

30x30scenario, so that there would not be a demand constraint. 

The BSM tracks the number of vehicle and the efficiency of each vehicle type. Using the 

number of vehicles and efficiency of the fleets, the BSM calculates the potential ethanol 

demand for total, E-85 and E-10 vehicles. The demand is then compared to the total 

production to measure the attractiveness for investors to build additional plants. 

Table B-3 below list the important assumptions made in the BSM as well as sources for 

information that went into the model. 

TableB-3. Assumptions Used in Modeling the BSM 

Supply Chain Element Assumptions 

Biomass Production 

Ag residues supply per ORNL 2000, Biomass Feedstock Supply Curves 

U. Tennessee POLYSYS land competition model for switchgrass on prime cropland 

Switchgrass yield of 5 tons per acre rising 1.5% per year to 13 tons per acre in 2050 

Corn at $1.90 per bushel with small increase as function of demand 

No arbitrary limit on corn supply for ethanol 

Only change in agricultural policy is allowance for energy crop harvesting on CRP land 

Biomass Distribution Truck delivery up to 50 miles to plant 

Biomass Conversion to 

Biofuels 

$1.07 per gallon ethanol nominal price target in 2012 at pilot scale 

Three year lag from pilot to full scale 

Maximum plant build-out 5 billion gallons per year (historical gasoline data) 

5,000 ton biomass per day commercial bioethanol plants 

Investor finance assumptions: 30% ROI for new technology declining to 10% ROI as 

industry experience reaches maturity; Debt: equity ratio of 40/60; Debt finance 

assumption of 25% capital cost growth for new plant, declining to zero for mature 

industry or set to zero for loan guarantee scenarios. 

Biofuels Distribution No modeling done (assumes infrastructure development not rate-limiting) 

End Use Market 

Two vehicle scenarios: 

1) business-as-usual rate of FFV introduction 

2) aggressive FFV introduction per Union of Concerned Scientists vehicle fleet model 

Fuel market based on AEO 2006 Oil Projections 

ORNL refinery model analysis to predict ethanol blending value as a function of demand 

B-11 
B-12 
Appendix C: Assessment of Uncertainty and Risk for 

Biomass Conversion Technologies 

The goal of risk assessment is to investigate factors that can affect the commercial success of 

processes and technologies and then to plan for them. For biomass conversion, risk 

assessment can be applied to the development of biorefinery processes that will provide the 

ethanol needed to replace 30% of 2004 U.S. motor gasoline demand by 2030. 

Risk assessment translates uncertainty into a risk rating by incorporating qualitative 

judgments. Risk assessment is not a single activity but rather encompasses a range of 

activities that require a range of tools and methods. 

There are various aspects of risk assessment, but a core component is uncertainty analysis. 

Types of uncertainty include: 

• Financial uncertainty

How do the financial assumptions affect the estimate of production costs? Can better 

financial assumptions offset technology targets that have not been fully met? 

• Market uncertainty

If all technology and cost targets are met, will the technologies be accepted in the 

marketplace and commercialized? 

• Process uncertainty

If pieces of process technology (such as yields) are not obtained, how will it affect 

production costs? What if some pieces are better than expected? How much can this 

offset pieces that are not as good as expected? 

Other aspects of risk assessment are more difficult to quantify. These include: 

• R&D risk

What is the ability to achieve R&D targets? On what does success depend? Will 

increased funding necessarily result in successful R&D? Are there differences in the 

outcomes of R&D funded by government and private industry? 

• Deployment risk

What is the likely process configuration to provide a positive internal rate of return? 

Can processes be guaranteed? How likely is industry growth? 

• External factors

What other factors (such as fuel and market turns and policymaking) could play a part 

in development and deployment? 

Methodology can also be difficult to determine. Difficulties include: 

C-1 
• Using past experience

Can historical data for similar industries be used to judge this industry? Have underlying 

conditions changed so that analogies are no longer valid? How are the opinions of experts 

incorporated—especially when those opinions are in opposition to each another? 

• Developing useful results

Is this a high- or low-risk venture? How does it compare with other avenues? What is 

its ranking on a scale with other options?

C.1 Uncertainty Analysis 

The first step of a risk assessment is to identify the process aspects that can be quantified. 

Process and financial uncertainty are relatively straightforward quantitative measures. For 

these, a single-value uncertainty interval analysis can be performed. This analysis involves: 

• Estimating the range in which a process or financial variable may occur 

• Calculating the cost effect at each extreme 

• Holding all other variables the same as the benchmark value. 

A more sophisticated analysis involves adjusting multiple variables simultaneously while 

taking into account the probability of a variable having a certain value. This forms the basis of 

Monte Carlo analyses. For these analyses, one can calculate not only the range of costs 

possible but also the costs that are most probable. This type of analysis can uncover 

interdependencies among process and financial variables, but the accuracy is dependent on 

how accurately one characterizes the probability of the input values. The validity of a Monte 

Carlo analysis is also dependent on a robust and mature model to combine the effects of 

variables. A model that is not robust can give misleading results. For example, the models for 

the biochemical conversion operations are more mature than the models for feedstock 

delivery systems and thermochemical conversion operations. A Monte Carlo analysis of these 

processes would not give as-accurate results. The same situation applies for the advanced 

technology process scenarios. More process detail is needed before a multivariate analysis 

would be useful. 

C.1.1 Uncertainty Analysis for $1.07/Gallon Target Technologies 

Uncertainty analysis was performed on the feedstock assembly, biochemical conversion 

processes, and thermochemical conversion processes that make up the $1.07/gallon target 

cases. The results are loosely grouped into three types of uncertainty: 

• Financial uncertainty, including capital and operating costs and 

investment assumptions

Equipment, supplies, and loans make up this type of uncertainty. However, by using 

experts such as vendors and engineering/construction firms, information can be 

gathered to reduce equipment uncertainty. Chemical and other operating expenses can 

also be determined with relative ease. Investment assumptions are the most difficult to 

determine but the easiest to evaluate via uncertainty analysis. 

C-2 
Here, ethanol production costs have been expressed as MESPs. An MESP is the result 

of a specific financial scenario that ties together levels and types of technologies, 

operating and installed capital costs, and the time-value of money. The particular 

financial scenario for an MESP attempts to be an average of debt versus equity 

financing and a reasonable internal rate of return. The MESP provides a mechanism 

for comparing one type of technology with another, especially if they have different 

relative operating and capital costs. 

• Market uncertainty, including product values and plant size

Product value and volume data are relatively easy to obtain; however, the introduction of 

a new or replacement product into an established market affects the market in uncertain 

ways. It can reduce or inflate value and change the volume of both demand and supply. 

Because co-products will be important to the economic health of individual processing 

plants, it is important that plant developers look closely at their effects. However, the cost 

analyses presented here represent industry averages. In this case, significant co-product 

credits are justified only if: 

o The market for the co-product is on the same order as the fuels market (such as 

co-produced electricity) 

o The amount of co-product is a fraction of that material’s market (and the 

production of that co-product by all processing plants will not swamp the market). 

Significant co-product credits can be justified only in near-term scenarios. In the near 

term, a small number of processing plants are expected to produce these co-products. 

These plants will ensure that they can sell the co-product into the market at a price 

approaching market value. 

Overall ethanol market acceptance has been estimated in the past using market models 

(such as MARKAL and the National Energy Modeling System). These models attempt 

to answer questions about how competing energy sources and technologies will be 

used to supply the needs of the marketplace. 

Plant size is dictated by supply and demand economics. There is an economy-of-scale 

effect that, when taken advantage of, significantly reduces the cost of capital. 

• Process uncertainty, including research targets and other process parameters

The cost implications of missing, hitting, or exceeding a target or set of targets are 

easily seen with process uncertainty analysis. Combinations of sensitivity analysis can 

provide several ways to achieve the same cost target, which reduces the overall risk of 

the process. Quantifying the relative cost savings of process improvements allows 

work to be directed to the most cost-effective R&D. 

Newer transition models attempt to combine the effects of market “pull,” operating cost 

“push,” policy, and financial factors. These models are the most complete at incorporating 

multiple aspects of uncertainty. 

C-3 
These are not exhaustive collections of items of uncertainty but rather those thought a priori

to have the greatest effects. However, analysis shows that not all of these items have as large 

of an effect as might be expected. 

C.1.1.1 Feedstock Assembly 

Feedstock assembly core R&D involves developing and applying technologies that address 

the barriers identified in the Roadmap for Agricultural Biomass Feedstock Supply in the 

United States (Cushman 2003). Overcoming these barriers is necessary for the achievement of 

the interim (2012) tonnage, cost, and regional accessibility targets. For R&D purposes, the 

barriers have been grouped into harvest and collection, storage, pre-processing, and 

transportation and handling unit operations. Figure C-1 summarizes the technical parameters 

and their feedstock assembly unit operations. 

Figure C-1. Technical Parameters Associated with Feedstock Assembly System 

Unit Operations 

The delivered cost and quality of biomass feedstocks is dependent on market and policy 

drivers as well as technical parameters. The sensitivity and risk associated with market and 

policy drivers are handled at the industry scale with appropriate models. However, the implied 

risks associated with feedstock assembly core R&D and analysis are assessed by applying a 

single-value uncertainty interval analysis to feedstock technical parameters. The results of this 

analysis are shown in figures C-2 and C-3. 

To perform this analysis, a model capable of quantitatively determining the integrated and 

individual costs of feedstock assembly and pre-processing operations must exist. This model 

must then be used to evaluate the high, mid, and low ranges of each technical parameter to 

provide the relative sensitivity of these parameters to the feedstock assembly and preprocessing system. 

C-4 
The dry feedstock assembly system is the most mature in terms of engineering data and assembly 

system models that incorporate the R&D needs of the conversion processes. These data and 

models were used to determine the range of sensitivity of the dry feedstock technical parameters 

as well as the relative costs associated with different unit operations and cross-cutting costs. 

The tornado chart in Figure C-2 presents the ranges of sensitivity for each of these parameters. 

The ranges are based on the 2012 delivered feedstock target of $35/dry ton and illustrate the 

potential of each parameter to affect the overall assembly system cost through engineering and 

quality impacts. The chart is split into three regions: (1) specific research technical parameters 

that directly affect the cost and quality of delivered feedstocks, (2) unit operations that make up 

the assembly system, and (3) variable costs that cross-cut all assembly system operations. The 

technical parameters are the primary focus of the risk analysis because their effect will 

determine how and why the cost targets are met. The other regions show the relative effects the 

technical parameters have on the assembly system as a whole. 

The ranking of each parameter with respect to another implies some degree of R&D risk. 

Those parameters with large cost ranges have a greater potential to affect the 2012 targets 

(positively and negatively). However, inherent with the single-value analysis approach is the 

inability to identify the interdependence of parameters. Although a Monte Carlo analysis could 

determine this interdependency, the feedstock assembly system models are too immature to 

provide the input ranges required. Thus, Figure C-2 will assist in identifying and directing 

R&D based on relative potential but cannot be used to determine the effect of changing a 

single parameter on other parameters. Nevertheless, it is expected, based on the nature of each 

feedstock assembly unit operation, that synergistic credits or debits will be realized. 

Figure C-2. Sensitivity Analysis of Technical and Logistical Parameters 

in the Dry Feedstock Assembly $35/dry ton Case 

C-5 
Figure C-3 shows the sensitivity of R&D technical parameters specific to a wet feedstock 

assembly system. As before, each parameter is based on the 2012 delivered feedstock target 

of $35/dry ton and illustrates a relative effect on assembly system costs and feedstock 

qualities. Unique to the wet feedstock type is its potential to use in-storage pre-processing 

technologies to significantly affect feedstock cost and quality. Specifically, the free sugar 

capture and reduced pretreatment severity parameters have the potential to offset much of the 

storage, pre-processing, and transportation costs (which are higher than the respective dry 

system costs because of the water content of the feedstock). 

Figure C-3. Sensitivity Analysis of Technical and Logistical Parameters 

in the Wet Feedstock Assembly $35/dry ton Case 

C.1.1.2 Biochemical Conversion Technologies 

Sensitivity analysis of biochemical processes has been a cornerstone of R&D in the OBP and, 

prior to that, EERE biofuels programs. These analyses have provided information about the 

most cost-sensitive parameters of the technology. In recent years, groupings of parameters 

have identified “barrier areas” in the conversion process. These have proved useful in the 

quantification of the challenge of biomass processing. Figure C-4 highlights these challenges. 

C-6 
Figure C-4. Barrier Areas for the Biochemical Ethanol Process 

Figure C-5. Single-point Sensitivity Analysis of Process Parameters 

in the Biochemical Conversion $1.07/gallon Target Case 

C-7 
Many market factors are possible, but the two selected, plant size and electricity value, 

demonstrate the range of effect. Plant size, because of economies of scale for capital 

equipment, has a large effect. It is determined by factors such as feed availability and cost, 

market demand, and technology level (e.g., pilot or demonstration). However, the electricity 

credit has a small effect on production costs. 

Of the financial parameters evaluated, ROI has the greatest effect. A higher required ROI can 

result from higher risk perception by financiers. This can be reduced by piloting and 

demonstrating facilities to prove the technology at larger and larger scales prior to building a 

commercial plant. Getting a no- or low-cost loan through a guarantee program can reduce the 

financial burden of capital investment, especially when the loan is lower than the hurdle rate 

required by equity holders. 

However, of most interest to the R&D program is the effect of uncertainty in the technology. 

Feedstock cost and composition have significant effects on production costs. Moisture that 

does not affect solid loading in the process appears to have a minor effect. As higher-solids 

processing is achieved, feedstock moisture will begin to drive process loadings. Yields, 

whether in pretreatment or fermentation, can have a large effect on cost. For the ranges 

studied, which were developed from state-of-technology and target values, pretreatment 

yields of xylose were the largest factor, in part because larger pretreatment yields ultimately 

lead to larger ethanol yields. 

Table C-1 shows abbreviated process targets and is color-coded to Figure C-5 by barrier area. 

Of the process targets that have not yet been met, feedstock and enzyme cost have the largest 

effects. Pretreatment yields of xylose and fermentation yields of all sugars combined also 

have large effects on the production cost of ethanol. Conversely, some achieved targets, such 

as solids loading in saccharification, could be improved for cost savings or to offset 

problematic targets. Other parameters, such as capital costs and feedstock composition, are, at 

best, identified as areas of uncertainty that must be tracked and, if possible, better quantified 

as the process is developed. 

C-8 
2005

Post EnzymeSubcontract

2007 2009 2011

2012 

Market 

Target

Change 

to MESP 

($/gal)

Minimum Ethanol Selling Price $2.26 $1.07

Installed Capital per Annual Gallon $3.04 1.85

Yield (Gallon/dry ton) 65 90

Feedstock

Feedstock Cost ($/dry ton) $53 $35 $0.26

Pretreatment

 Solids Loading (wt%) 30% 30% 30% 30% 30%

 Xylan to Xylose 63% 68% 77% 86% 90% $0.10

 Xylan to Degradation Products 13% 12% 9% 6% 5%

Conditioning

 Xylose Sugar Loss 13% 11% 7% 2% 0%

 Glucose Sugar Loss 12% 10% 6% 2% 0%

Enzymes

Enzyme Contribution ($/gal EtOH) $0.32 $0.32 $0.32 $0.10 $0.10 $0.23

Saccharification & Fermentation 

 Total Solids Loading (wt%) 20% 20% 20% 20% 20%

 Combined Saccharification & Fermentation Time (d) 7 7 6 3 3 $0.04

 Overall Cellulose to Ethanol 86% 86% 86% 86% 86%

 Xylose to Ethanol 76% 76% 80% 80% 85% $0.03

 Minor Sugars to Ethanol 0% 40% 40% 80% 85%

Table C-1. Possible Interim Targets to $1.07/Gallon Ethanol for Stover 

Combining the results of single-point sensitivities with the area results in Figure C-6 provides 

a snapshot of overall process challenges and areas in which R&D may be lagging. 

C-9 
Figure C-6. Combined Sensitivity Analysis Provides Barrier Area Cost Effects 

Monte Carlo analysis was used to evaluate the likely production cost of feedstock and process 

yield variations. Figure C-7 shows the range of production costs possible and the likely 

values. Feedstock variation had a larger effect on production cost than process yield for the 

ranges studied. 

Figure C-7. Monte Carlo Analysis of Feedstock Composition and Yield 

Uncertainties 

C-10 
C.1.1.3 Thermochemical Conversion Technologies 

Figure C-8. Barrier Areas for the Thermochemical Ethanol Process 

Figure C-9. Sensitivity Analysis of Process Parameters 

in the Thermochemical Conversion $1.07/gallon Target Case 

C-11 
Figure C-9 depicts the results of uncertainty analysis of the thermochemical conversion 

process. The market and financial uncertainties are essentially the same as those explored for 

the biochemical process. The research items are applicable to thermochemical technology 

and, therefore, are different. 

The market (plant size and value of co-products) and financial (required ROI) factors have 

large effects on production costs. The benchmark plant size was established as 2,000 dry 

tonnes/day. For a commercial plant, it will be determined by factors such as feed availability 

and cost, market demand, and the level of technology (e.g., pilot or demonstration). In general, 

the larger the plant, the lower the capital costs on a unit-produced basis (per gallon of ethanol). 

For the thermochemical process, the co-product credit for higher alcohols has a significant effect 

on process economics. If higher alcohols can be sold in the chemical market, then they will have 

a value 3–4 times greater than their fuel value ($3.70–$4.20/gallon). This seems reasonable for 

the first few processing plants or if the amount is small compared with the total market (such as 

the case for n-butanol). The alternate use for these alcohols is, at worst, as a non-spec fuel (such 

as residual fuel oil); this would set their value at about 80% of that for gasoline. 

A positive characteristic of the thermochemical process is its relative insensitivity to 

feedstock composition. High-carbohydrate feedstocks (such as wood and corn stover) process 

at essentially the same cost. Low-carbohydrate feedstocks (such as lignin residues) should 

give good yields at nearly the same cost. Indeed, low-carbohydrate feedstocks may give 

higher yields because of their higher heating values and lower oxygen contents. 

The process uncertainty results can aid in qualitative risk assessment based on feedstock 

characteristics. For example, feedstock moisture content has a smaller effect on biochemical 

processing costs (because water is added anyway) than on thermochemical processing costs 

(because the feedstock is dried before gasifying). However, actual carbohydrate composition 

of feedstock has a much larger effect on biochemical processing costs (because only the 

carbohydrates are converted to ethanol) than on those of thermochemical processing. 

C.1.2 Advanced Technology Scenarios 

Both process uncertainty and risk assessment of the 2030 scenarios are tenuous at best 

because there is not enough information to do useful process uncertainty analysis. Production 

yields and costs are based on estimates of what the technology might look like. The best 

assessment is to consider the large drivers of advanced technology development. 

The overall picture of the advanced technology must consider: 

• Feedstock cost versus process cost

The highest-cost component in a mature process is feedstock. There is then a trade-off 

of how low processing costs should be viewed. Will the process produce an 

inexpensive fuel from a cheap feedstock? Will the process be able to economically 

produce a fuel using a larger range of more expensive feedstocks? 

• Future technology advances

Will existing technology be able to incorporate high-tech advances (such as would be 

C-12 
provided from systems biology and GTL)? Or will the facility need to completely reinvent to incorporate this? 

• Similar external factors as for near-term technology deployment (such as 

emphasis of funding sources, competing fuel prices, and public policy). 

C.2 Risk Assessment 

The next step is to translate, via qualitative methods, the results of uncertainty analysis into a 

risk assessment of the R&D and deployment paths envisioned to meet the $1.07/gallon target. 

C.2.1 Translating Process Uncertainty into R&D Risk 

Process uncertainty analyses can help drive the qualitative assessment toward determining the 

likelihood of achieving targets by identifying: 

• High- and low-risk activities (revolutionary and evolutionary) 

• The most probable R&D scenario 

• Uncertainty in translation to other feedstocks 

• Uncertainty in translation to other scales. 

There are many risk assessment methods. Most of them are subjective or qualitative. A review 

of the most applicable is performed, and one, or possibly a mix, is employed. At best, risk 

assessment is semi-qualitative when coupled with uncertainty analysis to provide some 

quantitative information. Coupling that information with expert opinion on likelihood of 

success and historical information is probably the best choice. For the first cut, identifying the 

risk as high or low provides significant information. Alternatively, classifying the R&D path a 

set of known activities or completely new activities may enable clarification of which parts 

are truly revolutionary or evolutionary (Roussel 1983). 

 

The best use of risk assessment results is to mitigate risk via strategies. Several strategies are 

already being used at some level in the biomass R&D community. A few examples from 

biomass and other technology communities include: 

• Develop multiple conversion technologies and pathways. 

Combined scenarios help reduce the effects of failing to meet targets in one platform 

by achieving successes in another. For example, one of the technology risks in the 

biochemical conversion process is the conversion of C5 sugars to ethanol. The risk of 

not converting these sugars biochemically is reduced if a thermochemical conversion 

process is integrated with the biochemical conversion process. C5 sugars that are not 

converted biochemically can still be converted thermochemically by routing the waste 

syrups through the gasifier. 

• Encourage the development of “open source technology.” 

Advocating open sharing of technology improvements, analogous to the computer 

industry, would make certain discoveries available to all. This would allow research 

groups to minimize redundant areas of research and speed the overall development of 

new technologies. The sharing of research results would speed the implementation of 

C-13 
C-14 

• Use what is learned on previous feeds to jumpstart new feedstocks of similar type 

or classification. 

• Plan for both pilot and demonstration projects. 

This reduces costs in two ways. First, it reduces process uncertainty and minimizes 

the over-design of equipment. Second, and perhaps more importantly, it provides 

demonstrations near scale so the required ROI can be reduced, which lowers financial 

cost (which has been shown to be a significant portion of the overall cost of 

producing ethanol). 

C.2.2 Translating Process Uncertainty into Deployment Risk 

Even with the best R&D plans, there are deployment risks for new technology. Costs 

invariably are higher for the first plants because of unforeseen difficulties at the commercial 

scale. A methodology for estimating cost overruns of pioneer plants, outlined in the 1981 

Rand Report, was applied to the biochemical process by Wallace (2003). The analysis showed 

that one of the key areas that drives costs above what are expected is the number of new 

(unproved at the commercial scale) steps in an overall process. 

Near-scale demonstrations are essential to reducing deployment risk. The more processes that are 

demonstrated near scale, the lower their expected cost is at full scale. Near-scale demonstration 

also suggests ways to simplify the overall process. The number of actual processing steps is 

another key area for unexpected cost growth in a pioneer processing facility. 

C.2.3 External Factors 

External factors that are expected to play a part in the deployment and possibly development 

of technology include: 

• Private industry

Private industry is less likely to share technology with competitors. This will 

potentially slow the implementation of new technology. 

• Fuel prices

If the price of petroleum-derived fuels remains high, there will be less pressure to 

reduce the production costs of alternative fuels. This may speed up the time frame in 

which alternative fuels become competitive and widely available. 

• Policy

Government policy—including loan guarantees, solicitations, incentives, and taxes—

will affect the outcome of development and deployment activities. 
Appendix D: Feedstocks Technology R&D 

Some of the biggest unanswered questions related to the deployment of cellulosic ethanol 

biorefineries involve feedstocks. The joint U.S. Department of Energy and U.S. Department 

of Agriculture “Billion Ton” study (Perlack et al. 2005) identified a domestic cellulosic 

feedstock resource potential sufficient to displace more than 30% of U.S. 2004 finished motor 

gasoline demand. However, these questions—e.g., “How can biomass be delivered cost 

effectively and sustainably to biorefineries?” and “How much biomass is available at what 

cost?”—represent significant barriers to the 2012 ethanol cost target of $1.07/gallon and the 

2030 goal of 30% fuel displacement. 

The 2030 goal of 60 billion gallons of ethanol will require the production of 40–45 billion 

gallons from cellulosic feedstocks, assuming 15–20 billion gallons will be produced from 

starch feedstock resources. This quantity of cellulosic ethanol will require an estimated 500 

million dry tons of biomass. Although the goal of producing, supplying, and converting 500 

million dry tons of cellulosic biomass is important, it is also useful to have an intermediate 

target that: 

• Represents a realistic near-future biomass demand 

• Includes every major type of cellulosic feedstock 

• Enables feedstock at a price that allows the resulting ethanol to be competitive in the 

near-future transportation fuels market 

• Can be described and achieved with technology currently under development. 

Specifically, the intermediate target is the demonstration of $35/dry ton cellulosic biomass 

feedstock for all major feedstock types at the pilot scale by 2012. A more detailed explanation 

of feedstock types is provided in section D.2. 

D.1 The 2012 Feedstock Cost Target 

The feedstock R&D path is based on the 2012 feedstock cost target of $35/dry ton. This target 

includes all aspects of feedstock supply up to the point of insertion into the conversion 

process (see Figure D-1). As such, the biomass feedstock cost includes purchase (the grower 

payment in the case of crop residues and energy crops or the stumpage cost in the case of 

forest residues), harvest and collection, storage, pre-processing, transportation, and additional 

handling, queuing, and other operations. Also included is the interface between supply and 

conversion (i.e., feedstock optimization for conversion and delivery to the “throat of the 

reactor”). This interface definition holds regardless of the conversion technology or feedstock 

type. From a cost perspective, the $35 can be divided into: 

• Feedstock value – $10 for 130 million dry tons (or about 10% of the 1.3 billion) 

• Farm gate price – variable based on the supply system scenario 

• Biorefinery plant gate price – variable based on the supply system scenario 

D-1 
• Feedstock cost to the starting point of the conversion process (or “throat of the 

reactor”) – $35/dry ton (which includes the $10 feedstock value and is based on 2002 

U.S. dollars). 

Biomass Production

• Agricultural Resources

• Forest Resources

Harvest 

and 

Collection

Storage Preprocessing Transportation

Handling 

and 

Queuing 

at the 

Biorefinery

Production Interface:

• Resource characterization / assessment

• Biomass Quality / Composition

• Fractional Removal

Biomass Conversion:

• Biochemical

• Thermochemical

Feedstock-Conversion Interface:

• Biomass Composition (or functional yield)

• Dockage

• Fractionation and Blending (Depot concept)

• Biomass conversion efficiency 

characteristics

Grower payment threshold 

cost = $10 / dry ton 2012 feedstock threshold cost delivered to 

the “throat” of the conversion reactor = $35 / 

dry ton (including grower payment)

Feedstock Interface Boundary

Figure D-1. The 2012 Feedstock Supply Scope (green boxes) and Cost Target 

(red box) 

Feedstock value is the price that must be paid for biomass, on the land, to purchase it from the 

producer (e.g., a farmer or forester). Different feedstocks have different median and average 

values (Perlack et al. 2005), and their price ranges vary from less than $10/dry ton to $40/dry 

ton and more in some cases (Perlack and Hess 2006). The reasons for this variability are as 

diverse as the geographic regions and growers that produce biomass. However, the single 

largest variable is tied to the tonnage demanded and competing demands such as 

soil/agronomic sustainability. 

Using POLYSYS (De La Torre Ugarte and Ray 2000), it was estimated that 10% of the 

potential 1.3 billion ton domestic biomass resource could be purchased at or less than $10/dry 

ton (see also Section 4.1.1). The analysis demonstrated that this 10% resource availability was 

not uniform on a per-acre basis or across resource type; rather, the 10% resource availability 

comes from the sum of all acres and biomass resources. This analysis is important to the 2012 

feedstock R&D and cost targets for two reasons. First, based on the market demand estimates 

for cellulosic ethanol, the cellulosic biomass resource demand will not exceed 130 million dry 

tons until sometime after 2015 (Chapter 2). Thus, the $10/dry ton feedstock threshold value 

for an estimated 130 million dry tons is a practical feedstock cost estimate for the 2012 cost 

target. Second, the 130-million-dry-ton estimate includes all major feedstock types, so 

feedstock supply system technologies must be developed and validated for each of the major 

feedstock types. 

The farm gate and processing plant gate feedstock pricing structures are commonplace in 

agriculture supply systems, but they can be dynamic and variable based on the farmagribusiness relationship. In the integrated feedstock assembly scenario, farmers are 

responsible for harvesting, collection, and delivery to storage. These assembly functions are 

integral to production and, thus, remain under the producer’s control (even if he chooses to 

have them performed by others). Often, farm gate pricing is based on feedstock value plus 

these on-farm operations. The agribusiness is then responsible for feedstock procurement and 

storage, pre-processing, transportation, and handling. However, if the biorefinery uses a 

D-2 
distributed on-farm storage system (or some other on-farm value process), the agribusiness 

may arrange for the farmer to store the material, which will change farm gate pricing. The 

agribusiness may also choose to subcontract feedstock assembly operations before the 

biomass reaches the biorefinery gate, which also would alter plant gate pricing. 

Cumumlative Ethanol Production

0

5

10

15

20

25

30

35

40

45

50

55

60

2000 2005 2010 2015 2020 2025 2030

Year

Ethanol (Billion Gallon per Year)

Corn Grain

Cellulose

Combined Total

Figure D-2. Projections for Growth of Ethanol Production 

Each of the business elements of the feedstock supply chain must work seamlessly with the 

others to provide biomass to the biorefinery. However, the seamless integration of business 

elements does not mean the entire biomass production, supply, and conversion system must 

employ common technologies and decision criteria. In fact it likely will not, which makes 

farm gate and plant gate pricing variable across feedstocks and regions. As such, it is 

important that the feedstock R&D plan consider technologies and costs in terms of farm gate 

and biorefinery plant gate interfaces. However, the variable costs and technology 

characteristics of these interface points do not make good cost targets. 

Because farm gate and plant gate costs do not represent feedstock supply system end-state 

costs, the biomass feedstock cost of $35/dry ton delivered to the “throat of the reactor” has 

been established as the feedstock R&D 2012 interim cost target. This is the feedstock cost 

assumption for the interim ethanol cost target of $1.07/gallon ethanol by 2012. By setting the 

biomass feedstock cost target at the entry point of the conversion process, all feedstock supply 

system unit operations are included in the feedstock R&D scope. This also allows R&D 

technology development to be optimized across all feedstock supply business elements and 

unit operations. 

D-3 
D.2 Feedstock R&D Pathways to Achieve the 2012 $35/Dry Ton Cost Target 

The goal of feedstock R&D is to create technology that provides a feedstock supply of 10% of 

the 1.3 billion dry ton feedstock potential (about 130 million dry tons) accessible at $35/dry 

ton. To be successful, near-term R&D should: 

• Specifically target the remaining production, harvest and collection, pre-processing, 

storage, and transportation barriers for dry, wet, and woody feedstock supply systems 

• Employ a range of participants to capture expertise in diverse areas 

• Maximize interfaces between participants and other program elements 

• Include pilot-scale demonstration to optimize process integration and identify scale-up 

issues. 

The key activities and milestones are shown in Figure D-3. 

Project Target

Project Activity

Accessible Feedstock Tonnage Target

G

2007 2008 2009 2010 2011 2012

1 2 G3

T1:

Dry (<15% moisture) biomass 

system pilot-scale validation 

resulting in $35/dry ton feedstock

T2:

Woody biomass system pilot-scale 

validation resulting in $35/dry ton 

feedstock

T3:

Wet (silage) biomass system 

pilot-scale validation resulting 

in $35/dry ton feedstock

Goal:

State of Technology case with pilot 

data resulting in $35/dry ton feedstock 

for dry, wet and woody feedstock 

types, thereby making 130 M dry tons 

of feedstock accessible.

22 M dry ton accessible at 

$35/ton (Dry Biomass)

67 M dry ton accessible 

at $35/ton (Dry + Woody 

Biomass)

Pilot runs for dry 

feedstock system

Pilot runs for woody 

feedstock system

Pilot runs for wet 

feedstock system

2007 dry and wet 

feedstock quality work 

initiated.

2010 woody feedstock 

supply system validation 

partnerships established

2008 dry feedstock supply 

system validation partners 

established

Figure D-3. Timeline of Near-term Feedstock Cost and Tonnage Goals 

D.2.1 The Feedstock Supply System 

Figure D-4 is a general process diagram of the feedstock supply system. It also includes 

barriers to the feedstock cost target. The barriers represent feedstock supply system 

performance metrics that underpin feedstock cost and supply. The performance metrics 

include: 

• Efficiency – The operational costs—as influenced by materials, supplies, labor, 

logistical issues, and material losses—associated with particular equipment 

configurations 

• Capacity – The throughput of particular equipment or sets of equipment 

D-4 
• Quality – The product specifications, value, and functional end-product yields of the 

biomass passing through the supply system. Quality is intrinsically linked to capacity 

and efficiency. 

These metrics relate to the delivered feedstock cost according to the following 

equation: 

Feedstock Cost 

($/ton) = ( Grower Payment ($/ton) )+( Efficiency [$/hr] )+Quality [$/ton] 

Capacity [ton/hr] 

Since these metrics encompass the supply system factors that affect feedstock cost, all 

feedstock R&D activities focus on one or more of these metrics in order to reduce feedstock 

cost and achieve the $35 cost goal. 

Biomass Production

• Agricultural Resources

• Forest Resources

Harvest 

and 

Collection

Storage Preprocessing Transportation

Handling 

and 

Queuing 

at the 

Biorefinery

Biomass Conversion:

• Biochemical

• Thermochemical

Feedstock Interface Boundary

• Equipment Capacity

• Compositional Impacts

• Pretreatment Impacts

• Shrinkage

• Compositional Impacts

• Pretreatment Impacts

• Soluble Sugar Capture

• Equipment Capacity

• Material Bulk Density

• Compositional Impacts

• Pretreatment Impacts

• Truck Capacity

• Loading compaction

• Loading efficiencies

• Handling efficiencies

• Handling compaction

• Material Bulk Properties

Biomass Production

• Agricultural Resources

• Forest Resources

Harvest 

and 

Collection

Storage Preprocessing Transportation

Handling 

and 

Queuing 

at the 

Biorefinery

Biomass Conversion:

• Biochemical

• Thermochemical

Feedstock Interface Boundary

• Equipment Capacity

• Compositional Impacts

• Pretreatment Impacts

• Shrinkage

• Compositional Impacts

• Pretreatment Impacts

• Soluble Sugar Capture

• Equipment Capacity

• Material Bulk Density

• Compositional Impacts

• Pretreatment Impacts

• Truck Capacity

• Loading compaction

• Loading efficiencies

• Handling efficiencies

• Handling compaction

• Material Bulk Properties

Figure D-4. Feedstock Supply Schematic for $35/dry-ton Feedstock 

The performance metrics are distributed across the elements of the feedstock supply system. 

These elements encompass all the unit operations necessary to move biomass feedstocks from 

the land resource to the conversion process of the biorefinery (Cushman et al. 2003). 

• Biomass production is the beginning of the feedstock supply chain. It involves 

producing feedstocks to the point of harvest. Production includes the determination of 

factors such as feedstock type, land use, policy, and agronomic practices that drive 

biomass yield and directly affect harvest and collection operations. 

• Harvest and collection encompasses all operations associated with getting the biomass 

from its source to the storage or queuing location. In addition to obvious operations 

such as cutting (or combining, swathing, or logging) and hauling, harvest and 

collection often includes some form of densification (such as baling, bundling, or 

chipping) to facilitate handling and storage. 

• Storage and queuing are essential operations in the feedstock supply system. They are 

used to deal with seasonal harvests and variable yields and delivery schedules. The 

objective of a storage system is to provide the lowest-cost means of holding biomass 

D-5 
material in a stable, unaltered form (i.e., neither quality improvements nor reductions) 

until it is called for by the biorefinery. 

• Pre-processing occurs prior to the conversion process to physically transform feedstock 

into the format required by the biorefinery. Pre-processing can be as simple as grinding 

and formatting biomass for increased bulk density and improved conversion efficiency 

or as complex as improving feedstock quality through fractionation, tissue separation, 

and blending. 

• Transportation generally consists of moving biomass from a storage location to a 

biorefinery via truck, rail, barge, or pipeline. The system used directly affects how the 

feedstock is handled and fed into the conversion process. Because the transportation 

and handling of feedstock are dependent on the format and bulk density of the material, 

these operations are tightly coupled to each other and all other operations in the 

feedstock supply chain. 

D.2.2 Feedstock Classifications, Targets, and Performance Metrics 

Achieving the 2012 objective requires that multiple supply systems, representative of the 

major feedstock types, be developed. These types, categorized according to feedstock 

resource, are: 

• Dry herbaceous (model pathway – straw and stover primarily from irrigated lands) 

• Wet herbaceous (model pathway – stover and switchgrass primarily from rain fed 

lands) 

• Woody (model pathway – logging residues) 

• Energy Crops (model pathway – switchgrass). 

This categorization is defined by the major technology and capital equipment changes 

required for each feedstock type. For example, regardless of the crop, all herbaceous biomass 

with less than 15% moisture is handled and managed with similar technologies and processes. 

Differences among these crops tend to be compensated for through equipment tuning and 

management. However, those same biomass crops in a high-moisture (more than 50% 

moisture) system require different technologies and capital equipment. The same is true for 

woody biomass. Energy crops, which can be herbaceous or woody, justify a separate 

feedstock classification because of production and crop development and introduction 

considerations. However, supply logistics and conversion issues for energy crops are similar 

to those of herbaceous (wet and dry) and woody crops. 

D.2.2.1 Dry Herbaceous Supply System 

Dry herbaceous feedstocks (e.g., corn stover, cereal straws, milo stover, and switchgrass) 

consist of crop residues and herbaceous energy crops with moisture contents less than 15%. 

Because the moisture content limit is dictated mainly by storage requirements, biomass that is 

more than 15% moisture at the time of harvest may be dried prior to storage and included in 

the dry herbaceous classification. An analysis of biomass resources in the “Billion Ton” study 

showed the net potential supply of dry crop residue feedstocks to be about 22 million dry tons 

by 2012 (Perlack et al. 2005). 

D-6 
The primary challenge of the feedstock assembly core R&D in the dry herbaceous feedstock 

supply system is migrating from traditional technologies, which primarily serve the smaller 

distributed livestock and forage industries, to an assembly system specifically designed for the 

biorefinery industry. This new design considers policy, logistics, and agricultural practices 

that affect feedstock supply; alternatives to the delivery of bales to the biorefinery; preprocessing as an integrated component of feedstock assembly; and feedstock quality impacts 

on the biorefinery conversion processes. 

Dry herbaceous feedstocks have been the model feedstocks of the feedstock platform. As 

shown by the 2006 state-of-technology assessment in Figure D-5, the dry herbaceous 

feedstock supply system is well-developed, and significant progress has been made toward 

achieving the $35/dry-ton cost target. A significant aspect of dry feedstocks core R&D has 

been the transition from a bale to a bulk feedstock system. Thus, the challenge for dry 

feedstock core R&D is not to find new, undeveloped technologies but to pursue 

improvements to the bulk technology already under study. The R&D activities required to 

accomplish these technology advancements and to achieve the cost improvements shown in 

Figure D-5, are shown in Table D-1. 

$52.00 $53.00

$50.75 $49.20

$47.30

$45.00

$42.25

$38.95

$35.00

$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

$55

$60

$65

$70

$75

$80

2004 2005 2006 2007 2008 2009 2010 2011 2012

Delivered Feedstock Cost ($/dry ton)

Storage

Preprocessing

Transportation & Handling

Harvest & Collection

Production

Threshold

Figure D-5. Dry Feedstock Assembly Threshold Cost Curve 

and Associated Unit Operation Cost Reductions 

D-7 
Feedstock cost and quality are dependent on the targets and performance metrics associated 

with the R&D activities identified in Table D-1 as well as market and policy drivers. The 

sensitivity and risk associated with market and policy drivers are handled at the industry scale 

with appropriate models. However, the implied risks associated with feedstock assembly core 

R&D and analyses are assessed by applying a single-value uncertainty interval analysis to key 

feedstock technical parameters. This analysis requires a model that can quantitatively 

determine the costs of feedstock assembly operations. The model is then used to evaluate the 

high, mid, and low range of each technical parameter and provide the relative sensitivity of 

each parameter within the feedstock assembly system. The dry feedstock assembly system is 

the most mature in terms of engineering data and assembly system models that specifically 

incorporate the R&D needs of the conversion processes. These data and models were used to 

determine the range of sensitivity of each of the dry feedstock technical parameters as well as 

the relative costs associated with unit operations and other cross-cutting costs. 

Table D-1. Timeline of Key Feedstock R&D Activities for the Dry Feedstock 

Supply System 

DRY Completion Year 

R&D Area 2006 2007 2008 2009 

2009 Validation of the Dry Feedstock Supply System for 22 Mtons at $35/dry ton 

2010 2011 2012 

Interface 

Feedstock 

Conversion 

Interface 

Establish value and 

quality requirements for 

dry preprocessed and 

stored biomass for 

delivery to a biorefinery 

 Verify pretreatment and 

conversion technoeconomic values 

associated with fractions 

produced within core 

R&D unit operations 

(i.e., define dockage 

values) 

 

Feedstock Infrastructure Core R&D 

Sustainable 

Harvest & 

Collection 

Quantify selective 

harvest value by 

identifying quality 

impacts (both 

compositional and 

functional) on the 

conversion process 

 

Preprocessing 

Increase grinder 

efficiencies and resulting 

material bulk densities 

by coupling the 

understanding of 

biomass deconstruction 

and biomass rheological 

properties 

Build, instrument, and 

test size selective 

grinder configurations to 

optimize machine 

capacity and material 

bulk density to meet 

established performance 

targets 

Build, instrument, and 

test optimum grinder 

configuration based on 

FY07 results that include 

a multi-feed input 

system, dust control, 

safety features, etc., to 

meet FY09 validation 

tests 

Validate preprocessing 

performance targets 

based on established 

analysis designs. 

Storage & 

Queuing 

Quantify storage options 

and mitigation strategies 

to minimize dry matter 

losses (<5%) and 

maximize potential 

compositional and 

functional biomass 

improvements 

Evaluate dry biomass 

storage configurations 

and protection strategies 

for high moisture 

environments 

 Validate storage and 

queuing system 

performance targets 

based on established 

analysis designs 

Transportation 

& Handling 

 Build, instrument, and 

test High throughput 

biomass compaction 

systems (i.e., tapered 

auger) to meet 

established transport, 

load-out, and handling 

performance targets 

Build, instrument, and 

test optimum 

compression 

configuration based on 

FY07 results to meet 

FY09 validation tests 

Validate storage and 

queuing system 

performance targets 

based on established 

analysis designs 

Analysis 

Integration/ 

Modeling 

Analysis 

Completion of integrated 

supply system model in 

support of draft $35/dt 

analytical engineering 

design 

Employ integrated 

supply system model to 

evaluate unit operation 

designs for multiple 

regions 

 Completion of integrated 

supply system model in 

support of $35/dt 

validation per unit 

operation specifications 

D-8 
The tornado chart shown in Figure D-6 presents the range of sensitivity for each of these 

parameters. The ranges are based on the 2012 delivered feedstock target of $35/dry ton and 

illustrate the potential of each parameter to affect overall assembly system cost through 

engineering and quality. The chart is split into three regions: (1) specific research technical 

parameters that directly affect the cost and quality of delivered feedstocks, (2) unit operations 

that make up the assembly system, and (3) variable costs that cross-cut assembly system 

operations. The research technical parameters are the primary focus of the risk analysis 

because their effects determine if and how the cost targets are met. The other two regions 

show the relative effects the technical parameters have on the assembly system as a whole. 

The ranking of each parameter implies some degree of R&D risk. Those parameters with 

large cost ranges have greater potential to affect the 2012 targets (positively or negatively). 

However, inherent in the single-value analysis approach is the inability to identify the 

interdependence of parameters. Although a Monte Carlo analysis could determine this 

interdependency, the feedstock assembly system models are too immature to provide the input 

ranges required. 

Thus, Figure D-6 assists in identifying and directing R&D based on relative potential but 

cannot be used to determine the effect of changing a single parameter on related parameters. 

Nevertheless, it is expected, based on the nature of each feedstock assembly unit operation, 

that synergistic credits or debits will be realized. This suggests that an integrated R&D 

approach is necessary. 

($6) ($4) ($2) $0 $2 $4 $6 $8 $10 $12 $14

Fuel Cost (0.57 to 4.28 $/gal)

Average Labor Cost (6.04 to 24.14 $/hr)

Dry Storage Cost (-3% to +6%)

Transportation & Handling Cost (-2% to +9%)

Production - Grower Payment (8 to 15 $/dt)

Harvest & Collection Cost (-11% to +11%)

Preprocessing Cost (-9% to +39%)

Plant Conveying Bulk Density (12 to 2.6 dry lbs/cu-ft)

Multi-pass Capacity (100% to 50% grain harvest capacity)

Plant Storage Bulk Density (18 to 7.1 dry lbs/cu-ft)

Preprocessing Bulk Density (21 to 6.5 dry lbs/cu-ft)

Plant Queuing (4% to 0% soluable sugar capture)

Field Handling Bulk Density (10.4 to 3.9 dry lbs/cu-ft)

In-Storage Shrinkage (0.85% to 25% dry matter loss)

Pretreatment Impacts (±$0.05 MESP)

Selective Harvest Composition Impacts ($1.06 to $1.18 MESP)

Preprocessing Composition Impacts ($1.10 to $1.25 MESP)

Single-pass Capacity (85% to 50% grain harvest capacity)

Preprocessing Capacity (0.071 to 0.009 dry tons/kW-hr)

Change in Delivered Feedstock Cost ($ per dry ton)

Research

Unit Operations

Variable Costs

Figure D-6. Sensitivity Analysis of Technical and Logistical Parameters 

in the Dry Feedstock Assembly $35/dry-ton Case 

D-9 
D.2.2.2 Wet Herbaceous Supply System 

Wet herbaceous feedstocks (e.g., corn stover, milo stover, and switchgrass) consist of crop 

residues and herbaceous energy crops that primarily require anaerobic storage and have 

enough moisture at the time of storage to support anaerobic stability. These feedstocks would 

degrade if a dry storage technique were applied. Wet feedstocks are the result of: 

• Harvesting in wet conditions because of agronomic and environmental factors 

• Assembly and storage in locations with high humidity or rainfall throughout the year 

• Large supplies of wet biomass (e.g., bagasse) made available as a byproduct of another 

process. 

An analysis of biomass resources in the “Billion Ton” study (Perlack et al. 2005) showed the 

net potential supply of wet herbaceous feedstocks to be about 38 million dry tons by 2012. 

However, the use of wet herbaceous feedstocks is currently limited by a host of infrastructure 

barriers. As a result, wet feedstock costs are well beyond the $35/dry-ton cost target, Figure 

D-7. Because wet herbaceous feedstocks represent a significant portion of the overall 

feedstock resource, overcoming these barriers provides the greatest potential for achieving the 

tonnage targets. 

$52.00 $53.00

$50.75 $49.20

$47.30

$45.00

$42.25

$38.95

$35.00

$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

$55

$60

$65

$70

$75

$80

2004 2005 2006 2007 2008 2009 2010 2011 2012

Delivered Feedstock Cost ($/dry ton)

Storage

Preprocessing

Transportation & Handling

Harvest & Collection

Production

Threshold

Figure D-7. Wet Feedstock Assembly Threshold Cost Curve and Associated 

Unit Operation Cost Reductions 

D-10 
Once again, one of the challenges of feedstock assembly core R&D in the wet herbaceous 

feedstock supply system is migrating from traditional technologies to an assembly system 

specifically designed for the biorefinery industry. An additional challenge is the development 

of value-added operations that improve feedstock quality for the biorefinery. Without valueadded operations to offset high feedstock costs, a large percentage of feedstock resources will 

not be available at the $35/dry-ton cost target. These value-added operations—which include 

quality (composition and pretreatment) enhancements and soluble sugar capture in storage—

combined with equipment capacity and efficiency improvements form the basis of the R&D 

activities for the wet herbaceous supply system. This R&D plan is shown in Table D-2. 

Based on the R&D activities shown in Table D-2, specific targets have been identified for key 

technical parameters of the wet herbaceous supply system. Figure D-8 shows the sensitivity of 

these R&D parameters specific to a wet feedstock assembly system. Each parameter is based 

on the 2012 delivered feedstock target of $35/dry ton. The figure illustrates the relative effect 

of each parameter on assembly system cost and feedstock quality. Unique to wet feedstock is 

its potential to use value-added technologies to affect feedstock cost and quality. Specifically, 

the free sugar capture and reduced pretreatment severity parameters have the potential to 

offset storage, pre-processing, and transportation costs, which are higher than the respective 

dry system costs because of the water content of the feedstock. 

D-11 
Table D-2. Timeline of Key Feedstock R&D Activities for the Wet Feedstock 

Supply System 

DRY Completion Year 

R&D Area 2006 2007 2008 2009 

2009 Validation of the Dry Feedstock Supply System for 22 Mtons at $35/dry ton 

2010 2011 2012 

Interface 

Feedstock 

Conversion 

Interface 

Establish value and quality 

requirements for dry 

preprocessed and stored 

biomass for delivery to a 

biorefinery 

 Verify pretreatment and 

conversion technoeconomic values 

associated with fractions 

produced within core R&D 

unit operations (i.e., define 

dockage values) 

 

Feedstock Infrastructure Core R&D 

Sustainable 

Harvest & 

Collection 

Quantify selective harvest 

value by identifying quality 

impacts (both 

compositional and 

functional) on the 

conversion process 

 

Preprocessing 

Increase grinder 

efficiencies and resulting 

material bulk densities by 

coupling the 

understanding of biomass 

deconstruction and 

biomass rheological 

properties 

Build, instrument, and test 

size selective grinder 

configurations to optimize 

machine capacity and 

material bulk density to 

meet established 

performance targets 

Build, instrument, and test 

optimum grinder 

configuration based on 

FY07 results that include a 

multi-feed input system, 

dust control, safety 

features, etc., to meet 

FY09 validation tests 

Validate preprocessing 

performance targets 

based on established 

analysis designs. 

 

Storage & 

Queuing 

Quantify storage options 

and mitigation strategies 

to minimize dry matter 

losses (<5%) and 

maximize potential 

compositional and 

functional biomass 

improvements 

Evaluate dry biomass 

storage configurations and 

protection strategies for 

high moisture 

environments 

 Validate storage and 

queuing system 

performance targets 

based on established 

analysis designs 

 

Transportation & 

Handling 

 Build, instrument, and test 

High throughput biomass 

compaction systems (i.e., 

tapered auger) to meet 

established transport, 

load-out, and handling 

performance targets 

Build, instrument, and test 

optimum compression 

configuration based on 

FY07 results to meet FY09 

validation tests 

Validate storage and 

queuing system 

performance targets 

based on established 

analysis designs 

 

Analysis 

Integration/ 

Modeling 

Analysis 

Completion of integrated 

supply system model in 

support of draft $35/dt 

analytical engineering 

design 

Employ integrated supply 

system model to evaluate 

unit operation designs for 

multiple regions 

 Completion of integrated 

supply system model in 

support of $35/dt 

validation per unit 

operation specifications 

 

D-12 
($9) ($6) ($3) $0 $3 $6 $9 $12 $15 $18 $21

Average Labor Cost (6.04 to 24.14 $/hr)

Fuel Cost (0.57 to 4.28 $/gal)

Transportation & Handling Cost (-4% to +7%)

Production - Grower Payment (8 to 15 $/dt)

Harvest & Collection Cost (-11% to +11%)

Preprocessing Cost (-22% to +45%)

Wet Storage Cost (-15% to +55%)

Multi-pass Capacity (100% to 50% grain harvest capacity)

Plant Storage Bulk Density ( to dry lbs/cu-ft)

Field Handling Bulk Density ( 5.3 to 3.8 dry lbs/cu-ft)

Plant Conveying Bulk Density (8.0 to 4.0 dry lbs/cu-ft)

In-Storage Soluble Sugar Capture (5% to 0%)

Preprocessing Composition Impacts ($ to $ MESP)

Preprocessing Bulk Density (14 to 6 dry lbs/cu-ft)

Selective Harvest Composition Impacts (1.02$ to 1.10$ MESP)

Pretreatment Impacts (±$0.05 MESP)

In-Storage Shrinkage (5% to 30% dry matter loss)

Single-pass Capacity (85% to 50% grain harvest capacity)

In-Storage Composition Impacts ($1.09 to $1.39 MESP)

Preprocessing Capacity (0.028 to 0.006 dry tons/kW-hr)

Change in Delivered Feedstock Cost ($ per dry ton)

Research

Unit Operations

Variable Costs

Figure D-8. Sensitivity Analysis of Technical and Logistical Parameters 

in the Wet Feedstock Assembly $35/dry ton Case 

D.2.2.3 Woody Supply System 

The “Billion Ton” study estimated that 225 million dry tons of forest residues are potentially 

available for bioenergy. This excludes the 35 million dry tons of fuel wood used for 

residential and commercial space heating, 8 million dry tons of urban wood wastes used to 

generate power, and nearly 100 million dry tons of forest biomass residues used by the forest 

products industry. Also excluded are perennial woody crops grown on crop land, as opposed 

to forest land (Perlack et al. 2005). Forest residues come from the following sources: 

• Primary 

o Logging residues from conventional harvest operations and residues from forest 

management and land-clearing operations 

o Forest thinnings from wildfire prevention projects (fuel treatments) 

o Fuel wood extracted from forest lands 

• Secondary 

o Wood residues from forest products industry mills that process primary wood 

products 

D-13 
o Pulping liquors (black liquor) 

o Wood residues from secondary processing mills 

• Tertiary 

o Urban wood residues (e.g., construction and demolition debris, tree trimmings, 

packaging wastes, and consumer durables). 

Logging residues and forest thinnings will account for the major portion of forest residues for 

ethanol production (see Figure D-9). The near-term woody feedstock available consists 

largely of logging residues, based on the expectation that some aspects of harvesting costs are 

predominantly deferred to the primary logging operations producing the residues. That is, the 

recovery of logging residue is presumed to be carried out concurrently with the harvesting of 

conventional forest products. The available logging residue resource is estimated at about 41 

million dry tons by 2012, taking into account recovery and equipment limitations. Additional 

resources may be collected if more efficient and cost-effective equipment designed 

specifically for residue collection becomes available. 

Figure D-9. Potential Woody Biomass Resources 

D-14 
The annual recoverable forest residue resource from thinning operations for wildfire 

prevention is estimated to be at least 50% more than the logging residue resource. However, 

forest thinnings—especially those associated with the reduction of fire hazards—are not 

considered available in significant quantities within the 2005–2012 period. This is primarily 

because of the need for more concrete implementation policies for the Healthy Forest 

Restoration Act of 2003 and the need for technologies that would allow cost-effective 

recovery. 

The three key areas to the achievement of the $35/dry-ton cost target and the feedstock supply 

targets for woody feedstocks are: 

• Cost and efficiency 

The current supply system for markets of woody biomass (e.g., timber, pulp and paper, 

fuel wood, and urban residues) varies with respect to equipment, products, land use, 

markets, and revenues. In addition, the resource environment varies with respect to 

policy, accessibility, slope, and compaction. It is therefore clear that the cost and 

efficiencies of woody supply systems will vary significantly. However, the woody 

supply system has two advantages: (1) The markets for woody resources and the 

infrastructure to support those markets are well established, and (2) it is possible to 

store the biomass “on the stump.” One remaining challenge is the equipment used in 

the wood products supply systems. It may not be the best equipment for a wood residue 

supply system. Thus, assessing the cost and efficiency of the woody feedstock supply 

system is a critical component of this research. 

• Supply security and accessibility

The factors that affect woody feedstock supply are much different than those that affect 

herbaceous feedstocks. The supply and availability of crop residues and energy crops 

are determined by the risk, profit, and agronomic/environmental decisions of individual 

growers. The supply of woody residues is affected by a multitude of factors such as 

land use policies and economics, crop maturity rates, and sustainability and water 

quality factors. Consequently, analysis of supply security and accessibility of woody 

feedstocks is a critical component of this research. 

• The feedstock conversion interface

The feedstock conversion interface is the basis for feedstock quality upgrade 

components. This interface expanded the feedstock key research areas from cost and 

efficiency elements to include quality elements critical to achieving the cost and 

tonnage targets of 2012 and beyond. The potential for quality upgrades through 

biomass fractionation and other innovative pre-processing options is assumed to exist. 

Therefore, developing, validating, and exploiting (through engineered systems) 

feedstock quality impacts is a critical component of woody feedstock research. 

Although these are overarching R&D issues of the woody supply system, a woody supply 

system state of technology (SOT) must be conducted to identify specific R&D targets and 

performance metrics for this feedstock type. This SOT, as well as other important R&D 

activities is included in the woody feedstock R&D plan shown in Table D-3. 

D-15 
Table D-3. Timeline of Key Feedstock R&D Activities for the Woody Feedstock 

Supply System 

Woody Completion Year 

R&D Area 2006 2007 2008 2009 2010 2011 

2011 Validation of the Woody Feedstock Supply System for 45 Mtons at $35/dry ton 

2012 

Interfac

e Feedstock 

Conversion 

Interface 

 Verify pretreatment and 

conversion technoeconomic values 

associated with woody 

biomass fractions 

 

Feedstock Infrastructure Core R&D 

Sustainable 

Harvest & 

Collection 

 Baseline harvest and 

collection systems 

costs and assess 

quality upgrade 

potential to identify 

infrastructure 

needs/modifications 

Establish harvest and 

collection design 

configurations that meet 

cost and quality 

performance targets 

Develop and test 

innovative equipment 

specific to the recovery 

of wood residues for 

each resource class 

and conditions where 

existing equipment is 

too costly and inefficient 

Validate the 

achievement of 

established 

performance targets for 

harvest and collection 

systems 

Preprocessing 

 Test the bulk densities, 

rheological properties 

and composition of 

standard formatted 

material produced by 

current woody biomass 

industries 

Assess performance 

target improvements 

and potential equipment 

designs necessary to 

achieve the unit 

operation cost target 

Establish preprocessing 

system design 

configurations that meet 

cost and quality 

performance targets 

Test preprocessing 

system designs to 

determine hardware 

configurations capable 

of meeting established 

performance targets 

Validate predetermined 

efficiency, capacity, and 

quality targets for a wet 

preprocessing system 

Storage & 

Queuing 

 Baseline storage 

systems costs and 

assess quality upgrade 

potential to identify 

infrastructure 

needs/modifications 

 Build, instrument, and 

test prototype storage 

and queuing system 

configurations that meet 

established 

performance targets 

Validate the 

achievement of 

established 

performance targets for 

storage and queuing 

systems 

Transportation & 

Handling 

 Quantify the impact 

feedstock bulk densities 

and rheological 

properties have on the 

cost of transportation 

and handling systems 

and establish design 

configurations to meet 

performance targets 

Establish transportation 

and handling design 

configurations that meet 

cost and quality 

performance targets 

Validate the 

achievement of 

established 

performance targets for 

transportation and 

handling systems 

Analysis 

Integration/ 

Modeling Analysis 

 State of Technology 

(SOT) assessment to 

establish unit operation 

performance targets 

 Completion of 

integrated supply 

system model in 

support of $35/dt 

analysis engineering 

design 

Refine technoeconomic model based 

on built and tested unit 

operation systems in 

support of $35/dt 

engineering design 

validation 

 

D.2.2.4 Energy Crops Supply System 

Energy crops include perennial grasses, forage-type crops, and trees grown specifically as 

feedstocks for bioenergy and bio-product facilities. Both herbaceous grasses and woody 

energy crops can grow on most of the approximately 400 million acres of land classified as 

crop land in the United States. These crops can also grow on land not suitable for 

conventional crops while providing the protection from erosion recommended for agricultural 

“set aside” or Conservation Reserve Program lands. 

Switchgrass is a model herbaceous bioenergy crop. It is a high-yielding, native, perennial, 

warm-season grass that can be grown on a variety of sites. It is planted and harvested like a 

traditional hay crop and managed with existing agricultural equipment. Once established, it 

can produce for about 10 years before replanting is required. Switchgrass can be harvested dry 

using conventional baling equipment and delivered to biorefining facilities as large round or 

rectangular bales. The crop is also amenable to a wet feedstock supply system. Switchgrass 

was chosen as a model herbaceous bioenergy crop because it has relatively low energy and 

resource requirements, a high yield potential, and ecological value in the protection and 

improvement of soil quality and wildlife habitat. In addition, it is compatible with 

conventional farming equipment and management practices. Although many varieties of 

switchgrass are commercially available from forage seed companies, the best-producing 

D-16 
varieties for energy crops may not be readily available because their production is not yet 

commercial. It may be necessary to establish a seed production stand within a project or 

contract 1–2 years in advance with seed production firms to obtain sufficient seed for largescale plantings (see Figure D-10). 

Cultivar 

Choices

Grow 

seed for 

seed 

Spring 

Planting 

(Crop 

establishmen

t year) 

No Harvest 

Minimal 

Harvest 

 (Second 

growing 

year) 

Determine 

Yield 

First Mature 

(Second 

growing 

year) 

Harvest 

Mature 

Harvest 

Multiple 

Crops 

YEAR 1 YEAR 2 YEAR 3 YEAR 4 YEAR 5 YEAR 6 

YEAR 1 YEAR 2 YEAR 3 YEAR 4 YEAR 5 YEAR 6 

YEAR 1 YEAR 2 YEAR 3 YEAR 4 YEAR 5 YEAR 6 

NOW 

2010+Gap 

Regional 

Partnerships 

Developed 

2006 

Time 

Gap?

2011+Gap 2012+Gap 

Farmer 

Contracts, 

Seed Co. 

grow seed 

for planting 

100,000 

acres 

Figure D-10. Timeline for the Development of Herbaceous Energy Crops 

Energy crops also include short-rotation woody crops such as hybrid poplar and hybrid 

willow. Hybrid poplar (or Populus spp.) is a widely distributed and genetically diverse species 

that can grow throughout the United States. It is established and managed with conventional 

agricultural equipment and harvested with existing forestry equipment (i.e., feller-bunchers, 

skidders, and whole-tree chippers). Willow (or Salix spp.) is also a widely distributed and 

diverse tree species. It, however, is recommended only for the Northeast and Lake States 

regions. Hybrid willow is planted at higher densities than poplar and harvested after 4 years of 

growth. Hybrid willow stands are regenerated by coppice or resprout. The planting and 

harvesting of willows require specially designed machinery that is commercially available in 

Europe. Both woody crops are usually delivered as whole-tree chips. 

With the exception of production practices employed to grow perennial grasses, herbaceous 

energy crops can easily be accommodated by dry or wet feedstock supply systems. However, 

the woody energy crops, such as poplar and willow, are harvested in a manner similar to that 

of woody feedstocks. To the degree that energy crop feedstock systems mimic dry or wet 

systems, the sensitivity and risk associated with the dry and wet systems apply. Those energy 

crop systems that mimic the woody system require detailed data and models to assess the 

sensitivity of associated technical parameters. 

D.3 The Comprehensive Feedstock R&D Pathway 

The preceding sections introduced key issues and R&D activities associated with the 

development of supply systems for each of the feedstock types (i.e., dry, wet, woody and 

energy crops). While the scope and focus of feedstock R&D activities may differ for each 

feedstock type, the R&D pathways for the feedstock supply systems share the common 

elements and barriers illustrated in Figure D-4. Furthermore, the feedstock supply systems 

share performance metrics (i.e., efficiency, capacity, and feedstock quality). Therefore, to 

D-17 
avoid the redundancy of presenting an R&D pathway for each of the feedstock supply 

systems, a single, comprehensive R&D pathway is presented here to represent, at a high level, 

the R&D activities needed to meet the intermediate feedstock cost and supply targets. 

D.3.1 Production 

Production is a critical component of the feedstock supply system, and it is a key research 

area for ensuring an adequate and sustainable feedstock supply. Through the USDA and 

regional partnership collaborations, validated assessments of feedstock resource type and 

potential will be accomplished. Specific research needed to address production issues will: 

• Assess the cost and availability of the feedstock resource on a local basis to define 

production costs (e.g., grower payments) and identify regional tonnages available 

within each feedstock type or classification at or less than the feedstock threshold costs 

• Identify and validate sustainable agronomic practices specific to feedstock types and 

regional variables to ensure sustainable production of the feedstock resource 

• Investigate crop production improvements (e.g., increased yields, decreased yield 

variability, and consistent quality) through genetic modification 

• Develop a perennial crop program that matches varieties to site conditions, establishes 

optimum agronomic practices, and creates a seed production capability. 

D.3.2 Harvest and Collection 

The feedstock R&D plan requires harvest and collection advancements in three key areas: (1) 

selective harvest (including forest-thinning operations), (2) single-pass or minimum impact 

harvest, and (3) harvest and collection efficiencies. The primary drivers for improved harvest 

system technologies are reduced costs and access to larger tonnages of biomass through 

increasing producer participation. For example, improved harvest technologies that address 

the soil quality issues of no-till farming (such as soil carbon sequestration, soil nutrient/water 

retention, erosion control, and soil compaction) will become increasingly important for 

enticing grower participation and accessing agricultural residues. Performance metrics for 

new harvest and collection systems include efficiency, equipment capacity, and quality. 

Without these improvements, the accessible biomass tonnage will remain restricted because 

of limited or niche producer participation. Specific research needed in this area will: 

• Develop innovative harvest and collection methods for all resource types to eliminate 

or reduce unit operations and agronomic/operational impacts 

• Quantify and validate harvesting-specific quality related to compositional impacts, 

pretreatment impacts, contaminant reductions, and bulk-handling improvements 

• Develop and test innovative equipment specific to the recovery of wood residues for 

each resource class and conditions in which existing equipment is too costly and 

inefficient. 

D.3.3 Pre-Processing 

In the transition from a bale-based system to a bulk feedstock system, significant advances 

have been made in dry herbaceous pre-processing. However, additional advances are needed 

D-18 
in three key areas: pre-processing equipment capacity, feedstock bulk density, and feedstock 

quality. Equipment capacity and bulk density directly affect feedstock cost and are important 

technical parameters to be addressed. Furthermore, a key component of feedstock R&D is to 

extend pre-processing beyond feedstock size reduction to include value-added operations that 

improve feedstock quality to the biorefinery. These operations involve fractionation and 

separation of higher-value feedstock components. Without value-added operations to offset 

high feedstock costs, a large percentage of feedstock resources will not be available at $35/dry 

ton. Specific research needed in this area will: 

• Develop the pre-processing requirements for each feedstock type (This includes 

identifying the biorefinery feedstock requirements, pre-processing logistics, and 

storage, transportation, and handling requirements. 

• Reveal the relationship between biomass structure and composition for the assessment 

of quality upgrade potential and the development of equipment and methods to achieve 

upgrades 

• Establish an understanding of biomass tissue deconstruction in pre-processing and the 

relationships of the grinder configuration, tissue fractions, tissue moisture, and grinder 

capacity to optimize the grinder configuration for fractionation, capacity, and 

efficiency 

• Increase bulk densities by coupling the understanding of biomass deconstruction and 

biomass rheological properties with innovative bulk compaction methods 

• Control feedstock rheological properties resulting from pre-processing operations to 

provide a product that minimizes handling problems associated with transportation, 

handling, and queuing operations. 

D.3.4 Storage 

Feedstock shrinkage (or dry matter loss) is the main problem of feedstock storage, and 

shrinkage risks and mitigation strategies vary from region to region. DOE OBP’s core R&D 

program has demonstrated that annual dry matter loss can be as low as 0.85%. But in wetter 

regions, dry matter loss may exceed 25%. To achieve the 2012 cost and supply targets, dry 

matter losses are targeted at less than 5% for all feedstock types. Specific research needed to 

achieve this will: 

• Assess storage options and their effects on dry matter losses, compositional changes, 

and functional biomass changes (specific to resource type and regional variables) 

• Establish baseline costs of storage systems at scales from 0.8 million tons/year to 10 

million tons/year to identify key cost and infrastructure issues and develop paths to 

minimize industrial-scale storage costs 

• Provide an understanding of soluble sugar and carbohydrate loss and evaluate the 

feasibility of preventing such losses or reclaiming soluble sugars and carbohydrates 

from feedstock during storage. 

D-19 
D.3.5 Transportation and Handling 

Transportation operations can account for nearly 50% of the capital investment for a 

feedstock assembly system. Therefore, transportation costs can be a barrier to the use of 

feedstock resources. Because transportation operations do not add value to the feedstock, 

their costs must be reduced to the absolute amount feasible to achieve the $35/dry ton cost 

target. The feedstock R&D plan identifies feedstock bulk density as the key technical 

parameter that must be addressed to decrease transportation costs. As such, methods for 

increasing bulk density are a focus of feedstock R&D. In addition, because bulk handling is 

affected by feedstock rheological properties, this is an area of focus. Specific research needed 

to reduce transportation costs is: 

• Provide an understanding of feedstock physical and rheological properties (including 

bulk density) as they relate to handling systems for optimizing handling and 

transportation efficiencies 

• Provide an understanding of feedstock rheological properties in bulk storage to predict 

and minimize adverse feedstock physical changes that may affect plant processing 

• Evaluate innovative transportation and handling methods. 

D.3.6 Validation and Demonstration 

As the dry, wet, and woody supply system technologies are improved and the milestones in 

Figure D-3 are achieved, the supply systems will be validated to demonstrate that resources 

can be supplied at the $35/dry-ton cost target. The validation will be performed in an 

integrated, pilot-scale facility that includes the supply system equipment and unit operations 

to demonstrate the capacities, bulk densities, rheological properties, composition, and quality 

that contribute to the $35/dry-ton feedstock cost for dry, wet, and woody feedstock systems. 

D.3.7 Conclusions 

An economically viable feedstock assembly system capable of delivering lignocellulosic 

biomass to a biorefinery for the production of ethanol must integrate many complex factors. 

These include five key unit operations (production, harvest and collection, storage, preprocessing, and transportation and handling), four feedstock types (dry, wet, woody, and 

energy crops), and various grower-specific agronomic and crop production practices as well 

as regional weather and topography constraints. The successful integration of these factors is 

dependent on technology advances made possible through core R&D activities and guided by 

core R&D and strategic analysis. Three fundamental R&D elements—efficiency, capacity, 

and feedstock quality—form the standard by which feedstock assembly technologies are 

quantified and implemented. These elements are inherently tied to one another such that 

changing one element for a specific part of the assembly system will affect the value of the 

other elements (positively or negatively). Thus, the purpose of the feedstock core R&D and 

analysis effort is to assess and advance the SOT of each unit operation within the assembly 

system specific to each of the four feedstock types and regional production constraints such 

that overall improvements in efficiency, capacity, and feedstock quality are realized. 

Between now and 2012, feedstock assembly core R&D will addresses the barriers identified 

in the Roadmap for Agricultural Biomass Feedstock Supply in the United States (Cushman et 

D-20 
al. 2003). Overcoming these barriers is necessary for the achievement of the intermediate 

target of demonstrating the delivery of $35/dry-ton cellulosic biomass feedstocks—including 

the four major feedstock types (dry, wet, woody, and energy crops)—at pilot scale by 2012. 

D.4 Advanced Feedstock R&D Pathway to Achieve 2030 Tonnage Goals 

By 2012, the biomass feedstock state of technology will include functional feedstock supply 

systems for all major types of biomass resources. That is, for agriculture resources, feedstock 

supply systems will be available for low-moisture (dry) crop residue, high-moisture 

(wet/silage) crop residue, and herbaceous energy crops. For woody resources, logging residue 

feedstock supply systems will be demonstrated and adapted to short-rotation woody energy 

crops and other high-potential forest resources (such fuel treatment thinnings). All of the 

feedstock supply systems will have demonstrated harvest, collection, storage/queuing, preprocessing, and transportation and handling at an integrated cost performance target of 

$35/dry ton. The biomass feedstock available at the $35 cost target is shown in Figure D-11, 

with the annual tonnages coinciding with the validation of the dry, woody and wet feedstock 

supply systems that will occur in 2009, 2011 and 2012, respectively. 

Because every region of the United States has some biomass that could be accessed for a 

$10/dry-ton grower payment in 2012, feedstock supply technologies should not be a limiting 

factor for biorefinery development in any region or for any biomass resource. However, as the 

biorefining industry expands, economies of scale will drive biorefineries from 2,000-ton/day 

facilities to 5,000- and possibly 10,000-ton/day facilities. While such scaling favors 

conversion processes, larger scale biomass supply systems begin to incur greater costs. 

Increasing costs include, but are not limited to, those related to the transportation, handling, 

and integration of an expanded diversity of feedstocks that must be segregated or blended. 

Therefore, the longer-term feedstock supply R&D challenge is to develop technologies that 

control these costs so feedstock supply system costs will not limit achievable biorefinery scale 

efficiencies or consume biorefinery profits that could be used to purchase higher-value 

feedstocks. 

An additional focus of the advanced feedstock platform R&D, is to ensure that the available 

biomass tonnages keep pace with the growth in ethanol demand (see Figure D-2 and 

Appendix J), with the ultimate goal of producing, supplying and converting in excess of 500 

M dry tons of cellulosic biomass by 2030 (Figure D-11). For this to occur, the grower 

payment, which is influenced by production and sustainability issues, must increase 

substantially beyond the $10/ton level. In order to support the increased grower payment 

while maintaining profitability, additional advancements in supply system and conversion 

technologies must occur. The implementation of favorable policies (e.g., Farm Bill policies) 

may play a role as well. 

With the focus on mitigating increasing supply system costs associated with larger scale 

biorefineries as well as on mitigating the increasing grower payment needed to insure 

adequate feedstock supplies, the advanced feedstock R&D goal will transfer from the 2012 

$35/ton cost goal to the 2030 60M gallon ethanol production goal. The projected magnitude 

of the feedstock and conversion technology advancements needed to achieve the 2030 goal, 

D-21 
measured in terms of allowable feedstock cost (i.e., the price the conversion process can pay 

for the feedstock without exceeding the 1.07/gallon production cost) is shown in Figure D-11. 

22 67

130

250

446

582

675

$10.00 $10.00 $10.00

$20.29

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$51.15

$71.15

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$44.04

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$0

$5

$10

$15

$20

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2009 2011 2012 2015 2020 2025 2030

0

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400

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1,000

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Resource Tonnage Available (MM tons)

Feedstock Cost to Throat of Reactor ($/dT)

Feedstock Grower Payment ($/dT)

Resource Tonnage Demand (MM tons)

Figure D-11. Substantial Improvements in Supply System and Conversion 

Technologies will be Needed to Cover Increased Feedstock Costs without 

Exceeding Ethanol Cost Targets 

New ideas have been proposed to control, and possibly reduce, feedstock supply system costs 

in large-tonnage systems by increasing the available biomass tonnage per square mile by 

increasing yields per acre and improving land use efficiency and productive capacity per 

square mile. These ideas include developing sustainable production systems that allow greater 

residue removal from existing crops and forest resources, putting more land into optimized 

sustainable production by integrating perennial energy crops with traditional crops and woody 

resources, and developing higher-yield crops and biomass cropping alternatives. 

The immediate effect of increasing land area productivity is that a 5,000-ton/day biorefinery 

could operate on the same land-area footprint as a 2,000-ton/day biorefinery, thereby 

mitigating scale-up transportation issues. However, increased land-area productivity solutions 

are inherently multi-crop, and the feedstock supply system must be able to handle the 

diversity of those biomass resources. Fortunately, the United Sates grain handling and country 

grain elevator system provides a large-scale supply system model for a similar biomass “depot 

concept” to maximize the efficiency of delivering feedstock to large-scale biorefineries, 

Figure D-12. 

D-22 
Figure D-12. Large-scale, Flexible Feedstock Supply System or “Depot 

Concept” to Handle and Merchandise a Diversity of Feedstocks across Large 

Geographical Areas 

To develop a large-scale feedstock supply system that will enable conversion technologies to 

achieve optimum economic scales, feedstock supply long-term R&D will improve land use 

productivity/efficiency and advance feedstock supply technologies to handle larger volumes 

of biomass while controlling supply system costs. In addition, the production and removal of 

biomass on an annual basis can result in reduced crop yield. This presents a potential barrier 

to the use of biomass crop residues and energy crops as biorefinery feedstocks. Because these 

agricultural resources make up the largest biomass resource, this issue must be considered in 

large-scale biorefining. The factors that limit biomass removal will be addressed through 

partnerships with the USDA and biomass regional partnerships. 

D.4.1 Increased Land Use Efficiency 

Improved land use efficiency is dependent on the improvement of yields of existing crops and 

the development of new cropping alternatives. The joint DOE-USDA “Billion Ton” study 

(Perlack et al. 2005) assumed that historic trends of crop development and yield increase will 

continue. This assumption is dependent on the continued progress of the USDA, the land 

grant university system, and the agriculture industry through 2030, which is a reasonable 

assumption. Needed research for short-rotation woody crops includes the characterization of 

existing and potential woody crops in terms of yield, adaptation zones, and cost estimates for 

production and scale-up; the determination of practical aspects of scale-up such as 

establishment, cultural practices, and weed control strategies; and the development of genetics 

and targeted traits to accelerate species domestication. Needed research for perennial crops 

must address regional yields, genetic improvement, production management and costs, 

sustainability, and environmental production issues. 

D-23 
D-24 

D.4.2 Advanced Large-Tonnage Feedstock Supply Systems 

The advanced feedstock supply system must not only handle large tonnages of biomass over 

possibly larger geographical areas but also collect and assemble a multiplicity of feedstocks 

and convert those feedstocks into a “commodity spec” that can supply a common large-scale 

biorefinery. Primary research elements will include storage and queuing, pre-processing, and 

transportation and handling. The diversity of on-farm and in-forest systems must be 

accommodated not changed. The interface between producers and the commodity biomass 

system is a subtle but extremely important interface for large-scale feedstock supply 

technology development. 

Additional research will include the development of depot-based feedstock pre-processing 

technologies to reformat and condition different feedstocks into a common, acceptable format 

and quality for improved transportation, handling, and conversion operations. Depot-based 

technologies will enable the feedstock assembly system to take advantage of advanced valueadd operations such as fractionation and separation of biomass into secondary co-products for 

local markets (e.g., feed products). The co-products may be of higher value than the 

biorefinery feedstock and thereby will offset a larger percentage of supply system costs. In 

addition, depot-based technologies will enable feedstock blending to produce large-scale, 

commodity biomass at refinery-specified quality. 

Advanced feedstock supply systems that handle large tonnages of biomass over wide 

geographical areas will rely heavily on the development of new transportation methods and 

technologies that incorporate value-added pre-processing and merchandising of raw feedstock 

material. Traditional biomass transportation modes, such as trucks, may not be economically 

possible because of transport distances, traffic congestion, and community opposition. Rail 

transport of biomass reduces the frequency of loads, but it is often more expensive than truck 

transport because of infrastructure constraints. Advanced transportation systems will likely 

incorporate technologies that not only provide infrastructure and operational cost savings but 

also incorporate in-transit value-added processes. 
Appendix E: Biochemical Conversion Technology R&D 

Needs 

Replacing 30% of U.S. 2004 finished motor gasoline demand (which equates to roughly 60 

billion gallons) with ethanol by 2030 will require a significant increase in ethanol production 

over today’s corn starch-based industry. Put simply, it will require the commercialization of 

cellulosic ethanol technology. 

Currently, this is technically feasible for corn stover (Aden et al. 2003) and possibly poplar 

(Wooley et al. 1999) using biochemical conversion technology that includes pretreatment, 

enzymatic hydrolysis, and fermentation. However, the process remains inefficient and is 

therefore costly to commercialize. But ultimately, cellulosic ethanol production via 

biochemical conversion could provide fuel at prices commensurate with historical gasoline 

prices (i.e., less than $1/gallon) by taking advantage of breakthroughs in biotechnology. 

Although this is the ultimate goal, it is useful to have an intermediate goal to provide ethanol 

that is competitive in the near-future transportation fuels market and that can be described and 

achieved with technology now under development. Taken together, these goals direct the path 

forward for biochemical conversion R&D. The intermediate target can be succinctly 

described as the demonstration of $1.07/gallon biomass-to-ethanol technology at pilot scale 

by 2012. The R&D needed to achieve this target is outlined here. 

Achieving the intermediate target requires technology advancements in the key areas of 

pretreatment and enzymatic hydrolysis and sugar fermentation as well as the integration of 

these technologies. For the near-term R&D pathway to the $1.07/gallon target, an implicit 

assumption is that the lignin residue component of the process is burned to supply the heat 

and power needs of the plant using currently available technology. Detailed mass and energy 

balances validate this assumption (Aden et al. 2002). However, long-term, research is needed 

for better use of the lignin component to add value for enhanced fuel production. This is 

addressed in the context of advanced technology research (see Appendix G). In addition, the 

capital investment for cellulosic ethanol technologies is a significant hurdle. The research 

needs identified directly address this issue by reducing the total installed capital per annual 

gallon of output from the current 2005 state-of-technology estimate of $3.04/gallon to 

$1.85/gallon for the 2012 market target case. 

The overall goal of biochemical conversion R&D is to create technology that provides 

market-competitive ($1.07/gallon), moderate-risk (evolutionary) technology tested at pilotscale (1 ton/day) on a model feedstock (corn stover). 

To be successful, near-term R&D should: 

• Specifically target the remaining pretreatment, hydrolysis, fermentation, and 

integration barriers 

• Employ multiple methods of overcoming a barrier (i.e., chemical and biological 

catalysis) 

E-1 
E-2 

• Be based primarily on moderate risk or evolutionary development of the technology 

• Include some revolutionary or novel technology development that complements the 

evolutionary emphasis 

• Employ a range of participants to capture required expertise in diverse areas 

• Maximize interfaces between participants and other program elements (e.g., feedstock 

development) 

• Include 1 year of pilot production to optimize process integration and identify 

scale-up issues. 

The key activities to achieve the target include: 

• Meeting cost targets for delivered feedstock 

• Developing pretreatment technologies that produce high xylose yields 

• Developing new accessory enzymes to aid pretreatment efficacy 

• Further reductions of cellulase cost via improvements in activity or other elements 

• Developing a commercially viable, co-fermenting micro-organism capable of 

producing ethanol at high yields, rates, and titers in pretreatment hydrolyzates. 

Figure E-1 depicts these critical activities on a timeline. Table E-1 provides more 

detailed information. 

Figure E-1. Critical Targets and Activities for Pilot Demonstration of 

$1.07/gallon Technology 
Table E-1. Timeline of Key Activities to 2012 

Completion Year 

R&D Area Current 2007 2008 2009 2010 Feedstock Interface Corn stover 

Determine which feedstock types will be used in pioneer plants and have the potential to provide significant volumes (>100 million tons/year) 

Develop/adapt dry corn stover analytical methods for diverse samples (>3) of another feedstock type (e.g. switchgrass) achieving mass balance closure of 100% ± 5% 

Define the relationships between variations in the feedstock composition and key processing parameters 

Develop/adapt dry corn stover analytical methods for diverse samples (>3) of another feedstock type (e.g. ensiled corn stover) achieving mass balance closure of 100% ± 5% 

Define betwethe feecompoproces

Pretreatment 

63% xylan yields and 13% sugar degradation in continuous reactor with > 30% solids from corn stover 

1) Achieve 75% xylose yield in laboratory scale high solids pretreatment reactor on corn stem internode 2) Define the relationships between pretreatment conditions and the chemical/ultrastructural changes in corn stover stems that result in biphasic xylan hydrolysis 

Validate > 75% xylan yield & < 8% degradation from corn stover using a continuous reactor 

1) Understand sugar degradation kinetics & how to reduce degradation to < 6% for corn stover 2) Test accessory enzymes' effect on reducing pretreatment costs for corn stover in lab equipment 

1) Achieve > 85% xylan yields and < 6% sugar degradation from corn stover using a continuous reactor with > 30% solids 2) Make final decision on pretreatment process to use in 2012 pilot operation 

Providpretretwo ot(e.g. sensiledevelocorrelacorn s

Hydrolyzate Conditioning 

13% sugar losses in overliming conditioning step on corn stover 

Define the relationships between corn stover hydrolyzate conditioning and fermentation performance in lab equipment 

Reduce sugar losses in corn stover hydrolyzate conditioning step to < 7% in laboratory equipment (accessory enzymes is one option) 

Reduce sugar losses in corn stover hydrolyzate conditioning step to < 2% in laboratory equipment 

Providconditother fswitchcorn scost cthe co

Enzyme Production 

$0.32/gallon of ethanol 

Develop the first generation computational model of CBH I capable of describing structure and function, and verify CBH I structure 

1) Baseline commercial cellulases' specific activity 2) Determine how cellulase enzymes move along cellulose chains 

Conduct targeted substitutions of cellobiohydrolase to increase specific activity by 2-fold relative to native 

1) Define cellulase interactions at the plant cell wall 2) Validate the cost contribution of purchased enzyme at $0.16/gal EtOH 

Validacontribpurcha$0.10/

E-3 
E-4 

Enzymatic Saccharification and Fermentation 

> 85% Cellulose to EtOH, > 75% xylose to EtOH, 0% other sugars to EtOH in a total of 7 days in lab equipment with > 20% total solids from corn stover 

Demonstrate > 85% cellulose to EtOH, > 80% xylose to EtOH, > 40% other sugars to EtOH in a total of 7 days in lab equipment with > 20% total solids 

Define the relationships between lignin redeposition and enzyme kinetics 

Demonstrate > 85% cellulose to EtOH, > 80% non-glucose sugar to EtOH in a total of 5 days in lab equipment with > 20% total solids 

1) Demcellulo85% nto EtOdays iwith > 2) DeSSF dfeedstensiledevelocorrelacorn s

Integration/Modeling 

Research state-oftechnology utilizing current data and modeling shows a $2.26/gal ethanol selling price with total capital of $3.04/gal of annual installed capacity for corn stover 

Complete rapid analysis method to predict component concentrations in pretreated slurry stream to same accuracy as wet chemistry methods 

Completed biochemical pilot facility with 2 or more pretreatment trains and occupancy formally handed over to operating entity 

1) Biochemical pilot facility shakedown completed (For NREL, demonstrated by successful operation of new pretreatment train(s) on corn stover) 2) Process cost estimate updated with latest data and engineering consultations 

Providoperatcorn sbaselicorrelafeedst
Figure E-2 shows a general process flow diagram of the $1.07/gallon target biochemical 

technology and the major barriers that must be addressed to accomplish the target. Appendix C 

provides the results of uncertainty analysis performed on the biochemical conversion process to 

understand the cost impacts of the technology barriers, associated targets, and other process 

parameters. In addition, financial and market parameters such as ROI and processing facility 

size were evaluated. From this information, a qualitative assessment of R&D and deployment 

risk can be made. 

Figure E-2. Major Research Areas and Barriers for Biochemical $1.07/gallon 

Ethanol 

E.1 Feedstock/Process Interface R&D Needs 

Interface activities between feedstock development (assumed to be corn stover or a very similar 

feedstock for the $1.07/gallon target) and biochemical processing R&D will ensure the chemical 

and physical characteristics of the feedstock are optimized for the process and vice versa. Different 

types of feedstocks are likely to require different processing conditions but not necessarily new 

processes. Although corn stover will be used as the model feedstock, it will be necessary to adapt 

the conversion processes for the other feedstocks identified in the “Billion Ton” study to 

accomplish the 30x30 goal. To realize large-volume ethanol production, multiple feedstocks must 

be processed. This will require understanding the feed-process interactions, translating model 

agricultural residue feedstock data to other feedstocks, and identifying and working on new model 

feedstocks to cover other feedstock groups such as woody biomass. 

The targets here are to determine the sensitivity of the overall process to differences in 

feedstock type and quality and determine how to adjust and modify the process to accommodate 

changes in feedstock. 

E-5 
Research will: 

• Develop an understanding of the range of feedstock types expected to be used in pioneer 

plants 

• Develop an understanding of and improve or make more suitable the quality (physical 

and chemical characteristics) of each feedstock through work with feedstock suppliers 

and researchers 

• Determine the effects on all downstream unit operations of different feedstocks 

• Develop an understanding of how to adjust the process to maintain optimal yields and 

productivities with varying feedstock quality or different feedstocks. 

This is a relatively immature area of R&D, and specific targets need to be developed. Some 

important parameters and issues for the feedstock-process interface area include: 

• Quality of the feedstock 

o How is carbohydrate content affected by formatting? 

o Do differences among feedstock fractions suggest different processing schemes? 

o Can digestibility or another aspect of feed be enhanced via a “pre-” pre-treatment? 

• Cost of the delivered feedstock 

o What is the best trade-off between field processing and in-plant processing? 

o Can process limits, such as moisture in the feedstock, be managed with process 

requirements (minimum and maximum solids content)? 

• Toxicity of the feedstock 

o Can acetyl groups be altered to reduce toxicity of feed during storage? 

o What compounds are created in storage practices such as ensiling? 

• Handling in the processing facility 

o What equipment and manpower are required for the feedstock? 

In relation to agricultural residues, this reveals several important considerations. Agricultural 

practices such as wet harvesting require different storage formats, and the conversion process 

must be able to accommodate these formats. For example, dry feedstock is generally straight 

from the field and undergoes minimal formatting other than baling or loafing. For this 

format, the most likely parameters important to the conversion process are quality and cost. 

Wet feedstock, however, may have been adapted via ensiling or other pre-processing 

methods, and as such, it may undergo maximum formatting before it arrives at the processing 

facility. The most likely important parameters for ensiled feedstock are quality, cost, 

handling, and toxicity. 

E-6 
E.2 Pretreatment and Hydrolyzate Conditioning R&D Needs 

Pretreatment is a critical component of the biochemical conversion process, and it represents a 

significant cost component. The purpose of the pretreatment step is to generate a pretreated 

feedstock that is highly digestible in enzymatic hydrolysis and, in the case of dilute acid 

pretreatment, to convert a significant fraction of the hemicellulose to soluble sugars. 

Although dilute acid pretreatment is a potential approach to achieving the process yields 

necessary to reach the $1.07/gallon target, recent advances in the understanding of plant cell 

wall structure and the hydrolytic enzyme system could allow for less severe and less costly 

pretreatment processes. In addition, hydrolyzates resulting from different feedstock and 

pretreatment approaches will have different conditioning requirements to allow for ethanologen 

performance that meets ethanol yield, rate, and concentration targets. Although significant 

advances have been made in pretreatment technology to increase sugar yields and decrease 

costs, additional advances are needed to achieve the $1.07/gallon target. 

To achieve the 30x30 goal, a variety of biomass feedstock types—including agricultural 

residues, woody forest land residues, and herbaceous and woody energy crops—will be needed. 

With such a broad feedstock base, there is a range of compositional and structural features. The 

feedstock types respond differently to pretreatment processes and conditions, so experimental 

studies are needed to properly match feedstock types with pretreatment conditions and enzyme 

preparations. Parametric studies can help reveal these relationships to ensure that a variety of 

biomass feedstocks can be economically processed. 

The target here is to maintain or increase the solids loading of 30% while increasing the xylanto-xylose conversion to 90% and reducing the xylan lost to degradation products to 5% in a 

continuous, pilot-scale reactor. 

Research will: 

• Determine the location of xylan in the plant cell wall and optimize pretreatments that 

selectively remove and hydrolyze it to xylose 

• Reduce sugar degradation to minimal levels by developing an understanding of the 

kinetic mechanisms that lead to undesirable degradation products and systematically 

blocking the pathways 

• Make a down-selection to one primary pretreatment technology to focus research. 

Knowledge of how specific pretreatment conditions and processes influence the reaction and 

re-arrangement of biomass feedstock components will enable cost-effective pretreatments 

that are properly matched to specific feedstock types and required enzyme activities and 

loadings. Cell wall imaging provides information about xylan, cellulose, and lignin location 

and reactivity to pretreatment methods, which enables the development of more targeted (i.e., 

efficient) pretreatments. 

The target is to reduce the capital cost of pretreatment through the use of ancillary enzymes. 

E-7 
Research will determine if other enzymes, such as xylanases, can be used to improve xylose 

yields, minimize formation of degradation products, reduce costs associated with the 

pretreatment process by reducing the required severity, and reduce the need for conditioning. 

Employing cost-effective hemicellulases and other accessory enzymes in conjunction with 

cellulases could allow for less severe and less costly pretreatment with lower sugar degradation 

losses and the ability to achieve a combined 90% xylose yield from pretreatment and enzymatic 

hydrolysis. A “hemicellulase/accessory enzyme” subcontract solicitation would engage 

commercial enzyme suppliers in the development and testing effort and include validation and 

knowledge transfer activities to ensure applicability to the biochemical process. Sugar 

degradation can also be addressed by developing an understanding of the kinetic mechanisms 

that lead to undesirable degradation products and then systematically blocking the pathways. 

Finally, parametric studies can determine relationships between sugar degradation and 

pretreatment conditions. The findings can be used to identify pretreatment conditions to reduce 

xylose degradation reactions to target levels. 

The target here is to eliminate or greatly reduce the need for conditioning. 

Research will develop an understanding of: 

• The role of hydrolyzate conditioning to eliminate sugar losses 

• Degradation kinetics to allow the minimization or elimination of the formation of 

inhibitory compounds. 

A hydrolyzate conditioning step adds cost and complexity to the process and results in the 

potential loss of sugars for fermentation to ethanol. The need for conditioning is intrinsically 

related to the pretreatment process and conditions, which influence the formation of sugar 

degradation products that are potentially inhibitory and toxic compounds from lignin, portions 

of which could be solubilized under certain pretreatment conditions. A systematic approach 

that includes the release or formation of fewer inhibitory compounds during pretreatment and 

the development of more robust ethanologen strains is needed to eliminate the hydrolyzate 

conditioning step. 

E.3 Enzyme Production R&D Needs 

The enzymatic hydrolysis step saccharifies the cellulose found in energy plant cell walls to 

glucose using cellulase enzymes as part of simultaneous saccharification and co-fermentation 

(SSCF). As in the case of pretreatment, significant advances have been achieved in the enzymes 

used in this step over the past decade. An example is the recently achieved 30-fold reduction in 

enzyme cost. However, additional advances are required to achieve the $1.07/gallon target. 

The target is to reduce the cellulase enzyme cost to $0.10/gallon of ethanol produced for a 90% 

conversion of cellulose to glucose within 3 days in a hybrid saccharification and fermentation 

(HSF) system. 

E-8 
Research will: 

• Develop an understanding of cellulase interactions at the plant cell wall ultrastructural 

level to optimize hydrolysis processes and enzyme kinetics and, ultimately, cellulase use 

and cost 

• Determine how cellulase enzymes move along the cellulose chain and the roles of 

enzyme sub-structures 

• Employ targeted substitutions of enzyme components to increase specific activity, 

guided by molecular modeling of cellulase-substrate interactions 

• Identify the enzyme production process and logistics that minimize processing and 

transportation costs of enzyme products. 

To achieve the desired near-term cellulase cost reduction (a factor of approximately 2–5) 

scenario, further enzyme development work is critical (Xu 2006). This work is likely to follow 

the new and developing fields of protein engineering and include an even greater element of 

bioinformatics. The recently developed tools and databases that are now available to 

bioinformaticists show exciting potential to achieve the additional required 2 – 5 X cost 

reductions. Also, methods for conducting high-throughput directed evolution have been refined 

considerably over the past 2 years. Cellulases have been difficult to improve by directed 

engineering primarily because proper screens were not available. The adage “you get what you 

screen for” is especially true for such enzymes (Himmel and Georgiou 1993). Cellulases in the 

biorefinery must work on the complex structure of the cellulose buried deeply in the plant cell 

wall. Thus, conducting a robotic screening program on cellulose surrogates or modified 

celluloses usually leads to failure in the context of a biomass-degrading enzyme. Aspects of 

cellulase function to be improved by this program include cellulase-specific performance (e.g., 

bonds broken per unit of time per gram of enzyme), cellulase thermal tolerance, and cellulase 

resistance to substances found in biomass pretreatment hydrolyzates. Although there is yet no 

evidence that cellulose hydrolysis occurs more rapidly at elevated temperatures, the objective to 

increase the stability of cellulases to denaturation by increasing thermal tolerance is reasonable. 

Also, cellulases are known to be affected by high sugar concentrations, phenolics, and other 

extractives and sugar degradation products. Therefore, a program to increase the resistance of 

cellulases to these agents is likely to have a positive effect on the $1.07/gallon target. As part of 

this study, the enzyme companies are well placed to determine “best guess” scenarios for the 

costs and constraints to be expected for delivery and storage of improved process enzymes. 

This activity will likely target the non-informational approaches to improving enzyme 

economics. In addition, it will be necessary to understand cellulase interactions at the plant cell 

wall ultrastructural level to optimize hydrolysis processes and enzyme kinetics (Himmel, Ruth, 

and Wyman 1999). 

Some key pieces of information are needed to enhance the effectiveness of near-term enzyme 

engineering activities. One is a direct measurement of the effectiveness of individual CBH I 

cellulase molecules acting on cell wall cellulose. To date, no direct experimental method 

permits such measurement, but this information is needed to assess the quality of enzyme 

engineering strategies (Zhang, Himmel, and Mielenz 2006). The complexity and structural 

uncertainty of real plant biomass is primarily responsible for this. New methods for imaging 

E-9 
and measuring the rates of motion of these processive cellulases are needed. Computer 

modeling can also deliver important information about the mechanism of complex enzymes, 

such as CBH I (Nimlos et al. 2006, Matthews et al. 2006). 

Although this work is likely to contribute primarily to the post-2012 objective, it is 

reasonable to expect some benefit to near-term enzyme engineering activities. One example 

of a near-term benefit is an understanding of the molecular basis for cellulose binding 

afforded by the cellulose binding domains, which are found in essentially all commercialgrade cellulases. Near-term activities designed to probe and map the cellulose binding 

domain binding affinities on cellulose (or the microfibril) for natural enzymes will permit 

design strategies for improving existing commercial enzyme components and ultimately 

reducing cellulase cost. 

Lignin is known to migrate from its native configuration and compartmentalization during 

and immediately after application of thermal/chemical pretreatment processes such as dilute 

acid. The attempt to apply classical enzyme kinetics to assess the nature of the interference 

lignin poses for cellulase has not been especially useful for process engineers seeking to 

improve the pretreatment step or biochemists seeking to improve the tolerance of the 

enzymes. Again, the problem is the complexity of biomass. Classical kinetics provides only 

“ensemble average” information, and to improve this process, the actual cause for ligninbased enzyme inactivation must be determined at the molecular scale (Vinzant, Ehrman, and 

Himmel 1997). Once this level of understanding is obtained, pretreatment and enzyme use 

process parameters can be changed to minimize the deleterious impact released lignin has on 

remaining unit operations. 

NREL’s Biomass Surface Characterization Laboratory affords new tools for this historical 

problem. New biomass surface imaging methods are being developed to permit the direct 

mapping of re-deposited lignin on pretreated biomass. Important directions to pursue include 

the development of lignin chemistry-specific molecular probes, which permit the 

visualization of lignins in plant cell walls, and probes for all the critical cell wall polymers, 

including the hemicelluloses and proteins (Matthews et al. 2006). Again, some of this work 

will continue past the 2012 target and build a foundation for the 2030 goal. 

 

E.4 Enzymatic Saccharification and Fermentation R&D Needs 

The development of an organism capable of producing ethanol from mixed biomass sugars at 

the required rates, yields, and titers to achieve the $1.07/gallon cost target is the biggest 

remaining technical challenge for biochemical conversion of biomass to ethanol. Significant 

advances in fermentation technologies must be achieved to meet the projected demand for 60 

billion gallons of ethanol in 2030. 

The target is to develop a robust, commercially viable biocatalyst (micro-organism) capable of 

fermenting 85% of the hemicellulose sugars and 95% of the glucose to a concentration of at 

least 6% ethanol in 3 days in a combined hybrid saccharification and fermentation (HSF) 

system. 

E-10 
Research will: 

• Identify strain candidates that exhibit superior “wildtype” performance 

• Use metabolomics, proteomics, and other tools to develop an understanding of 

metabolic bottlenecks in the carbon assimilation pathways that limit rates of pentose 

sugar uptake and the ability to withstand fermentation inhibitors such as organic acids, 

low pH, and increased temperature 

• Extend “omics” studies to identify and aid understanding of secondary pathway 

limitations related to reaction co-factors and regulation of metabolism 

• Increase pentose uptake rates by applying protein and metabolic engineering to increase 

sugar transporter efficiency, pentose specificity, and expression 

• Improve strain robustness by manipulating cell membrane composition to reduce its 

permeability to organic acids and improve its temperature stability 

• Use a combination of metabolic engineering, mutagenesis, and long-term culture 

adaptation strains on actual pretreatment hydrolyzate to achieve the target fermentation 

performance 

• Perform parametric analysis of such factors as lignin re-deposition to minimize 

detrimental effects using structural and surface analysis tools. 

Robust strains that ferment all hemicellulose sugars at high rates with minimum byproduct 

formation must be developed for commercial use. Improving the xylose-to-ethanol process 

yield (currently 25%–50%) to 85% is essential to meeting the $1.07/gallon target. Enhancement 

of the xylose fermentation rate is critical to achieving a high xylose-to-ethanol yield. The xylose 

fermentation rate needs to be enhanced 3- to 10-fold to approach the glucose fermentation rate. 

Reducing the toxic effect of inhibitors on pentose fermentation by improving microbial 

resistance to the hydrolysate or minimizing toxic levels during pretreatment and subsequent 

process treatment must also be achieved. Improved microbial resistance can be achieved by 

traditional adaptation or a more rational approach using advanced biological tools. It would be 

beneficial to develop an understanding of the toxic mechanisms to help in the development of 

superior strains and to provide guidance in the pretreatment process. 

The application of effective metabolic engineering tools is critical to meeting the $1.07/gallon 

target in 2012. One challenge is to develop better cost estimates of current best fermentation 

organisms. Such performance data are generally not publicly available. Current plans are to 

conduct a “fermentation organism” solicitation, similar to the enzyme subcontracts, to develop 

and validate the performance of this organism. 

The “fermentation organism” solicitation will encourage industry, academic research 

organizations, and national laboratories to work together to overcome the technical barriers that 

limit current strain performance. Research breakthroughs and innovative approaches are critical 

to this goal. Parallel efforts with the industry-driven organism solicitation and fundamental 

toolbox building (core R&D) will ensure success. Capabilities built by the Biomass Program at 

the national laboratories can contribute to the fundamental toolbox building significantly. 

Concerted, multi-year efforts will be crucial. 

E-11 
The development of robust, industrially useful fermentation strains to meet these targets will 

require substantial knowledge of the fundamental factors that limit efficient sugar 

bioconversion in hydrolysate. A collective knowledge of strain improvement—including a 

deeper understanding of strain physiology, metabolic engineering options, hydrolysate toxicity, 

and process considerations—is required. 

The target is to develop an HSF process capable of saccharifying 90% of the cellulose to glucose 

and fermenting 85% of the hemicellulose sugars and 95% of the glucose to ethanol in 3 days 

while maintaining the solids concentration necessary for the ethanol concentration target above. 

Research will: 

• Use information about the enzyme capabilities and fermenting strain’s performance to 

develop and test strategies for efficiently integrating enzymatic hydrolysis with 

biomass sugar fermentation to maximize cellulose hydrolysis and sugar fermentation 

rates and yields 

• Quantify the effect of enzyme loading, strain inoculation time, and inoculum charge on 

batch process performance 

• Use reactor designs and operational schemes to maximize the solids loading and 

conversion of cellulose and other sugars to ethanol. 

Working in a solids environment presents special challenges for organisms. To meet the 

$1.07/gallon target, an organism must be able to ferment hydrolysate with a minimum total 

solids content of 20% (with 11%–15% total sugars), with minimal nutrient supplement and 

hydrolysate conditioning. A tolerance to at least 5% ethanol, and preferably to 8%–10% (w/w), 

is needed to achieve higher ethanol titer. 

Improving the pentose fermentation rate in hydrolysate, as described above, is key to shortening 

fermentation time. Enhancing the enzyme activity (enzymatic hydrolysis by cellulases and 

hemicellulases) and the organism’s tolerance to hydrolysates will also contribute to the 

residence time reduction goal. 

The organism must be produced at low cost via onsite production with hydrolysate sugars and 

minimal nutrients or supplied at low cost (pennies per gallon of ethanol), as in the corn ethanol 

industry. Inhibitors in hydrolysate, such as acetic acid, can severely inhibit cell growth, so 

overcoming such growth inhibition is critical. 

Enhancement of the specific productivity of the fermentation organism, particularly on xylose 

and arabinose, is necessary to keep the inocula at acceptable cost levels. This is critical to 

achieving ethanol productivity using industrial-size inocula to achieve the $1.07/gallon target. 

Cell recycle may not be an option in biomass hydrolysate fermentation because of the lignin 

residue after fermentation.

E-12 
E.5 Integration/Process Engineering R&D Needs 

Combining individual unit operations into an integrated, systematic process is a significant 

challenge. Individual pilot-scale operations that demonstrate performance of unit operations and 

complete, integrated pilot development runs will be required to demonstrate the $1.07/gallon 

technology. A specific challenge will be to continue to demonstrate high solids processing at 

the pilot scale to reduce capital costs throughout the process (at least 30% in pretreatment and 

20% in hydrolysis). 

The targets are to: 

• Optimize the key unit operations, pretreatment, hybrid saccharification and 

fermentation, product recovery, and residue processing (separations only) in an 

integrated pilot plant with appropriate recycles 

• Obtain the data necessary to update the conceptual process design and cost estimate to 

validate the 2012 technical target. 

Research will: 

• Set up all appropriate unit operations in a safe, integrated system capable of continuous 

operation, 24 hours per day, 7 days per week, with data-gathering capabilities 

• Develop analytical methods and equipment to monitor the process and collect data 

• Test the integrated process and optimize conditions to maximize performance 

• Use data from the operating pilot plant to complete a conceptual full-scale process design 

and cost estimate to validate the 2012 technical target. 

Integration work needs to continue efforts to advance core process knowledge, with an 

emphasis on understanding the factors that affect integrated process performance and produce 

process-relevant residues and waste streams for testing. Ultimately, this research reduces risk as 

well as capital and operating cost by overcoming technical barriers associated with high-solids 

processing, developing an understanding of the effects of feedstock variability, and developing 

a better understanding of the key interactions that control process efficiency and performance 

(process integration). 

Following adequate bench-scale testing, the integrated process must be tested at the pilot scale 

(1 ton of dry biomass/day). It is envisioned that one to three feedstocks—such as stover, 

switchgrass, and wood chips—could be run through available pilot systems. Data collected 

from bench and pilot runs will be used to assess the state of technology annually. 

Additional work is needed to improve and further develop analytical methods to facilitate 

research efforts that require accurate compositional information. Realizing the goals of the 

30x30 initiative will require the development, standardization, and validation of hundreds of 

new analytical methods specifically for biomass feedstocks and intermediate process streams. 

The data generated by these analytical methods will be used to obtain performance information 

to accurately evaluate process economics. This will improve the efficiency of R&D activities in 

E-13 
the biomass community and support the emerging industry needs for accurate and rapid analysis 

methods for quality monitoring and process control. 

Specific activities are to: 

• Develop and validate standardized wet chemical methods for analysis of biomass 

feedstocks and intermediate products 

o Develop improved analytical methods that more accurately quantify biomassderived components in the liquid phase 

o Complete development of analytical methods for analysis of corn stover that 

accurately quantify more than 97% of its dry mass 

o Develop compositional analysis methods for a new near-term feedstock (e.g., 

switchgrass or other material) that accurately quantify more than 97% of its 

dry mass 

o Publish industry-wide consensus standards 

• Develop and validate low-cost, rapid compositional analysis methods to support 

research efforts and facilitate online compositional measurements required for quality 

monitoring and process control 

o Develop rapid analysis methods for a new near-term feedstock 

o Develop rapid analysis methods for liquid and solid components in intermediate 

process streams. 

Process design is a critical part of technology development. Without it, the technology pieces 

are just individual operations. By incorporating process design, standard engineering 

methodology, and economic analysis techniques into an R&D plan, the resulting work is 

targeted to create a cost-effective, technically viable process. 

A large body of work has already been developed to direct biomass R&D efforts. Including 

process engineering and analysis in R&D provides: 

• Quantification of the critical targets and activities 

• Development of intermediate metrics that contribute to the critical targets 

• Progress tracking to the targets via annual state-of-technology assessments 

• An understanding of uncertainty in process parameters and the resulting cost effect 

• Early feasibility evaluation of alternate processes that might improve the process 

economics/feasibility. 

Table E-2 shows an example of the progress that could be achieved against each technology 

target in the biochemical process for $1.07/gallon ethanol. For simplicity, one “path to get there” 

has been identified for the process design and economic analysis. This does not mean that if the 

E-14 
listed targets are not met or this specific process design is not piloted that target cannot be 

achieved. Other targets may be exceeded, or other process designs (e.g., an alternate 

pretreatment) may become viable before 2012. A fluid design and analysis scheme must be 

employed to allow these breakthroughs. It is also important to recognize that R&D rarely 

progresses linearly and improvements are likely to come in a more step-wise fashion. 

Table E-2. Possible Interim Targets to $1.07/Gallon Ethanol for Stover 

2005

Post EnzymeSubcontract2007 2008 2009 2010 2011

2012 

Market 

Target

Minimum Ethanol Selling Price $2.26 $1.07

Installed Capital per Annual Gallon $3.04 1.85

Yield (Gallon/dry ton) 65 90

Feedstock

 Feedstock Cost ($/dry ton) $53 $45 $35

Pretreatment

 Solids Loading (wt%) 30% 30% 30% 30% 30% 30% 30%

 Xylan to Xylose 63% 68% 72% 77% 81% 86% 90%

 Xylan to Degradation Products 13% 12% 10% 9% 8% 6% 5%

Conditioning

 Xylose Sugar Loss 13% 11% 9% 7% 4% 2% 0%

 Glucose Sugar Loss 12% 10% 8% 6% 4% 2% 0%

Enzymes

 Enzyme Contribution ($/gal EtOH) $0.32 $0.32 $0.32 $0.32 $0.16 $0.10 $0.10

Saccharification & Fermentation

 Total Solids Loading (wt%) 20% 20% 20% 20% 20% 20% 20%

 Combined Saccharification & Fermentation Time (d) 7 77653 3

 Overall Cellulose to Ethanol 86% 86% 86% 86% 86% 86% 86%

 Xylose to Ethanol 76% 76% 76% 80% 80% 80% 85%

 Minor Sugars to Ethanol 0% 40% 40% 40% 80% 80% 85%

To understand how much development is still required, DOE/NREL tracks the research state of 

technology of the biochemical conversion process by extrapolating current-year laboratory results 

to a conceptual process design and cost estimate. The design and cost estimates are based on 

engineering company consultations and ASPEN modeling (Aden et al. 2002). The state of 

technology includes only ethanol and excess electricity sales (i.e., no co-product credits). It does 

not include any proprietary, company-specific enhancements over the baseline conversion 

technology demonstrated at DOE’s core biomass research program. 

Figure E-3 shows state of technology advances from 2001 to 2005 and looks to the future with 

R&D targets to understand what is required to achieve the technical target in 2012. Much of the 

cost reductions from 2001 through 2005 were because of DOE-industry partnerships formed to 

reduce the cost of enzymes (Harris, Teter, and Cherry 2006; Mitchinson 2006). As shown in the 

figure, the 2012 target was accelerated by DOE to accommodate the president’s Advanced 

Energy Initiative. The research outlined here addresses the more aggressive target.

E-15 
E-16 

Figure E-3. Research State of Technology for Biochemical Conversion 
Appendix F: Thermochemical Conversion 

F.1 Gasification Scenarios for the $1.07/Gallon Ethanol Cost Target 

The target of $1.07/gallon thermochemical ethanol by 2012 was evaluated by constructing 

process models based on currently available technologies to benchmark a state-of-technology 

case. The process design for ethanol via gasification/mixed alcohol synthesis is documented 

in the draft Thermochemical Ethanol via Indirect Gasification and Mixed Alcohol Synthesis 

of Lignocellulosic Biomass design report (Phillips et al. 2007). Techno-economic analyses 

provide a biofuel production cost based on capital and operating cost estimates for individual 

unit operations in the mixed alcohol process design. A sensitivity analysis helps identify 

variables and, thus, R&D areas that, when overcome, have the largest effect on the cost of 

thermochemical ethanol. The thermochemical ethanol cost estimates are based on the process 

design parameters summarized in Figure F-1 and listed in Table F-1. A more detailed view of 

the process is shown in Figure F-2. 

Figure F-1. Process for Thermochemical Ethanol Production at $1.07/gallon 

F-1 
Table F-1. Thermochemical Ethanol Production From Biochemical Lignin-Rich 

Residues and Forest and Wood Residues: Path to $1.07/Gallon Ethanol 

2002 2005 2008 2010 2012

Process Description Tar Removal & 

Disposal

Tar & Light 

Hydrocarbon 

Reforming

Tar & Light 

Hydrocarbon 

Reforming — 

Increased 

Hydrocarbon 

Conversion

Consolidated Tar 

& Light 

Hydrocarbon 

Reforming

Improved Alcohol 

Catalysis & 

Synthesis

Minimum Ethanol Selling Price ($/gal ethanol) $2.41 $1.98 $1.73 $1.55 $1.07

Higher Alcohol Co-Product Value ($/gal alcohol) $1.15 $1.15 $1.15 $1.15 $1.15

Installed capital cost ($/annual gal MA) $4.42 $2.97 $2.75 $2.66 $1.97

Operating cost ($/annual gal MA) Excluding 

Feed and CoProduct $0.41 $0.47 $0.37 $0.30 $0.24

Ethanol Yield (gal/dry ton) 64.5 62.7 65.1 66.1 75.8

Mixed Alcohol Yield (gal/dry ton) 75.9 74.0 76.7 77.9 89.1

Feedstock

Feedstock Price ($/dry ton) $53 $53 $45 $45 $35

Thermochemical conversion 

Syngas yield (lb/lb dry feed) 0.82 0.82 0.82 0.82 0.82

Raw syngas methane (mol% - dry basis) 15.09 15.09 15.09 15.09 15.09

Cleanup and Conditioning

Tar reformer exit methane (mol% - dry basis) N/A 13.5 4.39 1.19 1.38

Tar reformer light HC reforming - % CH4 

conversion N/A 20% 50% 80% 80%

Tar reformer heavy HC reforming - % benzene N/A 70% 90% 99% 99%

Tar reformer heavy HC reforming - %tar 

conversion N/A 95% 97% 99.9% 99.9%

SMR Light HC reforming - % CH4 conversion 85% NA NA NA NA

Sulfur removal 1ppmv (SMR) 50 ppmv(MA) 50 ppmv(MA) 50 ppmv (MA) 50 ppmv (MA)

Catalytic Fuel Synthesis

Compression for fuel synthesis (psia) 2,000 2,000 2,000 2,000 1,000

Single pass CO conversion 40% 40% 40% 40% 60%

Overall CO conversion 40% 40% 40% 40% 60%

CO selectivity to alcohols (CO2-free basis) 80 80 80 80 90

Biomass gasification is a complex thermochemical process that consists of a number of 

elementary chemical reactions. It begins with the thermal decomposition of a lignocellulosic 

fuel which is followed by partial oxidation of the fuel with a gasifying agent—usually air, 

oxygen, or steam (Tabatabaieraissi and Trezek 1987). The initial heating of the biomass leads 

to water evaporation. A further increase in temperature initiates biomass pyrolysis, which is 

followed by the partial oxidation of pyrolysis vapors. The char remaining after a biomass 

particle is devolatilized is also gasified. 

The biomass gasification product gas is a low- to medium-energy-content gas (depending on 

the gasifying agent) that consists mainly of carbon monoxide, hydrogen, carbon dioxide, 

water, nitrogen, and hydrocarbons. Minor components of the product gas include tars, sulfur 

and nitrogen oxides, alkali metals, and particulates. These minor components potentially 

threaten the success of downstream syngas use. 

Gas composition and quality are dependent on a range of factors, including feedstock 

composition, type of gasification reactor, gasification agents, stoichiometry, temperature, 

pressure, and the presence or lack of catalysts. The energy content of the gasification product 

F-2 
gas ranges from 5 MJ/Nm3

 to 15 MJ/Nm3 and is considered a low- to medium-energy-content 

gas compared with natural gas (at 35 MJ/Nm3

) (Littlewood 1977; Maschio, Lucchesi, and 

Stoppato 1994). The relative amount of carbon monoxide, carbon dioxide, water, hydrogen, 

and hydrocarbons depends on the stoichiometry of the gasification process and the selected 

gasification medium. The air-fuel ratio in a gasification process generally ranges 0.2–0.35, 

and if steam is the gasifying agent, the steam-biomass ratio is less than 1. If air is used as the 

gasifying agent, then roughly half of the product gas is nitrogen gas (De Bari et al. 2000). 

Air-blown, or directly heated, gasifiers use the exothermic reaction between oxygen and 

organics to provide the heat necessary to devolatilize biomass and convert residual carbonrich chars. In directly heated gasifiers, the heat that drives the process is generated within the 

gasifier. Thus, when air is used, the product gas is diluted with nitrogen and typically has a 

dry-basis calorific value of about 5–6 MJ/Nm3

. The dry-basis calorific value of the product 

gas can be increased to 13–14 MJ/Nm3

 through the use of oxygen instead of air. Oxygen 

production is expensive, however, and its use has been proposed only for direct-heating 

gasification applications that involve the production of synthesis gas when nitrogen is not 

permitted in downstream synthesis conversion operations. Oxygen-blown gasifiers typically 

operate at high pressures (~30 bar), similar to the outlet delivery of the air separation unit. 

Indirectly heated gasifiers accomplish biomass heating and gasification through heat transfer 

from a hot solid or through a heat transfer surface. Because air is not introduced into the 

gasifier, little nitrogen diluent is present, and a medium calorific gas is produced. Dry basis 

values of 18–20 MJ/Nm3

 are typical. 

Gas phase impurities in syngas include ammonia, hydrogen cyanide, other nitrogencontaining gases, hydrogen sulfide, other sulfur gases, hydrogen chloride, alkali metals, 

organic hydrocarbons (tar), and particulates. The concentration of these non-syngas 

components depends on feedstock composition. For example, gasification of biomass with 

high nitrogen and sulfur content yields high levels of ammonia and hydrogen sulfide in the 

syngas stream. Hydrogen chloride concentration in biomass-derived syngas directly correlates 

to the chlorine content of the feedstock. Alkali metal, mostly potassium, in syngas is related to 

the alkali content in the biomass ash. Ash particles entrained in syngas affect the alkali metal 

content of syngas. The concentration of alkali vapors or aerosols in syngas depends on the ash 

chemistry of the selected biomass feedstock and the temperature of the gasification process. 

The organic impurities in syngas range from low-molecular-weight hydrocarbons to highmolecular-weight polynuclear aromatic hydrocarbons. The lower-molecular-weight 

hydrocarbons can be used as fuel in gas turbine or engine applications, but they are 

undesirable for mixed-alcohol synthesis. The higher-molecular-weight hydrocarbons are 

collectively known as “tar.” Tar yields in biomass-derived syngas can range from 0.1% 

(downdraft) to 20% (updraft) or greater (pyrolysis). 

F.2 Syngas to Mixed Alcohols (Ethanol) 

Mixed alcohols are an attractive gasoline blending stock for octane enhancement or as a fuel 

because they have better fuel properties than methanol and can use the existing fuel 

infrastructure. Several companies rigorously pursued the commercial development of mixed 

alcohol synthesis in the 1980s and early 1990s; however, no commercial mixed alcohol 

F-3 
synthesis from syngas plants are currently operating. The commercial success of mixed 

alcohol synthesis has been limited by poor selectivity and low product yields. Single-pass 

yields are on the order of 10% syngas conversion to alcohols, with methanol typically being 

the most abundant alcohol (Wender 1996; Herman 2000). Commercialization efforts were 

largely discontinued because of the low-cost petroleum available at the time, which led to 

unfavorable process economics. Current economic conditions—in which crude oil is around 

$60/barrel and natural gas prices around $7–$9/MMBtu, make mixed alcohols from biomassderived syngas an attractive renewable blending stock for transportation fuels. 

R&D has shown that methanol can be recycled to produce higher alcohols or removed and 

sold separately. To date, modified methanol and modified Fisher-Tropsch catalysts have been 

more effective in the production of mixed alcohols; the sulfide-based catalysts tend to be less 

active than the oxide-based catalysts (Herman 2000). For an economic commercial process, 

improved catalysts are needed to increase the productivity and selectivity to higher alcohols 

(Fierro 1993). 

Techno-economic analyses of thermochemical ethanol scenarios show that the separation of 

the higher alcohols (e.g., n-propanol, n-butanol, and n-pentanol) from the mixed alcohol 

product to enable selling them into their respective chemical markets (Phillips et al., 2007) 

could provide the initial economic benefit to accelerate the deployment of thermochemical 

technologies by making the first plants more economically attractive. 

The thermochemical route to ethanol and other mixed alcohols is depicted in Figure F-2. 

Figure F-2. Block Flow Diagram of Syngas Conversion to Mixed Alcohols 

The capital cost breakdown for the major process areas is shown in Table F-2. 

F-4 
Table F-2. Capital Investment Distribution of Major Process Areas 

 Process 

(From Figure F-1) % of Total Capital Cost 

Syngas Production 50% 

Mixed Alcohol Synthesis 29% 

Carbon Dioxide Removal 17% 

Product Fractionation 4% 

Table F-3 shows the market volume and market price for each alcohol co-product. Assuming 

a 3% per year market growth in 2012, it would take close to 10 plants to saturate the market 

for n-propanol and n-pentanol. As the number of plants increases, the options for other uses of 

the co-product alcohols will need to be investigated. Other uses include recycling of the coproduct alcohols to the synthesis reactor, using the co-product alcohols internally as fuel for 

steam and power, and using the entire ethanol and co-product alcohol stream as a 

transportation fuel. 

Table F-3. Alcohol Co-Product Market Size 

Alcohol 

2001 Market 

Volume 

(Thousand 

Tonnes/Year)(a)

2001 Market 

Price 

($/Gallon)(b)

In 2012 % of Market 

for One Plant at 

2,000 Tonnes/Day 

In 2012 % of Market 

for Ten Plants at 

2,000 Tonnes/Day 

Methanol 31,000 $0.60 0.01% 0.1% 

n-Propanol 142 $3.69 13% 133% 

n-Butanol 1,226 $3.72 3% 30% 

n-Pentanol 35 $4.23 10% 95% 

(a) Bizzari, Gubler, and Kishi 2002. 

(b) Bain (2005). 

In a recent review of mixed alcohol synthesis processes (Spath and Dayton 2003), the cost of 

mixed alcohols from natural gas-derived syngas ranged $0.50–$1.20/gallon, depending on the 

process assumptions and the cost of natural gas. Improved catalyst yield and selectivity, better 

process integration (to reduce energy losses), and economies of scale will improve overall 

process economics and opportunities. These technical and economic barriers need to be 

addressed by R&D efforts aimed at demonstrating integrated biomass gasification, gas 

cleanup and conditioning, and high-pressure catalytic synthesis of mixed alcohols. 

F-5 
Other R&D efforts led to the pilot-scale demonstration of several processes to convert syngas 

derived from natural gas to mixed alcohols. 

• A 12,000-ton/year facility based on the partial oxidation of natural gas to make syngas 

converted in a fixed-bed adiabatic reactor charged with a modified methanol synthesis 

catalyst was built in Italy in 1982 (Olayan 1987) as a result of a joint venture between 

Snamprogetti and Haldor Topsoe. 

• In 1984, Dow Chemical developed a process based on a novel alkali-promoted 

molybdenum disulfide catalyst that is inherently resistant to sulfur poisoning. Gas 

cleanup and conditioning requirements for syngas used in this process should be less 

stringent compared with those of other catalytic conversion processes. 

• The Octamix process developed by Lurgi was based on a modified low-pressure 

methanol synthesis catalyst and demonstrated in a 2-ton/day plant built in May 1990 at 

the Institute of Energy Process Engineering at the Research Centre in Julich, 

Germany. 

F.3 R&D to Achieve Syngas-to-Ethanol Cost Targets 

The following sections provide detailed descriptions of the R&D activities presented in Table 

F-1. 

F.3.1 2002: Tar Removal and Disposal 

The 2002 case presents thermochemically derived ethanol prior to tar destruction, gas 

cleanup, and conditioning research. Without gas cleanup and conditioning for tar removal, the 

gasification system removes tars and other contaminants (e.g., ammonia, alkalis, and residual 

particulates) via water quench. [A high-pressure gasification system might consider a physical 

solvent such as Rectisol instead, but it is unknown whether a physical solvent can remove the 

tars to a low enough level (Nexant 2005). Plus, operational problems such as foaming are 

known to occur from hydrocarbons in the physical solvent.] The tars are entrained in the 

scrubber water, which must be treated and disposed of. This increases capital and operating 

costs. In addition, ethanol yield is reduced because of the loss of hydrocarbon tar components. 

More downtime is required to clean the tars from the heat exchange and cooling systems, so 

the operating capacity factor is lower. The lighter hydrocarbons are converted to carbon 

monoxide and hydrogen using conventional SMR. 

F.3.2 2005–Present: Sequential Tar and Light Hydrocarbon Reforming 

Clearly, the physical removal of tar from biomass-derived syngas is economically and 

environmentally unattractive. Therefore, the 2005 state-of-technology design incorporates 

sequential tar and light hydrocarbon reforming to separately convert the heavy (tar) and light 

hydrocarbons to additional syngas. In this design, a fluidized bed tar reformer is placed 

downstream of the gasifier to convert tars and other hydrocarbons in the raw syngas to carbon 

monoxide and hydrogen. 

The tar reformer unit operation works well for tar reforming, but it has not been validated 

experimentally to efficiently reform light hydrocarbons. This un-optimized tar reformer 

converts only 20% of methane, so an additional SMR step is required downstream. This is 

F-6 
still an improvement over the 2002 case because the capital and operating costs of this system 

are less—primarily because the reduced tar loadings mean a smaller quench system is needed. 

In addition, there is a reduction in waste water. Alcohol yields are also slightly higher because 

the tars are converted to additional syngas. 

F.3.3 2008 Target: Improved Sequential Tar and Light Hydrocarbon Reforming 

Clearly, capital and operating costs can be reduced if tars and light hydrocarbons can be 

converted in a single unit operation. This is an intermediate cost goal on the way to 

achieving tar and light hydrocarbon reforming in one step. In this case, methane conversion 

increases from 20% to 50% in the fluid bed tar reformer, benzene conversion is increased 

from 70% to 90%, and tar conversion is increased from 95% to 97%. This results in a 

decrease in capital and operating costs because of a reduction in the size and energy 

requirements of the downstream SMR. 

F.3.4 2010 Target: Consolidated Tar and Light Hydrocarbon Reforming 

The 2010 design represents the R&D goal of consolidated tar and light hydrocarbon reforming. 

For this case, the tar reformer changes from a single fluidized bed to a two-vessel circulating 

tar reformer/catalyst regenerator system. This is to maintain optimum catalyst reforming 

activity and maximize tar and hydrocarbon conversion efficiency. In this system, tar 

conversion increases to 99.9%, benzene conversion increases to 99%, and methane conversion 

increases to 80%. Because methane conversion is essentially as high as the conversion for the 

conventional SMR step, the SMR can be eliminated from the process design. 

This case is the basis for the R&D activities to develop fluidizable tar-reforming catalysts for 

demonstration in integrated biomass gasification systems. 

F.3.5 2012 Target: Consolidated Tar and Light Hydrocarbon Reforming plus 

Improved Alcohol Catalysis and Synthesis 

The deployment of thermochemical ethanol technologies can begin after consolidated gas 

cleanup and conditioning technology is demonstrated to generate a clean syngas suitable for 

fuels synthesis. Therefore, the 2012 design presents incremental process improvements to 

optimize synthesis catalyst performance to increase ethanol/mixed alcohol yields. 

The advances in catalysis and alcohol synthesis result in an increase in mixed alcohols singlepass carbon monoxide conversion from today’s value of 38.5% to 50%. In addition, the 

carbon monoxide selectivity to alcohols increases from 80% to 90%, and the alcohols 

synthesis pressure is reduced from 2,000 psia to 1,000 psia. The product yield increases by 12 

gallons/dry ton. The energy requirement is reduced because, in addition to a lower syngas 

pressure for alcohol synthesis, less unconverted syngas is recompressed and recycled. All of 

this contributes to a reduction in overall capital cost. 

F.4 Technical Barriers to Achieving the Syngas-to-Ethanol Cost Targets 

To facilitate the development of advanced thermochemical conversion systems in gasification, 

advanced R&D is needed to address technical barriers. The technical approach for the OBP 

thermochemical platform involves core research to address identified technical barriers. This 

leads to demonstration in integrated biorefineries, which, in turn, leads to commercialization. 

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A conceptual block-flow diagram of thermochemical conversion is depicted in Figure F-3. 

Each technical barrier area has technology options. R&D plans are being developed to 

overcome these barriers and achieve low-cost biofuel production to meet the 30x30 goal. 

Figure F-3. Conceptual Block Flow Diagram of Thermochemical Conversion 

Technical Barriers 

F.4.1 Feed Processing and Handling 

For biorefineries, it is important that feedstock requirements be met while preparation 

requirements are minimized to reduce costs. This will require balancing the cost of plant-gate 

feedstock with the handling and processing required for reliable operation. The objective is to 

develop methodologies that allow biomass to be collected, prepared, and converted in a costeffective manner. 

Although specific R&D activities to supply biomass feedstocks to integrated biorefineries at 

specified cost targets are covered in Appendix D, feedstock processing as it relates to the 

thermochemical conversion process will need to be addressed. In the wide application of 

biomass, feedstocks such as forest and wood residues must be compatible with 

thermochemical processing technologies. Also, the use of wet residues requires appropriate 

pretreatment for de-watering and drying useable particle-size materials (for gasification and 

pyrolysis systems). This becomes an even greater challenge for pressurized gasification 

systems. To address this barrier, refinements in dry biomass feeder systems for use with 

gasification and pyrolysis will be required. 

F-8 
F.4.2 Thermochemical Processing 

The crucial first step in the R&D process is to resolve technical questions related to the 

operability and reliability of biomass gasification systems. Fundamental gasification studies, 

process modeling, and techno-economic evaluations will help identify opportunities for 

technology improvements. Research, development, and deployment (RD&D) on advanced 

gasification technologies for long-term applications will lead to lower-cost, more efficient, 

cleaner gasification systems appropriate for a variety of biomass feedstocks. 

F.4.3 Cleanup and Conditioning 

Techno-economic analysis has shown that syngas cleanup and conditioning to remove 

chemical contaminants such as tar, ammonia, chlorine, sulfur, alkali metals, and particulates 

has the greatest effect (the potential for a 28% ethanol cost reduction) on the cost of clean 

syngas. To date, gas cleanup and conditioning technologies are unproved in integrated 

biorefinery applications. 

R&D activities are addressing catalytic gas cleanup and conditioning strategies to produce a 

clean syngas from a range of biomass feedstocks and the validation of syngas quality suitable 

for mixed alcohol synthesis. These R&D activities are being pursued to validate the cost 

targets determined from the techno-economic analyses of the thermochemical ethanol 

scenarios via gasification/mixed alcohol synthesis. 

The target conversions for impurities in biomass-derived syngas are listed in Table F-4. The 

table presents the 2005 state-of-technology case and the 2010 tar-reformer goal case. The 

conversion percentage for current performance is what can be achieved in the laboratory for 

short time periods prior to catalyst deactivation. The goal case conversions were selected to 

yield an economically viable, clean syngas that is suitable for use in a catalytic fuel synthesis 

process without further hydrocarbon conversion steps. 

F-9 
Table F-4.Tar Reformer Performance – % Conversion 

Compound Current Goal 

Methane (CH4) 20% 80% 

Ethane (C2H6) 90% 99% 

Ethene (C2H4) 50% 90% 

Tars (C10+) 95% 99.9%

Benzene (C6H6) 70% 99% 

Ammonia (NH3) 70% 90% 

“Gas conditioning” is a general term for removing the unwanted impurities from biomass 

gasification product gas. It generally involves an integrated, multi-step approach that depends 

on the end use of the product gas. The goal is to demonstrate catalytic gas cleanup and 

conditioning strategies to produce a clean syngas from a range of biomass feedstocks and 

validate the syngas quality for mixed alcohol synthesis. For this report, the focus is on 

removing or eliminating tars without regard to acid gas, ammonia, alkali metal, and 

particulate removal. 

R&D activities related to cleanup and conditioning of intermediates from thermal processing 

of biomass include: 

• Evaluating the chemistry and kinetics of biomass gasifier tar destruction 

• Examining catalytic reforming of tars 

• Analyzing large-scale gas conditioning with catalysts 

• Developing advanced systems for clean gas production through the use of membranes 

and circulating fluid beds of catalyst/adsorbant 

• Developing advanced concepts for particulate and tar removal in existing test-bed 

facilities 

• Developing options for new thermal and catalytic removal and treatment technologies 

and materials. 

One promising hot gas conditioning method for tar destruction is catalytic steam reforming. 

Catalytic steam reforming offers several advantages: 

 

F-10 
• Catalyst reactor temperatures can be thermally integrated with the gasifier exit 

temperature. 

• The composition of the product gas can be catalytically adjusted. 

• Steam can be added to the catalyst reactor to ensure complete reforming of tars. 

Hot gas conditioning using current or future commercially available catalysts offers the best 

solution for mitigating biomass gasification tars. Tars are eliminated, methane can be 

reformed if desired, and the water-to-carbon monoxide ratio can be adjusted in a single step. 

Research is expected to improve gas cleaning and conditioning to allow biomass syngas to be 

effectively used for liquid fuels production. 

F.4.4 Tar Reforming Catalyst Development 

Improvements in tar reforming catalyst performance and long-term activity following many 

deactivation-regeneration cycles are being investigated. Fundamental catalyst studies are 

being performed to determine deactivation kinetics and mechanisms by probing catalyst 

surfaces to uncover molecular-level details. Combining this fundamental information should 

lead to improved catalyst formulations on robust support materials. 

Alternative tar reforming catalysts are being developed in ongoing projects at several 

institutions with the goal of improving catalyst performance to realize consolidated tar and 

light hydrocarbon reforming. The Gas Technology Institute is using a novel submerged 

combustion melting technology developed for the glass industry to generate engineered 

catalysts by inserting catalytically active metals into olivine crystal structures. The Research 

Triangle Institute is developing attrition-resistant tar reforming catalysts by impregnating 

zeolites structures with active metals and testing them in a laboratory-scale integrated cleanup 

and conditioning unit called the Therminator. NREL is developing fluidizable, attritionresistant reforming catalysts by depositing active metals onto the surface of hardened alumina 

spherical substrates. 

Optimized catalyst formulations and materials are also being evaluated at the pilot scale at 

NREL to demonstrate catalyst performance and lifetime as a function of process conditions 

and feedstock. The goal of these efforts is to provide chemical and engineering data to enable 

the design and successful demonstration of a regenerating tar-reforming reactor for long-term, 

reliable gas cleanup and conditioning. The cost target will be met when tar and light 

hydrocarbons are sufficiently converted to additional syngas, technically validating the 

elimination of an additional unit operation to separately reform methane and other light 

hydrocarbons. This consolidated gas cleanup and conditioning concept is shown in the block 

flow diagram in Figure F-4. 

F-11 
Figure F-4. Block Flow Process Diagram Showing Consolidated Gas Cleanup 

and Conditioning 

Additional reforming catalyst development will provide incremental improvements in catalyst 

activity, lifetime, and resistance to impurities. Consolidated tar and light hydrocarbon 

reforming with nickel-based catalysts demonstrates the technical feasibility for this gas 

cleanup and conditioning strategy. Alternative catalyst formulations can be developed to 

optimize reforming catalyst activity and lifetime and expand functionality. Further process 

intensification is possible by designing catalysts with higher tolerances to sulfur and chlorine 

poisons. Further reductions in gas cleanup costs could be realized by lowering or eliminating 

the sulfur and chlorine removal cost prior to reforming. Optimizing the water gas shift activity 

of reforming catalysts could also reduce or eliminate the need for an additional downstream 

shift reactor. 

R&D partnerships will focus on the development of materials, catalysts, manufacturing 

technologies, and combinatorial catalyst designs. Technical validation of tar and light 

hydrocarbon reforming is paramount to developing thermochemical technologies. Fuel 

synthesis and reforming catalyst activity and lifetimes can be optimized by including costeffective, efficient means of mitigating sulfur-, chlorine-, and nitrogen-containing species in 

syngas. Alternative, dry reforming chemistry to mitigate tars and light hydrocarbons could 

improve process integration with a biochemical ethanol process by effectively using a highvolume, low-value waste stream from the fermentation process. 

F.4.5 Advanced Catalysts and Process Improvements for Mixed Alcohol 

Synthesis 

Improved mixed alcohol catalysts would increase the single-pass carbon monoxide 

conversion from 38.5% to 50%, and potentially higher, and improve the carbon monoxide 

selectivity to alcohols from 80% to 90%. Improved mixed alcohol catalysts with higher 

activity could require a lower operating pressure (1,000 psia compared with 2,000 psia), 

which could reduce process operating costs. The energy requirement for the improved 

F-12 
synthesis loop is therefore reduced because of the combination of lower syngas pressure for 

alcohol synthesis and less unconverted syngas to recompress and recycle. Overall, product 

yields could increase substantially by improving synthesis catalyst performance, thus 

simplifying the overall fuel synthesis process. 

Alternative mixed alcohol synthesis reactors and catalysts should also be explored. Greatly 

improved temperature control of the exothermic synthesis reaction has been demonstrated for 

methanol synthesis in slurry-phase reactors. The suspension of catalysts in a heat-transfer fluid 

maintains the reaction temperature precisely, which results in higher selectivity to ethanol and 

improved yields. Slurry-phase reactors for mixed alcohol synthesis should be evaluated as part 

of future R&D. Optimizing catalyst performance in slurry-phase reactors should result in 

enhanced thermochemical ethanol synthesis processes. Other novel reactor designs, such as 

microchannel reactors, may also have the potential to improve thermochemical ethanol 

production. 

Improvements in mixed alcohol synthesis catalysts could potentially increase alcohol yields 

and selectivity of ethanol production from clean syngas. Partnerships with catalyst 

manufactures and reactor design engineers are expected to achieve this goal. Technoeconomic analyses and process design for other potential fuels (e.g., Fisher-Tropsch gasoline, 

Fisher-Tropsch diesel, and methanol) from syngas will also be investigated. 

F.4.6 Process Sensors and Controls 

Better monitoring sensors are needed for biomass feed systems to ensure reliable feed supplies to 

thermochemical processes. Thermochemical conversion of biomass also creates unique gas 

impurities, such as heteroatom tars and alkali, for which there are currently no adequate realtime, online sensors. Partnerships among national laboratories, universities, and instrumentation 

developers to establish concepts for monitoring these and other gas-phase species during system 

operation should be pursued. The development of such sensors and automated controlling 

systems is expected to improve the operability and reliability of biorefineries. 

F.4.7 Thermochemical Platform Analysis 

Techno-economic analysis and process modeling of the thermochemical conversion processes 

will determine the cost of biofuels production using currently available and developing 

technologies. This analysis is used to evaluate major process steps, highlight the most important 

technical barrier areas, and provide options for reducing biofuel production costs. Lifecycle 

assessments will be conducted along with techno-economic analysis to determine the 

sustainability of thermochemical pathways. Analysis results will be provided to decision makers. 

F.4.8 Fundamental Gasification Studies 

To maximize the efficiency of biomass conversion, future biorefineries will require optimized 

integration of biochemical and thermochemical conversion processes. R&D efforts are 

focused on establishing baseline information for gasification of residue streams generated by 

current and future biorefineries. This will enable the development of gasification technologies 

that are cleaner, more efficient, and more appropriate for a variety of feedstocks. Fundamental 

thermochemical conversion of biorefinery residues will be performed to determine how 

residue composition affects syngas composition and quality. Residues of interest include 

those that are out-of-spec and undesirable for the fermentation of corn stover, lignin-rich 

F-13 
F-14 

streams, and other in-process sugar biorefinery streams and materials such as corn fiber and 

distillers dry grain. 

Other biomass feedstocks considered for integrated biorefineries include wheat straw, 

switchgrass, poplar, and forest and wood residues. Each of these streams has a different 

elemental composition. Of particular interest will be elements such as nitrogen, sulfur, and 

chlorine. They also have very different compositions in terms of carbohydrate-to-lignin ratios, 

which could influence tar formation. All of these features will affect the quality and value of 

the syngas as an intermediate for mixed alcohol production. 

Alternative gasification technologies, such as pressurized oxygen-blown systems for dry 

feedstocks and wet gasification or hydrothermal processing for wet feedstocks, should also 

be evaluated. Pilot-scale data for these systems will provide input for techno-economic 

analyses that optimize process integration, heat integration, thermal efficiency, and overall 

process economics. 
Appendix G: Advanced Conversion Technology R&D for 

Integrated Biorefineries 

The technology advancements needed to achieve $1.07/gallon lignocellulosic ethanol are 

milestones on the path to energy independence. A well-defined R&D approach addresses the 

major technical barriers to cost reductions in biochemical and thermochemical conversion. 

However, achieving 30 x 30 will require continued efforts to reduce production costs by 

employing advanced, integrated biorefinery technologies beyond 2012. Projections for 

advanced biofuels estimate ethanol production costs ranging from $0.59/gallon to 

$0.91/gallon of gasoline equivalent by the 2030 timeframe, given a robust R&D program 

(Greene 2004, Ruth and Ibsen 2005). 

Preliminary market analysis, based on results from the Biomass Transition Model, suggests that 

achieving the goal will require additional technology advancement and demonstration to drive 

down production costs and increase ethanol market penetration and consumer acceptance. 

Future R&D efforts will focus on four areas to achieve the 30 x 30 goal: 

• Advanced, large-tonnage feedstock supply systems 

• Technology integration, economics of scale, and evolutionary process optimization 

• Systems biology for biochemical processing improvement 

• Selective thermal transformation for thermochemical processing improvement. 

Taken independently, these areas of research will not result in technology improvements that 

will meet the 30 x 30 goal. Together, however, they will yield quantifiable process 

improvements that will guide future biorefinery technology development to meet and 

hopefully exceed the 30 x 30 goal. 

G.1 Advanced, Large-Tonnage Feedstock Supply Systems 

Advanced feedstock supply systems will be needed to collect the large tonnages of feedstocks 

required for large-scale biorefineries. By 2012, functional feedstocks supply systems will 

have been demonstrated, and feedstock R&D needs will shift to increasing the accessible 

biomass tonnage sufficient to produce 60 billion gallons of ethanol/year. 

An efficient interface between producers and the commodity biomass systems will be varied, 

based on biomass resources and local practices. Primary research needs include storage, preprocessing, and transportation systems that are suited to the varied systems. Advanced 

feedstock supply systems will also rely heavily on the development of new transportation 

methods and technologies to take advantage of value-added pre-processing and 

merchandising of raw feedstock material. Advanced transportation systems will likely 

incorporate technologies that not only provide infrastructure and operational cost savings but 

also incorporate in-transit value-added processes. 

G-1 
G.2 Process Improvement and Optimization 

Process improvement R&D and plant optimization are low risk-approaches for technology 

development beyond 2012. The construction and operation of demonstration-scale 

biorefineries will reveal unit operations that require optimization and identify opportunities for 

process integration to maximize energy efficiency and biomass use. The operating experience 

and engineering data accumulated from demonstration plants will be used in the design of 

larger-scale biorefineries to reduce production costs by leveraging economies of scale. 

Three advanced, integrated biorefineries were identified as models for future R&D: 

1. Integrated biochemical-thermochemical biorefineries that use lignin for 

combined heat and power and fuel production 

As plant scales increase, the volume of lignin-rich residues increases. At a certain 

point, these residues can be gasified to provide combined heat and power and 

converted to ethanol and mixed alcohols through fuel synthesis. Biorefinery ethanol 

yield will increase after the integration of thermochemical processing to convert 

lignin-rich residues to ethanol, as shown in Figure G-1. 

Better use of corn stover also results in higher yields. Integrating the gasifier/fuel 

synthesis steps raises yield 20% while supplying heat and power for all processes. The 

integrated biorefinery in Figure G-1 assumes 10,000 MT/day of biomass is processed and 

that the biorefinery capitalizes on all process improvements identified. Capital and 

operating costs associated with integrated biorefineries are partially offset by the 

economies of scale of the biochemical ethanol process and integration of the processes. 

Figure G-1. Integrated Biochemical-Thermochemical Biorefinery Gasification 

Scenario with Excess Lignin Converted to Ethanol 

G-2 
2. Integrated biochemical-thermochemical biorefineries that maximize lignin 

ethanol production 

If the goal of advanced, integrated biorefineries is to maximize liquid fuel yields from 

lignocellulosic feedstocks, then an alternative, low-cost fossil fuel such as coal can be 

used to meet heat and power demands (Figure G-2). This scenario yields maximum 

lignocellulosic ethanol per ton of corn stover delivered to the plant gate by using all 

available lignin-rich residues from the biochemical conversion process in 

thermochemical gasification to produce ethanol. 

This scenario is a subset of the first scenario. The same process improvements and 

integration opportunities in the first scenario are accounted for here. 

Figure G-2. Integrated Biochemical-Thermochemical Biorefinery Gasification 

Scenario Maximizing Ethanol Production 

3. Advanced, integrated biochemical-thermochemical biorefineries in the “E85” 

scenario 

A scenario in which the thermochemical conversion technology is based on the 

conversion of lignin to gasoline- and diesel-range hydrocarbons is also considered. 

The process scenario shown in Figure G-3, in which the biomass is converted to 

ethanol biochemically and the excess lignin-rich residues are converted to gasoline, is 

dubbed the “E85” scenario because the approximate ethanol-to-gasoline ratio is 85%. 

The pyrolysis technology is based on the thermochemical conversion scenario that used 

the pyrolytic lignin fraction of bio-oils as a feedstock for a high-temperature, highpressure catalytic hydrotreating process. The assumption in this scenario is that the 

lignin-rich residues can be transformed into a feedstock for the hydrotreating process and 

converted to gasoline with identical yields as the conversion of the pyro-lignin. As a first 

G-3 
approximation, the lignin-rich residues are mixed with the diesel fuel from the 

thermochemical process to form a slurry that can be pumped into the 

hydrotreating process. 

The impact of the diesel-range hydrocarbons on yields and conversion efficiencies in this 

step are unknown, and the actual conversion efficiency of the lignin-rich residues to 

gasoline have not been measured experimentally. This scenario clearly contains unproven 

assumptions; however, it demonstrates the potential process flexibility of advanced, 

integrated biorefineries to combine biochemical and thermochemical processes to 

maximize fuel production. 

Figure G-3. Advanced, Integrated Biochemical-Thermochemical Biorefinery – 

“E85” Scenario 

G.3 Advanced Biological Processing 

To maximize ethanol potential, a base of new fundamental science must be engaged. To this 

end, research activities must be coordinated between EERE and the Office of Science. To 

achieve an advanced state of biofuels production, fundamental R&D in systems biology and 

thermal transformations and applied R&D are required. 

G.3.1 Biological Processing: A Systems Biology Approach Using Translational 

Science 

Translational science, a concept familiar to the biomedical industry, is an integrated approach 

to connecting basic research and industrial application. Regardless of the technical topic, 

fundamental science must be well integrated with applied objectives to ensure success. 

Figure G-4 and Figure G-5 illustrate the advances in biorefinery processing from a systems 

biology perspective. Figure G-4 depicts the $1.07/gallon case for the biochemical conversion 

G-4 
of biomass to ethanol; Figure G-5 depicts the advanced case. To enable this advancement, 

fundamental research in systems biology integrated with applied R&D in biomass conversion 

is needed. 

Figure G-4. Process Schematic of $1.07/gallon Ethanol Production 

Figure G-5. Process Schematic of an Advanced State of Ethanol Production 

G-5 
The advanced state of technology depicted in Figure G-5 projects a capital cost savings from 

the $1.07/gallon case of approximately 50% per installed gallon (Wallace 2006). It projects a 

cost savings of 38% compared with the same-size plant using advanced technology. Process 

intensification brought about by incremental process improvements and breakthrough 

technologies will reduce the constraint of available capital and enable the large-scale 

deployment of biorefineries necessary to achieve the 30 x 30 goal. 

In general, a full and detailed integration of science and engineering research will lead to the 

most efficient process development plan. In the case of biomass conversion, work in the 

fundamental area must be targeted to process improvements based on technical barriers. An 

integrated fundamental and applied research program in biochemical conversion must include 

advancements in the following three areas of biomass conversion: 

• Area 1: Energy plant engineering 

The goal of plant engineering R&D is to maximize lignocellulose accumulation by 

energy crops per acre in an environmentally and economically sustainable way. 

Optimized energy crops for biorefinery use must minimally produce “useful” carbon 

in the form of polysaccharides that can be efficiently hydrolyzed to fermentable 

sugars. With directed R&D in plant engineering, energy plants may also produce 

byproducts important to the economic viability of the process. Such additional 

revenue-generating products may include animal feed supplements, chemicals, lipids 

and oils, and other industrial enzymes such as laccases, proteases, and amylases. 

The objectives of the R&D are to: 

o Address GTL strategies to maximize the biomass yield and agronomics of 

developing energy crops 

o Design and manipulate plant cell wall composition and structure to maximize 

yield of fermentable sugars. 

Plant engineering R&D needs to be directed to the following areas: 

o Expression of hydrolytic enzymes in the plant tissue 

o Regulation of lignin levels in plants 

o Increases in plant polysaccharide levels 

o Fundamental genomic research in potential dedicated bioenergy crops 

and trees. 

Genetically engineering plants to express cellulase enzymes has begun with the 

expression of Acidothermus E1 endoglucanse in the model plant species Arabidopsis, 

which was expressed to 2%–5% of the soluble protein (Stricklen 2006). In addition, 

the lignin-modifying enzyme laccase has been successfully expressed in maize 

apoplast by industry (Hood 2004). 

G-6 
The development of plants with reduced or modified lignin has long been a goal for 

improving biomass feedstocks because of the inhibitory effects of lignin degradation 

products on the bioconversion process. Also, reduced lignin may positively influence 

polysaccharide levels. For example, transgenic aspen produced with reduced lignin 

showed a 15% increase in cellulose with no negative effects (Dean 2005). 

Furthermore, tobacco showed an increased yield upon insertion of a flowering locus 

from Arabidopsis (Rose and Bennett 1999), apparently because delayed flowering 

permitted longer vegetative growth. 

A longer-term approach for plant feedstock improvement uses the latest biological tools 

with the sequencing and annotation of the Populus genome. Access to the genetic 

portfolio of this fast-growing tree species will help extend the basic work with the 

model dicot plant species, Arabidopsis, and permit the comparative analysis of selected 

genes and pathways for other plant crop genomes such as corn, soybean, and rice. 

• Area 2: Plant cell wall deconstruction 

Advances in GTL and biochemistry tools can improve plant productivity and chemical 

composition. Novel and more efficient technologies will also facilitate analysis of 

plant cell wall compositions for breeding and basic research. In many cases, methods 

capable of higher throughput and increased precision are required for improved GTLbased research. 

The objectives of plant wall deconstruction R&D are to: 

• Develop a more detailed understanding of plant cell wall structure, including 

elucidating glycosyl hydrolase structure/function 

• Develop improved engineered enzymes for advanced biochemical conversion 

technologies. 

Recent cellulase cost reduction advances achieved by DOE/OBP-funded research have 

provided, for the first time, a real opportunity for enzymatic saccharification of 

lignocellulose processes. Although a 30-fold reduction in enzyme cost has been 

achieved by Genencor International and Novozymes Biotech, further cost reductions 

are required to achieve an economic and robust biorefinery industry. For example, the 

costs of amylases for starch to ethanol processes are about $0.01–$0.02/gallon of 

ethanol produced, and the most optimistic estimates for the cost of cellulase 

preparations is now about 10-fold more. 

More than 95 families of polysaccharide-degrading enzymes have been identified. 

However, only seven harbor cellulases, and only 12 are linked to hemicellulose 

disassembly (Boeckmann et al 2003, Coutinho and Henrissat 1999). Developing an 

understanding of the structure/function controls of these protein machines, their 

diversity in nature, and the changes they promote on the surfaces of plant cell walls is 

critical to the attainment of the 30 x 30 goal. Research needs to address the synthesis 

and structure of the plant cell wall with a new outlook toward disassembly. 

G-7 
Research in this field has traditionally looked at cell wall synthesis from a botanical 

perspective (Persson et al. 2005; Anderson-Gunneras et al. 2006). New research should 

target ways to modify the cell wall assembly apparatus to produce plants that are more 

easily digested and yield an optimal mixture of fermentable sugars. To meet these 

objectives, an understanding of cell wall engineering and cellulase action is needed. 

There are challenges associated with acquiring a new understanding of the substrate at 

the molecular level and the mechanisms of action of the glycosyl hydrolases that act 

on these surfaces. Plant biomass has evolved superb mechanisms for resisting assault 

on its structural sugars from the microbial and animal kingdoms (Himmel, Baker, and 

Overend 1994; Himmel, Baker, and Saddler 2001). 

• Area 3: Biomass sugars conversion 

The lack of a robust fermentation organism that can convert all of the sugars derived 

from lignocellulosic biomass to ethanol at sufficient rates, yields, and titers is a barrier 

that must be overcome if the $1.07/gallon target is to be achieved (Himmel et al. 1997). 

Although most of the strains in the public sector perform reasonably well at low sugar 

concentrations (solids loadings less than 10%–5%), the market target calls for the use of 

20% (or more) solids with the conversion of the majority of sugars to ethanol (Toon et al. 

1997; Bothast et al. 1994). Clearly, under these conditions, the available strains cannot be 

used at a commercial scale. Therefore, the development of robust, industrial fermentation 

strains will require substantial knowledge about the factors that limit efficient sugar 

bioconversion. The collective knowledge of strain improvement will be critical to 

overcoming the technical barriers that limit current strain performance. 

The goal of this R&D is to target advanced conversion technologies of sugars released 

during biomass deconstruction to liquid fuels and products, wherein consolidated bioprocessing is achieved. 

To achieve strain improvement goals, new and novel research must be conducted in 

aspects of cell morphology and physiology. For example, information about the 

structure and function of sugar transport enzymes is critical to improving the ability of 

strains to efficiently use all biomass-derived sugars. A new understanding of cell 

membrane function is also needed to improve the cells resistant to process-produced 

toxic substances. Finally, a deeper understanding of the gene- and protein-level 

metabolic controls of energy pathways in ethanologenic strains is critical. Special 

attention must also be given to metabolic pathway engineering and consolidation 

approaches to reducing cost in the long term. For example, the production of new 

generations of microbial strains able to ferment biomass sugars at high concentrations 

directly from pretreated biomass, with no exogenous hydrolytic enzymes added, 

should be pursued (Himmel et al. 1997). 

Advanced scenarios have been proposed to combine key process steps and thus reduce 

overall process complexity and cost. A reduction of process steps can be accomplished 

biologically by engineering a single micro-organism to perform the roles now 

associated with three or four strains. Although this goal is clearly longer-term, R&D 

G-8 
has been proposed for investigation. One notable example is the consolidated biomass 

processing technology proposed by Zhang and Lynd (2005) for the Clostridium 

thermocellum case. Zhang and Lynd believe these benefits exceed the bioenergetic 

cost of cellulase synthesis, which supports the feasibility of anaerobic processing of 

cellulosic biomass without added saccharolytic enzymes. 

Another option for consolidated biomass processing is to enable yeast, already 

ethanologenic, to produce cellulases (VanRensburg, Zyl, and Pretorius 1998). In this 

case, expression of some active and effective cellulases from yeast has proved 

challenging (Godbole et al. 1999); however, endoglucanses and beta-glucosidases 

appear more amenable to yeast processing (VanRooyen et al. 2003). 

Analysis bridges all research functions of the program. Analysis to support the three 

areas of R&D must include advancements in process application knowledge at two 

levels. The first will address key process-related engineering research that converts 

new understanding from fundamental research to the biorefinery context. The second 

will use process-related engineering information to make recommendations to industry 

about the selection of process parameters, equipment, and operating conditions. 

• Engineering research 

This work targets the interface between fundamental science and process-scale 

integration engineering. It carries elements of both science and engineering research 

for that reason. This work will bring an engineering-level understanding to process 

unit operations by using computational modeling and experimental methods. The 

objective will be to acquire new understanding in broad-based aspects of applied 

process engineering research. 

• Process integration and modeling 

This work addresses the barrier of process integration and will focus initially on 

integrating enzymatic hydrolysis process technology based on dilute acid pretreatment 

that incorporates advanced, lower-cost cellulase enzymes being developed. These 

activities will also include the continued development of rapid analysis techniques to 

generate timely mass balance data for application at the process scale. Analysis of 

process options will also be performed under this effort. 

G-9 
G.3.2 Thermochemical Technologies for 30 x 30 and Beyond 

Achieving the near-term ethanol cost target of $1.07/gallon via biomass gasification/mixed 

alcohol synthesis will require improvements in catalytic tar and light hydrocarbon reforming 

to increase conversion efficiencies and reduce the capital costs of syngas cleanup and 

conditioning. This is the first step in an attempt to define barrier areas and unit operations that 

can be improved, combined, or eliminated to minimize capital and operating costs by 

intensifying thermochemical conversion processes. 

Thermochemical conversion R&D from 2010 to 2030 will include two complementary 

approaches: 

• Engineering approach: Process improvement and optimization 

• Scientific approach: Advanced thermal processing. 

G.3.2.1 Process Improvement and Optimization 

The objective of this R&D path is to improve yields and efficiencies and maximize process 

integration opportunities in existing thermochemical processes. 

Advances in catalysis and mixed alcohol synthesis will produce cost reductions. For example, 

improved mixed alcohol catalysts can increase the single-pass carbon monoxide conversion 

from 38.5% to 50% and improve carbon monoxide selectivity to alcohols from 80% to 90%. 

Improved mixed alcohol catalysts with higher activity could require a lower operating 

pressure—1,000 psia compared with 2,000 psia. The energy requirement for the improved 

synthesis loop is therefore reduced because of the combination of lower syngas pressure for 

alcohol synthesis and less unconverted syngas to recompress and recycle. Overall, the product 

yield increases by 12 gallons/dry ton of biomass. Combined, these improvements contribute 

to a reduction in the overall capital cost as well as operating costs. 

Additional technical R&D includes improvements to the thermochemical conversion process 

to move the technology toward catalytic gasification to increase carbon conversion 

efficiencies to syngas and decrease tar formation. Within the gasifier, this design converts 

50% of the methane produced during biomass gasification to carbon monoxide and hydrogen, 

thus reducing the methane in the raw syngas. The throughput of the gasifier also increases by 

25%. 

The block flow diagram in Figure G-6 illustrates the technical barriers that must be addressed 

to advance thermochemical conversion technology. 

G-10 
Figure G-6. Selective Thermochemical Processing 

Future R&D efforts will focus more on the front end of thermochemical processes as the 

downstream unit operations are optimized. To this point, biomass feedstocks will be prepared 

for introduction into a specific thermochemical conversion process 9.(gasification or 

pyrolysis), and the output of the conversion process will be conditioned and optimized for 

subsequent fuel production. 

There is also an opportunity to tailor the feedstock for thermal conversion to eliminate 

downstream intermediate conditioning and conversion. Feedstock processing and handling 

then involves not only size reduction and drying but also deconstruction to fractionate 

biomass into components with compositions most suitable for a specific conversion 

technology. This is analogous to the separation of crude oil into individual components at the 

front end of a petroleum refinery. With tighter limits on the composition of fractionated 

biomass materials, it seems plausible that selective thermal transformation of specific 

fractions could be developed for high-yield biofuels production with high selectivity. These 

selective thermal transformations effectively combine heat, catalysts, and chemical reagents 

to optimize the conversion of biomass to fuels. Advanced feedstock processing acts as the link 

between the engineering and scientific approaches defining thermochemical conversion R&D. 

G.3.2.2 Advanced Thermal Processing 

Biochemical processes for the conversion of biomass into sugars produce a wet, lignin-rich 

residue that contains unconverted carbohydrates, proteinaceous material from the enzymes 

and fermentative organisms used, and ash. Conversion of the lignin in this residue to 

higher-value products is challenging because of the high moisture content and presence of 

the other components. 

Alternative strategies for lignin use need to be evaluated to make future biorefinery concepts 

technically and economically competitive. These strategies include gasification, pyrolysis, 

solvation, hydrotreatment, and hydrothermal treatment. The integration of lignin 

G-11 
thermochemical conversion processes into biorefineries will require analysis and experimental 

validation to identify the most promising, highest-value options. 

Developing robust, economic, and selective thermal transformation processes will leverage 

the R&D successes of both biomass gasification and pyrolysis. Establishing a rigorous 

research program to investigate fundamental biomass thermochemical conversion will help 

erase the lines between gasification and pyrolysis as separate technology options. 

• Area 1: Catalytic gasification and pyrolysis 

Catalytic process improvements must be cost-effective to be commercially viable. 

Since the beginnings of coal gasification, catalysts have been sought to improve carbon 

conversion to products and increase gasification rates while minimizing temperature to 

increase process efficiency. Alkali metals have long demonstrated catalytic activity in 

steam gasification of solid fuels, and metal-based catalysts—particularly nickel-based 

materials—are active and effective for hydrocarbon reforming. 

R&D must investigate and identify active catalytic agents (e.g., potassium, calcium, 

phosphorous, nickel, sulfur trioxide, manganese, magnesium, and titanium) and 

determine how they can be exploited for technical and economic application in 

biomass gasification systems. Previous efforts to apply catalysis to biomass 

gasification through techniques for coal gasification have been, in general, 

unsuccessful. A targeted approach to understanding the effect of primary catalysts on 

gasification chemistry is required to optimize syngas compositions and minimize the 

effect of impurities (e.g., tars, sulfur, nitrogen, chlorine, and potassium) on 

downstream cleanup and conditioning unit operations. Ultimately, the successful 

implementation of gasification catalysts could lead to process intensification by 

eliminating the need for certain downstream cleanup and conditioning steps. 

Depending on the feedstock, these gasification catalysts may already be inherently 

part of the mineral matter of the solid fuel. This is the case for low-rank coals such as 

lignites and many biomass fuels. Many woody biomass fuels contain high fractions of 

calcium in ash, but the overall ash content is usually quite low (~1%), so the “catalyst” 

concentration is also low. Agricultural residues such as rice and wheat straws, alfalfa 

stems, and corn stover have relatively high ash content with high concentrations of 

potassium that can lead to higher “catalyst” concentrations. 

• Area 2: Lignin utilization 

Lignin is present (at 15–30 wt%) in all lignocellulosic biomass. It is the component 

with the highest energy content (9,000–11,000 Btu/lb versus 7,300–7,500 for 

cellulose). Any process for making products from the carbohydrates in lignocellulosic 

biomass will have lignin-rich residues as a byproduct. Integration and use of lignin 

residues will be key for the commercial viability of lignocellulosic biorefineries and to 

maximize biomass utilization for fuel production. 

The phenolic nature of lignin makes it chemically different from all other renewables 

components. This makes it a potential source of products for the chemicals industry. 

G-12 
Although lignin is presently a low-value and complex biomass component, there is 

potential for new technology development to increase the value of this residue stream 

and significantly enhance the competitiveness of integrated biorefineries. The chemical 

and physical nature of lignin residues means that some fraction of the lignin will likely 

be resistant to conversion into higher-value products via traditional biochemical 

processing. Lignin pyrolysis is an alternative that could yield an additional, highervalue option for the production of aromatic fuel components and chemicals. 

Advanced thermochemical conversion of lignin, such as integrated gasification 

combined-cycle systems, offers the possibility of increasing the efficiency of lignin 

use and providing excess power for additional revenue. Gas turbine systems offer 

electrical conversion efficiencies approximately double those of steam-cycle 

processes, and developing fuels cells are projected to be nearly three times as efficient. 

• Area 3: Selective thermal transformation of fractionated biomass 

A range of alternative conversion options are possible through the fractionation of 

biomass into specific components. A narrower, uniform-composition biomass fraction 

enables thermochemical conversion options with high yields and selectivities. These 

selective thermal transformations combine heat, catalysts, and chemical reagents to 

optimize the conversion of biomass to fuels. Developing robust, economic, and 

selective thermal transformation processes will leverage the R&D successes of both 

biomass gasification and pyrolysis—and integrate the biomass pretreatment processes 

being developed for lignocellulosic ethanol production—into a single, consolidated 

biomass conversion process to meet or exceed long-range biofuel cost targets. 

G-13 
G-14 
Appendix H: Life Cycle Environmental Analysis of Biofuels: 

GREET and VISION (vehicle needs) Simulation Results 

 

H.1 Life-Cycle Analysis of Bioethanol 

H.1.1 Bioethanol Production Cases for Life Cycle Analysis 

This study examined three biofuel production options: an advanced biochemical ethanol 

production process, a thermochemical gasification process to produce mixed alcohols, and an 

advanced biorefinery process that integrates biochemical and thermochemical processes. 

These processes—all of which co-produce heat and electricity—were designed and simulated 

by NREL and Dartmouth College-Princeton University. For the project, Argonne National 

Laboratory conducted a mobility chains (“well to wheels,” or WTW) analysis using the 

GREET model (Wu, Wu, and Wang 2005). 

The mixed-alcohol thermochemical process and the biorefinery are in the early stages of 

quantification, and key parameters of the life cycle analysis may be subject to uncertainties. 

However, the analysis provides an indication of the potential energy and emission benefits of 

these processes relative to those offered by the biochemical ethanol production process. All 

three processes are being pursued by government and industry. The comparison of energy and 

emission benefits provides information to help researchers and decision makers evaluate the 

merits of biofuel production pathways. 

Four feedstocks were considered for bioethanol production: corn, corn stover, switchgrass, 

and forest residues. To take into account process and technology advances over time, the 

energy and environmental effects of cellulosic ethanol were evaluated for the near term 

(2012) and the long term (2020–2030). To compare ethanol with gasoline, the analysis 

included gasoline production from conventional crude in 2012 and from a mix of 

conventional crude and oil sands in 2030. Thus, 10 cases were analyzed: 

1. Conventional crude to gasoline in 2012 

2. Corn to ethanol through conventional corn ethanol technology in 2012 

3. Switchgrass to ethanol through biochemical conversion in 2012 

4. Corn stover to ethanol through biochemical conversion in 2012 

5. Forest residues to mixed alcohols through thermochemical (gasification) conversion 

in 2012 

6. Conventional crude and oil sands to gasoline in 2030 

7. Corn to ethanol through conventional corn ethanol technology in 2030 

8. Switchgrass to ethanol through a biorefinery with consolidated bioprocessing and 

gasification turbine combined cycle (GTCC) in 2030 

9. Corn stover to ethanol through a biorefinery with consolidated bioprocessing and 

GTCC in 2030 

H-1 
10. Forest residues to mixed alcohols through the biochemical-thermochemical 

biorefinery in 2030. 

Figures H-1 through H-4 are flow diagrams for the cellulosic ethanol production scenarios. 

All cellulosic ethanol cases were based on Aspen Plus simulations. Of the 10 cases, the 

simulations for cases 1, 2, 6, and 7 (crude oil to gasoline and corn to ethanol in 2012 and 

2030) relied on data collected at Argonne during its life cycle analysis using the GREET 

model (Wang, Wu, and Elgowainy 2005; Brinkman et al. 2005). Cases 3 and 4 (biochemical 

process with switchgrass and corn stover in 2012) are for a biochemical process that was 

simulated by NREL for a 2,000-dry-ton/day ethanol plant (see Figure H-1) (Jechura 2006b). 

Case 5 is a 2,000-dry-ton/day ethanol plant with thermochemical gasification (see Figure H-2) 

(Jechura 2006b). Cases 8 and 9 are the consolidated bioprocessing-GTCC process simulated 

by Dartmouth College and Princeton University for a 5,000-dry-ton/day cellulosic biomass 

feedstock biorefinery (see Figure H-4) (Laser and Jin 2004). Case 10 is a 2,000-dry-ton/day 

biorefinery with the biochemical and thermochemical process simulated by NREL (see Figure 

H-3) (Jechura 2006b). 

(Jechura 2006b) 

 Figure H-1. Simplified Process Flow Diagram of Biochemical Conversion 

of Switchgrass and Corn Stover to Ethanol with Steam and Electricity 

Cogeneration 

H-2 
(Jechura 2006b) 

Figure H-2. Simplified Process Flow Diagram of Thermochemical Conversion 

of Forest Residues to Ethanol and Other Alcohols with Steam and Electricity 

Co-generation 

(Jechura 2006b) 

Figure H-3. Simplified Process Flow Diagram of a Biorefinery 

that Co-produces Ethanol, Steam, Electricity, and Other Chemicals from Forest 

Residues 

 

H-3 
(Laser and Jin 2004) 

Figure H-4. Simplified Process Flow Diagram of a Biorefinery 

that Co-produces Ethanol, Steam, and Electricity from GTCC 

Ethanol production process data from Aspen Plus simulations that serve as GREET inputs are 

listed in Table H-1. Co-product credit partitioning is discussed in Section G.1.3.3. 

H.1.2 GREET Model 

Since 1995, with support primarily from EERE, Argonne has been developing the GREET 

model. Argonne released its first version, GREET 1.0, in June 1996. 

GREET is a Microsoft® ExcelTM-based multidimensional spreadsheet model that addresses the 

WTW analytical challenges associated with alternative fuels and vehicle technologies. The 

latest version, GREET 1.7, can analyze more than 90 transportation fuel pathways and 75 

vehicle/fuel systems (Wang, Wu, and Elgowainy 2005). A licensed software product is 

available free of charge to the public, GREET has more than 3,000 registered users 

worldwide. Users include government agencies, automotive companies, energy companies, 

universities and research institutions, and non-governmental organizations. 

H-4 
Table H-1. Key Ethanol Production Process Data Used to Determine GREET 

Inputs 

 Fuel and Chemical Yield (Gal/dt) Electricity 

Export 

(KWh/dt 

Case Feed) a Ethanol

nPropanol nButanol nPentanol Methanol 

2012 

Case 3: 

Switchgrass to Ethanol 

Through Biochemical 

Conversion 

75.97 NAb NA NA NA 362.4 

Case 4: 

Corn Stover to Ethanol 

Through Biochemical 

Conversion 

89.84 NA NA NA NA 215.5 

Case 5: 

Forest Residues to 

Mixed Alcohols Through 

Thermochemical 

Conversion 

66.65 12.11 4.67 2.13 2.45 -53.8c

2030 

Case 8: 

Switchgrass to Ethanol 

Through Biochemical 

Conversion and GTCC 

105 NA NA NA NA 604.3 

Case 9: 

Corn Stover to Ethanol 

Through Biochemical 

Conversion and GTCC 

105 NA NA NA NA 604.3 

Case 10: 

Forest Residues to Mixed 

Alcohols Through 

Biochemical/Thermochemical Biorefinery 

103.92 1.81 0.70 0.32 0.37 765.6 

a

 Process data for cases 1, 2, 6, and 7 are default GREET values, so these cases are not included in 

the table. 

b

NA = not applicable. 

c

 A negative value indicates electricity import from the grid is required. 

H-5 
For a given vehicle and fuel system, GREET separately calculates: 

• Consumption of total energy (energy in non-renewable and renewable sources), fossil 

fuels (petroleum, natural gas, and coal), and petroleum 

• Emissions of carbon dioxide-equivalent GHGs (primarily carbon dioxide, methane, 

and nitrous oxide) 

• Emissions of five criteria pollutants: 

o VOCs 

o Carbon monoxide 

o Nitrogen oxide 

o Particulate matter of diameter smaller than 10 microns (PM10) 

o Sulfur oxides. 

The criteria pollutant emissions are further separated into total and urban emissions. 

Although this work covers the full fuel cycle (from cellulosic feedstocks production to fuel 

processing to vehicle operations), it does not include energy or emissions associated with farm 

equipment and vehicle manufacturing, capital equipment and infrastructure in manufacturing 

facilities, and construction of transportation infrastructures. For a fair comparison, if energy use 

and emissions from these activities are to be included for biofuels, energy use and emissions 

from the construction of oil field rigs, ocean tankers, pipelines, and petroleum refineries should 

be included in gasoline life cycle analysis. Overall, the completed studies that include these 

infrastructure-related activities show that their contribution to total life cycle energy use and 

emissions is relatively small per unit of fuel. 

H.1.3 Methodology and Assumptions 

H.1.3.1 Modeling Boundary 

Fuel pathways simulated in this study are divided into five stages: biomass farming; biomass 

feedstock transportation; fuel production, fuel product transportation, distribution, and 

storage; and fuel use during vehicle operation. 

The GREET modeling boundary for this study is depicted in Figure H-5. As the figure shows, 

bioethanol life cycle analysis begins with the manufacture of fertilizer. Cellulosic biomass is 

transported via trucks to the fuel production facility, where it undergoes biochemical or 

thermochemical processing for fuel production. The demand for heat and power (through 

steam and electricity) from the biochemical and thermochemical plant is met by employing a 

biomass fluidized bed combustion boiler, GTCC, or importing electricity and natural gas. Fuel 

products are then transported to refueling stations via rails, barges, and trucks. Bioethanol is 

used as E85 (a mixture of 85% ethanol and 15% gasoline by volume) to fuel flexible-fuel 

vehicles (FFVs) or in low-level ethanol blends such as E10 (10% ethanol and 90% gasoline 

by volume) in gasoline vehicles. 

H-6 
Gasoline life cycle analysis, on the other hand, begins with crude oil recovery in oil fields and 

ends with gasoline combustion in gasoline-powered vehicles. 

 Figure H-5. GREET WTW Modeling Boundary

H.1.3.2 Data Sources and Assumptions 

GREET modeling input parameters are collected from literature, published reports, field 

expertise, and ASPEN simulations (as described in Section H.1.1). Table H-2 lists major 

processes and operations and their data sources. Detailed GREET input parameters and 

assumptions for the cellulosic biomass feedstocks are provided in Tables H-3 through H-5. 

For ethanol use in vehicles, both E10 and E85 were assumed. E85 was assumed to achieve the 

same fuel economy per gallon of gasoline equivalent in FFVs as gasoline in gasoline-powered 

vehicles (Table H-6). The crude oil source for gasoline production in 2030 includes oil sands 

as well as conventional crude. 

Fertilizer is a major source of the energy use and emissions associated with corn farming 

operations. There is a baseline value for fertilizer use when corn grain is the only product 

harvested. Additional fertilizer will be needed when corn stover, an agriculture residue, is 

H-7 
collected to produce ethanol because corn stover (with an assumed mass-to-grain ratio of 

about 1:1) has traditionally been left in corn fields as a carbon and nutrient source. In current 

practice, almost 100% of stover is left in the field. The removal of corn stover will therefore 

require additional fertilizer (e.g., nitrogen, phosphorus, and potassium) to supplement its 

nutrient value. The additional demand for fertilizer is included in the two corn stover cases 

and is accounted for in the corn stover-based pathways. 

The issue of energy and emission partitioning between corn and corn stover arises in the 

estimation of baseline fertilizer use. In previous corn ethanol life cycle analyses, as in this 

study, all of the baseline fertilizer use is allocated to corn grain. However, it is conceivable 

that some portion of baseline fertilizer use could be allocated to corn stover if it becomes a 

vital feedstock for ethanol production. Consequently, the energy and emission benefits of corn 

stover to ethanol should be examined when stover is no longer an agricultural residue but a 

commercial feedstock. In this analysis, corn stover is treated as an agricultural residue, and no 

baseline fertilizer use was allocated to corn stover. 

Table H-2. Major Processes and Their Data Sources 

Process Data Sources 

Corn Stover Collection Sheehan et al. (2002), Spatari, Zhang, and 

Maclean (2005), Kim and Dale (2005) 

Switchgrass and Corn Stover Transportation to 

Ethanol Plant 

Sheehan et al. (2002), Hess and Perlack (2006) 

Forest Residue Collection and transportation Hess and Kelley (2006) 

Corn to Ethanol Conversion GREET default values with incremental 

technology advancement to 2030 

Biochemical Conversion to Ethanol (2012) Jechura (2006b) 

Thermochemical/Gasification Conversion to 

Ethanol (2012) 

Jechura (2006b) 

Biorefinery Consolidated Bioprocessing to Ethanol 

with GTCC (2030) 

Dartmouth College and Princeton University, 

RBAEF (Laser and Jin 2004) 

Biorefinery Biochemical-Thermochemical 

Conversion to Ethanol (2030) 

Jechura (2006b) 

Ethanol Transportation GREET default values 

Vehicle Operation GREET default values 

H-8 
Table H-3. Detailed Assumptions: Switchgrass to Ethanol 

Assumptions

Farming Operation 

Switchgrass Yield Estimated from national average yield increase of 0.165 

ton/acre/year through breeding from the base-year yield of 4.96 

ton/acre/year (2004) based on Greene (2004) 

Switchgrass Transportation 

Distance From Farm to Ethanol 

Plant 

Ethanol plant surrounded by farmland with 2.2% acreage 

coverage for switchgrass farming in 2012 and 3.8% in 2030, 

based on Dartmouth College and Princeton University data (Laser 

and Jin 2004) 

 50-mile one-way distance by heavy trucks with a payload of 17 

tons 

Ethanol Production

Ethanol Yield See Table H-1 

Steam and Power Generation 

From Biochemical Plant in 2012 

Small industrial boiler used for power generation from residue of 

switchgrass and corn stover 

EPA AP-42 emission factors and the National Emissions 

Inventory for the boiler used (U.S. EPA 1995; 1999) 

Criteria Pollutant Emissions 

From Biorefinery in 2030 

RBAEF simulation results (Wu et al. 2005) 

Criteria Pollutant Emissions 

From Biochemical Process in 

2012 

GREET default values based on EPA’s AP-42 report and the 

National Emissions Inventory used (U.S. EPA 1995; 1999) 

Table H-4. Detailed Assumptions: Corn Stover to Ethanol 

Assumptions 

Farming Operation 

Corn Yield 

 

Corn yield: 154 bu/acre in 2012; 180 bu/acre in 2030 (Perlack 

2006) 

Corn Grain Mass to Corn Stover 

Mass Ratio 

1:1 

Additional Nitrous Oxide Emissions 

From Stover Removal 

Based on Intergovernmental Panel on Climate Change (1996) 

Nitrogen content in stover is 0.45% (Sheehan et al. 2002)

 

Additional nitrogen, Phosphorous, 

and Potassium Fertilizer Required 

0.005 g nitrogen/g stover collected per year (Sheehan et al. 

2002) 

H-9 
Assumptions 

Because of Stover Removal 

0.0018 g phosphorous/g stover collected per year (Sheehan et 

al. 2002; Kim and Dale 2005) 

0.0092 g potassium/g stover collected per year (Kim and Dale 

2005) 

Soil Carbon Change Because of 

Land Use 

Zero (Kim and Dale 2005) 

Corn Stover Collection Operation includes harvesting, bailing, and moving to the edge 

of field and stack 

Corn and soybean crop rotation for farms and 50% of corn 

stover available for collection in the year corn is planted (Kim 

and Dale 2005) 

Fuel Use During Stover Collection Diesel used in this operation 

Fuel use based on stover collection rate by regression model 

in NREL report (Sheehan et al. 2002) 

Lubricant Oil Use Energy and emissions associated with lubricant oil use during 

stover collection are small [volume used is less than 1% that of 

diesel, according to Sheehan et al. (2002)]; they are thus 

ignored 

Corn Stover Transportation From 

Corn Field to Ethanol Plant 

75% acreage use; the 2,000-dry-ton/day ethanol plant is 

surrounded by farmland in a circular area (Aden et al. 2002) 

Stover bail loaded on a trailer pulled by a heavy truck with 

payload of 10 short tons, based on stover bail size and bulk 

density provided by NREL report (Sheehan et al. 2002) 

Transportation Distance 21-mile one-way distance in 2012; 33-mile one-way distance 

in 2030 (based on above assumptions and 2,000 dry tons/day 

in 2012; 5,000 dry tons/day in 2030) 

Ethanol Production

Ethanol Yield See Table H-1 

Steam and Power Generation in 

Biochemical Plant in 2012 

Same as for switchgrass (Table H-3) 

Criteria Pollutant Emissions From 

Biorefinery in 2030 

RBAEF simulation results (Wu et al. 2005) 

Criteria Pollutant Emissions From 

Biochemical Process in 2012 

GREET default values based on EPA’s AP-42 report 

H-10 
Table H-5. Detailed Assumptions: Forest Residues to Ethanol 

Assumptions

Farming Operation 

Forest Residue Harvesting

 

Operation includes stumpage and harvesting for pine and hardwood 

(Hess and Kelley 2006) 

Fuel Consumption During 

Harvesting 

4.77 gallons diesel/ton of wood in 2012; 2.38 gallons/ton in 2030 

Data derived from operation cost data provided by Idaho National 

Laboratory (Hess and Kelley 2006) assuming 2003 diesel price 

Share of Types of Wood 

Harvested 

2012: pine 54%, hardwood 46%; 2030: pine 59%, hard wood 41% 

(Haynes 2003) 

Transportation From 

Collection Site to Ethanol 

Plant 

75-mile one-way distance (Hess and Kelley 2006) 

Heavy trucks with payloads of 17 tons 

Ethanol Production

Ethanol Yield See Table H-1 

Natural Gas Use as 

Process Fuel 

Ethanol production process fed with forest wood (cases 5 and 10) 

requires natural gas to provide additional heat and power: 45,976 

Btu/gallona in 2012; 3,539 Btu/gallon in 2030 (Jechura 2006b) 

Heat and Power Generation 

in Thermochemical Plant in 

2012 

Natural gas utility industrial boiler (>100 mmBtu/h) used for syngas 

and natural gas power generation for thermochemical plant 

Emission factors from EPA AP-42 report 

Criteria Pollutants Emissions 

From Thermochemical 

Process 

Based on NREL (2006) Aspen simulations and GREET default boiler 

emission factors (AP-42 data) 

Emissions from thermochemical process were very small (i.e., 10-

7

g/gallon ethanol); therefore, they were ignored in GREET analysis 

a

 This value represents an un-optimized process, and full heat integration for energy efficiency is currently 

ongoing. Therefore, it is not used in this study. Instead, 3,539 Btu/gallon was assumed for the 2012 woody 

biomass thermochemical process. 

Table H-6. Gasoline-Equivalent Fuel Economy of Light-Duty Vehicles 

Flexible-Fuel 

Vehicles with E85

Gasoline 

Vehicles 

2012 24.9 24.9 

2030 26.6 26.6 

H-11 
Although Table H-6 presents fuel economy for FFVs and gasoline vehicles, the life cycle 

simulations for ethanol and gasoline were conducted on the basis of per-million British 

thermal unit of ethanol and gasoline produced and used. Thus, the life cycle analysis results, 

in the end, were not affected by vehicle fuel economy. 

H.1.3.3 Co-Product Credit Allocation 

The energy allocation method was used to determine the energy and emission credit for 

export electricity and chemical co-products (i.e., n-propanol, n-butanol, and n-pentanol) for 

the cases in which these products were produced with cellulosic ethanol. Energy allocation is 

based on output product energy share. For each fuel production case, total energy and 

emissions of bioethanol and co-products were first estimated. Next, their energy shares were 

determined on the basis of product energy content. Finally, the total energy and emissions 

from the fuel production process and upstream feedstock activities were allocated by 

multiplying the total energy and emissions by their energy shares. 

The energy allocation approach tends to be conservative in determining energy and emission 

credits for electricity. This approach treats all energy products from the production process as 

equal—regardless of form and quality differences. The complexity of this issue is recognized. 

Until additional data become available and resources allow for other methods (such as the 

displacement method), the allocation approach is appropriate for this study. 

Methanol is consumed internally in ethanol plants; therefore, it is not treated as a co-product. 

The energy partitioning results serve as GREET inputs. Table H-7 lists the output energy 

shares (as percentages) for each production option. The energy shares were calculated from 

ASPEN Plus results (Table H-1). 

Table H-7. Energy Allocation for Ethanol, Electricity, and Chemicals 

Case Output Energy Share (%)

Ethanol Electricity Chemicals

Case 3: Switchgrass to Ethanol Through Biochemical 

Conversion 

84.48 16.22 0 

Case 4: Corn Stover to Ethanol Through Biochemical 

Conversion 

91.20 8.80 0 

Case 5: Forest Residues to Mixed Alcohols Through 

Thermochemical Conversion 

73.75 NAa 26.25% 

Case 8: Switchgrass to Ethanol Through Biochemical and 

GTCC 

79.6 20.4 0 

Case 9: Corn Stover to Ethanol Through Biochemical and 

GTCC 

79.6 20.4 0 

Case 10: Forest Residues to Mixed Alcohols Through 

Biochemical/Thermochemical Biorefinery 

91.45 5.42 3.13% 

a NA = Not applicable; this option requires grid electricity input. 

H-12 
H.1.4 WTW Results 

H.1.4.1 Energy Use and GHG Emissions 

Figures H-6 and H-7 present WTW fossil energy and petroleum energy consumption per 

million British thermal unit of fuel produced and used for each of the 10 cases. Corn grain and 

stover- and switchgrass-based ethanol scenarios achieve substantial reductions in petroleum 

energy use (more than 90% compared with gasoline; see Figure H-6) in both 2012 and 2030. 

Savings for the forest residue case are 80%–86%. The reduced petroleum savings by forest 

residue-based ethanol are primarily the result of diesel use for forest wood harvesting, 

collection, and transportation. Furthermore, for the forest residues case, hardwood operations 

require about 25% more fuel than do softwood operations. Therefore, when the share of 

hardwood decreases in 2030 (Table H-5), diesel fuel use will decrease for the forest residuebased ethanol case. 

Figure H-6. WTW Petroleum Energy Use by Bioethanol Case 

Compared with Gasoline in 2012 and 2030 

For fossil energy use (see Figure H-7), although each biofuel case shows a net reduction 

compared with gasoline, the differences among biofuel options are quite large. Corn ethanol 

achieves a moderate reduction (about 40%), while cellulosic ethanol cases could reduce fossil 

fuel use by an additional 30%–50%. This is because lignin is burned in cellulosic ethanol 

plants to meet internal power and heat demands. (In corn ethanol plants, coal or natural gas is 

burned to provide heat and power, which contributes to fossil energy use). 

H-13 
Figure H-7. WTW Fossil Energy Use by Bioethanol Case 

Compared with Gasoline in 2012 and 2030 

There is a marked reduction in fossil energy use in the forest residue cases from the near-term 

(Case 5) to the long-term (Case 10). In 2012, forest residue-based ethanol is produced through 

a thermochemical process that requires natural gas and grid electricity input. The woody 

ethanol production in 2030 is through a biochemical-thermochemical biorefinery, in which 

the ethanol plant becomes a net electricity exporter and, thus, reduces fossil energy use. 

Apparent high fossil energy savings by corn stover is partially attributable to the fact that the 

baseline fertilizer use in corn fields is allocated to corn grains, as indicated in Section H.1.3.3. 

The nitrogen fertilizer production process is the major fossil fuel use for farming operations. 

Regardless of the feedstocks and ethanol production process, cellulosic ethanol could reduce 

GHG emissions by 80%–88% in 2012 and up to 96% in 2030 (see Figure H-8). Ethanol 

produced from forest residues in 2030 could reduce GHG emissions by an additional 16%, 

which is also an attribute of the biochemical-thermochemical biorefinery process. 

Biorefinery production of cellulosic ethanol from corn stover and switchgrass also 

contributes to GHG emission reductions. The GHG emissions reductions for the corn ethanol 

cases are moderate (24%–25%). GHG emissions here are carbon dioxide-equivalent 

emissions of carbon dioxide, nitrous oxide, and methane, weighted with their global warming 

potentials (1, 23, and 296, respectively). 

H-14 
Figure H-8. WTW GHG Emissions by Bioethanol Case Compared with Gasoline 

in 2012 and 2030 

(Net results here are the sum of WTP and PTW emissions. A positive value means net emissions, 

and a negative value means a net uptake of carbon dioxide from the air.) 

WTW results for carbon dioxide emissions only are presented in Figure H-9. The carbon 

dioxide data are presented to allow comparison with the results from other studies, which only 

estimate carbon dioxide emissions. Apparently, ignoring nitrous oxide and methane emissions 

gives fuel ethanol some unwarranted additional benefits. 

H-15 
Figure H-9. WTW Carbon Dioxide Emissions by Bioethanol Case Compared 

with Gasoline in 2012 and 2030 

(Net results here are the sum of WTP and PTW emissions. A positive value means net emissions, 

and a negative value means a net uptake of carbon dioxide from the air.)

H.1.4.2 Criteria Pollutant Emissions 

Criteria pollutant emissions results are presented in figures H-10 through H-19. These 

emissions are separated into total and urban emissions. Urban emissions have long been an 

environmental and health concern because of their potential for human population exposure. 

Total emissions are the sum of urban and rural emissions. 

Results show across-the-board net reductions in urban criteria pollutant emissions by ethanol, 

with the exception of carbon monoxide (no change), relative to gasoline. This can be 

explained by the location of bioethanol plants. Corn and cellulosic ethanol plants are most 

likely to be built near farms to minimize feedstock transportation costs. Criteria pollutants 

emitted from farming, feedstock transportation, and ethanol production steps contribute to 

rural emissions only. In contrast, a sizable portion of petroleum refineries (up to 60%) are 

situated in or near urban areas, which results in a high share of urban emissions. 

The figures show that biorefineries that incorporate biochemical and thermochemical 

processes with integrated heat and power generation could avoid significant total sulfur oxide, 

H-16 
nitrogen oxide, PM10, and VOC emissions. For example, the production step for the forest 

residue-based ethanol case in 2030 results in total sulfur oxide emissions that are 86% lower 

than those in 2012—a direct result of the biochemical-thermochemical biorefinery (see 

figures H-18 and H-19). This refinery design is responsible for half of the reduction in total 

nitrogen oxide emissions from 2012 to 2030 (see figures H-14 and H-15). 

For corn stover- and switchgrass-based cases, additional 77%–80% reductions in total 

nitrogen oxide emissions from 2012 to 2030 are attributable to the switch from biochemical 

conversion plants to biorefinery plants. The reductions in nitrogen oxide emissions between 

2012 and 2030 are also achieved by reduced diesel use during wood harvesting and 

transportation; reduced emissions from combustion technologies fueled with natural gas-, 

coal-, and petroleum-based fuels during feedstock farming and ethanol production; and 

reduced tailpipe emissions from vehicles in 2030. 

The trend for PM10 emissions closely follows that of nitrogen oxide emissions. Total PM10

emissions decrease 63%–71% from 2012 to 2030; the majority of the decrease results from 

the change to biorefinery production. 

Similarly, total VOC emissions decrease 14%–25% from 2012 to 2030 for the three cellulosic 

ethanol cases (see figures H-10 and H-11). Results suggest that, with a well-integrated heat 

and power cogeneration ethanol production plant, net reductions of total sulfur oxides and 

PM10 could be realized for corn stover-, switchgrass-, and forest residue-based ethanol. 

A potential problem with the cellulosic ethanol production cases is an increase in total 

emissions of VOCs, carbon monoxide, PM10, and nitrogen oxide in the near term (i.e., 2012), 

although these emissions are reduced significantly in 2030 because of the use of advanced 

ethanol production technologies. The only exception is sulfur oxide emissions from corn 

stover- and switchgrass-derived ethanol. Ethanol produced from biochemical processes with 

these two feedstocks outperformed other ethanol-feedstock combinations and achieved 37%–

45% reductions in total sulfur oxide emissions in 2012 relative to the sulfur oxide emissions 

of gasoline. 

Corn-based ethanol shows increased emissions of criteria pollutants in both 2012 and 2030. 

The corn ethanol plants analyzed employ a conventional fermentation process that relies on 

coal and natural gas-fired combustion systems to supply heat and on-grid electricity (U.S. 

mix) to supply power. The U.S. electricity mix is produced from a mixture of coal (50%), 

natural gas (20%), and other sources. The heat and power supply is responsible for the major 

criteria pollutants emissions: nitrogen oxide (from natural gas and coal), PM10 (from coal), 

and sulfur oxide (from coal). 

From the figures with results for 2030, readers may mistakenly conclude that the corn-based 

ethanol cases have worse results than cellulosic ethanol cases. This is only because different 

scales are used for 2012 and 2030 for the same pollutant in the cellulosic ethanol cases. In 

fact, criteria pollutant emissions associated with corn-based ethanol remain relatively constant 

between 2012 and 2030 while those of cellulosic ethanol are reduced significantly between 

2012 and 2030. 

H-17 
Figure H-10. WTW Total and Yrban VOC Emissions 

by Bioethanol Case Compared with Gasoline in 2012 

Figure H-11. WTW Total and Urban VOC Emissions 

by Bioethanol Case Compared with Gasoline in 2030 

H-18 
Figure H-12. WTW Total and Urban Carbon Monoxide Emissions 

by Bioethanol Case Compared with Gasoline in 2012 

Figure H-13. WTW Total and Urban Carbon Monoxide Emissions 

by Bioethanol Case Compared with Gasoline in 2030 

H-19 
Figure H-14. WTW Total and Urban Nitrogen Oxide Emissions 

by Bioethanol Case Compared with Gasoline in 2012 

Figure H-15. WTW Total and Urban Nitrogen Oxide Emissions 

by Bioethanol Case Compared with Gasoline in 2030 

H-20 
Figure H-16. WTW Total and Urban PM10 Emissions 

by Bioethanol Case Compared with Gasoline in 2012 

Figure H-17. WTW Total and Urban PM10 Emissions 

by Bioethanol Case Compared with Gasoline in 2030 

H-21 
Figure H-18. WTW Total and Urban Sulfur Oxide Emissions 

by Bioethanol Case Compared with Gasoline in 2012 

Figure H-19. WTW Total and Urban Sulfur Oxide Emissions 

by Bioethanol Case Compared with Gasoline in 2030 

H-22 
H.2 Estimation of Annual Reductions in Oil Use and GHG Emissions by 

Ethanol Use in Light-Duty Vehicles through the VISION Model 

The previous section presented estimates of WTW energy and emissions of producing and 

using one million British thermal units of bioethanol versus gasoline. This section presents 

estimates of annual reductions in oil use and GHG emissions as a result of using ethanol in the 

U.S. light-duty vehicle fleet by applying the WTW results per unit of ethanol from the 

previous section and annual ethanol supply for the 30x30 scenario 

NREL provided two sets of annual ethanol supply projections. Argonne’s VISION model was 

then used to simulate the effects of increased ethanol use in light-duty vehicles on energy use 

and GHG emissions. 

The VISION model was used to generate ethanol demand by motor vehicles to match ethanol 

supply under the two ethanol-supply cases. The VISION model uses vehicle survival and agedependent usage procedures to track vintage-specific vehicle stock and usage. The model 

develops estimates of light- and heavy-duty vehicle stock composition, vehicle miles traveled, 

and energy use. The current version of the model can simulate conventional vehicles as well 

as six new-technology vehicles (Singh, Vyas, and Steiner 2004). The model was calibrated to 

annually match the EIA’s projections in its 2006 Annual Energy Outlook, which covers up to 

2030. The EIA’s projections were extended to 2050 through a collaborative effort with DOE 

EERE Office of Planning and Budget Analysis. EERE has used the VISION model 

extensively to evaluate the impacts of new technology. 

H.2.1 Ethanol Supply 

The ethanol supply projections for the 30x30 scenario were evaluated to determine the inputs 

to the VISION model. The two sets of estimates are shown in Figure H-20. The low ethanol 

supply case was developed under the reference case of the EIA 2006 Annual Energy Outlook

(EIA 2006a), and the high ethanol supply case was developed under the high oil price case of 

the 2006 Annual Energy Outlook. As Figure H-20 shows, ethanol supply rises slowly during 

2005–2014. The supply then remains nearly stable for almost 8 years before beginning to rise 

significantly. 

H-23 
Figure H-20. Ethanol Estimates 

H.2.2 VISION Inputs 

In the design of VISION simulations, it was assumed that during the period through 2022, 

ethanol would be used by FFVs designed to use either E85 or gasoline. With this assumption, 

the FFV market penetration schemes were developed for both ethanol supply cases so that at 

any year between 2010 and 2050, the amount of ethanol supplied would be consumed by 

light-duty vehicles. 

The ethanol use in light-duty vehicles would occur two ways: 

• In low-level blends (10% or less by volume) with gasoline for use by conventional 

vehicles 

• In high-level blends (up to 85% by volume) for use by FFVs. 

As of now, the low-level ethanol blends are E5.7 in California and E10 in the rest of the 

country. On average, about 30%–40% of U.S. gasoline is blended with ethanol at low levels. 

The majority of ethanol is now used in low-level blends. Ethanol use in E85 for FFVs is tiny. 

For VISION simulations, a penetration scheme of low-level blends in the gasoline market was 

developed. It assumed that by 2020, all gasoline will contain 10% ethanol. Table H-8 shows 

the percent of fuel ethanol of the total gasoline market with the low-level blend market. As the 

table shows, the volumetric share of ethanol in the form of low-level blends increases slowly 

from 1.5% in 2000 to 10% in 2020. 

H-24 
Table H-8. Fuel Ethanol Share of Motor Gasoline 

for Use in Low-Level Ethanol Blends by Conventional Vehicles 

Year Ethanol Share (%) Year Ethanol Share (%) 

2000 1.5 2010 4.5 

2001 1.7 2011 4.9 

2002 2.0 2012 5.3 

2003 2.3 2013 5.7 

2004 2.6 2014 6.2 

2005 3.0 2015 6.7 

2006 3.3 2016 7.3 

2007 3.5 2017 7.9 

2008 3.8 2018 8.5 

2009 4.1 2019 9.2 

2020–2050 10.0 

(Data for 2000–2005 were to match ethanol use; data for 2006 and on were 

based on a penetration of ethanol use in low-level blends to 10% of the 

gasoline market by 2020.) 

After taking into account the low-level ethanol blend market, market penetration profiles were 

then specified for new FFVs to use high-level blends up to E85. It was assumed that, when 

available, these vehicles will use E85 fuel. However, because of fluctuations in ethanol 

production, they may not always use E85. The resulting average fuel ethanol share of FFV 

fuel could be less than 85% by volume, depending on the ethanol supply in a given year. 

It was also assumed that E85 FFVs would have the same fuel economy, on a British thermal 

unit basis, as conventional vehicles. 

The projections were assumed to represent neat ethanol. All calculations assume that fuel 

ethanol contains 5% gasoline as the denaturant. Thus, ethanol production projections 

represented 95% of fuel ethanol that would be made available. 

The E85 FFV market penetration profiles were developed on the basis that the resulting FFV 

stock in a given year would consume the supply volume of ethanol for the given year 

projected. FFVs have been on the market since the late 1990s. The EIA provides the number 

of ethanol FFVs made available (EIA 2006). Data relating to sales through 2004 were used. 

On the basis of press releases by automakers and personal conversations with auto industry 

representatives, new light-duty FFV sales through the year 2010 were estimated. After 2010, 

two new FFV sales profiles were developed for the period 2011–2050 by fitting logic models 

to generate enough ethanol demand to consume the ethanol supply under the two ethanolsupply cases (minus the ethanol demand by the low-level blend market). Although it would be 

difficult to exactly match ethanol production estimates if all FFVs use E85 only, it was 

assumed that the ethanol content of FFV fuel would vary, depending on ethanol availability in 

a given year. This simulation logic reflects the reality of FFV operations, as long as E85 is 

cost-competitive. With this assumption, the market penetration profile would provide ethanol 

consumption estimates very similar to production estimates. 

H-25 
Figure H-21 shows FFV market penetration profiles used in this analysis. Under the low 

ethanol supply case, FFV share of the new vehicle market increases slowly, reaches the 50% 

level in 2038, and has a 57% share by 2050. Under the high ethanol supply case, the 50% 

market share is achieved by 2032, and the 2050 share is 67%. Current policy discussion 

suggests that automakers could be required to produce FFV shares much larger and faster than 

these FFV penetration schemes suggest—the latter reflects the constraint of ethanol supply. 

This result implies that ethanol supply, not ethanol demand by FFVs, will be the constraint on 

the future fuel ethanol market. 

Figure H-21. Assumed E85 FFV Market Penetrations of the New Light-duty 

Vehicle Market 

H.2.3 VISION Results 

The VISION model was executed, and its energy use results were analyzed. The ethanol 

production estimates, as shown in Figure H-20, rise through 2014 and are nearly stable 

during 2014–2022. During 2000–2020, fuel ethanol share of blended gasoline also rises. 

Even with the rising ethanol content of gasoline for use by conventional vehicles, some 

E85 FFVs must be on the road to consume the available fuel ethanol. The light-duty 

vehicles have very high survival probability through the first 10 years. Consequently, the 

FFVs sold during 2000–2014 may not have enough fuel ethanol available to operate at 85% 

volume when ethanol production stabilizes during 2014–2022. In this situation, it was 

assumed that FFVs would use ethanol blends lower than 85% ethanol or that some of the 

FFVs may operate on gasoline only (as most of FFVs do now). In the end, the ethanol 

consumption will match the production estimates, but the average ethanol content of FFV 

fuel may be less than 85%. 

H-26 
Figures H-22 and H-23 show ethanol use patterns projected by VISION under the two ethanol 

supply cases. The upper curves in the figures show the pattern of ethanol consumption if all 

FFVs could use fuel ethanol at 85%. The lower curve shows the pattern of ethanol 

consumption that matches the production estimate by assuming that all FFVs use ethanol 

blends lower than E85. 

Figure H-22. Ethanol Consumption Patterns from VISION – low ethanol supply 

case 

Figure H-23. Ethanol Consumption Patterns from VISION – high ethanol supply 

case 

H-27 
The difference between the VISION-estimated ethanol demand and NREL-estimated ethanol 

supply in each of the figures shows that larger volumes of E85 will be used if all FFVs 

operate on E85, as assumed under the FFV market penetration schemes shown in Figure H21. The gap between ethanol production and ethanol consumption is very small through 2012. 

It then widens after 2012 and closes by 2050. The shortfall (i.e., the difference between 

ethanol demand if all FFVs use E85 and the production estimate) under the low ethanol 

supply case is wider than that of the high ethanol supply case. 

The average ethanol contents of FFV fuel were estimated under both cases. Figures H-24 and 

H-25 show average ethanol contents of FFV fuel and actual ethanol consumption by FFVs 

under the two cases. Under the low ethanol supply case, the average volume of fuel ethanol 

rises to 61% by 2012 (see Figure H-24) and then drops as ethanol production stagnates. 

Because more FFVs continue to be sold with very little increase in ethanol fuel production, 

the average ethanol volume drops to 21% by 2020. The average ethanol volume then rises as 

more ethanol is produced. The implication is that, to fully take advantage of the FFVs already 

on the road, ethanol production should increase during this period. Under the high ethanol 

supply case, the average ethanol volume rises to 66% by 2011 (see Figure H-25) and then 

suddenly drops. The V-shaped drop is caused by a sudden change in ethanol supply (see 

Figure H-20). The average ethanol volume in FFV fuel drops to 27% by 2020 and then starts 

rising to 85% by 2050. 

Figure H-24. Average Ethanol Share in FFV Fuel and FFV Ethanol Use 

under the Low Ethanol Supply Case 

H-28 
Figure H-25. Average Ethanol Share in FFV Fuel and FFV Ethanol Use 

under the High Ethanol Supply Case 

The projected total ethanol demand is made up of low-level ethanol blends for use by 

conventional vehicles and higher-level ethanol blend for use by FFVs. Figures H-26 and H-27 

show that the low-level blend would use only a small amount of ethanol after 2025. As ethanol 

production increases, FFVs capable of using E85 are necessary to consume the remaining 

ethanol supply. The implication is that in the future, FFVs and E85 refueling stations will have 

to be introduced to accommodate the volume of potential ethanol supply. Under the low 

ethanol supply case, ethanol use by FFVs rises through 2014 and then stays below the 2014 

volume for 9 years before rising again. Under the high ethanol supply case, the FFV ethanol 

use pattern is different. The FFV use of ethanol rises through 2011, drops for 2 years, rises for 

1 year, and then stays below the 2014 volume through 2021 before rising again. 

 

H-29 
Figure H-26. VISION-projected Ethanol Volumes used in E10 and E85 Fuels – 

low ethanol supply case 

Figure H-27. VISION-projected Ethanol Volumes used in E10 and E85 Fuels – 

high ethanol supply case 

The VISION model also projected the number of FFVs on the road. It computed their share of 

the total light-duty vehicle stock and analyzed it. Figure H-28 shows the number of FFVs on 

road under the two ethanol-supply cases. The numbers of FFVs on the road are nearly the 

same for the first 15 years under the two ethanol-supply cases. They diverge only after 2018. 

H-30 
This outcome is expected because new FFV sales were very similar through 2013, and small 

differences after that were not significant enough to show visible change from the surviving 

prior-year FFVs. The number of FFVs on the road reaches 207 million under the low ethanol 

supply case and 244 million under the high ethanol supply case. 

Their share of light-duty vehicles was also analyzed. The FFV share of the total light-duty 

fleet reaches 52.6% under the low ethanol supply case and 62.1% under the high ethanol 

supply case in 2050. Figure H-29 shows FFV share of new light-duty vehicle sales and FFV 

share of light-duty vehicle stock. The FFVs reach 50% share of the new light-duty vehicles 

market in 2038 under the low ethanol supply case and in 2032 under the high ethanol supply 

case. The 50% share of the light-duty stock is reached in 2048 under the low ethanol supply 

case and in 2041 under the high ethanol supply case. Thus, a 7–8 year delay is observed for 

the FFV stock to reach the 50% level. This lag would be different if the rate of change in new 

FFV sales is modified or new FFV sales during the 2014–2022 period are changed to account 

for stagnant ethanol production. 

Figure H-28. Number of FFVs on the Road Under the Two Ethanol Supply 

Cases 

H-31 
Figure H-29. FFV Share of New and On-the-road Light-duty Vehicles 

Under the Two Ethanol Supply Cases 

H.2.4 Impacts of Ethanol Use by Light-Duty Vehicles 

For VISION simulations, the average energy use and GHG emissions of the ethanol types are 

needed for each given year between 2010 and 2050. The split between corn ethanol and 

cellulosic ethanol was based on supply estimates from Chapter 2. For the three types of 

cellulosic ethanol, it was assumed that 30% is produced from forest residues, 35% from 

agricultural residues, and the remaining 35% from switchgrass. 

The simulated ethanol consumption will replace a part of the projected gasoline consumption 

if no ethanol were produced. Gasoline consumption by light-duty vehicles was estimated 

under a base case. GREET model estimates of upstream petroleum use for producing gasoline 

and ethanol were used to estimate total WTW petroleum, or oil, demand under three cases: (1) 

the base case, (2) the low ethanol supply case, and (3) the high ethanol supply case. 

Figure H-30 shows the petroleum energy demand in million barrels per day oil equivalent. 

Because the two ethanol supply patterns from Chapter 2 are similar through 2022, the 

incremental impact of the high ethanol supply case is not visible until then. Although the base 

case oil demand continuously rises, both ethanol supply cases begin to reduce oil demand 

immediately. Even with increasing ethanol supply, oil consumption continues to increase 

through 2028 under the low ethanol supply case and through 2025 under the high ethanol 

supply case. The pattern is then reversed, and oil demand declines. The 2050 oil demand 

values, under the two ethanol-supply cases, fall below the 2005 oil demand by light-duty 

vehicles. 

H-32 
Figure H-30. WTW Oil Use Under the Base and Ethanol Supply Cases 

The production of gasoline and ethanol, as well as their use in light-duty vehicles, will 

generate GHG emissions. The projected gasoline and ethanol demand were used with 

GREET-generated GHG emissions rates to produce WTW GHG emissions. 

Figure H-31 shows WTW GHG emissions under the base, low ethanol supply, and high 

ethanol supply cases. The WTW GHG emissions show a pattern somewhat different from that 

of oil use. This is because of the different biomass types (with considerably different GHG 

profiles) used in ethanol production. The GHG emissions reduction is small through 2022. 

The GHG reduction is significant after 2022 because of increased ethanol use and greater 

share of ethanol supply from cellulosic biomass. 

H-33 
H-34 

Figure H-31. WTW GHG Emissions Under the Three Cases 

 
Appendix I: Storage, Distribution, Transport, Blending, and 

Refueling Infrastructure Needs 

I.1 Introduction 

This appendix addresses the assumptions, calculations, and references for the storage, 

distribution, and transportation of ethanol. It also addresses the blending, refueling station 

infrastructure, and vehicle fleet dynamic issues related to the 30 x 30 goal. 

The major assumption that underlies all calculations and results in this appendix is the 

projected annual ethanol production rate. The ethanol production rates used here are based on 

the availability of biomass as presented in the “Billion Ton” study and conversion technology 

advances. 

The ethanol production curves are based on aggressive and moderate growth scenarios. Each 

growth curve includes the contribution of ethanol from starch. 

• The aggressive scenario reaches 60 billion annual gallons by 2030 and grows to more 

than 135 billion gallons by 2050. The major assumption behind this curve is that 

ethanol from cellulosic biomass enters the market early by utilizing fiber and residues. 

• The moderate growth curve delays the introduction of ethanol production from 

cellulosic biomass until 2020 and hits 60 billion gallons in 2045. The corresponding 

growth rate is not as large as that of the former case. 

I.2 Current Practices 

Fuel ethanol is used primarily as a gasoline blend. It accounts for slightly less than 3% of the 

gasoline pool, and typical RFG contains 5%–10% ethanol by volume. Ethanol increases fuel 

octane and helps users meet gasoline oxygenate requirements in U.S. EPA urban nonattainment areas to improve air quality. Demand for ethanol as a fuel additive has increased 

sharply as the use of methyl tertiary butyl ether has decreased because of its negative 

environmental effects. (Methyl tertiary butyl ether was identified as a major aquifer pollutant 

in 1996 primarily because of leaking underground gasoline tanks.) 

Ethanol is currently produced in wet mills and dry mills. Wet mills are typically larger and 

produce 100 million gallons or more of ethanol per year. Wet mills also produce significant 

quantities of co-products such as corn oil, corn germ, corn gluten feed, and high-fructose corn 

syrup. Dry mills are typically smaller. They produce 35–70 million gallons of ethanol 

annually and the animal feed distillers dried grain. 

I.3 Storage 

There are two major assumptions used for the storage of ethanol. These assumptions are as 

stated by Reynolds (Reynolds 2006) and are based on current storage practices for petroleum 

products. 

1. The storage volume required is 10% of the cumulative production rate plus 30% (of 

the 10%) for inventory receipts and extra working space. 

I-1 
2. The installed cost for storage in conventional tanks is $22.40/barrel. 

The cost per gallon of storage for ethanol was calculated without any depreciation. According 

to the formula from Reynolds, for 60 billion gallons, the storage cost would be $4.16 billion, 

which equates to $0.07/gallon. 

I.4 Distribution 

The Midwest has the greatest potential for biomass production and, therefore, the potential to 

be the largest source of ethanol. Currently, nearly all the ethanol produced from starch is 

produced in the Midwest. 

It is assumed that the pattern of light vehicle transportation fuel distribution shown in Table I1 will essentially remain the same for this study. Because of this assumption, ethanol use can 

be compared with finished gasoline use via PADDs (Petroleum Administration Defense 

Districts) using EIA data. 

TableI-1 shows the percentage of 2004 finished motor gasoline consumption by PADD. 

Figure I-1. PADDs 

Table I-1. EIA Finished Motor Gasoline Consumption by PADD in 2004 

PADD % Total Gasoline Consumption

1 35.7% 

2 28.7% 

3 14.8% 

4 3.1% 

5 17.6% 

I-2 
Tables I-2 and I-3 show biomass resources and ethanol production by PADD. The data in 

these tables come from POLYSIS, a biomass feedstock database used by Oak Ridge National 

Laboratory and the USDA. 

Table I-2. Cellulosic Biomass Resources by PADD 

 2020 2030 2040 2050 

PADD 1 14.7% 11.9% 13.6% 12.7% 

PADD 2 66.8% 71.3% 69.3% 69.7% 

PADD 3 11.3% 10.5% 12.1% 12.9% 

PADD 4 2.8% 2.5% 2.2% 2.1% 

PADD 5 4.4% 3.8% 2.9% 2.7% 

Table I-3. Ethanol Production by PADD* 

 

Aggressive Scenario 

(Billion Gallons) 

 2020 2030 2040 2050 2060

PADD 1 0.8 5.6 13.7 16.0 15.1 

PADD 2 15.7 45.6 81.8 100.8 101.3 

PADD 3 0.6 5.0 12.2 16.2 17.0 

PADD 4 0.2 1.2 2.2 2.6 2.5 

PADD 5 0.3 1.8 3.0 3.3 3.0 

Total 17.6 59.2 112.8 139.0 139.0 

 

 

Moderate Scenario 

(Billion Gallons) 

 2020 2030 2040 2050 2060 

PADD 1 0.5 4.4 12.4 15.6 15.1 

PADD 2 13.2 37.1 74.0 96.1 99.2 

PADD 3 0.4 3.9 11.0 15.7 17.0 

PADD 4 0.1 0.9 2.0 2.5 2.5 

PADD 5 0.2 1.4 2.7 3.2 3.0 

Total 14.4 47.7 102.0 133.1 136.9 

These figures assume that all the ethanol from starch comes from PADD 2 (see 

above). 

The distribution pattern is estimated in Figure I-2. The major gathering areas for ethanol in 

PADD 2 (as indicated by blue stars in the diagram) are Chicago, Des Moines, Minneapolis, 

and Pierre. Figure I-2 also shows end-user markets. 

I-3 
Figure I-2. Sample Ethanol Distribution Map 

I.5 Transportation 

There are four possible modes of transport to move ethanol from the plant gate to the 

distribution terminal: 

• Truck 

• Rail 

• Barge 

• Pipeline. 

Calculations for truck, rail, and barge transportation were taken from Reynolds (Reynolds 

2006) and extrapolated to 60 billion gallons from 43 billion gallons. A spreadsheet that 

accompanied the report calculated the cost of delivering ethanol as a function of distance 

traveled for truck, rail, and barge. This spreadsheet from Reynolds used MACRS depreciation 

over a 15-year period (Table I-4, Column 1). 

To compare pipeline costs with conventional transportation methods, the same depreciation 

period was used with projected ethanol production rates over the depreciation period. Table I-4 

provides figures for capital payment for pipeline installation. 

I-4 
Table I-4. Installation Costs 

 Miles of 

Pipeline 20,000 30,000 40,000 50,000 60,000 70,000 

15-Year MACRS 

Depreciation 

Period 

Payment 

(Billion $) 

5.00% 1.0 1.5 2.0 2.5 3.0 3.5 

9.50% 1.9 2.9 3.8 4.8 5.7 6.7 

8.55% 1.7 2.6 3.4 4.3 5.1 6.0 

7.70% 1.5 2.3 3.1 3.9 4.6 5.4 

6.93% 1.4 2.1 2.8 3.5 4.2 4.9 

6.23% 1.2 1.9 2.5 3.1 3.7 4.4 

5.90% 1.2 1.8 2.4 3.0 3.5 4.1 

5.90% 1.2 1.8 2.4 3.0 3.5 4.1 

5.91% 1.2 1.8 2.4 3.0 3.5 4.1 

5.90% 1.2 1.8 2.4 3.0 3.5 4.1 

5.91% 1.2 1.8 2.4 3.0 3.5 4.1 

5.90% 1.2 1.8 2.4 3.0 3.5 4.1 

5.91% 1.2 1.8 2.4 3.0 3.5 4.1 

5.90% 1.2 1.8 2.4 3.0 3.5 4.1 

5.91% 1.2 1.8 2.4 3.0 3.5 4.1 

2.95% 0.6 0.9 1.2 1.5 1.8 2.1 

Using Table I-4 and the ethanol production rates in Table I-5, the average cost per gallon was 

calculated as a function of the cumulative miles of pipeline installed. The graph showing these 

results is shown in Figure I-3 and also in the body of the report. 

 

20 30 40 50 60 70

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Cents/gallon

Pipeline (Thousand Miles)

2050 2040 2030

Figure I-3. Ethanol Pipeline Costs 

I-5 
Table I-5. Ethanol Production Rates (Aggressive Scenario) 

Year Billion Gallons Year Billion Gallons Year Billion Gallons 

2030 59.2 2040 112.8 2050 139.0 

2031 65.3 2041 115.5 2051 139.0 

2032 71.4 2042 118.6 2052 139.0 

2033 77.5 2043 122.2 2053 139.0 

2034 83.6 2044 127.0 2054 139.0 

2035 89.6 2045 131.8 2055 139.0 

2036 95.7 2046 136.9 2056 139.0 

2037 100.5 2047 139.0 2057 139.0 

2038 104.7 2048 139.0 2058 139.0 

2039 109.0 2049 139.0 2059 139.0 

2040 112.8 2050 139.0 2060 139.0 

2041 115.5 2051 139.0 2061 139.0 

2042 118.6 2052 139.0 2062 139.0 

2043 122.2 2053 139.0 2063 139.0 

2044 127.0 2054 139.0 2064 139.0 

2045 131.8 2055 139.0 2065 139.0 

I.6 Blending 

Costs for blending terminals were taken from Reynolds (Reynolds 2006). Reynolds reports 

that blending equipment at each terminal costs roughly $300,000. Further, adding blending at 

600 terminals would cost $180 million. It is assumed that these costs would be the same 

whether ethanol is blended into gasoline or visa versa. 

I-6 
I.7 Refueling 

I.7.1 Vehicle and Retail Refueling Infrastructure to Support E10 

Approximately 4 billion gallons of ethanol were used in blends in 2005, and the industry 

plans to increase production capacity in the near future. In fact, the construction of new 

ethanol plants and expansion of existing facilities will increase production capacity to more 

than 6 billion gallons by the end of 2006. 

As noted in the main body of the report, expanding ethanol consumption through the 

widespread use of E10 will have relatively few effects on the existing vehicle and refueling 

infrastructure and should require no additional capital investment. 

There are, however, concerns about ozone-forming emissions from ethanol blended with 

RFG. When ethanol is blended with gasoline, the fuel’s Reid Vapor Pressure (RVP) increases. 

This leads to an increase in evaporative emissions from the vehicle and during refueling. This 

can be controlled by reducing the RVP of the gasoline blend stock. This is already done for 

reformulated gasoline (RFG) in geographic areas that use ethanol and are in non-attainment 

with EPA air quality requirements for ozone (see Figure I-2). In areas that are in nonattainment for carbon monoxide (CO) only , the EPA generally grants a waiver for RVP 

requirements to allow the use of ethanol as an oxygenate to reduce carbon monoxide 

emissions in winter months. 

Figure I-4. Air Quality Non-attainment Areas That Could Affect E10 Deployment 

I-7 
Recent studies conducted by the Coordinating Research Council indicate, however, that there 

may be ethanol-related increases in VOC emissions independent of RVP because of 

permeation through rubber and plastics on today’s conventional vehicles (Inside Fuels 2006). 

This requires further study, and indications are that today’s FFVs do not have this problem. 

If ethanol were excluded from RFG because of the permeation emission concern, the potential 

market for blends would be reduced by 30% (the percentage of RFG in the gasoline pool) to a 

little more than 10 billion gallons annually by 2015. Figure I-3 shows this change. Both 

cases—one with and one without ethanol in RFG—achieve the 60-billion-gallon goal in 2030. 

Assuming there is no ethanol in RFG while achieving the goal in 2030 has two effects: 

1. It reduces the amount of ethanol used in blends while increasing the amount of ethanol 

used in E85. 

2. It reduces the total amount of ethanol in the earlier years. 

There is also uncertainty regarding the impact of ethanol on tail-pipe emissions of NOx and 

aldehydes. This is mainly an issue for modern FFVs, state regulatory agencies are also 

concerned about the impact of E10. While some studies show little or no effect on these 

emissions, other studies suggest that increases might be significant. Very little emission test 

data are available for recent technology vehicles, and in addition, most available test data are 

for ethanol blended with gasoline that is not representative of that actually being used. 

Additional testing is warranted to alleviate the concerns of air quality regulators. 

Figure I-5. Effect of Excluding Ethanol in RFG on Projected Ethanol Use 

I-8 
I.7.2 Vehicle and Retail Refueling Infrastructure to Support E85 

Increasing the use of ethanol via E85 involves a different set of hurdles. These hurdles include 

the availability of FFVs and the ability of drivers to select E85 rather than gasoline based on 

convenience, price, and performance. 

 

I.7.2.1 Retail E85 Refueling Infrastructure 

In the United States today, roughly 169,000 retail outlets (National Petroleum News 2005) 

sell more than 380 million gallons of gasoline each day (EIA 2006b). This means that the 

average retail gasoline station sells 828,000 gallons annually. In contrast, only 619 (DOE 

2006a) stations dispense E85. To increase the sale of E85 beyond the roughly 31 million 

gallons sold in 2004, the number of E85 refueling stations must greatly increase so drivers are 

not inconvenienced. 

The percentage of retail refueling stations that must sell E85 to be convenient for FFV drivers 

is not known. However, if a driver could count on every other station in an area to sell E85, it 

should be reasonably convenient. To reach 50% E85 station coverage nationwide, 84,000 

more stations must be equipped to dispense E85. However, targeting specific regions for E85 

use would reduce the required number. This strategy can also have other advantages related to 

the cost of E85 at the pump. 

Figure I-4 shows the five U.S. PADDS. Figure I-5 shows the most likely areas of biomass 

feedstock availability. A comparison of these maps shows that the heaviest concentrations of 

biomass feedstocks are in the Midwest, South, and Northeast regions. Targeting the sale of 

E85 and FFVs in these three regions would place ethanol end markets nearest its feedstocks. 

Because feedstock transportation is expensive, ethanol production facilities will be located in 

these areas to minimize costs. In addition, the proximity of end markets to production 

facilities will minimize the cost of distributing ethanol to the end markets. 

Targeting the central, South, and Northeast regions for E85 use also takes advantage of 

existing E85 infrastructure. These regions have the bulk of current ethanol production and 

E85 refueling stations, and their drivers are more familiar with E85. In fact, there are 580 E85 

stations in the targeted regions. These represent 94% of the current E85 retail infrastructure. 

I-9 
Figure I-6. PADDs 

Figure I-7. Switchgrass and Forest Residue Resources for Ethanol 

Figure I-6 shows projections for the growth of E85 stations and E85 use in the three-region 

area through 2030. An E85 station growth rate of 2% per year was assumed from 2008 to 

2030. Based on the number of E85 stations and the total volume of E85 sold, the average 

station in 2030 would sell 842,000 gallons of E85. This is considerably more than the average 

I-10 
E85 sales per station of roughly 95,000 gallons in 2005, but it is close to the 828,000 gallons 

of gasoline sold by the average station in 2005. 

It was assumed that the number of stations will remain roughly constant over time while 

station throughput will increase based on two countervailing trends: 

1. The number of stations has contracted in recent years (195,455 in 1995 to 168,987 in 

2005) as stations have moved to higher-volume pump dispensers (National Petroleum 

News 2005). 

2. Annual sales of gasoline are projected by the EIA to increase from 2005’s 138 million 

gallons to 185 million gallons in 2030. 

If these trends bear out, then an average refueling station will dispense approximately 1.1 

million gallons in 2030. 

Figure I-8. Projections for Growth in E85 Stations and E85 Consumption 

The effect of E85 on gasoline sales in the three-region area will be significant. Projecting 78% 

of national fuel use in 2001 to 2030 shows that the three-region fuel market in 2030 will be 

the equivalent of 146 billion gallons of gasoline. (See Figure I-7 for a breakdown by division). 

The projected 2030 use of ethanol in E85 in these regions is 45 billion gallons; this would 

displace 20.5% of the regions’ 146 billion gallons of gasoline. 

I-11 
Figure I-9. Gasoline Consumption by Census Division 

Converting an existing gasoline pump to be compatible with E85 requires nominal changes. 

• Storage tanks 

Most metal underground storage tanks that meet U.S. EPA December 1998 codes can 

store E85. Double-walled fiberglass tanks are generally compatible as well, but 

single-walled tanks installed before 1992 may not be compatible. However, a few are 

in use today. Compatible storage tanks will need to be cleaned, at a cost of roughly 

$2,000/tank. 

• Dispensing pumps 

Because ethanol is highly corrosive, E85 pumps must be equipped with a nonaluminum (i.e., nickel-plated or steel) nozzle, compatible breakaways, and a Teflonlined hose with stainless steel ends and fittings. Pumps and dispensing equipment are 

highly variable and depend on the performance and safety features selected. 

• Installation costs 

Permitting, planning, and electrical and mechanical installation are important parts of 

the conversion process. 

Table I-1 shows a sample of nine bids for refueling station retrofits as reported to the 

Alternative Fuels Data Center at NREL. Because each station owner decides the quality and 

features of station equipment, the costs vary significantly. The data in Table I-1 indicate that 

the average cost to convert a station (generally one dispenser with two nozzles) is $24,690. To 

satisfy demand, stations would require about two dispensers/four nozzles per station to meet 

I-12 
I-13 

2030 demand for E85. Therefore, the conversion of 60,000 stations in the targeted regions 

would cost roughly $3 billion. 

Table I-4. Sample of E85 Refueling Station Retrofits 

Bid 

Number 

Number 

of Tanks 

Above or 

Below 

Ground 

Tank 

Cost 

Number of 

Dispensers 

Dispensing 

Cost Labor 

Total 

Cost 

1 0 N/A 1 $3,000 $3,965 $15,231 

2 1 Below $8,872 0 $0 N/A $27,218 

3 1 Below $2,825 1 $4,970 N/A $20,196 

4 1 Below $3,961 1 $5,500 $12,365 $27,321 

5 2 Below N/A 2 N/A $57,922 

6 0 N/A 1 $11,223 $1,078 $4,118 

7 1 Above $8,704 1 $2,957 $12,000 $32,574 

8 1 Below $3,340 1 $2,130 $3,000 $13,525 

9 1 Below $4,662 1 $4,404 $6,000 $24,105 

Average Costs $5,394 $4,273 $6,401 $24,690 

 
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30x30 Scenario 

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 NREL report assessing market drivers and technology needs required to supply 30% of 2004 motor gasoline 

demand with biofuels by 2030. Goal will require a 60 billion gallon/year ethanol industry by 2030 and necessitates a 

target price of $1.07/gallon ethanol by 2012. Report discusses meeting those targets through biochemical and 

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biomass; ethanol; 30x30; biochemical conversion; thermochemical conversion; ethanol market drivers; ethanol 

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