NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Process Design and Economics 

for the Conversion of 

Lignocellulosic Biomass to 

Hydrocarbons:

Dilute-Acid and Enzymatic 

Deconstruction of Biomass to Sugars 

and Biological Conversion of Sugars to 

Hydrocarbons

R. Davis, L. Tao, E.C.D. Tan, M.J. Biddy, 

G.T. Beckham, and C. Scarlata

National Renewable Energy Laboratory

J. Jacobson and K. Cafferty

Idaho National Laboratory

J. Ross, J. Lukas, D. Knorr, and P. Schoen

Harris Group Inc.

Technical Report

NREL/TP-5100-60223

October 2013
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Process Design and 

Economics for the Conversion 

of Lignocellulosic Biomass to 

Hydrocarbons: 

Dilute-Acid and Enzymatic 

Deconstruction of Biomass to Sugars 

and Biological Conversion of Sugars 

to Hydrocarbons

R. Davis, L. Tao, E.C.D. Tan, M.J. Biddy, 

G.T. Beckham, and C. Scarlata

National Renewable Energy Laboratory

J. Jacobson and K. Cafferty

Idaho National Laboratory

J. Ross, J. Lukas, D. Knorr, and P. Schoen

Harris Group Inc.

Prepared under Task No. BB07.2410

Technical Report

NREL/TP-5100-60223

October 2013
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

 Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 
i 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Executive Summary

The U.S. Department of Energy (DOE) promotes the production of a range of liquid fuels and 

fuel blendstocks from lignocellulosic biomass feedstocks by funding fundamental and applied 

research that advances the state of technology in biomass collection, conversion, and 

sustainability. As part of its involvement in this program, the National Renewable Energy 

Laboratory (NREL) investigates the conceptual production economics of these fuels. 

Between 1999 and 2012, NREL conducted a campaign to quantify the economic implications 

associated with measured conversion performance for the biochemical production of cellulosic 

ethanol, with a formal program between 2007–2012 to set cost goals and to benchmark annual 

performance toward achieving these goals, namely the pilot-scale demonstration by 2012 of 

biochemical ethanol production at a price competitive with petroleum gasoline based on modeled 

assumptions for an “n

th” plant biorefinery. This goal was successfully achieved through NREL’s 

2012 pilot plant demonstration runs, representing the culmination of NREL research focused 

specifically on cellulosic ethanol, and a benchmark for industry to leverage as it commercializes 

the technology. This important milestone also represented a transition toward a new Program 

focus on infrastructure-compatible hydrocarbon biofuel pathways, and the establishment of new 

research directions and cost goals across a number of potential conversion technologies. 

This report describes in detail one potential conversion process to hydrocarbon products by way 

of biological conversion of lignocellulosic-derived sugars. The pathway model leverages 

expertise established over time in core conversion and process integration research at NREL, 

while adding in new technology areas primarily for hydrocarbon production and associated 

processing logistics. The overarching process design converts biomass to a hydrocarbon 

intermediate, represented here as a free fatty acid, using dilute-acid pretreatment, enzymatic 

saccharification, and bioconversion. Ancillary areas—feed handling, hydrolysate conditioning, 

product recovery and upgrading (hydrotreating) to a final blendstock material, wastewater 

treatment, lignin combustion, and utilities—are also included in the design. Detailed material and 

energy balances and capital and operating costs for this baseline process are also documented.

This benchmark case study techno-economic model provides a production cost for a cellulosic 

renewable diesel blendstock (RDB) that can be used as a baseline to assess its competitiveness 

and market potential. It can also be used to quantify the economic impact of individual 

conversion performance targets and prioritize these in terms of their potential to reduce cost. The 

analysis presented here also includes consideration of the life-cycle implications of the baseline 

process model, by tracking sustainability metrics for the modeled biorefinery, including 

greenhouse gas (GHG) emissions, fossil energy demand, and consumptive water use. 

Building on prior design reports for biochemical ethanol production in 1999, 2002, and 2011, 

NREL, together with the Harris Group Inc., performed a feasibility-level analysis for a plausible 

conversion pathway to RDB to meet an intermediate DOE cost goal of $5/gallon gasoline 

equivalent (GGE) by 2017. The modeled biorefinery processes 2,205 dry ton biomass/day and 

achieves an RDB selling price of $5.10/GGE in 2011$ as determined by modeled conversion 

targets and “n

th-plant” project costs and financing, associated with a process RDB yield of 45.4 

GGE/dry ton. In addition, the report includes a high-level discussion on improvements needed to 

achieve a final 2022 DOE target of $3/GGE moving forward, focused on coproducts from lignin.
ii

Figure ES-1. Economic summary for diesel blendstock production

Minimum Fuel Selling Price (MFSP): $5.35 /gal

MFSP (Gasoline-Equivalent Basis): $5.10 /GGE

Contributions: Feedstock $1.85 /gal ($1.76/GGE)

Enzymes $0.39 /gal ($0.37/GGE)

Non-Enzyme Conversion $3.11 /gal ($2.96/GGE)

RDB Production 31.3 MMgal/yr (at 68 °F) (32.9 MM GGE/yr)

RDB Yield 43.3 gal / dry U.S. ton feedstock (45.4 GGE/ton)

Bioconversion Metabolic Yield 0.284 kg FFA/kg total sugars (79% of theoretical)

Feedstock + Handling Cost $80.00 /dry U.S. ton feedstock

Internal Rate of Return (After-Tax) 10%

Equity Percent of Total Investment 40%

Capital Costs Manufacturing Costs (cents/gal RDB product)

Pretreatment $51,400,000 Feedstock + Handling 184.9

Neutralization/Conditioning $2,200,000 Sulfuric Acid 6.2

Enzymatic Hydrolysis/Conditioning/Bioconversion $75,400,000 Ammonia (pretreatment conditioning) 3.6

On-site Enzyme Production $12,400,000 Caustic 6.5

Product Recovery + Upgrading $26,600,000 Glucose (enzyme production) 21.7

Wastewater Treatment $60,100,000 Hydrogen 8.4

Storage $3,400,000 Other Raw Materials 19.2

Boiler/Turbogenerator $76,000,000 Waste Disposal 4.5

Utilities $8,800,000 Net Electricity -16.3

Total Installed Equipment Cost $316,300,000 Fixed Costs 44.9

Capital Depreciation 58.7

Added Direct + Indirect Costs $266,400,000 Average Income Tax 34.1

 (% of TCI) 46% Average Return on Investment 158.5

Total Capital Investment (TCI) $582,700,000 Manufacturing Costs ($/yr)

Feedstock + Handling $57,900,000

Installed Equipment Cost/Annual Gallon $10.09 Sulfuric Acid $1,900,000

Total Capital Investment/Annual Gallon $18.59 Ammonia (pretreatment conditioning) $1,100,000

Caustic $2,000,000

Loan Rate 8.0% Glucose (enzyme production) $6,800,000

Term (years) 10 Hydrogen $2,600,000

Capital Charge Factor (Computed) 0.135 Other Raw Materials $6,000,000

Waste Disposal $1,400,000

Carbon Retention Efficiencies: Net Electricity -$5,100,000

 From Hydrolysate Sugar (Fuel C / Sugar C) 49.5% Fixed Costs $14,100,000

 From Biomass (Fuel C / Biomass C) 26.2% Capital Depreciation $18,400,000

Average Income Tax $10,700,000

Maximum Yields (100% of Theoretical)a Average Return on Investment $49,600,000

 FFA Production (U.S. ton/yr) 172,465

Current FFA Production (U.S. ton/yr)b 117,587 Specific Operating Conditions

Current Yield (Actual/Theoretical) 68.2% Enzyme Loading (mg/g cellulose) 10

Saccharification Time (days) 3.5 aComplete conversion of biomass carbohydrates to C16 fatty acid Bioconversion Time (days) 2.9 bRecovered FFA yield after concentration, sent to hydrotreating Bioconversion FFA titer (wt%) 9%

 (Theoretical yields above do not consider refining to final RDB Excess Electricity (kWh/gal) 2.6

 product, as refining yield varies with catalyst and conditions) Plant Electricity Use (kWh/gal) 11

All Values in 2011$

Dilute Acid Pretreatment, Enzymatic Hydrolysis, Hydrocarbon (FFA) Bioconversion, Hydrotreating to Paraffins (RDB)

Biological Renewable Diesel Blendstock (RDB) Process Engineering Analysis
iii

Table of Contents

Executive Summary..................................................................................................................................... i

Acronyms ..................................................................................................................................................... v

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

1.1 Background and Motivation ............................................................................................................. 1

1.2 Process Overview ............................................................................................................................ 3

1.3 Techno-Economic Analysis Approach ............................................................................................. 6

1.4 About nth-Plant Assumptions............................................................................................................ 7

1.5 About the NREL Aspen Model......................................................................................................... 8

2 Design Basis and Conventions........................................................................................................... 9

2.1 Plant Size ......................................................................................................................................... 9

2.2 Feedstock Composition.................................................................................................................... 9

2.3 Updated Modeling Basis ................................................................................................................ 10

2.4 Design Report Conventions ........................................................................................................... 11

2.4.1 Units ................................................................................................................................ 11

2.4.2 Total Solids Loading........................................................................................................ 11

2.4.3 Product Density and Heating Value ................................................................................ 11

3 Process Design and Cost Estimation Details.................................................................................. 12

3.1 Area 100: Feedstock Storage and Handling.................................................................................. 12

3.1.1 Overview ......................................................................................................................... 12

3.1.2 Design Basis ................................................................................................................... 15

3.1.3 Cost Estimation ............................................................................................................... 16

3.2 Area 200: Pretreatment and Conditioning...................................................................................... 16

3.2.1 Overview ......................................................................................................................... 16

3.2.2 Design Basis ................................................................................................................... 18

3.2.3 Cost Estimation ............................................................................................................... 21

3.2.4 Achieving the Design Case ............................................................................................. 22

3.3 Area 300: Enzymatic Hydrolysis, Hydrolysate Conditioning, and Bioconversion .......................... 23

3.3.1 Overview ......................................................................................................................... 23

3.3.2 Design Basis ................................................................................................................... 24

3.3.3 Cost Estimation ............................................................................................................... 36

3.3.4 Achieving the Design Case ............................................................................................. 37

3.3.4.1 Biomass Deconstruction (Pretreatment + Enzymatic Hydrolysis)...................................38

3.3.4.2 Bioconversion..................................................................................................................39

3.3.4.3 Low-Cost Media ..............................................................................................................42

3.4 Area 400: Cellulase Enzyme Production ....................................................................................... 42

3.4.1 Overview ......................................................................................................................... 42

3.4.2 Design Basis ................................................................................................................... 43

3.4.3 Cost Estimation ............................................................................................................... 45

3.4.4 Enzyme Cost Discussion ................................................................................................ 46

3.5 Area 500: Product Recovery and Upgrading ................................................................................. 47

3.5.1 Overview ......................................................................................................................... 47

3.5.2 Design Basis ................................................................................................................... 48

3.5.3 Cost Estimation ............................................................................................................... 52

3.6 Area 600: Wastewater Treatment.................................................................................................. 53

3.6.1 Overview ......................................................................................................................... 53

3.6.2 Design Basis ................................................................................................................... 55

3.6.3 Cost Estimation ............................................................................................................... 57

3.7 Area 700: Product and Feed Chemical Storage ............................................................................ 58

3.7.1 Overview ......................................................................................................................... 58

3.7.2 Design Basis ................................................................................................................... 58

3.7.3 Cost Estimation ............................................................................................................... 59

3.8 Area 800: Combustor, Boiler, and Turbogenerator........................................................................ 59

3.8.1 Overview ......................................................................................................................... 59

3.8.2 Design Basis ................................................................................................................... 59
iv

3.8.3 Cost Estimation ............................................................................................................... 60

3.9 Area 900: Utilities ........................................................................................................................... 60

3.9.1 Overview ......................................................................................................................... 60

3.9.2 Design Basis ................................................................................................................... 61

3.9.3 Cost Estimation ............................................................................................................... 63

4 Process Economics ........................................................................................................................... 64

4.1 About Cost-Year Indices ................................................................................................................ 64

4.2 Total Capital Investment ................................................................................................................ 65

4.3 Variable Operating Costs............................................................................................................... 67

4.4 Fixed Operating Costs ................................................................................................................... 69

4.5 Discounted Cash Flow Analysis and the Minimum Selling Price of Fuel....................................... 70

4.5.1 Discount Rate.................................................................................................................. 70

4.5.2 Equity Financing.............................................................................................................. 71

4.5.3 Depreciation .................................................................................................................... 71

4.5.4 Taxes............................................................................................................................... 72

4.5.5 Construction Time ........................................................................................................... 72

4.5.6 Start-Up Time.................................................................................................................. 73

4.5.7 Working Capital............................................................................................................... 73

5 Analysis and Discussion ................................................................................................................... 76

5.1 Carbon and Water Balances.......................................................................................................... 76

5.2 Sustainability Metrics for 2017 Base Model................................................................................... 79

5.3 Cost Sensitivity Analysis ................................................................................................................ 82

5.3.1 Single-Point Sensitivity Analysis ..................................................................................... 82

5.3.2 Evaluation of Alternative Metabolic Pathways ................................................................ 85

6 Paths Forward to $3/GGE .................................................................................................................. 88

6.1 Lignin Utilization: Technical Context.............................................................................................. 89

6.2 Lignin Utilization: TEA Modeling/Analysis...................................................................................... 93

6.3 Lignin Utilization: Sustainability Analysis ....................................................................................... 97

7 Concluding Remarks........................................................................................................................ 102

7.1 Summary...................................................................................................................................... 102

7.2 Future Work ................................................................................................................................. 103

References............................................................................................................................................... 105

Appendix A. Individual Equipment Cost Summary ............................................................................. 113

Appendix B. Discounted Cash Flow Rate of Return Worksheet........................................................ 120

Appendix C. Process Parameters/Operating Summary...................................................................... 122

Appendix D. Aspen Plus Properties...................................................................................................... 123

Appendix E. Process Flow Diagrams.................................................................................................... 128
v 

Acronyms

BETO Bioenergy Technologies 

Office 

BFW boiler feed water

BLS Bureau of Labor Statistics

CBP consolidated bioprocessing

CIP clean-in-place 

COD chemical oxygen demand

CSL corn steep liquor

DAP diammonium phosphate

DCFROR discounted cash flow rate of 

return 

DCO decarboxylation

DOE U.S. Department of Energy

FCI fixed capital investment

FFA free fatty acid

FGD flue gas desulfurization

GBT gravity belt thickener

GGE gallon gasoline equivalent

GHG greenhouse gas

HDO hydrodeoxygenation 

HHV higher heating value

HMF 5-hydroxymethyl furfural

INL Idaho National Laboratory

IS insoluble solids

IRR internal rate of return

ISBL inside battery limits (of the 

plant) 

LCA life cycle assessment

LHV lower heating value

MESP minimum ethanol selling 

price

MFSP minimum fuel selling price

MM million

MVR mechanical vapor 

recompression

MYPP BETO’s Multi-Year Program 

Plan

NPV net present value

NREL National Renewable Energy 

Laboratory

OTR oxygen transfer rate

OUR oxygen uptake rate

PFD process flow diagram

PSA pressure swing adsorption

RDB renewable diesel blendstock

SCFM standard cubic feet per 

minute 

SHF separate (or sequential) 

hydrolysis and fermentation

SOT annual State of Technology 

case

SSCF simultaneous 

saccharification and cofermentation

TAN total acid number

TCI total capital investment

TDC total direct cost

TEA techno-economic analysis

TS total solids

VOC volatile organic compound

VVM volume (of gas) per volume 

(of liquid) per minute

WWT wastewater treatment
1 

1 Introduction

1.1 Background and Motivation

The U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) promotes the 

production of liquid fuels from lignocellulosic feedstocks by sponsoring programs in 

fundamental and applied research that aim to advance the state of biomass conversion 

technology. These programs include laboratory research to develop improved cellulose 

hydrolysis enzymes and metabolic conversion microorganisms through synthetic biology, 

detailed engineering studies of potential processes, and construction of pilot-scale demonstration 

and production facilities. This research is conducted by national laboratories, universities, and 

private industry in conjunction with engineering and construction companies. 

To support the DOE program, the National Renewable Energy Laboratory (NREL) investigates 

the process design and economics of cellulosic biofuel manufacturing in order to develop an 

absolute plant-gate price for fuels and fuel blendstocks based on process and plant design 

assumptions consistent with applicable best practices in engineering, construction, and operation. 

This plant-gate price is referred to as the minimum fuel selling price or MFSP. The MFSP can be 

used by DOE to assess the cost-competitiveness and market penetration potential of a given 

cellulosic biofuel technology pathway in comparison with petroleum-derived fuels and 

established biofuel technologies such as starch- or sugar-based ethanol. 

The techno-economic analysis (TEA) effort at NREL also helps to direct biomass conversion 

research by examining the sensitivity of the MFSP to process alternatives and research advances.

Proposed research and its anticipated results can be translated into a new MFSP that can be 

compared to the benchmark case documented in this report. Such comparison helps to quantify 

the economic impact of core research targets at NREL and elsewhere and to track progress 

toward meeting competitive cost targets. It also allows DOE to make more informed decisions 

about research proposals that claim to reduce MFSP.

For more than 10 years, NREL has developed design case models and associated reports that 

document process and cost targets for ethanol production from cellulosic feedstocks via 

biochemical conversion (e.g., deconstruction to monomeric sugars followed by fermentation to 

ethanol), based on the best understanding of the technology and equipment costs at the time.

Early reports were established in 1999 [1] and in 2002 [2] to establish a target model aimed 

initially at producing ethanol at a price competitive with traditional corn-based ethanol. Over the 

following 9 years, a great deal of progress was made to better quantify R&D conversion 

performance and process economics (namely equipment costs) for a commercial-scale cellulosic 

biorefinery. To reflect these learnings, NREL revisited the biochemical ethanol design report in 

detail in 2011 to incorporate recent research progress in the conversion areas (pretreatment, 

conditioning, enzymatic hydrolysis, and fermentation), optimizations in product recovery, and an 

increased understanding of the ethanol plant’s back end (separation, wastewater, and utilities), in 

conjunction with an engineering subcontractor (Harris Group Inc.) to refine cost estimates based 

on more thorough vendor feedback. This updated report detailed a more rigorous and robust 

pathway model with a new minimum ethanol selling price (MESP) target of $2.15/gal [3], 

intended to approximate cost competitiveness with petroleum gasoline (versus corn grain 

ethanol) consistent with new DOE cost targets for 2012 [4] (see Figure 1). Subsequently, NREL 

did meet the important milestone of demonstrating achievement of the $2.15/gal MESP target 
2 

based on a modeled 2,000 dry metric ton/day integrated “n

th-plant” facility using process 

performance data garnered from NREL pilot-scale demonstration runs in 2012 [5, 6]. 

Figure 1. Ethanol State of Technology, 2001–2012, using updated 2011 model framework [3, 7–9] 

While further room for improvement exists to continue reducing ethanol costs, the achievement 

of the 2012 MESP target represented the culmination and closeout of NREL’s primary charter to 

demonstrate technology improvements required to de-risk the technology in support of moving it 

toward commercialization, and to provide a benchmark for private industry to expand upon. 

Moving forward, DOE-BETO is transitioning its core platform focus from ethanol to 

hydrocarbon fuel and blendstock products (fungible/infrastructure-compatible fuels), including a 

new pathway MFSP cost target of $3/gallon gasoline equivalent (GGE) in 2022 for the biological 

sugar conversion platform, with an interim target of $5/GGE in 2017 [10]. The focus of this 

report is to document a plausible pathway model for biological conversion of cellulosic sugars to 

hydrocarbon blendstock products in the diesel boiling range, primarily to enable achieving the 

2017 interim cost goal, but with additional insight provided for a path forward to ultimately meet 

the 2022 target of $3/GGE. 

This analysis leverages the decades of experience that NREL has established in biochemical 

conversion research, primarily with respect to the front end of the process for biomass 

pretreatment and enzymatic deconstruction to sugars, which largely remain the same as 

previously documented but with more aggressive targets. Likewise, the work also builds upon 

NREL’s earlier models, including the 2011 ethanol design model, as well as continuing the 

practice of consulting with vendors through the assistance of an engineering company to assist in 

design and cost estimation for critical unit operations. For the present report, NREL worked with 

Harris Group to provide engineering support primarily for new or modified unit operations 
3 

relative to the 2011 ethanol basis, which is based in large part on vendor quotations. Thus, the 

economics of this conceptual process uses the best available equipment and raw material costs 

and an “n

th-plant” project cost structure and financing. The projected 2017 nth-plant MFSP 

computed in this report is $5.10/GGE ($5.35/gal) in 2011$. 

Modifications to the conceptual process design presented here will be reflected annually through 

NREL’s State of Technology (SOT) reports. These ensure that the process design and its cost 

benchmarks incorporate the most current data from equipment vendors, NREL, and other DOEfunded research. 

Similar to caveats noted in prior NREL design reports, we stress that this design report serves to 

describe a single, feasible biological conversion process and to transparently document the 

assumptions and details that went into its design. This report is not meant to provide an 

exhaustive survey of process alternatives or cost-sensitivity analyses. Furthermore, it is 

important to note that the various technologies and metabolic pathways to diesel-range 

hydrocarbon products from biomass are generally less well-understood and more complex than 

cellulosic ethanol production (which is supported by decades of research), and thus intrinsically 

carry a higher degree of uncertainty in the model inputs and assumptions utilized here. Moving 

forward, as the science and technology progress for less well-defined areas of the process such as 

metabolic conversion and lignin deconstruction and upgrading, the process models and economic 

tools developed for this report may be updated in a similar fashion as the ethanol design report 

iterations have evolved. 

1.2 Process Overview

The process described here uses preprocessing (deacetylation) and co-current dilute-acid 

pretreatment of lignocellulosic biomass (primarily corn stover), followed by enzymatic 

hydrolysis (saccharification) of the remaining cellulose, followed by hydrolysate conditioning 

and bioconversion of the resulting hexose and pentose sugars to diesel-range fatty acids. The 

process design also includes feedstock handling and storage, product purification, product 

upgrading (hydrotreating) to straight-chain paraffin blendstocks, wastewater treatment (WWT), 

lignin combustion, product storage, and required utilities. The process is divided into nine areas 

(see Figure 2).

Area 100: Feed handling. The feedstock, in this case milled corn stover blended with 

switchgrass, is delivered to the feed handling area from a uniform-format feedstock supply 

system. Only minimum storage and feed handling are required. From there, the biomass is 

conveyed to the pretreatment reactor (Area 200). 

Area 200: Pretreatment and conditioning. In this area, the biomass is processed in an alkaline 

deacetylation step to solubilize and remove acetate and other non-fermentable components, then 

drained and treated with dilute sulfuric acid catalyst at a high temperature for a short time to 

liberate the hemicellulose sugars and break down the biomass for enzymatic hydrolysis. 

Ammonia is then added to the whole pretreated slurry to raise its pH to ~5 for enzymatic 

hydrolysis. 

Area 300: Enzymatic hydrolysis, hydrolysate conditioning, and bioconversion. Enzymatic 

hydrolysis is initiated in a high-solids continuous reactor using a cellulase enzyme prepared 
4 

onsite. The partially hydrolyzed slurry is next batched to one of several parallel bioreactors. 

Hydrolysis is completed in the batch reactor with a total hydrolysis time of 3.5 days between the 

continuous and batch steps. The slurry is then fed to a vacuum filter press to remove insoluble 

solids (namely lignin), and the remaining soluble sugar stream is split into a small fraction that is 

sent directly to the fed-batch bioreactors to initiate conversion and a larger fraction that is 

concentrated in a vacuum evaporation system to concentrate the sugar components. The solids 

fraction exiting the filter press is sent to the combustor (Area 800). The concentrated sugar slurry 

exiting the evaporators is cooled and inoculated with the generic bioconversion microorganism. 

The conversion step proceeds in fed-batch mode under aerobic reactor conditions. After a total of 

2.9 days of conversion in the bioreactor, most of the cellulose and xylose have been converted to 

free fatty acids (FFAs). The resulting broth is sent to the product recovery train (Area 500).

Area 400: Cellulase enzyme production. The on-site enzyme production section was maintained 

in this design, consistent with the details provided in the 2011 ethanol report. Purchased glucose 

(corn syrup) is the primary carbon source for enzyme production. Media preparation involves a 

step in which a portion of the glucose is converted to sophorose to induce cellulase production. 

The enzyme-producing fungus (modeled after Trichoderma reesei) is grown aerobically in fedbatch bioreactors. The entire fermentation broth, containing the secreted enzyme, is fed to Area 

300 to carry out enzymatic hydrolysis. 

Area 500: Product recovery and upgrading. The FFA product is secreted from the organism 

during the conversion step in A300 and is phase-separated from water via decantation and 

centrifugation. The heavy liquid (water) phase is sent to WWT (Area 600), while the recovered 

FFA product (> 99% purity) is sent to product upgrading in an on-site hydrotreating facility 

(including reactors, fresh and recycle gas compressors, flash columns, and product fractionation, 

utilizing purchased hydrogen). The primary product from the hydrotreating section is a dieselrange paraffinic product suitable as a diesel blendstock. The hydrotreating section also includes a 

pressure swing adsorption (PSA) unit in the recycle gas loop to remove carbon dioxide (CO2) 

generated during decarboxylation. 

Area 600: Wastewater treatment. Plant wastewater streams are treated by anaerobic and aerobic 

digestion. The methane-rich biogas from anaerobic digestion is sent to the combustor (Area 800), 

where sludge from the digesters is also burned. The treated water is suitable for recycling and is 

returned to the process. 

Area 700: Storage. This area provides bulk storage for chemicals used and produced in the 

process, including corn steep liquor (CSL), ammonia, sulfuric acid, nutrients, water, and product. 

Area 800: Combustor, boiler, and turbogenerator. The solids from the filter press and WWT are 

combusted along with the biogas from anaerobic digestion and the tailgas from the hydrotreater 

PSA unit to produce high-pressure steam for electricity production and process heat. Most of the 

process steam demand is in the pretreatment reactor. The boiler produces excess steam that is 

converted to electricity for use in the plant and for sale to the grid. 

Area 900: Utilities. This area includes a cooling water system, chilled water system, process 

water manifold, and power systems.
5 

Figure 2. Simplified flow diagram of the overall process (key streams only; 

see Appendix E for more detailed schematic and process flow diagrams)
6 

1.3 Techno-Economic Analysis Approach

Figure 3 describes the engineering approach used for modeling the conversion of biomass 

to biofuels, including process design, process modeling, and economic analysis. This 

approach was largely followed for this study as well, albeit under a condensed timeline 

and with additional external inputs from literature for areas of the process in which 

NREL does not yet have sufficient in-house data (primarily the microbial conversion 

step). As such, this report is less prescriptive in some sections than are previous reports, 

because of the early stage of understanding for new areas of the process and the 

somewhat more preliminary nature of the associated models. 

Figure 3. NREL’s approach to process design and economic analysis

Starting from the general process flow diagram shown in Figure 2 and the more detailed 

diagrams contained in Appendix E, a process simulation is developed using Aspen Plus software 

[11]. This process model computes thermodynamically rigorous material and energy balances for 

each unit operation in this conceptual biorefinery. 

Process Flow Diagrams

Rigorous

Material & Energy Balances

(Aspen Plus)

Capital & Project Cost 

Estimation

Discounted Cash Flow 

Economic Model

Minimum Fuel Selling Price

Engineering Companies 

Consulting on Process 

Configuration

Estimates of Other 

Commercial Technology

Cost Estimation 

Software (e.g., ICARUS)

Engineering Company

Cost Estimations

NREL or other

DOE-Sponsored

Research

Vendor Cost Quotations

Outside Engineering

Studies (e.g., feed handling, 

separations)

Published Literature
7 

The material and energy balance data from the Aspen simulation are next used to assist in 

determining the number and size of capital equipment items. As process conditions and flows 

change, baseline equipment costs are automatically adjusted in an Excel spreadsheet using a 

scaling factor. These baseline costs come from vendor quotes (a favored procedure for larger or 

nonstandard unit operations and packaged or skid-mounted subsystems) or from Harris Group’s 

proprietary cost database (for secondary equipment such as tanks, pumps, and heat exchangers). 

Harris Group provided updated design and cost estimates based largely on vendor quotations for 

critical new or modified areas of the current process model, primarily deacetylation, sugar 

conditioning (lignin removal and sugar concentration), aerobic bioreactors, product separation, 

hydrotreating, and additional cost information on the pretreatment reactor. Final equipment costs 

for this report are tabulated in Appendix A. 

Once equipment costs are determined, direct and indirect overhead cost factors (e.g., installation 

costs and project contingency) are applied to determine a total capital investment (TCI). The 

TCI, along with the plant operating expenses (also developed using flow rates from the Aspen 

model), is used in a discounted cash flow rate of return (DCFROR) analysis to determine a plantgate price for refined renewable diesel blendstock (RDB) for a given discount rate. This plantgate price is also called the minimum fuel selling price (MFSP, in $/gallon), required to obtain a 

net present value (NPV) of zero for a 10% internal rate of return (IRR) after taxes.

The product of the analysis described above is a techno-economic model that reasonably 

estimates a product price for a pre-commercial process. The resultant MFSP is unique for the set 

of process conditions simulated, and it should be emphasized that a certain percentage of 

uncertainty always exists around these chosen conditions, as well as around the assumptions 

made for capital and raw material costs. Without a detailed understanding of the basis behind it, 

the absolute computed MFSP carries a risk of being taken out of context. While the MFSP can be 

used to assess the marketplace competiveness of a given process, it is best suited for comparing 

technological variations against one another or for performing sensitivity analyses that indicate

where economic or process performance improvements are needed. 

1.4 About nth-Plant Assumptions

The techno-economic analysis reported here uses what are known as “n

th-plant” economics. The 

key assumption implied by n

th-plant economics is that our analysis does not describe a pioneer 

plant; instead, it assumes several plants using the same technology have already been built and 

are operating. In other words, it reflects a mature future in which a successful industry of n plants 

has been established. Because the techno-economic model is primarily a tool for studying new 

process technologies or integration schemes in order to comment on their comparative economic 

impact, n

th-plant analysis avoids artificial inflation of project costs associated with risk financing, 

longer startups, equipment overdesign, and other costs associated with first-of-a-kind or pioneer 

plants, lest these overshadow the real economic impact of research advances in conversion or 

process integration. At the same time NREL also continues to work on quantifying economic 

factors associated with first-of-a-kind implementation. At the very least, these n

th-plant 

economics should help to provide justification and support for early technology adopters and 

pioneer plants.

The n

th-plant assumptions in the present model apply primarily to the factored cost model used to 

determine the total capital investment from the purchased equipment cost and to the choices 
8 

made in plant financing. The n

th-plant assumption also applies to some operating parameters, 

such as process uptime of 90%. These assumptions were agreed upon by NREL and DOE for 

this report and reflect our best estimates at the time of publication. It should be emphasized, 

however, that these assumptions carry a degree of uncertainty and are subject to refinement.

1.5 About the NREL Aspen Model

While Aspen Plus can be thermodynamically rigorous, such detail is not always warranted in the 

simulation, whether for lack of data or introduction of additional complexity for little gain in 

accuracy. Some unit operations, particularly solid-liquid separation and FFA product 

concentration, were modeled with a fixed performance determined by experimental testing or by 

standard engineering practices confirmed with vendors. Bioreactors were modeled using 

experimentally determined conversions of specific reactions (e.g., cellulose to glucose) rather 

than rigorous kinetics or rate expressions. This simple stoichiometric model still satisfies mass 

and energy balances.

The Aspen Plus simulation uses component physical properties internal to the software as well as 

property data developed at NREL or from the literature [12, 13]. Similar to the 2011 model, the 

current model does not rely on external property databanks and minimizes the number of 

custom-defined components within reason. A discussion of the components and properties used 

is given in Appendix D. 
9 

2 Design Basis and Conventions

2.1 Plant Size

The plant size in the present design is the same as that used in prior designs: 2,205 dry U.S. 

ton/day (2,000 metric tonne/day). With an expected 7,880 operating hours per year (90% 

uptime), the annual feedstock requirement is 724,000 dry U.S. ton/year. The present model 

assumes that feedstock is delivered under a uniform-format logistics system, which includes a 

blended feedstock consisting of multi-pass corn stover, single-pass corn stover, and switchgrass, 

to enable achieving cost and composition requirements set for 2017 targets by Idaho National 

Laboratory (INL) [14]. A strategy to reduce the cost of obtaining biomass is to “blend” or use 

numerous types of feedstocks to design an aggregated or formulated feedstock. This strategy 

assumes that the formulation of multiple feedstocks will perform as well as, or better than, a 

singular feedstock. There are research plans for FY 2014 to test various blended feedstocks in 

both bio-oil and sugar conversion pathways. Under the new uniform format system, 

opportunities may exist to justify a larger facility scale with an increased feed rate to the 

biorefinery, thus allowing for economy of scale advantages; however, the specifics are not yet 

defined and we retain the 2,000 dry tonne/day basis here. 

2.2 Feedstock Composition

The conversion target feedstock composition remains the same as was assumed in the 2011 

design basis; however, the biomass supply composition has been altered. As noted by INL, the 

previous requirements for feedstocks included volume and cost targets only. There were no 

quality requirements; further analysis indicated a mismatch on biomass characteristics and 

conversion in-feed specifications. Improved analysis highlighted the need to develop the 

technologies in the logistics supply chain that can address quality in addition to stability and 

densification requirements. The updated composition assumed in the 2011 design basis improved 

on the initial estimate in the 2002 basis document. This update further improves on the 2011 

design basis and the associated price of $58.50/dry ton (2007$), which included a more uncertain 

grower payment, to a more reasonable target at an increased price of $80/dry ton including 

grower payment (2011$, used as the new basis here). This new price is more appropriate for a 

large commodity scale going beyond a “niche market” price. 

As has been described in prior design reports, the feedstock composition (see Table 1) plays a 

critical role on overall process design and economics, primarily with respect to carbohydrate 

components (cellulose and hemicellulose), lignin, and increasingly acetate and ash, given 

modifications being made to the pretreatment strategy such as the use of deacetylation. The 

blended uniform-format feedstock composition assumed here for purposes of 2017 targets is 

shown below, with supporting details (in the context of corn stover compositional variability) 

described in the 2011 ethanol report [3]. Also consistent to the 2011 design, the moisture content 

for the delivered feedstock is 20%.

When converting the analytical composition to components used in the Aspen Plus model, the 

nonstructural component fractions from the compositional analysis were combined under 

“extractives.” The extractives component is assumed to be primarily organic, with an average 

composition of CH2O, and consists primarily of sugars, sugar alcohols, and organic acids, as well 

as some nonstructural inorganics [15]. The presence of extractives in the biomass depends on the 

time of harvest and in part to how much microbial degradation of the material occurs after 
10

harvest; the amount of extractives in a given sample may therefore be indicative of its age. 

Additionally, where the mass balance did not sum to 100%, the extractives component was used 

to close it by difference. Sucrose is another extractive component, but it is measured separately 

in laboratory analysis and has been added as a separate feedstock component in the present 

design. The amount of sucrose present in the biomass is dependent on harvesting and handling 

practices. In pretreatment, this sucrose is assumed to be fully hydrolyzed to glucose and fructose. 

The labile fructose is further converted to degradation products in pretreatment, but the less 

reactive glucose resists degradation and thus is available for fermentation [16]. 

Table 1. Delivered Feedstock Composition Assumed in the Present Design

Component Composition

(dry wt %)

Glucan 35.1

Xylan 19.5

Lignin 15.8

Ash 4.9

Acetatea 1.8

Protein 3.1

Extractives 14.7

Arabinan 2.4

Galactan 1.4

Mannan 0.6

Sucrose 0.8

Total structural carbohydrate 59.0

Total structural carbohydrate + sucrose 59.8

Moisture (bulk wt %) 20.0 a

Represents acetyl groups present in the hemicellulose polymer; converted to acetic acid in pretreatment.

In the present design, the lignin fraction ultimately will also become essentially of equal 

economic importance as the fermentable carbohydrates because of the need to improve overall 

carbon efficiency to product(s) in order to ultimately achieve the 2022 $3/GGE target. This is 

described further in Section 6. 

2.3 Updated Modeling Basis

A number of important adjustments were made to the model basis assumptions from an 

operational and a financial standpoint, relative to previous modeling efforts. These adjustments 

were made consistently to all new pathway models being developed under the DOE BETO 

Program to allow for slightly more conservative operational assumptions and to update the costyear basis to a more recent and relevant timeframe [10, 17]. These updates are as follows:

1. Facility start-up time: 0.5 year (6 months) rather than previous 0.25 year (3 months) 

2. Cost-year dollar basis: 2011 dollars rather than previous 2007 dollars (all cost results 

presented here will be in 2011-year dollars)

3. Facility on-stream time: 90% (7,884 hours/year) rather than 96% (8,406 hours/year); 

note, this is still intended to represent an “n

th-plant” facility, but allows for slightly more 

operational downtime in part to reflect the increased process complexity of the new 

pathway model (e.g., sugar clarification and concentration steps, aerobic fed-batch 

conversion, hydrotreating, etc.) relative to the ethanol baseline. 
11

Additionally, the feedstock cost for the biochemical pathway is updated from a previous basis of 

$58.50/dry ton (2007$) to $80/dry ton (2011$) for the 2017 target case, to reflect a more 

commercially relevant pricing model at a larger national scale (described in more detail below).

2.4 Design Report Conventions

2.4.1 Units

The Aspen Plus model we developed is, by legacy, based on the set of units required by Aspen 

for specifying custom component properties: kg, kmol, atm,°C for materials, and MMkcal (Gcal) 

for energy. Values in this report that were pulled directly from the Aspen model therefore tend to 

be reported in these units. Harris Group preferred to use U.S. standards (lb, Btu,°F, gal, etc.) 

when communicating with equipment vendors. Therefore, equipment specifications tend to be 

cited in these U.S. units. 

Note that in the present report, certain quantities (e.g., yields and costs) are computed and 

reported in terms of “tons.” To avoid ambiguity, tonne will denote a metric tonne (1,000 kg) and 

ton will denote a short or U.S. ton (2,000 lb). In general, the U.S. ton is the standard for this 

document. Ton also appears in Section 3.9 in the context of refrigeration, but this usage should 

be clear from the discussion.

2.4.2 Total Solids Loading

The process described here converts a solid feedstock (corn stover blended with switchgrass) 

into a liquid product (FFA/RDB). Most material streams in the process therefore have a solid 

fraction and a liquid fraction. The relative amount of solids in a given stream is called its “solids 

loading.” Total solids (TS) loading is defined as the total weight percent of soluble solids (e.g., 

sugars and salts) and insoluble solids (IS; e.g., cellulose and lignin) in a given material stream. 

Where useful, the TS loading and the IS loading will be reported together. Note that in our 

convention, sulfuric acid, acetic acid, and ammonia are not considered soluble solids but 

ammonium acetate and ammonium sulfate are. Therefore, around some unit operations, e.g., 

hydrolysate conditioning, TS loading is not a conserved quantity. 

2.4.3 Product Density and Heating Value 

The results from this analysis are reported in terms of volume of product (RDB) produced: $/gal, 

gal/yr, gal/ton, etc., as well as energy produced in gallons-gasoline-equivalent: $/GGE, GGE/yr, 

GGE/ton, etc. As the final RDB product exiting the refining step is a mixture of more than one 

component, the blended product density as computed by Aspen Plus is utilized as the most 

accurate estimate for setting the RDB product volume flow rate. In the baseline model this 

density is computed as 0.769 kg/L at 20°C (68°F); this is a reasonable density value for a 

mixture of pentadecane and hexadecane (assumed to be produced stoichiometrically from the 

hydrotreating section under a simplifying scenario of palmitic acid upgrading) based on 

published literature values for each respective component [18, 19]. Similarly, component heating 

values were set at 10,047 KJ/mol and 10,699 KJ/mol (higher heating value [HHV] basis) for 

pentadecane and hexadecane, respectively [20, 21], for translating to an energy basis. An HHV

basis was selected in this case as this is a more commonly published basis regarding heating 

values of the above-listed components (relative to lower heating value), thus minimizes room for 

error in translating to a GGE basis. To subsequently translate to a GGE basis, a conventional 

gasoline heating value of 124,340 Btu/gal (46.54 MJ/kg, HHV basis) was applied [22].
12

3 Process Design and Cost Estimation Details

The process design described in this study is based upon NREL’s demonstrated performance 

during 2012 pilot-scale trials for ethanol production (specifically with respect to front-end steps 

for pretreatment and hydrolysis) to project plausible future improvements in these pertinent 

areas. For downstream operations, including bioconversion and microbial product secretion and 

purification, plausible targets are set based upon information presented in public literature as 

well as to consider the impact of similar performance targets originally set for ethanol (e.g., 

glucose, xylose, and arabinose utilization) to maintain consistency in translating to new 

hydrocarbon pathways. As noted previously, these targets are merely one set of conditions that 

would enable achieving the interim 2017 MFSP cost goal of $5/GGE and will help to inform 

near-term research directions, but are not necessarily strict targets for NREL or other DOE 

biochemical research at this point. Additionally, given the scope and timeframe under which the 

present analysis effort was conducted, the report is more concise in some areas (such as detailed 

process flow diagrams and stream-level information), and summarizes rather than repeats in 

detail the process areas that were unchanged relative to the 2011 ethanol design basis. This 

section describes the process as modeled and discusses the influence of specific R&D goals in 

the decision-making process. 

Two caveats are noted here in the context of process and cost estimates utilized in the present 

analysis. First, given the more preliminary scope and nature of the design models established 

here, the engineering support provided by Harris Group focused exclusively on the primary unit 

operations (e.g., reactors, separation and concentration equipment, upgrading operations, etc.), 

and did not cost out individual minor supporting equipment such as pumps, heat exchangers, and 

agitators (except for bioreactor agitators, as these are considered critical operations). For these 

minor equipment, Harris Group added a percentage factor to the provided cost estimate for the 

respective primary operations based on the associated costs in the 2011 ethanol design. Second, 

at the request of Harris Group who provided all vendor estimates for cost quotations, vendor 

company names associated with a particular unit operation will not be provided in this report. 

The equipment list in Appendix A may note vendors that have already been identified as 

discussed in the 2011 ethanol report; however, vendor names associated with new or modified 

operations for which estimates were provided for this design case are not shown. 

3.1 Area 100: Feedstock Storage and Handling

3.1.1 Overview 

Area 100 handles incoming biomass feedstock. For a biochemical conversion pathway, 

herbaceous biomass is the preferred feedstock. The present design assumes a blend of corn 

stover and switchgrass delivered according to the specifications detailed in the INL design report 

for the uniform-format feedstock supply system [23]. In this envisioned design, biomass is stored 

in a central depot and is preprocessed, densified, stabilized, and homogenized to a degree before 

delivery, such that the biorefinery receives feedstock with known, uniform-format specifications 

including particle size, ash content, and moisture content. All analysis and cost implications 

associated with Area 100, and with feedstock logistics in general, are outside the scope of the 

present effort focusing on the conversion biorefinery, but are summarized in the discussion 

below to convey information attributed to the updated feedstock cost of $80/dry ton delivered to 

Area 200. 
13

Feedstock logistics includes operations that take place after the biomass is produced in a field or 

forest, but before it is introduced into a conversion process. All activities related to feedstock 

logistics are directed at reducing the delivered cost of sustainably produced feedstock, improving 

and preserving the quality of harvested feedstock to meet the needs of biorefineries, and/or 

expanding the volume of feedstock materials accessible to the bioenergy industry. Feedstock 

logistics efforts are primarily focused on identifying, developing, demonstrating, and validating 

efficient and economic systems to harvest, collect, transport, store, and preprocess1 raw biomass

from a variety of crops to reliably deliver high quality, high volume, affordable feedstocks to 

biorefineries. Sustainably supplying the required quantities of quality, affordable feedstock to the 

emerging biorefining industry will be achieved through a transition from logistics systems that 

have been designed to meet the needs of conventional agriculture and forestry systems—termed 

“conventional” logistics systems—to more advanced, purpose-designed systems in the 2013 to 

2022 timeframe, termed “advanced” logistics systems [24]. In the context of biochemical 

conversion using herbaceous feedstocks, the current focus is on the U.S. Midwest Corn Belt 

given its abundance of corn stover. However, future design cases will move away from the niche 

areas to areas with lower yields and more difficult logistic cost challenges.

As illustrated in Figure 4, the advanced Terrestrial Feedstock Processing Supply and Logistics

System is envisioned to draw in presently inaccessible and/or underused resources via local 

biomass preprocessing depots that format biomass into a stable, bulk, densified, and flowable 

material. The formatted biomass will be transported to one or more networks of much larger 

supply terminals, where the material aggregated from a number of depots may be blended and/or 

further preprocessed to meet the specification required by each biorefinery conversion process 

(note that feedstock specifications may differ substantially, depending upon the actual 

conversion process). The advanced biomass logistics system design incrementally incorporates 

technology and other system improvements as the industry matures. This improved series of 

feedstock supply and logistics system designs will couple to, and build from, current systems and 

address science and engineering constraints that have been identified by rigorous sensitivity 

analyses as having the greatest impact on feedstock supply and logistics system efficiencies and 

costs. The introduction of advanced preprocessing operations and sequences, including the 

implementation of blending and formulation strategies, and preprocessing strategies, is critical to 

achieving feedstock cost, quality, and volume targets to meet BETO 2017 and 2022 goals. 

 

1 Note that some preprocessing research takes place under the conversion technology areas, while other 

research is funded under feedstock logistics.
14 

Figure 4. The depot concept behind the advanced Terrestrial Feedstock Processing Supply and 

Logistics System 

To address the high cost of biomass procurement, lower cost resources could be supplemented 

from other regions, but this strategy would increase transportation costs. Another strategy to 

reduce the cost of obtaining biomass is to “blend” or use numerous types of feedstocks within a 

region to design an aggregated or formulated feedstock. This strategy assumes that the 

formulation of multiple feedstocks will perform as well as or better than a singular feedstock. To 

demonstrate this strategy, Figure 5 exemplifies farmgate price functions for three feedstocks in a 

region. At $40/dry ton only around 250,000 tons of corn stover are accessible, but by including 

switchgrass and wheatstraw the available biomass at $40/dry ton increases to 600,000 tons. 

Figure 5. Typical farm gate price function for three feedstocks: corn stover, switchgrass, and 

wheatstraw, to demonstrate cost and/or scale advantages of feedstock blending (courtesy of INL) 

0

200

400

600

800

1000

1200

1400

1600

1800

0 20 40 60 80 100

Tons (x1000)

$/Dry Ton

Generic Farmgate Cost Curves for Multiple Feedstocks

Switchgrass

Wheatstraw

Cornstover
15

Blending strategies could also improve overall feedstock quality to help meet biofuel quality 

specifications and conversion in-feed requirements. Quality targets can have a large impact on 

whether or not a particular feedstock is cost effective. One aspect of the inherent spatial and 

temporal variability of biomass resource quality is illustrated by the ash, xylan, and glucan 

distributions for biomass shown in Figure 6. Note: The distributions include not only biological 

variability but impacts from various field management, including different harvesting and 

collection practices such as single-pass and multi-pass harvesting, bar rake, wheel rake, etc. 

Formulation allows the use of low-cost, but typically low-quality, biomass blended with higher 

cost and quality biomass to achieve minimum conversion process quality specifications. The use 

of low-cost biomass allows the supply chain to implement additional preprocessing technologies 

that actively control feedstock quality and bring more biomass into the system. 

Figure 6. Example of the spatial and temporal variability of biomass characteristics

Preliminary results on feedstock supply chains delivering material to cellulosic biorefineries 

demonstrate that with the ability to blend multiple feedstocks and include some preprocessing 

operations, it is possible to acquire high volumes of material, reduce feedstock variability to meet 

biorefinery in-feed specifications, and meet the required price of feedstock material to the throat 

of the biorefinery. Much more research needs to be done on the performance of blended material 

and the blending strategies, as well as on other technologies incorporated into advanced designs. 

In addition, current pre-conversion systems are expensive, and reducing those costs is a 

significant barrier to meeting future cost targets. However, there is ongoing research at the 

national laboratories, universities, and industry to address these challenges. 

3.1.2 Design Basis

In the new uniform-format feedstock supply system design, feedstock would be stored in a 

satellite depot location with delivery to the biorefinery occurring 6 days per week by truck, or 

possibly by rail. At the depot, material would be milled to a mean size of 0.15–0.25 in. (with a 

high content of fines), in order to achieve a mean bulk density of 9–11 lb/ft3 to maximize the 

biomass load per trailer. The incoming uniform format feedstock (corn stover/switchgrass blend) 

is assumed to have 20% moisture when it reaches the biorefinery; this is representative of a 

mixture of field-dried material having < 15% moisture and co-harvested material having > 20% 

moisture. Because the preprocessing operation is designed to lose very little dry matter and does 

not include any rinsing of biomass, it is assumed that the biomass composition discussed in 

Section 2.2 is valid for the delivered material. 

The as-received feedstock requirement for a plant is 2,750 U.S. ton/day (104,200 kg/h; 229,700 

lb/h) including moisture. In the projected design, refinery receiving operates on the same 
16

schedule as the biomass depot: 24 hours per day, six days per week. Each truck trailer holds 10 

U.S. tons of biomass. To satisfy production and storage requirements, the plant must receive 12 

trucks every hour. Incoming trucks are weighed by electronic scale and unloaded using a wholetruck dumper capable of a 7–10 minute unloading time. The dumpers empty into dedicated 

hoppers that meter the biomass to a series of conveyors. The conveyors carry material from the 

truck tipper to short-term storage. The minimum receiving rate is 250 ton/h to maintain 110 ton/h 

of continuous processing. In order to process 250 ton/h, and assuming a relatively constant flow 

of trucks, a pair of scales (one inbound and one outbound) and two truck dumpers are required. 

On-site storage is kept to a minimum of 72 hours to allow for a weekend buffer. Open piles are 

not favored due to concerns of fire, rodent infestation, and moisture degradation. Instead, the 

unloaded feedstock is stored in concrete domes. Two domes (each with a 36-hour capacity) are 

required so that one can be loaded while the other empties to the conversion process. 

Conveyors connect the storage domes to the feedstock receiving bins on the pretreatment reactor 

in Area 200. A dust collection system integrated with the conveyors and domes handles airborne 

particles released during the unloading and conveying processes. No dry matter is assumed lost 

in Area 100. 

3.1.3 Cost Estimation

The feedstock cost assumed in this report is $80/dry ton (2011$), a cost increase over the 2011 

ethanol design that is attributed to the new uniform format logistics system described above. This 

cost comes from the Multi-Year Program Plan (MYPP) published by DOE-BETO [10]. This is a 

rolled-up cost which includes both grower payment as well as logistics considerations for all 

collection, processing, storage, and transportation costs between the field and the receiving bin 

on the pretreatment reactor (e.g., Table B-2 of the referenced MYPP document). It should be 

stressed that these costs are 2017 and 2022 DOE research targets, like the conversion 

performance targets used in the Aspen Plus model. 

3.2 Area 200: Pretreatment and Conditioning

3.2.1 Overview

Pretreatment of biomass for biofuel production is a crucial step. Its primary role is to disrupt the 

matrix of polymeric compounds that are physically and chemically bonded within lignocellulosic 

biomass cell wall structures, including cellulose microfibrils, lignin, and hemicellulose. 

Pretreatment has a significant impact not only on enzymatic hydrolysis, fermentation, and 

downstream processing [25], but also on overall process economics and sustainability. Acidbased pretreatments target hydrolysis of significant fractions of hemicellulose and small fractions 

of cellulose. The dilute acid pretreatment process converts most of the hemicellulose 

carbohydrates in the feedstock to soluble sugars (xylose, arabinose, galactose, and mannose) by 

hydrolysis reactions. Alkaline pretreatments break bonds between lignin and carbohydrates and 

disrupt lignin structure, making carbohydrates more accessible to enzymatic hydrolysis [25]. 

Dilute alkaline pretreatment normally results in better delignification than dilute acid 

pretreatment by cleaving lignin-hemicellulose linkages and swelling cellulose [26], with minimal 

hydrolysis of the cellulose or hemicellulose. 

Due to complementary advantages of acid and alkaline pretreatment, a combination of both 

dilute acid and alkaline may reduce not only overall chemical loadings, but more importantly 
17

provide high fermentable sugar yields for downstream bioconversion. The use of dilute alkaline 

deacetylation combined with low acid pretreatment process steps has shown improvements in 

ethanol yields and calculated MESP for cellulosic ethanol production [27, 28]. Acetyl groups in 

the hemicellulose are liberated and removed as acetic acid by sodium hydroxide during 

deacetylation, greatly reducing inhibition from acetate to improve hydrolysis and fermentation 

performance (in the context of ethanol production; implications for hydrocarbon production are 

noted below). Sugar degradation products such as furfural and 5-hydroxymethyl furfural (HMF) 

can be reduced using low severity acid pretreatment. These compounds can also have adverse 

effects on the fermenting organisms in sufficiently high concentrations. 

In the present design, pretreatment reactions are catalyzed first using dilute sodium hydroxide, 

then using dilute sulfuric acid. This two-stage design results in a reaction severity that is milder 

than what has been modeled in previous designs. In the first step (deacetylation), biomass is 

soaked in dilute (~0.4%) sodium hydroxide solution (pH 8–10) at 20% TS and 80°C for 1 hour in 

a stirred tank. After deacetylation, the liquor is drained and the deacetylated biomass solid stream 

is charged with dilute sulfuric acid into a horizontal screw-feed reactor with a short residence 

time (5–10 minutes). A subsequent “oligomer conversion” step is no longer necessary in this 

design, as was utilized in the 2011 ethanol design basis. This is because deacetylation can 

significantly improve conversion of oligomeric to monomeric xylose, with one study showing a 

decrease in oligomeric xylose yield from 21%–23% for non-deacetylated corn stover to 7%–10% 

for deacetylated corn stover [28]. After the pretreatment reactors, the hydrolysate slurry is flashcooled, vaporizing a large amount of water along with some of the acetic acid and furfural. The 

flash vapor is condensed and sent to the WWT area. The hydrolysate slurry is cooled by dilution 

water and sent to a conditioning reactor, where ammonia is used to raise its pH from 1 to 5. 

Figure 7 shows a simplified flow diagram of the pretreatment area. It bears noting that the twostep pretreatment process (deacetylation and dilute acid pretreatment) was a modification made 

relative to the 2011 ethanol design case as a more optimized approach for biomass 

deconstruction, and not a consequence of changing to a hydrocarbon product. 

Figure 7. Simplified flow diagram of the deacetylation, pretreatment and conditioning process

Uniform formatted feedstock

High-pressure steam

NaOH 

STEAM

Dilution water

& NH3

Vertical presteamer

Horizontal reactor

Blowdown

tank

Ammonia

conditioning

To wastewater

To enzymatic hydrolysis

Acid

Deacetylation 

reactor Black liquor
18

3.2.2 Design Basis

Milled biomass (corn stover and switchgrass blend) is conveyed to the pretreatment area and 

treated with dilute sodium hydroxide (for deacetylation) and sulfuric acid catalyst at moderate to 

high temperature (see Table 2) to liberate the hemicellulose sugars and break down the biomass 

for enzymatic hydrolysis. Solids exiting the alkaline deacetylation step are sent on to dilute acid 

pretreatment, followed by ammonia conditioning to raise the pH of the whole pretreated slurry 

from ~1 to 5, which is then sent to enzymatic hydrolysis. 

The deacetylation step uses a sodium hydroxide soaking process at 80°C for 1 hour, with a 

NaOH loading of 17 mg/g dry biomass. The deacetylated material is dewatered by draining 

through screens at the bottom of the deacetylation reactor. A vertical pressure vessel is used for 

the deacetylation tank with 316 stainless steel as the material of construction based on the 

reaction conditions. Three identical batch reactors are used with square trough live bottoms 

consisting of four 24-inch horizontal discharge screws each. Each tank volume is 44,000 gal with 

a total cycle time of 2.4 hours. The drained liquor, often referred to as “black liquor,” contains 

20%–25% of the original dry biomass constituent material, including water extractives (100% 

solubilization), soluble ash constituents (75% ash solubilization), 20% of the lignin, 2% of the 

xylan, 50% of the sucrose, and 88% of the acetate that was originally present in the feedstock 

(dry basis). In addition to the benefit of reducing acetate inhibition during bioconversion, the 

removal of lignin, ash, and other primarily non-fermentable components provides an important 

secondary benefit of reducing the total flow rate to downstream operations, thereby reducing 

equipment size and cost. This black liquor stream is pumped to WWT (A600) in the baseline 

process design. The remaining biomass solids are discharged from the deacetylation reactor and

transported to the acid pretreatment reactor system. 

The acid pretreatment reactor system is described in detail in the 2011 ethanol design report, and 

will not be repeated in such detail here; in summary, this system includes a feedstock receiving 

system, followed by a vertical vessel with a long residence time for steam heating and potential 

acid impregnation of the biomass, followed by the horizontal pretreatment reactor, which 

operates at a higher pressure and a short residence time. The 26-inch plug screw feeder is a 

rugged, high-compression screw device designed to form a pressure-tight plug of material 

through axial compression. Dilute sulfuric acid is metered at the discharge spool of each plug 

screw feeder, shown in Figure 8. Feedstock drops from the plug screw discharge into a mixing 

and heating screw, which discharges the feedstock into the top of the presteamer. High-pressure 

steam is injected into this vessel to maintain temperature, while hot water is added at this point to 

control the pretreatment effluent at 30 wt% TS. The current model assumes operation of the 

presteamer at 100°C such that no significant hydrolysis reactions occur in this section. It can be 

used if additional hold up is required for acid hydrolysis. With a 2,205 U.S. ton/day throughput, 

the presteamer can add up to 10-minute retention time at 165°C , using a single vertical vessel. 

Feedstock flows downward through the vertical presteamer with uniform temperature 

throughout, discharging through a dual screw outlet device to two plug screw feeders. The plug 

screw feeders meter feedstock to the horizontal pretreatment reactor, with a horizontal reactor 

configuration chosen to allow tighter residence time distribution control than with a vertical 

reactor. 
19

Figure 8. Horizontal acid pretreatment reactor design scheme

Upon deacetylation and solid-liquid separation by draining the black liquor, the actual sulfuric 

acid loading present in the horizontal acid pretreatment reactor is only 9 mg/g dry feedstock. The 

reactor pressure is held at the bubble point for the mixture. Heat losses from the reactor are not 

accounted for in the energy balance calculations. The residence time in the pretreatment reactor 

is nominally 5 minutes. The effective sulfuric acid concentration in the pretreatment reactor 

(estimated at 0.3–0.4 wt% after dilution by condensing steam is accounted for) may allow for the 

use of lower-cost metallurgies in the reaction zone (such as 904L or other duplex stainless 

alloys) instead of Incoloy-clad carbon steel, which could reduce pretreatment equipment costs. 

However, relevant corrosion data under these conditions are not available, so in the model, 

Incoloy–825 cladding is still conservatively assumed for the material of construction (consistent 

with the 2011 design report). The potential impact of lower-cost metallurgy and other 

pretreatment reactor cost savings is addressed below in the Sensitivity Analysis section. 

The direct cost benefit of lower acid loading in pretreatment is the reduction of the amount of 

neutralization ammonia usage, along with reducing the formation of inhibitors (such as HMF and 

furfural) while maintaining effective pretreatment performance. Additionally, much less acetic 

acid is formed due to acetate removal during deacetylation, thus further reducing ammonia 

neutralization demand. Additional acid is used to pre-impregnate the deacetylated biomass (the 

cost of this usage is included in the purchased sulfuric acid cost in the techno-economic model), 

but much of this acid is removed in pilot plant operation upon dewatering of the impregnated 

biomass and does not enter the pretreatment reactor. Acid pre-impregnation and its associated 

dewatering step are not explicitly modeled in our design, but it is envisioned that these could 
20

take place in the vertical presteamer, similar to the rationale in the 2011 design report [3]. The 

pretreatment reactor operating conditions are summarized in Table 2. 

Table 2. Pretreatment Conditions Applied in the 2011 Design Model, Compared to This Design

Parameter 2011 design model This design

Total sulfuric acid loading 

present in pretreatment reactor

22 mg/g dry biomass 

(includes 4 mg/g for 

oligomer conversion)

9 mg/g dry biomass

Residence time 5 minutes 5 minutes

Temperature 158°C 160°C

Pressure 5.5 atm 5.5 atm

Total solids loading 30 wt % 30 wt %

Table 3 summarizes the reactions and conversions that take place in pretreatment. Glucan 

contained in the hemicellulose is converted to glucose along with a small portion of the cellulose. 

Minor hemicellulose carbohydrates (arabinan and galactan) are assumed to have the same 

reactions and conversions as xylan. The xylan-to-xylose conversion is a total hydrolysis value, 

which also may include an enzymatic component that will be discussed later. The sucrose 

reaction to HMF and glucose reflects 100% hydrolysis of sucrose to fructose and glucose, 

followed by complete degradation of the fructose to HMF. 

Table 3. Pretreatment Hydrolysis Reactions and Assumed Conversions

Reaction Reactant % Converted to 

Product

(Glucan)n + n H2O→ n Glucose Glucan 9.9%

(Glucan)n + n H2O → n Glucose Oligomera Glucan 0.3%

(Glucan)n → n HMF + 2n H2O Glucan 0.3%

Sucrose → HMF + Glucose + 2 H2O Sucrose 100%

(Xylan)n + n H2O→ n Xylose Xylan 90.0%

(Xylan)n + m H2O → m Xylose Oligomera Xylan 2.4%

(Xylan)n → n Furfural + 2n H2O Xylan 5.0%

Acetate → Acetic Acid Acetate 100%

(Lignin)n → n Soluble Lignin Lignin 5.0% a Sugar oligomers are considered soluble but not fermentable.

The pretreatment reactor is discharged to a flash tank. The pressure of the flash is controlled to 

keep the temperature at 100°C (212°F). The flash is condensed and routed to WWT (Area 600), 

containing more than 30% of the furfural along with other volatile and potentially inhibitory 

organics from pretreatment. After the flash, the hydrolysate whole slurry containing 30% TS and 

16% IS is sent to conditioning, namely neutralization by ammonia in stoichiometric quantities. 

Ammonia gas is mixed into dilution water to raise the hydrolysate pH to 5. The residence time 

for neutralization is 30 minutes and the dilution cools the slurry to 75°C (167°F). The slurry is 

diluted with water to slightly greater than 20 wt% TS to ensure miscibility through enzymatic 

hydrolysis and bioconversion. The composition of the stream at this point, and other major points 

throughout the process, is shown in the stream table information in Appendix E. The material 

from conditioning is conveyed to a saccharification storage tank. 
21

3.2.3 Cost Estimation

Each deacetylation tank was quoted by Harris Group at $890,000 for this design, including 

reactor tanks and conveyors. The acid pretreatment reactor design and cost basis is similar to that 

described in the 2011 ethanol design report; however, we have expanded the cost analysis in the 

present work to allow for a more robust cost estimate with higher flexibility in operational 

variables. Namely, an evaluation of pretreatment reactor pricing in terms of metallurgy, reactor 

conditions, and reaction residence time was conducted by Harris Group based on input from a 

vendor. In addition to the base case Incoloy-clad metallurgy used in the 2011 report and again 

here as the baseline assumption, a less costly stainless steel option was considered as an option. 

The stainless steels (duplex or 904) are acceptable for low-temperature and low-acid loadings

only. Based on discussions with the vendor, stainless steel 904 may be acceptable in a 

temperature range of 135°–150°C with acid concentration below 0.5%. Duplex stainless steels 

may be acceptable if temperature is below 135°C and acid concentration is below 0.25%. An 

important outcome from this exercise is a quantified validation that acid concentration is not 

necessarily the primary factor for metallurgy selection, but rather the high temperature at the acid 

loadings considered here. 

An additional outcome from the follow-up vendor consultation was a more detailed quantitative 

understanding of how the horizontal reactor cost may vary according to throughput and/or 

residence time. At issue is the fact that the horizontal reactors are discrete pieces of equipment 

that are additive in cost, rather than scalable, given a finite maximum capacity at the stipulated 

horizontal reactor design. As such, Harris Group worked with the vendor to establish cost 

boundaries for a low (2-minute) and high (20-minute) horizontal reactor residence time. Using 

the information provided, Harris Group then interpolated the resulting cost values to establish a 

single equation for total pretreatment reactor cost as a function of horizontal reactor residence 

time. The equations were further adjusted to the new pretreatment throughput basis of 1,516 dry 

tonne/day, which reflects the fractional biomass solubilization upstream during deacetylation 

from an initial feed rate of 2,000 dry tonne/day. The resulting equations are provided below, and 

will permit continued use of the same reactor quotation in our analyses as pretreatment severity 

and operating conditions change based on future research: 

Total Equipment Cost (2011 $MM); Incoloy 825 = 16.4+7.4×m (1)

Total Equipment Cost (2011 $MM); Duplex steel = 16.8+3.9×m (2)

Where, m = integer (minutes of resident time/3.3)

For the conditions used in this design, which are milder than earlier designs, the recommended 

metallurgy from the vendor continues to be Incoloy 825, with a total installed capital cost of 

approximately $46.8MM (2011$) for the system including feedstock receiving bin, pre-steaming, 

pressurized heating, reaction, and flash cooling. The reactor schematic is shown in Figure 8, and 

is similar to the configuration presented in the 2011 report. 

Area 200 contributes about $0.78/gal (or $0.74/GGE) to the MFSP, including deacetylation, acid 

pretreatment, and conditioning. About 55% of the $0.74/GGE contribution is attributed to capital 

cost, of which deacetylation and acid pretreatment equipment accounts for 96% of total capital 

expenses. 
22

3.2.4 Achieving the Design Case

Table 4 shows demonstrated performance results achieved during NREL’s 2012 pilot-scale 

demonstration runs, as well as the 2011 ethanol report assumptions and the present 2017 

conversion targets (i.e., those used in the current Aspen model) for the pretreatment area. 

Table 4. Research Status and 2017 Targets in the Pretreatment Area

2011

Design report

2012

SOT

2017

Targets

Pretreatment

 Solids loading (wt %) 30% 30% 30%

 Xylan conversion to xylose (%) 90% 82% 90%

 Xylan conversion to furfural (%) 5% 6% 5%

Conditioning

 Ammonia loading (g/L of hydrolysate) 4.8 1.6 1.6

 Hydrolysate solid-liquid separation no no no

 Xylose sugar loss (% entering conditioning) 1% 0% 0%

 Glucose sugar loss (% entering conditioning) 0% 0% 0%

As shown above, the 2017 target is 90% conversion of xylan to monomeric xylose with 5% loss 

to degradation products. Total xylan-to-xylose yield was demonstrated to range from 81%–88% 

in four out of five trials during NREL’s 2012 pilot demonstration runs, with a fifth run achieving 

93% [29]. Thus, further room for improvement exists with a reasonable goal to achieve 90% 

conversion better and more consistently in a short timeframe by 2017 under mild pretreatment 

conditions. It is also noteworthy that NREL’s 2012 pilot runs demonstrated a higher xylose-toethanol yield, exceeding the 85% target in a number of cases, largely because of the addition of 

deacetylation which allowed for more complete sugar utilization. Another metric of note is the 

high arabinan-to-arabinose yields, but because of the small quantity of arabinan in the biomass 

there is more variability in demonstrated results. Nevertheless, an average arabinan-to-arabinose 

yield of 90% continues to be a reasonable assumption [29]. 

The deacetylation step also enables less sulfuric acid usage in pretreatment, resulting in lower 

furfural formation and less salt formation by requiring less ammonium hydroxide for 

neutralization, in addition to dramatically lowering acetic acid concentration and reducing total 

stream throughput in downstream operations. Thus, deacetylation is an important addition here, 

and the ability to achieve the deacetylation yields discussed above at reasonable conditions 

(primarily 17 mg/g caustic loading) will be another important performance metric. It is also 

recognized that a number of hydrocarbon-producing organisms may possess the ability to 

tolerate or even utilize acetate [30], thus negating inhibition concerns traditionally associated 

with ethanol fermentation, but the value gained in other benefits from deacetylation listed above 

(primarily reduction in non-fermentable biomass components and subsequent reduction in 

throughput and size for all downstream equipment) is expected to outweigh tolerance or 

utilization of acetate. This will be confirmed in further analysis moving forward.

Further research topics in the pretreatment area include optimizing deacetylation with acid 

pretreatment, additional size reduction after acid pretreatment and prior to enzymatic hydrolysis, 

and optimizing enzyme preparation and loading to accommodate deacetylated biomass. These 
23

are aimed at overall process optimization for chemical loadings (alkaline, acid, and enzyme), 

process conditions (time, temperature, residence time) and other process steps to improve 

digestibility. 

3.3 Area 300: Enzymatic Hydrolysis, Hydrolysate Conditioning,

and Bioconversion

3.3.1 Overview

In this process area, cellulose is converted to glucose using cellulase enzymes. This process is 

known as enzymatic saccharification or enzymatic hydrolysis. A cellulase enzyme preparation is 

a mixture of enzymes (catalytic proteins) that work together to break down cellulose fibers into 

cellobiose and soluble gluco-oligomers and ultimately into glucose monomers. The resulting 

glucose and other sugars hydrolyzed from hemicellulose during pretreatment are then 

conditioned to remove insoluble solids, partially concentrated, and converted to hydrocarbon 

molecules (this final step is commonly referred to as “fermentation” and would be the analog to 

ethanol fermentation in the previous design case, however the process stipulated here is formally 

defined as aerobic respiration, thus we are avoiding the terminology “fermentation” here and 

instead using “bioconversion”).

The enzymatic hydrolysis portion of this process area follows the same fundamental process 

schematic described in the 2011 ethanol report; namely carrying out hydrolysis at elevated 

temperature (which provides higher enzyme activity) so the reaction proceeds faster and requires 

lower enzyme loading, with hydrolysis split between two sequential reactions: a high-solids 

continuous flow reactor followed by batch hydrolysis in a stirred tank. Bioconversion of the 

released sugars occurs separately at lower temperature and in separate vessels, thus the process 

configuration is akin to separate hydrolysis and “fermentation” (SHF). 

After hydrolysis is complete, the hydrolysate material containing soluble sugars and insoluble 

residual solids (primarily lignin) is sent to a solid-liquid separation step where the insoluble 

fraction is removed using a vacuum filter press to enable more efficient gas-liquid mass transfer 

downstream in the aerobic bioreactors. This operation includes a wash step to recover soluble 

sugars carried over into the solids fraction. The washed lignin-rich solids fraction is then sent to 

the boiler for combustion. An important implication of this added solid separation step is that 

enzymes will also be removed prior to bioconversion, thus negating additional hydrolysis 

activity that could take place during bioconversion. A portion of the liquid fraction exiting the 

filter press is sent directly to the bioconversion step, which is carried out in fed-batch mode, 

while the majority of the stream is sent to an evaporator system to concentrate the sugars. The 

concentrated sugars are then cooled and fed to the bioreactors as the conversion reaction 

proceeds through the batch cycle. The bioreactors are inoculated with the conversion organism 

from an inoculum seed train, and the bioconversion process proceeds for approximately 2.9 days 

until the target conversion of sugars to FFA product is achieved. The product broth from the 

bioreactors is emptied to a storage tank before being pumped to purification. Figure 9 shows a 

simplified flow diagram of the enzymatic hydrolysis, conditioning, and bioconversion process. 
This report is available at no cost from the 24 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications. 

Figure 9. Simplified flow diagram of the enzymatic hydrolysis, hydrolysate conditioning, and 

bioconversion process 

3.3.2 Design Basis 

The process and design basis for enzymatic hydrolysis is the same as described in the 2011 

ethanol report, thus will only be briefly summarized here. In short, enzymatic hydrolysis is 

initiated in a continuous, high-solids vertical tower reactor with the slurry flowing down the 

reactor by gravity; this first step is required as the feed material at 20% solids (or more) is not 

pumpable until the cellulose has been partially hydrolyzed. After mixing in the cellulase enzyme, 

the total solids loading entering the continuous column reactor is 20 wt% (12.2 wt% insoluble) 

and the temperature is 48°C (118°F). The residence time in the continuous reactor is 24 hours. 

After this point, the slurry is pumpable and is batched to one of six 1 MM gal vessels (950,000 

gal working volume), where enzymatic hydrolysis continues for another 60 hours. The batch 

reactors are agitated and temperature controlled at 48°C using a pump-around loop with cooling 

water heat exchange. 

The amount of enzyme used (the enzyme loading) is determined based on the amount of 

cellulose present in the hydrolysate and the specific activity of the enzyme. In the 2011 design 

report, the enzyme loading was set to a 2012 target of 20 mg enzyme protein/g cellulose to 

achieve a 90% conversion to glucose. During NREL’s 2012 state of technology demonstration 

runs, both the enzyme loading target and the cellulose conversion target were achieved 

independently, although not at the same time: two runs at 19 mg/g enzyme loading resulted in 

78% and 82% cellulose to glucose conversion, while a third run at 26 mg/g enzyme loading 

resulted in 89% cellulose conversion, very near the 90% target [5]. These particular results 

translated to a tradeoff in calculated MESP nearly identical to each other for all three runs, 

indicating that the increase in glucose yield paid for the higher enzyme loading in the latter case. 

Moving forward to 2017, this metric will be targeted to aggressively improve to a loading of 10 

mg/g cellulose while maintaining a 90% conversion target. Given ongoing advancements in 

enzyme preparations, discussions with enzyme manufacturers suggest this is not an unreasonable 

projection. Furthermore, new enzyme cocktails with increasing xylanase activity may become 

available to allow for a greater degree of xylan-to-xylose conversion during enzymatic 

Broth

To A500

High-solids hydrolysis

(continuous flow)

Hydrolysis and Fed Batch Biological Conversion

Seed train

Storage tank

CW

Enzyme

From Pretreatment

To combustor

To process water

Fed-batch 

reactor

Evaporator

Filter

Batch hydrolysis 

reactor

Splitter

Air

Air blower
25

hydrolysis, taking some of the burden off the pretreatment section to achieve extremely high 

levels of xylan deconstruction to xylose (potentially allowing milder pretreatments at reduced 

cost). This tradeoff has not been considered here for lack of specifics to set quantitative targets, 

but may be considered in future state of technology cases or design updates. The target design 

conditions for enzymatic hydrolysis are summarized in Table 5. 

Table 5. Enzymatic Hydrolysis Conditions

Temperature 48°C (118°F) 

Initial solids loading 20 wt % total solids (12.2% insoluble/7.8% soluble) 

Residence time 3.5 days total (84 h) 

Number and size of continuous vessels 8 @ 950 m3 (250,000 gal) each

Number and size of batch vessels 6 @ 3,600 m3 (950,000 gal) each

Cellulase loading 10 mg protein/g cellulose 

The reactions and conversions taking place during enzymatic hydrolysis are listed in Table 6. As 

noted above, although the use of more advanced enzymes may achieve some xylan conversion 

during enzymatic hydrolysis, for modeling purposes all xylan conversion is assumed to be 

achieved upstream during dilute acid pretreatment.

Table 6. Enzymatic Hydrolysis Reactions and Assumed Conversions

Reaction Reactant % Converted to 

Product

(Glucan)n → n Glucose Oligomer Glucan 4.0%

(Glucan)n + 1⁄2n H2O → 1⁄2n Cellobiose Glucan 1.2%

(Glucan)n + n H2O → n Glucose Glucan 90.0%

Cellobiose + H2O → 2 Glucose Cellobiose 100%

Once sugar production is complete, the hydrolysate is processed through a series of conditioning 

steps to purify and concentrate the sugars prior to conversion. This represents a deviation from 

the ethanol design case where the sugars are immediately converted to ethanol by reducing 

temperature and initiating fermentation in the same vessels that were used for enzymatic 

hydrolysis, without any intermediate conditioning. The first conditioning operation is a solidliquid separation step to remove lignin and other residual insoluble solids from the hydrolysate. 

As discussed in NREL’s recent technical report, Biological Conversion of Sugars to 

Hydrocarbons Technology Pathway, unlike anaerobic ethanol fermentation, the conversion 

pathway modeled here is aerobic and the solids may adversely interfere with necessary gasliquid oxygen mass transfer and limit oxygen uptake rates (OURs), and may also impede 

complete hydrocarbon product recovery and purification [31, 32]. However, the requirement for 

removing solids as a prerequisite to aerobic bioconversion is not yet fully understood. The 

relatively limited literature on hydrocarbon metabolic pathways focuses almost exclusively on 

using glucose or standard commodity hexose-based sugars (e.g., sugarcane juice, corn syrup) 

[33]; thus, performance with lignocellulosic hydrolysate, also including pentose sugars and 

solubilized lignin-derived compounds, is not yet quantified. From a cost standpoint it may be 

preferential to allow solids to pass through the bioconversion step to be removed downstream (as 

in the case of the ethanol process), as this may allow for the use of a lower-cost separation 

system and ultimately increase yield by avoiding sugar losses that are incurred in the upstream 
26

separation; however, a tradeoff could be incurred by increasing the complexity of downstream 

product recovery in such a scenario. Namely, the hydrophobic lignin material may more readily 

partition into the hydrocarbon phase than the aqueous phase, potentially confounding 

downstream product recovery costs and/or efficiencies. 

The design of the solid separation step was set based on extrapolating from NREL experimental 

data as well as input from Harris Group and vendors. NREL has evaluated the potential process 

and economic tradeoffs between three hydrolysate clarification technologies in the context of 

post-enzymatic hydrolysis material, namely a pressure belt filter, vacuum belt filter, and basket 

centrifuge [34]. Each option exhibits differences in capacity, sugar recovery, energy efficiency, 

and cost. Between the filter options, the vacuum belt filter was shown to accommodate a larger 

solids processing capacity (thus requiring lower total filtration area), but the pressure filter 

allowed for slightly higher retention of sugars (i.e., lower carryover loss of sugar into the solid 

stream) at a given wash ratio. The centrifuge exhibited similar sugar retention efficiency as the 

pressure filter, but at considerably lower capacity and increased cost than either filter press 

option; therefore, the centrifuge option was not explored further. 

Based on the preliminary NREL data and Harris Group estimates, it was determined that the 

vacuum filter option would provide the most cost-efficient processing operation for hydrolysate 

clarification, while maintaining reasonably high sugar recovery (up to 99% at a wash ratio of 2.5 

L water/L of liquor remaining in the filter cake). Although this is a relatively high wash ratio and 

incurs a cost penalty accordingly, the stipulation for 99% sugar recovery is also fairly aggressive, 

and not yet well understood in the context of separating solids in post-saccharification slurries, 

which is more challenging than prior to saccharification. The economic impact incurred by 

varying the assumption around sugar recovery is considered below in the Sensitivity Analysis 

section. Alternatively, to achieve good solids separation in this step after 90% hydrolysis of 

cellulose to glucose, additional filter area could plausibly be required relative to the baseline 

assumption. The cost impact associated with varying the vacuum filter capital cost (attributed to 

varying the total filtration area) is also investigated in the Sensitivity Analysis section. The 

design and performance metrics assumed for the vacuum belt filter are summarized in Table 7. 

Table 7. Vacuum Belt Filter Specifications for Hydrolysate Solids Separation

Number of stages 2

Wash ratio 2.5:1 (L water : L liquor in filter cake)

Capacity (Insoluble Solids basis) 20 kg/hr-m2 (IS basis)

Soluble sugar recovery 99.0%

Maximum unit size 170 m2

Power demand 490 KW (660 hp) per unit

As described in NREL’s recent technical memo on biological hydrocarbon production [32], 

following solids removal, the sugar stream may be sent directly to the biological conversion step 

or may be further processed to concentrate the sugars by evaporation or other means (e.g., 

reverse osmosis or nanofiltration). While utilizing dilute (100–150 g/L) sugars is the approach 

taken in the 2011 ethanol design model, concentrated commodity sugars (≥ 500 g/L) are utilized 

in many literature reports on hydrocarbon biofuels production [35]. Different processing schemes 

for the biological conversion step also require different optimum sugar concentrations, with 
27

higher sugar concentrations being more conducive to fed-batch operation versus batch operation 

[36, 37]. Although overall bioreactor volumes can be similar in either case if volumetric 

productivities (i.e., g/L/h) are similar, there are additional impacts to downstream unit operations 

within the context of an integrated process if concentrated sugars are used; for example, employing 

an evaporation step prior to bioconversion will allow the evaporated water to be recycled directly 

as a high-purity water source, thus reducing total water feed rate to and cost of WWT. 

The present model utilizes a fed-batch mode of operation in the bioreactors (described below), 

thereby favoring inclusion of a sugar concentration step prior to bioconversion. The process 

design detailed here assumes that 30% of the clarified hydrolysate material (13.8 wt% total sugar 

concentration) is sent directly to the bioreactors, which are filled to a 50% initial fill level 

(including inoculum addition) to commence bioconversion. After the sugar bioconversion step 

has been initiated, concentrated sugars (46.3 wt% concentration) are continuously fed to the 

bioreactors; fed-batch operation maintains high product titers and better control over the reaction 

[36, 37]. Sugar consumption and product formation then continue through the production cycle. 

Hydrolysate concentration is thus utilized for the 70% of the clarified hydrolysate stream fed into 

the production bioreactors during fed-batch mode. Moving forward further tradeoffs will be 

investigated in hydrolysate concentration, for example concentrating the entire clarified 

hydrolysate without first initiating bioconversion or seed growth with a dilute fraction. 

The concentration step utilizes mechanical vapor recompression (MVR) evaporators. MVR 

evaporators were deemed to be preferable over standard thermal (steam)-driven evaporators 

given preliminary heat and power balance information, where MVR evaporators are primarily 

electricity-driven rather than steam-driven. Four effects are required to concentrate the sugars 

from 14% to 46% (i.e., reduce water content to 50%). While further concentration to 60%–70% 

may more closely replicate commodity sugar streams such as corn syrup, such a high 

concentration could become detrimental in the context of cellulosic-derived sugars as other 

impurities such as salts and inhibitors would also become more concentrated. Thus, the model 

conservatively concentrates to only 46% for lack of more quantitative in-house data at this time.

To avoid the possibility of sugar degradation at high temperatures [38], the evaporators are 

assumed to be operated under slight vacuum to keep the maximum temperature below 80°C 

(176°F). The vapor exiting the evaporation system is nearly all water (~99.9% purity) and can be 

recycled directly to the process water manifold after being condensed by air cooling. This has an 

added benefit of reducing the total water flow rate to the WWT section (A600), which reduces 

cost for WWT units, which are dependent on the hydraulic flow rate. The vendor-provided 

evaporation system design specifications are summarized in Table 8. 

Table 8. Hydrolysate Evaporator Specifications

Feed sugar concentration 14 wt%

Product sugar concentration 46 wt% (50% water, 4% other solubles)

Maximum operating temperature 80 °C

Evaporator technology MVR

Number of effects 4

Electricity usage 3,600 KW (4,830 hp)

Steam usage (low-pressure steam) 1,550 kg/hr (0.8 MM kcal/hr)
28

The inoculum seed train design was assumed to be similar to that described in the 2011 ethanol 

report, with the addition of aeration to produce the conversion organism. To summarize, 10% of 

the hydrolysate stream (here, the clarified and concentrated hydrolysate) is diverted to seed 

production. Each inoculum production train consists of five reactors in series operating in batch 

mode with a 24-hour batch time and an additional 12-hour turnaround time. The seed reactors are 

cooled with chilled water from Area 900 to maintain the temperature at 32°C (90°F). The first 

vessel (40 gallons) is inoculated with a seed culture from the lab. Its broth is used to inoculate a 

larger reactor, and so on. After five iterations, the cell mass from the last vessel (200,000 

gallons) is sufficient to inoculate the production vessel. Two parallel seed production trains are 

utilized as described in the 2011 design. Batch sequencing calculations were performed to verify 

adequate seed train capacity. These calculations confirmed that that the seed production train 

does have enough capacity to accommodate the given bioreactor operational assumptions, with 

two seed trains and sizing/operational conditions as shown in Table 9. However, this must be reevaluated as different operational factors such as volumetric productivity and bioreactor vessel 

size are introduced in the bioreactors. 

Unlike prior designs for ethanol where the conversion organism (ethanologen) was known, 

namely Zymomonas mobilis, the current design intentionally avoids selecting a specific 

organism, as this would introduce a level of subjectivity given that the current design is intended 

strictly to establish a representative biochemical pathway to hydrocarbons from sugars without 

getting ahead of NREL or other DOE research, which has not yet publicly settled on a specific 

metabolic pathway, production host organism, or product (this is described more below). As 

such, Z. mobilis is maintained in the current design exclusively for purposes of satisfying mass 

and element balances, but is intended to serve as a placeholder that will be modified as additional 

specifics for targeted research become known. It is recognized that Z. mobilis may not be a 

realistic choice organism for an aerobic pathway, and a number of important caveats are noted 

further below recognizing the differences in metabolic demands for aerobic production 

organisms. While the fundamental design aspects for the seed train remain the same as the 2011 

basis (e.g. consecutively larger vessels as described above), an important modification was made 

to introduce aeration for aerobic seed growth. Consistent with the early nature of the 

representative pathway model evaluated here (agnostic of organism selection and metabolic 

specifics), oxygen demands for seed growth were set by translating the same aeration demand on 

a volumetric basis (volume [of gas] per volume [of liquid] per minute or VVM) as was 

established for the main FFA production bioreactors (discussed below), with aeration for both 

the seed and main production vessels provided by a common air blower. 

Table 9 summarizes the seed train design specifications. Similar to the 2011 design basis, 0.1% 

(w/v) sorbitol is used in the final seed fermentor to improve cell viability at high sugar 

concentrations [39, 40]. 
29

Table 9. Seed Train Specifications

Inoculum level 10 vol % of production vessel size 

Batch time 24 h 

Vessel turnaround time 12 h 

Number of trains 2

Number of vessel stages 5

Maximum vessel volume 200,000 gal (757 m3

); 80% maximum working 

volume (160,000 gal) 

Aeration rate

0.4 VVM (based on total seed train working 

volume; supplied by air blower shared with 

production bioreactors, see discussion 

below)

Corn steep liquor (CSL) loading 0.50 wt % 

Diammonium phosphate (DAP) loading 0.67 g/L broth (whole slurry)

Table 10 gives the reactions and conversions used to describe the microorganism growth and 

sugar metabolism in the seed vessels, providing a comparison against assumptions employed in 

the 2011 model for anaerobic seed growth for ethanol production. As described in the 2011 

report, the fraction of sugar converted to cell mass for anaerobic Z. mobilis inoculum for ethanol 

was quite small as is typical for this organism, namely 4% conversion of both glucose and xylose 

(of the 10% sugar stream diverted to seed growth; i.e., 0.8% diversion of total monomeric 

hydrolysate sugars to biomass seed growth), with a high ethanol yield even in the seed step at 

90% conversion of glucose and 80% conversion of xylose to ethanol. However, because species 

such as E. coli or yeast could be expected to consume more sugar resulting in cell mass yields 

roughly 2 times higher than Z. mobilis under anaerobic conditions [3, 41], and because of the 

switch to aerobic production, the total sugar diversion to biomass growth during seed culture 

propagation is likely to be higher than in the ethanol case. Indeed, an excellent analysis of the 

subject by Huang and Zhang notes that sugar diversion to biomass growth in semi-aerobic 

systems may be at least 4–6 times higher than for anaerobic ethanol production by way of Z. 

mobilis due to increased energetic (ATP) demands for metabolism [41]. In a commercial system 

this will require strict control of reaction conditions, namely dissolved oxygen, to control cell 

growth (this may also be achieved by biological engineering and/or process configuration). 

To account for increased cell mass production (assumed for modeling purposes to primarily 

occur in the seed train), a much larger fraction of glucose and xylose is diverted toward organism 

production in the current model, as shown below. These assumptions translate to a diversion of 

8% of total glucose and 4% of total xylose in the original hydrolysate material being consumed 

for biomass organism growth, relative to 0.4% of each in the 2011 design basis. Combined with 

the cell growth assumed to occur downstream in the main bioreactor stage, these values result in 

a cell mass-to-total feed sugar ratio of 0.064 g/g, and a cell mass-to-FFA product ratio (exiting 

the bioreactors) of 0.226 g/g (for reference, the 2011 ethanol design report values are 0.017 g/g 

sugars and 0.038 g/g ethanol respectively). The model conservatively assumes no FFA 

production in the seed train, as the reactions in Table 10 are intended to account for the majority 

of all cell growth. This large diversion of substrate, which detracts from ultimate fuel yields, will 

be an important issue to better understand and quantify moving forward, and also represents a 

potential area for future improvement to incorporate possible cell recycling methods. 
30

Table 10. Seed Train Reactions and Assumed Conversions

Reaction Reactant % Converted to Product

2011 design 2013 design

Glucose → Ethanol/FFA product (stoichiometry varies) Glucose 90.0% 0%

Glucose + 0.047 CSLa + 0.018 DAP → 6 Z. mobilis + 2.4 H2O Glucose 4.0% 80.0%

Glucose + 2 H2O→ 2 Glycerol + O2 Glucose 0.4% 0.4%

Glucose + 2 CO2→ 2 Succinic Acid + O2 Glucose 0.6% 0%

Xylose → Ethanol/FFA product (stoichiometry varies) Xylose 80.0% 0%

Xylose + 0.039 CSL + 0.015 DAP → 5 Z. mobilis + 2 H2O Xylose 4.0% 40.0%

3 Xylose + 5 H2O → 5 Glycerol + 2.5 O2 Xylose 0.3% 0.3%

Xylose + H2O→ Xylitol + 9.5 O2 Xylose 4.6% 4.6%

3 Xylose + 5 CO2 → 5 Succinic Acid + 2.5 O2 Xylose 0.9% 0% a Corn steep liquor (CSL) and diammonium phosphate (DAP) are both nitrogen sources required for Z. mobilis

growth. The stoichiometry shown above is only used to balance the compositions assumed for nonstandard 

components such as cell mass.

Sugar may be lost to side products by contaminating microorganisms in addition to being 

converted to cell mass and hydrocarbon (FFA) product. Sugar contamination losses were set 

consistent with the 2011 report basis, namely modeling a diversion of 3% sugar losses to lactic 

acid upstream of the production bioreactors. These contamination reactions are shown in 

Table 11. While it is recognized that sterility is more problematic in aerobic bioconversion 

systems with increased risk of contamination than for anaerobic fermentation, the assumption of 

similar contamination losses as for the ethanol design case is reasonable as an “n

th-plant” target 

assuming a robust microbe may be developed to accommodate these values. Additionally, the 

vessel turnaround time is increased considerably longer than for the ethanol design case to 

account for more cleaning time between runs (discussed below). The cost impact for varying the 

contamination loss is considered below in the Sensitivity Analysis section.

Table 11. Bioreactor Contamination Loss Reactions 

Reaction Reactant % Converted to 

Product

Glucose → 2 Lactic Acid Glucose 3.0%

3 Xylose → 5 Lactic Acid Xylose 3.0%

3 Arabinose → 5 Lactic Acid Arabinose 3.0%

Galactose → 2 Lactic Acid Galactose 3.0%

Mannose → 2 Lactic Acid Mannose 3.0%

Bioconversion of sugars to product occurs in a system of 264,000 gal (1 MM L) stirred-tank 

aerated vessels. The design and operation of these vessels represents an important change from 

the 2011 anaerobic ethanol fermentation basis, which utilized simpler, larger vessels of 1 MM 

gal capacity (950,000 gal working volume). As noted previously, ethanol is produced by 

anaerobic fermentation (via organisms such as Z. mobilis as per NREL’s research). Long-chain 

hydrocarbon biofuel precursors such as farnesene, fatty alcohols, or fatty acids are typically 

produced by aerobic bioprocesses, although anaerobic pathways to hydrocarbons also exist [42, 

43]. Such anaerobic processes likely require longer research timeframes, however, to achieve 

performance required for economic viability within the context of fuels. Thus, this representative 

model pathway focuses on aerobic bioconversion. 
31

In aerobic processes, oxygen is a nutrient used by microorganisms for cell growth, maintenance, 

and metabolite production. Scarcity of dissolved oxygen negatively impacts process performance 

and reduces product yields. Molecular oxygen (O2) has low solubility in aqueous media and the 

solubility also decreases with elevated temperature and increases in broth osmolarity and 

viscosity [44]. These factors create new challenges beyond those observed for cellulosic ethanol 

production, primarily related to economically scaling up oxygen mass transfer to a commercial 

level sufficient for biofuels applications. As noted in NREL’s recent technical memo cited 

previously [32], it is anticipated that the ability to maintain effective gas-liquid mass transfer 

(i.e., sufficiently high volumetric oxygen transfer rates [OTRs]) will ultimately limit the size at 

which microbial fuel production can be operated to considerably smaller volumes than are 

possible for anaerobic processes. For example, the largest bioreactor scale reported publicly for 

biological upgrading of sugars to hydrocarbons is approximately 500,000 L (130,000 gal) [45], 

although it is expected that with further process optimization and continued scale-up it will be 

possible to increase maximum vessel size beyond this value (even the smaller A400 section 

enzyme aerobic bioreactors are sized at 300,000 L, discussed below). Thus to establish a 

baseline, the production bioreactors were assumed to be 1 MM L (264,000 gal); a reasonable, if 

not conservative, extrapolation to a commercial scale that provides economy of scale and still 

allows for good control of system and reaction parameters.

Fed-batch operations are commonly used for large-scale bioprocesses whereby the vessels are 

inoculated and filled to an initial level, and substrate is continuously fed to the bioreactors over 

the course of the batch cycle as sugars are continuously consumed [46] (the enzyme bioreactors 

also operate in fed-batch mode as described below). Fed-batch operation of aerobic bioprocesses 

can help balance the carbon feed rate to limits set by the maximum OTR [47]. In the present 

model the bioreactors are filled to a 50% initial level (including inoculum), using the clarified 

dilute sugar material obtained after solids separation (representing 30% of the total hydrolysate 

liquor). After this point, as the reaction proceeds the remaining 70% of the hydrolysate liquor is 

delivered to the bioreactors after first being concentrated through the sugar evaporation step 

described previously. This fed-batch delivery of concentrated sugars enables a high product titer 

and less downstream purification/concentration demand. A maximum fill level of 80% is 

specified to ensure adequate head space for vapor-liquid disengagement and to mitigate foaming 

issues associated with the aerobic nature of the process. Thus, to calculate the number of 

required bioreactors, an average vessel working volume of 65% (650,000 L) is assumed over the 

duration of the fed-batch cycle. In a realistic system the initial, final, and average working 

volume set-points are more complex, with dependencies on detailed bioreactor design including 

number and location of draws, draw volumes, gas holdup, etc.; however, these design details are 

currently beyond the scope of this analysis. 

The agitation and aeration rates for the production bioreactors were set based on vendor feedback 

via Harris Group, using a calculated oxygen uptake rate (OUR, mmol/L-h), which was set 

stoichiometrically from the FFA production rate (similar to the basis for setting OUR in the 

A400 enzyme section). The vendor evaluated estimates for multiple stirred tank vessel sizes. All 

sizes evaluated were set at a maximum liquid height-to-diameter ratio of near 1:1, which tends to 

minimize agitator cost as well as blower cost and power demand relative to increased liquid 

heights (pressure head), albeit at the expense of aeration efficiency (degree of oxygen 

solubilization) relative to increased aspect ratios. This design assumption will continue to be 

evaluated in further detail moving forward to better quantify appropriate design optimization, 
32

including to consider bubble columns and stirred tank vessels of higher aspect ratios relative to 

the 1:1 basis utilized here. For the vessel sizes evaluated by the equipment vendor, the quoted 

agitation power demand varied between roughly 0.2–0.4 hp/1,000 gal of working volume (based 

on the average working volume of 65% for any given vessel size); while already an order of 

magnitude higher than the anaerobic ethanol fermentor agitator in the 2011 design report (0.03 

hp/1,000 gal), during external peer review of this report it was decided that such an agitation 

power may still likely be optimistically low for submerged aerobic systems. Thus, the agitation 

power was conservatively increased from the vendor basis up to 1.0 hp/1,000 gal (200 W/m3) of 

working volume, or 0.65 hp/1,000 gal of total bioreactor vessel volume for the 1 MM L (264,000 

gal) vessel size (41 ft height × 33 ft diameter, maximum liquid level of 33 ft). This value is 

applicable during vessel operation only, but not during vessel downtime for turnaround 

(draining, cleaning, and refilling). This mixing power is roughly 20 times higher than the 

anaerobic ethanol model on a working volume basis, but is to be expected given that agitation is 

required merely to maintain bulk mixing of the slurry in the ethanol case, while it is an important 

aspect of achieving necessary oxygen gas-liquid mass transfer in the present design. 

In addition to agitation demands, aeration rate is another critical design parameter in aerobic 

systems, as this sets air compressor cost and power, both of which can be significant [48]. 

Required aeration rate is a function of OTR, which is the product of the volumetric mass transfer 

coefficient kla (h-1) and the log mean oxygen concentration gradient (the difference between the 

saturated dissolved oxygen concentration and the desired dissolved oxygen concentration) at the 

top and bottom of the bioreactor operating volume: 

OTR = kla

(Cb

∗ − CD) − (Ct

∗ − CD)

ln �

Cb

∗ − CD

Ct

∗ − CD

�

where Cb* and Ct* are the saturated dissolved oxygen concentrations (mmol/L) at the bottom and 

top of the vessel, respectively, and CD is the desired dissolved oxygen concentration. The 

volumetric mass transfer coefficient and dissolved oxygen concentration values are in turn 

dependent on gas solubilities, pressures, agitator power consumption, and vessel working 

volume; these parameters have been described in detail in the 2011 ethanol design report for the 

A400 enzyme section [3] and will not be repeated in such detail here, suffice it to reiterate the 

inter-dependencies that design considerations such as vessel size, agitation power, and oxygen 

transfer rate exhibit on each other, all of which are dependent on specific oxygen uptake rates 

and metabolic needs for a given product pathway. 

Using the combination of the stoichiometric reactions for the bioreactor shown below in Table 

14, as well as the assumed FFA volumetric productivity of 1.3 g/L/h (discussed below), the 

resulting stoichiometric OUR for FFA production would be relatively low at approximately 5.0 

mmol/L-h as an average over the course of the fed-batch cycle (based on 65% average working 

volume), with a minimum of 4.1 at maximum working volume of 80% and maximum of 6.6 at 

minimum (initial) working volume of 50%. When setting OTR to this OUR value, the aeration 

demand required to achieve this value would be relatively low, e.g. < 0.1 VVM (volume 

air/working broth volume/minute) of air, which is less than what may be practical for 

commercial scale aerobic systems operating in a range of 0.3–0.5 VVM (a range which was 

confirmed during peer review of this report). Thus, the aeration rate was increased to 0.4 VVM 
33

based on average working volume of the bioreactors (0.3 at maximum working volume and 0.5 

at initial fill volume). This is delivered by air blowers operating at a pressure of 15 psig (2 atm) 

based on a maximum vessel liquid height of 33 ft (with a common air blower supplying the main 

bioreactors as well as the seed train, also operating on a basis of 0.4 VVM). It is recognized that 

the OTR is realistically governed by the complex operational parameters described above, and 

that the achieved OTR will in turn set achievable FFA volumetric productivity; however, the 

combination of the OTR, agitation power, and vessel design (1:1 aspect ratio) originally 

provided by the vendor appeared overly-optimistic, thus agitation and aeration were increased as 

described above. Relative to the 2011 ethanol design report with a total power demand of 2.6 

MW for the A300 section, the new A300 power demand has increased by an order of magnitude 

to 20.0 MW, primarily due to bioreactor agitation and aeration power demands. The cost impact 

associated with varying the aeration rate is explored below in the Sensitivity Analysis section. 

Unlike the ethanol system which contained significant amounts of ethanol in the fermentor vent 

gas (caused by ethanol’s relatively high volatility), which in turn required the use of a vent 

scrubber to recover the volatilized ethanol, the hydrocarbon product targeted here has much 

lower volatility so there is nearly no FFA product in the vent gas and a scrubber is not required. 

Heat exchange assumptions are maintained the same as for the ethanol case, namely cooling the 

bioreactors using a pump-around loop with the chiller system, with cooling demands calculated 

in the Aspen model to maintain the reactors at 32°C (96°F). While reasonable for anaerobic 

ethanol production, this pump-around arrangement could plausibly be detrimental to the aerobic 

organism, either by damaging the organism itself or by introducing a contamination point. 

Moving forward, alternative cooling arrangements may be considered such as cooling coils or 

vessel jackets. Aerobic reactions typically produce more heat than anaerobic fermentation, with a 

published rule of thumb presented as 110 kcal of heat produced per mole of oxygen consumed 

[49], considering only the heat of reaction itself; this compares to 194 kcal/mol estimated in the 

Aspen model, although this includes other factors beyond strictly the reaction (e.g. aeration, 

etc.); in addition, the heat generated during agitation adds another 33 kcal/mol to this value in the 

model. Finally, to mitigate potential foaming issues associated with the aerobic nature of the 

present system, a corn oil antifoam agent is added to the bioreactors at a basis of 890 mg/L of 

clarified hydrolysate feed to the bioreactors (1 mL/L at 0.89 kg/L corn oil density). Table 12

provides a summary of bioreactor design parameters utilized in this design. 

Table 12. Guiding Bioreactor Design Basis Assumptions 

Operating mode Fed-batch

Temperature 32°C (96°F)

Reactor size 1 MM L (264,000 gal) operating at 50% initial working 

volume, 80% final working volume

Reactor height 12.5 m (41 ft)

Reactor diameter 10 m (33 ft)

Agitation power 110 KW each; 2,110 KW total (150 hp each, 2,830 hp total) 

Average aeration rate 0.4 VVM (based on average working volume)

Inoculum level 10 vol %

Corn steep liquor (CSL) level 0.25 wt %

Diammonium phosphate (DAP) level 0.33 g/L broth (whole slurry) 

Corn oil antifoam level 890 mg/L (1 mL/L) of hydrolysate feed to bioreactors
34

A number of potential metabolic pathways and products exist for biological conversion of sugars 

to long-chain hydrocarbons. Such products include isoprenoids, fatty acids, triglycerides, and 

paraffins [41, 50-52]. Each pathway exhibits varying theoretical yields, dictated by underlying 

metabolic mass and energy yields. Based on a survey of literature, a summary of theoretical 

metabolic yields to the product classes noted above is provided in Table 13. 

As evidenced in Table 13, ethanol remains a superior product for bioconversion of sugars in the 

context of fuel molecules, exhibiting a much higher theoretical metabolic mass yield (e.g., 

weight of product relative to sugar), but also a higher energy yield (e.g., heating value of product 

relative to sugar), with the energy yield for hydrocarbon products closer to that of ethanol, but 

still 5%–24% lower. This demonstrates the increased burden placed on the present approach to 

achieve cost targets for hydrocarbon pathways relative to ethanol. These example products are all 

diesel-range molecules; however, products in the gasoline or jet-range (e.g., isobutanol, C6-C10 

alcohols, etc.) generally compare similarly to the energy yields below [33, 52]. 

Table 13. Theoretical Metabolic Yields for Various Product Pathway Classes 

Compared to Ethanol [41, 50–53] 

Mass yield Carbon yield Energy yield 

(HHV basis)

Ethanol 51% 67% 98%

Pentadecane 29% 62% 88%

Farnesene (DXP pathway) 29% 64% 85%

Farnesene (MVA pathway) 25% 56% 74%

Fatty Acid (Palmitic acid) 36% 67% 89%

FAEE (Ethyl palmitate) 35% 67% 90%

Fatty Alcohol (Hexadecanol) 34% 67% 93%

It is worth repeating here that the present technology pathway is generally in a much earlier state 

of development and understanding relative to ethanol, which has a number of decades of 

research and experience to pull from (both internal and external to NREL research) in developing 

detailed process and economic models. As there is not yet a single pathway, organism, or product 

selected at NREL or DOE for targeted research in biological hydrocarbon production (nor will 

there necessarily be a single such pathway selected in the near term as “the best” option, given 

the constantly evolving efforts in metabolic engineering in the field), the intention here is to 

select what may be viewed as a representative product pathway to provide quantitative insight as 

to technical and economic challenges and future potential of the general “biological sugar 

conversion” technology approach. As such, the FFA product pathway was selected for evaluation 

here, primarily because (a) it exhibits a potential metabolic energy yield (an indicator of ultimate 

GGE fuel yield) toward the middle of the range shown in Table 13, thus serving as a reasonable 

mid-point proxy for the general product suite discussed above, and (b) fatty acid biosynthesis 

(e.g., in E. coli) is a fundamental pathway that has been extensively investigated [54–56]. A 

high-level sensitivity analysis to product selling price associated with a number of alternative 

product pathways is presented and described below in the Sensitivity Analysis section. 

FFAs have desirable properties such as high energy density and low water solubility. FFAs are 

precursors of lipids and they play a vital role in cell physiology. FFAs are typically used within 
35

the cell to form phospholipids for incorporation into cellular membranes, or to form 

triacylglycerides (TAGs) for energy storage. Many bacteria and some plants synthesize fatty 

acids through the highly conserved type II fatty acid synthase system. Biosynthesis of fatty acids 

is energy intensive and carefully regulated.2 Regulation is required to ensure that cells do not 

accumulate excess, energy-rich lipids. Any excess lipids are rapidly degraded by oxidative 

cleavage to acetyl-CoA. These regulatory mechanisms need to be overcome in order to overproduce free fatty acids. Microbes tend to predominantly produce palmitate, a C16 fatty acid. 

Palmitate and other FFAs can be modified ex vivo to produce alkanes; i.e., by esterification, 

reduction to fatty alcohols, decarboxylation to olefins, or hydrotreating. 

The type II fatty acid synthesis system in bacteria begins with the enzyme catalyzed formation of 

a three-carbon intermediate, malonyl-CoA, by carboxylation of acetyl-CoA. The elongation of 

fatty acid molecules progresses through the iterative addition of two-carbon units derived from 

malonyl-CoA with the elimination of CO2. The process is generally complete when the chain 

length reaches 16 carbons (i.e., palmitate). However, other longer or shorter fatty acids may also 

be formed, with varying degrees of saturation. Palmitate (palmitic acid, C16H32O2) is assumed in 

the present model to be the representative FFA component produced during the bioconversion 

step for purposes of setting mass balances and energy yields. It is recognized that in reality, a 

mixture of saturated and unsaturated FFA components would be produced without targeted 

organism manipulation (which would also lower the net melting point of the combined FFA 

hydrocarbon mixture to the point that hydrocarbon-phase processing is not expected to be 

problematic, thus special measures are not taken in the present model to address FFA congealing 

issues). 

Hydrocarbon components produced by biological pathways may be present as intracellular 

products (for example, many heterotrophic algae species), or may be secreted into the broth [57]. 

Intracellular production typically requires a dedicated extraction step to disrupt cells and extract 

stored hydrocarbon/oil product, and this step has been shown in the literature to incur significant 

energy and/or cost penalties [41], which is consistent with preliminary NREL modeling of each 

pathway option. For this reason, a secreted FFA product is assumed in this baseline model 

pathway to permit easier product recovery and post-conversion processing (described further 

below). 

Aside from engineering and design challenges, from a process standpoint the primary cost 

drivers for the conversion stage are product yield (dictated by sugar conversions) and volumetric 

productivity (g/L/h of product being produced). The present design case model assumes 

conversion performance targets for glucose, xylose, and arabinose consistent with the 2011 

ethanol design case, shown in Table 14. Additionally, the batch time in this process is set by 

translating from volumetric productivity, which is targeted at a basis that is also consistent with 

the ethanol design case at 1.3 g/L/h (based on average working volume for the FFA bioreactors). 

This translates to a batch time of 69 hours, with an additional turnaround time of 10 hours for 

draining, cleaning, and refilling, requiring 19 bioreactor vessels (see Table 15). Important 

implications around these assumptions are discussed in Section 3.3.4. The turnaround time 

 

2 Biosynthesis of one palmitate requires 7 ATP and 14 NADPH
36

assumed here is considerably longer than in the anaerobic ethanol case (2 hours), even with 

smaller bioreactors, primarily to account for increased cleaning time associated with typical 

requirements to maintain a higher level of sterility for most aerobic bioconversion systems 

relative to anaerobic fermentation. 

Table 14. Bioconversion Reactions and Assumed Conversions

Reaction Reactant % Converted to 

Product

4 Glucose + O2 → 1 Palmitate (FFA) + 8 CO2 + 8 H2O Glucose 95.0%

Glucose + 0.047 CSLa + 0.018 DAP → 6 Z. mobilis + 2.4 H2O Glucose 2.0% b

4.8 Xylose + O2 → 1 Palmitate (FFA) + 8 CO2 + 8 H2O Xylose 85.0%

Xylose + 0.039 CSL + 0.015 DAP → 5 Z. mobilis + 2 H2O Xylose 2.0%

4.8 Arabinose + O2 → 1 Palmitate (FFA) + 8 CO2 + 8 H2O Arabinose 85.0%

Arabinose + 0.039 CSL + 0.015 DAP → 5 Z. mobilis + 2 H2O Arabinose 2.0% a Corn steep liquor (CSL) and diammonium phosphate (DAP) are both nitrogen sources required for Z. mobilis

growth. The stoichiometry shown above is only used to balance the compositions assumed for Z. mobilis cell mass. 

Nutrient demands have not been optimized and a minimal, low-cost nutrient formulation has yet to be defined. b Additional sugar diversion to biomass (represented as Z. mobilis) is assumed to occur at high rates in the seed 

train step, although some of the biomass growth modeled in the seed train may instead occur in the product

bioreactors 

Table 15. Bioconversion Productivity and Impacts on Bioreactor Vessels

FFA Volumetric productivity 1.3 g/L-hr (based on average working volume)

Bioconversion residence time 69 hours

Vessel turnaround time 79 hours

Number of vessels required 19 (1 MM L vessels, 65% average working volume)

3.3.3 Cost Estimation

For equipment which remains the same as the 2011 ethanol design case, as well as new 

equipment that was modified or added in the present analysis, the material of construction for 

most operations in this section remains 304SS, which is the most cost-effective material for 

fermentation service. The design and cost basis assumptions for all enzymatic hydrolysis 

equipment were left unchanged from the 2011 ethanol case, namely empty towers for the 

continuous hydrolysis reactor based on a vendor quotation for flat-bottomed plug-flow reactors 

with a 10:1 height:diameter ratio; as well as 1 MM-gal batch hydrolysis reactors and agitators. It 

is worth noting that the required number of batch hydrolysis vessels has decreased from 8 in the 

2011 ethanol design case (i.e., 8 of the 12 1 MM-gal vessels were for the hydrolysis portion of 

the operation in the combined hydrolysis/fermentation step) to 6 in the present model, due to a 

reduction in total volumetric throughput into the hydrolysis section associated with the addition 

of up-front deacetylation to solubilize and remove a portion of non-fermentable biomass 

components. 

The vacuum belt filter design and cost information was provided by a vendor, who provided a 

design basis of 170 m2 maximum filtration area per individual filter, with filter sizing based on a 

filtration rate of 20 kg/h m2

 based on insoluble solids. The cost includes a two-stage 

countercurrent water wash step at a wash ratio of 2.5 L/L liquor in the filter cake. A filtered 

hydrolysate storage tank with a residence time of 20 minutes is included to provide intermediate 
37

storage for filtrate from the belt filter. The sugar concentration (evaporation) equipment was also 

based on vendor-provided design and cost estimates. As noted above, the system utilizes MVR 

evaporators in a series of four effects to concentrate a fraction (70%) of the filtered hydrolysate 

from 14 wt% to 46 wt% (reducing water content to 50%). A concentrated sugar storage tank with 

a residence time of 20 minutes is included downstream of the evaporator. 

The bioreactor vessels (using stirred tank agitators) were costed by scaling down from the 

previous vendor estimate for 1 MM-gal ethanol fermentation vessels, an approach that Harris 

Group confirmed to be reasonable for the bioreactor vessels themselves (i.e., not including new 

agitation and aeration considerations, to be included separately). As described above, the aerobic 

bioreactor vessels were selected to be sized at 1 MM L (264,000 gal, rather than 1 MM gal) as a 

reasonable extrapolation from the understanding of current, non-commercial scale capacity for 

similar service operations while restricting the size from becoming too large to enable effective 

oxygen mass transfer and uptake. Thus, while the cost basis for the large 1 MM-gal ethanol tanks 

was applied for scaling to the smaller 264,000-gal bioreactor vessel size, the smaller size loses

economy-of-scale benefits, which results in a cost per reactor volume higher than that exhibited 

in the ethanol design ($370/thousand L purchased cost per vessel versus $240/thousand L in 

2011$ for larger 1 MM-gal tanks). Blower and agitator costs were scaled from information 

provided by Harris Group based on vendor quotations. The vendor indicated that for applications 

requiring higher air flow rates than those estimated here, more costly specialized impellers with a 

parabolic blade design would be required to achieve good dispersion; however, the air flow rate 

considered here is sufficiently low that conventional agitator blades were deemed appropriate.

The cost and design bases for the seed train equipment were left unchanged from the 2011 

ethanol design. Finally, a 1.2 MM-gal storage tank is provided downstream of the bioreactors. 

Harris Group also provided cost estimates for bubble column reactors as a plausible alternative to 

standard stirred-tank reactors, but based on a preliminary analysis of power and cost implications 

for these units, the stirred tank basis was deemed more cost-effective. Thus, bubble columns are 

not utilized here in the design base case, but will be evaluated in more detail moving forward to 

better understand tradeoffs in design details, OTR demands, agitation/air compression power, 

and maximum plausible vessel size for either vessel option. Another anticipated benefit of 

bubble columns would be to negate the need for dedicated agitators, as the bubbles may provide 

the necessary mixing in-situ.

3.3.4 Achieving the Design Case

For the enzymatic hydrolysis and bioconversion area, Table 16 shows the 2011 design case 

ethanol target assumptions, the 2012 State of Technology demonstrated values, and the new 

target values used here for production through the FFA intermediate product. 
38

Table 16. 2017 Targets for Enzymatic Hydrolysis and FFA Production, Compared to 2012 Targets 

and State of Technology Results for Ethanol

2012

Targets 

(Ethanol)

2012

State of 

Technology 

(Ethanol)

2017

Targets (FFA)

Total solids loading (wt %) 20% 20% 20%

Enzymatic hydrolysis substrate Whole-slurry Whole-slurry Whole-slurry

Hydrolysis time (days) 3.5 3.5 3.5

Enzyme loading (mg/g cellulose) 20 19 10

Bioconversion time (days) 1.5 1.5 2.9

Combined cellulose-to-glucose × glucose-toproduct conversion (through hydrolysis and 

bioconversion,%)a

85.5% 74% 85.5%

Total xylan to xylose conversion (%) 90% 81% 90%

Xylose to product conversion (%) 85% 93% 85%

Arabinose to product conversion (%) 85% 54% 85% a Assumes 95% glucose-to-product conversion (product = ethanol or FFA respectively). 

Note, this was previously stated as “overall cellulose-to-product (ethanol)” conversion; however the metric is 

strictly a combination of fractional cellulose conversion to glucose multiplied by fractional glucose conversion 

to product, and does not include losses to fermentation, cell mass growth, or processing inefficiencies (i.e., 

the amount of glucose ultimately available for conversion to product is less than the amount of glucose 

produced from upstream cellulose conversion)

3.3.4.1 Biomass Deconstruction (Pretreatment + Enzymatic Hydrolysis)

During NREL’s 2012 ethanol demonstration runs, the addition of deacetylation improved the 

performance of a number of downstream unit operations, namely pretreatment (milder conditions 

via reduced acid loading), hydrolysis, and fermentation (increased yields caused by reduced 

inhibition in both steps). However, a number of operations did not quite achieve the 2012 targets 

or the performance observed in prior SOT demonstrations, namely glucose yield during 

enzymatic hydrolysis, which had been performed on a washed-solids substrate rather than wholeslurry prior to 2012. Xylan-to-xylose yield was also somewhat below the target, but this was 

offset by a higher xylose-to-ethanol yield such that overall xylan-to-ethanol yield was similar to 

the target. Arabinose-to-ethanol conversion was low, and is an area for continued research 

moving forward, albeit for a different product pathway. 

Biomass deconstruction to sugars is an important research area that will continue to progress by 

further improving monomeric sugar yields at increasingly less severe conditions (e.g., milder 

pretreatment and lower enzyme loadings). The FFA target production pathway stipulates 90% 

xylose yield from xylan, which may be observed from a combination of reduced severity 

pretreatment (combined with the use of deacetylation) as well as new advanced enzyme cocktails 

with xylanase activity to further reduce the burden on hemicellulose deconstruction exclusively 

in pretreatment. The target model also assumes that the stipulated solubilization of nonfermentable components will be achieved during deacetylation while maintaining xylan loss to 

no more than 2%. There is early indication that this is a reasonable set of targets for 

deacetylation, but will require further optimization of deacetylation conditions. The FFA target 
39

pathway also stipulates 90% glucose yield at 50% of the enzyme loading (10 mg/g) relative to 

current 2012 performance, with constant enzymatic hydrolysis batch time. As evidenced by the 

lower glucose yield observed in the 2012 SOT demonstration even at higher (19 mg/g) enzyme 

loading, this will be an aggressive improvement, but again based on discussions with NREL 

researchers and enzyme companies, is a reasonable target for a 2017 timeframe given 

performance recently observed using the latest enzyme preparations. 

3.3.4.2 Bioconversion

There remains considerable room for improvement in the efficiency of sugar conversion to fuel, 

particularly for utilization of pentose sugars, across a variety of hydrocarbon-producing 

microorganisms [58-60], as well as in improving specific productivity rates (g product/g cell/h), 

targeting a lower diversion of sugar to microbial cell growth and/or engineering ways to recover 

and reuse microbial cells (e.g., cell retention or cell recycle bioreactor configurations) and 

mitigate potential hydrocarbon product toxicity effects [50]. Likewise, volumetric productivities 

also solicit much room for improvement beyond publicly-demonstrated performance. Recent 

literature suggests that the current state of technology for microbial conversion to hydrocarbon 

components includes product titers ranging from 0.1- 24 g/L of long-chain hydrocarbons [35, 52, 

61-63], with times for batch or fed-batch production of 2–7 days [59]. Relative to ethanol 

fermentation with demonstrated productivities of 1.2–1.6 g/L/h or higher, maximum reported 

productivities for long-chain hydrocarbons are on the order of 0.11–0.19 g/L/h for fatty acids 

(the highest rates were achieved using a fed-batch process) [61] [64, 65], and less than 0.1 g/L/h 

for other components such as fatty acid ethyl esters or isoprenoids [33, 51]. Even after adjusting 

for differences in theoretical metabolic yields, these productivities leave much room for 

improvement compared to established ethanol performance. This point is recognized by DOEBETO as well, as microbial catalyst development is identified as a critical technical R&D barrier 

in the May 2013 MYPP [66]. 

While this information is strictly a subset of published literature values, and does not include 

NREL in-house data (which has not yet been established for the conversion step), it begets a 

question regarding the timeframe required to achieve organism performance targets. The most 

direct response to this point is that the intent of the present report is to document a transparent, 

quantitative path to achieve an RDB selling price of $5/GGE based on rigorous process 

modeling, which is in fact achieved via the combination of process targets described here. We 

feel it is not our place to comment on the “state of the industry” (which includes a number of 

commercial entities that have already made significant progress in developing an organism and 

product pathway, which may or may not reflect similar values to those presented here from 

literature), to make an authoritative judgment call regarding specific timing of out-year 

achievements of such targets and how closely these achievements correlate between published 

literature and private industry. Additionally, moving forward as more detailed technical targets 

are developed, validated, and refined, if certain targets are not met in a given timeframe by 

NREL or other partners, the shortfall may be compensated for by achievements in other areas; 

for example, progress demonstrated for fractional conversion of lignin to value-added coproducts 

(which is considered for the time being in the ultimate “2022” path to $3/GGE below in Section 

6, but not here in the $5/GGE model). Alternatively, a cell recycling step could plausibly be 

added, which could potentially increase per-pass sugar conversions as well as reduce the amount 

of sugar diversion to cell mass growth relative to FFA production. The cost impacts associated 
40

with variations in both the sugar conversion efficiencies as well as volumetric productivity are 

considered below in the Sensitivity Analysis. 

Advances in systems biology and metabolic engineering have enabled the development of 

microbes to produce hydrocarbon biofuels with properties similar to petroleum-based fuels. A 

variety of hosts, including microalgae, bacteria, and fungi, are available to produce biofuels. The 

discussion below focuses on E. coli as a model organism for the bioconversion of cellulosic 

sugars to free fatty acids. Figure 10 shows a comparison of the concentration, reported yield, and 

volumetric productivity of several hydrocarbon biofuel products produced by E. coli with those 

for ethanol as reported in literature and summarized in [33]. The figure illustrates that even with 

a well-understood microbe such as E. coli, further work is needed to put the production of 

hydrocarbons on par with ethanol. A brief description of those opportunities follows, but it is 

with the acknowledgment that other microbes such as oleaginous yeast or ethanologens such as

Z. mobilis can be more productive than E coli [67–70]. 

Figure 10. Comparison of titer, sugar utilization, and volumetric productivity for ethanol versus 

selected hydrocarbon products produced by E. coli, as reported in [33] 

Knowledge of transcriptional and translation regulation of fatty acid biosynthesis is incomplete 

[71]. Approaches to producing fatty-acid derived fuel from E. coli require overcoming regulation 

of fatty acid biosynthesis, and upgrading fatty acids to meet fuel-grade specifications [51]. There 

have been a number of publications detailing approaches to increasing the production and quality 

of fatty acids from E. coli: 

Short-Chain Fatty Alcohols

Isoprene

Fatty Acids

Fat ty acid ethyl esters

Farnesol

Ethanol

60

50

40

30

20

10

0

Short -Chain Fatty Alcohols

Isoprene

Fatty Acids

Fatty acid ethyl esters

Farnesol

Ethanol

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Short -Chain Fat ty Alcohols

Isoprene

Fatty Acids

Fatty acid ethyl esters

Farnesol

Ethanol

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Concentration [g/L] Reported Yield [g/g] Vol. Prod. [g/L/h]

Concentration, Reported Yield, Volumetric Productivity by Product
41

• The composition of fatty acids from E. coli has been modified by heterologous 

expression of plant thioesterases [72]. This enables the production of fatty acids at a 

variety of chain lengths and degrees of unsaturation—an important step to producing 

fuel-compatible FFAs.

• A multi-step metabolic engineering approach has shown encouraging results. Production 

of FFAs was increased by knocking out fatty acid degradation, expression of plant 

thioesterase, increasing malonyl-CoA production, and decreasing feedback inhibition by 

over-expressing an endogenous thioesterase [73–75]. When combined, these 

modifications resulted in a 19-fold increase in total fatty acid production compared to the 

original strain (0.048 g FA/g glycerol). Subsequent studies have mimicked this approach 

and the results were summarized by Lennen and Pfleger [71]. Zhang et al. reported one of 

the higher yield results from this approach at 0.19 g FA/g glucose [76]. 

• A promising result was reported by Dellomonaco et al. where they engineered the 

reversal of the beta-oxidation pathway that normally degrades fatty acids [61]. The 

advantage of this approach is that it eliminates the need to consume ATP when 

synthesizing malonyl-CoA. The result was an increase in product yields to 0.23 g FA/g 

glucose. 

Lennen and Pfleger summarized four additional opportunities to improve fatty acid production 

[71]. First, increase FFA export rates. No transporter has been identified to export FFAs from 

within E. coli. Genetic engineering can improve export rates once transporters are identified. 

Second, regulate membrane saturation. Thioesterase can improve FFA yields but it can also 

impact cell physiology. Thioesterase can alter the relative amounts of saturated and unsaturated 

FFAs. The result is that cell membranes incorporate more saturated phospholipids that are found 

in a normal cell, decreasing cell viability. Third, address metabolic and regulatory bottlenecks. 

The barriers to prevent FFA production to reach near-theoretical yields remain unknown. Further 

work is needed, particularly on developing anaerobic production of FFAs to remove the 

operating costs associated with aeration. Fourth, additional structural, biochemical, and genetic 

studies are required. Basic science studies are needed to support metabolic engineering efforts, 

including enzyme kinetics, enzyme structure, and translational/transcriptional regulation. 

In addition to organism performance factors driven by sugar utilization efficiency and volumetric 

productivity to hydrocarbons, a number of additional process issues will also be important in 

demonstrating achievement of the design case in the bioconversion area. These include:

a. Bioconversion performance and tolerance to cellulosic hydrolysate impurities. This 

includes salts, acetate, lignin, and other byproducts formed upstream. As noted above, 

most public data on biological conversion of sugars to hydrocarbons focuses on glucose 

or other clean commodity sugars. Today’s organisms may require such clean sugars, 

which in the present context could require even more hydrolysate conditioning 

equipment; e.g., further purification through ion exchange or other similar costly 

techniques. Additionally, little is known about the specific degree to which lignin and 

other insoluble solids impede oxygen mass transfer and bioconversion performance. 

Economically it may be preferable to remove lignin solids after bioconversion rather than 

before to allow for a lower-cost separation system and to avoid minor losses of sugars 

when employing this step prior to bioconversion. Furthermore, while acetate is a known 

inhibitor to ethanol fermentation, it may be tolerated or utilized by some hydrocarbon-
42

producing organisms. Much of the design of Area 200 is focused on deacetylation and the 

resulting acetate is sent to WWT. If acetate could be used as a carbon source for 

bioconversion to hydrocarbons, it could enable further optimization and cost reduction in 

upstream processes.

b. Effective secretion of the FFA or other hydrocarbon product(s) outside the cell 

membrane. While a secretion pathway is selected as the baseline approach in the current 

model given inherent energy and cost savings rather than extraction of intracellular 

product, the effectiveness of such an approach warrants validation through continued 

research. Additionally, it will be important to quantify impurities such as polar membrane 

lipids or factors that may lead to emulsions as the targeted product is secreted across the 

membrane cell and into the surrounding broth, as this will have impacts on downstream 

processing/purification needs. 

c. Consideration of anaerobic pathways. Although most hydrocarbon-producing pathways 

occur aerobically, not all such pathways must inherently proceed this way. For example, 

as proof of concept, NREL researchers have demonstrated using anaerobic fermentation 

in the lab to produce low quantities of hydrocarbon fuel using engineered Z. mobilis [42]. 

Additionally, many anaerobic conversion pathways to produce intracellular and 

extracellular hydrocarbon products and intermediates exist in various bacteria and yeast 

microorganisms [43]. Making a switch to anaerobic bioconversion would allow for the 

use of larger, simpler equipment without the need for costly aeration capabilities, albeit 

likely requiring longer timeframes to achieve performance required for economic 

viability within the context of fuels. 

3.3.4.3 Low-Cost Media

In keeping with the 2011 ethanol design case, corn steep liquor (CSL) is again assumed as a 

nutrient source for the seed and product bioconversion steps. While NREL does not use CSL in 

routine laboratory or pilot plant experiments, its use is assumed in the techno-economic model as 

a placeholder for a low-cost source of nitrogen and trace minerals. CSL, a by-product of the corn 

wet milling process, is high in protein, vitamins, and minerals and is usually mixed with the corn 

fiber by-product and sold as gluten feed [77]. On its own, CSL is used as a nutrient source in 

many fermentation industries, including wet-mill corn ethanol, pharmaceuticals, and industrial 

enzyme production. Because CSL is a wet mill by-product, and the corn starch business is 

relatively static, there is in effect a fixed amount of CSL available in the United States. Finding 

another low-cost and scalable nutrient source will be a continued subject of future research and 

TEA. Additionally, different nutrient demands and feeding strategies may be incurred for 

different types of organisms; for example, oleaginous yeast and heterotrophic algae may perform 

most optimally with replete nitrogen availability for organism growth, then switched to nitrogen 

depletion (starvation) to trigger lipid/product accumulation.

3.4 Area 400: Cellulase Enzyme Production

3.4.1 Overview

This process area produces cellulase enzyme that is used in Area 300 to hydrolyze cellulose into 

glucose. Cellulase refers to a mixture of enzymes (catalytic proteins) that includes: (1) 

endoglucanases, which attack randomly within the cellulose fiber, reducing polymer chain length 

rapidly; (2) exoglucanases, which attack the ends of highly crystalline cellulose fibers; and (3) βglucosidase, which hydrolyzes the small cellulose fragments (cellobiose, a glucose dimer) to 
43

glucose. Cellulase is produced industrially using (among other microorganisms) Trichoderma 

reesei, a filamentous fungus that secretes high levels of cellulase enzymes when grown 

aerobically in the presence of cellulose or other cellulase inducers.

The present analysis maintains consistency with the fundamental assumptions for enzyme 

production and cost estimation as detailed in the 2011 ethanol report, first and foremost being the 

use of on-site enzyme production rather than a purchased-enzyme model. As stipulated in the 

2011 ethanol report, again we note that by including an on-site enzyme production section, 

NREL and DOE are not making a judgment about whether or not the cellulosic biofuel industry 

should align to this mode of enzyme distribution. Rather, the model on-site enzyme section is 

intended to improve transparency in determining the true cost of cellulase enzymes for largescale cellulosic biofuel production.

As in the 2011 basis, the present design considers submerged aerobic cultivation (“aerobic 

fermentation”) of a T. reesei-like fungus on a feedstock of glucose and fresh water. Producing 

cellulase enzymes with glucose is not straightforward, because the absence of cellulose does not 

encourage the microorganism to secrete cellulase enzymes. Here, we have assumed a media 

preparation step where a small fraction of glucose is converted to sophorose, a powerful inducer 

of cellulase, using a small amount of the cellulase enzyme itself. When grown on this substrate, 

T. reesei has been shown to productively secrete cellulase [78]. Using glucose as the substrate is 

not necessarily more expensive than using hydrolysate slurry from pretreatment because the 

expected enzyme titer is significantly higher with glucose, reducing the capital and utility costs 

compared with using hydrolysate. Figure 11 is a simplified flow diagram of the enzyme 

production section. 

Figure 11. Simplified flow diagram of the enzyme production process

3.4.2 Design Basis

The design for the enzyme production operations is described in detail in the 2011 ethanol report, 

and will not be repeated in such detail here. In summary, the design for the system was based in 

part on the claims in U.S. Patent 4,762,788 (Example 5) [79] [80] in combination with a number 

of reasonable assumptions to develop a rudimentary process that is adequate for our needs. The 

key assumptions used in the current design are summarized in Table 17. 

Glucose + water

Sophorose conversion enzyme

To hydrolysis

Media prep

Aerobic culture

(fed-batch)

Holding tank

Glucose/sophorose Air

Seed train

(Trichoderma reesei)
44

Table 17. Area 400 Guiding Design Basis Assumptions

Parameter Assumption

Protein loading to enzymatic hydrolysis 10 mg protein/g cellulose

Reactor size 300,000 L @ 80% final working volume

Enzyme titer at harvest 50 g/L

Mass yield of enzyme from glucose 0.24 kg enzyme/kg glucose

Enzyme production cycle time 120 h online, 48 h offline, 168 h total

The assumed cellulase loading to enzymatic hydrolysis is reduced in the present pathway model 

to 10 mg of enzyme protein per g of cellulose, from 20 mg/g in the 2011 ethanol report. The 

rationale for this decision (keeping in mind this is a target for 2017) is described above in 

Section 3.3.2. “Protein” here refers to the total amount of high molecular weight protein in the 

enzyme broth as determined by assay; not all of this protein is active cellulase. The total protein 

demand was thus calculated to be 260 kg/h (570 lb/h). An additional 10% is produced to account 

for the slipstream provided to the media preparation tank to make the glucose/sophorose mixture. 

The size of the cellulase production vessels was set at 300 m3

 (80,000 gal) with a height-todiameter ratio (H/D) of 2. Fermentation is assumed to be a fed-batch process starting at 50% 

working volume and ending at 80%. Over one week, each bioreactor will see a 24-hour cell 

growth period, a 96-hour protein production period, and a 48-hour offline period for draining, 

cleaning, and refilling. With a one-week total cycle time and the production parameters listed in 

Table 17, one bioreactor is capable of producing 12,000 kg of protein in a week, or 71.4 kg/h 

(157 lb/h). The equivalent enzyme volumetric productivity is 0.30 g protein/L-h. Five reactors 

were therefore required to deliver the 260 kg/h of protein needed for enzymatic hydrolysis. 

The reactors are loaded initially with the glucose/sophorose carbon source and nutrients 

including CSL, ammonia, and SO2. After the initial cell growth period, additional substrate is 

added to maintain protein production. The bioreactors are sparged with compressed and cooled 

air and corn oil is added as an antifoam. The reactors are temperature-controlled by chilled water 

flowing through internal coils. 

Aeration and agitation requirements for the production bioreactors are functions of OTR and 

OUR, which have been discussed above in Section 3.3.2 and in more detail in the 2011 ethanol 

report. Thus, the details and governing principles for these metrics will not be repeated again 

here. In the modeled bioreactors, the reaction stoichiometry balances the reactions of substrate, 

oxygen, ammonia, and SO2 to cell mass and enzyme (plus CO2 and water) using an elemental 

composition for commercial cellulase provided by Novozymes [80]. The composition of cell 

mass was taken as the average of a generic cell mass composition [81] and the enzyme 

composition, with the assumption that cell mass includes some unreleased protein. 

Cellulase protein: CH1.59N0.24O0.42S0.01

T. reesei cell mass: CH1.645O0.445N0.205S0.005

In the production bioreactors, it is assumed that 90% of the carbon source is converted via the 

protein reaction and 10% is converted via the cell mass reaction. In the seed reactors, 85% of the 

carbon is converted via the cell mass reaction and 5% via the protein reaction, with 10% 
45

unreacted. This represents an overall molar selectivity of glucose to 31% protein, 4% cell mass, 

and 65% CO2, yielding 0.24 kg enzyme protein/kg glucose. The average aeration rate to the 

bioreactors remains the same as in the 2011 basis at 0.83 standard m3/s (including two days of 

offline time), as does the average impeller power P at 260 kW (350 hp), with a maximum of 600 

kW (800 hp). The final specifications for the enzyme production reactors are shown in Table 18. 

Table 18. Specifications of the Enzyme Production Bioreactors

Total volume 300 m3 / 80,000 gal

Maximum working volume 80%

H/D ratio 2

Height 11.5 m

Diameter 5.75 m

Operating pressure 1 atm

Operating temperature 28°C (82°F)

Material 316SS

Agitator 800 hp

Total electricity demand per kg protein 

(air compressors, agitators, chillers, pumps)

9 kWh/kg

Four trains of three seed fermentors provide inoculum to the production bioreactors. Each vessel 

in the seed trains is run batchwise on the same substrate as the production vessels. Air is also 

sparged through each of the seed vessels, which are cooled with chilled water. The seed 

bioreactors are each sized at 10% of the next bioreactor volume, i.e., 0.3 m3, 3 m3, and 30 m3. 

The aeration demand is assumed to be 10% of the production aeration rate. Four trains were 

chosen because each production fermentor has a total cycle time of 7 days; each seed fermentor 

should have a cycle time of 2 days (including cleaning and sterilization) to get through the cell 

growth phase only. 

Like the oxygen uptake rate, the glucose demand is also computed stoichiometrically from the 

required protein production rate. Ammonia and SO2 are fed to the reactors stoichiometrically and 

CSL, trace nutrients, and antifoam (corn oil) are added to the substrate based on flow rate. The 

required nutrient concentrations are based on Schell et al. [82] and remain the same as presented 

in the 2011 ethanol report. Glucose, the carbon source for cell mass and protein, is the most 

significant enzyme production expense in this model. The cost for glucose was maintained 

consistently with the 2011 ethanol design case (after adjusting to 2011$), but may be revisited 

moving forward as it may err slightly on the conservative side. Electricity also remains a 

significant contributor due to the power requirements of air injection, agitation, and refrigeration. 

Total electricity demand for these operations is shown in Table 18. 

3.4.3 Cost Estimation

The cost estimation for all equipment in A400 was left unchanged from the basis values provided 

in the 2011 ethanol report. Most equipment in this area is stainless steel. The air compressor and 

some of the nutrient delivery equipment items are specified as carbon steel. Quotes for the 

production bioreactors, internal cooling coils, production agitators and motors, skid-mounted 

seed fermentors, and air compressor were provided by vendors through Harris Group, which

developed costs for the pumps in this area using their internal database.
46

Not included in the enzyme production model are any costs for concentration, stabilization, or 

transportation of the enzyme to the plant. At the very least, one expects to have to pay licensing 

fees for the cellulase production microorganism, but these costs are not included because we lack 

information on what they might be (though an educated guess might be ≤ $0.05/gal of RDB 

product). Additionally, while the design and cost assumptions for the enzyme production vessels 

themselves were left unchanged from those described in the 2011 ethanol report, the 300,000 L 

vessel size could potentially be on the large side for realistic commercial operation of a highly 

viscous system such as that for T. reesei. The implications for variations in assumed vessel size 

(e.g., capital cost) are considered below in the Sensitivity Analysis section. The enzyme 

production system is also sized strictly to provide the amount of enzyme required for the 

hydrolysis step, for example, is not oversized to accommodate occasional poor-yielding runs. A 

reasonable backup measure for this risk would be to either over-design the enzyme system or to 

add supplemental purchased enzyme capacity. These strategies are not considered here for 

consistency with the n

th-plant approach taken elsewhere (e.g., avoiding over-design of equipment 

operations), but may be considered in future analyses exercising this design model. 

3.4.4 Enzyme Cost Discussion

Based on the economics of the on-site enzyme section described above, the predicted cost of 

enzymes to the facility is $0.39/gal of RDB. This is broken down as shown in Figure 12, and 

follows a very similar fractional breakdown in cost contributions as presented in the 2011 

ethanol report, with small variations due to different rates of escalation for capital and material 

costs when translating to 2011 dollars. The carbon source (glucose) makes up 55% of the cost of 

the enzyme, i.e., $0.22/gal of the $0.39/gal is just for glucose. We can back out the unit cost of 

the enzyme from $0.39/gal by multiplying by the RDB production rate and dividing by the 

enzyme flow rate, which works out to $5.38/kg protein. On an absolute cost basis, this is higher 

than the cost of $4.24/kg presented in the 2011 ethanol report primarily due to the adjustments 

made to underlying TEA assumptions, namely updating to 2011 dollars and reducing on-stream 

operating factor to 90% (7,884 hours/year). 

Figure 12. Enzyme production cost breakdown

Capital 

25%

Glucose

55%

Other 

Nutrients

5% Electricity

10%

Fixed Costs

5%

$0.39/gal RDB

$5.38/kg protein
47

While the fundamental process and design aspects of the enzyme production and enzymatic 

hydrolysis steps remain the same as stipulated in the 2011 ethanol report (e.g., size and cost of 

enzyme production and hydrolysis equipment, 3.5 day total hydrolysis time, etc), an important 

factor in achieving the $5/GGE 2017 target for the present hydrocarbon pathway model is a 

reduction in required enzyme loading to 10 mg/g cellulose while maintaining cellulose-toglucose yield at 90%. This is predicated on continued improvement in advanced enzyme 

formulations to achieve such an aggressive reduction in loading and concomitant increase in 

activity, but as noted previously this does not appear to be an unreasonable target for a 2017 

timeframe, based on feedback from NREL researchers and enzyme companies. If enzyme 

loading of 20 mg/g were used in this design case, the resulting MFSP would be $5.45/GGE of 

RDB product. Additional discussion regarding enzyme loading implications is provided below in 

the “Sensitivity Analysis” section. 

As noted previously, advanced enzyme preparations may increasingly exhibit levels of xylanase 

activity to enable fractional increases in xylose yield during hydrolysis rather than exclusively in 

pretreatment, thus removing some of the burden for xylan conversion during pretreatment and 

potentially allowing for more mild pretreatment conditions (acid loading, temperature, and/or 

residence time) at lower cost. This benefit is not explicitly targeted in the present design, but may 

be considered in future iterations of the model as additional information becomes available to 

assist in setting associated targets.

The caveat discussed in the 2011 ethanol report bears repeating that the enzyme cost contribution 

modeled here is lower than one would expect for an enzyme preparation purchased from a 

separate, non-adjacent production facility. Transportation of the enzyme to the biorefinery 

facility could add $0.09–$0.18/kg of product, even if formulation costs could be avoided. 

Furthermore, by lumping the enzyme production equipment in with the biorefinery, some key 

items are inherently shared; e.g., the land and buildings, cooling tower, and utilities 

infrastructure. Overhead and fixed costs, especially labor and management, would also be higher 

for a standalone facility. Eliminating the shared aspects between the enzyme unit and the 

biorefinery could easily add another $0.05/gal of RDB to the enzyme contribution. Additionally, 

an external enzyme production facility would probably demand a higher rate of return than the 

10% IRR assumed for the biorefinery plant because it is a higher-risk and lower-volume 

business. Including all these extra costs would likely bring the total enzyme cost contribution in 

line with numbers cited publicly (on the order of $0.50/gal for ethanol under today’s loading near 

20 mg/g as demonstrated in NREL’s 2012 SOT work, which could translate to $0.50–$1.00/gal 

of RDB for 10–20 mg/g loading, respectively). Such differences between off-site and on-site 

production make an argument for eventual integration and on-site production; however, it is 

more likely that cellulosic biorefineries (whether targeting sugars, ethanol, or hydrocarbons) will 

purchase enzyme from an external supplier with an organization dedicated to improving enzyme 

performance and reducing costs. 

3.5 Area 500: Product Recovery and Upgrading

3.5.1 Overview

Area 500 separates the bioreactor broth from A300 into a hydrocarbon (FFA) phase and an 

aqueous phase containing water, soluble solids (including unconverted sugars), and organism 

biomass. Aside from the bioconversion organism, the suspended solids fraction of the broth is 
48

small as the lignin and other insoluble solids were already removed upstream (see Figure 13). 

The relatively high immiscibility of the FFA material is exploited to utilize simple phase 

separation to purify and concentrate the FFA product from the aqueous broth, rather than 

requiring cost- and energy-intensive distillation and dehydration steps as were required in the 

ethanol design model. Following phase separation, the concentrated FFA material is sent directly 

to on-site hydrotreating to upgrade the material into diesel-range RDB product. 

Figure 13. Simplified flow diagram of product recovery and upgrading

3.5.2 Design Basis

The broth from the bioconversion area contains approximately 9 wt% FFA (a function of sugar 

feed concentration as well as sugar utilization efficiencies described previously). As noted above, 

the FFA material is assumed for modeling purposes to consist of palmitate (palmitic acid), a fully 

saturated 16-carbon fatty acid; in reality, the FFA material will likely consist of a mixture of 

saturated and unsaturated fatty acids in the diesel carbon number range (C12–C20) [83]. These 

components will largely exhibit similar properties as palmitic acid, namely low density relative 

to water (palmitate density = 0.86 kg/L) and essentially immiscible with water. Thus, it is 

anticipated that this material will readily phase-separate from the rest of the fermentation broth 

using standard equipment such as decantation or centrifugation. For lack of in-house data 

currently available on the subject, this is a point that will require experimental validation to 

confirm, and that liquid-liquid mixing behavior or the formation of emulsions will not pose 

insurmountable problems requiring alternative purification techniques to those assumed here. 

Under guidance from Harris Group with input from a vendor knowledgeable in processing of 

similar material (e.g., biodiesel processing), a standard biodiesel-type oil water separation 

decanter was selected for primary concentration of the hydrocarbon phase; subsequently to the 

engineering design conducted by Harris Group, it was also decided to follow the decanter 

purification step with centrifugation. Both Harris Group and the vendor felt comfortable with the 

use of decanters to achieve primary oil separation/purification, assuming the separation behaves 

similarly to ideal fatty acid/fatty acid methyl ester (FAME, i.e. biodiesel) processing. However, 

given uncertainties listed above regarding the possible presence of other impurities, emulsions, 

or lysed cells which may complicate the separation process, secondary centrifuge purification 

was also included to increase the likelihood of achieving desired separation performance (namely 

high oil recovery and purity). 

Broth 

Storage Tank

Decanters

Storage 

tank

Broth

To WWT

H2

Product 

storage Hydrotreating

Centrifuge
49

The gravity decanter vendor estimated that a 15- to 30-minute residence time would be adequate 

for the decantation operation; however, based on Harris Group’s experience in similar 

applications this was extended to a 60-minute residence time to err on the conservative side, 

particularly in ensuring high FFA product recovery, which was set to 97% (3% product 

carryover loss into the aqueous phase, combined between the decantation and centrifugation 

steps). Based on the flow rate provided, this resulted in four decanter vessels operating in parallel 

to process the full bioreactor broth stream, concentrating the material from approximately 9% to 

34% (fourfold concentration factor). At this outlet concentration, the flow rate of the light liquid 

phase containing the partially concentrated FFA material is roughly 200 gal/min. This phase is 

then sent to a disk stack centrifuge with a maximum capacity of 250 gal/min (based on vendor 

feedback) for final concentration to > 99%. The aqueous stream from both unit operations, which 

contains cell biomass, unconverted sugars, ammonium sulfate salts, and excess nutrients, is 

routed to the WWT section (A600) for cleanup and energy recovery. The combination of 

decantation and centrifugation is assumed to achieve > 99% product purification with 97% 

product recovery. The cost impact associated with varying the assumed product recovery is 

investigated below in the Sensitivity Analysis section.

As noted above, depending on specific fatty acid components or impurities present in the 

secreted FFA product, additional processing considerations could be required to allow for 

effective handling or further purification of the product. For example, fully saturated fatty acids 

in the C12–20 range exhibit melting points between 45°–75°C , but introducing even one degree 

of unsaturation (one double bond) reduces these melting points to less than 25°C [84, 85]. As 

such, the present model assumes that special conditions such as elevated temperature to maintain 

a liquid FFA state are not required. Alternatively, if the secreted FFA product were to include 

carryover of significant levels of impurities such as polar lipids (present in the cell membrane) or 

binding with extracellular proteins, a separate cleanup step such as product bleaching and 

degumming or washing to remove such impurities could be required prior to hydrotreating; such 

impurities could also lead to emulsions making further purification more difficult. Dedicated 

cleanup or washing steps are not assumed to be required currently, but the question of impurities 

or emulsions will be an important subject of future research and quantification. 

Following FFA product concentration, the material is upgraded in a hydrotreater to refine the 

oxygenated intermediate material into saturated paraffin components suitable for blending as a 

diesel blendstock. Harris Group consulted with a number of vendors, including catalyst suppliers 

and process licensors, to obtain budgetary estimates for a hydrotreating facility that may be 

expected to process a high-FFA feed as modeled here into deoxygenated diesel-range paraffins.

A number of cost estimates were provided for such a facility, but the vendors declined to provide 

specifics without confidentiality restrictions regarding associated process and design conditions 

(e.g., reactor sizing dictated by space velocities, operating temperatures and pressures, hydrogen 

partial pressure, etc). Thus, to estimate reasonable process conditions for modeling purposes, a 

combination of literature and Harris Group estimates were applied. Namely, a prior NREL study 

for hydrotreating triglycerides was leveraged [86], which in turn was based in large part on a 

report jointly authored by UOP, NREL, Pacific Northwest National Laboratory, and Michigan 

Technological University [87]. This report described relatively mild hydrotreating temperature 

and pressure conditions at 350°C (660°F) and 35 atm (500 psig), but a high hydrogen feed ratio 

at 6,000 standard cubic feet/barrel of feed (this translates to a hydrogen partial pressure of 

approximately 460 psig at reactor inlet). As confirmed by Harris Group, high hydrogen feed is 
This report is available at no cost from the 50 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications. 

necessary in part to provide a quench to control the large exotherm attributed to deoxygenation. 

A reasonable liquid hourly space velocity associated with this process would likely be in the 

range of 0.9–1.4 h-1 [87], which was confirmed by an estimated value of 1.2 from Harris Group. 

A schematic diagram of the FFA concentration and hydrotreating process operations is shown in 

Figure 14. This diagram includes an assumption of a two-stage hydrotreater, with the first stage 

including a guard bed for metals, as well as a possible mechanism for handling the high acid 

(total acid number [TAN]) content that may be expected to be present in a concentrated FFA 

feedstock (based on vendor feedback). Additionally, a cost estimate was also included for a 

pressure swing adsorption (PSA) unit in the hydrotreater gas recycle loop, primarily to remove 

CO2 formed by decarboxylation reactions in the hydrotreater (discussed below). The design and 

cost estimates for the PSA unit were also provided by vendor input. In the Aspen model, the gas 

recycle feed stream to the PSA consists primarily of H2 and CO2 (after cooling the reactor 

effluent and knocking out most of the produced water, which is routed to WWT). Given this gas 

composition, the PSA vendor provided a cost estimate for a patented PSA unit which utilizes a 

zeolite-based sorbent to provide a high expected hydrogen recovery: the expected hydrogen loss 

noted by the vendor was only about 1%. As this recovery is higher than standard PSA operations 

(even if based on a more novel PSA technology), and given the relative uncertainty inherent to 

the early status of the model including details around product and recycle gas compositions, the 

hydrogen loss in the PSA tailgas was conservatively increased to 5% (95% recovery). The 

overall economic sensitivity to this assumed PSA hydrogen recovery is evaluated below in the 

Sensitivity Analysis section. The PSA tailgas, containing approximately 54 vol% H2 and 46 

vol% CO2, is split to provide the necessary heat to the hydrotreater furnace (thus avoiding 

external natural gas to drive this operation), with the remainder sent to the boiler (A800). 

Figure 14. Schematic diagram of hydrotreating operations included in vendor design estimate 

(Note: vendor cost estimate includes distillation column for product separation, but present 

design model produces only diesel-range hydrocarbons) 
51

Oxygen removal from the FFA feed may occur by way of hydrodeoxygenation (HDO) or 

decarboxylation (DCO) reactions in the hydrotreater. The relative degree of one reaction 

pathway over another may be tailored by catalyst and reaction conditions, with a tradeoff 

exhibited between higher hydrogen consumption to reject oxygen as water via HDO (this carries 

important implications particularly on the life cycle assessment (LCA)/sustainability metrics of 

the system when sourcing hydrogen from conventional fossil sources) and decreased carbon 

yields to reject oxygen as CO2 via DCO (with decreased product yield translating directly to cost 

impacts). Again for lack of more specific details selected at this stage on targeted product 

pathways and reaction conditions consistent with the emphasis on a representative technology 

pathway, the oxygen removal mechanism is assumed to be split evenly between HDO and DCO 

reactions. A more likely scenario may be for the HDO versus DCO reactions to favor one 

reaction pathway over another (for example, 80/20 split favoring one reaction or another); 

however, again for purposes of setting a representative approach, and given that some potential 

pathway products (e.g., farnesene or other isoprenoids) are not even oxygenated in the first place, 

a 50/50 split is reasonable at this stage. Thus, the governing equations employed in the model for 

FFA upgrading are provided in Table 19. 

Table 19. Governing Stoichiometry Assumed for Hydrotreating Reactions

Reaction Reactant % Converted to 

Product

1 Palmitate (FFA) + 3 H2 → 1 C16H34 + 2 H2O (HDO reaction) FFA 50%

1 Palmitate (FFA) → 1 C15H32 + 1 CO2 (DCO reaction) FFA 50%

As shown in Table 19, the hydrotreating reactions were assumed to occur stoichiometrically to 

remove oxygen in the palmitate feedstock equally by HDO and DCO mechanisms, producing 

hexadecane and pentadecane in equimolar amounts. These reactions also set the hydrogen 

consumption across the hydrotreater, with additional hydrogen losses primarily in the recycle gas 

PSA unit (5% loss into the PSA tailgas). For the present preliminary model, no overcracking is 

assumed to light gases or naphtha-range material aside from the two reactions presented above.

Thus, for modeling and mass balance purposes, the product is assumed to be composed of 

hexadecane and pentadecane, both straight-chain paraffins with high cetane value, making them 

valuable as diesel-range blendstocks. It is recognized that to produce a stand-alone final fuel 

product meeting ASTM diesel specifications, the straight-chain paraffinic product from 

hydrotreating would require further isomerization to address potential cloud point issues [88] 

however, the high cetane and low sulfur content expected to be exhibited by the RDB product is 

expected to make such a product valuable for diesel blending purposes, and the removal of 

oxygen during hydrotreating suffices to meet DOE requirements consistent across other 

technology pathways for a finished product [10]. The assumptions discussed above for the FFA 

hydrotreating section are summarized in Table 20. 
52

Table 20. Summary of Hydrotreating Process Assumptions 

FFA feed rate 99,540 gal/day (377 m3/day)

Average reaction temperature 660 °F (350 °C)

Pressure 500 psig (35 atm)

Hydrogen partial pressure 460 psig (32 atm) / 6,000 SCF/bbl

Reactor LHSV ~1.2 hr-1 (estimated) a

Hydrogen loss across recycle PSA 5%

Makeup hydrogen rate 2.2 MM SCFD (59,030 Nm3/day)

RDB product rate 95,400 gal/day (361 m3/day) a Reactor LHSV is not used in the present model (total hydrotreater facility cost was used based on 

vendor feedback, which implicitly includes hydrotreater sizing which was not provided by vendor)

3.5.3 Cost Estimation

The gravity decanter units for FFA concentration consist of horizontal cylindrical vessels with 

internal baffles that improve product separation by making flow more laminar. Each vessel is 

sized at 9,840 gal (96 in. × 35 ft) to provide one-hour residence time. The vendor-supplied cost 

estimate for each vessel is $560,000 for equipment purchase. The centrifuge purchase cost is 

based on a previous vendor-supplied estimate of $450,000 per unit with maximum unit capacity 

of 250 gpm [89], thus only one unit is required in the present design at a basis of 200 gpm 

exiting the decanters. The centrifuge is assumed to operate at a vendor-supplied power demand 

of 0.2 KW/gpm processed. 

The hydrotreating facility cost is an “inside battery limits” (ISBL) cost estimate, including costs 

for reactors, makeup and recycle gas compressors, fired heater, separation vessels, and 

distillation (which is included to reflect a slate of products which may be produced from 

hydrotreating, although only pentadecane and hexadecane are assumed to be produced in the 

current model which both fall into the diesel range and would not require distillation under this 

simplistic scenario). Additionally, catalyst replacement cost is not explicitly specified, but one 

vendor noted that catalyst would be an insignificant part of the total cost estimate for this 

particular system, at a typical catalyst lifetime of two years and the given FFA feed specification. 

Two vendor cost estimates were provided for the hydrotreating section, which varied 

considerably in magnitude, in part due to the preliminary nature of the modeling and the lack of 

specificity regarding FFA feed characteristics (more detailed FFA composition, presence of 

impurities, acid level, etc.); as such, it is important to emphasize that at this stage the cost 

estimate for the hydrotreating system is most appropriately viewed as an order-of-magnitude 

estimate, and should be treated as such in drawing conclusions as to the cost that may be incurred 

for upgrading the FFA material or other similar mildly oxygenated products from biological 

conversion pathways. Indeed, the uncertainty range associated with the cost estimate which was 

selected ($23MM installed cost) was listed as –50% /+100%. The overall cost impact associated 

with this given range of uncertainty in the hydrotreating cost estimate is evaluated below in the 

Sensitivity Analysis section. Even with such a relatively high margin of uncertainty in the 

hydrotreating cost estimate, we feel it is more prudent to leverage this information than 

attempting a cost estimate on our own (e.g., using cost estimation software) as the vendor 

estimates bring valuable insight as to important considerations such as reactor design, number of 

stages, and operating conditions. 
53

The lower vendor cost estimate of $23MM installed cost was selected here over a second, higher 

estimate of $62MM as the lower estimate appeared to be supported more by what might be 

reasonably predicted both based upon literature as well as Harris Group’s expertise borrowing 

primarily from conventional petroleum hydrotreating, under an expectation of a relatively clean 

FFA feed stream low in impurities. While supporting detail behind the two vendor estimates was 

sparse, a primary reason for such a large cost discrepancy may be in how the acidity is addressed 

in a high-TAN feedstock such as FFA material. The vendor who provided the higher cost 

estimate indicated the necessity for exotic metallurgy to handle expected TAN content, while the 

vendor providing the lower cost number indicated a capability to deal with this issue through 

careful catalyst selection including an upstream guard bed, and specifically mentioned that exotic 

metallurgy was not required. Given the relatively mild hydrotreating conditions listed in Table 

20, which are in line with a relatively high-quality material as would be expected to be produced 

through most biological pathways (e.g., fatty acids, fatty acid esters, isoprenoids, etc.), both 

Harris Group and NREL felt comfortable utilizing the $23MM estimate as the baseline here. 

The PSA unit was provided as a separate cost item, as this is not a typical unit operation in 

conventional hydrotreating systems, but is required here primarily to remove CO2 formed during 

hydrotreating (e.g., via the DCO reaction which diverts carbon away from product yields to 

produce CO2). The cost estimate for this operation was provided by a PSA vendor. As noted 

above, the design basis for this operation was based on a patented zeolite sorbent system, 

allowing for high expected hydrogen recoveries. Harris Group also evaluated the cost for an 

amine system as an alternative CO2 removal option, and found that this alternative may be more 

cost effective relative to the PSA option, but this also is likely to exhibit a higher heat demand 

for amine regeneration. As more definition becomes available for the biological production 

pathway, intermediate product characterization, and hydrotreating specifics, the options for CO2

removal in the recycle gas line may be revisited.

3.6 Area 600: Wastewater Treatment

3.6.1 Overview 

All the wastewater generated in the conversion process is sent to the wastewater treatment

system in Area 600. The treated water is assumed clean and fully reusable by the process, which 

reduces both the fresh makeup water requirement and discharge to the environment. In 2010, 

NREL placed a subcontract with Harris Group and Brown and Caldwell (a WWT technology 

vendor) to design a treatment system capable of handling wastewater material associated with 

NREL’s 2011 biochemical ethanol design report model. The emphasis of the collaboration was 

primarily to ensure that the wastewater design could accommodate the large volume, highsalt/high-nitrogen material generated at the specific process conditions used in the design report 

model. Following this effort, a new subcontract was placed in 2012 to consult directly with 

Brown and Caldwell to revisit the WWT design and cost estimates, primarily to address process 

modifications being implemented. These modifications primarily included the addition of 

deacetylation and new black liquor stream to WWT, as well as the move to lower severity acid 

pretreatment, resulting in lower nitrogen and salt levels reaching the WWT section. We felt this 

second subcontract was necessary as the earlier 2010 subcontract provided very specific design 

and cost estimates associated with the design report basis conditions, and the basis for scaling all 

WWT equipment was established as the total kg/h feed rate to WWT; such a basis does not allow 

for accurately capturing cost impacts associated with different WWT feed characteristics such as 
54

nitrogen or chemical oxygen demand (COD) loading. The 2012 subcontract also provided more 

flexibility in this regard, costing out equipment on a more individual basis with associated 

scaling factors provided based both on hydraulic rate as well as COD load. The updated 

simplified flow diagram is shown in Figure 15; the process schematic itself is similar to the 

previous design used in the 2011 ethanol report, but a number of unit-level costs and design 

assumptions are different. A key factor behind the updated wastewater equipment design 

provided by Brown and Caldwell was the use of actual representative material generated in the 

pilot plant to accurately characterize the WWT feed to be treated, forming a better-informed 

basis from which cost and design estimates are scaled in the present analysis.

Figure 15. Simplified flow diagram of the WWT process

Condensed pretreatment flash vapor, black liquor from deacetylation, boiler blowdown, cooling 

tower blowdown, and the bioreactor broth water streams are mixed together. The combined 

wastewater stream is processed in A600 consisting of anaerobic and aerobic digestion, 

membrane filtration, reverse osmosis, evaporation, dewatering, and gravity belt thickening. Each 

process component and its function are summarized in Table 21. Anaerobic digestion produces a 

biogas stream that is rich in methane, so it is fed to the combustor. The aerobic system utilizes a 

membrane bioreactor which is essentially an aeration tank followed by an ultrafiltration 

membrane system with biomass recycle. The effluent of the membrane system is of sufficient 

quality to feed a reverse osmosis system directly (i.e., without additional filtration). The 

dewatered sludge and biogas are sent to the boiler in A800. The dewatering process for the 

anaerobic sludge is centrifugation. The aerobic sludge is thickened by gravity belt thickeners 

(GBTs) prior to centrifugation in order to reduce the number of centrifuges required. In general, 

centrifugation provides higher solids capture and a drier biomass cake compared to other 

dewatering systems thus reducing the size of the biomass burner system. It also provides a 

margin of safety against changes in sludge quality which could impact dewatering processes 

relying upon filtration (e.g., belt filter press). Reverse osmosis is required to remove dissolved 

salts, so that cleaned water can be recycled to upstream processes while mitigating salt 

accumulation. 

This process produces a relatively clean water stream that can be reused in the process. The 

water stream is routed to the common process water header (A900) to be used in other process 

areas, such as dilution water in pretreatment or enzymatic hydrolysis. 

From centrifuge

Cooling tower blowdown

Pretreatment flash 

and black liquor

Boiler blowdown Biogas CH4/CO2

Air Vent to atmosphere

Digester sludge to 

combustor

Treated water

Aerobic

Digestion

Anaerobic

Digestion Clarifier

Dewatering

RO

Waste brine

Evaporator
55

Table 21. Process Components and Functions in Area 600

Process Component Functions

Anaerobic Treatment Organic load reduction, Biogas generation

Aerobic treatment Organic load reduction

Membrane Filtration

Total suspended solids removal

Colloidal organic material removal

Protection of reverse osmosis system

Reverse Osmosis Removal of dissolved inorganic salts

Evaporation Volume reduction of reverse osmosis reject flow

Reduction of heat needed in boiler

Gravity Belt Thickener Biological sludge thickening prior to centrifugation

Centrifugation Biological sludge dewatering

Reduction of heat needed in biomass boiler

3.6.2 Design Basis

The WWT system was designed by and Brown and Caldwell under the 2012 subcontract 

mentioned above. The design includes anaerobic digestion, aerobic digestion, sludge dewatering, 

reverse osmosis, evaporation, and centrifugation. The relevant design parameters of the 

wastewater system are summarized in Table 21, based on actual material generated in the pilot 

plant in 2012. 

Evaporated water from the sugar concentration step is of high purity (> 99.5% water), and thus is 

not routed to A600 for further cleanup but is recycled directly for process water use. This helps 

reduce the total hydraulic flow and cost of a number of WWT unit operations. The deacetylation 

black liquor, condensed pretreatment flash vapor, bioreactor aqueous phase after FFA separation, 

boiler blowdown, and cooling tower blowdown are mixed together and pumped through a heat 

exchanger and cooled to 35°C before being sent to the anaerobic digester. 

The total COD entering anaerobic digestion in the current design case is approximately 120 g/L 

(30,700 kg/h). This COD concentration represents an increase from both the 2011 and 2012 

WWT designs, primarily because of increased diversion of sugars to bioconversion cell mass, 

with a lower total volume flow caused by upstream sugar concentration and water removal. As 

noted above, the updated subcontract design/cost basis now allows for costing unit operations on 

a hydraulic load and COD basis (as appropriate), so these variations from the design are 

accurately captured in the cost estimates. Low-rate Bulk Volume Fermentors (BVF reactors) are 

used as the anaerobic digester vessels, capable of removing 89% of incoming COD. 85% of the 

COD is converted to biogas (methane and carbon dioxide) and 5% is converted to cell mass. 

Methane is produced from the organic matter at 238 g/kg COD removed. Carbon dioxide is also 

produced at a nearly equimolar rate to methane, such that the biogas from the digester is 51% 

CH4/49% CO2 on a dry molar basis; this ratio is lower than typical digester biogas at 60%–70% 

CH4, but is consistent with the vendor suggestions. Cell mass is produced at a yield of 48 g cell 

mass per kg COD removed. Biogas blowers pull a negative pressure to remove the produced gas. 

The preferred use of the biogas is in the on-site combustor for combined heat and power benefits. 

However, economic factors may drive the sale of the gas via pipelines. In either case, an 
56

emergency biogas flare is required and designed into the process to burn the biogas in case of a 

process upset in the combustor. Sulfate is converted to hydrogen sulfide (H2S) and leaves the 

system in the biogas. Since the COD-to-sulfate ratio in the design basis is ~2000, the methaneproducing organisms will out-compete the sulfate-reducing organisms, so upstream removal of 

sulfate is unnecessary. The H2S and any other sulfur species in the biogas are converted to 

SO2 downstream in the combustor, which is mitigated using flue gas desulfurization (FGD) 

(Area 800). 

Table 22. WWT System Design Basis

2011 WWT design (used 

as basis for cost estimates 

in 2011 ethanol report)

2012 WWT design 

(used as scaling basis 

for cost estimates here)

This design (all values 

estimated by Aspen)

Hydraulic load 2.15 MMgal/d 2.7 MMgal/d 1.6 MMgal/d

Wastewater inlet 

temperature

50°C 60°C 60°C

Inlet pH 5.2 7.8 -

Total COD 87 g/L 66 g/L 120 g/L

Soluble COD 85 g/L 66 g/L -

Total solids 68 g/L 33 g/L 130 g/L

Total suspended solids 1500 mg/L 56 g/L 27 g/L

Total alkalinity 2,750 mg/L as CaCO3 0 mg/L as CaCO3 -

Ammonia-N 1,060 mg/L 0 mg/L -

Total Kjeldahl N 1,200 mg/L 404 mg/L 724 mg/L

Sulfate 4,400 mg/L 36 mg/L -

Silica 1,600 mg/L 754 mg/L -

Potassium 500 mg/L 1060 mg/L -

Phosphate 805 mg/L 3260 mg/L -

The liquid from the anaerobic digester is pumped to the aerobic activated-sludge basin with 

surface aerators. In the concrete and steel basin, 96% of the remaining soluble organic matter is 

removed, with 74% producing water and carbon dioxide and 22% forming cell mass. With the 

89% COD reduction in anaerobic digestion followed by 96% reduction of the remaining COD in 

aerobic digestion, the total COD reduction is 99.6%. It is estimated by Brown and Caldwell that 

a non-degradable COD of 1,300 mg/L would be generated in the effluent of the aerobic basin. 

The fully digested material is pumped to a membrane bioreactor for clarification. The membrane 

unit removes additional COD along with colloidal particles (especially silica). Coarse- and ultrafiltration upstream of the membrane unit separate the aerobic biomass sludge. The sludge is 

pumped to a dewatering device to reduce the sludge volume and water content to the boiler. 

Typically, a centrifuge is used for dewatering. A GBT was evaluated by Brown and Caldwell as 

a pre-dewatering step prior to the centrifuge in order to reduce of the number of centrifuges 

required. In this design, the GBT produces sludge with a solid content of 4%. The centrifuge 

units are then periodically operated to provide a solids capture rate of 95% and a cake 

concentration of 20%. The centrate flow is recycled with polymer addition to the activated 

sludge aerobic system. Most of the dewatered sludge is recycled to the aeration basin to maintain 

a high cell mass loading. The remaining portion is removed to a holding tank, where it is mixed 

with sludge from anaerobic digestion. Dewatered sludge is then routed to the combustor in A800. 

The sludge solid is primarily cell mass and unconverted biomass components. 
57

The treated water is pumped to a reverse osmosis (RO) membrane system for salt removal. RO 

produces a brine (217 gpm) containing primarily sodium nitrate along with all remaining ions 

and organics. The brine is further concentrated in a mechanical-vapor-recompression evaporator 

to 50% solids and the condensate is also recycled to the process. We do not further process the 

brine in our model at this time, and it is assumed waste. 

3.6.3 Cost Estimation

Brown and Caldwell estimated the cost for the wastewater system, drawing on support from 

technology vendors. Unlike the previous study by Harris Group and Brown and Caldwell, which 

was utilized in the 2011 ethanol design basis and scaled all WWT costs according to total mass 

flow rate into the WWT system, the cost estimation provided in the updated analysis from Brown 

and Caldwell allows for greater detail to adjust individual unit costs either based on hydraulic 

flowrate or by COD, thus resulting in improved accuracy (e.g., to accommodate differences in 

COD concentration even at similar total hydraulic flow rate). This is an important improvement 

given the relatively high cost of the WWT system. 

The anaerobic system is scaled based on total COD and the installed cost in this design is 

$30.9MM, shown in Table 23. For the aerobic digestion step, liquid volume per basin is 4.2 MM

gal, recommended by Brown and Caldwell. The total size of the aerobic basins is determined by 

hydraulic flow rate and mean cell residence time, which is set at 15 days by Brown and Caldwell 

and installed capital of the aeration basin is estimated at $7.5MM here, including capital costs for 

the surface aerators. The membrane bioreactor, dewatering units, reverse osmosis system, and 

evaporators are also scaled based on hydraulic flow rate, and their capital costs are shown in 

Table 23, in comparison with the 2011 design report, based on 2011-dollars. It can be seen that 

although some cost savings are realized here, such as the centrifuge, which is less costly because 

of a reduction in hydraulic load as well as the addition of GBT dewatering prior to centrifugation 

(included here in the “other costs”), other costs have increased, namely the anaerobic and aerobic 

systems, both of which have cost elements which depend on COD loading (the entire anaerobic 

system cost, as well as the blowers for the aerobic system); these units were also both re-costed 

in the updated Brown and Caldwell subcontract effort, so a direct comparison as shown below is 

somewhat artificial.

Table 23. Equipment Capital Costs for WWT (A600), in Comparison With 2011 Design 

Equipment WWT Installed cost ($MM; 2011$)

2011 Design Model Present Case

Evaporator System 4.2 5.9

Membrane Bioreactor 5.7 4.8

Reverse Osmosis System 2.4 2.6

Centrifuge 7.1 2.0

Anaerobic Digester 29.6 30.9

Aeration Basin 5.1 12.0

Others (pumps, conveyer, etc.) 1.0 2.0

Total WWT 55.1 60.2
58

Influent Total Kjeldahl Nitrogen (TKN) was analyzed at 404 mg-N/L in the 2012 wastewater 

sample, and it is 724 mg-N/L in this design estimated by Aspen Plus simulation. Both TKN

numbers are low enough that a nitrification step is no longer needed in this design, as it was in 

the 2011 design basis (which was based on a TKN level of 1,200 mg/L). This is due to the shift 

toward increasingly more mild pretreatment conditions and acid loading (with subsequent lower 

ammonia neutralization demand), beyond the basis established in the 2011 report. Thus, caustic 

which was required in the 2011 design for pH adjustment during nitrification is no longer 

utilized in the WWT section here, resulting in a considerable operating cost savings. Instead, 

supplemental ammonia would be needed to make up for a slight nitrogen deficit now present in 

the model to meet the optimum nitrogen demand (1,055 mg-N/L) for microorganisms to 

metabolize organic compounds in the aerobic digestion unit. The resulting ammonia demand in 

the aerobic basin is calculated to be 140 kg/h. In addition, polymer is added to the centrifuge to 

increase dewatering efficiency. Polymer requirements are set at 9 lb/ton total solids feeding into 

the aeration basin, estimated at 3 kg/h in the present model. The replacement of caustic in the 

2011 design with a smaller amount of ammonia and polymer in the present design results in a net 

variable operating cost savings of $2.3 MM/yr (2011$), with operating expenses discussed in 

more detail in Section 4. 

3.7 Area 700: Product and Feed Chemical Storage

3.7.1 Overview

This portion of the plant provides bulk storage for process chemicals and the RDB product. The 

chemicals stored in this area include ammonia, CSL, sulfuric acid, sodium hydroxide, and 

purchased glucose for enzymes. Water for fire suppression is also stored here.

3.7.2 Design Basis

Table 24 shows the major storage requirements for the present design. The design and cost bases

for all tanks maintained in the current design were left unchanged, and are scaled to a new cost if 

needed. The only exception is the product storage tank (RDB product, formerly the ethanol 

storage tank), which is cut by 50% to only require one tank for seven days of storage as the total 

volumetric product rate has decreased by nearly 50% relative to the ethanol design basis.

Table 24. Storage Requirements

Material Size (size basis maintained from 2011 ethanol basis)

RDB product Sufficient to contain 7 days of production: 1 carbon steel tank @ 

750,000 gal

Sulfuric acid (93%) 1 carbon steel tank @ 12,600 gal

Caustic (as pure) Duplicated from sulfuric acid tank: 1 carbon steel tank @ 12,600 gal

Ammonia 2 SA-516-70 tanks @ 28,000 gal. Ammonia is stored anhydrous at 

250 psig

Fire water 4 hours of fire suppression @ 2,500 gpm: 1 glass-lined carbon steel 

tank @ 600,000 gal

Corn steep liquor 1 glass-lined carbon steel tank @ 70,000 gal

Glucose syrup for A400 Duplicated from CSL tank: 1 glass-lined carbon steel tank @ 70,000 

gal

Diammonium phosphate (DAP) 1 SS304 tank @ 12,800 gal
59

Other supplemental tanks and pumps were left unchanged relative to the 2011 ethanol design 

basis, with more details provided in the 2011 report. 

3.7.3 Cost Estimation

The costs for the A700 storage section were left unchanged relative to the 2011 ethanol design 

basis (with the exception of the main product tank as noted above), and costs are scaled 

according to new material flow rates to estimate new tank prices if applicable.

3.8 Area 800: Combustor, Boiler, and Turbogenerator

3.8.1 Overview

The purpose of the combustor, boiler, and turbogenerator subsystem is to burn various organic 

byproduct streams to produce steam and electricity. Combustible byproducts include all of the 

lignin and the unconverted cellulose and hemicellulose from the feedstock, biogas from 

anaerobic digestion, biomass sludge from WWT, and PSA offgas from the hydrotreating unit. 

Burning these byproduct streams to generate steam and electricity allows the plant to be selfsufficient in energy (“thermal-neutral”), reduces solid waste disposal costs, and generates 

additional revenue through sale of excess electricity. Sulfur oxide (SOx) emissions are mitigated 

from the combustor via flue-gas desulfurization. 

The fuel streams are fed to a combustor capable of handling the wet solids. A fan moves air into 

the combustion chamber. Treated water enters the heat exchanger circuit in the combustor and is 

boiled and superheated to high-pressure steam. A multistage turbine and generator are used to 

generate electricity. Steam is extracted from the turbine at two different conditions for use in the 

process. In the final stage of the turbine, the remaining steam is taken down to a vacuum and 

condensed with cooling water for maximum energy conversion. The condensate is returned to 

the boiler feed water system along with condensate from the various process heat exchangers. 

The steam turbine turns a generator that produces AC electricity for all users in the plant. The 

balance of electricity is assumed to be sold back to the grid, providing a co-product credit.

3.8.2 Design Basis

For the A800 section equipment, the fundamental design and cost basis assumptions as provided 

by a boiler vendor employed in the 2011 ethanol design report were maintained in the present 

model. Thus, details of the boiler and turbogenerator design will not be repeated here, but can be 

found in the 2011 report. To briefly summarize, the combustor system features a live-bottom 

grated fuel bin to ensure drying and complete combustion of the wet solid fuel, with a boiler 

efficiency near 80%. Flue gas from the combustor preheats the entering combustion air then 

enters a spray dryer for flue gas desulfurization (FGD) (with the majority of sulfur entering the 

boiler system in the form of ammonium sulfate salt introduced upstream during pretreatment and 

conditioning operations). Although the ammonium sulfate salt level has decreased in the present 

model relative to the 2011 design case because of lower acid and ammonia loadings upstream, 

the FGD capital cost was included as part of the original quoted overall boiler cost, thus is not 

decoupled here to reflect a cost differential for lower FGD demands (although the FGD lime 

operating cost is reduced here to reflect less SO2 in the boiler flue gas). Ash is removed in a 

baghouse and disposed to a landfill. Finally, the boiler raises steam at 875 psig, which is 

superheated and then sent through a multistage steam turbine with two extraction ports and a 

final condenser.
60

While the design and cost bases for the boiler and turbogenerator system have not changed, a 

number of process parameters are somewhat different from the 2011 bases. Primarily, the 

moisture content of the combined solid feed to the combustor has increased from 44% in the 

2011 ethanol model to 60% in the current model. This is primarily due to the new lignin removal 

step, which is now placed upstream of the bioreactors. This necessitates the use of a solids wash 

step to recover carryover losses of sugars into the solids material from the filter press. This wash 

step adds more water to the lignin stream, which diverts more heat toward drying and reduces the 

amount of heat generated in the combustor. This is somewhat offset by the addition of the new 

offgas stream from the hydrotreater PSA unit, although this is a relatively small stream. The net 

result is a decrease in steam cycle flow rate of 1% from 235,000 kg/hr to 233,000 kg/hr. 

However, the net power generation in the steam cycle is increased by 29% relative to the 2011 

basis (from 41.3 MW to 53.1 MW) because heat demand and thus turbine steam extraction has 

decreased substantially. While the fraction of steam removed from the first turbine extraction 

point for feeding to the pretreatment reactor is similar to the 2011 basis, the fraction of steam 

subsequently removed in the second turbine extraction point (at a pressure of 125 psig or 9.5 

atm) is much less at roughly 2.5% rather than 35% in the 2011 basis. In the 2011 ethanol model, 

the steam extracted at this stage was required in large part to drive the distillation reboilers, 

which are no longer required in this pathway model (instead using simple phase separation for 

product purification). A small new stream is taken off at this extraction point to provide 

supplemental heating for the sugar evaporator system, but as this is an MVR evaporation unit, it 

is driven primarily by electricity rather than heat; thus, the steam requirement is fairly low at 

1,550 kg/h. After the intermediate extraction point, the remainder of the steam is sent through a 

final turbine stage to vacuum pressure, and is condensed at –13 psig (0.1 atm) and pumped back 

to the boiler. The turbine generator efficiency is assumed to be 85% as in the 2011 basis. The 

resulting power generation is 53 MW. The process uses 43 MW, leaving about 10 MW to be sold 

to the grid.

3.8.3 Cost Estimation

The cost basis for the A800 equipment remains the same as described in the 2011 ethanol report. 

Namely, the combustor/boiler system is based on a vendor quote and includes the boiler feed 

water preheater, FGD spray dryer, and baghouse. For the baghouse, bag replacement appears as a 

periodic charge in the cash flow worksheet. The turbogenerator is also based on a vendor quote 

for an industrial generator which remains suitable for this project. Harris Group also obtained 

quotes from a third vendor for support equipment including the deaerator, chemical injection 

system, tanks, and pumps. 

3.9 Area 900: Utilities

3.9.1 Overview

Area 900 contains the utilities required by the RDB production facility (except for steam, which 

is provided by Area 800). Area 900 tracks cooling water, chilled water, plant and instrument air, 

process water, and the clean-in-place (CIP) system. In the model, Area 900 also tracks the 

electricity usage throughout the plant. 

The process water manifold in Area 900 mixes fresh water with treated wastewater and 

condensate from the sugar evaporation system (assumed suitable for all plant users) and provides 

this water at a constant pressure to the facility. Water is provided to the cellulase production unit, 
61

boiler and cooling tower makeup, the CIP system, and the wash for the lignin filter press. Fresh 

water is also mixed with some internally-recycled water for dilution before pretreatment and 

enzymatic hydrolysis. The plant and instrument air systems provide compressed air for general 

use (pneumatic tools and clean-up) and instrument operation. Larger users of compressed air, 

namely the cellulase system and aerobic bioreactors, have their own compressors specified. The 

CIP system provides hot cleaning and sterilization chemicals to hydrolysis, bioconversion, and 

the enzyme production section. 

3.9.2 Design Basis

The cooling water system is designed for a 28°C supply temperature with a 9°C temperature rise 

in coolers throughout the facility. This is an assumed average rise; the actual cooling water rises 

across each exchanger are not explicitly modeled in Aspen. The primary cooling water users in 

this process are listed in Table 25. The percentage of cooling duty contributed by each user is 

shown in Figure 16. Compared to the 2011 ethanol design basis, the total cooling water demand 

has increased by 34% from 97 to 130 MMkcal/h, due primarily to increased steam turbine 

condenser duty for a larger steam flow reaching the condensing turbine (due in turn to lower 

facility steam/heat demand), the addition of the bioconversion air compressor cooler, and the 

addition of a water-cooled exchanger to cool the sugar evaporator condensate. This is discussed 

and analyzed in further detail below in Section 5. 

Table 25. Cooling Water Users

M-811 Condenses the steam turbine exhaust at a vacuum.

M-908 The chilled-water loop requires cooling water to condense the refrigerant. The 

cooling water duty to M-908 is set equal to the total load on the chilled-water loop.

H-301 Cools the pretreated slurry to enzymatic hydrolysis temperature (48°C).

F-300 Before bioconversion is initiated, the slurry must be cooled to bioreactor 

temperature. It is assumed that cooling water would be used for the initial drop from 

48°C to 32°C and that chilled water would afterward be substituted to sustain this 

temperature. 

QC-EVCND Provides cooling of the sugar evaporator condensate.

M-401 Uses cooling water to cool the compressed air used in enzyme production.

QC-AIRCL Uses cooling water to cool the compressed air used in bioreactor aeration.

H-244 Condenses residual pretreatment flash vapor before it enters WWT. Most of the 

vapor is condensed by heat exchange with the incoming dilution water and boiler 

feed water.

As was the case in the 2011 ethanol model, the largest user of cooling water is the condensing 

turbine. Aspen computes the cooling tower evaporation rate based on a temperature drop from 

37°C to 28°C. It was assumed that windage would be 0.005% of the total flow to the tower. The 

tower blowdown was assumed to be 0.15% of the flow leaving the tower basin. 

Chilled water is provided by two 2,350-ton Trane centrifugal chillers. Per the chiller spec sheet, 

the compressor electricity demand for the chiller was estimated at 0.56 kW/ton of refrigeration. 

The cooling water demand for the chiller system was assumed to be equal to the heat removed in 

the chilled-water loop. The chiller provides cooling to the bioreactors in Area 300 and Area 400. 
62

Figure 16. Cooling water heat duty distribution between major users

Fresh water is assumed to enter the facility at 13°C and is used to cool the wastewater entering 

Area 600 to digestion temperature before entering the process water tank. The fresh water is 

mixed with the treated wastewater effluent and the evaporation condensate in the process water 

tank (T-914) and then split several ways. Clean water must be provided to biomass dilution in 

the front end, to the cellulase production unit, to lignin filter press wash step, and to the boiler 

and cooling tower water makeup. The process water tank is designed for an 8-hour residence 

time. The process water pump (P-914) pumps water from the tank into the facility and is 

designed to handle 1.5 times the process water flow requirement. 

The plant and instrument air systems provide compressed air for pneumatic tools and clean-up 

and instrument operation (not including major air demands such as enzyme and product 

bioreactors). The plant air compressor is sized for 400 SCFM at 125 psig. An instrument air 

dryer and surge tank were designed to provide clean dry air at a consistent pressure to the 

instrument air system. The surge tank was sized at 3,800 gal. 

About 80% of the electricity generated by the boiler in Area 800 is used throughout the plant to 

power pumps, agitators, compressors, etc. The surplus is sold to the grid for credit. The 

distribution of total plant power utilization among all areas is shown in Figure 17. Note that the 

cost of the power required by Area 100 is already assumed to be included in the feedstock cost 

but must be subtracted from the plant’s electricity export. This is reflected in the economics by 

an operating cost credit equal to this amount of electricity. 

Condensing 

Turbine

67%

Chiller 

condenser

13%

Hydrolysis cooler

6%

Hydrolyzate flash 

condenser

1% Pre-fermentation 

cooler 

2% Enzyme 

production 

compressor

0%

Bioreactor air 

compressor

6%

Sugar evaporator 

condensate

5%

Total = 130 MMkcal/hr (516 MM BTU/hr)
63

Figure 17. Distribution of plant electricity utilization by process area

3.9.3 Cost Estimation

All cost estimates for the utility equipment in A900 were maintained consistent with the basis 

values used in the 2011 report. To summarize, the cooling tower was based on a cost estimate 

from a vendor for a fiberglass cooling tower capable of handling 44,000 gpm; this cost is scaled 

to the increased cooling water throughput estimated here. Harris Group had estimated costs for 

the cooling water circulation and makeup pumps using their internal database. The material of 

construction for the cooling water loop is carbon steel. The cost for the chiller came from a 

recent quote for a similarly-sized system. Harris Group had also used their internal database to 

estimate costs for the remaining equipment: the process water tank and pump; the 

plant/instrument air compressor, dryer, and surge tank; and the CIP system. 

 to A100 Feedstock 

Handling

2% 

 to A200 Pretreatment

15% to A300 Hydrolysis/ 

Conditioning/ 

Bioconversion

38% 

 to A400 Enzyme 

Production

5% 

 to A500 Recovery + 

Upgrading

1% 

 to A600 

Wastewater

9% 

 to A700 Storage

0% 

 to A800 

Boiler/Turbogen

3% 

 to A900 

Utilities

7% 

Excess electricity to 

grid

20% 

Total Power Generated = 53 MW
64

4 Process Economics

The ultimate purpose for developing such a detailed process design, simulation model, and cost 

estimate is to determine the economics of biofuel production. This information is used either as 

an absolute cost to assess the product’s potential in the marketplace or as a relative cost that can 

be used to guide research by examining the change in production cost associated with a process 

modification or other core research activity.

The total capital investment (TCI) is first computed from the total equipment cost. Next, variable 

and fixed operating costs are determined. With these costs, we use a discounted cash flow 

analysis to determine the minimum fuel selling price (MFSP) required to obtain a zero net 

present value (NPV) with a finite internal rate of return (IRR). This section summarizes the 

assumptions made in completing the discounted cash flow analysis, with more details and 

supporting description available in the 2011 ethanol report for assumptions that are unchanged. 

Our analysis does not take into account any policy factors such as subsidies, mandates, or carbon 

credits, because these would be purely speculative. The purpose of this analysis is to demonstrate 

the process requirements needed to achieve specific DOE cost targets which are set from the topdown, and to demonstrate how the technology pathway described here is able to achieve such 

targets on its own merits (through bottom-up TEA modeling) and, if it cannot, to give 

policymakers a sense of the magnitude of incentive required to make it so.

4.1 About Cost-Year Indices

The cost-year of 2011 was chosen for this analysis to provide more updated and relevant cost 

output information relative to the 2007-year basis which had been consistently utilized for a 

number of years in prior analyses [3, 90]. This new basis is being applied consistently across all 

DOE-BETO platforms for which similar “design case target” reports are being established during 

2013–2014 efforts, and it is expected that performance goals and TEA outputs will remain in 

2011$ through 2017 to permit comparison of future feedstocks, conversion technologies, and 

other alternative scenarios. However, the present equipment costs were obtained in 2012$ or 

2013$ for new pieces of key equipment which were added or replaced in the current design 

model (as part of the subcontract with Harris Group), and in 2009$ or 2010$ for unit operations 

that were previously provided by Harris Group and vendors in support of NREL’s 2011 ethanol 

design report, and are still maintained in the present model. Cost-years for chemicals range from 

1999 to 2012. 

The methods used for determining MFSP in another year’s dollar value and for scaling capital, 

operating, and labor cost estimates to a desired target year remain the same as described in the 

2011 ethanol report. Thus, the details will not be repeated here, but will be summarized briefly.

Capital costs provided in a year other than 2011$ were adjusted using the Plant Cost Index from 

Chemical Engineering Magazine [91] to a common basis year of 2011. The final cost index for a 

given year is generally not made available until the spring of the following year. Therefore, for 

the small number of equipment items that were quoted in 2013$, we assumed the same Plant 

Cost Index value from 2012 (all cost quotes which fall in this category were provided in the first 

half of 2013). Similarly, for chemical costs we used the Industrial Inorganic Chemical Index 

from SRI Consulting [92]. Employee salaries were maintained from 2009$ and were scaled using 

the labor indices provided by the U.S. Department of Labor Bureau of Labor Statistics [93]. The 

general formula for year-dollar back-casting is:
65

2011 Cost = (Base Cost) �

2011 Cost Index

Base Year Index�

Since NREL’s biochemical ethanol report was released in 2011 (which included cost estimates 

and analysis collected between 2009 and 2011), the cost indices have remained more stable than 

during the five-year period between 2005 and 2008, which experienced a significant run-up in 

the price of materials such as steel and petroleum, as well as the considerable fluctuation that 

ensued between 2008 and 2011. Thus, the indexing that takes place to discount costs back to 

2011$ from 2012$ or 2013$ for the newest price estimates utilized in the present report does not 

introduce large price differences for these specific years. To maintain consistency with the 2011 

ethanol report, capital costs and material prices for items which were unchanged in the present 

analysis were not modified except to update to 2011-year dollars. The only new “feed materials” 

that were added to the present design that were not required in the ethanol case were hydrogen 

and WWT chemicals, whose assumptions on delivery and pricing are described below. The 

electricity export price, taken to be the average wholesale price as determined by the North 

American Electric Reliability Corporation, was left unchanged from the ethanol report, set at 

$0.0572/kWh. This corresponds to roughly 84% of the EIA 2011 average industrial retail price 

for electricity of $0.068/kWh [94], which is consistent with published values for wholesale 

electricity as a fraction of retail prices [95]. 

4.2 Total Capital Investment

Section 3 of this report describes the details of the conceptual process design and how the 

purchased cost of the equipment was determined. The next step is to determine the installed cost 

of that equipment. The installation cost can be determined by performing a detailed study of 

everything required to install the necessary equipment and make it operational (e.g., foundation, 

piping, and wiring). This type of detail is not warranted at this level of analysis, and a factored 

approach in which multipliers are applied to the purchased equipment cost is considered 

satisfactory. The methodology and rationale for applying unit-level installation costs remain the 

same as described in the 2011 ethanol report, and again further detail can be found there that will 

not be repeated here. In summary, each type of equipment utilizes a different installation factor 

to scale the given direct equipment purchased cost to a final installed cost, with these factors 

generally varying between 1.5 and 3.0. A complete list of the equipment is provided in Appendix 

A, along with equipment purchased and installed costs. As described in the 2011 ethanol report, 

Harris Group obtained many package quotes for recent cost estimates, in which a given unit 

operation and all of its support equipment were quoted under one price. The installation factor 

for such packages can be relatively low because most of the engineering is already included in 

the price. Additionally, equipment designed as a pre-fabricated skid generally has a lower 

construction cost. Also, components that are more highly machined and have higher quality 

metallurgy tend to be more expensive per unit mass and therefore have a lower installation factor 

as a function of purchase price than a less sophisticated component does. 

The purchased cost for a given component reflects a baseline equipment size. As changes are 

made to the process, the equipment size required may be different than what was originally 

designed. Instead of re-costing in detail, an exponential scaling expression was used: 

New Cost = (Base Cost) �

New Size

Base Size�

n
66

In this equation, the scaling exponent n varies depending on the type of equipment to reflect 

economy-of-scale dependencies (more detail on reasonable scaling values for different types of 

equipment is provided in the 2011 ethanol report). The basis for scaling is typically some 

characteristic of the equipment related to production capacity, such as flow or heat duty. Some 

equipment does not follow such a scaling-factor approach, namely when the capacity for a given 

operation is exceeded and requires multiple units in parallel (thus losing economy of scale 

benefits which are captured in the exponential expression above). A good example of this is the 

new pretreatment reactor cost information discussed in Section 3, which has resulted in a 

modified cost estimate to utilize multiple discrete sections of the horizontal portion of the 

pretreatment reactor (e.g., the portion which is in contact with acid) to allow for variations in 

residence time through this section of the reactor.

Once the total equipment cost has been determined in the year of interest, we must add several 

other direct and indirect costs to determine the total capital investment (TCI). Site development 

and warehouse costs are based on the inside-battery-limits (ISBL) equipment costs (Areas 200, 

300, 400, and 500) and are considered part of the total direct cost (TDC). Beyond ISBL Areas 

200–500, the other process areas are considered outside-battery-limits (OSBL) including Areas 

100 (rolled up into feedstock costs) and 600-900. Project contingency, field expenses, homeoffice engineering and construction activities, and other costs related to construction are 

computed relative to the TDC and give the fixed capital investment (FCI) when summed. The 

sum of FCI and the working capital for the project is the TCI. Table 26 summarizes these 

categories and additional factors. The values assumed for each respective factor were maintained 

consistently with those discussed in the 2011 ethanol report. 

Table 26. Additional Costs for Determining TCI

Item Description Amount

Additional direct costs

Warehouse On-site storage of equipment and supplies. 4% of ISBL a

Site 

development 

Includes fencing, curbing, parking lot, roads, well drainage, rail system, 

soil borings, and general paving. This factor allows for minimum site 

development assuming a clear site with no unusual problems such as 

right-of-way, difficult land clearing, or unusual environmental problems. 

9% of ISBL

Additional piping To connect ISBL equipment to storage and utilities outside battery limits. 4.5% of ISBL

Indirect costs

Prorateable 

costs 

This includes fringe benefits, burdens, and insurance of the construction 

contractor. 

10% of total direct 

cost (TDC)

Field expenses Consumables, small tool and equipment rental, field services, temporary 

construction facilities, and field construction supervision. 

10% of TDC 

Home office and 

construction 

Engineering plus incidentals, purchasing, and construction. 20% of TDC

Project 

contingency 

Extra cash on hand for unforeseen issues during construction. 10% of TDC

Other costs Start-up and commissioning costs. Land, rights-of-way, permits, surveys, 

and fees. Piling, soil compaction/dewatering, unusual foundations. Sales, 

use, and other taxes. Freight, insurance in transit, and import duties on 

equipment, piping, steel, instrumentation, etc. Overtime pay during 

construction. Field insurance. Project team. Transportation equipment, 

bulk shipping containers, plant vehicles, etc. 

10% of TDC

a ISBL = installed cost of equipment inside battery limits (A200,300,400,500)
67

Table 27. Project Cost Worksheet Including TDC and TCI 

Process Area Purchased Cost Installed Cost

 Area 100: Feedstock handlinga $ 15,800,000 $ 26,900,000

 Area 200: Deacetylation + Pretreatment $ 33,900,000 $ 51,400,000

 Area 200: Conditioning $ 1,100,000 $ 2,200,000

 Area 300: Enzymatic hydrolysis/conditioning/bioconversion $ 58,100,000 $ 75,400,000

 Area 400: Enzyme production $ 7,300,000 $ 12,400,000

 Area 500: Recovery + Upgrading $ 16,300,000 $ 26,600,000

 Area 600: Wastewater $ 43,700,000 $ 60,100,000

 Area 700: Storage $ 1,900,000 $ 3,400,000

 Area 800: Boiler $ 42,000,000 $ 76,000,000

 Area 900: Utilities $ 5,100,000 $ 8,800,000

Totals (excl. Area 100) $ 209,300,000 $ 316,300,000

 Warehouse 4.0% of ISBL $ 6,700,000 

 Site development 9.0% of ISBL $ 15,100,000

 Additional piping 4.5% of ISBL $ 7,600,000

Total Direct Costs (TDC) $ 345,700,000

 Prorateable expenses 10.0% of TDC $ 34,600,000

 Field expenses 10.0% of TDC $ 34,600,000

 Home office & construction fee 20.0% of TDC $ 69,100,000

 Project contingency 10.0% of TDC $ 34,600,000

 Other costs (start-up, permits, etc.) 10.0% of TDC $ 34,600,000

Total Indirect Costs $ 207,400,000

Fixed Capital Investment (FCI) $ 553,200,000

 Land $ 1,800,000

 Working capital 5.0% of FCI $ 27,700,000

Total Capital Investment (TCI) $ 582,700,000

Lang Factor (FCI/purchased equip cost)b 3.2

TCI per annual gallon $18.59/gal

2011 dollars

BC1307A a Feedstock handling not included in this calculation.

b Area 600 not included in Lang Factor.

4.3 Variable Operating Costs

Variable operating costs, which include raw materials, waste handling charges, and byproduct 

credits, are incurred only when the process is operating. Quantities of raw materials used and 

wastes produced were determined using the Aspen material balance. Table 28 documents the 

costs and sources of chemicals used in the process, and Table 29 summarizes the variable costs 

on a per-year and per-gallon-of-RDB basis. 

As noted above, the cost basis for all material costs in the present model which were also used in 

the 2011 ethanol model were left unchanged, except to update to 2011$. The only new materials 

in this list are WWT chemicals (polymer), which was added during NREL’s subcontract with 

Brown and Caldwell in 2012, corn oil antifoam (added to the A300 bioreactors to mitigate 

foaming due to aeration and product secretion) and hydrogen, which was assumed here to be 

purchased as a product from standard natural gas-derived steam methane reforming (SMR). This 
68

approach for hydrogen sourcing was taken given the relatively small hydrogen demand for 

hydrotreating this particular FFA material, as well as to maintain consistency with the intention 

of evaluating a representative pathway model (with different hydrogen demands to be required 

for upgrading different products other than the FFA pathway), so to not place too much burden 

on an alternative on-site hydrogen production system without more definition on a specific 

product pathway and associated upgrading requirements. Moving forward, NREL will evaluate 

implications in cost and sustainability metrics associated with producing the required hydrogen 

internally (e.g., from anaerobic digestion biogas or other internal sources) rather than sourcing 

from natural gas off-site. Hydrogen price was set based on a recent DOE Hydrogen Program 

report, which presented a current price for natural gas-based SMR hydrogen of $1.57/kg 

(assumed in 2012$) associated with a natural gas price of $4/MM Btu [96]. This price is likely to 

err on the conservative side, as the associated report was for forecourt production (smaller scale 

distributed systems rather than large central production), and today’s U.S. industrial natural gas 

price is even lower at $3.54/MM Btu in 2012 [97] (while the 2011 natural gas price was higher at 

$4.70/MM Btu [97], the $4/MM Btu basis is a reasonable average of the two most recent years).

The overall cost sensitivity to the assumed hydrogen price is presented below in the Sensitivity 

Analysis section, using a reasonable maximum and minimum hydrogen cost range as presented 

in the referenced DOE report, namely $1.10–$2.00/kg associated with a natural gas price range 

of $2–$7.37/MM Btu, respectively. Prices for other new and repeated materials are summarized 

below. 

Table 28. Chemical Costs and Sources

Component Cost (2011$) Source

Biomass uniform-format feedstock $0.0320/lb 2013 MYPP, $80/dry ton @ 20% moisture [10]

Sulfuric acid, 93% $0.0499/lb Basic Chemical of Omaha via Harris Group

Ammonia $0.2496/lb Terra Industries via Harris Group

Corn steep liquor $0.0316/lb Corn Products via Harris Group

Diammonium phosphate $0.5492/lb Ronas Chemicals via Harris Group

Corn oil (antifoam) $0.6077/lb SRI Chemical Economics Handbook (CEH)

Sorbitol $0.6269/lb Coast Southwest via Harris Group

Glucose $0.3230/lb USDA ERS [98]

SO2 $0.1690/lb SRI Chemical Economics Handbook (CEH)

Enzyme nutrients $0.4570/lb SRI CEH (See 2011 design report for details)

Hydrogen $0.6838/lb DOE report, SMR H2 @ $4/MM BTU NG [96]

Caustic $0.0832/lb Brown and Caldwell 2011 WWT design [99]

Polymer for WWT $2.4806/lb Brown and Caldwell 2012 WWT design [100]

Lime $0.1109/lb Harris Group

Boiler chemicals $2.7788/lb 2002 Design Report [2]

Cooling tower chemicals $1.6653/lb 2002 Design Report [2]

Fresh water $0.0001/lb Peters & Timmerhaus [101]
69

Table 29. Variable Operating Costs

Process 

Area

Stream Description Usage

(kg/h)

Usage 

(lb/h)

Cost 

($/ton)

$/hour MM$/yr 

(2011$)

Cent/Gal 

RDB 

(2011$)

Raw materials

N/A Feedstock (wet) 104,167 229,688 64.00 7,350.00 57.95 184.90

A200 Sulfuric acid, 93% 2,240 4,940 99.85 246.62 1.94 6.20

Caustic (as pure) 1,406 3,101 166.42 258.02 2.03 6.49

Ammonia 260 574 499.27 143.38 1.13 3.61

A300 Corn steep liquor 1,404 3,095 63.24 97.86 0.77 2.46

Diammonium phosphate 127 279 1,098.38 153.40 1.21 3.86

Corn oil antifoam 122 269 1,215.42 163.77 1.29 4.12

Sorbitol 18 40 1,253.71 25.04 0.20 0.63

A400 Glucose 1,213 2,675 645.94 863.86 6.81 21.73

Corn steep liquor 83 182 63.24 5.75 0.05 0.14

Corn oil antifoam 7 15 1,215.42 8.98 0.07 0.23

Ammonia 58 127 499.27 31.75 0.25 0.80

Host nutrients 34 75 913.94 34.05 0.27 0.86

Sulfur dioxide 8 18 338.08 3.07 0.02 0.08

A500 Hydrogen 221 488 1,367.51 333.84 2.63 8.40

A600 Ammonia 173 381 499.27 95.08 0.75 2.39

Polymer 3 6 4,961.25 14.97 0.12 0.38

A800 Boiler chems <1 1 5,557.64 1.40 0.01 0.04

FGD lime 191 420 221.90 46.64 0.37 1.17

A900 Cooling tower chems 3 7 3,330.66 11.51 0.09 0.29

Makeup water 217,059 478,615 0.29 68.95 0.54 1.73

Subtotal 9,889.19 78.51 250.51

Waste disposal

A800 Disposal of ash 4,542 10,015 35.39 177.22 1.40 4.46

Subtotal 177.22 1.40 4.46

By-products and credits

Grid electricity 10,490 kW $0.0572/kWh 599.71 4.73 15.09

Area 100 electricity 859 kW $0.0572/kWh 49.13 0.39 1.24

Subtotal 648.84 5.12 16.32

Total variable operating costs 9,417.57 74.79 238.64

4.4 Fixed Operating Costs

Fixed operating costs are generally incurred in full whether or not the plant is producing at full 

capacity. These costs include labor and various overhead items. The assumptions on fixed 

operating costs were maintained consistently from the 2011 design basis (after updating to 

2011$), which in turn were based in large part on the 2002 ethanol design report [2] and/or 

Peters and Timmerhaus [101]. 

Table 30 shows the recommended number of employees and associated salaries. The number of 

employees was estimated by considering the likely degree of automation for each area and 

adding a reasonable number of management and support employees. Details behind the assumed 

number of employees and associated salaries are provided in the 2011 ethanol report. Because 

the model feedstock is predominately corn stover, salaries were estimated for rural regions of the 

U.S. Midwest (Iowa, Missouri, etc.). These estimates may vary depending on location. 
70

Table 30. Fixed Operating Costs

Position 2011 

Salary

# Required 2011 Cost MM$/yr 

(2011$)

Cent/Gal 

RDB (2011$)

Labor and supervision

Plant manager 155,400 1 155,400

Plant engineer 74,000 2 148,000

Maintenance supervisor 60,257 1 60,257

Maintenance technician 42,286 12 507,429

Lab manager 59,200 1 59,200

Lab technician 42,286 2 84,571

Lab tech-enzyme 42,286 2 84,571

Shift supervisor 50,743 4 202,971

Shift operators 42,286 20 845,714

Shift operators-enzyme 42,286 8 338,286

Yard employees 29,600 4 118,400

Clerks and secretaries 38,057 3 114,171

Total salaries 2,718,971 2.72 8.68

Labor burden (90%) 2,447,074 2.45 7.81

Other overhead

Maintenance 3.0% of ISBL 5,039,796 5.04 16.08

Property insurance 0.7% of FCI 3,872,126 3.87 12.36

Total fixed operating costs 14.08 44.92

A 90% labor burden is applied to the salary total and covers items such as safety, general 

engineering, general plant maintenance, payroll overhead (including benefits), plant security, 

janitorial and similar services, phone, light, heat, and plant communications. The 90% estimate is 

the median of the general overhead range suggested in the 2008 PEP Yearbook produced by SRI 

Consulting [92]. Annual maintenance materials were estimated as 3% of the installed ISBL 

capital cost and property insurance and local property tax were estimated as 0.7% of the fixed 

capital investment, based on the 1994 Chem Systems report described in NREL’s 2011 ethanol 

report. These factors are all consistent with those used in the 2011 ethanol report. 

4.5 Discounted Cash Flow Analysis and the Minimum Selling Price of 

Fuel

4.5.1 Discount Rate

For this analysis, the discount rate (which is also the IRR in this analysis) was set to 10% and the 

plant lifetime was set to 30 years. The discount rate was also used in previous design reports and 

was based on the recommendation in Short et al. [102] on how to perform economic evaluations 

of renewable energy technologies for DOE. His view was that, “In the absence of statistical data 

on discount rates used by industrial, transportation and commercial investors for investments 

with risks similar to those of conservation and renewable energy investments, it is recommended 

that an after tax discount rate of 10%...be used.” 
71

4.5.2 Equity Financing

For this analysis, it was assumed that the plant would be 40% equity financed. The terms of the 

loan were taken to be 8% interest for 10 years. The principal is taken out in stages over the 3-

year construction period. Interest on the loan is paid during this period, but principal is not paid 

back. (This is another n

th-plant assumption, which says that this cash flow comes from the parent 

company until the plant starts up.) This is all consistent with the assumptions used in the 2011 

ethanol report. Figure 18 illustrates the sensitivity of MFSP to the percentage of equity financing 

and the after-tax discount rate (the IRR). 

Figure 18. Sensitivity of MFSP to IRR and % equity (8% interest on a 10-year loan)

4.5.3 Depreciation

To determine the capital depreciation amount for the calculation of federal taxes to be paid, we 

used the IRS Modified Accelerated Cost Recovery System (MACRS). Within the MACRS 

system is the General Depreciation System (GDS), which allows both the 200% and 150% 

declining balance (DB) methods of depreciation. This offers the shortest recovery period and the 

largest tax deductions. According to IRS publication 946 [103], a cellulosic biorefinery plant 

would fall under Asset Class 49.5, “Waste Reduction and Resource Recovery Plants.” This class 

uses a 7-year recovery period, not including the steam plant equipment, which has a 20-year 

recovery period (Asset Class 49.13). IRS publication 946 contains a special provision for 

cellulosic biofuels plants that allows them to write off 50% of the capital investment in the first 

year. This was not implemented in our cost model because it does not ultimately affect the 

MFSP. (Although the provision affects the cash flow in the first few years of the analysis, it does 

not change the year in which the plant goes into the black and must start paying taxes.) 

$4.50

$5.00

$5.50

$6.00

$6.50

$7.00

$7.50

$8.00

$8.50

0% 20% 40% 60% 80% 100%

Minimum RDB Selling Price ($/GGE)

Equity %

10% IRR

15% IRR

20% IRR
72 

4.5.4 Taxes

The federal corporate tax rate used in our analysis is 35%. Income tax is averaged over the plant 

life and that average is calculated on a per-gallon basis. The amount of income tax to be paid by 

a potential fuel producer varies annually due to changes in the volume of product produced and 

the allowable depreciation deduction. In fact, no income tax is paid in the first eight years of 

operation because the depreciation and loan interest deductions are greater than the net income. 

State taxes are not considered, primarily because the location of the plant has not been 

determined and tax rates vary from state to state (from 0% to 12%).

4.5.5 Construction Time

The construction time is important to the cash flow analysis because no income is earned during 

construction, but huge sums of money are being expended. Construction time assumptions were 

left unchanged from the 2011 ethanol basis. Perry and Green [31] indicate that small projects 

(less than $10 million investment) can be constructed in fewer than 18 months and that larger 

projects can take up to 42 months. An overview of petroleum refining economics indicates that 

large refineries (on the order of $1.5 billion investment) can be constructed in 24 months [104]. 

Certainly this FFA/RDB process is much smaller than a petroleum refinery, so using a 

construction time of 24 months fits within these references, although an important difference 

between this type of facility and a refinery is the large number of field-erected vessels. These are 

constructed on-site and have a longer construction time than if the tanks were delivered finished. 

Table 31 summarizes the schedule for construction and the cash flow during that time. Twelve 

months are added before construction for planning and engineering. 

Table 31. Construction Activities and Cash Flow

Project 

Start 

Month

Project 

End 

Month

Activity Description

% of 

Project 

Cost

0 12 Project plan and schedule established; conceptual and basic design 

engineering, permitting completed. Major equipment bid packages issued, 

engineering started on selected sub-packages, P&IDs complete, preliminary 

plant and equipment arrangements complete. 

8%

12 24 All detailed engineering including foundations, structure, piping, electrical, 

site, etc. complete; all equipment and instrument components purchased 

and delivered; all site grading, drainage, sewers, rail, fire pond, foundation, 

and major structural installation complete; 80% of all major process 

equipment set (all except longest-lead items), all field fabricated tanks built, 

and the majority of piping and electrical materials procured. 

60%

24 36 Complete process equipment setting, piping, and instrumentation 

installation complete; all electrical wiring complete; all building finishing and 

plumbing complete; all landscaping complete; pre-commissioning complete; 

and commissioning, start-up, and initial performance test complete. 

32%

TOTAL 100%

Note: The above assumes no utility or process equipment orders placed prior to month seven. Expenditures based 

on typical 60 MMgal/yr grain-to-ethanol facility.
73

4.5.6 Start-Up Time

Perry and Green [31] indicate that for a moderately complex plant, start-up should be about 25% 

of the construction time, or 6 months in this case. While the 2011 ethanol model assumed a startup time of 3 months under an n

th plant assumption, this is extended back to 6 months in the 

present analysis. The start-up period is not completely wasted, however. We expect that an 

average of 50% production could be achieved during that period while incurring 75% of variable 

expenses and 100% of fixed expenses. 

4.5.7 Working Capital

Peters and Timmerhaus [101] define working capital as money available to cover (1) raw 

materials and supplies in inventory, (2) finished product in storage, (3) accounts receivable, (4) 

cash on hand for monthly payments such as wages and maintenance supplies, (5) accounts 

payable, and (6) taxes payable. The present analysis applies the same basis for working capital as 

was used in the 2011 ethanol report, namely 5% of FCI; this translates to a slightly higher 

working capital cost value given the increased direct capital costs and FCI in the present model. 

Table 32 summarizes the parameters used in the discounted cash flow analysis. Using these 

parameters, plus the cost information in Table 27, Table 29, and Table 30, the resulting MFSP of 

pure RDB is $5.35/gal or $5.10/GGE (2011$). Table 33 summarizes the yields and conversion 

costs for the present design. According to the methodology of Cran [105], the expected accuracy 

of the TCI analysis is 25%. If we apply this uncertainty to the TCI, the impact on the cost of 

RDB is ± $0.65/gal ($0.61/GGE). The complete discounted cash flow summary worksheet is 

shown in Appendix B. The MFSP can be further broken down into the cost of each process area. 

Figure 19 illustrates the contribution to the overall cost by process area and capital, operations, 

and fixed costs. (The bar for feedstock + handling reflects the single feedstock cost of $80.00/dry 

U.S. ton delivered to pretreatment and has not been decomposed.) 

Table 32. Discounted Cash Flow Analysis Parameters

Plant life 30 years

Discount rate 10%

General plant depreciation 200% declining balance (DB)

General plant recovery period 7 years

Steam plant depreciation 150% DB

Steam plant recovery period 20 years

Federal tax rate 35%

Financing 40% equity

Loan terms 10-year loan at 8% APR

Construction period 3 years

 First 12 months’ expenditures 8%

 Next 12 months’ expenditures 60%

 Last 12 months’ expenditures 32%

Working capital 5% of fixed capital investment

Start-up time 6 months

 Revenues during start-up 50%

 Variable costs incurred during start-up 75%

 Fixed costs incurred during start-up 100%
74

Table 33. Summary of Yields, Rates, and Conversion Costs

Feedstock rate 2,205 dry U.S. ton/day

On-line time 7,884 h/yr (90% on-line factor)

FFA intermediate yield 0.162 ton/dry ton feedstock 

(68% theoretical based on feedstock carbohydrates)

RDB yield 43.3 gal/dry U.S. ton feedstock (45.4 GGE/ton)

RDB production rate 31.3 MMgal/yr (32.9 MM GGE/yr)

Total equipment cost $316MM

Total capital investment (TCI) $583MM

TCI per annual gallon $18.59/gal

Minimum Fuel Selling Price $5.35/gal ($5.10/GGE)

 Feedstock contribution $1.85/gal ($1.76/GGE)

 Enzyme contribution $0.39/gal ($0.37/GGE)

 Non-enzyme conversion contribution $3.11/gal ($2.96/GGE)

Figure 19. Cost contribution details from each process area (per GGE RDB product)

It is worth drawing a comparison here between the results presented above and those shown in 

the 2011 ethanol report. Both cases utilize similar assumptions regarding sugar yields (albeit at 

improved process conditions such as reduced enzyme loading) and subsequent sugar utilization 

efficiencies (90% glucose, 85% xylose, and 85% arabinose of the total sugars fed to the 

bioreactors and converted to product), but the MFSP cost is considerably increased here; the 

ethanol MESP would be $3.61/GGE when updated to 2011$ (all other variables held constant; 

the original ethanol MESP was $3.27/GGE in 2007$). This difference is primarily due to: 
75

a. Higher facility capital costs (TCI = $583 MM versus $471MM for ethanol when updated 

to 2011$): Due primarily to increased complexity in A300 (hydrolysate conditioning and 

aerobic bioreactors) and the addition of hydrotreating in A500 

b. Lower yields (45.4 GGE/dry ton versus 52.0 GGE/dry ton for ethanol): Due to lower 

metabolic energy efficiency for FFA relative to ethanol (Table 13), higher diversion of 

sugars to biomass propagation (Table 10), thus a lower fraction of total sugars amenable 

to conversion to the desired FFA product, and additional yield losses across the 

hydrotreater for FFA upgrading to the RDB product 

c. Higher feedstock cost ($80/dry ton versus $58.50/dry ton in the ethanol case): At the 

yields presented here, the feedstock cost contributes $1.76/GGE to the MFSP, compared 

to $1.13/GGE in the ethanol case 

d. Decreased facility on-line time (7,884 h/yr vs 8,410 h/yr in the ethanol case): This 

parameter was adjusted externally to allow for a slightly more conservative n

th plant 

assumption, intended to be applied consistently across all new hydrocarbon pathways in 

the DOE-BETO platform work. 

At the same time, other costs have decreased relative to the 2011 ethanol basis. Namely, the 

2017 targeted enzyme loading has been reduced by 50% to 10 mg/g cellulose, relative to the 

2012 ethanol target of 20 mg/g; this results in a considerable reduction in total enzyme cost 

contribution to the MFSP, which is traditionally a very large economic driver at historical 

enzyme loadings of 20 mg/g or higher. Indeed, allocated enzyme cost shown in Figure 19 is now 

the fifth-largest contributor to MFSP of the nine facility process areas, while it was second only 

to feedstock cost in the 2011 ethanol report. 

While the analysis presented here demonstrates a viable and realistic path to achieve DOE’s 

intermediate 2017 target of $5/GGE via biological conversion, additional improvements will be 

needed to achieve the final 2022 target of $3/GGE for this pathway. Although it is premature to 

establish a design case in the same level of detail as described in this report for the 2017 

($5/GGE) pathway, a high-level discussion of such improvements is provided below in 

Section 6. 
76

5 Analysis and Discussion

5.1 Carbon and Water Balances

Table 34 shows the overall flow of carbon inputs and outputs, with a carbon balance closure very 

near unity. As shown in the table, > 98% of all carbon in the process enters in the biomass feed, 

with small amounts of additional carbon coming from glucose (for enzyme production) and 

bioconversion nutrients such as CSL. Given the reduced enzyme loading relative to the 

2011 ethanol report, the glucose contribution to total input carbon flow was reduced from 

2.5% to 1.3%. 

Table 34. Biorefinery Overall Carbon Balance

Stream Carbon Flow (kmol/hr) % of Carbon Flow

Carbon inlets

Biomass feedstock 3,117 98.1%

Glucose 40 1.3%

A300/400 nutrients 19 0.6%

Total 3,176 100.0%

Carbon outlets

RDB product 818 25.8%

Combustion exhaust 1,796 56.5%

Ash 10 0.3%

WWT brine 33 1.0%

Aerobic lagoons 58 1.8%

A400 vent 27 0.9%

A300 vent 438 13.8%

Hydrotreater furnace flue gas 10 0.3%

Total 3,189 100.4%

Of the carbon inputs to the process, 26% of carbon atoms leave as RDB after the inclusion of 

hydrotreating, which rejects a portion of the carbon in the FFA intermediate to additional CO2 by 

way of decarboxylation. Major exit points for the balance of carbon (accounting for 70%) are the 

combustor stack and bioreactor vent. We expect the amount of carbon exiting in the combustion 

exhaust to be rather large because most byproducts (lignin, etc.) of this process are burned to 

form CO2. The majority of the carbon exiting the bioreactor vent is also in the form of CO2

inherent to the bioconversion process. 

Only the carbohydrate components of the biomass are assumed to be convertible to product by 

bioconversion. Of the carbon in the feed stream, 1,857 kmol/h (59.6%) is carbohydrates (glucan, 

xylan, arabinan, mannan, galactan, and sucrose); converting all of this carbon to RDB represents 

the theoretical yield. According to Table 34, 1,256 kmol/h of the feed stream carbon is actually 

converted by the RDB pathway (carbon in the product plus CO2 in the bioreactor vent), so the 

actual yield is 68% of the theoretical yield. This has decreased from the 2011 ethanol basis of 

76% due to (a) minor losses of sugars attributed to the solid-liquid separation step now being 

placed before bioconversion, (b) additional diversion of sugars to biomass organism propagation 
77

inherent to aerobic bioconversion, and (c) loss of carbon to CO2 via decarboxylation for oxygen 

removal during hydrotreating. Carbon efficiency to the RDB product from initial hydrolysate 

sugars is 50%, dictated by a metabolic yield of 0.284 kg FFA intermediate/kg total sugars 

reaching the bioconversion step (79% of the theoretical metabolic yield, 0.36 kg FFA/kg). 

The overall flow of water throughout the model is presented below in Table 35. The primary 

point of entry for dedicated process water demand is the “makeup water” stream, shown in the 

current model to be 217,100 kg/h or 14.4 gal/gal RDB product. This is considerably higher than 

the 2011 ethanol demand of 147,100 kg/h (5.4 gal/gal), primarily because of increased cooling 

water demands and associated cooling tower evaporation losses, as well as the reintroduction of 

a solid-liquid separation unit which requires a washing step to recover soluble sugars. Cooling 

tower evaporation accounts for 85% of the total process makeup water demand, and has 

increased in magnitude attributed to a cooling demand of 130 MMkcal/h in the present design 

versus 97 MMkcal/h in the 2011 ethanol design; this in turn is due to considerably increased 

flow of steam through the condensing stage of the steam turbine cycle, higher chiller condenser 

duty (primarily due to increased heat removal for the bioreactors), and an added exchanger to 

cool the sugar evaporator condensate material for recycle purposes. A lignin filter press with a 

dedicated washing step was utilized in the 2002 ethanol design report for pretreatment 

conditioning, but removed in the 2011 ethanol design report and replaced with a lignin press on 

the beer stillage after fermentation (with no washing required). In the present design, a vacuum 

filter press is utilized after hydrolysis to remove lignin and other insoluble solids for downstream 

bioconversion, which again requires a wash step to recover lost sugars; this wash step results in a 

lignin material with considerably higher moisture content, and thus water loss, as described 

previously. 

Table 35. Biorefinery Water Balance

Inputs kg/hr gal/gal Outputs kg/hr gal/gal

Moisture in feedstock 20,833 1.38 Water in fuel product 30 0.00

Water in glucose syrup 214 0.01 Cooling tower evap. 184,167 12.23

Water in raw chemicals 1,442 0.10 Stripped in enzyme aeration 388 0.03

Generated in enzyme prodn. 648 0.04 Bioreactor vent 13,042 0.87

Generated in bioconversion 8,750 0.58 Consumed in Pretreatment 2,394 0.16

Generated in WWT -637 -0.04 Consumed in Enz. Hydr. 2,640 0.18

Generated in Combustor 26,968 1.79 WWT evap 2,857 0.19

Enzyme air intake 320 0.02 WWT Brine 3,767 0.25

Lignin cake dryer intake 0 0.00 Combustor stack 72,468 4.81

WWT air intake 2,878 0.19 Boiler blowdown vent 2,558 0.17

Combustion air intake 6,298 0.42 Upgrading flue gas 210 0.01

Makeup water 217,059 14.41 Upgrading produced water 917 0.06

Sum of Inputs 284,773 18.90 Sum of Outputs 285,407 18.95

Additional water enters the process via the feedstock and raw chemicals, both as free water and 

as “potential” water, i.e., the combustion product of lignin and unconverted sugars. Water is also 

consumed in hydrolysis reactions, and these are accounted for as well. As discussed in the 2011 

ethanol report, again we note that describing water usage with the units of gallons of water per 

gallon of product (whether ethanol or RDB) can be somewhat misleading, because this quantity 
78

obviously depends on product yield, and the unadjusted volumetric yield to RDB in the present 

design is about 45% lower than in the 2011 design to ethanol (43.3 gal RDB/ton versus 79.0 gal 

ethanol/ton). However much of this difference is artificial, due to differences in metabolic mass 

yield and product densities; a more consistent comparison of water consumption would be on a 

GGE basis, which would be 13.7 gal/GGE RDB or 8.2 gal/GGE ethanol for the biorefinery 

consumptive water use. 

As noted above, utilities are responsible for a large majority of the water loss, with 85% of the 

net water loss due to cooling tower evaporation. Table 36 expresses the individual cooling water 

users (see Figure 16) in terms of their water loss responsibility in gal/gal.

Table 36. Individual Contributors to Cooling Water Evaporation

Cooling Water User MMkcal/h % of duty gal/gal

Condensing Turbine 86.8 66.9% 8.18

Chiller condenser 16.9 13.1% 1.60

Hydrolysis cooler 7.5 5.8% 0.71

Hydrolysate flash condenser 1.5 1.1% 0.14

Pre-fermentation cooler 2.6 2.0% 0.25

Enzyme production compressor 0.6 0.5% 0.06

Bioreactor air compressor 7.9 6.1% 0.75

Sugar evaporator condensate 5.8 4.5% 0.55

Total 130.0 100% 12.23

The largest cooling water user is the condensing turbine, which is responsible for 8.2 gal/gal of 

water loss. A condenser on the turbine allows the steam to discharge from at a vacuum (about 0.1 

atm) for maximum conversion of compressive energy to electricity. With the condensing turbine 

in place, the process generates enough electricity to supply all users in the plant plus a significant 

surplus, which is assumed to be sold to the grid. Without the ability to let the steam down to such 

low pressure (and temperature), the total amount of generated electricity may decrease to the 

point that either a power coproduct is no longer produced, or the plant no longer makes enough 

electricity to support all of its users and would have to purchase the balance from the grid 

(particularly given the increased facility electricity demand relative to the 2011 ethanol basis). 

The present design model presents an interesting comparison with the 2011 ethanol model, in 

that facility power demand has increased but heat demand has decreased; thus, considerably less 

steam is removed from intermediate turbine stages to supply facility heating demands (such as 

distillation which is no longer utilized here), resulting in a larger amount of steam ultimately 

reaching the final condensing turbine stage, resulting in a larger cooling water demand for this 

stage as previously noted. While this allows for increased electricity generation from the turbine 

(53.1 MW versus 42.3 MW in the ethanol case), the increased facility power demand (42.6 MW 

versus 28.5 MW in the ethanol case) results in a marginally higher amount of facility power use 

relative to generation (80% versus 69% in the ethanol case). 

Moving forward, we will continue to evaluate ways to reduce the biorefinery consumptive water 

use. One promising possibility may be in direct air-cooled condensing of the steam turbine 

exhaust. The current conversion process employs largely wet evaporative cooling as the means 

of satisfying plant heat rejection requirements. The wet evaporative system is the most common 
79

system used industrially. It requires a water-cooled surface condenser, a cooling tower, cooling 

tower basin, a circulating water system to carry cooling water to and from a cooling tower, and 

associated water treatment facilities. This system requires continuous makeup water to offset 

water lost through evaporation, drift, and blowdown (discharge). On the other hand, a direct aircooled condenser is a completely closed-circuit heat exchange system in which the turbine steam 

is condensed on the inside of the tubes as a result of colder ambient air flowing across the outside 

of the finned tube surfaces. Because the heat rejection is achieved using only air, no water is 

consumed in the condensing process. A previous preliminary NREL study suggested that 

replacing the water cooler with an air-cooled condenser system on the steam turbine exhaust may 

effectively reduce the total consumptive water usage by about 50%, which in this model would 

reduce water consumption from 14 to 6 gal/gal RDB product. This improvement in water usage 

would not necessarily be at the expense of the RDB production cost. Cursory analysis suggests 

that the higher capital investment for the air cooler is offset by a lower total variable operating 

cost. Further study is warranted to confirm that replacing the water-cooled steam turbine 

condenser with an air-cooled condenser (either in full or in part) to reduce consumptive water 

use is technically and economically feasible.

5.2 Sustainability Metrics for 2017 Base Model

Beyond the carbon and water balance considerations discussed above, an important aspect of 

evaluating biomass-derived fuel processes is the quantification of life cycle resource 

consumption and environmental emissions. Life cycle assessment (LCA) provides a framework 

from which the environmental sustainability of a given process may be quantified and assessed. 

This section presents the salient sustainability metrics of the current conceptual process at the 

conversion stage. Direct CO2, NO2, and SO2 emissions, consumptive water use (discussed 

above), and other process-related metrics were derived from the integrated process model. 

SimaPro v.7.3 software [106] was used to develop and link units quantifying life cycle impacts 

as previously documented by Hsu et al. [107]. Ecoinvent v.2.0 [108] and the U.S. Life Cycle 

Inventory (LCI) [109] processes were used to fill the data gaps. The Ecoinvent processes were 

modified to reflect U.S. conditions (e.g., replacing the default European electricity mix with the 

U.S. electricity mix), and the U.S. LCI processes were adapted to account for embodied 

emissions and fossil energy usage. The LCI of the conversion step captures the impacts of input 

raw materials, and outputs, such as emissions and waste as predicted by the process model, as 

shown in Table 37. 
80

Table 37. Input and Output Data for Biorefinery Processing 

2,000 Dry Metric Tonnes of Biomass per Day 

Products Production rate

kg/hr gal/hr MJ/hr (HHV)

RDB 11576 3975 492812

Grid electricity 10490 kW

Resource Consumption Flow rate Per gal fuel Per MJ fuel

kg/hr kg/gal kg/MJ

Feedstock (corn stover) 104167 26.20 0.21

Sulfuric acid (93%) 2240 0.56 4.5E-03

Caustic (as pure) 1406 0.35 2.9E-03

Ammonia 491 0.12 1.0E-03

Corn steep liquor 1486 0.37 3.0E-03

Diammonium phosphate 127 0.03 2.6E-04

Corn oil 129 0.03 2.6E-04

Sorbitol 18 4.6E-03 3.7E-05

Glucose 1213 0.31 2.5E-03

Host nutrients 34 0.01 6.9E-05

Sulfur dioxide 8 2.1E-03 1.7E-05

Hydrogen 221 0.06 4.5E-04

Polymer 3 6.9E-04 5.6E-06

Boiler chemicals 2.3E-01 5.7E-05 4.6E-07

FGD lime 191 0.05 3.9E-04

Cooling tower chemicals 3 8.0E-04 6.4E-06

Makeup water 217059 54.60 0.44

Air demand 469468 118.10 0.95

Air Emissions

Water (H2O) 296665 74.63 0.60

Nitrogen (N2) 366641 92.23 0.74

Oxygen (O2) 142388 35.82 0.29

Carbon dioxide (CO2)* 102227 25.72 0.21

Methane (CH4) 2.0 5.1E-04 4.1E-06

Nitrogen dioxide (NO2) 76 0.02 1.5E-04

Carbon monoxide (CO) 67 0.02 1.4E-04

Sulfur dioxide (SO2) 11 2.9E-03 2.3E-05

Waste Streams

Disposal of ash 4542 1.14 0.01

Wastewater (Brine) 7531 1.89 0.02

* Direct carbon dioxide (CO2) emission is 100% biogenic CO2. 

In addition to the primary hydrocarbon RDB product, the process also produces an excess 

amount of electricity from lignin combustion (described in detail above). Electricity produced 

exceeds the on-site power demand for the biorefinery and is assumed in the TEA model to be 

sold back to the grid for a co-product credit. Given the large impact that electricity co-production 

exhibits on greenhouse gas (GHG) and fossil energy profiles, we evaluate the sustainability 

metrics both with and without consideration of the excess electricity co-product credits for 
81

avoided GHG emissions and fossil energy consumption. This exported electricity is treated as an 

avoided product using the product displacement method [110]. Co-product displacement (also 

termed system boundary expansion) is based on the concept of displacing the existing product 

with the new product. The excess electricity co-product displaces an equivalent amount of grid 

electricity, thus avoiding a significant amount of GHG emissions as well as fossil energy 

consumption, assuming an average U.S. electricity grid mixture. The GHG and fossil energy 

consumption credits attributed to the displacement of an average U.S. electricity grid mixture is 

0.78 kgCO2-eq/kWh and 9.1 MJ/kWh, respectively, as defined by Ecoinvent. The associated 

breakdown of the average U.S. electricity grid mix as utilized in Ecoinvent are as follows: coal 

(47%), natural gas (17%), oil (3.3%), nuclear power (20%), biomass (1.1%), wind (0.35%), solar 

(0.015%), hydrolelectric (8.2%), and others (2.5%). 

Details of contributions to GHG emissions and fossil energy consumption at the conversion stage 

are presented in Table 38, which corresponds to the information in Table 37. GHG emissions 

associated with the conversion stage for the cases with and without electricity co-product 

displacement credits are estimated to be 0.07 kg CO2-eq/GGE and 2.02 kg CO2-eq/GGE, 

respectively. Thus, the electricity co-product is responsible for a GHG offset of 1.95 kg CO2-

eq/GGE. There is minimal contribution to GHG emissions directly from the biorefinery. Direct 

CO2 emission from the conversion process (shown in Table 37) is biogenic CO2 (i.e., CO2

absorbed from the atmosphere and incorporated as biomass during the feedstock production 

phase, which is then re-released through lignin combustion, bioconversion to CO2, etc.). With its 

biomass origin, biogenic CO2 does not contribute to the increase of GHG in the atmosphere 

[111] and is not considered in the IPCC global warming methodology [112]. Hence, the 

contributions to GHG at the conversion stage are solely from the associated underlying processes 

(e.g., material inputs/outputs to and from the facility to support process operations). 

Table 38. Conversion Process GHG Emissions and Fossil Energy 

Consumption per Unit of RDB Product 

GHG Emission Fossil Energy Input

kg CO2-eq/GGE MJ/GGE

Electricity credit No Yes No Yes

Direct refinery emission 0.012 0.012 0.00 0.00

Pretreatment chemicals 0.522 0.522 5.73 5.73

Ammonia conditioning 0.136 0.136 2.60 2.60

Bioconversion (nutrients) 0.200 0.200 2.32 2.32

Infrastructure 0.0164 0.0164 0.172 0.172

Enzyme production 0.834 0.834 9.5 9.5

Hydrogen 0.089 0.089 9.9 9.9

Boiler water chemicals 8.62E-05 8.62E-05 1.06E-03 1.06E-03

Flue gas desulfurization chemicals 0.036 0.036 0.198 0.198

Wastewater treatment chemicals 0.1602 0.1602 3.07 3.07

Cooling tower Chemicals 1.63E-03 1.63E-03 1.84E-02 1.84E-02

Waste disposal 0.00827 0.00827 0.213 0.213

Electricity credit 0.00 -1.95 0.00 -22.8

Total 2.02 0.07 33.60 10.79
82

Table 38 also shows the fossil energy consumption for the biorefinery conversion process. Fossil 

energy consumption associated with the conversion stage for the cases with and without 

electricity co-product displacement credits are estimated to be 10.8 MJ/GGE and 33.6 MJ/GGE, 

respectively. Thus the electricity co-product is responsible for a fossil energy offset of 22.8 

MJ/GGE. Because the current biorefinery in the base case does not require any direct fossil 

energy input such as natural gas or fossil-derived electricity from the grid, the fossil energy 

consumption is entirely associated with the underlying process material inputs and outputs. 

Table 39 summarizes the key sustainability metrics for the process both including and excluding 

electricity co-product displacement credits. On the basis of RDB energy content, the GHG 

emissions at the conversion stage are 15.9 kg CO2-eq/GJ without co-product credits and 0.5 kg 

CO2-eq/GJ with co-product credits. Similarly, the fossil energy consumption is 0.27 MJ/MJ 

without co-product credits and 0.09 MJ/MJ with co-product credits. Clearly, the GHG and fossil 

energy metrics for the biological conversion pathway are strongly influenced by the co-product 

credit gained through the export of excess electricity, which provides considerable reductions in 

these metrics when assumed to displace the current U.S. electricity grid mix which is relatively 

carbon-intensive attributed in large part to coal usage.

In addition to GHG emissions and fossil energy consumption discussed above, consumptive 

water use, total fuel efficiency (e.g. yield), and carbon-to-fuel efficiency are also repeated for 

reference in Table 39. As described previously, the total RDB product yield is 43.3 gal/dry ton 

and the corresponding carbon-to-product efficiency is determined to be 26.2%. The net water 

consumption (i.e., makeup water) for the biorefinery is 14.4 gal/gal. The U.S. Geological Survey 

defines consumptive water use as “water that is evaporated, transpired, incorporated into 

products or crops, consumed by humans or livestock, or otherwise removed from an immediate 

water environment” [113]. Biorefinery consumptive use may occur through incorporation into 

the product, and evaporation from cooling and heating processes. Based on this definition, the 

consumptive water use for the conversion process is equal to the makeup water to the plant, 

described previously to be 14.4 gal/gal RDB (13.7 gal/GGE), with implications discussed above. 

Table 39. Summary of Sustainability Metrics for the Baseline Process

Electricity credit No Yes

GHG emissions (kg CO2e/GJ) 15.9 0.5

Consumptive water use (m3/day) 5209 5209

Consumptive water use (gal/GGE) 13.7 13.7

Total fuel efficiency (gal/dry ton) 43.3 43.3

Carbon-to-fuel efficiency (C in fuel/C in biomass) 26.2% 26.2%

Net fossil energy consumption (MJ/MJ) 0.27 0.085

5.3 Cost Sensitivity Analysis

5.3.1 Single-Point Sensitivity Analysis

A single-point sensitivity was performed on the Aspen model using the variables and limits 

shown in Table 40. The baseline for all variables used in the design case is described previously 

in this report. Reasonable minima and maxima for each variable were chosen to understand and 

quantify the resulting cost impact on overall MFSP. Each variable was changed to its maximum 
83

and minimum value with all other factors held constant. The sensitivities of (a) MFSP and (b) 

product yield are displayed as tornado charts in Figure 20. 

The variation of xylose-to-product yield during bioconversion, volumetric productivity during 

bioconversion, uncertainty in capital costs resulting from the factored approach we used (± 

25%), and enzyme loading (mg protein/g cellulose) have the largest impact to MFSP. Enzyme 

loading plays a less critical role in overall cost impacts at a 10 mg/g basis than in the 2011 

ethanol report at 20 mg/g, but still carries a higher cost impact over the loading range evaluated 

than the remainder of the parameters evaluated here.

Conversion-related parameters have the largest (and only) impacts on RDB yield; to simplify the 

yield plot, all other parameters considered in the cost sensitivity plot that did not have an impact 

on the yield sensitivity plot were excluded from the latter plot. Conversion parameters also factor 

heavily into production cost, because these parameters not only affect product final yield, but 

also affect capital and other operating costs. We note that this is an imperfect comparison 

because the TCI bar represents a true uncertainty while the other bars are more accurately called 

a risk, in the sense that they represent a range of results that we might expect to see in future 

pilot-scale demonstration runs. 

Table 40. Assumptions Varied in the Sensitivity Analysis

Assumption Name Min Baseline Max

Pretreatment Deacetylation xylan loss 0% 2% 10%

PT reactor metallurgy Stainless Steel High Alloy

PT residence time 2 5 10

PT acid loading (mg/g) 5 9 20

PT temperature °C 150 158 170

PT xylan to xylose 80% 90% 95%

PT xylan to furfural 3% 5% 8%

PT glucan to glucose 6% 10% 12%

Enz hydrolysis EH % solids 17.5% 20% 25%

+ conditioning EH cellulose to glucose 75% 90% 95%

EH enzyme loading mg/g 5 10 20

EH time (d) 2 3.5 6

Sugar loss in S/L separation - 1% 5%

S/L separation capex -50% - +50%

Enz production A400 capex -50% - +50%

Bioconversion CONV contamination losses 0% 3% 6%

CONV xylose to FA 50% 85% 90%

CONV arabinose to FA 50% 85% 90%

CONV productivity (g/L/hr) 0.4 1.3 3.0

Aeration (VVM) 0.1 0.4 1.0

FFA product recovery 95% 97% 99%

Upgrading Catalytic upgrading capital -50% - +100%

H2 price ($/kg) 1.10 1.57 2.00

PSA H2 Recovery 85% 95% 99%

Capital Total capital investment -25% - 25%
84

Figure 20. Single-point sensitivity tornado charts for (a) MFSP and (b) production yield
85

5.3.2 Evaluation of Alternative Metabolic Pathways

The diversity of biological pathways presents a challenge in regard to the selection of which 

molecules or which pathway may best serve as the “representative” baseline for the general 

biological conversion technology platform. Fatty acids are assumed in the base-case model to be 

a representative molecule (using palmitic acid as the representative component) for purposes of 

setting mass balances and energy yields. For comparison we evaluated six pathways for four 

molecules for a preliminary cost sensitivity analysis to account for variations on the governing 

metabolic yields and carbon efficiencies, shown in Table 41. The theoretical metabolic yield and 

carbon efficiency information are repeated from the discussion above shown in Table 13, based 

on a survey of literature [41, 50, 52, 53, 114, 115]. To compare with the base case using common 

assumptions, the bioconversion yield of glucose-, xylose-, and arabinose-to-product are set at 

95%, 85%, and 85% respectively, with all other operational assumptions also held constant (e.g. 

aeration rate, batch time, etc.). While such aggressive yield data have not yet been reported 

publicly for any pathway to the best of our knowledge, this assessment stands to serve primarily 

as a consistent comparison across metabolic pathways and subsequent product upgrading 

demands. 

The pathway for pentadecane production exhibits a moderate theoretical metabolic yield, in 

comparison with other pathways listed in Table 41. However, since pentadecane itself is a 

finished fuel blendstock and does not need any further upgrading, the resulting MFSP (per GGE 

basis) is lower than the $5.10/GGE base case. There are two metabolic pathways leading to the 

production of the precursor farnesene; the mevalonic acid (MVA) pathway and the more recently 

characterized deoxyxylulose-5-phosphate (DXP) pathway, which is also known as the 

methylerythritol phosphate (MEP) pathway [116]. Additionally, we have also modeled a 

hypothetical anaerobic pathway to farnesene, to reflect early research conducted at NREL on 

anaerobic farnesene production, and to understand potential process and cost savings in 

switching from the aerobic baseline. Thus, this study considers farnesene production via MVA, 

DXP, and anaerobic pathways. 

Table 41. Sensitivity Analysis for Various Pathways to Hydrocarbons, Based on Sugar Conversion 

Efficiencies Consistent With the Baseline FFA Model 

Pathway

Theoretical 

metabolic 

mass yield

Theoretical 

carbon 

efficiency

MFSP 

($/gal) 

MFSP 

($/GGE)

Production 

(MMgal/yr) 

Production yield 

(gal/dry ton biomass)

Pentadecane 0.29 0.63 $5.19 $4.96 30.0 41.4

Farnesene (MVA) 0.25 0.56 $6.25 $5.97 27.0 37.3

Farnesene (DXP) 0.29 0.64 $5.44 $5.20 31.1 43.0

Farnesene (Anaerobic) 0.32 0.71 $4.61 $4.41 34.8 48.0

Fatty ester (palmitate 

ethyl ester) 0.35 0.67 $5.83 $5.55 28.9 40.0

Fatty acid (palmitate, 

base case) 0.36 0.67 $5.35 $5.10 31.3 43.3

Fatty alcohol 

(hexadecanol, 

anaerobic)

0.34 0.67 $4.94 $4.70 32.3 44.5
86

The hypothetical anaerobic farnesene pathway exhibits the lowest MFSP, also shown in Figure 

21, not only because of its reduced cost for anaerobic fermentation, but also because of its high 

metabolic mass yield. In comparison with aerobic conversion, anaerobic fermentation enables 

reduced capital cost for the bioreactor vessels (e.g., 1 MM-gal anaerobic fermentors versus 1 

MM-L aerobic systems), as well eliminating the need for air compression and associated 

compressor power demand. Among all three farnesene pathways, the hypothetical anaerobic 

pathway has the highest metabolic mass yield and carbon efficiency. Farnesene or other 

isoprenoids may also possess additional advantages in allowing for a more simplistic 

hydrotreating reactor and/or lower hydrogen demands, as these molecules only require saturation 

and not oxygen rejection; such nuances are not captured here in this high-level comparative 

analysis. Fatty alcohol (hexadecanol as the representative molecule) also may proceed 

anaerobically, so the resulting MFSP for this pathway is the second lowest under this 

assumption. The metabolic mass yield for fatty alcohol is lower than that of the anaerobic 

farnesene pathway. The farnesene production route via the MVA pathway exhibits the highest 

MFSP due to considerably lower theoretical energy yields than most other pathways considered. 

The fatty ester (palmitate ethyl ester) pathway also exhibits a cost on the upper range of those 

considered here. Although carbon efficiency from the biological conversion step is reasonably 

high for this pathway, carbon loss to carbon dioxide as well as rejection of the ethyl group to 

ethane in the catalytic upgrading step reduces the overall product yield. 

Since the sugar to product utilization efficiency is fixed constant for each evaluated pathway, the 

MFSP is closely correlated with metabolic mass yield. The correlation, however, is found to be 

nonlinear, given other tradeoffs in system design and cost (e.g., aerobic versus anaerobic 

bioconversion) as well as upgrading demands in hydrotreating (e.g. additional carbon losses 

during FAEE conversion, no deoxygenation demand for farnesene, and no hydrotreating demand 

at all for pentadecane). Additionally, while all process performance metrics such as sugar 

utilization efficiencies and volumetric productivity were held constant to isolate the economic 

implications for different theoretical metabolic pathways, in reality most anaerobic pathways to 

long-chain hydrocarbons proceed considerably more slowly today than aerobic pathways which 

are better developed [32]; this translates to lower practical volumetric productivities and 

increased bioreactor capital costs, which are not reflected in this theoretical exercise with such 

factors held constant. Figure 21 also validates the intended rationale for the selection of FFA as a 

representative mid-point on an overall MFSP cost basis, relative to other potential product 

pathways. 
87

Figure 21. MFSP ($/GGE) estimates for alternative pathways

$5.97

$5.55

$5.20 $5.10 $4.96 $4.70 $4.41

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

Farnesene 

(MVA)

Fatty ester 

(palmitate 

ethyl ester)

Farnesene 

(DXP)

Fatty acid 

(palmitate)

Pentadecane Fatty alcohol 

(hexadecanol, 

anaerobic)

Farnesene 

(Anaerobic)

MFSP ($/GGE)
88

6 Paths Forward to $3/GGE

To meet future 2022 cost target goals of $3/GGE for this pathway, further cost reductions from 

the 2017 design case baseline will be needed. One potential route for economic improvement is 

to maximize the overall carbon efficiency by converting underutilized fractions of the biomass. 

As detailed in this section, finding routes for converting lignin from biomass to value added 

commodity chemicals and fuels with large global markets has the potential to meet and surpass 

the $3/GGE cost target goals. 

In addition to the conversion of lignin to chemicals and fuels, there are other opportunities that 

are not explored in detail here to achieve the $3/GGE cost target goals, for example by reducing 

the overall carbohydrate conversion costs. As highlighted in the 2017 design case, the sugar 

production metrics are tied to pretreatment and enzymatic hydrolysis. Reductions in sugar costs 

will continue to be important areas for further R&D and may be achieved through the use of 

alternative or milder pretreatment options and/or improved enzyme performance (higher 

conversion yields and/or lower enzyme doses or cost), including the incorporation of new 

enzyme classes and enzymatic hydrolysis mechanisms [50]. Developing methods to utilize 

biomass-derived intermediates beyond monomeric sugars could also help to improve overall 

carbon conversion efficiencies in the process. Tailoring the hydrolysate stream to the 

microorganism tolerance will be essential for improving overall yields and lowering production 

costs. Another potential pathway to optimize process integration is “direct microbial conversion” 

(also known as consolidated bioprocessing), whereby enzymatic hydrolysis and bioconversion 

occur in a single step without the need for externally added enzymes. Given the still relatively 

high cost of enzyme addition, this approach also warrants continued evaluation and research. 

Reduction of hydrolysate conditioning costs could also be realized through a better 

understanding of the tolerance of hydrocarbon-producing microbes to lignin and other cellulosic 

sugar substrate impurities, including organic acids, salts, and other potential inhibitors. The 

current 2017 design basis is potentially a conservative design approach that is informed by the 

majority of literature sources for hydrocarbon production by utilizing clean, solids-free sugar 

sources. The performance and cost tradeoffs between sugar stream concentration and purity and 

microbial hydrocarbon production must be quantified to be able to develop optimal process 

designs. These tradeoffs are complex and will vary for different microbes, product pathways, and 

hydrocarbon production process configurations. 

Furthermore, reductions in bioconversion costs may be possible through the production of 

hydrocarbon biofuels by anaerobic microbial conversion (e.g., anaerobic fermentation). 

Reducing the need to incorporate more complex and costly aeration capabilities into bioreactor 

systems should also be considered in parallel to improve process economy. The demonstration 

and development of organisms that can produce hydrocarbons at high rates and yields via 

anaerobic pathways would be a breakthrough for this field. 

The remainder of this section is intended to provide more details on technical context, as well as 

TEA and sustainability implications, specifically around the opportunity for lignin co-product 

utilization and upgrading to value-added materials, primarily to demonstrate quantitatively one 

potential path which could ultimately achieve the $3/GGE target. However, it is recognized that 

there are additional areas of potential improvement as discussed immediately above, and 
89

although not considered explicitly here, they will be important areas to continue evaluating 

moving forward, both from a research and a modeling standpoint. 

6.1 Lignin Utilization: Technical Context

As mentioned above, lignin utilization is of paramount importance for the biological conversion 

of sugars to hydrocarbons pathway to meet the 2022 cost target of $3/GGE. Here, we briefly 

discuss several options and technical considerations concerning lignin utilization in the context 

of this pathway. We also highlight potential benefits and drawbacks of well-known options for 

isolating lignin from carbohydrate streams, many of which will require further research and 

development to quantify their impact on the overall process via TEA and LCA. The upgrading of 

lignin is also briefly discussed, which is an area of significant research need for the economic 

viability of this conversion pathway. 

Lignin is a heterogeneous, alkyl-aromatic polymer comprised of three phenylpropanoid 

monomers that are primarily thought to polymerize via radical coupling reactions during plant 

cell wall formation [117], as illustrated in Figure 22. Lignin is connected by a diverse collection 

of C-C and C-O bonds, including linkages to hemicellulose, and possesses a broad range of 

reactivity to thermochemical environments and chemical and biological catalysts. 

Figure 22. Illustration of an intact lignin bio-polymer, taken from Kim et al. [118] 

The heterogeneous nature of lignin significantly imparts the two primary challenges associated 

with its utilization in the context of this pathway: namely (1) separation of lignin from biomass 

without negatively impacting carbohydrate yields, and (2) conversion of lignin from a 

heterogeneous feedstock to a product of substantial purity, quality, and/or quantity, depending on 

the fuel, chemical, material, or product of interest. These challenges, coupled to the need for 

lignin utilization to approach economic viability for hydrocarbon production, require a co-design 

approach for carbohydrate and lignin utilization. Lignin isolation is currently feasible at multiple 

process locations (illustrated in Figure 23), including: 
90

• Prior to carbohydrate deconstruction, as an in situ lignin deconstruction step utilizing an 

alkaline catalyst that partially removes lignin and acetate, followed by dilute acid 

pretreatment and enzymatic hydrolysis for the stream primarily comprised of 

carbohydrates (this scenario was used as the design basis for the model developed here)

• As a single fractionation step that utilizes a pretreatment catalyst that can (partially) 

remove lignin and (partially) solubilize hemicellulose into separate liquid-phase process 

streams, for example, using an Organosolv or acid pulping approach 

• After carbohydrate deconstruction by recovering and either deconstructing the recovered 

solid, lignin-rich residue remaining after processing via, e.g., base-catalyzed 

depolymerization or catalytically with a method such as hydrogenolysis, or utilizing this 

lignin-rich residue as a structural material.

Figure 23. There are multiple options for selective lignin utilization. (Top) Lignin can be 

deconstructed to a soluble, separable stream with, for example, sodium hydroxide, and the 

remaining carbohydrate solid can be converted subsequently. (Middle) Lignin can be fractionated 

from the carbohydrate fractions for subsequent deconstruction and upgrading in a parallel 

fashion to carbohydrate conversion with organic solvents and an acid catalyst. (Bottom) The 

lignin-rich residue after carbohydrate deconstruction can be isolated and upgraded to products of 

interest. 
91

There are myriad approaches within each of the aforementioned general options for isolating or 

removing lignin from biomass, and likely others that are not adequately captured in those generic 

isolation options. Regardless of the specific technology, catalyst, and processing conditions 

chosen for obtaining lignin in an upgradeable form, virtually all processing options will affect 

lignin chemistry, structure, and degree of polymerization. Additionally, the form of lignin 

necessary for upgrading to the desired fuel, material, chemical, or product of interest will also 

necessitate consideration of the entire process, as the nature of the lignin-enriched stream will 

likely dictate yield and product quality. 

Regarding lignin removal or fractionation before or concomitantly with carbohydrate 

deconstruction, there are many processing options available for this type of work, including 

biological pretreatment [119-125], alkaline or acid-based pulping-like processes [120, 126], 

numerous Organosolv processes [127-130], and use of novel solvents such as ionic liquids [131-

133]. Regardless of the upstream lignin isolation strategy, there may be significant changes to the 

carbohydrate fraction of the biomass, including potential carbohydrate loss to the resulting lignin 

stream, which will impact yields and thus MFSP. Conversely, significant reductions in cellulase 

enzyme loadings may be feasible with lower lignin content in the carbohydrate stream. This 

additionally may warrant further cocktail engineering for these carbohydrate streams that vary 

from those described as the basis of this specific design case (i.e., not derived from dilute-acid 

pretreatment). Moreover, there are additional potential benefits to lignin removal upstream for 

the carbohydrate train, including a lower mass flow through the pretreatment and enzymatic 

hydrolysis units [134], which was paramount in meeting the 2012 ethanol cost target, resulting 

from the deacetylation step. 

Alternatively, for lignin conversion processes that follow carbohydrate deconstruction, such as 

pretreatment and enzymatic hydrolysis, the nature of the pretreatment process will likely impact 

the lignin chemistry significantly. For example, high severity dilute acid pretreatment is thought 

to catalyze aryl-ether (C-O) bond cleavage in lignin and promote recondensation of C-C 

linkages, likely rendering lignin more recalcitrant [135, 136]. Thus, the nature of the upstream 

pretreatment chemistry intended to (in some cases) remove hemicellulose, redistribute lignin, 

and render biomass more digestible by enzymes should be considered in light of the intended 

downstream lignin conversion process. Multiple types of processes have been considered, 

beyond combustion of residual lignin for heat and power, for converting lignin to value-added 

molecules after carbohydrate removal. For example, pyrolysis and gasification of lignin-enriched 

streams following pretreatment and enzymatic hydrolysis have been examined at length, but both 

strategies exhibit significant technical hurdles primarily around feeding lignin to hightemperature reactors [137]. Beyond this, a plethora of catalysts and processing technologies have 

been demonstrated for depolymerization of biomass-derived lignin such that lignin 

deconstruction products can then be subsequently upgraded to fuels and chemicals. These 

include (but are not limited to) depolymerization in the presence of catalysts in aqueous or 

organic media [138], in high severity alkaline environments [139-144], and via hydrogenolysis 

over hydrogen-activating catalysts in the presence of high pressures of H2 or a hydrogen donor 

molecule [145-147]. These processes are typically followed by further catalytic upgrading and/or 

finishing to value-added chemicals. Other means for lignin utilization may involve retaining the 

natural or modified polymeric structure of lignin. Instead of needing to overcome the 

heterogeneity of lignin, it can be utilized directly or with some modifications in multiple 

materials applications [148]. Essentially, many process options have been examined in the 
92

context of lignin conversion technologies, and as with carbohydrate utilization, the appropriate 

technology for lignin utilization will depend on both the upstream processing of lignin and the 

desired value-added molecules.

To that end, there are multiple potential product classes for lignin utilization, many of which are 

reviewed in the seminal review products-from-lignin report [148]. A general approach of this 

integrated design for a standalone lignin upgrading process is illustrated in Figure 27, starting 

with examples of the heterogeneous lignin deconstruction product slate through upgrading to 

potential products from lignin. The variety of products from lignin allows for potential process 

flexibility and a design focus in line with the BETO programmatic goals toward displacing the 

whole barrel of oil. The largest—and perhaps most scale-relevant—is the conversion of lignin to 

fuels, likely as gasoline-range aromatics [148]. Given the heterogeneity of resulting lignin 

deconstruction products, this will likely require significant efforts into development of selective 

deoxygenation chemistries across multiple functional groups, as shown in Figure 27 as a 

“catalytic defunctionalization” step. This challenge is similar to that faced in bio-oil conversion 

pathways, and it is likely that work therein can be leveraged and adapted for lignin utilization. 

Additionally, it may be possible with sufficient hydrogenation and severity to obtain ring-opened 

products, which could expand the range of fuel types to distillates with more desirable fuel 

properties. 

On a smaller scale, there are many opportunities to develop commodity and specialty chemical 

streams from lignin, as reviewed in detail [148]. Upgrading lignin to chemicals is a challenge 

that many groups have examined over prior years, with limited and small-scale success to date. 

To make a broad slate of chemicals from lignin, the development of “catalytic 

defunctionalization” methodologies will be essential to reduce the heterogeneity of the ligninderived streams. In terms of target molecules, aromatics such as phenol, benzene, toluene, 

xylenes, terephthalic acid, cumene, and other functionalized aromatics are significant in the 

commodity chemicals market, and are primary targets from lignin. Additionally, ring-opened 

chemicals that have developed routes already from aromatics, such as adipic acid as examined in 

the design case reported here and recently reviewed [149], will likely be a component of the 

renewable chemical portfolio derived from lignin. Further, there are novel chemical conversion 

routes to specialty chemical products from lignin that with further research and development 

have the potential to grow the market in renewable chemical products, including polymer 

precursors and advanced materials such as carbon fibers. Generally, however, the development 

of processes to produce a specific molecule will hinge on the ability to control and tune the 

lignin-derived feedstock and lignin upgrading chemistry to obtain the desired product yields and 

required product purity. 
93

Figure 24. There are multiple options for selective lignin upgrading to fuels and chemicals via 

standalone lignin upgrading processes. Many of these pathways will require intense research, 

development, and deployment going forward to demonstrate pathway viability and TEA/LCA 

efforts to understand the economic and sustainability tradeoffs between them.

6.2 Lignin Utilization: TEA Modeling/Analysis

It should be noted that the lignin utilization process design is in an early stage of development 

and understanding. This outlined design is meant to serve as a representative, preliminary 

pathway to provide insight for the potential economic and sustainability impacts for lignin 

conversion to commodity chemicals and fuels, as well as to highlight key bottlenecks, design 

uncertainties, and data gaps for future research and development. 

The conceptual design basis for lignin conversion is a multistep process. A high-level block flow 

diagram of the process is shown in Figure 25, which highlights the three key conversion areas: 

lignin deconstruction, lignin upgrading to chemicals, and product recovery and purification. 

Figure 25. Schematic diagram for lignin deconstruction/upgrading pathway 

utilized for TEA analysis
94

In the current design basis, the lignin deconstruction process is an alkaline pretreatment 

approach. The alkaline pretreatment process modeled here is a direct extension of the 

deacetylation biomass pre-conditioning process, and was chosen for the initial economic analysis 

to be in line with the 2017 target base design. For the alkaline pretreatment step, the biomass is 

conveyed into the pretreatment reactor and mixed with sodium hydroxide. Medium pressure 

steam is introduced into the batch reactor system to reach the targeted 130°C temperature. The 

reaction conditions and process yields are summarized in Table 42 and are based on current 

conditions and yields from ongoing NREL research efforts and literature data [120]. To 

minimize loss of the carbohydrate biomass fraction, anthraquinone is added at a loading of 0.5 

mg/g dry biomass to the pretreatment process. Anthraquinone has been shown to catalytically 

inhibit carbohydrate loss due to peeling reactions when added in low dosages to alkaline pulping 

processes, such as in soda pulping [120]. The slurry is agitated in the batch alkaline pretreatment 

reactor for 30 minutes. Solubilized lignin is then drained from the residual carbohydrates 

streams, consistent with the previously outlined deacetylation process. The solid carbohydrate 

stream continues to dilute acid pretreatment and upgrading consistent with the 2017 target base 

design as previously outlined. The solubilized lignin stream is sent to the catalytic lignin 

upgrading process. 

As highlighted in the overview of the lignin section, there are a multitude of routes for the 

deconstruction of biomass that results in delignification. Both experimental work and detailed 

TEA are under development at NREL for alternative lignin deconstruction processes, and the 

most promising technologies will be carried forward for further research and development. 

Table 42. Comparison of Deacetylation and Alkaline Pretreatment Processing Conditions 

Deacetylation Alkaline pretreatment

Process Details

Temperature (°C) 80 130

Time (hr) 1 0.5

NaOH loading (mg/g dry biomass) 17 39.6

Anthraquinone loading

(mg/g dry biomass) 0 0.5

Solids loading (wt%) 20 20

Process Results

Xylan loss (wt%) 2 9

Acetate removal (wt%) 88 88

Extractives removal (wt%) 100 100

Sucrose removal (wt%) 50 100

Ash removal (wt%) 75 75

Lignin solubilized (wt%) 20 80

The solubilized lignin stream is sent to catalytic upgrading to produce commodity chemicals and 

fuels. In the current design, it is assumed that minimal preprocessing steps are required prior to 

upgrading. However, depending on the tolerance of the catalyst to any poisons in the lignin 

deconstruction stream, further preprocessing will likely be necessary and will be an important 

consideration in future designs. 
95

As described in Figure 24, there are multiple products possible from the upgrading of lignin to 

fuels and chemicals. A subset of four commodity products was selected as representative of this 

large slate of possible products, with lower cost hydrocarbon products and higher valued 

oxygenated commodity chemicals included in the evaluation. These specific products, outlined 

in Table 43, have significant global market demand and positive projected market growth. The 

product prices utilized in this analysis are based on recent cost information and care was taken to 

ensure that these prices were not the high point of recent commodity market curves but more 

mid-range estimates. The goal of this analysis is to understand both the economic and 

sustainability tradeoffs for each product. The catalytic upgrading to produce commodity 

chemicals and fuels from the solubilized lignin deconstruction product is an ongoing research 

and development project at NREL. The specific pathways and detailed yields are currently being 

characterized. 

Table 43. Summary of Lignin Upgrading Products Considered Here [92, 150–155] 

Product

World Production 

(thousand tons/year)

Price 

($/ton)

Projected 

growth rate Primary Usage

1,3 Butadiene >12,000 3200 5% Synthetic rubber

1,4 Butanediol >1,000 3170 5% Tetrahydrofuran,

specialty chemicals

Adipic Acid >3,000 1700 4-4.5% Nylon-6,6

Cyclohexane >5,700 1000 2.5% Nylon-6,6 precursors

The upgraded lignin products and residual unconverted products are sent to product recovery and 

purification. A minimum product purity specification of 95% was utilized for each of the 

products. There is a high degree of uncertainty around this purification specification as the 

preliminary process models have focused on a small slate of model components for the lignin 

deconstruction products. Further research is needed to better understand and quantify the ability 

to meet the high purity specifications of these commodity products. Furthermore, even more 

stringent product purification specifications can be required for commodity products and future 

work should consider any required higher product purity specifications and the subsequent 

process intensification and cost impacts. 

Harris Group provided preliminary capital cost estimates for the lignin utilization process 

configuration by considering the costs of the major process units. Harris Group provided cost 

estimates for the alkaline pretreatment reactor with a design a 127 m3 vertical pressure vessel 

similar to the pre-steaming section of the pretreatment reactor. The lignin upgrading process is 

estimated to cost $67MM for the total installed capital cost. The pretreatment reactor accounts 

for ~37% of the overall cost, the lignin upgrading process is ~43% of the overall cost, and the 

remaining ~20% of costs are associated with the product separations processes.

Applying this estimate for capital costs and high-level process models, a preliminary TEA

estimate was developed to understand the economic and sustainability drivers associated with the 

conversion of lignin to commodity products. Figure 26 summarizes this preliminary economic 

analysis to illustrate the potential for the conversion of lignin to meet the program DOE BETO 

cost target goals of $3/GGE. This diagram illustrates the MFSP for the four commodity chemical 

products as a function of the percentage of lignin converted to the product. As highlighted in this 
96

figure, the addition of the capital and operating costs associated with inserting the new lignin 

process train increases the base-case MFSP from $5.10 to $5.57/GGE, assuming no lignin 

conversion to products; this increase is driven primarily by increased cost of caustic, new cost of 

anthraquinone, and added capital cost. As seen in Figure 26, there are a number of product 

pathways that show promise to meet and potentially surpass the overall economic targets, 

including adipic acid and 1,4 butanediol. Both of these products are oxygenated species with 

high market value. Based on the preliminary analysis, the lower cost hydrocarbon products such 

as cyclohexane will not be able to achieve the $3/GGE target goal.

Figure 26. Minimum RDB selling price as a function of lignin conversion to selected coproducts

To begin to identify key cost drivers for the lignin utilization process design, a series of 

sensitivity analyses were performed on the $3/GGE adipic acid design (as outlined in Figure 27). 

To capture the price fluctuations of the commodity chemicals market, the high point and low 

point of the adipic acid cost curve were considered. As shown in Figure 27, at the lowest cost of 

adipic acid in the past 15 years, the MFSP would increase by $0.67/GGE while at the highest 

cost point, the MFSP would decrease by more than $0.27/GGE. The preliminary capital cost 

provided by Harris Group was an AACE Class 5 analysis with capital cost uncertainty ranging 

from –20% to +100%. If capital costs associated with the lignin utilization design doubled, the 

MFSP would increase by $0.45/GGE. Further, it should be noted that a mid-range price point 

from recent price quotes from Harris Group was utilized for this overall analysis. Key process 

targets for anthraquinone loadings and xylose loss are also shown. Anthroquinone loadings have 

a significant impact if loadings are raised to 2 mg/g. If xylose losses are increased by more than 

double, the impact on the MFSP is $0.06/GGE. 
97

Figure 27. Cost sensitivity analysis for a range of uncertainties in the preliminary TEA estimate for 

lignin deconstruction and conversion to adipic acid; base case = $3/GGE RDB

Further opportunities exist for cost reductions when investigating the integrated design of a 

biorefinery that co-utilizes carbohydrates and lignin. For example, similar to deacetylation, it is 

expected that the alkaline pretreatment will help break down the biomass structure and has the 

potential to further reduce the required severity of the dilute acid pretreatment process and 

overall process costs. Additionally, it is well established that lignin removal will aid in the 

digestibility of polysaccharides, and thus it is likely that reductions in enzyme loadings could be 

realized through significant up-front lignin removal in conjunction with engineering of new 

cellulase cocktails. Alternative lignin deconstruction routes are under investigation and could 

also lead to improvements to the overall cost of lignin conversion. Additionally, in this 

feasibility-level analysis we focus only on conversion of lignin in the alkaline pretreatment 

liquor stream; however, additional carbon utilization could be gained through conversion or 

recovery of acetate and “lost” carbohydrate species that are also fractionated into this stream. 

6.3 Lignin Utilization: Sustainability Analysis

To understand the sustainability drivers associated with lignin conversion to commodity 

products, GHG emissions associated with the processes described above for lignin 

deconstruction and conversion to coproducts are quantified using SimaPro as described in 

Section 5.2. As with exported electricity, lignin-derived co-products (adipic acid, 1,3 butadiene, 

1,4 butanediol, and cyclohexane) are treated as avoided products using the product displacement 

method [110], e.g. co-products are assumed to displace the equivalent existing material produced 

by conventional methods (largely derived from petroleum), thus avoiding a degree of GHG 

emissions as well as fossil energy consumption. The displacement credits for the lignin-derived 

co-products are summarized in Table 44. 

 $(0.40) $(0.20) $- $0.20 $0.40 $0.60 $0.80

Xylose loss 2%:9%:20%

Anthraquinone loading 0.05:0.5:2.0 mg/g

biomass

Lignin utilization total capital investment

+20%:0%:+100%

Adipic acid cost range 1800:1650:1260 $/ton

ΔMFSP ($/GGE)

Lignin Case $3.00
98

Table 44. GHG Emissions and Fossil Energy Consumption for Traditional Production of 

Petroleum Based Co-products as Well as Average U.S. Grid Electricity, 

as Modeled in SimaPro and Defined by Ecoinvent

GHG emissions Fossil energy Displacement ratio

Adipic acid 26.2 kg CO2-eq./kg 134.6 MJ/kg 1 kg of adipic acid from 

biorefinery displaces 1 kg of the 

same from petroleum

1,3-Butadiene 1.16 kg CO2-eq./kg 65.04 MJ/kg 1 kg of 1,3-Butadiene from 

biorefinery displaces 1 kg of the 

same from petroleum

1,4-Butanediol 5.05 kg CO2-eq./kg 92.92 MJ/kg 1 kg of 1,4-Butanediol from 

biorefinery displaces 1 kg of the 

same from petroleum

Cyclohexane 2.40 kg CO2-eq./kg 74.35 MJ/kg 1 kg of cyclohexane from 

biorefinery displaces 1 kg of the 

same from petroleum

Average U.S. 

electricity mix*

0.78 kg CO2-eq./kWh 9.1 MJ/kWh 1 kWh of electricity from 

biorefinery displaces 1 kWh of 

US average grid electricity

* U.S. grid mix: Coal (47%), natural gas (17%), Oil (3.3%), nuclear power (20%), biomass (1.1%), wind (0.35%), 

solar (0.015%), hydrolelectric (8.2%), and others (2.5%).

Figure 28 shows the GHG emissions profile as a function of lignin conversion associated with 

the specific lignin coproduct pathways evaluated for economic potential above. These processes 

utilize a lignin deconstruction step followed by a lignin conversion step and in turn require 

additional input of resources, including anthraquinone, hydrogen, and eventual import of 

electricity (upon becoming power limited), the latter of which comes into consideration as more 

lignin is diverted away from the boiler and toward the given coproduct. The GHG emissions 

associated with the production of these inputs are accounted for. Additionally, the co-products 

displace an equivalent amount of the same products derived from petroleum, and the resulting 

GHG emissions associated with the displacement are credited. Thus, the net GHG emissions are 

shown to increase for some products and decrease for others as a result of the interplay between 

electricity (imported/exported) and hydrogen consumption, as well as the magnitude of the 

individual lignin-derived co-product GHG credits (see Figure 29). 
99

Figure 28. Comparison of GHG Emissions versus percent lignin conversion to co-products. The 

dashed line represents the GHG emissions for the $5/GGE base case (0.07 kg CO2-eq/GGE) which 

assumes combustion of lignin and conversion to power coproduct. The 0% lignin conversion 

represents the case with lignin deconstruction but without any conversion to chemical 

coproducts. 

Figure 28 shows that the GHG emissions for the $5/GGE base case (without utilizing lignin 

conversion to commodity products) is 0.07 kg CO2-eq/GGE. When the lignin deconstruction step 

is included but not subsequent conversion to chemical coproducts (i.e., 0% lignin conversion 

point), the GHG emissions is found to be comparable to the $5/GGE baseline, as the increased 

GHG emissions associated with the addition of anthraquinone and the increase of caustic during 

lignin deconstruction is offset by increased power generation due to more carbon and less water 

reaching the boiler (demonstrated in Figure 29). 

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

0% 25% 50% 75% 100%

GHG Emissions (kg CO2-eq./GGE)

Lignin conversion to product

1,3 Butadiene

1,4 Butanediol

Cyclohexane

$5/GGE Base Case

-60

-50

-40

-30

-20

-10

0

10

0% 25% 50% 75% 100%

GHG Emissions (kg CO2-eq./GGE)

Lignin conversion to product

Adipic acid

$5/GGE Base Case
100

Increasing the yields of adipic acid and 1,4 butanediol both translate to a decrease in GHG 

emissions. While more resources are required to achieve higher lignin co-product yields 

(simultaneously reducing electricity co-product credit), the high chemical co-product 

displacement credits outweigh the additional GHG emissions associated with the underlying 

processes, including hydrogen and electricity import. In other words, the conventional 

production techniques for adipic acid and 1,4 butanediol are more carbon-intensive and offer 

greater coproduct offset credits than the equivalent amount of standard U.S. grid electricity 

displaced when converting the same amount of lignin to either the chemical product or 

electricity. 

Adipic acid is considerably more GHG-intensive to produce by conventional methods than all 

other products considered here, and thus exhibits the highest co-product displacement credit, 

26.2 kg CO2-eq/kg. Consequently, the GHG emissions at the conversion stage for the adipic acid 

pathway are significantly below the GHG profile of the $5/GGE base case. Similarly, the GHG 

emissions for the conversion of lignin to 1,4 butanediol also show the potential to be lower than 

the $5/GGE base case, and continue on this trend as additional lignin is converted to coproduct.

Conversely, because of the relatively low co-product displacement credits, both 1,3 butadiene 

and cyclohexane exhibit GHG emissions increasingly higher than the $5/GGE case as additional 

lignin is diverted to either product, and thus do not offer the same GHG benefits over lignin 

combustion. 

Figure 29 reveals in further detail the tradeoffs between the minimization and eventual loss of 

excess power co-production as more lignin is diverted away from the boiler towards alternate coproducts (resulting in the import of electricity), and the GHG profile of the specific co-product 

which is being displaced. It is noted here that given the very large GHG offset associated with 

the adipic acid pathway (more-so than all other products evaluated), to maintain a reasonable 

scale for the plot, the adipic acid coproduct GHG credits are multiplied by a factor of 0.1; thus, a 

reading on the y-axis of “–2” kg CO2-eq/GGE is actually –20 for this specific coproduct. 
101

Figure 29. GHG emissions contributions versus percent lignin conversion to coproducts. The 

$5/GGE case is the base case with excess electricity credit and without lignin deconstruction or 

conversion. The 0% case adds lignin deconstruction but no lignin conversion. Note: For the adipic 

cases, the co-product credits are multiplied by a factor of 0.1 to maintain a reasonable scale in the 

plot alongside the other products.

While the estimates discussed here for TEA and sustainability analyses are preliminary in nature 

and certainly do not consider an exhaustive list of all potential coproducts which may be 

produced from lignin (and should not be taken as such), the primary point to emphasize here is 

that credible, realistic pathways exist to further decrease economics from the $5/GGE base case 

to a final $3/GGE target by way of increasing overall biomass carbon efficiency via utilization of 

biomass components that are not amenable to traditional carbohydrate-based bioconversion. A 

number of important tradeoffs exist between economics and sustainability for specific lignin 

conversion coproduct options, and it will be critical to be mindful of such tradeoffs as further 

data become available for these coproduct pathways. The market sustainability (e.g., market 

volume) of such products will also play a key role in the practicality of a given coproduct 

pathway; we have performed an early high-level analysis for market volume of the given subset 

of coproducts evaluated here to ensure that sufficient market volumes exist to warrant their 

consideration, but will continue to evaluate this important issue moving forward.

-8

-6

-4

-2

0

2

4

6

$5 

Base

0% Lig 

Conv

25 50 75 100 25 50 75 100 25 50 75 100 25 50 75 100

GHG Emissions (kg CO2-eq/GGE)

Lignin conversion to products (%)

Direct refinery emission

Other chemicals

Enzyme production

Bioconversion (nutrients)

Pretreatment chemicals

Hydrogen

Anthraquinone

Electricity

Lignin-derived chemicals

Net GHG Emissions

Adipic acid 1,3 Butadiene 1,4 Butanediol Cyclohexane

adipic acid x 0.1
102

7 Concluding Remarks

7.1 Summary

The present report establishes a plausible case for achieving an intermediate 2017 cost goal of 

$5/GGE of upgraded renewable diesel blendstock via biological production, based on a single set 

of process R&D metrics and associated cost estimates. Namely, the pathway discussed here 

assumes continued improvements are made in the biomass deconstruction areas, including 

deacetylation, dilute-acid pretreatment, and enzymatic hydrolysis, beyond performance 

demonstrated during NREL’s 2012 State of Technology trials. This includes demonstrating high 

removal of acetate and other non-fermentable components at low loss of fermentable 

components under reasonable conditions during deacetylation, high xylose and arabinose yields 

under mild acid loading and other process conditions during pretreatment, and high glucose 

yields at low enzyme loadings during hydrolysis. These front-end unit operations leverage NREL 

expertise and prior work in these areas, and will continue to be important areas of future research 

for continued improvement. 

In addition to biomass deconstruction to sugars, the design also sets targets for sugar upgrading 

to fuels, primarily sugar conversion (95% glucose, 85% xylose, and 85% arabinose) and FFA 

volumetric productivity (1.3 g/L/h). While these targets for bioconversion are consistent with 

NREL’s 2011 ethanol design report targets, many of which were demonstrated at NREL in 2012 

for ethanol, current literature suggests that today’s state of technology is notably lower for these 

parameters in the context of biological hydrocarbon production, and thus still represents a 

reasonable set of targets to maintain for this new pathway by 2017. Even so, achieving this set of 

targets is anticipated to support an intermediate DOE cost goal of $5/GGE, but additional 

improvements elsewhere will be required to achieve the ultimate goal of $3/GGE. These include 

further integration and optimization to reduce process costs (e.g. direct microbial conversion or 

other options to reduce the number of processing steps) as well as increasing carbon yield likely 

beyond that achievable with the fermentable fraction of biomass alone (e.g. pursuing 

opportunities for value-added coproducts from lignin and acetate). 

The end result of the techno-economic analysis was a predicted minimum fuel selling price 

(MFSP) of $5.10/GGE (2011$) at a final upgraded product yield of 45.4 GGE/dry ton of 

biomass. This reflects a $1.76/GGE contribution from feedstock, a $0.37/GGE contribution from 

enzymes, and a $2.96/GGE contribution from the remainder of the conversion process. This 

modeled selling price is strictly representative of n

th-plant assumptions regarding biorefinery 

design, operation, and financing, and is not intended to reflect first-of-a-kind or early-entry 

commercial facilities. While the yield presented here is notably lower than that presented in prior 

design reports for ethanol (even after adjusting for energy content), the technology pathway 

described in this report carries a distinct advantage of producing a molecule or mixture of 

components which may be directly utilized by the existing fuel infrastructure and blended into 

existing fuel markets (namely diesel in this case), likely with select desirable fuel properties such 

as cetane value.

Beyond the detailed modeling and analysis conducted to establish the $5/GGE pathway, this 

report also describes viable improvements to ultimately achieve the $3/GGE target by 2022. 

While the analysis for the latter case is not detailed, due to insufficient data available given the 

early status of work at NREL on lignin co-product utilization, a higher level analysis does indeed 
103

suggest that lignin deconstruction and conversion to a small subset of many possible coproducts 

exhibits the potential to ultimately achieve the $3/GGE target, while remaining within realistic 

limits for total carbon conversion efficiency. 

In addition to providing an economic analysis, the present report also considers sustainability 

metrics for the baseline $5/GGE and alternative $3/GGE target scenarios, by tracking and 

reporting on greenhouse gas emissions, fossil energy demand, and direct consumptive water use 

at the modeled biorefinery facility. The sustainability analysis for the baseline scenario indicates 

a GHG profile of 0.07 kg CO2-eq/GGE, fossil energy consumption of 10.8 MJ/GGE, and water 

demand of 13.7 gal/GGE. The GHG emissions profile shows the potential to either increase or 

decrease in the alternative $3/GGE scenarios relative to the baseline $5/GGE case, which is a 

function primarily of the tradeoff between the minimization and eventual loss of excess power 

co-production as more lignin is diverted away from the boiler towards alternate coproducts, and 

the GHG profile of the specific coproduct which is being displaced. Given these important 

results in both cost and sustainability implications for co-production of value-added products 

from lignin (or other potential carbon sources), NREL will continue to evaluate this scenario as 

further experimental data become available.

It is worthwhile to reiterate that cellulosic ethanol production via fermentative pathways is 

supported by decades of research and process understanding, both at NREL and elsewhere, thus 

carrying a level of certainty and credibility when making target projections for biochemical 

ethanol TEA modeling, as in the case of the 2011 and even 2002 ethanol design reports. In 

contrast, biological hydrocarbon production is a much newer and more novel approach to 

biological conversion of sugars, with doors that have only recently been opened to a number of 

product pathways given recent advances in metabolic engineering, which continues to be a 

rapidly evolving field in this context. Thus, the absolute cost values established here, as well as 

the timeframe required to achieve these outcomes, inherently carries a somewhat higher degree 

of uncertainty given the nascent stage of research as presented in the public domain. 

7.2 Future Work

Moving forward, to ultimately achieve cost goals as well as reduce uncertainty in key areas for 

the biological conversion pathway, a number of important bottlenecks, uncertainties, and areas 

for further development are summarized below. These points are repeated from NREL’s recent 

technical memo for biological hydrocarbon production [32]: 

• Investigate synergistic opportunities for sugar/intermediate production and process 

integration: Tailoring the hydrolysate stream to the microorganism tolerance will be 

essential to improving yield and lowering production cost, and there continue to be 

further opportunities for synergistic improvement in combining unit operations or 

otherwise simplifying the fully integrated process. 

• Develop separation and conditioning requirements for hydrolysate: A better 

understanding is needed of the tolerance of hydrocarbon-producing microbes to soluble 

lignin and other cellulosic sugar substrate impurities, including organic acids, salts, and 

other potential inhibitors. Improving this understanding would allow for a better 

optimized bioconversion process. 

• Optimize design and scale for aerobic fuel production: While Harris Group has 

provided NREL with preliminary insight into process and design tradeoffs for submerged 
104

aeration systems, including plausible vessel sizes, oxygen transfer rates, and agitation 

demands, more room for improvement exists to identify the most cost-effective 

engineering design for commercial-scale aerobic bioconversion operations.

• Maximize sugar (and/or carbon) utilization and microbe metabolic performance: 

There is currently a dearth of literature and high-quality data in the public domain on 

sugar uptake/conversion and microbial productivity, particularly with respect to 

production using cellulosic feedstock-derived substrates containing pentose sugars. Better 

understanding is needed regarding the productivity of the fuel production microorganism 

and the potential of genetic engineering to significantly increase metabolic production 

rates and yields, minimize side-product formation, mitigate substrate and/or product 

toxicity effects, and otherwise develop robust microbes [50]. 

• Define product separation and final polishing/upgrading requirements: Given the 

early status of the pathway model described here, further insight is needed as to the ease 

or difficulty in recovering products secreted directly into the aqueous broth, given 

potential challenges with retention of product on cell mass, the potential for emulsions, 

and phase separation. Furthermore, a more detailed quantification of the product 

components and impurities is required to better quantify upgrading requirements and 

associated costs to refine the intermediate product(s) into final fuel blendstocks.

• Evaluate co-product opportunities: To ultimately achieve cost targets, particularly a 

level of $3/GGE to compete with petroleum fuels, co-products from lignin, acetate, or 

other non-fermentable components will become an increasingly important focus in the 

biological conversion pathway. These co-product opportunities must be carefully 

evaluated beyond strictly an economic context, including considerations for sustainability 

metrics as well as co-product market volumes and saturation potential. 

Beyond the new focus on hydrocarbon fuel production by biological conversion, NREL is next 

planning to expand the analysis work to also consider an alternative route to hydrocarbons via 

catalytic conversion of sugars and other hydrolysate intermediates. This technology pathway 

brings a new set of advantages and challenges, as documented in [156]. NREL will work with 

DOE and other partners to define logistics for establishing the model and associated targets for 

the catalytic conversion pathway during FY 2014. 
105

References

NREL milestone reports cited below cannot be accessed outside of NREL and DOE. Readers 

may contact the authors of the specific reference to determine if this information has been made 

public since publication of this design report. 

1. Wooley, R.J., et al., Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing 

Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis Current and Futuristic Scenarios, 

1999.

2. Aden, A., et al., Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing CoCurrent Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover, in Other Information: 

PBD: 1 Jun 20022002. p. Medium: ED; Size: 154 pages Available from: 

http://www.nrel.gov/docs/fy02osti/32438.pdf. 

3. Humbird, D., et al., Process Design and Economics for Biochemical Conversion of Lignocellulosic 

Biomass to Ethanol: Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover, 2011 

Available from: http://www.nrel.gov/docs/fy11osti/47764.pdf. 

4. MYPP, Biomass Program Multi-Year Program Plan - November 2011, U.S.D.o. Energy, Editor 

2011, Bioenergy Technologies Office Available from: 

http://www1.eere.energy.gov/biomass/pdfs/mypp_november_2011.pdf. 

5. Schell, D., Biochemical Processing Integration, in 2013 DOE Bioenergy Technologies Office (BETO) 

Project Peer Review2013: Alexandria, VA Available from: 

https://www2.eere.energy.gov/biomass/peer_review2013/Portal/Biochemical/#.

6. Davis, R., Biochemical Platform Analysis, in 2013 DOE Bioenergy Technologies Office (BETO) 

Project Peer Review2013: Alexandria, VA Available from: 

https://www2.eere.energy.gov/biomass/peer_review2013/Portal/Biochemical/#.

7. Humbird, D. and A. Aden, Biochemical Production of Ethanol from Corn Stover: 2008 State of 

Technology Model, 2008 Available from: http://www.nrel.gov/biomass/pdfs/46214.pdf. 

8. Humbird, D., et al., Economic Impact of Total Solids Loading on Enzymatic Hydrolysis of Dilute 

Acid Pretreated Corn Stover. Biotechnology Progress, 2010. 26(5): p. 1245-1251.

9. Humbird, D., FY 2011 Biochemical Platform State of Technology Update. 2011.

10. MYPP, Biomass Multi-year Program Plan - May 2013, U.S.D.o. Energy, Editor 2013, Bioenergy 

Technologies Office Available from: 

http://www1.eere.energy.gov/biomass/pdfs/mypp_may_2013.pdf. 

11. ASPEN, Release 7.2, in AspenPlus2007, Aspen Technology Inc.: Cambridge MA Available from: 

www.aspentech.com. 

12. Wooley, R.J., V. Putsche, and K. Ibsen, Development of an ASPEN PLUS Physical Property 

Database for Biofuels Components, 1999. p. 18.

13. Wooley, R.J. and V. Putsche, Development of an ASPEN PLUS Physical Property Database for 

Biofuels Components, in NREL Technical Memo1996, National Renewable Energy Laboratory: 

Golden, CO Available from: http://biodev.nrel.gov/pdfs/3955.pdf. 

14. Muth, D., et al., Feedstock Pathway for Cellulosic Sugar Conversion Pathway, 2008, Idaho 

National Laboratory: Idaho Falls, ID.

15. Chen, S.F., et al., Compositional analysis of water-soluble materials in corn stover. Journal of 

Agricultural and Food Chemistry, 2007. 55(15): p. 5912-5918.

16. Bower, S., et al., Modeling sucrose hydrolysis in dilute sulfuric acid solutions at pretreatment 

conditions for lignocellulosic biomass. Bioresource Technology, 2008. 99(15): p. 7354-7362.
106

17. MYPP, Biomass Multi-year Program Plan - November 2012, U.S.D.o. Energy, Editor 2012, 

Bioenergy Technologies Office Available from: 

http://www1.eere.energy.gov/biomass/pdfs/mypp_november_2012.pdf. 

18. DDBST GMbH, Density of Hexadecane, Last visited 2013 Available from: 

http://www.ddbst.com/en/EED/PCP/DEN_C516.php. 

19. Chemical Book, N-PENTADECANE, 2010 Available from: 

http://www.chemicalbook.com/ChemicalProductProperty_EN_CB4217947.htm. 

20. NIST. Hexadecane. Material Measurement Laboratory [cited 2013 June 24, 2013]; Available 

from: http://webbook.nist.gov/cgi/cbook.cgi?Name=hexadecane&Units=SI. 

21. NIST. Pentadecane. Material Measurement Laboratory [cited 2013 June 24, 2013]; Available 

from: http://webbook.nist.gov/cgi/cbook.cgi?Name=pentadecane&Units=SI. 

22. U.S. DOE Hydrogen Analysis Resource Center. Lower and higher heating values of hydrogen and 

fuels. 2012; Available from: 

http://hydrogen.pnl.gov/cocoon/morf/hydrogen/site_specific/fuel_heating_calculator. 

23. Hess, J.R., Uniform Format Design and Depot Preprocessing, in PNWER 2010 Annual 

Summit2010, Idaho National Laboratory: Calgary, Alberta Available from: 

http://www.pnwerarchive.org/LinkClick.aspx?fileticket=wAARiq9I5sI%3D&tabid=1892&mid=353

1. 

24. Hess, J.R., et al., Uniform-Format Solid Feedstock Supply System:A Commodity-Scale Design to 

Produce an Infrastructure-Compatible Bulk Solid from Lignocellulosic Biomass, 2008, Idaho 

National Laboratory: Idaho Falls, ID Available from: www.inl.gov/bioenergy/uniform-feedstock. 

25. Galbe, M. and G. Zacchi, Pretreatment of lignocellulosic materials for efficient bioethanol 

production, in Biofuels, L. Olsson, Editor. 2007. p. 41-65.

26. Shill, K., et al., Ionic Liquid Pretreatment of Cellulosic Biomass: Enzymatic Hydrolysis and Ionic 

Liquid Recycle. Biotechnology and Bioengineering, 2011. 108(3): p. 511-520.

27. Tao, L., et al., Improved ethanol yield and reduced MESP by modifying low severity dilute acid 

pretreatment with deacetylation and mechanical refining: 2) Techno-economic Analysis.

Biotechnology for Biofuels, 2012. 5(69).

28. Chen, X., et al., Improved ethanol yield and reduced Minimum Ethanol Selling Price (MESP) by 

modifying low severity dilute acid pretreatment with deacetylation and mechanical refining: 1) 

Experimental. Biotechnology for Biofuels, 2012. 5(1): p. 60.

29. Tao, L., et al., FY 2012 Biochemical State of Technology Report 2012 Available from: 

https://biodev.nrel.gov/bcfcdoc/11106.pdf.

30. NABC. National Advanced Biofuels Consortium News Release: Amyris Successfully Makes Biofene 

from Cellulosic Hydrolysate. 2012; Available from: 

http://www.nabcprojects.org/pdfs/amyris_makes_biofene_from_cellulosic_hydrolysate.pdf. 

31. Perry, R.H. and D.W. Green, Perry's Chemical Engineers' Handbook 7th Edition ed. 1997: 

McGraw-Hill. 

32. Davis, R., et al., Biological Conversion of Sugars to Hydrocarbons Technology Pathway, 2013 

Available from: http://www.nrel.gov/docs/fy13osti/58054.pdf. 

33. Huffer, S., et al., Escherichia coli for biofuel production: bridging the gap from promise to 

practice. Trends in Biotechnology, 2012. 30(10): p. 538-545.

34. Siever, D., L. Tao, and D. Schell, Performance and Technoeconomic Assessment of Several SolidLiquid Separation Technologies for Processing Dilute-Acid Pretreated Corn Stover. To be 

submitted to Bioresource Technology, 2013.

35. Renninger, N.S. and D.J. Mcphee, Fuel Compositions Comprising Farnesane and Farnesane 

Derivatives and Method of Making and Using Same, 2008.

36. Pandey, A., et al., Biofuels - Alternative Feedstocks and Conversion Processes. 2011: Elsevier.
107

37. Nag, A., Biofuels Refining and Performance. 2008: McGraw-Hill. 

38. Fagerson, I.S., Thermal degradation of carbohydrates; a review. Journal of Agricultural and Food 

Chemistry 1969. 17(4): p. 747-750.

39. Loos, H., et al., Sorbitol Promotes Growth of Zymomonas-Mobilis in Environments with HighConcentrations of Sugar - Evidence for a Physiological-Function of Glucose-Fructose 

Oxidoreductase in Osmoprotection. Journal of Bacteriology, 1994. 176(24): p. 7688-7693.

40. Dowe, N., D. Humbird, and D. Schell, Joule Milestone: Status Report on Overall Corn Stover to 

Ethanol Yield Improvement Effort, 2009, National Renewable Energy Laboratory: Golden, CO 

Available from: http://biodev.nrel.gov/bcfcdoc/10457.pdf. 

41. Huang, W.D. and Y.H.P. Zhang, Analysis of biofuels production from sugar based on three 

criteria: Thermodynamics, bioenergetics, and product separation. Energy & Environmental 

Science, 2011. 4(3): p. 784-792.

42. Zhang, M., Metabolic Engineering of Zymomonas mobilis for Hydrocarbon Fuels Production.

Presented at the 2012 SIMB Annual Meeting, 2012.

43. Ladygina, N., E.G. Dedyukhina, and M.B. Vainshtein, A review on microbial synthesis of 

hydrocarbons. Process Biochemistry, 2006. 41(5): p. 1001-1014.

44. Garcia-Ochoa, F. and E. Gomez, Bioreactor scale-up and oxygen transfer rate in microbial 

processes: An overview. Biotechnology Advances, 2009. 27(2): p. 153-176.

45. Solazyme. Solazyme Successfully Scales Renewable Oil Fermentation to 500,000 Liters at ADM 

Clinton, Iowa Facility. 2013 July 2013]; Available from: http://solazyme.com/media/2012-12-13. 

46. Yang, S.-T., Bioprocessing for Value-Added Products from Renewable Resources - New 

Technologies and Applications. 2007: Elsevier.

47. Lidén, G., Understanding the Bioreactor. Bioprocess and Biosystems Engineering, 2002. 24(5): p. 

273-279.

48. Kollaras, A., et al., Multiple Coproducts Needed to Establish Cellulosic Ethanol Industry, in 

Ethanol Producer Magazine 2012.

49. Benz, G.T., Bioreactor Design for Chemical Engineers. Chemical Engineering Progress, 2011. 

107(8): p. 21-26.

50. Department of Energy, Conversion Technologies for Advanced Biofuels, 2012, Department of 

Energy, Office of the Biomass Program in the Office of Energy Efficiency and Renewable Energy, 

Conversion Technologies for Advanced Biofuels Workshop.

51. Liu, T.G. and C. Khosla, Genetic Engineering of Escherichia coli for Biofuel Production. Annual 

Review of Genetics, Vol 44, 2010. 44: p. 53-69.

52. Rude, M.A. and A. Schirmer, New microbial fuels: a biotech perspective. Current Opinion in 

Microbiology, 2009. 12(3): p. 274-281.

53. Dugar, D. and G. Stephanopoulos, Relative potential of biosynthetic pathways for biofuels and 

bio-based products. Nature Biotechnology, 2011. 29(12): p. 1074-1078.

54. Magnuson, K., et al., Regulation of Fatty-Acid Biosynthesis in Escherichia-Coli. Microbiological 

Reviews, 1993. 57(3): p. 522-542.

55. White, S.W., et al., The structural biology of type II fatty acid biosynthesis. Annual Review of 

Biochemistry, 2005. 74: p. 791-831.

56. Fujita, Y., H. Matsuoka, and K. Hirooka, Regulation of fatty acid metabolism in bacteria.

Molecular Microbiology, 2007. 66(4): p. 829-839.

57. Westfall, P.J. and T.S. Gardner, Industrial fermentation of renewable diesel fuels. Current 

Opinion in Biotechnology, 2011. 22(3): p. 344-350.

58. Zhou, H., et al., Xylose isomerase overexpression along with engineering of the pentose 

phosphate pathway and evolutionary engineering enable rapid xylose utilization and ethanol 

production by Saccharomyces cerevisiae. Metabolic Engineering, 2012. 14(6): p. 611-622.
108

59. Zhang, F.Z., S. Rodriguez, and J.D. Keasling, Metabolic engineering of microbial pathways for 

advanced biofuels production. Current Opinion in Biotechnology, 2011. 22(6): p. 775-783.

60. Hawkins, R.L., Utilization of xylose for growth by the eukaryotic alga, Chlorella. Current 

Microbiology, 1999. 38(6): p. 360-363.

61. Dellomonaco, C., et al., Engineered reversal of the beta-oxidation cycle for the synthesis of fuels 

and chemicals. Nature, 2011. 476(7360): p. 355-359.

62. Peralta-Yahya, P.P., et al., Identification and microbial production of a terpene-based advanced 

biofuel. Nat Commun, 2011. 2: p. 483.

63. Steen, E.J., et al., Microbial production of fatty-acid-derived fuels and chemicals from plant 

biomass. Nature, 2010. 463(7280): p. 559-U182.

64. Liu, T., H. Vora, and C. Khosla, Quantitative analysis and engineering of fatty acid biosynthesis in 

E. coli. Metabolic Engineering, 2010. 12(4): p. 378-386.

65. Lu, X., H. Vora, and C. Khosla, Overproduction of free fatty acids in E. coli: Implications for 

biodiesel production. Metabolic Engineering, 2008. 10(6): p. 333-339.

66. MYPP, Biomass Multi-year Program Plan, 2013 Available from: 

http://www1.eere.energy.gov/biomass/pdfs/mypp_may_2013.pdf. 

67. Kosa, M. and A.J. Ragauskas, Lipids from heterotrophic microbes: advances in metabolism 

research. Trends in Biotechnology, 2011. 29(2): p. 53-61.

68. Zhao, X., et al., Medium optimization for lipid production through co-fermentation of glucose 

and xylose by the oleaginous yeast Lipomyces starkeyi. European Journal of Lipid Science and 

Technology, 2008. 110(5): p. 405-412.

69. Ageitos, J., et al., Oily yeasts as oleaginous cell factories. Applied Microbiology and 

Biotechnology, 2011. 90(4): p. 1219-1227.

70. Althoff, K., Commercializing Cellulosic Ethanol Technology, 2010, Dupont Danisco Available from: 

http://www.epoverviews.com/oca/althoff.pdf. 

71. Lennen, R.M. and B.F. Pfleger, Engineering Escherichia coli to synthesize free fatty acids. Trends 

in Biotechnology, 2012. 30(12): p. 659-667.

72. Voelker, T.A. and H.M. Davies, Alteration of the Specificity and Regulation of Fatty-Acid Synthesis 

of Escherichia Coli by Expression of a Plant Medium-Chain Acyl-Acyl Carrier Protein Thioesterase.

Journal of Bacteriology, 1994. 176(23): p. 7320-7327.

73. Davis, M.S., J. Solbiati, and J.E. Cronan, Overproduction of acetyl-CoA carboxylase activity 

increases the rate of fatty acid biosynthesis in Escherichia coli. Journal of Biological Chemistry, 

2000. 275(37): p. 28593-28598.

74. Cho, H.S. and J.E. Cronan, Defective Export of a Periplasmic Enzyme Disrupts Regulation of FattyAcid Synthesis. Journal of Biological Chemistry, 1995. 270(9): p. 4216-4219.

75. Jiang, P. and J.E. Cronan, Inhibition of Fatty-Acid Synthesis in Escherichia-Coli in the Absence of 

Phospholipid-Synthesis and Release of Inhibition by Thioesterase Action. Journal of Bacteriology, 

1994. 176(10): p. 2814-2821.

76. Zhang, F.Z., J.M. Carothers, and J.D. Keasling, Design of a dynamic sensor-regulator system for 

production of chemicals and fuels derived from fatty acids. Nature Biotechnology, 2012. 30(4): p. 

354-U166.

77. Ethanol Co-Products. 2007 [cited 2013 June 1, 2013]; Available from: http://ethanolinformation.com/ethanol-coproducts.php. 

78. Meerman, H.J., A.S. Kelley, and M. Ward, Advances in Protein Expression in Filamentous Fungi, in 

Protein Expression Technologies: Current Status and Future Trends. , F. Baneyx, Editor. 2004, 

Horizon Bioscience: Norfolk, UK. p. 345.

79. Warzywoda, M., 1984.

80. Emme, B., Personal communication, D. Humbird, Editor 2009, Novozymes, Inc.
109

81. Wiseman, A., Biochemical-Engineering and Biotechnology Handbook - Atkinson,B, Mavituna,F.

Chemistry in Britain, 1983. 19(5): p. 423-424.

82. Schell, D., et al., Technical and Economic Analysis of an Enzymatic Hydrolysis Based Ethanol Plant 

1991, Solar Energy Research Institute: Golden CO Available from: 

http://biodev.nrel.gov/bcfcdoc/3892.pdf. 

83. Cuellar, M.C., J.J. Heijnen, and L.A. van der Wielen, Large-scale production of diesel-like biofuels - 

process design as an inherent part of microorganism development. Biotechnol J, 2013. 8(6): p. 

682-9.

84. Bart, J.C.J., N. Palmeri, and S. Cavallaro, Biodiesel Science and Technology - From Soil to Oil. 

2010: Woodhead Publishing.

85. Altman, P.L. and D.S. Dittmer, Biology Data Book. Vol. 1-3. 1972-1974: Federation of American 

Societies for Experimental Biology.

86. Davis, R., A. Aden, and P.T. Pienkos, Techno-economic analysis of autotrophic microalgae for fuel 

production. Applied Energy, 2011. 88(10): p. 3524-3531.

87. Marker, T.L., et al., Opportunities for Biorenewables in Oil Refineries, U.S.D.o. Energy, Editor 

2005, UOP LLC Available from: http://www.osti.gov/bridge/servlets/purl/861458-

Wv5uum/861458.pdf. 

88. Dirks, G., J. McGowen, and P.T. Pienkos, Sustainable Algal Biofuels Consortium, in 2013 DOE 

Bioenergy Technologies Office (BETO) Project Peer Review2013: Alexandria, VA Available from: 

https://www2.eere.energy.gov/biomass/peer_review2013/Portal/Algae/#.

89. Davis, R., et al., Renewable Diesel from Algal Lipids: An Integrated Baseline for Cost, Emissions, 

and Resource Potential from a Harmonized Model, 2012 Available from: 

http://www.nrel.gov/docs/fy12osti/55431.pdf. 

90. Dutta, A., et al., Process Design and Economics for Conversion of Lignocellulosic Biomass to

Ethanol, Thermaochemical Pathway by Indirect Gasification and Mixed Alcohol Synthesis, 2011, 

National Renewable Energy Laboratory.

91. Chemical Engineering Magazine, 2011, Chemical Engineering Magazine Plant Cost Index 

Available from: http://www.che.com/pci/. 

92. SRI Consulting, U.S. Producer Price Indexes – Chemicals and Allied Products/Industrial Inorganic 

Chemicals Index. Chemical Economics Handbook, 2008(Menlo Park, CA).

93. Bureau of Labor Statistics Data website, National Employment, Hours, and Earnings Catalog, 

Industry: Chemicals and Allied Products, 1980-2009., 2009 Available from: 

http://data.bls.gov/cgi-bin/srgate. 

94. U. S. EIA. Electricity Retail Price to Consumers. 2013 July 2013]; Available from: 

http://www.eia.gov/electricity/data.cfm#sales. 

95. U. S. EIA, Electricity Wholesale Market Data, 2013 Available from: 

http://www.eia.gov/electricity/wholesale/index.cfm. 

96. Dillich, S., T. Ramsden, and M. Melina, DOE Hydrogen and Fuel Cells Program Record, U.S.D.o. 

Energy, Editor 2012 Available from: 

http://www.hydrogen.energy.gov/pdfs/12024_h2_production_cost_natural_gas.pdf. 

97. Administration, U.S.E.I., U.S. Natural Gas Prices, 2013 Available from: 

http://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_a.htm. 

98. Table 7—U.S. wholesale list price for glucose syrup, Midwest markets, monthly, quarterly, and by 

calendar and fiscal year, in USDA ERS - Sugar and Sweeteners Yearbook Tables, USDA, Editor 

2013 Available from: 

http://www.ers.usda.gov/datafiles/Sugar_and_Sweeteners_Yearbook_Tables/World_and_US_S

ugar_and_Corn_Sweetener_Prices/TABLE07.XLS. 
110

99. Brown Caldwell, Process Design of Wastewater Treatment for the NREL Cellulosic Ethanol Model. 

, 2011, Brown and Caldwell for Harris Group Inc.

100. Brown Caldwell, Process Design of Wastewater Treatment for the NREL Cellulosic Ethanol Model. 

, 2012.

101. Peters, M.S., et al., Plant design and economics for chemical engineers. 2003: McGraw-Hill New 

York.

102. Short, W., D.J. Packey, and T. Holt, A Manual for the Economic Evaluation and Energy Efficiency 

and Renewable Energy Technologies, 1995, National Renewable Energy Laboratory: Golden, CO.

103. Service, I.R., How to Depreciate Property, D.o.t. Treasury, Editor 2009: Washington, D.C. 

Available from: http://www.irs.gov/pub/irs-pdf/p946.pdf. 

104. Gary, J.H. and G.E. Handwerk, Petroleum refining : technology and economics. 3rd ed. 1994, New 

York: M. Dekker. xii, 465 p.

105. Cran, J., Improved factored method gives better preliminary cost estimates. Chemical Engineer, 

1981. 88(7): p. 65-79.

106. SimaPro, SimaPro, v.7.3; Product Ecology Consultants: Amersfoort, the Netherlands, 2011., 2011.

107. Hsu, D., Heath, G., Wolfrum, E., Mann, M.K., and Aden, A., Life Cycle Environmental Impacts of 

Selected U.S. Ethanol Production and Use Pathways in 2022. Environ. Sci. Technol, 2010: p. 

5289–5297.

108. Ecoinvent, Ecoinvent, v.2.2; Swiss Center for Life Cycle Inventories: Duebendorf, Switzerland, 

2010., 2010.

109. LCI, U.S. Life-Cycle Inventory, v. 1.6.0; National Renewable Energy Laboratory: Golden, CO, 2008., 

2008.

110. Wang, M., H. Huo, and S. Arora, Methods of dealing with co-products of biofuels in life-cycle 

analysis and consequent results within the US context. Energy Policy, 2011. 39(10): p. 5726-5736.

111. Biorecro, A.B., Global Status of BECCS Projects 2010. Canberra, Australia: Global CCS Institute., 

2010.

112. Fischer, B.S., et al., Issues related to mitigation in the long term context. Climate Change 2007: 

Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 2007: Cambridge: Cambridge University Press.

113. Shaffer, K.H., Consumptive Water Use in the Great Lakes Basin, U.S. Geological Survey (USGS),

http://pubs.usgs.gov/fs/2008/3032/pdf/fs2008-3032.pdf., 2008, U.S. Geological Survey (USGS) 

Available from: http://pubs.usgs.gov/fs/2008/3032/pdf/fs2008-3032.pdf. 

114. Blanch, H.W., Bioprocessing for biofuels. Current Opinion in Biotechnology, 2012. 23(3): p. 390-

395.

115. Paap, S.M., et al., Biochemical production of ethanol and fatty acid ethyl esters from switchgrass: 

A comparative analysis of environmental and economic performance. Biomass and Bioenergy, 

2013. 49(0): p. 49-62.

116. Lange, B.M., et al., Isoprenoid biosynthesis: The evolution of two ancient and distinct pathways 

across genomes. Proceedings of the National Academy of Sciences of the United States of 

America, 2000. 97(24): p. 13172-13177.

117. Boerjan, W., J. Ralph, and M. Baucher, Lignin biosynthesis. Annual Review of Plant Biology, 2003. 

54: p. 519-546.

118. Kim, S., et al., Computational Study of Bond Dissociation Enthalpies for a Large Range of Native 

and Modified Lignins. The Journal of Physical Chemistry Letters, 2011. 2(22): p. 2846-2852.

119. Fernandez-Fueyo, E., et al., Comparative genomics of Ceriporiopsis subvermispora and 

Phanerochaete chrysosporium provide insight into selective ligninolysis. Proceedings of the 

National Academy of Sciences of the United States of America, 2012. 109(14): p. 5458-5463.
111

120. Li, Q., et al., Comparison of different alkali-based pretreatments of corn stover for improving 

enzymatic saccharification. Bioresource Technology, 2012. 125: p. 193-199.

121. Martinez, D., et al., Genome, transcriptome, and secretome analysis of wood decay fungus 

Postia placenta supports unique mechanisms of lignocellulose conversion. Proceedings of the 

National Academy of Sciences of the United States of America, 2009. 106(6): p. 1954-1959.

122. Martinez, D., et al., Genome sequence of the lignocellulose degrading fungus Phanerochaete 

chrysosporium strain RP78. Nature Biotechnology, 2004. 22(6): p. 695-700.

123. Salvachua, D., et al., Fungal pretreatment: An alternative in second-generation ethanol from 

wheat straw. Bioresource Technology, 2011. 102(16): p. 7500-7506.

124. Archibald, F.S., et al., Kraft pulp bleaching and delignification by Trametes versicolor. Journal of 

Biotechnology, 1997. 53(2-3): p. 215-236.

125. Breen, A. and F.L. Singleton, Fungi in lignocellulose breakdown and biopulping. Current Opinion 

in Biotechnology, 1999. 10(3): p. 252-258.

126. Kleppe, P.J., KRAFT PULPING. Tappi, 1970. 53(1): p. 35-&.

127. Aziz, S. and K. Sarkanen, Organosolv Pulping - A Review. Tappi Journal, 1989. 72(3): p. 169-175.

128. Johansson, A., O. Aaltonen, and P. Ylinen, Organosolv Pulping - Methods and Pulp Properties.

Biomass, 1987. 13(1): p. 45-65.

129. Bozell, J.J., et al., Solvent fractionation of renewable woody feedstocks: Organosolv generation of 

biorefinery process streams for the production of biobased chemicals. Biomass & Bioenergy, 

2011. 35(10): p. 4197-4208.

130. Bozell, J.J., An evolution from pretreatment to fractionation will enable successful development 

of the integrated biorefinery. Bioresources, 2010. 5(3): p. 1326-1327.

131. Li, C.L., et al., Comparison of dilute acid and ionic liquid pretreatment of switchgrass: Biomass 

recalcitrance, delignification and enzymatic saccharification. Bioresource Technology, 2010. 

101(13): p. 4900-4906.

132. Swatloski, R.P., et al., Dissolution of cellose with ionic liquids. Journal of the American Chemical 

Society, 2002. 124(18): p. 4974-4975.

133. Wang, H., G. Gurau, and R.D. Rogers, Ionic liquid processing of cellulose. Chemical Society 

Reviews, 2012. 41(4): p. 1519-1537.

134. Tao, L., et al., Improved ethanol yield and reduced minimum ethanol selling price (MESP) by 

modifying low severity dilute acid pretreatment with deacetylation and mechanical refining: 2) 

Techno-economic analysis. Biotechnology for Biofuels, 2012. 5. 

135. Kobayashi, T., et al., Molecular-Level Consequences of Biomass Pretreatment by Dilute Sulfuric 

Acid at Various Temperatures. Energy & Fuels, 2011. 25(4): p. 1790-1797.

136. Moxley, G., et al., Structural changes of corn stover lignin during acid pretreatment. Journal of 

Industrial Microbiology & Biotechnology, 2012. 39(9): p. 1289-1299.

137. Nowakowski, D.J., et al., Lignin fast pyrolysis: Results from an international collaboration.

Journal of Analytical and Applied Pyrolysis, 2010. 88(1): p. 53-72.

138. Zakzeski, J., et al., The Catalytic Valorization of Lignin for the Production of Renewable 

Chemicals. Chemical Reviews, 2010. 110(6): p. 3552-3599.

139. Shabtai, J., W. Zmierczak, and E. Chornet. Conversion of lignin to reformulated gasoline 

compositions. 1. Basic processing scheme. 1997. Elsevier.

140. Shabtai, J., et al., Conversion of lignin. 2. Production of high-octane fuel additives. Prepr. Symp. - 

Am. Chem. Soc., Div. Fuel Chem., 1999. 44(Copyright (C) 2013 American Chemical Society (ACS). 

All Rights Reserved.): p. 267-272.

141. Shabtai, J., et al. Lignin conversion to high-octane fuel additives. 1999. Elsevier Science.

142. Shabtai, J.S., W.W. Zmierczak, and E. Chornet, Process for conversion of lignin to reformulated 

hydrocarbon gasoline, 1999, The University of Utah Research Foundation, USA . p. 32 pp.
112

143. Vigneault, A., D.K. Johnson, and E. Chornet, Base-catalyzed depolymerization of lignin: 

separation of monomers. Can. J. Chem. Eng., 2007. 85(Copyright (C) 2013 American Chemical 

Society (ACS). All Rights Reserved.): p. 906-916.

144. Miller, J.E., et al., Batch microreactor studies of lignin and lignin model compound 

depolymerization by bases in alcohol solvents. Fuel, 1999. 78 (Copyright (C) 2013 American 

Chemical Society (ACS). All Rights Reserved.): p. 1363-1366.

145. Macala, G.S., et al., Hydrogen Transfer from Supercritical Methanol over a Solid Base Catalyst: A 

Model for Lignin Depolymerization. Chemsuschem, 2009. 2(3): p. 215-217.

146. Wang, X.Y. and R. Rinaldi, Exploiting H-transfer reactions with RANEY (R) Ni for upgrade of 

phenolic and aromatic biorefinery feeds under unusual, low-severity conditions. Energy & 

Environmental Science, 2012. 5(8): p. 8244-8260.

147. Song, Q., et al., Lignin depolymerization (LDP) in alcohol over nickel-based catalysts via a 

fragmentation-hydrogenolysis process. Energy & Environmental Science, 2013. 6(3): p. 994-

1007.

148. Holladay, J., et al., Top value-added chemicals from biomass volume II—results of screening for 

potential candidates from biorefinery lignin., 2007, Oak Ridge, TN, USA: Pacific Northwest 

National Laboratory.

149. Van de Vyver, S. and Y. Roman-Leshkov, Emerging catalytic processes for the production of 

adipic acid. Catalysis Science & Technology, 2013. 3(6): p. 1465-1479.

150. ICIS Chemical Business, 5/14/2007, Vol. 271, Issue 19, 2007. p. 43. 

151. ICIS Chemical Business, 10/10/2011, Vol. 280, Issue 11, 2011. p. 55.

152. ICIS Chemical Business, 2/28/2011, Vol. 279, Issue 8, 2011. p. 43.

153. ICIS Chemical Business, 9/6/2011, Vol. 280, Issue 9, 2011. p. 43.

154. ICIS Chemical Business, 10/1/2012, Vol. 282, Issue 9, 2012. p. 34.

155. Chemical Week, 10/3/2011, Vol. 173 Issue 24, 2011. p. 35 Available from: 

http://www.prweb.com/releases/adipic_acid_market/polyurethane_resins/prweb9410852.htm

156. Biddy, M.J. and J. SB, Catalytic Upgrading of Sugars to Hydrocarbons Technology Pathway, 2013 

Available from: http://www.nrel.gov/docs/fy13osti/58055.pdf. 

157. Keller and Heckmann LLP, Assessment Plan for Corn Steep Liquor (CAS #66071-94-1) in 

Accordance with the USEPA High Production Volume Chemical Challenge Program. 2006: 

Prepared for The Corn Refiners Association.

158. Verevkin, S.P., et al., Biomass-Derived Platform Chemicals: Thermodynamic Studies on the 

Conversion of 5-Hydroxymethylfurfural into Bulk Intermediates. Industrial & Engineering 

Chemistry Research, 2009. 48(22): p. 10087-10093.

159. Lewis Sr., R.J., Hawley's Condensed Chemical Dictionary. 15th Edition ed. 2007, New York: 

Hawley's Condensed Chemical Dictionary.

160. Merrill, A.L. and B.K. Watt, Energy Value of Foods: Basis and Derivation, U.A.H. #74, Editor 1973 

Available from: http://www.ars.usda.gov/SP2UserFiles/Place/12354500/Data/Classics/ah74.pdf. 

161. Roels, J.A., Energetics and Kinetics in Biotechnology. 1983, Amsterdam: Elsevier Biomedical 

Press.

162. Bailey, J.E. and D.F. Ollis, Biochemical Engineering Fundamentals. 2nd Edition ed. 1986, New 

York: McGraw-Hill. 
113

Appendix A. Individual Equipment Cost Summary

The following table shows abbreviated specifications, purchased cost and installed cost for each 

piece of equipment in this process design. Although each piece of equipment has its own line, 

many were quoted as part of a package. Updated or new equipment cost estimates provided from 

Harris Group for this analysis (beyond estimates that were utilized in the 2011 ethanol report) are 

highlighted in blue in the table below. NREL and Harris Group would like to acknowledge the 

equipment vendors who assisted us with the cost estimation effort for this design report. 
114
115
116
117
118
119
120

Appendix B. Discounted Cash Flow Rate of Return 

Worksheet
121
122

Appendix C. Process Parameters/Operating Summary
123

Appendix D. Aspen Plus Properties

The table below is a list of the components used in the Aspen model. Previous versions of the 

model used custom property databanks created at NREL. Where possible in the new model, these 

components have been replaced with components from Aspen’s native databanks. Property 

definitions for the few remaining custom components were moved into the model itself (i.e., 

inside the simulation file) and are discussed here. 

The component CSL (corn steep liquor) used in previous models was removed. CSL streams are 

now modeled as 50% water, 25% protein, and 25% lactic acid [157]. 
124

Component Property Quantity Units Reference

H2O - - - Native Aspen component

GLUCOSE - - - Native Aspen component (dextrose)

GALACTOS - - - Duplicate of GLUCOSE

MANNOSE - - - Duplicate of GLUCOSE

XYLOSE DHFORM -216752.65 cal/mol Native Aspen component (d-xylose) with DHFORM specified (5/6 of GLUCOSE DHFORM)

ARABINOS - - - Duplicate of XYLOSE

CELLOB - - - Cellobiose. Used native Aspen component sucrose

SUCROSE - - - Native Aspen component

GLUCOLIG MW 162.1424 Glucose oligomers. Most properties from GLUCOSE; MW is GLUCOSE minus H2O

DHFORM -192875.34 cal/mol Back-calculated to match ΔHc of CELLULOS

GALAOLIG - - - Galactose oligomers. Duplicate of GLUCOLIG

MANOLIG - - - Mannose oligomers. Duplicate of GLUCOLIG

XYLOLIG MW 132.11612 Xylose oligomers. Most properties from XYLOSE; MW is XYLOSE minus H2O

DHFORM -149412.58 cal/mol Back-calculated to match ΔHc of XYLAN

ARABOLIG - - - Arabinose oligomers. Duplicate of XYLOLIG

EXTRACT - - - Organic extractives. Duplicate of GLUCOSE

LGNSOL - - - Solubilized lignin. Native Aspen component vanillin (see note at LIGNIN)
125

Component Property Quantity Units Reference

HMF MW 126.11 (5-hydroxymethylfurfural) Properties for HMF were estimated within Aspen using NIST 

TB 532.7 K TDE routines. Specify molecular structure, MW, TB and DHFORM. From [158]. 

DHFORM -79774.53 cal/mol

DHVLWT-1 80550000 J/kmol

TC 731.012 K NIST TDE

PC 5235810 Pa

OMEGA 0.99364671

VC 0.3425 m3/kmol

RKTZRA 0.198177974

FURFURAL - - - Native Aspen component

AACID - - - Native Aspen component (acetic acid)

LACID - - - Native Aspen component (lactic acid)

XYLITOL - - - Native Aspen component

GLYCEROL - - - Native Aspen component

SUCCACID - - - Native Aspen component (Succinic acid)

NH3 - - - Native Aspen component

H2SO4 - - - Native Aspen component

NH4SO4 - - - Native Aspen component (ammonium sulfate)

NH4ACET PLXANT/1 -1.00E+20 atm Native Aspen component (ammonium acetate) forced non-volatile

DAP - - - Native Aspen component (diammonium phosphate)

HNO3 - - - Native Aspen component

NANO3 - - - Native Aspen component

NAOH - - - Native Aspen component

CNUTR - - - Cellulase nutrient mix. Duplicate of glucose

WNUTR - - - WWT nutrient mix. Duplicate of glucose

DENAT - - - Denaturant. Native Aspen component (n-heptane)
126

Component Property Quantity Units Reference

OIL - - - Corn oil antifoam. Native Aspen component (oleic acid)

O2 - - - Native Aspen component

N2 - - - Native Aspen component

NO - - - Native Aspen component

NO2 - - - Native Aspen component

CO - - - Native Aspen component

CO2 - - - Native Aspen component

CH4 - - - Native Aspen component

H2S - - - Native Aspen component

SO2 - - - Native Aspen component

CELLULOS DHSFRM -233200.06 cal/mol Native Aspen component with specified heat of formation; back-calculated 

GALACTAN - - - Duplicate of CELLULOS

MANNAN - - - Duplicate of CELLULOS

XYLAN Formula C5H8O4 (monomer)

MW 132.117

DHSFRM -182099.93 cal/mol From [13]. Assumes the ratio of ΔHc of glucose:xylose is the same for cellulose:xylan.

ARABINAN - - - Duplicate of xylan

LIGNIN - - - Used native Aspen component vanillin (C8H8O3). The HHV of this compound (-23,906 

Btu/kg) is very close to what we previously assumed for lignin as a custom component (-

24,206)

ACETATE - - - Used native Aspen component acetic acid

PROTEIN Formula CH1.57O0.31N0.29S0.007 Wheat gliadin [159]

MW 22.8396

DHSFRM -17618 cal/mol From literature value of gliadin ΔHc [160]

PLXANT/1 -1.00E+20 atm Forces non-volatility
127

Component Property Quantity Units Reference

ASH - - - Native Aspen component CaO

ENZYME Formula CH1.59O0.42N0.24S0.01 Provided by Novozymes [3]

MW 24.0156

DHSFRM -17618 cal/mol copied from PROTEIN

DENZ - - - Denatured enzyme. Duplicate of enzyme

ZYMO Formula CH1.8O0.5N0.2 Z. mobilis cell mass. Average composition of several microorganisms In [161]

MW 24.6264

DHSFRM -31169.39 cal/mol From [13] after [162]

TRICHO Formula CH1.645O0.445N0.205S0.005 T. reesei cell mass. Average of generic cell mass [81] and the ENZYME composition

MW 23.8204

DHSFRM -23200.01 cal/mol Copied BIOMASS

BIOMASS Formula CH1.64O0.39N0.23S0.0035 WWT sludge.

MW 23.238

DHSFRM -23200.01 cal/mol From [13] after [162]

TAR - - - Modeled as solid xylose

LIME - - - Native Aspen component (calcium hydroxide)

CASO4 - - - Native Aspen component

C - - - Native Aspen component (graphite carbon)

N-TETRADECANOIC-ACID Formula C14H28O2 Native Aspen component

ETHYL-PALMITATE Formula C18H36O2-N2 Native Aspen component

METHYL-OLEATE Formula C19H36O2 Native Aspen component

N-PENTADECANE Formula C15H32 Native Aspen component

N-HEXADECANOIC-ACID Formula C16H32O2 Native Aspen component

N-HEXADECANE Formula C16H34 Native Aspen component

HYDROGEN Formula H2 Native Aspen component
128

Appendix E. Process Flow Diagrams

High-level stream table information from Aspen Plus modeling output follows, for key streams 

associated with each process operation area. This is followed by high-level PFDs for the 

associated process areas. Space for stream tables was limited; below is a key to lumped 

components. As the stream table information focuses primarily on the high-level overall process 

and does not include every individual modeled stream within each process area, mass balance 

closure around a given unit area may not be 100%. 

Other sugars (SS) arabinose, mannose, galactose, cellobiose, sucrose

Sugar oligomers (SS) oligomers of glucose, xylose, and all minor sugars

Organic soluble solids (SS) ammonium acetate, solubilized lignin, organic extractives, lactic acid, 

cellulase and WWT chemicals

Inorganic soluble solids (SS) ammonium sulfate, diammonium phosphate, sodium hydroxide, nitric 

acid, sodium nitrate

Furfurals furfural, HMF

Other organics glycerol, gasoline denaturant, corn oil, sorbitol, succinic acid, xylitol

CO/SOX/NOX/H2S NO, NO2, SO2, CO, H2S

Other struct. carbohydr. (IS) arabinan, mannan, galactan

Protein (IS) corn protein, cellulase, denatured cellulase

Cell mass (IS) WWT sludge, Z. mobilis from biological conversion, T. reesei from 

cellulase production

Other insoluble solids (IS) tar, ash, carbon, lime, gypsum
129 

Figure 30. Overall process schematic for Aspen stream tables

A200

DEACETYLATION, 

PRETREATMENT

& CONDITIONING

A400

ENZYME 

PRODUCTION

A700

STORAGE

A800

BURNER/BOILER 

TURBOGENERATOR

A900

UTILITIES

JUNE 2013 PFD-P120-A000

OVERALL PROCESS:

BIOMASS TO HYDROCARBON

RAW 

FEEDSTOCK

AIR

RECYCLE 

WATER

100 200

210

220

230

240

PROCESS 

WATER 250

300

CELLULASE

FEEDSTOCK HYDROLYSATE

600

BLACK LIQUOR, 

FLASH CONDENSATE

400

PROCESS 

WATER 430

NUTRIENTS 410

420

440 VENT

330

350

DILUTE HYDROLYSATE

AIR 320

PROCESS 

WATER 310

BIOCONVERSION CONCENTRATED 

HYDROLYSATE

360

800

810

ENZYMATIC 

HYDROLYSIS, 

HYDROLYSATE 

CONDITIONING

340 390 TO RECYCLE 

WATER VENT

AIR 380

NUTRIENTS 370

BIOREACTOR BROTH

500

AIR 520

HYDROGEN 510

530 FLUE

GAS

A500

PRODUCT 

RECOVERY & 

UPGRADING 

HYDROCARBON 

PRODUCT

710

620

630

STILLAGE

PRODUCED WATER

HYDROTREATER OFF-GAS

820

SLUDGE

840

BIOGAS

830

AUX FUEL 880

AIR 870

FGD LIME 860

PROCESS 

WATER 850

610

BOILER BLOWDOWN

890A STACK FLUE 

GAS

890B ASH

WWT 

CHEMICALS

AIR 640

EVAPORATION A600

WASTEWATER

TREATMENT

660

670

A100

FEED HANDLING

CAUSTIC

AMMONIA

STEAM

ACID

GLUCOSE

LIGNIN

FILTER PRESS AIR

650

A300

220 STEAM TO A200

890C BLOWDOWN 

VENT
130 

Aspen Plus mass balance information for key stream tables:

COMPONENT UNITS 100 200 210 220 230 240 250 300 310 320 330 340 350 360 370 380

Total Flow kg/hr 104,167 104,167 260 23,889 806 1,406 305,359 325,613 67,245 163 6,942 175,101 106,886 74,300 1,670 421,150

Insoluble Solids % 67.7% 67.7% 0.0% 0.0% 0.0% 0.0% 0.0% 12.4% 0.0% 0.0% 5.3% 0.0% 0.0% 0.1% 0.0% 0.0%

Soluble Solids % 12.3% 12.3% 0.0% 0.0% 0.0% 100.0% 0.0% 7.9% 0.0% 0.0% 0.3% 0.0% 14.8% 49.5% 0.0% 0.0%

Temperature °C 25 25 20 268 20 20 33 76 20 25 29 35 43 63 20 25

Pressure atm 1.0 1.0 9.0 13.0 3.4 1.0 5.0 7.8 1.0 1.0 1.7 1.0 1.0 1.0 1.0 1.0

Vapor Fraction 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0

RDB kg/hr

FFA kg/hr

Water kg/hr 20,833 20,833 23,889 56 305,359 259,103 67,245 6,549 174,843 90,917 37,298 705

Glucose (SS) kg/hr 3,381 8,793 20,518

Xylose (SS) kg/hr 16,310 4,844 11,303

Other sugars (SS) kg/hr 642 642 3,682 1,192 2,782

Sugar Oligomers (SS) kg/hr 559 477 1,114

Organic Soluble Solids (SS) kg/hr 12,208 12,208 699 20 38 210 451 347

Inorganic Soluble Solids (SS) kg/hr 1,406 1,011 276 645 127

Ammonia kg/hr 260

Acetic Acid kg/hr

Sulfuric Acid kg/hr 750

Furfurals kg/hr 504 220 150 130

Other Organics kg/hr 7 2 5 140

Carbon Dioxide kg/hr 0.1

Methane kg/hr

H2 kg/hr

O2 kg/hr 38 98,093

N2 kg/hr 125 323,057

CO/SOX/NOX/H2S kg/hr

Cellulose (IS) kg/hr 29,205 29,205 26,226 2 4

Xylan (IS) kg/hr 16,273 16,273 415 1 1

Other Struct. Carbohydr. (IS) kg/hr 3,675 3,675 96 0.1 0.3

Acetate (IS) kg/hr 1,508 1,508

Lignin (IS) kg/hr 13,132 13,132 9,980 15 35

Protein (IS) kg/hr 2,583 2,583 2,583 311 4 10 351

Cell Mass (IS) kg/hr 54 0.1 0.2

Other Insoluble Solids (IS) kg/hr 4,108 4,108 1,064 2 4
131 

COMPONENT UNITS 390 400 410 420 430 440 500 510 520 530 600 610 620 630 640 650

Total Flow kg/hr 451,657 1,427 189 16,346 5,729 16,750 152,333 221 956 1,408 110,275 4,640 138,805 917 146,928 175

Insoluble Solids % 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.2% 0.0% 0.0% 0.0% 3.1% 0.0% 2.5% 0.0% 0.0% 0.0%

Soluble Solids % 0.0% 85.0% 0.0% 0.0% 0.0% 0.0% 6.5% 0.0% 0.0% 0.0% 16.2% 0.0% 7.1% 0.0% 0.0% 0.0%

Temperature °C 32 28 20 25 33 28 32 25 32 380 85 100 33 39 25 20

Pressure atm 1.0 1.0 1.0 1.0 5.0 1.0 1.0 1.0 1.0 1.2 1.0 5.4 6.0 1.1 1.0 1.0

Vapor Fraction 1.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0

RDB kg/hr

FFA kg/hr 13,946 418

Water kg/hr 13,042 214 41 320 5,729 388 124,630 210 88,640 4,640 124,630 917 2,878

Glucose (SS) kg/hr 1,213 784 784

Xylose (SS) kg/hr 1,945 1,945

Other sugars (SS) kg/hr 2,188 321 2,188

Sugar Oligomers (SS) kg/hr 1,592 1,592

Organic Soluble Solids (SS) kg/hr 8 54 2,381 16,169 2,381 175

Inorganic Soluble Solids (SS) kg/hr 998 1,406 998

Ammonia kg/hr 58 0.2

Acetic Acid kg/hr

Sulfuric Acid kg/hr

Furfurals kg/hr 46 234 332 234

Other Organics kg/hr 7 221 221

Carbon Dioxide kg/hr 19,144 1,195 5 428 5

Methane kg/hr

H2 kg/hr 221

O2 kg/hr 96,363 3,733 2,873 1 223 37 1 33,552

N2 kg/hr 323,055 12,293 12,293 2 733 733 2 110,499

CO/SOX/NOX/H2S kg/hr 8

Cellulose (IS) kg/hr 6 6

Xylan (IS) kg/hr 2 325 2

Other Struct. Carbohydr. (IS) kg/hr 0.5 0.5

Acetate (IS) kg/hr

Lignin (IS) kg/hr 50 50

Protein (IS) kg/hr 21 185 185

Cell Mass (IS) kg/hr 3,159 3,159

Other Insoluble Solids (IS) kg/hr 5 3,081 5
132 

COMPONENT UNITS 660 670 710 800 810 820 830 840 850 860 870 880 890A 890B 890C

Total Flow kg/hr 146,796 203,888 11,638 43,507 167 742 25,364 19,211 32,637 953 321,584 0 406,986 4,542 2,558

Insoluble Solids % 0.0% 0.0% 0.0% 36.0% 0.0% 0.0% 0.0% 44.5% 0.0% 100.0% 0.0% 0.0% 0.0% 100.0% 0.0%

Soluble Solids % 0.0% 0.0% 0.0% 1.4% 0.0% 0.0% 0.0% 1.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Temperature °C 25 27 40 29 29 40 37 30 33 25 25 25 156 0 100

Pressure atm 1.0 1.0 1.1 1.0 1.0 30.6 1.0 2.0 5.4 1.0 1.0 1.0 1.0 1.0 1.0

Vapor Fraction 1.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0

RDB kg/hr 11,576

FFA kg/hr 19 0.2

Water kg/hr 2,857 203,888 30 27,194 4 2 815 10,423 32,637 763 6,298 72,468 2,558

Glucose (SS) kg/hr 296 1

Xylose (SS) kg/hr 163 2

Other sugars (SS) kg/hr 40 45 0.4

Sugar Oligomers (SS) kg/hr 16 7

Organic Soluble Solids (SS) kg/hr 21 0.3 17 0.2

Inorganic Soluble Solids (SS) kg/hr 0.1 89 120 109

Ammonia kg/hr 0.1

Acetic Acid kg/hr

Sulfuric Acid kg/hr 24

Furfurals kg/hr 5 1 1

Other Organics kg/hr 0.3

Carbon Dioxide kg/hr 2,533 32 701 17,802 2 78,927

Methane kg/hr 2 6,738

H2 kg/hr 0.2 38 0

O2 kg/hr 30,899 0.2 38 2 0.1 73,435 12,216

N2 kg/hr 110,496 0.4 125 4 0.1 241,850 243,119

CO/SOX/NOX/H2S kg/hr 9 1 146

Cellulose (IS) kg/hr 1,253 6

Xylan (IS) kg/hr 413 328

Other Struct. Carbohydr. (IS) kg/hr 95 0.5

Acetate (IS) kg/hr

Lignin (IS) kg/hr 9,930 50

Protein (IS) kg/hr 2,879 131

Cell Mass (IS) kg/hr 54 4,947

Other Insoluble Solids (IS) kg/hr 1,059 3,086 191 4,542
133 

CORN STOVER TO 

PRETREATMENT

PFD-002

MILLED CORN STOVER

C-104

DOME RECLAIM SYSTEM

Rev:B Project No:

Harris Group Inc.

Engineering for Optimum Performance 

www.harrisgroup.com

Drawing:

PFD-001

®

Subconsultant:

Engr: 

Appr:

Check: 

Check:

PMgr: 

-

JAR/LT

JAR/LT

-

-

-

-

Drawn: 

PROCESS FLOW DIAGRAM

FEEDSTOCK STORAGE AND HANDLING

30374.00

ADVANCED BIOFUELS ENGINEERING DESIGN

BIOCHEMICAL CONVERSION OF CORN STOVER

NATIONAL RENEWABLE 

ENERGY LABORATORY

M-106

DUST COLLECTION 

SYSTEM

M-102

TRUCK DUMPER

M-101

TRUCK SCALE
134 

STOVER FROM 

STORAGE/HANDLING

PFD-001

PROCESS WATER

HP STEAM FROM

PFD-007

SULFURIC ACID

M-207

PRETREATMENT 

REACTORS

T-203

BLOWDOWN TANK

T-204

FLASH TANK

T-209

AMMONIA ADDITION TANK

HYDROLYSATE TO 

SACCHARIFICATION AND 

FERMENTATION PFD-003

AMMONIA 

PROCESS WATER

NAOH

SPENT CAUSTIC 

LIQUOR TO WASTEWATER

 PFD-006

Rev:B Project No:

Harris Group Inc.

Engineering for Optimum Performance 

www.harrisgroup.com

Drawing:

PFD-002

®

Subconsultant:

Engr: 

Appr:

Check: 

Check:

PMgr: 

-

JAR/LT

JAR/LT

-

-

-

-

Drawn: 

PROCESS FLOW DIAGRAM

PRETREATMENT

30374.00

ADVANCED BIOFUELS ENGINEERING DESIGN

BIOCHEMICAL CONVERSION OF CORN STOVER

NATIONAL RENEWABLE 

ENERGY LABORATORY

M-204

PRESTEAMERS

T-225

DEACETYLATION 

REACTORS
135 

HYDROLYSATE FROM 

PRETREATMENT

PFD-002

T-310

SACCHARIFICATION TANK

CELLULASE ENZYME

FROM PFD-004

F-300B

AEROBIC BIOREACTORS

DAP

CSL

PRODUCT TO

PFD-005

F-301 – 305

SEED BIOREACTORS

T-301

SEED HOLD TANK

T-306B

BIOREACTOR STORAGE 

TANK

S-205

LIGNIN FILTER 

LIGNIN TO BOILER

PFD-007

AIR

F-300A

ENZYMATIC HYDROLYSIS 

REACTORS

T-306A

ENZYMATIC 

HYDROLYSIS 

STORAGE TANK 

E-303

SUGAR 

CONCENTRATION

WATER 

T-305

FILTERED HYDROLYSATE

STORAGE TANK

T-303

CONCENTRATED SUGAR

STORAGE TANK

WATER 

E-644

Rev:B Project No:

Harris Group Inc.

Engineering for Optimum Performance 

www.harrisgroup.com

Drawing:

PFD-003

®

Subconsultant:

Engr: 

Appr:

Check: 

Check:

PMgr: 

-

JAR/LT

JAR/LT

-

-

-

-

Drawn: 

PROCESS FLOW DIAGRAM

SACCHARIFICATION AND FERMENTATION

30374.00

ADVANCED BIOFUELS ENGINEERING DESIGN

BIOCHEMICAL CONVERSION OF CORN STOVER

NATIONAL RENEWABLE 

ENERGY LABORATORY
136 

AMMONIA

F-401 - 403

CELLULASE SEED 

FERMENTERS CSL

SULFUR DIOXIDE

T-406

CELLULASE NUTRIENT 

MIX TANK

T-405

MEDIA PREP TANK

F-400

CELLULASE 

FERMENTER

NUTRIENTS

PROCESS WATER

GLUCOSE

Harris GEngineering for www.harrisgroup.c

Subconsultant:Engr: Appr:

CheChePM

-JARJAR-

Drawn: 
137 

PREHEATER

PRODUCT

DISTILLATION

COLUMN

HYDROTREATING

GUARD BED/TAN 

REDUCTION

HYDROTREATING

REACTOR

SEPARATOR

HYDROGEN 

COMPRESSOR

S-570

HYDROTREATING UNIT

PARTIAL

CONDENSER

REBOILER

OIL HEAT

EXCHANGER

NOTE 1

NOTES:1. NATURAL GAS FOR STARTUP AND AS NEEDED2. DISTILLATION COLUMN IS INCLUDED IN VENDOR-SUPPLIED COST FOR ISBL HYDROTREATER. IN PRESENT DESIGN CASE MODEL, HYDROCARBON PRODUCT IS ASSUMED TO BE 100% RDB AND DISTILLATION IS SKIPPED

NAPHTHA 

(N/A HERE)

AIR COOLER

OIL (FFA) PRODUCT 

FROM BIOREACTORS

PFD-003

HYDROGEN FROM STEAM

METHANE REFORMER

S-540A

PRODUCT 

DECANTER

MAKEUP NATURAL GAS

(NORMALLY NOT NEEDED)

SEPARATOR

WATER

COOLER

Projec

Harris Group Inc.Engineering for Optimum Performance www.harrisgroup.com

®

Subconsultant:Engr: JARAppr:

Check: Check:PMgr: 

----

---

Drawn: JAR

B

WATER 

(N/A HERE)

NOTE 2

S-540B

PRODUCT CENTRIFUGE
138 

T-606

ANAEROBIC BASIN

S-611

GRAVITY BELT 

THICKENERS

T-608

AERATION DIGESTER

R-609

MEMBRANE 

BIOREACTOR

S-610

REVERSE OSMOSIS 

SYSTEM

POLYMER

S-611

CENTRIFUGE

SPENT CAUSTIC LIQUOR 

FROM PFD-002

BYPRODUCTS FROM

HYDROTREATING

PFD-005

HEAVY PHASE FROM

DECANTER

PFD-005

Harris GEngineering for www.harrisgroup.c

Subconsultant:Engr: Appr:

CheChePM

-JARJAR-

Drawn: 

E-644
139 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Harris GEngineering for www.harrisgroup.c

Subconsultant:Engr: Appr:

CheChePM

-JARJAR-

Drawn: 

SLUDGE FROM

PFD-006

M-803

BOILER

LIGNIN FROM

PFD-003

NATURAL GAS

M-804

COMBUSTION GAS BAGHOUSE

ATM

BIOGAS FROM

PFD-006

COMBUSTION AIR

TAILGAS FROM

HYDROTREATING

PFD-005

M-811

TURBINE/GENERATOR