### Partnership for AiR Transportation

Noise and Emissions ReductionAn FAA/NASA/Transport Canadasponsored Center of Excellence



# Life Cycle Greenhouse

Gas Emissions from

Alternative Jet Fuels

PARTNER Project 28 report

Version 1. 1

prepared by


### Russell W. Stratton, Hsin Min Wong, James I. Hileman



## April 2010

REPORT NO. PARTNER-COE-2010-001

# Life Cycle Greenhouse Gas Emissions from

Alternative Jet Fuels

Partnership for Air Transportation Noise and Emissions Reduction 

# Project 28

Russell&W.&Stratton

,& Hsin&Min&Wong &and& James&I.&Hileman Massachusetts&Institute&of&Technology ,&Cambridge,&Massachusetts

PARTNER

- COE

- 2010

- 001 April 2010

Version 1.1

This work is funded by the U.S. Federal Aviation Administration Office of Environment and Energ y and U.S. Air Force Research Lab under FAA Award Number : 06

- C

- NE

- MIT, Amendment

Nos. 012 and 021

. The project is managed by Warren Gillette of FAA, Tim Edwards, and Bill Harrison, both of AFR L

.

Any opinions, findings, and conclusions or recommendations expressed in this material are those

of the authors and do not necessaril y reflect the views of the FAA, AFRL, NASA or Transport

Canada.

The Partnership for AiR Transportat ion Noise and Emissions Reduction

— PARTNER

— is a cooperative aviation research organization

, and an FAA/NASA/Transport Canada

- sponsored Center of Excellence. PARTNER fosters breakthrough technological, operational, poli cy, and workforce advances for the betterment of mobility, economy, national security, and the environment. The organization's operational headquarters is at the Massachusetts Institute of Technology.

There may be periodic updates provided for this report . Please visit the PARTNER Project 28 website, http://web.mit.edu/aeroastro/partner/projects/project28.html , to check for the latest

version.



### The Partnership for AiR Transportatio n Noise and Emissions Reduction

Massachusetts Institute of Technolo gy, 77 Massachusetts Avenue, 33

- 115 Cambridge, MA 02139 USA

http://www.partner.aero info@partner.aero

### i 

# Table of Contents

List of Figures

................................ ................................ ................................ ................................ ....... iv

List of Tables

................................ ................................ ................................ ................................ ......... vi

Executive Summary

................................ ................................ ................................ ............................ ix

Acknowledgements

................................ ................................ ................................ ............................ xi

Units, Notation, Abbreviations and Acronyms

................................ ................................ ...... xii

1 Introduction

................................ ................................ ................................ ................................ ...... 1

2 Alternative Jet Fuel Pathways

................................ ................................ ................................ .. 3

3 Procedural Overview of Life Cycle Analysis of GHG Emissions

............................... 5

3.1 Life cycle Greenhouse Gas Emissions

................................ ................................ .......................... 5

3.2 Analysis Procedure

................................ ................................ ................................ ................................ .... 8

3.3 Goals and Practices

................................ ................................ ................................ ................................ .. 9

3.4 Evaluation of Variability

................................ ................................ ................................ ......................... 10
3.5 Functional Unit: g CO 2 

### e/MJ and g CO 2 

### e/kg

- km

................................ ................................ ...... 10

4 Conventional Petroleum Pathways

................................ ................................ ..................... 12

4.1 Crude Oil Recovery and Transportation

................................ ................................ ...................... 12

4.2 Conventional Jet Fuel from Crude Oil

................................ ................................ .......................... 14

4.2.1 Top

- Down Approach (Baseline Case)

................................ ................................ ............. 15

4.2.2 Bottom

- up Approach (Low and High Emissions Cases)

................................ ........ 16

4.2.3 Impact of Crude Oil Quality Compared to Processing Technique

.................... 17

4.2.4 Conventional Jet Fuel Results

................................ ................................ ............................. 18
4.3 Ultra

- Low Sulfur Jet Fuel from Conventional Crude

................................ ............................. 19

4. 3.1 Top

- Down Approach (Baseline case)

................................ ................................ .............. 19

4.3.2 Bottom

- up approach (low and high emissions cases)

................................ ............. 19

4.3.3 ULS Jet Fuel Results

................................ ................................ ................................ ................ 20

5 Unconventional Petroleum Pathways

................................ ................................ ................ 22

5.1 Conventional Jet Fuel from Oil Sands

................................ ................................ .......................... 22

5.1.1 Bitumen Production through Surface Mining

................................ ................................ 22
5.1.2 Bitumen Production through In Situ Recovery

................................ ............................. 23

5.1.3 Bitumen Upgrading to Syncrude

................................ ................................ ......................... 23

5.1.4 Oil Sands to Jet Fuel Results

................................ ................................ ............................... 24

5.2 Conventional Jet Fuel from Oil Shale

................................ ................................ ........................... 26
5.2.1 Analysis Methodology

................................ ................................ ................................ ............... 27

5.2.2 Oil Shale to Jet Fuel Results

................................ ................................ ................................ . 28

6 Fischer

- Tropsch Jet Fuel

................................ ................................ ................................ ......... 30

6.1 Carbon Capture and Sequestration with Fischer

- Tropsch Facilities

.......................... 30
6.2 Fischer

- Tropsch Fuel from Natural Gas

................................ ................................ ...................... 31
6.2.1 Case Study: Imp act of Carbon Capture on GHG Emissions from GTL

Facilities

................................ ................................ ................................ ................................ ........................... 32
6.3 Fischer

- Tropsch Fuel from Coal

................................ ................................ ................................ ...... 33

6.3.1 CTL without Carbon Capture

................................ ................................ ................................ 33

6.3.2 CTL with Carbon Capture

................................ ................................ ................................ ....... 35

6.4 Fischer

- Tropsch Fuel from Biomass

................................ ................................ ............................. 35

### ii 6.4.1 Switchgrass

................................ ................................ ................................ ................................ .... 36

6.4.2 Corn Stover and Forest Residue

................................ ................................ ........................ 40
6.4.3 Analysis of Biomass Feedstock s

................................ ................................ ........................ 42

6.5 Fischer

- Tropsch Fuel from Coal and Biomass

................................ ................................ ........ 43

6.5.1 Allocation Methodology

................................ ................................ ................................ ............ 45

6.5.2 Results: F

- T Jet Fuel from Coal and Biomass

................................ ............................. 46
6.5.3 Case Study: Impact of Carbon Capture on GHG Emissions from CBTL

Facilities

................................ ................................ ................................ ................................ ........................... 49

7 Hydroprocessed Renewable Jet Fuel from Renewable Oils

................................ ..... 50

7.1 Energy Requirements for HRJ from Renewable Oils

................................ .......................... 52

7.2 HRJ from Soybean Oil

................................ ................................ ................................ ........................... 55

7.2.1 Cultivation of Soybeans

................................ ................................ ................................ ........... 56

7.2.2 Extraction of Oil from Soybeans

................................ ................................ .......................... 58

7.2.3 Co

- Product Usage and Allocation Methodology

................................ ......................... 59

7.2.4 Transportation of Soy Oil to HRJ Production Facilities

................................ ........... 61

7.2.5 Land Use Change Emissions from Soy Oil Production

................................ .......... 61

7.2.6 Results

................................ ................................ ................................ ................................ .............. 62
7.3 HRJ from Palm Oil

................................ ................................ ................................ ................................ ... 63

7.3.1 Cultivation of Palm Fresh Fruit Bunches (FFB)

................................ .......................... 63

7.3.2 Extraction of Oil from Palm FFB

................................ ................................ .......................... 64

7.3.3 Co

- Product Usage and Allocation Methodology

................................ ......................... 66
7.3.4 Transportation of Palm Oil and Palm Kernel Oil to HRJ Production Facilities

66

7.3.5 Land Use Change Emissions from Palm Oil Production

................................ ........ 66

7.3.6 Results

................................ ................................ ................................ ................................ .............. 67

7.4 HRJ from Rapeseed Oil

................................ ................................ ................................ ........................ 69

7.4.1 Cultivation of Rapeseed

................................ ................................ ................................ .......... 69

7.4.2 Extraction of Oil from Rapeseed

................................ ................................ ......................... 72
7.4.3 Co

- Product Usage and Allocation Methodology

................................ ......................... 73

7.4.4 Transportation of Rapeseed Oil to HRJ Production Facilities

............................. 73

7.4.5 Land Use Change Emissions from Rapeseed Oil Production

............................. 74

7.4.6 Results

................................ ................................ ................................ ................................ .............. 75

7. 5 HRJ from Jatropha Oil

................................ ................................ ................................ ........................... 76

7.5.1 Yield and Plant Characterization

................................ ................................ ........................ 76

7.5.2 Cultivation of Jatropha Fruit

................................ ................................ ................................ .. 77

7.5.3 Toxicity of Jatropha Fruit and Oil

................................ ................................ ........................ 78

7.5.4 Transportation of Jatropha Fruit to Production Facilities

................................ ....... 78

7.5.5 Extraction o f Oil from Jatropha Fruit

................................ ................................ ................. 79

7.5.6 Co

- Product Usage and Allocation Methodology

................................ ......................... 80

7.5.7 Results

................................ ................................ ................................ ................................ .............. 82
7.6 HRJ from Algae Oil

................................ ................................ ................................ ................................ .. 82

7.6.1 Algal Yie ld and Lipid Content

................................ ................................ ................................ 83

7.6.2 System Expansion (Displacement) for Electricity Emissions

............................... 83
7.6.3 Algal Carbonation Options and Technologies

................................ ............................. 85

7.6.4 Cultivation of Algae in Open Ponds

................................ ................................ .................. 86

7.6.5 Nutrient Recycling via Anaerobic Digestion

................................ ................................ .. 87

7.6.6 Dewatering and Drying

................................ ................................ ................................ ............. 89
7.6.7 Transportation of Algae to Production Facilities

................................ ......................... 92

7.6.8 Extraction of Oil from Algae

................................ ................................ ................................ ... 92

### iii 7.6.9 Sensitivity Analysis and System Design

................................ ................................ ......... 94

7.6.10 Results

................................ ................................ ................................ ................................ ........... 95

7.7 HRJ from Salicornia Oil

................................ ................................ ................................ ......................... 96

7.7.1 Biomass, Seed and Oil Yield

................................ ................................ ................................ 97

7.7.2 Cultivation of Salicornia

................................ ................................ ................................ ........... 97

7.7.3 Transportation of Salicornia Biomass and Seeds to Production Facilities

... 99

7.7.4 Oil Extra ction from Salicornia Seeds

................................ ................................ ................ 99

7.7.5 Co

- Product Usage and Allocation Methodology

................................ ......................... 99

7.7.6 Land Use Change Emissions from Salicornia Cultivation

................................ .. 103

7.7.7 Results

................................ ................................ ................................ ................................ ........... 104

8 Summary of Results

................................ ................................ ................................ ................ 106

8.1 Life Cycle GHG Emissions

................................ ................................ ................................ ............... 106

8.2 Life Cycle GHG Intensity

................................ ................................ ................................ ................... 109

8.3 Biofuel Yield

................................ ................................ ................................ ................................ .............. 110
8.4 Biofuel Impact on Domestic Water Resources

................................ ................................ ..... 112

8.5 Invasive Species with Respect to Biofuels

................................ ................................ ............. 114

9 Conclusions

................................ ................................ ................................ ................................ 116

Appendix A

– General Feedstock and Fuel Properties

................................ .................... 118

References

................................ ................................ ................................ ................................ .......... 119

### iv 

# List of Figures

Figure 1: Aspects of alternative fuels that are being considered for alternative fuel feasibility and sustainability. This report has an emphasis on life cycle greenhouse gas emissions 1 Figure 2: Steps considered, in the well

- to

- wake, life cycle GHG inventory of conventional jet fuel 5 Figure 3: Steps considered in the well

- to

- wake, life cycle GHG inventory of bio

- based alternative jet fuels 6 Figure 4: Steps considered in the well

- to

- wake, life cycle GHG inventory of bio

- based fuels from algae 7 Figure 5: Levels of life cycle GHG studies used to ensure that the adopted practices are

consistent with the goal of the work 9 Figure 6: Origin specific GHG emissions by species of crude oil entering US refineries in 2005 (fraction of total imports in parentheses). Based on country profiles published in Skone and Gerdes (2008, 2009) 13 Figure 7: Variation in jet fuel processing emissions induced by origin specific crude oil properties and processing technique 18 Figure 8: Distribution of reported switchgrass yields across the United States (data from Gunderson et al., 2008) 37 Figure 9: The effects of product slate composition on life cycle greenhouse gas emissions from

F

- T jet fuel 46 Figure 10: Sensitivity analysis of operational specifications and configurations of F

- T jet fuel from

coal and biomass 47 Figure 11: Dependence of cumulative life cycle emissions and biomass requirements for varied

biomass uti lization within CBTL 48 Figure 12: Schematic showing the change in hydrocarbon composition between HRD and HRJ fuels that results from additional hydroprocessing. 54 Figure 13: Temporal variation in the soy oil and soy meal allocation fractions when using market value allocation. Constant dashed lines correspond to default GREET prices 61 Figure 14: Yearly rapeseed yield for France and the UK between 1999 and 2009 70 Figure 15: Utilization of by

- products from jatropha cultivation and oil extraction (adapted from Reinhardt, 2007) 79 Figure 16: Sensitivity of life cycle emissions of HRJ from jatropha to co

- product uti lization and

allocation scheme. Scenarios are described in Table 74 81 Figure 17: System boundary expansion of the algal HRJ fuel pathway 84 Figure 18: Process flows for algae oil HRJ using anaerobi c digestion to recover nutrients from algae meal 88 Figure 19: Flow chart showing the steps involved in dewatering and drying of algae 89 Figure 20: Energy consumption of a centrifuge secondary harv esting mechanism. Line is a fit through four data points 90 Figure 21: An examination of the post

- dewatering moisture content impact on energy consumption of drying algae to 90% total suspended solids 91 Figure 22: Algal lipid content and post

- dewatering moisture content that result in a sustainable drying process. Points above each line require additional energy to be added (e.g. from natural gas) 92 Figure 23: Life cycle GHG of HRJ production from algae as a function of the extent of dewatering. Three different system configurations were explored which are described more fully in the figure 95 Figure 24: Sensitivity analysis of operationa l specifications and configurations of HRJ production from algae 95 Figure 25: System boundary definitions for system level energy allocation between HRJ production from salicornia oilseeds and electricity generation from salic ornia straw biomass

101

### v Figure 26: Comparison of life cycle GHG emissions from the allocation methodologies of Table 74 based on the production of HRJ from salicornia oil when using the straw biomass co

product for electricity generation 101 Figure 27: System boundary definitions for system level energy allocation of coupled HRJ and F

T fuel production from salicornia oilseeds and straw biomass 102 Figure 28: Life cycle G HG emissions for the alternative jet fuel pathways under consideration. Note: CCS denotes carbon capture and sequestration and the land use change (LUC)

scenarios are defined in Table 96 108 Figure 29: L ife cycle GHG emissions for the alternative jet fuel pathways under consideration. Uncertainty bars represent the low emissions, baseline, and high emissions scenarios. Please note the different scales for the top and bottom portions of the figure. Note: CCS denotes Carbon Captur e and Storage and Land Use Change (LUC) scenarios are defined

in Table 96 109 Figure 30: Fuel production potential for various alternative jet fuels that could be derived from

biomass. This is not an all

- encompassing list of alternative jet fuel options; it merely represents those examined by the authors as part of their ongoing research 110 Figure 31: Land area requirements to replace conventional jet fuel use within the US with 100% SPK and 50/50 blend of SPK with conventional jet fuel. Average US conventional jet fuel consumption in 2009 is 1.4 million bbl/day 111 Figure 32: Water consumption and water withdrawals in the US by sector (data adapted from DOE, 2006) 112 Figure 33: Water consumption for the production of various fuels (data adapted from DOE, 2 006 and King and Webber, 2008) 113

### vi 

# List of Tables

Table 1: Fuel pathways investigated 4
Table 2: Country specific average waterborne round trip travel distance of crude oil to US domestic ports 13

Table 3: Type of process fuel and fuel share in the refining of jet fuel and ULS jet fuel 15

Table 4: Energy requirement in the production of straight

- run jet fuel 16
Table 5: Process fuel and fuel shares for the production of straight

- run jet fuel 16

Table 6: Energy requirement in the production of jet fuel from hydroprocessing 17

Table 7: Process fuel and fuel shares for the production of jet fuel from hydroprocessing 17
Table 8: Summary of results for jet fuel from conventiona l crude and a comparison of results to the NETL petroleum baseline study 18

Table 9: Energy requirement in the production of straight

- run ULS jet fuel 19

Table 10: Process fuel and fuel shares for t he production of straight

- run ULS jet fuel 20
Table 11: Energy requirement in the production of ULS jet fuel from hydroprocessing 20

Table 12: Process fuel and fuel shares for the production of ULS jet fuel from hydroprocessing 20

Table 13: Su mmary of results for ULS jet fuel from conventional crude pathway 21
Table 14: Process energy assumptions for the production of bitumen through surface mining for the low emissions, baseline and high emissions cases 23
Table 15: Process energy assumptions for the production of bitumen through in situ recovery for the low emissions, baseline and high emissions cases 23
Table 16: Assumed parameters in the upgrading of bitum en to syncrude 24
Table 17: Life cycle GHG emissions for jet fuel from Canadian oil sands using surface

- mining

and in

- situ recovery 25

Table 18: Life cycle GHG emissions for the high emissions scena rio of jet fuel from Canadian oil sands with and without carbon capture and sequestration of emissions from asphaltene gasification 25

Table 19: Assumptions regarding the use of surface and in situ bitumen recovery in the low

e missions, baseline, and high emissions scenarios of jet fuel from Canadian oil sands 26
Table 20: Summary of result for jet fuel from Canadian oil sands 26
Table 21: Input assumptions for the production of jet fuel from oil shale for low emissions, baseline and high emissions cases 28

Table 22: Summary of results for jet fuel from in situ oil shale pathway 28

Table 23: Summary of inputs and results for F

- T jet fuel from natural gas pathway 32
Table 24: Life cycle GHG emissions from natural gas F

- T jet fuel with and without carbon capture

33
Table 25: Input assumptions for the production of F

- T jet fuel from coal (without carbon capture) for low emissions, baseline and high emissions cases 34

Table 26: Summary of results for F

- T jet fuel from coal pathway (without carbon capture) 34
Table 27: Input assumptions for the production of F

- T jet fuel from coal (with carbon capture) for low emissions, baseline and high emissions cases 35

Table 28: Summary of result s for F

- T jet fuel from coal pathway (with carbon capture) 35

Table 29: Switchgrass yields assumed in the low, baseline and high emissions scenarios 37

Table 30: Reported cultivation inputs for swit chgrass 38
Table 31: Cultivation inputs for switchgrass in the low, baseline and high emissions scenarios 39

Table 32: Input parameters for the recovery and transportation of corn stover 41

Table 33: Input parameters for the collection and transportation of forest residue. 41

Table 34: Variation of life cycle GHG emissions of BTL pathway with type of feedstock 42

T able 35: Summary of results for F

- T jet fuel from switchgrass with no soil carbon sequestration

43

Table 36: Summary of results for F

- T jet fuel from switchgrass with soil carbon sequestration 43
Ta ble 37: Input assumptions for the production of F

- T jet fuel from coal and biomass (with carbon capture) for low emissions, baseline and high emissions cases 45

### vii Table 38: Summary of results for F

- T jet fuel from coal and biomas s without soil carbon sequestration credit 47
Table 39: Summary of results for F

- T jet fuel from coal and switchgrass with soil carbon sequestration credit 48
Table 40: Life cycle GHG emissions from F

- T jet fuel from coal and switchgrass with and without

CCS 49

Table 41: Component fatty acid profiles for renewable oils considered in this work 53

Table 42 : Experimental and theoretical requirements for the creation of renewable diesel 53

Table 43: Energy requirements for the creation of HRJ 55

Table 44: Farming energy required for average US soybean production 56

Table 45: Farming energy for US soybean production for low emissions, baseline and high

emissions cases 57

Table 46: US soybeans agrichemical use 57

Table 47: Input assumptions for the cultivation of soybeans for the low emissions, baseline and high emissions cases 58

Table 48: Input, output and process energy for extraction of oil from soybeans 58

Table 49: Allocation of GHG emissions between soy oil and soy meal using various methodologies 60

Table 50: Land use change emissions arising from soybean production 62
Table 51: Life cycle emission s for the soy oil to HRJ assuming zero land use change emissions

62
Table 52: Life cycle emissions for the soy oil to HRJ assuming conversion of Cerrado grassland to soybean field 63

Table 53: Life cycle emissions for the soy oil to HRJ assuming conversion of tropical rainforest 63
Table 54: Assumptions in the cultivation of palm fresh fruit bunches for the low emissions, baseline and high emissions cases 64

Table 55: Inputs, outputs and process energy in the extraction of palm oil and palm kernel oil 65

Table 56: Methane emissions from POME treatment 65

Table 57: Assumptions in the tran sportation of palm oil and palm kernel oil from Southeast Asia

to the US 66

Table 58: Land use change emissions in the cultivation of palm fresh fruit bunches 67
Table 59: Life cycle emissions for t he palm oil to HRJ assuming zero land use change emissions

68
Table 60: Life cycle emissions for the palm oil to HRJ assuming conversion of logged over forest

68
Table 61: Life cycle emissions for t he palm oil to HRJ assuming conversion of tropical rainforest

68
Table 62: Life cycle emissions for the palm oil to HRJ assuming conversion of peatland rainforest

69
Table 63: Farming energy, fertil izer and herbicide usage for the production of rapeseed in the low emissions, baseline and high emissions scenarios 71
Table 64: Rapeseed drying and storage assumptions in the low emissions, baseline and high

emissions scenario 72

Table 65: Process inputs for extracting oil from rapeseeds 73
Table 66: Transportation profile of Rapeseed Oil from Europe to the United States 74

Table 67: Land use change emissions from rapeseed cultivation on set aside lands in Europe 75

Table 68: Summary of results from renewable jet fuel production and use from rapeseed 75

Table 69: Life cycle GHG emissions for production and use of renewable jet fuel from rapeseed assuming cultivation on set

- aside land 76

Table 70: Parts of the jatropha fruit including processed states 77

Table 71: Yields and mass fractions characterizing the jatropha fruit 77
Table 72: Cultivation inputs for the growth of jatropha 78

Table 73: Process inputs for extracting oil from jatropha f ruit 80

### viii Table 74: Co

- product creation and allocation scenarios from the oil extraction process for jatropha capsules. The corresponding life cycle emissions for each scenario are shown in Figure 16 81
Table 75: Assumptions used in establishing average US biomass conversion efficiency to electricity 81
Table 76: Life cycle emissions from the jatropha oil to HRJ pathway 82

Table 77: Impact of CO 2 source and electricity choice on the biomass credit given to algal HRJ 84

Table 78: US power generation data as it relates to algae cultivation 85

Table 79: Comparison of energy inputs of direct flue gas injection and MEA extraction 85

Table 80: Cultivation inputs per kilogram of algae for algae growth in an open pond 86

Table 81: Life cycle GHG emissions from the production of nutrients used in algae cultivation 87
Table 82: GHG emissions resulting from fertilizer production within the algae to HRJ pathway for

the three emissions cases 87
Table 83: Energy consumption and performance specifications for primary algae harvesting

mechanisms 90

Table 84: Outputs and process energy for N

- hexane oil extraction from algae 93

Table 85: Life cycle emissions from the algae oil to HRJ pathway 96

Table 86: Salicornia yield and oil fraction assumptions 97

Table 87: Input assumptions regarding the cultivation of sali cornia 98
Table 88: Process inputs for extracting oil from salicornia seeds. All values are in Btu/lb of oil 100
Table 89: Allocation methodologies examined for the production of HRJ from salicornia oil when using the straw biomass co

- product for electricity generation 101
Table 90: Input assumptions relevant to a BTL facility using salicornia straw biomass as

feedstock 102
Table 91: Allocatio n ratios and product slates describing a coupled HRJ and F

- T facility processing salicornia oilseeds and straw biomass 103
Table 92: Long term soil, root and charcoal carbon sequestration from the cultivation of salicornia

for fuel production 104
Table 93: Summary of results from renewable and F

- T jet fuel production and use from salicornia

104
Table 94: Life cycle GHG emissions for production and use of renewable and F

- T jet fuel salicornia assuming long

- term carbon sequestration 105

Table 95: Baseline life cycle GHG emissions for all fuel pathways studied. Land use change

scenarios are described in Table 96 107

Table 96: Land use change scenarios considered in this work 108
Table 97: Feedstock and Fuel Properties 118

### ix 

# Executive Summary

The long

- term viability and success of a transportation fuel depen ds on both economic and environmental sustainability. These include, but are not limited to, the environmental impacts on global climate and air quality, the efficient usage of water and land resources, technical feasibility and the economic cost of fuel p roduction. This report focuses on aspects of environmental sustainability, with an emphasis on life cycle greenhouse gas emissions as they relate to impacts on global climate. Through a life cycle accounting of the GHG emissions starting with the well,

fie ld, or mine where the fuel feedstock is extracted, and extending to the wake behind the aircraft, one can ascertain the change in GHG emissions that result from the use of an alternative fuel.

The focus of this manuscript is a comparison of the well

- to

- w ake, life cycle GHG emissions from

select feedstock

- to

- jet fuel pathways for the United States. Each fuel pathway has been considered using a consistent methodology to facilitate equitable comparisons. The emphasis throughout is on maximizing the transpare ncy of assumptions and establishing a fundamental understanding for the reader of the pivotal factors defining fuel production for each feedstock and how these relate to life cycle analysis. As there is considerable variability in the life cycle GHG

emissi ons from existing fuel production, and many of the fuel pathways considered in this report have not been commercialized, a range of life cycle GHG emissions has been provided for each

feedstock

- to

- fuel pathway. In addition to the examination of life cycle GHG emissions, the manuscript provides a first order examination of the land and water usage that could accompany the development of a biofuel industry for aviation.

The fuel options considered herein are “drop

- in” alternatives in that they have the potential to serve as a direct replacement for conventional jet fuel, requiring little or no modification to existing infrastructure or aircraft. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) framework (versions 1.8b a nd 1.8a) and its supporting data were the primary tool used in the well

- to

- wake life cycle GHG analysis. The GREET model was developed for ground transportation; hence, this analysis involved modifying the underlying data and

framework of the model to refl ect jet fuel production. In cases where a fuel production pathway was not preexisting within the GREET framework, the relevant process inputs and production characteristics were developed from the open literature. All results are presented in terms of GHG emissions (CO 2 , CH 4 and N 2 O) per unit of energy (lower heating value) consumed by the aircraft.

The fuel pathways considered herein span petroleum based jet fuel from conventional and unconventional sources (oil and sands and oil shale), Fisher

- Tropsch je t fuel from natural gas, coal and biomass and hydroprocessed jet fuels from soy oil, palm oil, rapeseed oil, jatropha oil, algae oil, and salicornia oil. In most fuel pathways, the choice of allocation methodology and potential for GHG emissions from land use change were found to have the largest impact on the results. The scope of this work was limited to only quantifying the impacts of direct land use change; emissions from indirect land use change were not considered. Many fuel pathways were

found to res ult in life cycle GHG emissions that are either lower or higher than conventional jet fuel depending on the specifics of fuel production. For this reason, it is essential not to simply assume that biofuels are environmentally beneficial without knowing the specifics of how the fuel is produced.

A few of the key results are outlined below:

o Life cycle GHG emissions are but one of many considerations when evaluating the feasibility and sustainability of an alternative fuel option.

o The data do not include all of the feedstock

- to

- fuel pathways that could be use to create jet fuel. Some interesting options not covered include camelina oil to jet fuel, fuels created from pyrolysis oils and advanced fermentation of sugars to

### x hydrocarbons. These will be addresse d as part of the ongoing work and will appear in future revisions to this report.

o Of the fuel options considered herein, conventional petroleum has the lowest emissions of any jet fuel pathway that that relies exclusively on fossil fuel resources.

o Few b iofuels have zero life cycle GHG emissions.

o There is considerable variability in the life cycle GHG emissions; emissions from land use change contribute the most to this for the biofuel pathways considered.

o Water availability could be a limiting factor f or biofuel production in certain regions of

the US.

o Biofuel expansion within the U.S. would require a significant increase in crop and feedstock production.

o The possibility that non

- indigenous species will be introduced into an unprepared ecosystem shoul d not be ignored.

o The most significant challenge in developing viable alternative fuels that could reduce aviation's GHG emissions lies in developing and commercializing large

- scale production of next generation biomass feedstocks that could be grown in a

sustainable manner.

As part of the ongoing research that is presented in this report, a more complete assessment of land use change emissions that includes indirect effects is being developed. Proper evaluation of

the indirect effects of alternative fuel s within aviation requires modeling of the demand for renewable energy resources within the transportation sector, including aviation, as well as the demand for renewable energy resources from the energy sector as a whole. Most indirect effects are expecte d to occur on an international scale; hence, domestic analyses, such as those in this work, should be done in the context of the global market.

Aviation is not the only potential user of renewable biomass resources, and it will have to compete for these l imited resources. Furthermore, large land area requirements indicate that it is unlikely that a single region could create sufficient biomass to meet worldwide demand for biofuels. Hence, it is probable that large

- scale implementation of biofuels would ari se as a superposition of regionally appropriate feedstocks. Current actions with regard to biofuel expansion are important in realizing the potential of this industry.

Biomass feedstocks do need to have the potential to displace large volumes of petroleu m fuel to have considerable benefit. Any feedstock that is produced in a sustainable manner today could not only provide a benefit to those involved in the supply chain of fuel production and use, but it would also lead to valuable experience to the biofue ls industry providing essential lessons in production and processing techniques. This experience should prove invaluable to the development of future sustainable feedstocks that could be used to create transportation fuels, such as jet fuel, with a minimum of arable land and fresh water.

### xi 

# Acknowledgements

Over the course of this research study

, the authors benefited from the expertise and insights of many individuals and organizations. The quality of the underlying research was greatly improved through the inputs of Dr. Malcolm Weiss, Mr. Matthew Pearlson, Prof. Ian Waitz, (all from MIT)

and Dr. David Ortiz (RAND Corporation)

.

The authors would like to thank the following individuals for their detailed reviews of all or parts of this manuscript: Michae l Griffin (Carnegie Mellon University), Ray Dums (Chevron), David Shonnard (Michigan Technological University), Dennis Bushnell (NASA), Bob Dillmore, Tim Skone (both from National Energy Technology Laboratory), Wagner Daraia Rocha, Joanna Bauldreay, Trevor Stephenson, and Paul Bogers (all from Shell Global Solutions), Carl Hodges (The Seawater Foundation), Tom Kalnes (UOP LLC) , and Kristin Lewis (Volpe National Transportation Systems Center). Their efforts contributed to a much improved finished product.

T he authors gained much insight from participating in regular meetings and workshops of the Aviation Fuel Life Cycle Assessment Working Group , led by Bill Harrison, where aspects of this work were presented

. The input of Maria Vera Morales (University of Ca mbridge) and Joyce Cooper (University of Washington) also contributed greatly to the treatment of algae within this

work.

The authors benefited from countless interactions with the Commercial Aviation Alternative Fuels Initiative (CAAFI) and its leadershi p, Richard Altman

, Nathan Brown , and Kristin Lewis . Through this forum, the authors were given deep insight into the aviation industry, including that of equipment manufacturers, fuel producers and providers, commercial airlines, environmental advocates, and regulators.

Finally, the authors would like to thank Warren Gillette and Lourdes Maurice , of FAA, and Tim

Edwards and Bill Harrison, both of AFRL, for their leadership in managing th is project.

### xii 

# Units, Notation, Abbreviations and Acronyms

Units

Bpd Barrels per day LHV Lower Heating Value

Btu British Thermal Unit Mg Megagram (metric tonne)

Bu Bushel MJ Megajoule

Ha Hectare (1000 0 m 2 ) mmBtu Million British Thermal Units

HHV Higher Heating Value Ton Imperial Ton (2000 pou n ds)

kWh Kilowatt

- hour Tonne Metric Ton (megagram)

Chemical Species Notation

C n Hydrocarbon chain of length ‘n’ K 2 O Potassium Oxide

CH 4 Methane N 2 O Nitrous Oxide

CO Carbon Monoxide NH 3 Ammonia
CO 2 Carbon Dioxide NO x Nitrogen Oxide

CO 2 e Carbon Dioxide equivalent P 2 O 5 Phosphorous Pentoxide

H 2 Hydrogen Gas SO x Sulfur Oxides

Acronyms and Abbreviations

AFLCAWG Air Force Life Cycle Analysis Working Group IPCC Intergovernmental Panel on Climate Change

API American Petroleum Institute ISO International Organization for

Standardization

ASP Aquatic Species Program LCA Life Cycle Analysis

BTL Biomass to Liquids (via F

- T) LPG Liquefied Petroleum Gas

CBTL Coal and Biomass to Liquids (via F

- T) LUC Land Use Change

CCS Carbon Capture and Sequestration MEA Monoethanolamine

CRP Conservation Reserve Program NETL National Energy Technology Laboratory

CTL Coal to Liquids (via F

- T) NREL National Renewable Energy Laboratory

DME Dimethyl Ether PARTNER Partnership for Air Transportation

Noise and Emissions Reduction
DOE Department of Energy (US) PFEI Payload Fuel Energy Intensity

EIA Energy Information Agency PKO Palm Kernel Oil

EISA Energy Independence and Security Act POME Palm Oil Mill Effluent

FAPRI Food and Agricultural Policy Research

Institute SAGD Steam Assisted gravity Drainage

F

- T Fischer Tropsch SOR Steam to Oil Ratio

FFB Fresh Fruit Bunches (from oil palm) SOC Soil Organic Carbon

GHG Greenhouse Gas SPK Synthetic Paraffinic Kerosene

GREET Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation TSS Total Suspended Solids (mass percent)

GTL Gas to Liquids (via F

- T) TTW Tank to Wake
GWP Global Warming Potential (IPCC) ULS Ultra Low Sulfur

HRD Hydroprocessed Renewable Diesel USDA United States Department of Agriculture

HRJ Hydroprocessed Renewable Jet WTT Well to Tank
ICP In

- situ Conversion Process (Shell) WTW Well to Wake
IGCC Integrated Gasification Combined Cycle

### 1 of 129 

# 1 Introduction

Both economic and environmental sustainability are required for any transportation fuel to be viable in the long term. An expansion of our energy portfolio to include alternative fuels would also result in the desirable consequence of energy diversity. This report presents results from ongoing research within the Partn ership for AiR Transportation Noise and Emissions Reduction at MIT on alternative fuels. As shown by the diagram of Figure 1, the PARTNER alternative fuels research portfolio is considering many aspects of alternative fuel sustainability. This report

focus es on aspects of environmental sustainability, with an emphasis on life cycle greenhouse gas emissions as they relate to impacts on global climate.

Figure 1 : Aspects of alternative fuels that are being considered for alternative fuel feasibility and sustainability. This report has an emphasis on life cycle greenhouse gas emissions

Alternative jet fuels created from renewable resources offer the potential to reduce the greenhouse gas (GHG) emissions from aviation. This is not due to a change in fuel composition nor is it due to a change in engine efficiency; instead the reduction is due to a change in the GHG emissions that result from the extraction, production and combustion of the alternative fuel relative to conventional jet f uel. Through a life cycle accounting of the GHG emissions that starts with the well, mine, or field where the fuel feedstock is extracted, and extending to the wake behind the aircraft, one can ascertain the change in GHG emissions that result from the use of an alternative fuel. To emphasize the importance of life cycle analysis for an accurate comparison of GHG emissions, consider that the combustion of synthetic fuels, which are covered extensively in this work, results in about 4% less CO 2 emissions (pe r unit mass of fuel) as compared to conventional jet fuel. However, as will be shown in this report, the life cycle GHG emissions from various alternative fuels can vary by two orders of magnitude depending on the feedstock and the details of production

.

There are many other issues that need to be considered when evaluating the potential of a specific alternative fuel. These include, but are not limited to, the efficient usage of water and land resources, the environmental impacts on air quality, and the e conomic cost of fuel production. This work touches on water and land usage, but not air quality or economic costs. The interested reader is directed to Hileman et al. (2009) for an extended discussion of these and other aspects of alternative jet fuel feas ibility. Furthermore, this work does not consider whether our limited biomass resources would provide greater societal benefit if they were used elsewhere, for example to create fuels for ground transportation or to generate heat and/or electricity. A rece nt analysis by Hedegaard et al. (2008) indicates that scarce biomass resources could be used more !"#$%"&&

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### 2 of 129 effectively, from perspectives of energy efficiency and CO 2 mitigation, for heat and electricity rather than ethanol for transportation. These topics are bein g considered as part of the ongoing PARTNER research portfolio.

The focus of this manuscript is a comparison of the well

- to

- wake, life cycle GHG emissions from

select feedstock

- to

- jet fuel pathways for the United States; this is a partial list of potentia l alternative jet fuel options and future revisions to this report will expand upon this list. In some

cases, details for a given fuel production pathway are expected to differ for other countries. The underlying data for each analysis have come from the l iterature, but each fuel pathways has been considered using a consistent methodology. This work is the first of its kind in that that a broad range of alternative fuels are analyzed for aviation using consistent methodologies that facilitate equitable comp arisons. Each pathway is presented in such a way that transparency of assumptions is maximized and the reader is able to identify the pivotal factors defining fuel production for each feedstock. As there is considerable variability in the life cycle GHG em issions from existing fuel production, and many of the fuel pathways considered in this report have not been commercialized, a range of life cycle GHG emissions has been provided for each feedstock

to

- fuel pathway. In addition to the examination of life cy cle GHG emissions, the manuscript provides a first order examination of the land and water usage that could accompany the development of a biofuel industry for aviation.

The contents of the report are structured as follows. Chapter 2 presents the fuel pat hways considered in this work while Chapter 3 provides backgrou nd information on creating a life cycle GHG emissions inventory. Chapters 4 through 7 present life cycle GHG estimates for conventional jet fuel and various other alternative jet fuel pathways. Chapter 4 considers standard and Ultra Low Sulfur (ULS) jet fuel from conventional petroleum. Chapter 5 considers conventional jet from unconventional petroleum resources of oil sands and oil shale. Chapter 6

examines Fischer

- Tropsch synthesis as a means to create synthetic paraffinic kerosene (SPK) fuel from natural gas, coal, and biomass. Chapter 7 presents the analysis of hydroprocess ed renewable jet fuels (HRJ) from soy, palm, rapeseed, jatropha, algae, and salicornia. Chapter 8

pr esents a comparison o f the life cycle GHG emissions established in Chapters 4 through 7 with

a discu ssion of some constraints that need to be considered to understand the viability of these pat h ways These include potential damages caused by the introduction of invasive species and land and water availability. Conclusions from this work are presented in Chapter 9.

### 3 of 129 

# 2 Alternative Jet Fuel Pathways

The fuel options considered herein are “drop

- in” alternatives in that they have the potential to serve as a direct replacement for conv entional jet fuel, requiring little or no modification to existing

infrastructure or aircraft. Alcohols and biodiesel (fa tty acid methy l esters) are not considered d ue to both the safety issues and the decrease in fuel efficiency that would accompany th eir use in aircraft operations (Hileman et al., 2009). The fuel compositions analyzed in this work are as

follows:

• Conventional jet fuel (e.g., Jet A) from conventional petroleum (crude oil) and unconventional petroleum (oil sands and oil shale)

• Ultra Low Su lfur (ULS) jet fuel from conventional petroleum

• Synthetic Paraffinic Kerosene (SPK) fuels created via Fischer

- Tropsch (F

- T) synthesis or hydroprocessing of renewable oils.

This should not be viewed an all

- encompassing list of potential fuel compositions t hat could be used as a replacement or a blend stock in aviation gas turbine engines. For example, advanced fermentation or pyrolysis can both be used to create a jet fuel blendstock, 1 but neither of these

fuels is considered here.

ULS jet fuels are consid ered as they could reduce aviation’s impact on air quality. The processing used to remove sulfur from conventional jet fuel (discussed in detail in Section 4.3) changes the fuel properties such that an additive package may be required to improve lubricity

. Considerable research is ongoing in regards to the analysis of ULS jet fuels (e.g., Miller et al, 2009; Hileman et

al. , 2010; PARTNER Project 27, 2010).

SPK fuels have similar molecular composition to conventional jet fuel with the primary difference being a lack of aromatic compounds; conventional jet fuel typically contains 20% aromatic compounds. Because certain types of seals require aromatic compounds for proper swelling, SPK fuels have been certified in blends up to 50% with conventional jet fuel to maintain a

minimum aromatic content.

The fuel production pathways analyzed in this work were jet fuel from conventional crude oil, jet

fuel from Canadia n oil sands, jet fuel from oil shale, Fischer

- Tropsch jet fuel from natural gas, coal and biomass, and jet fuel created by hydroprocessing soy oil, palm oil, rapeseed oil, algal oil, jatropha oil and salicornia oil. As of September 2009, at 50% blend of SP K fuel derived via

Fischer

- Tropsch synthesis with conventional jet fuel was certified for use in commercial aviation under ASTM spec D7566

- 09 (ASTM D7566

- 09, 2009). The fuel specification and certification

division of the Commercial Aviation Alternative Fu els Initiative (CAAFI) played a key role in the process of establishing D7566 and has outlined plans for a expanding the specification to include a 50% HRJ blend with conventional jet by 2010, 100% F

- T derived SPK by 2011 and 100% HRJ by 2013 (Rumizen, 201 0).

For each pathway, three potential scenarios (low emissions case, baseline case and high emissions case) were identified and life cycle GHG emissions were calculated for each of these scenarios. The fuel pathways are summarized in Table 1.

1 Note that pyrolysis is being examined to create synthetic aromatics that could be blended with SPK fuels to create a fully synthetic jet fuel while advanced fermentation is being examined to create a paraffinic fuel for aviation gas turbines.

### 4 of 129 Table 1 : Fuel pathways investigated

Source Feedstock Recovery Processing Final product

Conventional

crude Crude extraction Crude refining Jet Fuel

Conventional

crude Crude extraction Crude refining Ultra low Sulfur

Jet Fuel

Canadian oil

sands Bitumen mining/

extraction and

upgrading Syncrude refining Jet Fuel Petroleum

Oil shale In

- situ conversion Shale oil refining Jet Fuel

Natural

gas Natural gas Natural gas

extraction and

processing Gasification, F

- T

reaction and

upgrading F

- T Jet Fuel (GTL)

Coal Coal Coal mining Gasification, F

- T

reaction and upgrading (with and

without carbon capture) F

- T Jet Fuel (CTL)

Coal and

Biomass Coal and Biomass Coal mining and

biomass

cultivation Gasification, F

- T

reaction and upgrading (with

carbon capture) F

- T Jet Fuel (CBTL)

Biomass Biomass

cultivation Gasification, F

- T

reaction and

upgrading F

- T Jet Fuel (BTL)

Soy oil Cultivation and extraction of soy

oils Hydroprocessing HRJ Fuel (Hydroprocess

ed Renewable Jet)

Palm oil from

Southeast Asia Cultivation and extraction of palm

oils Hydroprocessing HRJ Fuel

Rapeseed Oil Cultivation and extraction of soy

oils Hydroprocessing HRJ Fuel

Algae oil Cultivation and extraction of algae

oils Hydroprocessing HRJ Fuel

Jatropha oil Cultivation and

extraction of jatropha oils Hydroprocessing HRJ Fuel Biomass

Salicornia oil and

solid biomass Cultivation of

salicornia and

extraction of

salicornia oils Gasification, F

- T

reaction and upgrading (with carbon capture); Hydroprocessing F

- T Jet Fuel

and HRJ Fuel

### 5 of 129 

# 3 Procedural Overview of Life Cycle Analysis of GHG

Emissions

3.1 Life cycle Greenhouse Gas Emissions

The life cycle analysis of alternative jet fuels encompasses emissions from the complete fuel cycle. This includes recovery and transportation of the feedstock from the well, field, or mine to the production facility, processing of these materials into fuels, transportation and distribution of the fuel to the aircraft tank, and finally, the combustion of the fuel in the aircraft. The steps of suc h

a well

- to

- wake life cycle analysis are shown schematically in Figure 2. These “well

- to

- wake” (WtW) steps can be broadly grouped into fuel production and distribution, “well

- to

- tank” (WtT), and fuel combustion, “tank

- to

- wake” (TtW). The interested reader is directed to a recent guidance document created by a consortium that was assembled by the U.S. Air Force (AFLCAWG, 2009) to learn more about the details of creating a life cycle GHG inventory for transportation fuels.

Figure 2 : Steps considered, in the well

- to

- wake, life cycle GHG inventory of conventional jet fuel

For each step of the life cycle, GHG emissions are assessed and reported on the basis of per

- unit

energy con sumed by the aircraft (per mega joule). The GHG covered i n this analysis are carbon dioxide, methane and nitrous oxide using their 100

- year global warming potentials (IPCC, 2007). This analysis did not cover non

- CO 2 or non

- CO 2 equivalent combustion emissions from aircraft

- for example NO X , SO X , soot, and water

- that directly or indirectly impact global climate change. These are currently being considered as an ongoing part of this work and will presented in a future report. This study also did not consider energy or GHG emissions associated with the initial

cre ation of infrastructure such as extraction equipment, transportation vehicles, farming machinery, processing facilities, etc. The impact of such emissions on the total life cycle GHG emissions of the pathway is usually relatively small, and within the unce rtainty range of the analysis. (Hill et al., 2006, Edwards et al., 2007).

Fossil feedstocks such as crude oil, coal or natural gas are created from geologically sequestered carbon sources, and the carbon is released as CO 2 when the fuel products are burned. Such

combustion CO 2 has to be taken into account in the life cycle analysis (see Figure 2). Biomass

feedstocks absorb CO 2 from the atmosphere when they grow and the CO 2 emitted during fuel combustion is equal to that abs orbed during biomass cultivation. Hence, many biofuels have a
“biomass credit” that offsets the combustion CO 2 in the life cycle analysis (see Figure 3). This biomass credit is the primary difference between biomass and fossil fuels in terms of their GHG

e missions. However, a biofuel does not necessarily have GHG emissions that are below a fossil

fuel. GHG = N 2 O, CH 4 , CO 2 Recovery &

Extraction Raw Material

Movement Jet Fuel

Production Jet Fuel Transportation Jet Fuel

Combustion

CO 2 , PM, NO X , SO X , H 2 O Aviation Productivity Other Liquid Fuel Products

### 6 of 129 Figure 3 : Steps considered in the well

- to

- wake, life cycle GHG inventory of bio

- based alternative jet fuels

Biomass feedstocks also have the potential for CO 2 emissions or CO 2 sequestration from changes in land use (see first step of Figure 3). The CO 2 emissions or sequestration are due to changes in the biomass, soil and organic waste contained on and within the land. In some

ins tances, these emissions can dominate the life cycle GHG emissions of the biofuel pathway. The land use change can be a direct land conversion, (e.g., tropical rainforest being cleared for cropland to grow feedstocks), or it can be an indirect conversion re sulting from land being converted elsewhere in the world due to economic signals induced by increased demand for agricultural products. In either case, it is assumed that a fixed quantity of biomass (e.g., vegetable oil) needs to be supplied to global food markets and that additional production (for biofuel creation) is met by land that has been converted from some previous use. The magnitude of land use change emissions depends primarily on the type of land being converted to cropland and the type of crops being grown. For fossil feedstocks, where conversion of land (e.g. forest land, grass land) for extraction of fossil resources (e.g. extraction of bitumen) or placement of fuel processing facilities (e.g. oil refineries) takes place, land use change emiss ions are negligible compared to other components of the fuel pathway. This is because a large throughput of fuel volume or mass (as well as energy) is created per unit area of converted land.

Only emissions from direct land use changes

, where land is con verted to facilitate biofuel production

, were considered in this work. No attempt was made to quantify the magnitude of indirect land use change emissions resulting from fluctuations in supply and demand of other crops because of increased biofuel producti on. In order to properly capture these effects, a detailed economic model is required which falls beyond the capabilities of GREET and the scope of this work. Other academic and government groups have invested a significant amount of effort to quantify ind irect land use change emissions and the reader is directed to their publications for estimates of these effects (EPA, 2010; Melillo et al., 2009).

For biofuels produced from algae, sufficient growth rates cannot be achieved without the direct feeding of CO 2 during growth. This is because the atmospheric concentration of CO 2 is too dilute to support an economically viable growth rate (Putt, 2007)

. The CO 2 used to feed the biomass must be abundant and come from a source external to the biofuel production sy stem. In this study, fossil energy resource

- based electricity generation was chosen to meet these needs. One can imagine a coupled system in which the CO 2 source and algae facility are linked to one another, where a fossil fuel is the primary input and bot h electricity and algal biofuel are primary outputs. This concept is shown schematically in Figure 4 with the system boundary for a conventional biofuel pathway expanded to include an outside source of CO 2

. In addition, direct land use changes should be small as compared to crop

- based biofuel s and indirect land use changes should be minimal because the necessary infrastructure can be created in wasteland and desert areas. Algae also have the capability to grow in saline water, meaning that fresh water

is not a pre

- requisite. Biomass Co-Product

GHG = N 2 O, CH 4 , CO 2 Recovery & Extraction

Raw Material

Movement Jet Fuel

Production Jet Fuel Transportation Jet Fuel

Combustion

CO 2 , PM, NO X , SO X , H 2 O Land

CO 2 Change in Land Usage Cultivation

GHG = N 2 O, CH 4 , CO 2 Aviation Productivity Other Liquid Fuel Products

### 7 of 129 Figure 4 : Steps considered in the well

- to

- wake, life cycle GHG inventory of bio

- based fuels from algae

Fuel production generally results in the creation of co

- products in addition to the primary fuel produc t. For example, the systems shown in Figures 2

- 4 generate biomass and liquid fuel co

products. These co

- products have embodied value that can be quantified based on physical metrics, or their ability to displace some other product elsewhere in the greater marketplace. Four methods can be used to assign life cycle GHG emissions between the primary fuel product and

any co

- products that are created:

• Mass allocation

• Energy allocation

• Market

- value allocation

• Displacement (or substitution, or system expansion)

The international organization for standardization (ISO) states in ISO 14044:2006(E) that processes shared with other product systems shall be identified and dealt with by preferentially using process disaggregation, system expansion, allocation by an unde rlying physical relationship and economic value, in this order. Inventories are based on material balances between input and output; therefore, allocation procedures should attempt to approximate such fundamental input/output relationships and characterist ics (ISO, 2006).

The mass and energy allocation approaches distribute the life cycle GHG emissions based on either the mass or energy content, respectively, of the co

- products and the fuel. In this work, the energy allocation method was used to allocate e nergy and emissions between co

- products of the

Fischer

- Tropsch process as well as those in the hydroprocessing of renewable oils to make Hydroprocessed Renewable Jet (HRJ); this is because these co

- products have utility as energy

resources.

The market allocation approach apportions emissions based on the market prices of the co

products and primary fuel product. Unlike the mass or energy allocation approaches, the market value allocation can change with time. The sensitivity to market forces could be pa rticularly useful

when co

- products are generated in quantities that stand to flood existing markets and drive the

co

- product price to zero. For example, if a fuel has a co

- product that displaces some existing product, then the market value method will capt ure the diminished utility of creating additional co

product by allocating more of the emissions to the fuel being produced. This is because increasing alternative fuel production will not change the price of the alternative fuel as this is set by the pric e of conventional fuel net subsidies and taxes. Co

- product creation does, however, have the potential to alter the price of similar commodities. In this work, market valuation was Electricity

Biomass Co-Product

GHG = N 2 O, CH 4 , CO 2 Raw Material

Movement Jet Fuel

Production Jet Fuel Transportation Jet Fuel

Combustion

CO 2 , PM, NO X , SO X , H 2 O Recovery &

Extraction Recovery &

Extraction Raw Material

Movement Electricity

Production

CO 2 GHG = N 2 O, CH 4 , CO 2

Aviation Productivity Other Liquid Fuel Products

### 8 of 129 used to allocate emissions between co

- products leaving the system after oil extraction within the HRJ pathways.

The displacement method assumes that the production of the incidental co

- product displaces the production of a substitute product. As a result, an emissions credit from the displaced product that is no longer produced i s applied to the primary product. Although this methodology is desirable

because it is time

- invariant and it could in theory be applied to any co

- product, it is hard to implement. This is because of difficulties in identifying a suitable product to be disp laced, calculating the life cycle GHG emissions of that displaced product and determining the displacement ratio (Huo et al., 2008). In the case of biofuels, the issue of how to appropriately allocate land use change emissions further complicates the appli cation of the displacement

method. The life cycle analysis of algae in this work applied the displacement method to the fossil based electricity used as a CO 2 source.

The use of different allocation methodologies can lead to substantially different results, particularly in regards to biofuel pathways where significant quantities of co

- products are being produced. The appropriate method may depend to a large extent on th e question that the life cycle analyst attempting to answer. Regardless of which method is applied, it is important that those conducting life cycle analyses for any purpose clearly state the allocation approach adopted.



### 3.2 Analysis Procedure

Analyses of lif e cycle GHG emissions for several jet fuel production pathways were carried out based on available information in the scholarly and technical literature. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) framework (version s 1.8b and 1.8a) and its supporting data, both developed and maintained by Argonne National Laboratory, was the primary tool used in the well

- to

- wake life cycle GHG analysis. 2 A simulation year of 2015

was used and d efault GREET assumptions were used in th e analysis of the pathways, except where more recent data were obtained. For example, the average efficiencies of coal

- fired power plants (utility boiler) and coal integrated gasification combined cycle (IGCC) plants were assumed to be 36 percent and 41.5 percent, respectively, on a lower heating value basis 3 (Deutch and Moniz, 2007).

A key limitation of the GREET framework is that it is designed for ground transportation fuels and vehicle systems and does not include jet fuel production pathways. Also, n ot all of the feedstocks analyzed in this work are available in GREET (e.g. oil shale). Hence, this work utilized data from the literature on jet fuel and jet fuel alternatives where available (e.g. fuel properties, refining efficiency) and incorporated th em into the GREET framework to derive life cycle GHG emissions. Where supporting data are presented in this report, mixed units are used for consistency with

GREET version 1.8a/1.8b.

In this work, the GREET framework was primarily used as a database and calculation platform where the quality of output energy and emissions values depended on the quality of input assumptions such as energy efficiencies, fuel properties, type and share of process fuels, and

emission allocation method for co

- products. Hence, a de novo approach was taken in identifying and reviewing key inputs and assumptions for each pathway. Specifically, default GREET input assumptions were examined for the fuel pathways available in GREET. Key parameters with a

2 The specif ic version of GREET used for each pathway within a section is stated at the beginning of the section; however, the impact on the results of this work of using GREET version 1.8a or 1.8b is negligible compared to the inherent uncertainties of life cycle ana lysis.

3 From Deutch and Moniz, 2007, the US coal fleet average generating efficiency is about 33% (HHV) and the generating efficiency for coal IGCC plants is 38.4% (HHV). Since the difference between HHV and LHV range from 2 to 4%, a 3%

difference is assu med in this report. Hence, the efficiency of an average coal

- fired power plant is assumed to be (33+3) 36% and the efficiency of a coal IGCC plants is assumed to be (38.4+3) 41.5%.

### 9 of 129 significant impact on the lif e cycle GHG emissions of the pathway were identified. Default GREET values for these key parameters were updated wherever necessary using reviews of recent information available in the literature. Where a specific pathway was not available in GREET, the

pa thway was built within the GREET framework with all relevant input parameters gathered from the open literature.

The analysis methodology used in this work differs from that adopted by the US Environmental Protection Agency (EPA) in their Renewable Fuel Standard (RFS2) recently released in February 2010. Specifically, life cycle assessments can be categorized as either attributional or consequential. As defined by the EPA:

“An attributional approach to GHG emissions accounting provides information about the GHG emitted directly by a product and its life cycle. The product system includes processes that are directly linked to the product by material, energy flows or services through the supply

- chain. A consequential approach to GHG emissions accounting in products provides information about the GHG emitted, directly or indirectly, as a consequence of changes in demand for the product. This approach typically describes changes in GHG emissions levels from affected processes, which are identified by linking causes with effects.” (EPA, 2010)

Attributional and consequential life cycle analyses will tend to yield different results for an identical product; hence, comparing results from the two methodologies is inappropriate. This work implements an attribution al methodology while the EPA has used a consequential analysis to more broadly consider the impact of future policy scenarios.



### 3.3 Goals and Practices

A life cycle analysis should be consistent with the goals of the study. As discussed in the

“Framework and Gu idance for Estimating Greenhouse Gas Footprints of Aviation Fuels”, different levels of analysis fidelity originate primarily from robustness of assumptions, data quality and level of model completeness (AFLCAWG, 2009). These differences are summarized in Figure 5. As indicated, this work focuses on conducting high quality screening

- level analyses using assumptions that attempt to capture industry averages.

Figure 5 : Levels of life cycle GHG studies used to ensure that the adopted practices are consistent with the goal of the work

Although a screening level analysis approach was chosen, uncertainty ranges we re established using optimistic, nominal and pessimistic scenarios such that the result is presented as a range instead of a single point. In most cases, the GHG emissions inventories established for each 

### !"#"$%&%

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!"#"$%-%

'./*0/)0%

!"#"$%1%

2345)"6"*7+#"% Conducted in support of a preliminary assessment of a technology alternative, to inform policy makers about research funding.

Conducted to meet regulation, such as

Section 526 of EISA 2007. All major operations examined, but with a lower degree of completeness and data quality than comprehensive LCA.Increasing data quality, effort, andconfidence in analysis results

### 10 of 129 pathway are not representative of an existing production configuration. As such, site

- specific

e xaminations of individual fuel pathways are still essential to quantifying specific GHG footprints such as would be required to meet Section 526 of EISA 2007. The results of this work do not replace such an analysis.



### 3.4 Evaluation of Variability

To explore th e impact of variability in key parameters values, three different scenarios

– low GHG emissions, baseline or nominal GHG emissions, and high GHG emissions

– were envisaged for each pathway. Key parameters were identified through examination of the GHG emis sions that resulted from each of the individual steps of the life cycle (see Figures 1 through 3). The engineering judgment of the authors was used to identify parameters that had both variability as well as a considerable influence on the life cycle GHG e missions. Input parameters such as process efficiency and biomass feedstock yield have both of these qualities in that they exert considerable influence on the life cycle GHG emissions of the fuel pathway and their value a
decade into the future could have considerable variability; hence, these parameters were varied as part of the three scenarios. Well defined parameters with a large impact on the life cycle emissions (such as the mass of CO 2 emitted per unit of fuel consumed by the jet engine) and paramet ers with large variability but only a small impact on the life cycle emissions (such as the

distance the feedstock needs to travel from th e source to the refinery) were generally not

examined.

By using key parameters to define the low, baseline, and high emissions scenarios, a range of GHG emissions, rather than a single value, was derived for each fuel pathway. Appropriate values for the key parameters were determined through literature review and consultation with relevant experts. In general, industry a verage values, rather than marginal values, were sought. If a marginal value for a key parameter was found that fell outside of typical values and if the marginal value indicates a potential industry trend, then the value was examined as a separate

case st udy. Variation of the key parameter values across the three scenarios could arise from differences in time frame (e.g. historical data versus future projections), different feedstocks (e.g.

bituminous coal versus sub

- bituminous coal), different technologie s or changes in process designs. While the upper and lower bounds of values found in the literature were generally used in the low and high emissions cases, baseline values were usually those which were deemed most likely, most frequently occurring, or wer e the average or mid

- point of the range of values reported in the literature.

Some of the pathways under consideration result in nitrous oxide emissions that represent more than 50% of the total life cycle GHG emissions. This work applied IPCC Tier 1 metho dology to

calculate N 2 O emissions from each fuel pathway and is therefore subject to the full range of uncertainty associated with IPCC correlations. In many cases, the emission factors developed by the IPCC have uncertainty ranges in excess of 100% (IPCC, 2006). While the focus of this work was not to assess the uncertainties within the IPCC methodology, the reader should be aware of their existence and that their impact is of larger consequence for pathways where N 2 O is a significant contributor to the to tal life cycle GHG emissions.



### 3.5 Functional Unit: g CO 2 

### e/MJ and g CO 2 

### e/kg

- km

Consistent with (AFLCAWG, 2009), the life cycle GHG emissions are presented using a metric that captures the mass of GHG per unit of energy (lower heating value) consumed by the aircraft. This is given in units of g CO 2 e/MJ wh e re



### € CO 2 

### e = CO 2 

### + GWP CH 4 

### ⋅ CH 4 

### + GWP N 2 O 

### ⋅ N 2 

### O 

# ( )

well

- to

- tank 

### + CO 2 

# ( )

tank

- to

- wake Equation 1

As discussed in Section 3.1, non

- CO 2 combustion emissions were not considered in this report.

### 11 of 129 A metric of life cycle GHG intensity, with un its of gCO 2 e/kg

- km, could be generated from this metric via multiplication by an energy efficiency metric such as Payload Fuel Energy Intensity (PFEI) with units of energy per payload distance flown (Hileman et al. , 2008). This metric will be

considered in Section 8.2.

### 12 of 129 

# 4 Conventional Petroleum Pathways

Because this work focuses on fuels that could be used in gas turbine powered aircraft, the baseline for analysis is jet fuel from conventional petroleum. For the purposes of this report, jet fuel could represent JP

- 8, Jet A, or Jet A

- 1, which are the fuels in use by the U.S. Air Force, commercial aviation in the US, and commercial aviation in Europe as well as much of the rest of the world, respectively. Se ction 4.1 considers the extraction of conventional crude oil for jet fuel while Section 4.2 considers petroleum refining to produce jet fuel. Section 4.3 examines an ultralow sulfur jet fuel (ULSJ) with a maximum fuel sulfur content of 15 parts per million . ULSJ differs from the other alternative fuels considered in this report in that it is derived from conventional petroleum and it would be used specifically to reduce aviation’s impact on air quality as opposed to diversifying energy sources or reducing g reenhouse gas emissions.

The steps involved in the production of jet fuel from conventional petroleum sources include crude oil extraction, transportation of crude oil to U.S. refineries, refining of crude oil to jet fuel, and the transportation of jet f uel to the aircraft tank. The GHG emissions resulting from crude oil extraction, crude oil transportation and jet fuel transportation were obtained using data from two recently published National Energy Technology Laboratory (NETL) studies on the life cycl e GHG emissions of petroleum

- based transportation fuels (Skone and Gerdes, 2008; Skone and Gerdes, 2009). The emissions that result from crude oil refining were calculated using both a top

- down

and a bottoms

- up perspective for conventional and an ultra low sulfur jet (ULSJ) fuel within GREET version 1.8a (Sections 4.2 and 4.3, respectively). This work differs from that of Skone and Gerdes (2008, 2009) in that the jet fuel pathway considers only jet fuel refined within the US and excludes jet fuel made from unconventional petroleum sources, such as oil sands, from the final result (jet fuel from unconventional sources is treated individually in section 5). Jet fuel refined within the US comprised 88.7% of all domestic jet fuel consumption in 2005 (Skone and

G erdes, 2008).



### 4.1 Crude Oil Recovery and Transportation

The source of crude oil is important in order to properly represent the range of resulting GHG emissions. The GHG emissions from crude oil recovery and crude oil transportation are designated origin speci fic GHG emissions. The variation in these emissions by crude oil source is primarily due to specific hydrocarbon flaring and venting practices during extraction, the emissions resulting from local electricity production, equipment efficiency and the transp ortation distance of crude oil to a US port.

Imported crude oils are on average heavier (lower API gravity 4 ) and contain higher levels of sulfur than domestic products (Skone and Gerdes, 2009). The changes in crude oil properties as well as processing tec hnique drive a variation in processing emissions of converting crude oil into finished fuel products. The GHG emissions associated with the processing of crude oil to jet fuel and ULS jet fuel were developed in this study using the GREET framework. These r esults are presented in conjunction with the origin specific GHG emissions and finished fuel transportation emissions derived from Skone and Gerdes (2008, 2009) to establish GHG inventories for conventional jet fuel and ULS jet fuel produced at US refineri es.

Of the crude oil mix fed into US refineries in 2005, only 34% was domestically produced. The other 66% was imported from other counties located around the world. When including domestic production, over 90% of the crude oil mix came from only 11 count ries (Skone and Gerdes, 2008). Throughout this work, the remaining fraction of imported crude is designated ‘other’ and corresponds to the weighted average of all imported crude (excluding Canadian oil sands).

4 API gravity is a measure of the density of a petroleum liquid relative t o water. An API gravity greater than 10 indicates lighter than water while an API gravity less than 10 indicates heavier than water. API gravity = 141.5/SG

- 131.5, where SG = specific gravity

### 13 of 129 Table 2 : Country specific average waterborne round trip travel distance of cr ude oil to US domestic ports

Crude Oil

Source Country

- Specific Average

Round

- Trip Travel

Dist a nce ( nautical miles) Fraction of Imported Crude Transported

by Ocean Tanker

Saudi Arabia 24036 100%

Kuwait 25052 100% Iraq 24740 100%

Venezuela 3578 100%

United States 0 0%

Ecuador 11306 100% Algeria 8904 100%

Canada 1350 28%

Mexico 2122 100%

Other 20000 100% Angola 13472 100% Nigeria 11344 100%

Notes: 1) Skone and Gerdes (2008) 2) Imported crude from Canadian oils sands ignored in this section because it is explicitly dealt with in Section 5.1

Figure 6 : Origin specific GHG emissions by species of crude oil entering US refineries in 2005 (fraction of

total imports in parentheses). Based on country profiles published in Skone and Gerdes (2008, 2009)

In their 2008 life cycle GHG analysis of petroleum based fuels, Skone and Gerdes developed crude oil extraction profiles, including methane flaring and venting data, for eac h of these 11 countries. They also developed a transportation profile for each country by accounting for the transport of imported crude oil from its point of extraction to foreign ports, ocean tanker transport of waterborne imported crude oil to domestic ports and crude oil transport within the US. !" #" $!" $#" %!" %#" &'('")*+,-"./0"122'345" (6+,/"7*68/6")*+,-"./0"19':45"

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### 14 of 129 Domestic crude oil is only subject to transport within the US and 72% of Canadian crude imports are transported via pipeline and do not incur waterborne transport emissions; all other imports are subject to all three forms of transportation. Listed in Table 2 are the round trip distances used to calculate waterborne GHG emissions and the fraction of imports transported by ocean tanker for

each crude oil source. The interested reader is directed to Skone and Gerde s (2008) for the emissions factors for each leg of the transportation process.

The significance of the data reported in Table 2 is that importing crude oil from Saudi Arabia and Kuwait results in substantially more GHG emissions from transportation than c rude from other sources. The recovery and total transportation GHG emissions (by species) from 2005 are given for crude oil from each source in Figure 6. Beside the label for each source, the volumetric fraction of total crude fed into US refineries import ed from that country is given in parentheses. The volumetric fractions do not add up to 100% because Canadian oil sands are not listed.

These data highlight that domestically produced crude oil results in lower GHG emissions than any other source because of reduced transportation emissions. Even though CO 2 emissions resulting from domestic oil extraction are higher than Iraq, Kuwait, Venezuela and Saudi Arabia, the combination of transportation emissions and methane venting causes these regions to have hig her origin specific GHG emissions than the US. In the cases of Iraq, Kuwait and Saudi Arabia, transportation emissions (primarily ocean transport emissions) represent 47%, 51% and 55% of the total origin specific GHG emissions.

The primary driver for cou ntries with excessive origin

- specific GHG emissions is methane venting. Methane emissions from Nigerian and Angolan crude extraction exceed all other origin specific GHG emissions. Mexican and Canadian crude also have non

- negligible methane

emissions from venting.

The variati on in profiles shown in Figure 6 was used to establish the low emissions, baseline and high emissions scenarios for jet fuel and ULS jet fuel from conventional crude. The low emissions scenario was composed of purely domestic crude oil , the baseline scenario adopted the weighted average of all crude oil fed into US refineries, excluding Canadian oil sands, and the high emissions scenario used only Nigerian crude. The transportation of jet fuel from US refineries to

the aircraft tank are independent of the source of crude oil; hence, a single result was used for all

three emissions scenarios.



### 4.2 Conventional Jet Fuel from Crude Oil

The conventional jet fuel production pathway forms the baseline against which the life cycle GHG
emissions of a lternative jet fuels are compared. The properties of conventional crude oil were based on the projected average crude oil received by U.S. refineries in 2015, obtained using historical data provided by the EIA. There is a definite trend for crudes to becom e heavier and more sour (more sulfur) in the future; therefore, a business as usual scenario would likely see jet fuel production becoming more energy intensive as more hydroprocessing is required to maintain current product quality. This means that the en ergy intensity of refining may increase beyond the values used in this study. Some discussion of the impacts of crude oil quality on GHG emission from refining is given section 4.2.3 (EIA, 2008a). 5

The key parameter in analyzing the GHG emissions associa ted with the production of jet fuel

from crude oil is fuel

- refining efficiency. Two methods were employed in the derivation of jet fuel refining efficiency. The first method was a top

- down approach, which derived the jet fuel refining energy efficiency fro m the overall U.S. refining energy efficiency. This formed the baseline case.

5 From data on sulfur content and API gravity of average crude oil input to U.S. refineries from 1995 to 2006 given by EIA (EIA, 2008a), it was estimated that there was approximately a 2% annual increase in sulfur content and 0.25% annual decrease in API gravity. From these trends, the average crude oil quality receiv ed by U.S. refineries in 2015 was

estimated.

### 15 of 129 The second method was a bottom

- up approach, which estimated jet fuel

- refining efficiency by summing the energy requirements for the individual refining processes. Specifically, t wo extreme cases were examined: straight

- run fuel production and hydroprocessing of crude. The refining efficiencies obtained using the bottom

- up approach were used for the low and high emissions cases, respectively, providing a bound on the range of possi ble values.

4.2.1 Top

- Down Approach (Baseline Case)

The overall U.S. refinery efficiency as estimated by Wang (2008 6 ) was 90.1% (LHV), based on statistics of process fuel use in U.S. refineries, and 2006 refinery fuel inputs and outputs provided by the EIA. Ove r all refinery efficiency is defined as (Wang, 2008):



### € η 0 

### = E products


### E inputs Eq uation 2

where η o = petroleum refinery energy efficiency,

E products = energy of all petroleum products,

E inputs = energy in crude input, other feedstock inputs, and process fuels.

The jet fuel

- specific refining efficiency was calculated from the overall refinery efficiency using the kerosene relative energy intensity 7 and Equation 3, developed by Wang et al. (2004 ). The relative energy intensity of the production of kerosene (jet fuel), X S , based on an energy

- content process allocation method, is 62.4 percent, and the overall refining efficiency, η 0 , is 90.1 percent.



### € η

s 

### = η 0



### η

0 

### + X

s 

### 1

− η 0 

# ( ) Equation 3

From Equation 3, the refining energy efficiency of jet fuel is 93.5% (LHV). This refining efficiency was used in the baseline case for the life cycle analysis of GHG emissions from the production of jet fuel from conventional petroleum.

Based on 2006 U.S. refinery data published by the EIA, Wang (2008) calculated the shares of process fuel used in U.S. petroleum refineries (see Table 3). These shares were used as inputs to the GREET model for the baseline case of jet fuel production.

Table 3 : Type of process fuel and fuel share in the refining of jet fuel and ULS jet fuel

Type of process fuel Process energy share (%) Electricity 3.5

Natural Gas 41.3 Refinery Gas 39.6

Coke 14.3

Residual Oil 1.3

Total 100

6 This work was used to update the refining efficiency of gasoline, diesel, LPG, residual oil and naphtha in GREET (version 1.8b). (GREET, 2008)

7 Relative energy intensity was defined as “the ratio of total energy use share to the mass share of a given fuel.” It provides a measure of how energy intensive the production of a particular fuel is relative to the mass share of that fuel produced. A rela tive energy intensity of more than 100% for a particular fuel means that the production of that fuel consumes a greater share of overall process energy than the mass share of that fuel produced. The energy intensity of the overall refinery is 100%.

### 16 of 129 4.2.2 Bottom

- up Approach (Low and High Emissions Cases)

The jet fuel produced by a refinery may be straight

- run, produced from hydroprocessed stocks, or a blend of both. The refining efficiency of each production method was estimated by summing the energy inputs of indivi dual processes. These two cases formed the low and high emissions scenarios, respectively, for the life cycle analysis of GHG emissions in the production of jet fuel from conventional petroleum.

The energy for the processes involved in refining jet fuel was taken from a 2007 Department of Energy sponsored report (Pellegrino et al., 2007). The report provided both a range of refining process energy use, as well as average energy use. The average energy use data for the relevant refining processes were used to calculate the jet fuel refining efficiency.

Straight

- Run Jet Fuel The main processes involved in the production of straight

- run jet fuel are crude desalting and atmospheric distillation, followed by chemical treatments (such as the Merox process) to remove contaminants like mercaptans and organic acids, etc. The estimated process energy in crude desalting and atmospheric distillation is shown in Table 4. As no data were found in the literature regarding the energy needed for chemical treatment, it was assumed that the energy needed for this process was negligible by comparison.

Table 4 : Energy requirement in the production of straight

- run jet fuel

Refining process Energy required (J/MJ product) Crude desalting and atmospheric

distillation 20,055

Chemical treatment Assumed to be negligible

Total 20,055 Overall refining efficiency (LHV) 98%

Under these assumptions, the refining efficiency of straight

- run jet fuel is about 98% (LHV). This refining efficiency likely represents the maximum efficiency for the production of jet fuel from conventional crude and was used in the low emissions scenario. The corresponding process fuel and fuel shares for the production of straight

- run jet fuel are shown in Table 5.

Table 5 : Process fuel and fuel shares for the production of straight

- run jet fuel

Type of process

fuel J/MJ of jet fuel Process fuel share (%) Electricity 423 2.1

Natural Gas 5,772 28.8 Refinery Gas 9,669 48.2

Coke 3,548 17.7

Residual Oil 643 3.2

Total 20,055 100

Hydroprocessed Jet Fuel The refining processes involved in producing jet fuel from hydroprocessing include crude desalting, atmospheric and vacuum distillation, and hydrotreating and/or hydrocracking. Since the production of this hydroprocessed jet fuel was considered as the high emissions scenario, it was assumed that all the above processes were required (including both hydrotreating and hydrocracking). The energy needed for the refining processes to produce hydroprocessed jet fuel

is shown in Tabl e 6

.

### 17 of 129 Table 6 : Energy requirement in the production of jet fuel from hydroprocessing

Refining process Energy required (J/MJ product) Crude desalting and atmospheric

distillation 20,055

Vacuum distillation 16,379

Hydrotreating (to S content of ~500ppm) 24,368 Hydrocracking 75,092

Total 135,894 Overall refining efficiency (LHV) 88%

The refining efficiency of hydroprocessed jet fuel is approximately 88% (LHV). As mentioned earlier, this refining efficiency was assumed in the high emissions scenario. The corresponding process fuel shares for the production of hydroprocessed jet fuel are shown in Table 7.

Table 7 : Process fuel and fuel shares for the production of jet fuel from hydroprocessing

Type of process

fuel J/MJ of jet fuel Process fuel share (%) Electricity 9,137 6.7

Natural Gas 82,683 60.9 Refinery Gas 30,713 22.6

Coke 11,294 8.3

Residual Oil 2,067 1.5

Total 135,894 100

4.2.3 Impact of Crude Oil Quality Compared to Processing Technique

The scope of section 4.2 thus far has been limited to establishing a range of processing emissions for conventional jet fuel while assuming US average crude oil properties. As shown by Skone and Gerdes (2009), imported crude oils are on average heavier and contain higher levels of sulfur than domestic products. Skone and Gerdes used the API gravity and sulfur content of crude oils fed into US refineries to establish origin

- specific processing GHG emissions for diesel

fuel in 2005. Bas ed on these data, the origin

- specific processing GHG emissions per barrel of crude oil were calculated and subsequently related to origin

- specific processing GHG emissions for jet fuel. 8 The upper and lower bounds on the variation from crude oil quality co rrespond to Mexican crude (API gravity of 23.8 and sulfur content of 3.0%) and Algerian crude (API gravity of 44.8 and sulfur content of 0.1%), respectively (Skone and Gerdes, 2009).

The variation in processing GHG emissions introduced from crude oil quality and processing technique are shown graphically in Figure 7. The variation in processing emissions resulting from processing techniques is 70% larger than the variation from crude oil quality. This analysis emphasizes that there are two factors that impact the processing emissions of making jet fuel. Although they have been considered separately, the impacts of crude oil quality and processing technique on processing emissions are not necessarily independent. Namely, the impact of crude oil quality o n straight run fuel production may not be the same as its impact on hydroprocessed jet fuel.

8 This calculation was based on the assumption that the difference in processing emissions per barrel of diesel between each source and the average is equal to the difference in processing emissions per barrel of jet between each source and the average. Average p rocessing emissions for diesel are 9.0 gCO 2 e/MJ while those for jet are 5.65 gCO 2 e/MJ.

### 18 of 129 Figure 7 : Variation in jet fuel processing emissions induced by origin specific crude oil properties and processing technique

4.2.4 Conventional Jet Fuel Results

The life cycle GHG emissions from the production of jet fuel from conventional crude are shown

in Table 8. These results incorporate the recovery (crude extraction) and transportation results discussed in Section 4.1 to comple te the life cycle GHG inventory. A comparison of the domestic results from this study with the average results presented by Skone and Gerdes (2008) is shown in the far right column of the table. Despite using a different approach to derive the GHG

emission s in the processing of feedstock in the baseline case (top

- down) from that used in the NETL study (bottom

- up approach), similar results were obtained. These results assume average crude oil properties in all three scenarios; hence, they do not include any variation in processing emissions from crude oil quality. In addition, the combustion CO 2 equivalent emissions used by Skone and Gerdes are slightly higher than those calculated in this study. This is due to their

estimates of CH 4 and N 2 O emissions from je t fuel combustion. These emissions were excluded in this study due to the high level of uncertainty associated with their estimation. Overall, the life cycle GHG emissions of jet fuel from conventional crude obtained by NETL (88.0 gCO 2 e/MJ) are
about 0.7% higher than the baseline results (87.5 gCO 2 e/MJ) developed herein

.

Table 8 : Summary of results for jet fuel from conventional crude and a comparison of results to the NETL petroleum baseline study

MIT Conventional Je t Fuel NETL

Low Baseline High Baseline Key Assumptions Crude oil origin US Average Ni g eria n/a

Processing Technique Straight

Run Average H y dro

processed n/a

Refining efficiency (LHV) 98.0% 93.5% 8 8.0% n/a Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 3.7 4.2 9.4 4.3 Transportation of feedstock (gCO 2 /MJ) 0.8 1.5 1.8 1.3 Processing of feedstock to fuel (gCO 2 /MJ) 1.6 5.5 11.0 5.5

Transportation of jet fuel (gCO 2 /MJ) 0.8 0.8 0.8 0.9

Combustion CO 2 (gCO 2 /MJ) 73.2 73.2 73.2 73.7

WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 7.0 11.9 22.9 12.0

WTT CH 4 emissions (gCO 2 e/MJ) 0.5 2.3 13.0 2.3

WTT N 2 O emissions (gCO 2 e/MJ) 0.1 0.1 0.1 0.1

Total WTW GHG emissions (gCO 2 e/MJ) 80.7 87.5 109.3 88.0

Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.92 1.00 1.25 1.01 !"#$%& !"#'%& "#'%& "#$%& (#'%&

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### 19 of 129 4.3 Ultra

- Low Sulfur Jet Fuel from Conventional Crude

The production of ULS jet fuel from conventional crude oil was approached with the same techniques and assumptions that were applied to conve ntional jet fuel. Specifically straight

- run fuel production and hydroprocessing of crude were considered for the low and high emissions scenarios, respectively. The additional hydroprocessing that would be required to desulfurize the petroleum stream to cr eate the ULS jet fuel was modeled based on that required for ULS diesel. The required hydroprocessing likely exceeds simple hydrotreatment, and it may instead involve deep hydrogenation and/or hydrocracking.

Although the technology to produce a ULS jet f uel is known, it is important to note that the current hydroprocessing capacity in the United States is only sufficient to meet ULS diesel needs. It would need to be expanded to have sufficient capacity for both ULS jet and ULS diesel fuels. Such an expans ion would require the construction of additional infrastructure, which was not included in the life cycle analysis.

4.3.1 Top

- Down Approach (Baseline case)

General Motors et al. (2001) estimated a 2% energy penalty for reducing sulfur content in gasoline and diesel fuel from 350ppm to 5ppm. The baseline assumption in this work assumed the same 2% energy penalty applies for the production of ULS jet fuel compared to conventional jet fuel. Hence, the refining energy efficiency of ULS jet fuel was assumed to be 2 % less than that of jet fuel, i.e. 91.5 percent (LHV). The process fuel shares outlined for conventional jet fuel (Table 3) were also used as inputs to the GREET model for the production of baseline ULS jet

fuel.

4.3.2 Bottom

- up approach (low and high emissions cases)

The bottom

- up approach for ULS jet fuel builds upon that from the baseline case wherein the energy for the processes involved in refining jet fuel was taken from a 2007 Department of Energy sponsored report (Pellegrino et al., 2007). The report prov ided both a range of refining process energy use and average energy use. The average energy use data for the relevant refining processes were used to calculate the jet fuel refining efficiency.

Straight

- Run ULS Jet Fuel The production of straight

- run ULS jet fuel requires crude desalting and atmospheric distillation followed by hydrotreatment to remove sulfur and other impurities. The estimated energy for these processes is shown in Tabl e 9

.

Table 9 : Energy requirement in the prod uction of straight

- run ULS jet fuel

Refining process Energy required (J/MJ product) Crude desalting and atmospheric

distillation 20,055

Hydrotreatment (to S content of ~5ppm) 48,184

Total 68,239 Overall refining efficiency (LHV) 93.5%

Hence, the refining efficiency of straight

- run ULS jet fuel is about 93.5% (LHV). This refining efficiency was used in the ULS jet fuel low emissions scenario. The corresponding process fuel shares for the production of straight

- run ULS jet fuel are shown in Table 10

.

### 20 of 129 Table 10 : Process fuel and fuel shares for the production of straight

- run ULS jet fuel

Type of process fuel J/MJ of jet fuel Process fuel share (%) Electricity 3,806 5.6

Natural Gas 42,570 62.4 Refinery Gas 15,235 22.3

Coke 5,603 8.2

Residual Oil 1,025 1.5

Total 68,239 100

Hydroprocessed ULS Jet Fuel The calculation of the process energy required in the production of hydroprocessed ULS jet fuel is similar to that of jet fuel, except that additional hydrotreating is required to reduce the sulfur content of the fuel from about 500ppm to 5ppm. The energy needed for the refining processes is

shown in Table 11

.

Table 11 : Energy requirement in the production of ULS jet fuel from hydroprocessing

Refining process Energy required (J/MJ product) Crude desalting and atmospheric

distillation 20,055

Vacuum distillation 16,379 Hydrotreating (to S content of ~5ppm) 47,578 Hydrocracking 75,092

Total 159,104 Overall refining efficiency (LHV) 86%

The refining efficiency of hydroprocessed ULS jet fuel is about 86% (LHV). This refining efficiency was assumed in the high emissions scenario for the production of ULS jet fuel from conventional

crude oil. The corresponding process fuel shares for the produc tion of hydroprocessed ULS jet
fuel are shown in Table 12.

Table 12 : Process fuel and fuel shares for the production of ULS jet fuel from hydroprocessing

Type of process

fuel J/MJ of jet fuel Process fuel share (%) Electricity 10767 6.8

Natural Gas 100408 63.1 Refinery Gas 33394 21.0

Coke 12284 7.7

Residual Oil 2251 1.4

Total 159104 100

4.3.3 ULS Jet Fuel Results

The life cycle GHG resulting from the production of ULS jet fuel from conventional crude are

shown in Table 13. These results incorporate the recovery and transportation results discussed in section 4.1 to complete the life cycle GHG inventory.

### 21 of 129 Table 13 : Summary of results for ULS jet fuel from conventional crude pathway

Low Baseline High Key Assu mptions Crude oil origin US Average Nigeria

Processing Technique Straight

Run Average H y dro

processed Refining efficiency (LHV) 93.5% 91.5% 86% Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 3.7 4.2 9.4 Transportation of feedstock (gCO 2 /MJ) 0.8 1.5 1.8 Processing of feedstock to fuel (gCO 2 /MJ) 5.5 7.3 13.1 Transportation of jet fuel (gCO 2 /MJ) 0.8 0.8 0.8

Combustion CO 2 (gCO 2 /MJ) 72.9 72.9 72.9 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 10.9 13.7 25.0

WTT CH 4 emissions (gCO 2 e/MJ) 0.7 2.4 13.2

WTT N 2 O emissions (gCO 2 e/MJ) 0.1 0.1 0.1 Total WTW GHG emissions (gCO 2 e/MJ) 84.6 89.1 111.2 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.97 1.02 1.27

In the baseline case, where a refining energy efficiency penalty of 2% was assumed in the production of ULS jet fuel relative to conventional jet fuel, the production of ULS jet fuel results in life cycle GHG emissions about 2% greater than those of conventional jet fuel. In the low emissions case, the life cycle GHG emissions from the production of ULS jet fuel are decreased by 3% relative to the baseline conventional jet fuel. This effect arises from reduced origin

- specific

emissions relative to the baseline case and an additional energy intensive hydrotreating step compared to the production of straight

- run conventional jet fuel. Conversely, the increase in life cycle GHG emissions between high emissions scenario ULS jet fuel and baseline conventional jet fuel is 27%. The energy pe nalty in this case results from further hydrotreating the jet fuel from about 500ppm to 5ppm (~2% difference in refining efficiency). The main reason driving the increase are the methane venting and transportation emissions associated with the Nigerian

cru de oil. In all cases, CO 2 emissions from combustion of ULS jet fuel are 0.3 gCO 2 e/MJ less that conventional jet fuel.

### 22 of 129 

# 5 Unconventional Petroleum Pathways

This manuscript considers two unconventional petroleum resources, oil sands from Alberta,

Canada and oi l shale from the Green River Formation in Colorado, Utah and Wyoming. While current production of oil sands is approximately 1.3 million bbl/day, oil shale development is presently limited to laboratory and pilot

- plant stages (Hileman et al., 2009). Oil sa nds are deposits

of tar

- like petroleum, known as bitumen, within sand or porous rock while oil shale is a solid sedimentary rock that contains kerogen, a mixture of organic compounds, including bitumen,

which can be refined to oil. Both of these resources require extensive work to convert the raw resource to a synthetic crude and this results in increased life cycle GHG emissions relative to conventional petroleum. All of the work regarding jet fuel from unconventional crude oil was performed with GREET ver sion 1.8a.



### 5.1 Conventional Jet Fuel from Oil Sands

Alberta’s oil sands resources constitute one of the world’s largest proven oil reserves, second only to Saudi Arabia (Lacombe and Parsons, 2007). Bitumen production from oil sands takes place primarily by two methods, depending on the depth of the oil sand deposits. Relatively shallow deposits (75m or less) are recovered through surface mining, which involves the removal of soil and rock overlying the mineral deposit, mining of the oil sands ore and extraction of bitumen from oil sands using hot water. Deeper deposits employ in

- situ production methods, which typically inject steam underground to reduce the viscosity of the bitumen and allow it to be pumped to the surface (AEUB, 2007). The bitumen produced can b e upgraded to lighter synthetic crude oil (syncrude) through hydrogen

- addition processes (e.g. hydrocracking) and/or carbon

subtraction processes (e.g. delayed coking).

The life cycle GHG emissions for the extraction of bitumen by both surface mining and in

- situ technologies were considered. In particular, steam assisted gravity drainage (SAGD) was the main technology assumed for the in

- situ production of oil sands. This technique has proven to be “technically feasible and economically attractive” for the extraction of bitumen oil sands deposits below 200 meters (Isaacs, 2007a). It was further assumed that all bitumen was upgraded to a light synthetic crude oil (syncrude). 9 Oil sands projects have traditionally used natural gas as the main source of proces s energy for bitumen extraction and upgrading as well as for producing hydrogen required for the upgrading process. This stems from the era when natur al gas was cheap and abundant; h owever, this practice may not be sustainable as bitumen production expands . To reduce the dependence on natural gas, alternative process energy and hydrogen sources are being explored. These include coal combustion, coal gasification, nuclear energy, and the use of internally generated fuels such as bitumen residues (ACR, 2004). Additional energy demands are also being met using diesel, gasoline and grid electricity.

5.1.1 Bitumen Production through Surface Mining

The GHG emissions from bitumen production through surface mining are largely dependent on the quantity and source of the process energy. This work explored the use of natural gas and coal combustion to generate the steam and electrical power required for bitumen extraction. The total process energy data for surface mining were taken from the average values of individual indu stry projects (Deer Creek Energy, 2006; Synenco Energy, 2006; Shell Canada, 2007a). The process energy sources and values for the production of bitumen through surface mining for the low emissions, baseline and high emissions scenarios are summarized in Ta ble 14.

9 It was assumed that about 1,600 standard cubic feet of hydrogen was used per barrel of syncrude produced through bitumen upgrading. The quality of syncrude produced (~ 35

- 49 ° API) is generally lighter than the average crude oil received by U.S. refineries (~30 ° API). However, syncrude tends to produce lower quality distillate oil due to its low hydrogen content and requires more complex refining operations to produce tra nsportation fuels. This report assumed that the refining efficiency of jet fuel from the lighter, but lower hydrogen content syncrude was the same as that from using heavier crude oil with higher hydrogen content.

### 23 of 129 Table 14 : Process energy assumptions for the production of bitumen through surface mining for the low emissions, baseline and high emissions cases

Parameter Low Baseline High Process energy for steam and power generation (J/MJ bitumen) 52058 26029 (NG) 26029 (coal) 52058

Source of process energy NG NG and coal Coal Grid electricity (J/MJ bitumen) 3213 3213 3213 Diesel (J/MJ bitumen) 10723 10723 10723 Gasoline (J/MJ bitumen) 319 319 319 Total process energy required (J/MJ bitumen) 66313 66313 66313

5.1.2 Bitumen Production through In Situ Recovery

The efficiency of bitumen recovery from oil sands using in situ recovery techniques is characterized by the steam

- to

- oil ratio (SOR). SOR is the volume of steam required to extract a given volume of bitumen. The SOR of SAGD technology ranges from 2.0 to greater than 3.0, (Nieuwenburg, 2006; Lacombe and Parsons, 2007) with a 2006 average of about 2.5 (Canadian National Energy Board, 2006). The lower and upper bounds were adopt ed in the low and high emissions scenarios while the average was used in the baseline scenario. These assumptions translate to natural gas usages of 840 scf, 1050 scf and 1260 scf per barrel of bitumen in the low baseline and high emissions scenarios, resp ectively. 10 Other energy sources, such as coal, can also be used to supply an equivalent amount of energy. Electricity used in the in

- situ production of bitumen was consistent with the average value provided by the National Energy Board (2006).

In the low and baseline cases, it was assumed that the process energy for steam generation was provided by natural gas, and in the high case, by coal. The process energy values for the production of bitumen through in

- situ production for the three scenarios are summ arized in Table

15.

Table 15 : Process energy assumptions for the production of bitumen through in situ recovery for the low emissions, baseline and high emissions cases

Parameter Low Baseline High SOR (steam

- to

- oil ratio) 2.0 2.5 3.5 Process energy needed to make steam (J/MJ bitumen) 138544 173180 242451

Source of steam energy NG NG Coal Grid electricity (J/MJ bitumen) 5700 5700 5700

5.1.3 Bitumen Upgrading to Syncrude

The parameters analyzed in the upgrading of bitumen to syncrude included the yield of syncrude, amount of hydrogen used for the upgrading process, the source of hydrogen and the process energy. The yield of syncrude from bitumen upgrading can vary depending on the upgrading processes. The average volumetric yield is ab out 1 barrel of syncrude per 1.16 barrel of bitumen input, or 1 MJ of syncrude for every 1.27 MJ of bitumen input (EIA, 2006). 11 This yield formed the baseline upgrading analysis of this work. In the low scenario, it was assumed that 1.1 MJ of bitumen input is required per MJ of syncrude, based on the Shell Scotford upgrading project

10 Assuming that about 420 standard cubic f eet of natural gas is needed to make a barrel of steam (Lacombe and Parsons, 2007).

11 Assuming that the LHV of bitumen is 39.5 MJ/L (Shell Canada, 2007b), and the LHV of syncrude is 36.1 MJ/L (from Table 97).

### 24 of 129 (Shell Canada, 2007b). The high scenario assumed that 1 MJ of syncrude was produced from 1.36 MJ of bitumen input based on the Synenco Energy Northern Lights Upgrading project.

In the low and baseline cases, it was assumed that 1626 scf of hydrogen was needed per barrel of syncrude produced (Isaacs, 2007b, ACR, 2004) 12 and that natural gas was the source of the hydrogen. In the high case, 1800 scf of hydrogen was assumed to be u sed per barrel of syncrude produced, as predicted by the Alberta Chamber of Resources as the amount needed to produce high quality light syncrude in the future (ACR, 2004). It was further assumed that this hydrogen was produced from the gasification of asp haltene, a by

- product of the bitumen upgrading process, with no carbon capture. 13 A sensitivity analysis was conducted on the high emissions case to examine the impact of carbon capture and sequestration on the CO 2 resulting from the asphaltene gasification

. 14

Natural gas and electricity are the main process energy requirements for the upgrading processes. The range of values used for the low to high cases is based on data from the Alberta Chamber of Resources (baseline case) and individual industry upgradin g projects (Low Case: Sturgeon upgrader; High case: Northern Lights upgrader 15 ) (ACR, 2004; Petro

- Canada, 2006; Synenco Energy, 2006). The parameters used in analyzed in the upgrading of bitumen to syncrude are summarized in Table 16

.

Table 16 : Assumed parameters in the upgrading of bitumen to syncrude

Parameter Low Baseline High Yield of SCO (MJ bitumen per MJ syncrude produced) 1.1 1.27 1.36

Amount of H 2 needed (J/MJ syncrude) 86468 86468 98780

Source of H 2 N G NG asphaltene gasification Process energy (NG) (J/MJ syncrude ) 4747 14400 25207

Electricity (J/MJ syncrude) 8323 4200 34846 Jet fuel refining efficiency from syncrude (%) 93.5 93.5 93.5

5.1.4 Oil Sands to Jet Fuel Results

The life cycle GHG emissions from the production of jet fuel from Canadian oil sands using

surface

- mining and in

- situ production are shown in Table 17.

Carbon

- capture and sequestration (CCS) of emissions from the gasification of asphaltene for hydrogen production was examined as a sensitivity study to the high emissions scenario of Table

12 From ACR (2004) ~0.4 volume units of natural gas are needed to produce a volume unit of hydrogen.

13 It is assumed that there are no energy use or emissions associated with the production of asphaltene, which is a by

product of bitumen upgrading. The efficiency of asphaltene gasification is also assum ed to be the same as that of coal gasification (default GREET value of 62%).

14 A 90% efficiency in the capture of carbon emitted from the gasification of asphaltene and carbon capture energy requirement of 250 kwh/ton C are assumed.

15 In addition to syncru de, the Northern Lights upgrader project also produces butane and excess hydrogen. The

emission credit for the butane co

- product was ignored because it is negligible compared to the other well

- to

- tank emissions (~1%). It was also assumed that the emissions associated with asphaltene gasification to produce any excess hydrogen are offset by the emissions credit given when this hydrogen displaces conventional hydrogen on the market. The net effect is to ignore the excess hydrogen produced in the process. Such assumptions are deemed appropriate for the high emissions scenario.

### 25 of 129 15. Only emissions from the upgrading of bitumen to syncrude are affected by the use of carbon capture on the hydrogen production process. The re sults for the high emissions scenario of surface and in situ production of bitumen are shown with and without CCS in Table 18.

Table 17 : Life cycle GHG emissions for jet fuel from Canadian oil sands using surface

- mining and in

- situ

recovery

Low Baseline High

Surface In situ Surface In situ Surface In situ Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 14.6 19.8 16.5 23.9 30.1 54.4 Transportation of feedstock (gCO 2 /MJ) 1.3 1.3 1.3 1.4 1.5 1.7 Processing of feedstock to fuel (gCO 2 /MJ) 5.5 5.5 5.5 5.5 5.5 5.5 Transportation of jet fuel (gCO 2 /MJ) 0.5 0.5 0.5 0.6 0.6 0.7

Combustion CO 2 (gCO 2 /MJ) 73.2 73.2 73.2 73.2 73.2 73.2 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 21.9 27.1 23.8 31.4 37.6 62.3

WTT CH 4 emissions (gCO 2 e/MJ) 2.7 3.1 2.7 3.5 2.5 3.3

WTT N 2 O emissions (gCO 2 e/MJ) 0.1 0.1 0.1 0.1 0.1 0.2 Total WTW GHG emissions (gCO 2 e/MJ) 97.9 103.6 99.8 108.2 113.5 139.0 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 1.12 1.18 1.14 1.24 1.30 1.59

Table 18 : Life cycle GHG emissions for the high emissions scenario of jet fuel from Canadian oil sands with and without carbon capture and sequestration of emissions from asphaltene gasification

Surface Mining In situ recov e ry High Emissions Scenario: w/o

CCS w/ CCS w/o

CCS w/ CCS

Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 30.1 19.4 54.4 43.8 Transportation of feedstock (gCO 2 /MJ) 1.5 1.3 1.7 1.6 Processing of feedstock to fuel (gCO 2 /MJ) 5.5 5.5 5.5 5.5

Transportation of jet fuel (gCO 2 /MJ) 0.6 0.5 0.7 0.7

Combustion CO 2 (gCO 2 /MJ) 73.2 73.2 73.2 73.2 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 37.6 26.7 62.3 51.5

WTT CH 4 emissions (gCO 2 e/MJ) 2.5 2.5 3.3 3.3

WTT N 2 O emissions (gCO 2 e/MJ) 0.1 0.1 0.2 0.2

Total WTW GHG emissions (gCO 2 e/MJ) 113.5 102.6 139.0 128.2

Life Cycle GHG Emissions Relative

to Baseline Conventional Jet Fuel 1.30 1.17 1.59 1.47

In developing a single value for each of the scenarios, the proportion of bitumen extracted through surface

- mined oil sands versus that of bitumen extracted through in

- situ production for processing into jet fuel was varied. Specifically, the low case assu mes that all bitumen was

extracted from surface

- mined oil sands and the high case assumes all bitumen was extracted entirely through in

- situ processes. The Alberta Energy and Utilities Board (AEUB) forecast that in 2015, about 56.5% of crude bitumen produc tion will come from surface mining and the remaining
43.5% from in

- situ production (AEUB, 2007). These proportions were adopted in the baseline case. The inputs for the scenario analysis are summarized in Table 19.

### 26 of 129 Table 19 : Assump tions regarding the use of surface and in situ bitumen recovery in the low emissions, baseline, and high emissions scenarios of jet fuel from Canadian oil sands

Low Baseline High Proportion of bitumen used in jet fuel production from surface

- mining processes 100% 56.5% 0%

Proportion of bitumen used in jet fuel production from in

- situ processes 0% 43.5% 100%

The corresponding life cycle GHG emissions from producing jet fuel from Canadian oil sands are

shown in Table 20. Recall that the low emissions value represents the low case for surface mining while the high emissions value represents the high case for in

- situ recovery.

Table 20 : Summary of result for jet fuel from Canadian oil sands

Low Baseline High Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 14.6 19.7 54.4 Transportation of feedstock (gCO 2 /MJ) 1.3 1.3 1.7 Processing of feedstock to fuel (gCO 2 /MJ) 5.5 5.5 5.5 Transportation of jet fuel (gCO 2 /MJ) 0.5 0.5 0.7

Combustion CO 2 (gCO 2 /MJ) 73.2 73.2 73.2 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 21.9 27.1 62.3

WTT CH 4 emissions (gCO 2 e/MJ) 2.7 3.0 3.3

WTT N 2 O emissions (gCO 2 e/MJ) 0.1 0.1 0.2 Total WTW GHG emissions (gCO 2 e/MJ) 97.9 103.4 139.0 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 1.12 1.18 1.59

The life cycle GHG emissions of the production and use of jet fuel from Canadian oil sands range from 1.12 to 1.59 times higher than those from the production and use of conventional jet fuel.

This analysis has only considered the use of natural gas as a process fuel and source of hydrogen. Although natural gas is currently the main source of energy for oil sands production, its use may not be sustainable in the long term. To reduce dependence o n natural gas, coal and asphaltene (a bitumen residue) were considered as an alternative energy sources. Though abundant, the use of coal and bitumen upgrading residues can result in greater GHG emissions compared to the use of natural gas. Specifically, u sing coal energy to power bitumen production and asphaltene gasification to provide hydrogen for bitumen upgrading, the life cycle GHG

emissions from surface

- mining and in

- situ production are 1.3 times and 1.6 times greater than those of conventional jet f uel, respectively. Even when carbon capture is used to reduce emissions from the gasification of asphaltene, the life cycle GHG emissions from surface

- mining

and in

- situ production are still about 1.2 times and 1.5 times greater than those of conventional jet fuel, respectively.



### 5.2 Conventional Jet Fuel from Oil Shale

Oil shale is a sedimentary rock formation of mostly carbonate and silicate minerals containing a solid organic material called kerogen. This analysis of jet fuel production from oil shale is base d on the Shell in situ conversion process (ICP), in which oil shale is heated while still in the earth using electrically heated wells for a prolonged period (two to three years). The heat releases a combination shale oil and shale gas from the kerogen in the oil shale. 16 Shale oil is a light crude

16 Note that oil shale is the geologic rock formation that contains kerogen, whereas shale oil is the light crude that is

released from the oil shale.

### 27 of 129 composed largely of middle distillates while shale gas is mixture of natural gas, propane and butane. The yield relationship between the two products is estimated as two

- thirds shale oil and

one

- third shale gas on an energy basis. The area surrounding the extraction site is frozen to form an impermeable barrier, preventing groundwater from disturbing the heating process and preventing products from escaping (Bartis et al., 2005; Mut, 2005).

Ex situ conversion is the alternative to the Shell ICP. Ex situ heating of the kerogen occurs above ground and requires the shale to be mined before it is processed (Brandt, 2008). The conversion of oil shale to shale oil and shale gas on the surface takes place at higher tempe ratures than the in situ technique. The higher temperatures could result in the decomposition of the carbonate minerals contained within the oil shale, potentially releasing considerably more CO 2 than the Shell ICP process. 17

5.2.1 Analysis Methodology
The pathway for the extraction of shale oil from oil shale using the Shell ICP is not available in GREET and was analyzed based on incorporating information available from the literature into the GREET framework. Specifically, the process energy for the pr oduction of shale oil from oil shale, as well as the yield of oil and gas products using the Shell ICP, were adopted from Brandt’s analysis of the Green River Formation in Colorado, Utah, and Wyoming (Brandt, 2008). This analysis was specifically focused o n the Green River Formation; however, many other oil shale resources are dispersed around the world. 18

Only major process energy demands, namely the in situ heating energy and the energy to maintain the frozen wall (about one order of magnitude less than the in situ heating energy) were

considered. 19 The effects of different amounts and sources of in

- situ heating energy on the overall GHG emissions of this pathway were explored with the low emissions, baseline and high

emissions scenarios. The low and basel ine cases assumed that 25% of the in situ heating energy needed was provided through the recycling of waste heat (as was assumed in the low carbon case in Brandt’s analysis), while no recycling of waste heat was assumed in the high case. As a large amount of electrical energy is needed to provide in

- situ heating for the Shell ICP, dedicated

coal

- based power generation facilities were assumed to be near the extraction site due to the vast abundance of coal resources in the Green River Formation. The locality of electricity was
assumed to reduce transmission losses to 5% from the GREET default value of 8%. For the low case, the use of coal IGCC electricity with carbon capture (90% efficiency) was assumed; 20 while the baseline and high cases employ traditional p ulverized coal

- fired electricity without carbon capture.

Brandt (2008) provided low and high estimates of the electrical energy required for in situ heating, the amount of shale oil output and the amount of shale gas co

- produced. There is no natural gas consumption within the process; therefore, energy and emission credits were given to the natural

17 Typical oil shale from the Green R iver Formation is composed of 23% dolomite (calcium

- magnesium bicarbonate) and 16% calcite (calcium carbonate). Surface conversion processes require up to 750°C while the decomposition of dolomite and calcite occur around 575°C and 650°C, respectively. The Shell ICP process takes place at temperatures between 340°C and 400°C. Because of this increased temperature, production of shale oil using surface retorting could result in between 1.2 to 3 times more GHG emissions than if the production had been perform ed using the Shell ICP process. (Hileman et al., 2009)

18 There are known oil shale formations in Australia, Brazil, Canada, China, Estonia, Israel, Jordan, Morocco, Russia, Sweden, Syria, Thailand, Turkey and the United States (Geology.com, 2009)

19 Other m inor process energy requirements like drilling and pumping energy (< 1% of retorting energy), as well as energy needed for infrastructural construction, are ignored in this study.

20 From Deutch and Moniz (2007), the estimated efficiency of an IGCC plant wi th 90% carbon capture is 31.2% (HHV) or about 34% (31.2+3) on a LHV basis. The efficiency of 34% (LHV) was adopted in this study.

### 28 of 129 gas co

- produced using the displacement method. 21 The low emissions scenario used the low value for in situ heating energy with high values for shale oil and sha le gas production; the baseline scenario used mean values; and the high emissions scenario used high value for in situ heating energy with low values for shale oil and shale gas production. In addition, shale oil is much lighter and contains almost no heav y ends compared to traditional crude oil; hence, the refining efficiency of processing shale oil to jet fuel was assumed to be higher (~2

- 3%) than that of refining traditional crude oil. 22

The key processes and assumptions involved in the production of jet fuel from oil shale using the

Shell ICP are summarized in Table 21.

Table 21 : Input assumptions for the production of jet fuel from oil shale for low emissions, baseline and high

emissions cases

Low Baseline High

Process conditions Use 25% recycled heat for retorting; use coal IGCC electricity

with CCS; capture efficiency of 90% Use 25% recycled heat for retorting;
use 100% coal

fired electricity; no

carbon capture No recycled heat used; 100%

coal

- fired electricity; no

car bon capture Electrical energy input (J/MJ shale oil produced) 134600 148100 211900

Natural gas co

- produced (J/MJ shale oil produced) (LHV) 223700 189600 152500

Refining efficiency (LHV) 96% 96% 96%

5.2.2 Oil Shale to Jet Fuel Results

The GHG emissions from the production and use of jet fuel from oil shale using the Shell ICP are

shown in Table 22.

Table 22 : Summary of results for jet fuel from in situ oil shale pathway

Low Baseline High Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 3.6 41.2 59.7 Transportation of feedstock (gCO 2 /MJ) 0.5 0.6 0.7 Processing of feedstock to fuel (gCO 2 /MJ) 3.3 3.3 3.3 Transportation of jet fuel (gCO 2 /MJ) 0.5 0.6 0.7

Combustion CO 2 (gCO 2 /MJ) 73.2 73.2 73.2 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 7.8 45.8 64.4

WTT CH 4 emissions (gCO 2 e/MJ) 2.4 2.5 3.2

WTT N 2 O emissions (gCO 2 e/MJ) 0.6 0.2 0.2 Total WTW GHG emissions (gCO 2 e/MJ) 84.1 121.6 141.0 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.96 1.39 1.61

21 The displacement (system expansion) method was used to account for energy and emission credits to natural gas, i.e.,

the na tural gas produced was assumed to displace the recovery and processing of conventional petroleum based natural gas in a separate, independent facility.

22 Jim Bartis, interview with Hsin Min Wong, July 16, 2007.

### 29 of 129 With carbon capture (low emissions case), the life cycle GHG emissions of jet fuel from oil shale are reduced to slightly less than baseline conventional jet fuel. Without the capture of carbon

dioxide from coal

- based electricity plants providing the in si tu heating energy, the production of jet fuel from oil shale produces life cycle GHG emissions 1.4 to 1.6 times greater than baseline conventional jet fuel.

In addition to increased GHG emissions, oil shale development also presents other adverse impacts to the environment. Though less intrusive to the surface topography than ex situ processes and not requiring the disposal of spent shale, in

- situ conversion will still cause displacement of all other land uses in the area and disruptions to the local ecol ogical community (Bartis et al., 2005). In situ methods also have the potential to cause ground water contamination. Though the freeze wall protects groundwater during production, contamination may occur post

production. As the Green River formation lies w ithin the Colorado River drainage basin, water contamination could impact millions of downstream users (Gruenwald, 2006).

### 30 of 129 

# 6 Fischer

- Tropsch Jet Fuel

The Fischer Tropsch (F

- T) process first involves the steam reforming or gasification of any carbon

contain ing feedstock (e.g. natural gas, coal or biomass) to synthesis gas (syngas), which is a mixture of hydrogen and carbon monoxide . The syngas is subsequently converted to paraffinic hydrocarbons in the presence of an iron

- or cobalt

- based catalyst (Fischer

- Tropsch synthesis). A third upgrading step cracks the longer hydrocarbon chains to maximize the production of synthetic paraffinic liquid fuels like diesel and jet fue l. Syngas must be cleaned before Fischer

Tropsch synthesis step to remove contaminants, particularly sulfur, to avoid poisoning the catalyst. Hence, the resultant Fischer

- Tropsch liquid fuels are virtually free of contaminants and the jet fuel fraction of the product slate falls into the category of synthetic paraffinic fuels.

All jet fuels produced using F

- T synthesis have similar characteristics, independent of feedstock type. Any small variations in fuel properties are primarily associated with the ope rating conditions (e.g., catalyst, temperature, and pressure) within the synthesis reactors and how the direct products of the synthesis are treated and processed. All jet fuels produced using the F

- T process share common characteristics with regard to com patibility with existing infrastructure and aircraft,

combustion emissions, and their relative merit for use in aviation. Feedstock choice, however, does have a strong influence on fuel production capacity, production cost, life cycle greenhouse gas emissi ons, and technology readiness (Hileman et al., 2009).

Fischer

- Tropsch fuels created from natural gas, coal, biomass and combinations of coal and biomass were analyzed in this work. Gas

- to

- Liquid (GTL) production is currently limited to Malaysia where She ll has been producing approximately 15,000 bbl/day since 1993. Sasol as well as Shell in collaboration with Qatar Petroleum are both constructing GTL facilities in Qatar with design capacities of 34 ,000 bbl/day and 140,000 bbl/day, respectively. Existing c oal

- to

liquids (CTL) capacity is limited to Sasol in South Africa where a production capacity of 160,000 bbl/day of oil equivalent has been consistently maintained. There is no commercial scale production of F

- T fuels using biomass as the feedstock (BTL). This technology is still in the development phase; however, a German firm, CHOREN, began start

- up operations of a 300 bbl/day facility in 2008 and Solena Group, with Rentech, announced plans f o r a 1 ,800 bbl/day BTL facility located in Gilroy, California. E xperience with simultaneously gasifying a combination of coal and biomass in a single gasifier is presently limited to successful tests at an IGCC plant in the Netherlands (Hileman et al., 2009). The analysis of F

- T jet fuel from coal and natural gas was

c onducted using GREET version 1.8a while F

- T jet fuel from biomass and the combination of coal and biomass was considered using GREET version 1.8b.



### 6.1 

## Carbon Capture and Sequestration with Fischer

- Tropsch

Facilities

By comparison to conventional power plants, F

- T plants are well suited for the implementation of carbon capture and sequestration (CCS). This opportunity occurs because the F

- T process results in a relatively pure stream of CO 2 upstream of the F

- T synthesis step, whereas coal power plants produce fl ue gas, which must be scrubbed to obtain a pure CO 2 stream.

This work considered cases with CCS implemented on a gas

- to

- liquids plant, a coal

- to

- liquids plant and a coal and biomass

- to

- liquids plant. Carbon dioxide is already captured within F

- T plants a s part of the process; therefore, the only difference for standard CCS implementation is

the addition of CO 2 compression, transport and storage capital and operating costs (Tarka, 2009).

I t was assumed that the energy needed for CO 2 compression was provide d by electricity internally generated within the F

- T process. This results in a reduction of the overall process efficiency because some of the ene rgy from the feedstock is used to generate this additional energy. The electrical energy needed to c ompress t he captured carbon dioxide to a pipeline

### 31 of 129 ready pressure of about 15 MPa was assumed t o be 250 kWh/ton carbon 23 (GREET, 2008; Kreutz et al., 2008). The transportation of compressed carbon dioxide and the energy required for sequestration in a storage site we re not considered.

The amount of carbon available for capture is equal to the difference between the carbon present in the feedstock and the carbon present in the final products. The term carbon capture efficiency is used to define the percentage of avail able carbon that is actually captured. Capture efficiencies of 80% (Kreutz et al., 2008), 85% (SSEB, 2005) and 90% (Bartis et al., 2008; Deutch and Moniz, 2007; Tarka, 2009) were assumed for the high emissions, baseline and low emissions cases, respectivel y. Recent analyses at National Energy and Technology Laboratory (NETL) have indicated that capital expenditures associated with systems designed to capture CO 2 at the 80% level would not vary significantly from those capturing at the 90% level (Dilmore and Skone, 2009). A capture efficiency of 85% was chosen for the baseline case in this work to reflect the lack of commercial experience surrounding widespread implementation of CCS. Carbon emitted from the combustion of process fuels is not captured.



### 6.2 

## Fischer

- Tropsch Fuel from Natural Gas

The life cycle GHG emissions for the production of F

- T fuels using natural gas sourced outside North America were considered. The Gas to Liquids (GTL) pathway is available in GREET and

well

- documented based on current industr y data (Wang, 1999; Wang and Huang, 1999; Wang, 2002). Specifically, this analysis assumed non

- North American natural gas was supplied to an internationally located stand

- alone F

- T liquid fuels plant designed to maximize liquid fuels production (e.g. throu gh recycling of tail gas from F

- T reactors). Sufficient electrical energy was produced to fuel all internal processes, with negligible excess electricity produced for export. The process included the upgrading (hydroprocessing) of long

- chain liquids to a f inal product slate of diesel (C 18 ), jet fuel (C 12 ) and naphtha (C 4 - C 6 ). The energy allocation method was adopted for assigning energy and emissions to various liquid products. Default GREET assumptions were adopted regarding the transportation profiles of F

- T fuel from international plants to the US (5000 miles by ocean tanker plus domestic transpor t via pipeline, barge and rail).

Most studies in the literature focus on F

- T reactor designs and conditions that produce diesel and naphtha. To produce jet fuel instead of diesel, additional hydrocracking and greater syngas recycle are needed, resulting in a small increase in hydrogen and power requirements for the plant. Furthermore, a moderate decrease in the CO 2 associated to jet fuel compared to diesel

would ensue due to changes in the allocation fractions. As these additional energy requirements

do not lead to substantial increases in CO 2 emissions from the facility (Gray et al., 2007), they were ignored in this analysis, (i.e. the production of F

- T jet fuel is assumed to have the same emissions as the production of F

- T diesel). This assumption applies to all of the F

- T jet fuel pathways analyzed in this study, namely the production of F

- T jet fuel from natural gas, coal and biomass. Although F

- T jet fuel can be made without added burdens, it is not possible to have a product slate of 100% F

- T jet fuel 24 (a value of 25% is taken as the preferred value in these studies, and a sensitivity study is shown for a co

- fed coal and biomass F

- T facility).

The F

- T proces s efficiency is a key parameter affecting the life cycle GHG emissions of the production of F

- T jet fuel from natural gas. Equation 4 defines process efficiency for a general F

- T facility. GTL process efficiencies from the literature vary from 60 to 65% (E dwards et al., 2007;

23 Kreutz et al. assume 90.5 kWh per tonne of CO 2 , which converts to 300 kWh per ton of carbon. GREET default value in 2010 is 300 kWh per ton of carbon but an improved efficiency of 250 kWh per ton of carbon is assumed for 2015.

24 An F

- T plant configured to produce 70% diesel and 30% naphtha should t heoretically be able to undergo modifications such that it could yield 60% jet fuel and 40% naphtha (Gray et al., 2007). Sasol is developing the ability to produce a joint Battlefield

- Use Fuel of the Future (BUFF) using F

- T synthesis

. This fuel could be used in place of JP

- 8 in military aircraft and they report a yield of ~30% that conforms to the freezing point standards of JP

- 8. The rest of the product slate is composed of “heavy” diesel and naphtha (Lamprecht, 2007).

### 32 of 129 Green Car Congress, 2006a; Wang, 2002). The low emissions, baseli n e and high emission scenarios assumed LHV process efficiencies of 65%, 63% and 60%, respectiv e ly.



### € Process Efficiency = 1 MJ fuel 

### 1 MJ feedstock 

### + Process Energy Equ ation 4

The important assumptions and corresponding life cycle GHG emissions in the production and
use of F

- T jet fuel from natural gas are shown in Table 23.

Table 23 : Summary of inputs and results for F

- T jet fuel from natural gas pathway

Low Baseline High Key Assumptions Process efficiency (LHV) 65% 63% 60% Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 4.6 4.6 4.6 Transportation of feedstock (gCO 2 /MJ) 0.0 0.0 0.0 Processing of feedstock to fuel (gCO 2 /MJ) 19.4 20.2 21.4 Transportation of jet fuel (gCO 2 /MJ) 1.2 1.2 1.2

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 25.2 26.0 27.2

WTT CH 4 emissions (gCO 2 e/MJ) 4.4 4.6 4.8

WTT N 2 O emissions (gCO 2 e/MJ) 0.0 0.0 0.0 Total WTW GHG emissions (gCO 2 e/MJ) 100.1 101.0 102.4 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 1.14 1.15 1.17

The life cycle GHG emissions of the production and use of F

- T jet fuel made from natural gas range from 1.14 times to 1.17 times higher than those from conventional petroleum

- based jet fuel.

6.2.1 Case Study: Impact of Carbon Capture on GHG Emissions from GTL Facilities

A distinguishing factor of GTL facilities is the reduced CO 2 emitted as compared to CTL and BT L. From the perspective of the operator this could potentially lead to higher capture costs per ton of carbon; hence the discussion of CCS with a GTL plant is often overlooked. Nonetheless, this work explored the effect of capturing CO 2 produced from GTL p lants in the baseline scenario configuration. A capture efficiency of 85% was assumed for consistency with the baseline assumptions in the other F

- T pathways.

A comparison of results from the baseline scenario with and without CCS is given in Table 24.

CC S could reduce the processing GHG emissions of F

- T jet fuel from natural gas by 15 gCO 2 /MJ, such that the WTT emissions from the GTL pathway become comparable to those from conventional jet fuel. As is the case with all fossil based fuels, combustion emiss ions dominate the life cycle GHG inventory, making it difficult to impr ove upon conventional jet fuel.

### 33 of 129 Table 24 : Life cycle GHG emissions from natural gas F

- T jet fuel with and without carbon capture

Baseline Baseline with

CCS Key Assumptions Carbon capture efficiency

-- 85%

Process Efficiency (LHV) 63% 62.7% (63% without CCS) Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 4.6 4.6 Transportation of feedstock (gCO 2 /MJ) 0.0 0.0 Processing of feedstock to fuel (gCO 2 /MJ) 20.2 5.4 Transportation of jet fuel (gCO 2 /MJ) 1.2 1.2

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 26.0 11.2

WTT CH 4 emissions (gCO 2 e/MJ) 4.6 4.6

WTT N 2 O emissions (gCO 2 e/MJ) 0.0 0.0 Total WTW GHG emissions (gCO 2 e/MJ) 101.0 86.2 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 1.15 0.99



### 6.3 Fischer

- Tropsch Fuel from Coal

This examination of life cycle GHG performance of F

- T jet fuel production from coal was conducted for scenarios both with and without carbon capture and sequestration. Similarly to GTL plants, a stand

- alone F

- T liquid fuels plant designed to maximize liquid fuels production with no excess electricity was examined. The upgrading (hydroprocessi ng) of long

- chain liquid products to desired fuel products was also included. Energy allocation was used to assign energy and emissions among liquid fuel products.

6.3.1 CTL without Carbon Capture

Process efficiency and coal type were judged to be key parameters having a significant impact on the overall GHG emissions of the Coal to Liquids (CTL) pathway. Both bituminous and sub

bituminous coal based processes were examined. 25 In 2007, almost 65% of bituminous coal production in the U.S. occurred via underground m ining while the remainder was surface

- mined. On the other hand, sub

- bituminous coals are almost exclusively surface

- mined. (EIA, 2008b) Underground mining results in substantially greater methane emissions compared to surface mining processes. Methane emis sions arising from the production of underground

- mined and

surface

- mined coals were estimated using aggregate coal mining methane emissions data (EIA, 2007; Bartis et al., 2008). 26

25 Lignite coal can possibly be used as a feedstock to CTL plants but lignite production in the US is much lower (~7% of total coal production in 2007, EIA 2008c) compared to bituminous and sub

- bituminous coal production (46.7% and 46.3% of total coal production in 2007, respectively, EIA 2008c). Reliable data on methane emissions associated with the mining of lignite coal are not available. For these reasons, lignite coal is not analyzed in this work.

26 Bartis et al., 2008 estimated methane emissions of 338 pounds of carb on dioxide equivalent per ton of underground

mined coal and methane emissions of 42.4 pounds of carbon dioxide equivalent per ton of surface

- mined coal based on EIA data (EIA, 2008b). The methane emissions per MJ of coal production were calculated from the lower heating values

of bituminous coal and sub

- bituminous coal.

### 34 of 129 The low emissions case assumed a lower heating value process efficiency of 53% with surface

mined bituminous coal. 27 In the baseline case, a process efficiency of 50% was used (Bartis et al., 2008; Deutch and Moniz, 2007; Van Bibber et al., 2007; Marano and Ciferno, 2002) with the 2007 average US coal mix of underground

- mined and surface

- mined bituminous coal and

surface

- mined sub

- bituminous coal 28 ; the use of anthracite or lignite coal was not considered in this analysis. The high emissions case assumed a process efficiency of 47% and underground

mined bituminous coal (based on ca se 1 of Southern States Energy Board CTL study, SSEB, 2005). For comparison, the first Sasol CTL plant built in the 1950s had process efficiencies under 40% (UK DTI, 1999; Gray and Tomlinson, 2001).

The inputs assumed for the three scenarios are summarize d in Table 25.

Table 25 : Input assumptions for the production of F

- T jet fuel from coal (without carbon capture) for low emissions, baseline and high emissions cases

Low Baseline High Process efficiency (LHV) 53% 50% 47%

Coal input Surface

- mined

bituminous coal Average

bituminous and

sub

- bituminous

coal mix 29 Underground

mined bituminous

coal

Coal mining methane emissions (g CO 2 e/MJ coal) 0.80 2.8 6.4

The life cycle GHG emissions from the production and use of F

- T jet fuel from a CTL plant without carbon capture and sequestration are shown in Table 26.

Table 26 : Summary of results for F

- T jet fuel from coal pathway (without carbon capture)

Low Baseline High Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 0.8 0.8 0.8 Transportation of feedstock (gCO 2 /MJ) 0.1 0.1 0.0 Processing of feedstock to fuel (gCO 2 /MJ) 100.5 117.2 122.4 Transportation of jet fuel (gCO 2 /MJ) 0.6 0.6 0.6

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 102.1 118.8 123.9

WTT CH 4 emissions (gCO 2 e/MJ) 1.5 5.7 13.7

WTT N 2 O emissions (gCO 2 e/MJ) 0.0 0.0 0.0 Total WTW GHG emissions (gCO 2 e/MJ) 174.0 194.9 208.0 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 1.99 2.23 2.38

27 Thomas Tarka, interview with David Ortiz, October 3, 2008.

28 Compared to 2007, the coal production mix in 2017 is projected to comprise a larger proportion of surface

- mined sub

bituminous coal from Western coal production, particularly the Powder River Basin. (EIA, 2008d)

29 From EIA (2008b) about 49% of total bitum inous and sub

- bituminous coal production in 2007 is made up of surface

mined sub

- bituminous coal, 18% is made up of surface

- mined bituminous coal, and the remaining 33% is made up of underground

- mined bituminous coal. Coal methane emissions, lower heating value and carbon content of the average coal mix used in the baseline case are based on weighted average values.

### 35 of 129 6.3.2 CTL with Carbon Capture

As discussed in Section 6.1, carbon capture efficiencies of 80%, 85% and 90% were assumed for the high emissions, baseline and low emissions cases, respectively. These input assumptions are

summarized in Table 27.

Table 27 : Input assumptions for the production of F

- T jet fuel from coal (with carbon capture) for low emissions, baseline and high emissions cases

Low Baseline High Carbon compression

energy 250 kWh/ton C 250 kWh/ton C 250 kWh/ton C

Process efficiency (LHV) 51.3% (53% without CCS) 48.4% (50% without CCS) 45.6% (47% without CCS)

Coal input Surface

- mined

bituminous coal Average

bituminous and

sub

- bituminous

coal mix Underground

mined bituminous

coal

Carbon capture efficiency 90% 85% 80%

The life cycle GHG emissions of the production and use of F

- T jet fuel from a CTL plant with carbon capture and sequestration are shown in Table 28.

Table 28 : Summary of results for F

- T jet fuel from coal pathway (with carbon capture)

Low Baseline High Life Cycle CO 2 Emissions by Stage Recovery of feedstock (gCO 2 /MJ) 0.8 0.8 0.8 Transportation of feedstock (gCO 2 /MJ) 0.1 0.1 0.1 Processing of feedstock to fuel (gCO 2 /MJ) 11.4 19.4 26.6 Transportation of jet fuel (gCO 2 /MJ) 0.6 0.6 0.6

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ) 12.9 20.9 28.1

WTT CH 4 emissions (gCO 2 e/MJ) 1.6 5.9 14.1

WTT N 2 O emissions (gCO 2 e/MJ) 0.0 0.0 0.0 Total WTW GHG emissions (gCO 2 e/MJ) 84.9 97.2 112.6 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.97 1.11 1.29

The production of F

- T jet fuel from a CTL plant without carbon capture results in life cycle GHG

emissions that are between 2.0 (low emissions case) to 2.4 (high emissions case) times higher than conventional jet fuel. When carbon sequestration is implemented, the life cycle GHG emissions relative to those of conventional jet fuel range from almost 1.0 to 1.3. The use o f carbon capture and sequestration can allow for an approximate 50% reduction in GHG emissions

for a coal

- to

- liquids facility.



### 6.4 Fischer

- Tropsch Fuel from Biomass

A self

- sufficient BTL plant was assumed for the GHG analysis presented here. This assumes that biomass feedstock is used to meet internal process energy needs, with little or no excess electricity produced for export. Similar to the other F

- T plants analyz ed above, the BTL plant was assumed to produce commercial quality liquid fuels like diesel and jet fuel, and the energy allocation method was adopted for assigning energy and emissions between liquid fuel products.

### 36 of 129 The biomass feedstocks examined include waste biomass (e.g. forest residue, agricultural residue) and non

- food energy crops (e .g. herbaceous biomass) that were assumed to be grown on land that would not incur adverse direct or indirect land use change emissions (e.g. idle or abandoned cropland) . Specifically, three types of biomass feedstocks were considered: switchgrass, corn stover and forest residue. Switchgrass was considered as the primary biomass feedstock in all cases of the scenario analysis (low emissions, baseline and high emissions)

b ecause of its better yiel d and improved scalability relative to waste products. Furthermore, if waste products were used on a large scale then they would become a market commodity, as they are no longer waste. Such a change in classification leads to questions as to where system boundaries should be drawn with respect to the crop that initially produced the waste. Such issues were not considered in the scope of this report.

6.4.1 Switchgrass

Switchgrass is a perennial warm season grass native to North America, found in remnant prairies

, native grass pastures , and naturalized along roadsides. Other forms of herb aceous biomass include mixed prairie grasses, wheat, hay and leaves, among others. As a replacement for annual crops, warm season grasses have also been shown to provide important habitat for wildlife, including game birds and other species threatened by t he loss of tall grass prairie habitat (McLaughlin et al., 2002). The assumptions regarding the yield, energy and emissions associated with switchgrass cultivation were based on a survey of existing cultivation data from the literature.

Also considered is t he potential for long term changes in carbon contained within the soil on which the switchgrass is grown. The greatest potential for long

- term soil carbon sequestration is in those situations where agricultural practices have led to a progressive and histo rical decline in soil carbon stocks (McLaughlin el al., 2002). The properties (e.g. lower heating value, carbon content) of switchgrass used in this work are reported in Table 97

6.4.1.1 Switchgrass Yield And Cultivation Trends
The approach used in this work is s imilar to that taken by the National Academies in their 2009 report on Liquid Fuels from Coal and Biomass. Figure 8 shows a distribution of annual switchgrass yields taken from Gunderson et al. (2008) that were used to establish predictive maps of potentia l yields across the continental United States. The data set comprises approximately 1400 observations with a mean of approximately 4.9 tons/acre/yr. For each data point, the specific cultivar, crop management information, ecotype, precipitation and tempera ture in the long

- term climate record were documented. Using their model, Gunderson et al. (2008) predicted yields in excess of 8.9 tons/acre/yr for lowland ecotypes 30 in the Appalachian region

and 5.4

- 6.2 tons/acre/yr in the Nebraska/South Dakota region. Si milarly, yields for upland ecotypes in the Appalachian region were predicted to be greater than 6.2 tons/acre/yr and 3.1

- 4.5 tons/acre/yr in the Nebraska/South Dakota region. Gunderson et al. (2008) openly discuss that their model predict s the theoretical maximum yield for a given set of input conditions; hence, experimental yields for these regions will most likely be lower in practice.

Other studies have focused on establishing estimates for a national average yield. Heaton et al. (2004) found an average switchgrass yield of 4.6 tons/acre/yr (+/

- 0.3 tons/acre/yr) and McLaughlin et al. (2002) projected a national average annual yield of 4.2 tons/acre/yr. Vadas et al. (2008) adopted a nominal yield of 4.0 tons/acre/yr and an optimistic yield of 5.8 tons/acre/yr based on data from large field plots in southern Wisconsin while Adler et al. (2007) simulated switchgrass production in Pennsylva nia as 4.3 tons/acre/yr using DAYCENT. 31 Finally, the GREET herbaceous biomass production pathway assumes a yield of 6.0 tons/acre/yr.

30 Lowland and upland ecotypes are defined by position relative to the level where water flows or where flooding occurs

31 DAYCENT is the daily t ime

- step version of the CENTURY biogeochemical model. From weather (daily maximum and minimum air temperature, precipitation), soil

- texture class, and land

- use inputs, DAYCENT simulates fluxes of carbon (C) and nitrogen (N) between the atmosphere, vegetati on, and soil while predicting crop production, soil organic

- matter changes, and trace

- gas fluxes.

### 37 of 129 Figure 8 : Distribution of reported switchgrass yields across the United States (data from Gun derson et al., 2008)

While numerous studies have estimated the potential yield of switchgrass, many of these results are based on small plots (less than 5m 2 ) and the results are not necessarily indicative of what can be expected of farm

- scale production (National Academies, 2009). Schmer et al. (2008) managed switchgrass as a biomass energy crop in field trials of 7.4 to 22.2 acres on marginal cropland from 10 farms across a wide precipitation and temperature gradient in the mid

- continental US.

The actual farm

- scale production resulted in harvested yields about 35 to 50 percent lower than
those of small

- scale plots. It is possible that the lower yields from large

- scale production can be attributed to farmers’ inexperience with the cropping system or differ ences in cropland quality; however, farmers worked closely with the researchers in collecting this data and the land had been in active crop production before being converted to switchgrass production (National Academies, 2009). Actual yield data from Schm er et al. (2008) ranged from 2.3 to 5.0 tons/acre/yr with a mean of 3.2 tons/acre/yr.

In their analysis, Gunderson et al. (2008) concluded that switchgrass yield is most influenced by ecotype (upland or lowland) and the relationship of precipitation and t emperature. Lower yields

were attributed to factors that were not quantified across the data set, such as soil pH, inherent soil fertility, total solar radiation (vs. long periods of cloud cover) and others that are artifacts of each individual growing sit e and cannot be aggregated for a generalized result. The specific rate of nitrogen application was found not to have a significant influence on yield. Very high levels of fertilization certainly did not guarantee increased biomass production, and in many c ases, cases the zero fertilizer plantings did as well as any fertilized stands. Based on these conclusions, the yields adopted for the low emissions, baseline and high emissions scenarios (as shown in Table 29) were assumed to be independent input paramete rs from other cultivation inputs (on a per ton basis).

Table 29 : Switchgrass yields assumed in the low, baseline and high emissions scenarios

Low 1 Baseline 2 High 3

Yield (tons/acre/yr) 5.8 4.6 3.2

Notes: 1) Optimistic yield from Vadas et al. (2008) based on large field plots in

southern Wisconsin 2) Projected national average from McLaughlin et al. (2002) 3) Average farm

- scale yield from Schmer et al. (2008) based on mid

continental US

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### 38 of 129 6.4.1.2 Cultivation and Transportation of Switchgrass The key inputs for switchgrass production are the process fuels and electricity used in farming, fertilizer inputs and herbicide usage. These parameters have been identified in several studies but are the subject of much uncer tainty. Although it was determined that these inputs do not have a substantial impact on yield, they are essential for estimating the GHG emissions associated with switchgrass production. A summary of the ava ilable data is given in Table 30

.

These data s how considerable variation in the application rates of non

- nitrogen fertilizers and herbicides. The phosphorous and potassium application rates quoted by Vadas et al. (2008) are an order of magnitude larger than those given by Adler, which are in turn an o rder of magnitude larger than the GREET default values.

Using the same arguments, which were previously made in considering yield as an independent parameter, the process fuel usage, nitrogen fertilizer application and other fertilizer and herbicide appl ication were decoupled from their respective data sets. Hence, they were also considered as independent parameters for the purposes of the scenario analysis. The input parameters used for the low emissions, baseline and high emissions scenario are outlined in Table 31. The GREET default parameters were not used at all in this work because they are a decade older than the other results and they are not consistent with t he 2015 timeframe of this study.

Default GREET transportation and distribution assumption s were adopted for switchgrass. Specifically that bailed switchgrass is transported 40 miles by truck from the field to an F

- T processing facility in loads of 24 to ns.

Table 30 : Reported cultivation inputs for switchgrass

Adler et al.

( 2007) 1 Vadas et al. (2008) 2 Schmer et al. (2008) 3 GREET (2008) 4

Process Fuels (Btu/ton)

Diesel 82874 113046 107533 201589

Gasoline 0 22609 0 0 Electricity 0 7536 0 15641 Crop Management (g/ton) Nitrogen 5218 11348 7701 10635

P 2 O 5 1236 10387 0 142

K 2 O 2488 24607 0 226

Limestone 9491 0 0 0

Herbicides 6.4 0 185 28

Notes: 1) Actual fertilizer application rates were only given for nitrogen. All others were given in terms of CO 2 e with application rates calculated using production

emissions from GREET 1.8b. 2) Actual phosphorous and potassium usage was given in terms of elemental weight and converted to P 2 O 5 and K 2 O using molar mass fractions. Lube oil consumed is expressed in terms of diesel equivalent on an energy basis. 3) Data is the average of 10 field scale plots in Nebraska, North Dakota and South

Dakota. 4) From Oak Ridge National Laboratory in 1998.

### 39 of 129 Table 31 : Cultivation inputs for switchgrass in the low, baseline and high emissions scenarios

Low Baseline High

Process Fuels (Btu/ton)

Diesel 82874 107533 113046

Gasoline 0 0 22609 Electricity 0 0 7536 Nitrogen Fertilizer (g/ton) 5218 7701 11348

Other Fertilizers (g/ton)

P 2 O 5 0 1236 10387

K 2 O 0 2488 24607

Limestone 0 9491 0

Herbicides 185 6.4 0

6.4.1.3 Nitrous Oxide Emissions Nitrous oxide emissions can either be estimated using specialty software or through simple IPCC emissions factors. Estimates from Adler et al. (2007) using DAYCENT included both direct

emissions of N 2 O through nitrification and denitrification processes in the soil and indirect

emissions of N 2 O through soil nitrogen losses in forms other than N 2 O (e.g. NO X , NH 3 , NO 3 ), which were subsequently converted to N 2 O elsewhere. Conversely, the GREET method employs

the IPCC 2006 conversion factor for direct and indirect N 2 O emissions from switchgrass production, as shown in using Equation 5.

€ N 2 O Emissions g N 2 O

ton "

# $ %

& '

= g nitrogen fertilizer

ton "

# $ %

& '

⋅ 0.01325

⋅ 44 g N 2 O 28g N "

# $ %

& ' Equation 5

The N 2 O emissions from Adler et al. (2007) were found to be 43% higher than those predicted by Equation 5 from the same application rate. The principle reason for this discrepancy is that

a nnual nitrogen in crop residues (abo ve

- ground and below

- ground) was not included

. Estimates of the nitrogen deposited on the soil in the form of crop residues was obtained u sing the IPCC Tier 1 methodology for perennial gras ses (IPCC, 2006) and implemented through Equation 6.

€ N 2 O Emissions g N 2 O

ton "

# $ %

& '

= g nitrogen fertilizer

ton + 6025 "

# $ %

& '

⋅ 0.01 direct + g nitrogen fertilizer

ton "

# $ %

& '

⋅ 0.001 indirect "

# $ %

& '

⋅ 44 g N 2 O 28g N "

# $ %

& ' Equation 6

where 6025 is the nitrogen in crop residues, 0.01 direct is the emissions factor for N 2 O from nitrogen

and 0.0 0 1 indirect is the emissions factor for volatilized NH 3 and NO
x from synthetic nitrogen

converted to N 2 O. T he N 2 O emissions as calculated using the new IPCC methodology are within 2% of those estimated by Adler et al. (2007) for the same nitrogen application rate. As such, Equation 6 was used for all N 2 O calculations within the switchgrass production pathway.

6.4.1.4 Long Term Soil Carbon Sequestration from Switchgrass

Soil carbon sequestration is a potential strategy for offsetting CO 2 emissions to the atmosph ere. The capacity of perennial energy crops to offset CO 2 emissions through soil carbon sequestration depends on the rate of soil carbon additions, the long

- term capacity of soil for carbon storage, and the stability of sequestered soil carbon over time. The greatest potential for long

- term soil carbon sequestrat ion is in those situations where agricultural practices have led to a progressive and historical decline in soil carbon stocks (McLaughlin el al., 2002). Two land use change scenarios were considered for switchgrass production. The first is switchgrass gro wn on Conservation Reserve Program (CRP) land and second is switchgrass grown on carbon

- depleted soils. The carbon contained within soil can become depleted over time as a result of farming traditional row crops with conventional tilling practices. The CRP compensates farmers for removing lands from crop production for environmental and economic reasons (National Academies, 2009). After stopping crop production, the land naturally re

- establishes its carbon content due to the growth of native perennial grass es; hence, switchgrass grown on CRP land

### 40 of 129 was assumed to cause no net change of carbon in the soil. The change in soil carbon resulting from switchgrass grown on carbon

- depleted soils was based on results from McLaughlin et al. (2002) and Adler et al. (2007 ).

McLaughlin et al. (2002) developed an empirically derived soil carbon dynamics model that estimates soil carbon accumulation rates for contrasting soil types and climates. After accounting for regional soil carbon gains by prior cropping history and c limatic region, they found the average soil carbon sequestration level of traditional cropland converted to switchgrass production to be 0.21 Mg of C/acre/yr over a 30

- year period. These sequestration rates are based on annual rates of aboveground producti on over a 6

- year period, ranging from 5.3 to 7.9 tons/acre/yr. The variation between individual sites in the fist decade ranged from a minimum of

- 0.2 Mg of C/acre/yr for southeastern pastures to a maximum of 0.57 Mg of C/acre/yr for south

- central cropland s (McLaughlin et al ., 2002). Therefore, increased soil carbon from switchgrass cultivation is not guaranteed and in some cases a carbon release could occur.

Adler et al. (2007) also estimated the net change in soil carbon associated with the cultivation of switchgrass. As was the case with McLaughlin et al. (2002), the land use change estimates made by Adler et al. (2007) were sensitive to initial soil carbon levels, which are influenced by previous vegetation cover and land management. To acquire realist ic modern soil carbon levels, 1800 years of native vegetation followed by tree clearing, plowing, and 200 years of cropping were simulated. The consequence of this effort is that the initial conditions included legacy effects of 215 years of conventional t illage cropping. The result of 15

- year switchgrass cycle in DAYCENT was an average carbon sequestration of approximately 0.17 Mg of C/acre/yr (Adler et al., 2007).

Changes in soil carbon arising from changes in land use can constitute a major component of the life cycle GHG emissions; however, their quantification involves high levels of uncertainty. Therefore, a single value was chosen to illustrate the potential impact of land use chang e emissions on this pathway. Specifically, the estimate of 0.17 Mg of C/acre/year presented by Adler et al. (2007) was adopted as it represents a conservative value with respect the work of McLaughlin et al. (2008).

6.4.2 Corn Stover and Forest Residue

Input ass umptions on the energy and emissions associated with the recovery and transportation of corn stover and forest residues were based on default GREET assumptions (GREET, 2008). Default GREET assumptions were also adopted for the energy and carbon content of corn stover

and forest residues 32 (see Table 97 . While forest residues require no fertilizer and their removal is assumed to have negligible impact on the surrounding environment, corn stover is usually left on the field to replenish soil nutrients and miti gate erosion; however, there is little consensus regarding the quantification of the impacts on productivity, soil structure, and nutrient cycling of removing stover from the field. In addition to maintaining soil carbon levels, stover is a source of

nitro gen; hence, synthetic fertilizer would be needed to supply the incremental amount of nutrients needed for corn cultivation when stover is removed (GREET, 2007).

The yield attainable for corn stover is directly proportional to the fraction of stover that must be left on the field to mitigate these aforementioned negative impacts. The quantity of available stover can then be calculated as the difference between the total stover yield and quantity left on the field. The National Academies reported an average yield of corn stover across the United States in 2007 of 151 bu/acre/yr 33 , or 4.24 tons/acre/yr. This yield is based on the assumption of a 1:1 ratio of dry weight of corn grain to stover (National Academies, 2009). Results from Wilhelm et al. (2007) have shown that the stover needed to maintain soil carbon, and thus productivity, is a greater constraint to an environmentally sustainable harvest than that needed to control water

32 A significant variation of forest residue properties can be found in the literature, due to different assumptions of the type of biomass (e.g. hard wood vs. soft wood), which constitute the forest residues.

33 A bushel of corn or corn stover is defined as 56 lbs at 15.5% moisture content by mass.

### 41 of 129 and wind erosion. They predicted that 3.4 tons/acre/yr are required to mitigate erosion and soil carbon loss with moldboard plowing while only 2.3 tons/acre/yr are required if no

- till practices were employed. Sheehan et al. (2004) considered the USDA’s regional tolerable soil

- loss limits as the constraint for leaving stover on the fi eld. They found that 2.2 tons/acre/yr are required with conventional tillage while only 1.1 tons/acre/yr are required if no

- till practices are employed.

The corn stover yield adopted in this work was based on the total yield from the National Academies ( 2009) and the constraints for soil carbon and erosion under no

- till practices from Wilhelm et al. (2007). These resulted in a yield of 1.65 dry tons/acre/yr (equivalent to 1.94 tons/acre/yr at 15% moisture). The key input assumptions for the recovery and t ransportation of corn stover and forest residues are shown in Tables 32 and 33, respectively.

Although the corn stover yields adopted in this work were chosen to prevent a reduction of the natural carbon stock of the soil, an examination of the magnitude of potential land use change emissions from improperly harvested corn stover is still relevant. The analysis from Sheehan et al. (2004) imposed no constraint on maintaining soil carbon levels and the evolution of carbon stored in the soil was a central par t of their results. Under maximum stover removal conditions subject to maintaining USDA’s tolerable soil constraints, an average emissions rate of 13 kg of C/acre/yr was calculated over the first 30 years. The emissions profile over that time period is strongly positive for the first 10 years but becomes negative after year 20 as the soil begins to approach a new equilibrium state.

Table 32 : Input parameters for the recovery and transportation of corn stover

Input parameter 1 Valu e
Yield 1.65 dry ton/acre Collection Energy 254,190 Btu/dry ton (100% diesel fuel use)

Fertilizer Use Nitrogen Phosphorus

Potassium 4.50 kg/dry ton 1.63 kg/dry ton 8.35 kg/dry ton

N 2 O emissions 2 13.3 g/dry ton Transportation distance 30 miles Truck payload 24 ton

Notes: 1) All input parameters were based on GREET 1.8b (GREET, 2008) simulation year of 2015. 2) Includes N 2 O emissions from nitrification and denitrification in the soil,

as well as N 2 O credit from corn stover removal based on GREET

def aults. 34

Table 33 : Input parameters for the collection and transportation of forest residue.

Input parameter 1 Value Diesel fuel consumption 3.4 gal/dry ton Collection energy 459,200 Btu/dry ton Transportation distance 75 miles Truck payload 17 ton

Note: 1) These input parameters were based on GREET 1.8b (GREET, 2008)

34 Incremental fertilizer use (including nitrogen fertilizer) is accounted for to make up for the loss in soil nutri ents from the removal of corn stover from the field. On the other hand, if left on the field, a fraction of the nitrogen in corn stover will be

converted to N 2 O and emitted from the soil. This N 2 O emission is avoided when corn stover is removed from the fi eld. In
this case, the N 2 O credit from the removal of corn stover slightly outweighs the N 2 O emissions resulting from the incremental application of nitrogen fertilizers, resulting in net negative N 2 O emissions.

### 42 of 129 6.4.3 Analysis of Biomass Feedstocks

A sensitivity analysis was carried out to investigate the effect of varying feedstock type (switchgrass, corn stover and forest residue) and the influence of changes in soil carbon due to switchgrass growth or corn stover removal on the overall life cycle GHG emissions of the BTL pathway. A process efficiency of 45% was assumed in all the cases. The results are summarized

in Table 34

.

The sensitivity study shows that the life cycle GHG emissions from the use of switchgrass, without considering soil carbon change credit, are the highest of the three feedstocks. This is largely due to the large N 2 O emissions arising from the use of nitrogen fertilizers. However, when a soil carbon change credit is included, the life cycle GHG emissions are

- 0.02 times those of conventional jet fuel. Hence, the pathway becomes carbon negative because the CO 2 sequestration arising from land use change can dominate the GHG emissions of the BTL pathway. Overall, the use of all three types of feedstock in a biomass to liquids facility results in significant reductions (~80

- 100% reduction) of life cycle GHG emissions compared to conventional jet fuel

.

Table 34 : Variation of life cycle GHG emissions of BTL pathway with type of feedstock

Feedstock Life cycle

CO 2 emissions (gCO 2 /MJ) Life cycle

N 2 O

emissions (gCO 2 e/MJ) Life cycle

GHG

emissions (gCO 2 e/MJ) Compared to

baseline

conven tional jet fuel Corn stover (without soil carbon change emissions) 7.8 1.0 9.0 0.10

Corn stover (with soil carbon change emissions) 12.4 1.0 13.6 0.16

Forest residue 11.7 0.1 12.2 0.14
Switchgrass (without soil carbon change credit) 7.3 10.3 17.7 0.20

Switchgrass (with soil carbon change credit) - 12.5 10.3

- 2.0

- 0.02

Note: 1) Process efficiency of 45% was assumed in all cases.

For the low emissions case, a process efficiency of 52% was assumed, based on the analysis by Kreutz et al. (2008) of a 4400 bpd BTL plant. The baseline case assumes a process efficiency of 45% from the Choren process based on the “self

- sufficient basis scenario” where all required process energy is provided by biomass feedstock (Baitz et al., 2004). Choren currently operates

th e only commercial

- scale BTL plant in the world, producing 300 bpd of F

- T liquids. This is further confirmed by estimates of roughly a 5% efficiency drop for BTL plants compared to CTL plants due to additional processing energy for biomass grinding and dryi ng. 35 The high emissions case directly applies a 5% drop in process efficiency compared to 47% assumed in the CTL case, resulting in a process efficiency of 42%.

The input assumptions and life cycle GHG emissions for the production and use of F

- T jet fuel from switchgrass with and without a soil carbon sequestration credit are shown in Tables 35 and 36 respectively. The ‘biomass credit’ represents the CO 2 that is absorbed from the atmosphere during biomass growth. Note that the CO 2 emitted during the combu stion of process fuels and the

F

- T fuel is approximately equal to the CO 2 absorbed from the atmosphere during g rowth of the

biomass feedstock.

35 Thomas Tarka, interview with David Ortiz, October 3, 2008.

### 43 of 129 Table 35 : Summary of results for F

- T jet fuel from switchgrass with no soil carbon sequestration

Land Use Change Scenario B0 Low Baseline High Key Assumptions

Feedstock Switchgrass Switchgrass Switchgrass Process Efficiency (LHV) 0.52 0.45 0.42 Life Cycle CO 2 Emissions by Stage Biomass Credit (gCO 2 /MJ)

- 192.7

- 222.7

- 238.6 Recovery of feedstock (gCO 2 /MJ) 3.8 6.4 11.4 Transportation of feedstock (gCO 2 /MJ) 0.6 0.6 0.6 Processing of feedstock to fuel (gCO 2 /MJ) 122.1 152.1 168.0 Transportation of jet fuel (gCO 2 /MJ) 0.5 0.5 0.5

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 65.8

- 63.1

- 58.2

WTT CH 4 emissions (gCO 2 e/MJ) 0.1 0.2 0.5

WTT N 2 O emissions (gCO 2 e/MJ) 7.2 10.3 13.3 Total WTW GHG emissions (gCO 2 e/MJ) 11.9 17.7 26.0 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.14 0.20 0.30

Table 36 : Summary of results for F

- T jet fuel from switchgrass with soil carbon sequestration

Land Use Change Scenario B1 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ)

- 13.6

- 19.8

- 30.5

WTW CO 2 emissions (gCO 2 /MJ)

- 9.0

- 12.5

- 18.2 Total WTW GHG emissions (gCO 2 e/MJ)

- 1.7

- 2.0

- 4.4 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel - 0.02

- 0.02

- 0.05

Notes: 1) All other input assumptions (cultivation of switchgrass, F

- T processing and carbon capture efficiency) are based on those in the B0 emissions case of the corresponding scenario.

The life cycle GHG emissions of the BTL pathway using switchgrass as a feedstock are 0.14 times to 0.3 times those of conventional j et fuel with no soil carbon sequestration. When the soil carbon sequestration credit is included, the emissions from the pathway range from

- 0.02 to

- 0.05 times those of conventional jet fuel. The larger value for land use change emissions in the high

scen ario occurs due to the lower assumed yield per acre.

Nitrous oxide emissions represent more than 50% of the total life cycle GHG emissions from the switchgrass to F

- T jet pathway. As a result, life cycle GHG emissions for this pathway are strongly influenc ed by any uncertainty associated with the IPCC correlations that were used in the
nitrous oxide estimates. The reader should be aware of these inherent uncertainties when comparing different pathways for GHG reduction potential in Sections 8.1 and 8.2.



### 6.5 Fi scher

- Tropsch Fuel from Coal and Biomass

While both CTL and BTL hold promise as alternative jet fuels, they also have flaws. Even with 85% carbon capture, a CTL plant has life cycle GHG emissions over 110% of conventional jet fuel (i.e., the life cycle GHG emissions are 10% higher than conventional jet fuel); without carbon capture, CTL has 220% of the emissions of conventional jet fuel. If the goal is to reduce GHG emissions, then coal alone appears to be a poor choice. Biomass

- to

- liquids plants without ca rbon

### 44 of 129 capture have life cycle GHG emissions that are less than 10% of conventional jet fuel; however, considerable logistical challenges exist in obtaining sufficient biomass to operate at large scales because of the relatively low energy density of biomass . Because current F

- T plant designs are capital intensive, it is not economically feasible to build many small plants that are dispersed among the regions where biomass is being grown. Biomass must therefore be accumulated from

considerable areas and trans ported to large central plants; hence, the infrastructure to move the biomass becomes a limiting factor. Emissions from the transportation of the biomass to the processing facility are included in the life cycle analysis, but represent a negligible fractio n of the

total emissions. Since both biomass and coal are processed into an F

- T fuel using similar technology, they could be processed at a single F

- T plant. The biomass offsets the high emissions from coal and coal offsets the low energy density and produ ction limitations of biomass. A coal and biomass to liquid (CBTL) plant with carbon capture and storage (CCS) was considered in this analysis. A configuration without CCS was examined as a case study; however, the focus herein is on a configuration with CC S as the primary goal is to reduce GHG

emissions.

The coal and biomass can be gasified either in parallel with the syngas streams being mixed afterwards, or in the same unit (co

- gasification). Since the parallel configuration is a superposition
of the re sults of sections 6.3 and 6.4, this section examines co

- gasification. Parallel processing also requires additional infrastructure, as separate gasifiers are needed for each feedstock. Co

gasification was assumed to occur in an entrained flow gasifier. Such technology is already commercially available for large scale processing of coal and biomass ( van der Drift et al., 2004 ). Before entering the gasifier, biomass must be milled down to particles of diameter 1mm or less. Currently, the most energy efficient method of milling the biomass is via torrefaction pre

treatment, which is a mild thermal treatment where the biomass is heated to ~250°C yielding a solid uniform product with lower moisture content and higher energy content. Studies have shown

that torrifi ed wood can be milled to the required size using only 10

- 20 kW electricity /MW biomass and that capacity expansion factors between 2 and 6.5 can be achieved (van der Drift et al., 2004; Bergman et al., 2005) . Moreover, torrified particles can be pneumatically transportable, which is considered impossible for a bed of untreated biomass particles. Efficiencies for torrefaction range from 85% to 97%, with 90% assumed in the baselin e case (Bergman et al., 2005).

Because of the pre

- pr ocessing of biomass, the overall F

- T plant efficiency dep ends on the weight percent of biomass that is being co

- gasified. This study explored a range from 10 % to 40% biomass feed with 40%, 25% and 10% chosen for the low, baseline and high emissions c a ses, respectively. Due to the CO to H 2 ratio resulting from gasification, feeds with biomass weight fractions below 50% lead to syngas favorable for methanol and hydrocarbon fuels whereas biomass weight fractions above 50% lead to syngas favorable for dimethyl ether (DME) synthesis. Additionally, higher biomass feeding rates lead to the formation of less char and tars ( Kumabe et al., 2007 ). CTL plant efficiencies were modified to account for the extra power consumption of pre

- processing the biomass; hence, the r eduction of the CTL plant efficiency,

η CTL , depends on both the fraction of biomass feed and the torrefaction efficiency. The plant is assumed to be self sufficient in terms of electricity production with no extra power exported to the grid. Higher efficie ncies could be possible if additional electricity were generated for grid export. The implementation for CCS comes at the cost of 250kWh per ton of carbon captured, as was the case with CTL. This energy requirement is also accounted for in the CBTL process efficiency plant. In total, the CBTL plant efficiency was examined as a function of CTL efficiency, biomass weight percentage, torrefaction efficiency, biomass grinding energy and CCS efficiency, as expressed in Equation 7.



## € η CBTL 

## = f η CTL 

## , biomass wt%, η torrefaction 

## , biomass grinding energy, η CCS 

# ( ) E quation 7

Assumptions about feedstock type are similar to those made when coal and biomass were considered separately. Specifically, surface

- mined sub

- bituminous coal and switchgrass were assumed in the low emissions case; average US coal from 2007 (mix of underground

- mined and

### 45 of 129 surface

- mined bituminous coal and surface

- mined sub

- bituminous coal 36 ; anthracite or lignite coal is not considered) and switchgrass were assumed in the baseline case and underground

- mined bituminous coal (from c ase 1 of Southern States Energy Board CTL study, SSEB, 2005) and switchgrass were assumed in the high case. Sub

- bituminous coal is used in the low emissions case because it reduces GHG emissions by increasing the fraction of input energy provided by

biomas s for a given biomass/coal mass ratio. Input assumptions for the combined coal and biomass pathway are summarized in Table 37.

Table 37 : Input assumptions for the production of F

- T jet fuel from coal and biomass (with carbon capture ) for low emissions, baseline and high emissions cases

Low Baseline High CTL Process Efficiency (LHV) 53% 50% 47%

Biomass Weight Fraction 40% 25% 10% Carbon Capture Efficiency 90% 85% 80% Carbon Compression Energy 250 kWh/ton C 250 kWh/ton C 250 kWh/ton C Torrefaction Efficiency 97% 90% 85%

Coal Input Surface

- mined

sub

- bituminous

coal Average bituminous

and sub

- bituminous

coal mix Underground

mined bituminous

coal Coal Mining Methane Emissions (gCO 2 e/MJ Coal ) 0.8 2.8 6.4

Biomass Type Switchgrass Switchgrass Switchgrass

6.5.1 Allocation Methodology

As previously stated, an F

- T facility can produce a wide product slate. For example, the study carried out by NETL on F

- T diesel from CBTL assumed an output of 70% diesel and 30% naphtha (Tarka, 2009). In this analysis, F

- T jet fuel is the product of interest but does not consist of more than roughly a third of the total plant output to prevent excessive naphtha production.

Since other F

- T fuels are made as a result of producing F

- T jet fuel, the emissions from processing and all other upstream activities must be allocated among the fuels being produced. Even when configured to make jet fuel, diesel fuel will likely be the primary output of the F

- T facility due to economic drivers. For this reason , it is sensible to allocate emissions among the liquid products (i.e., jet fuel, diesel fuel, and naphtha) on the basis of t heir respective energy

content.

Figure 8 demonstrates problems that could result from usin g the displacement method to assign

biom ass ‘credits’ to a fuel that is not the primary product. Whe n the yield of jet fuel is 25% by volume, there are 3 liters of other F

- T fuels produced for e very 1 liter of jet fuel. When the yield of jet fuel is reduced to 5% by volume, there are 19 liters o f other F

- T fuels produced for e very 1 liter of jet fuel. In the limit where the yield of jet fuel approaches zero, the quantity of other F

- T fuels asymptotically approaches infinity. If the other F

- T fuels represent a reduction in emissions as compared to their petroleum equivalents then the displacement method attributes these emissions reductions to the jet fuel; therefore, the displacement methodology results in a jet fuel which appears to have life cycle emissions that approach negative infinity as the yield of jet fuel is reduced towards zero. The results from Figure 9 show that an energy allocation scheme prevents a product that is responsible for a third, or less, of total output from the facility receiving the

emissions ‘credit’ from the biomass use d as feedstock for the entire product slate.

36 Compared to 2007, the coal production mix in 2017 is projected to comprise a larger proportion of surface

- mined sub

bituminous coal from Western coal production, particularly the Powder River Basin. (EIA, 2008d)

### 46 of 129 Figure 9 : The effects of product slate composition on life cycle greenhouse gas emissions from F

- T jet fuel

The diverging effect resulting from use of the displacement method is unavoidable because jet fuel is not the primary product from the F

- T plant. This highlights the important conclusion that life cycle GHG emissions from a given fuel are as much a function of subjective choices of the analyst/operator regarding allocation m ethodologies, as they are of specific production characteristics and process inputs. In order to maintain consistent results, which cannot be skewed by choices, such as what is the product slate that is being selected, energy allocation

was chosen. The res ults using this method come with the caveat that for each unit of jet fuel produced, substantial volumes of other fuels are also produced, such as diesel and naphtha, which could also carry environmental benefits. The specific product slate considered in t his analysis was 25% F

- T jet fuel, 55% F

- T diesel and 20% F

- T naphtha.

6.5.2 Results: F

- T Jet Fuel from Coal and Biomass

Local sensitivity analysis was conducted on the feedstock type, the quantity of GHG emissions from land use change, CTL process efficiency, biomass weight percentage, torrefaction efficiency, CCS efficiency and CCS compression energy. Each parameter was varied with all others held at their baseline values with the impact on life cycle GHG emissions quantified as a percent change from the base line value. Figure 10 presents this information in a manner that allows the magnitude of each change to be seen in comparison to the others. The biomass feed rate has the dominant influence. Since the biomass feed rate is a parameter that is chosen by the operator, the emissions from CBTL facilities will be dictated by practical, as opposed to technological, limitations. Overall, the choice of feedstock and the potential for soil carbon sequestration were found to have a larger impact on life cycle GHG emis sions than the process

efficiencies.

When examining the impacts of changing the plant efficiency (this can be achieved either directly or by changing the energy consumption for biomass pre

- processing or carbon dioxide compression) it was found that lower efficiencies lead to lower net emissio ns. This counter

intuitive result occurs because the plants were chosen to be self

- sufficient and CCS is used to capture emissions from gasifying additional biomass to supply syngas for process fuel. The capture and sequestration of carbon contained in the biomass leads to a net carbon removal from the atmosphere and hence having less biomass converted to fuel results in more of the carbon in the biomass going to sequestration. !"##$ !%##$ !&##$ !'##$ !(##$ !)##$ #$ )##$

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### 47 of 129 Figure 10 : Sensitivity analysis of operational spec ifications and configurations of F

- T jet fuel from coal and

biomass

The results for the low emissions, baseline and high emissions scenarios for CBTL using switchgrass as the biomass feedstock are shown in Tables 38 and 39. Table 38 gives the results

when no soil carbon sequestration credit is given to the switchgrass while Table 39 gives the results when the soil carbon sequestration credit is included. The ‘biomass credit’ represents the

CO 2 that is absorbed from the atmosphere during biomass growth. Incr easing biomass credit reflects larger amounts of biomass being used as feedstock.

Table 38 : Summary of results for F

- T jet fuel from coal and biomass without soil carbon sequestration credit

Land Use Change Scenario B0 Low Baseline High Key Assumptions Biomass Weight Fraction 40% 25% 10% Biomass Input Switchgrass Switchgrass Switchgrass Carbon Capture Efficiency 90% 85% 80%

CBTL Process Efficiency (LHV) 48.9% (53% without

CCS or

biomass processing) 46.0% (50% without

CCS or

biomass processing) 44.1% (47% without

CCS or

biomass processing) Life Cycle CO 2 Emissions by Stage Biomass Credit (gCO 2 /MJ)

- 78.6

- 44.3

- 15.3 Recovery of feedstock (gCO 2 /MJ) 1.2 1.2 1.1 Transportation of feedstock (gCO 2 /MJ) 0.3 0.2 0.1 Processing of feedstock to fuel (gCO 2 /MJ) 14.7 21.9 28.6 Transportation of jet fuel (gCO 2 /MJ) 0.5 0.5 0.5

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 62.0

- 20.5 14.9

WTT CH 4 emissions (gCO 2 e/MJ) 1.1 4.9 13.6

WTT N 2 O emissions (gCO 2 e/MJ) 2.9 2.0 0.9 Total WTW GHG emissions (gCO 2 e/MJ) 12.4 56.9 99.8 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.14 0.65 1.14 !"#$ !%#$ !&#$ #$ &#$ %#$ "#$

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### 48 of 129 Table 39 : Summary of results for F

- T jet fuel from coal and switchgrass with soil carbon sequestr at ion credit

Land Use Change Scenario B1 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ)

- 5.5

- 3.9

- 2.0

WTW CO 2 emissions (gCO 2 /MJ) 2.9 46.0 83.4

Total WTW GHG emissions (gCO 2 e/MJ) 6.9 53.0 97.8 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.08 0.61 1.12

Notes: 1) All other input assumptions (cultivation of switchgrass, F

- T processing and carbon capture efficiency) are based on those in the B0 emissions case of the corresponding scenario.

The life cycle GHG emissions of the CBTL pathway range from 0.14 to 1.14 times those of conventional jet fuel when no soil carbon sequestration credit is given. The emissions for this pathway range from 0.08 to 1.12 times those of conventional jet when the soil carbon sequestration credit is included. The large range of this pathway is primarily driven by the variation in biomass weight fraction of the feedstock.

In their assessment of F

- T diesel pro duction, Tarka (2009) used a displacement (system expansion) scheme instead of energy

- based allocation to account for the benefit of making a reduced carbon, biomass

- based F

- T naphtha in addition to the F

- T diesel. As such, their results

differ from those given here. When a common allocation approach and productions assumptions are implemented, both analyses yield similar results.

Figure 11 : Dependence of cumulative life cycle emissions and biomass requirements for varied biomass

utilization within CBTL

Figure 11 presents the implication of varying biomass weight over a range of 5% to 45%. Life cycle GHG emissions can be reduced to a fraction of conventional jet fuel with considerable biomass usage. For example, provided sufficien t CCS is available, a CBTL jet fuel created from 45% biomass could have life cycle GHG emissions that are only 20% of conventional jet fuel; however, roughly 245 railroad cars of biomass would be needed every day to create sufficient CBTL jet fuel to power the aircraft at Boston Logan airport. 37 This large amount of biomass

37 6,500 tonnes of biomass would be needed per day to provide 25,000 barrels per day of jet fuel at a biomass feed rate of 45% (this is roughly the consumption of Boston Logan Airport). A typical railroad car can carry 26.5 tonnes of biomass (Mahmudi, 2006). !" #!!!" $!!!" %!!!" &!!!" '!!!" (!!!" )!!!"

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### 49 of 129 highlights the importance of considering GHG reductions for high biomass weight percentages in conjunction with biomass feeding requirements; it also points to lower biomass percentages b eing

more realistic. Future work will consider the economics of CBTL fuels.

6.5.3 Case Study: Impact of Carbon Capture on GHG Emissions from CBTL Facilities

The analysis presented above assumed the use of CCS to reduce GHG emissions. Two

additional cases were ex amined to emphasize the importance of using CCS to reduce GHG

emissions from CBTL facilities . The first is the baseline CBTL case without CCS and wi thout soil carbon sequestration . The second is the baseline CBTL case without CCS where the biomass

feed rat e has been adjusted such that the life cycle GHG emissions are on parity with conventional jet fuel.

A comparison of results from the baseline scenario with and without CCS is given in Table 40. Not having CCS leads to an increase in GHG emissions of 106. 1 gCO 2 /MJ, such that the WTW

emissions from the CBT L pathway become 186 % of conventional jet fuel. Without CCS, one

needs to use 70 % biomass, by weight, for F

- T fuel from coal and switchgrass to reach GHG parity with conventional jet fuel. This understates the importance of CCS to getting reduced GHG

emissions from CBTL fuels.

Table 40 : Life cycle GHG emissions from F

- T jet fuel from coal and switchgrass with and without CCS

Baseline Base l ine ( w/o CCS ) Biomass for GHG Parity (w/o

CCS ) Key Assumptions Biomass Weight Fraction 25% 25% 70% Biomass Input Switchgrass Switchgrass Switchgrass Carbon capture efficiency 85% 0% 0% Life Cycle CO 2 Emissions by Stage Biomass credit (gCO 2 /MJ)

- 44.3

- 42.0

- 141.1 Recovery of feedstock (gCO 2 /MJ) 1.2 1.2 1.9 Transportation of feedstock (gCO 2 /MJ) 0.2 0.2 0.4 Processing of feedstock to fuel (gCO 2 /MJ) 21.9 126.1 146.5 Transportation of jet fuel (gCO 2 /MJ) 0.5 0.5 0.5

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 20.5 86.0 8.3

WTT CH 4 emissions (gCO 2 e/MJ) 4.9 4.7 2.3

WTT N 2 O emissions (gCO 2 e/MJ) 2.0 1.9 6.5 Total WTW GHG emissions (gCO 2 e/MJ) 56.9 163.0 87.5 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.65 1.86 1.00

### 50 of 129 

# 7 Hydroprocessed Renewable Jet Fuel from

Renewable Oils

Renewable oils can be processed into a fuel that has properties similar to those of F

- T fuels. The processing involves hydrotreatment to deoxygenate the oil with subsequent hydrocracking to create hydrocarbons that fill the distillation range of jet fuel (Hileman et al., 2009). This work examined the life cycle GHG emissions from the production and use of Hydroprocessed Renewable Jet fuel (HRJ) from soybean oil, palm oil, rapeseed oil, al gae oil, jatropha oil and salicornia oil. As of the writing of this report, the production of hydroprocessed jet fuel from any oil feedstock is still limited to quantities suitable for flight

- testing. Numerous flight tests have been successfully conducted of fuel composed of 50% HRJ from mixes of jatropha, algae and camelina blended with conventional jet fuel.

In all cases, HRJ will have to compete with hydroprocessed renewable diesel (HRD) and

biodiesel for feedstock availability. Biodiesel is currently the only biofuel produced at commercial scales from renewable oils; however, facilities to hydroprocess renewable oils are being built worldwide with a production capacity of nearly 60,000 bbl/day (Hileman et al., 2009). While biodiesel is not appropriate for use in gas turbines at any blending ratio, biodiesel production can be used to understand the resource potentia l of renewable oils for biofuel because it is made from
the same feedstock . Soy and palm oil were examined using GREET version 1.8a whereas a ll other HRJ pathways used GREET version 1.8b.

Soybean oil is of interest as it is used extensively in the US and Europe for biodiesel production. The US and Europe used a total of 48,700 bbl/day (2.6 million metric tons per year) of soy oil in

2009 for b iodiesel

, which represents only 20% of total soy oil use in these regions. Similarly, rapeseed oil is the main feedstock used for biodiesel production in Europe. Approximately 35% of all rapeseed oil used in Europe went to biodiesel, amounting to 58,000 bb l/day ( 3.1 million metric

tons per year) . Palm oil, on the other hand, has grown to become the most produced oil in the world. Its production has increased rapidly in the past 20 years and the production of palm oil surpassed soy oil for the first time in 2006. Almost 90% of global palm oil production occurs in Indonesia and Malaysia (FAPRI, 2010). Biodiesel production is expanding based on the palm oil

resources in this area. Malaysia has issued approval of 91 companies to build domestic palm oil

based bio diesel plants; however, total planned capacit y of the entire region is still less than 30,000 bbl/day (1.5 million metric tons per year) ( Planet Arc , 2007; Forbes.com , 2007; Mission Biofuels Limited, 2007).

Soybeans, palm and rapeseed are edible food cro ps requiring fertile cropland for cultivation. The use of these crops in fuel production could result in direct or indirect land use change emissions. As noted previously in the text, large uncertainties exist in estimating the GHG emissions from

both dire ct and indirect land use change. T his work only quantifies direct land use change

because the economic models required to analyze indirect effects are beyond the scope of this
effort. Similar to the treatment of switchgrass in Sections 6.3 and 6.4, the fol lowing sections consider a range of direct land use change scenarios using multiple land conversion scenarios to establish upper and lower bounds for these values. These scenarios were created based upon existing data from the literature. Since indirect la nd use change emissions are the integrated impac t of direct land use change resulting from increased crop prices, the range of direct land use change emissions presented herein should br acket any potential indirect land use change

e missions occurring because of these feedstocks.

Jatropha curcus is a small tree or large shrub, up to 5

- 7 meters tall, which can grow without irrigation in a broad spectrum of rainfall regimes. All subsequent references to jatropha are with regard to jatropha curcas . Under normal conditions, the jatropha plant flowers only once a year during the rainy season; however, in permanently humid regions or under irrigated conditions it can be made to flower almost all year round (Achten et al., 2008). The res ulting fruit is composed

### 51 of 129 of an outer capsule containing two o r three seeds. Each seed has a shell and a kernel, which contains oil. Jatropha plants have higher o il yields than many other oil yielding crops; however,

the husk and the seed shells result in m ore co

- product per unit mass of jatropha oil than both algae and palm fresh fruit bunches.

The potential for jatropha was recently demonstrated by successful gas turbine test flights using a hydroprocessed mix of jatropha and other oils

. Jatropha cultivat ion is ideal for regions of the world with the highest rates of poverty and plenty of hot, dry land because it is well adapted to the tropics and subtropics. India has been recently pushing to expand biodies el production from jatropha oil ; the government h as announced plans to subsidize an intensive program to plant jatropha for biofuels on 27 million acres of “wastelands”. Despite these recent efforts, global production in 2008 was limited to 242 cultivation projects, amounting to only 2.2 million acres.

F urthermore, most jatropha grown for biofuels is cultivated locally on plots of less than 12 acres (Yale Environment 360, 2009) . The major limitations to expanding from local cultivation to large

scale production is that farmers are in poverty and only allow jatropha to grow on their land

because of its ability to grow in the arid conditions . In areas where jatropha is not naturally occurring, farmers do not wish to take the initiative to restructure their farms to prevent other revenue generating specie s from dominating the natural growth of j atropha . New plantations

t akes 3

- 5 years for jatropha production mature such that it can be grown commercially and

f armers don’t have the cap ital to invest in projects where the potential for revenue is several

year s away. Due to the general poverty of the regions where jatropha is considered promising,

there is little to no loan availability or prese nce of long term contracts to guarantee a return on

their investment (Mani, 2010). While it appears that jatropha may be an appropriate solution to provide fuel to the villages in which it is grown, there is currently minimal evidence to indicate that jatropha will expand beyond the village level to become an energy resource on the global scale.

Algae were first examine d as a biofuel feedstock by the Department of Energy during the Aquatic Species Program (ASP) from 1978 to 1996. The ASP focused most of its attention on identifying a specific factor that would stimulate the algae to have a high lipid weight fraction. Muc h discussion still surrounds the possibility of genetically modifying certain strains of algae to produce more oils; however, the present analysis focuses only on previously documented strains that currently exist. Furthermore, it is important to different iate between micro

- algae and macro

algae. Microalgae, as the name suggests, are tiny organisms which grow in water with

concentrations ~0.2

- 0.4g/L and have the appearance of tinting the water green; these are the algae considered in this work. Macro algae are the classical long strands that grow on the bottom of ponds and lakes, (a.k.a. seaweed). While some research has been conducted using macro

algae as a fuel source, it is not considered further in this work; hereafter, a lgae will refer to microalgae.

N o company is currently producing commercial quantities of algal oil for use as a transportation fuel; instead they are producing quantities that are appropriate for various stages of research and development. Furthermore, the industry is not sufficiently d efined to have well defined best practices for cultivating and extracting the oil from the algal cells. This report examines relatively conventional methods of oil extraction wherein the algae are dried prior to chemical treatment ; however, several researc h efforts are ongoing to evaluate whether the oil could be extracted via

electric al pulses or if the algae can be genetically modified to naturally excrete oil. Although no commercial scale production of algae currently exists, significant investments are being made into developing it as a feedstock for alternative fuels. For example, the Department of Defense recently ordered 20,000 gallons of algae fuel from Solaz yme at a cost of $8.5 million USD ($425 per gallon) for advanced testing as F

- 76 Naval Distil late (Green Car Congress, 2009). This is a relatively small sum compared to the 600 million US$ that Exxon Mobil has invested in research and development of algal transportation fuels (New York Times, 2009).

Halophytes are considered an oil seed crop for fuel production because they prosper in sea or brackish water on marginal lands. Other benefits of halophyte agriculture include freeing up arable land for freshwater resources, cleansing the environment, decontaminating soils,

### 52 of 129 desalinating brackish water s and soil carbon sequestration. Considering that 43% of the earth’s landmass is arid or semi arid and 97% of the earth’s water is seawater (Hendricks and Bushnell, 2008), halophytes are an attractive option for large

- scale production. From the halophyte family, Salicornia bigelovii emerg ed as an oilseed crop from a screening of wild halophytes and was selected for seawater trials due to its seed yield and oil fraction (Anwar et al., 2002). All further references to halophyte or salicornia refer specifically to Salicornia bigelovii

.

The development of salicornia for fuel production is still in the experimental stage; no fuel testing has been conducted on fuel produced from the salicornia plant. Despite our inexperience, a major UNFCCC project in the Sonoran desert has been cultivating sal icornia on 30 hectares of coastal land since 1996 with the goal of developing cost

- effective cultivation processes on a commercial basis, and optimizing agronomic methods for irrigation and harvesting (UNFCCC, 1998). The GCC (Cooperation Council for the Ar ab States of the Gulf) countries have also established research programs for halophytes in the form of an experimental 200

- hectare integrated seawater agricultural system near Abu Dhabi. The Masdar Institute of Science and Technology (MIST) is

collaboratin g with UOP, Boeing and Etihad Airways to r ealize this project (Green Car Congress

, 2010).



### 7.1 Energy Requirements for HRJ from Renewable Oils

The hydrotreatment process for the production of HRJ from renewable oils was based on the UOP hydrodeoxygenation proce ss, which primarily produces “green diesel” (Marker et al., 2005; Huo et al., 2008). Other techniques are available to produce hydrocarbon fuels from renewable oils beyond the approach developed by UOP; however, the UOP process is currently the most

establ ished for jet fuel production. Similar to F

- T fuels, additional hydroprocessing is needed for the production of jet fuel instead of diesel, resulting in increased hydrogen and power requirements. The assumption that additional processing requirements for F

- T jet fuel relative to diesel are negligible is justified in the literature (Gray et al., 2007); however, in the case of HRJ, using diesel as a surrogate for jet is only appropriate for crude estimates.

The UOP process used for the c reation of Hydroproc essed Renewable Diesel (HRD) is described by the following chemical react i on:



### € C

n 

### H 2n + 1 

### COOH + 3H 2 

### → C

n + 1 

### H 2n + 4 

### + 2H 2 

### O Equation 8

where n is the carbon chain length of the fatty acids within the triglyceride molecules u sed as a feedstock for the process. Triglycerides are formed from a single molecule of glycerol, combined with three fatty acids. This simplified analysis assumed that all renewable oils, regardless of type (soy, pal, rapeseed, jatropha, algae or salicornia) are identical and contain only fatty acids with a

carbon chain length of 18. The error introduced by this assumption is likely small compared to the uncertainty in quantifying cultivation inputs for each feedstock ; however, not all oils are chemically

equa l and this analysis does not reflect the physical properties of any single oil type.

Table 41 presents the actual distributions of fatty acid carbon chain lengths for most of the oils

considered in this work. 38 An example of a shortcoming of the assumption used in this analysis is the treatment of oils containing unsaturated carbon chains

. The presence of double bonds within

a carbon chain would lead to additional hydrogen consumption during the deoxygenation process

in order to saturate t he molecule, ho wever, such effects are ignored herein.

38 The notation for deno ting carbon chain length and number of double bonds is (chain length):(number of double bonds). For example, a chain length of 18 with 2 double bonds is expressed as 18:2.

### 53 of 129 Table 41 : Component fatty acid profiles for renewable oils considered in this work

Fatty Acid Components (weight %)Caprylic (8:0)Capric (10:0)Lauric (12:0)Myristic (14:0)Palmitic (16:0)Palmitoleic (16:1)Stearic (18:0)Oleic (18:1)Linoleic (18:2)Linolenic (18:3)Arachidic (20:0)Gadoleic (20:1)Behenic (22:0)Erucic (22:1)

Soybean

— — — — 11

— 4 22 53 8

— — — — Palm

— — — — 44

— 4 39 11

— — — — — Palm Kernel 3 7 47 14 9

— 1 19 1

— — — — — Rapeseed (B. campestris)

— — — — 4

— 2 33 18 9

— 12

— 22 Rapeseed (B. napus)

— — — — 3

— 1 17 14 9

— 11

— 45 Jatropha curcas 3

— — — — 13

— 8 45 34

— — — — 1

Salicornia bigevolii 4

— — — — 7

— 3 18 73

— — — — — Notes: 1) Unless otherwise indicated, this information comes from: DeMan et al. (1999) 2) Additional data for fatty acid components of coconut oil, canola oil, sunflower oil, cottonseed oil, peanut oil, olive oil, mustard seed, lard, yellow grease is also available

from DeMan et al. (1999) 3) From Shweta et al. (2004) 4) Salicornia oil is similar to safflower oil in fatty acid composition (Glenn et al., 1998). This profile is averaged values for safflower oil from: Co ş ge et al. (2007)

Equation 8 can be re

- written in a mass balance form for easier comparison to experimental data.

1.12 lb Oil + 0.024 lb Hydrogen → 1.00 lb HRD + 0.14 lb Water

The feedstock, key products and process energy needed per pound of HRD are summarized in

Table 42

.

Table 42 : Experimental and theoretical requirements for the creation of renewable diesel

Experimental

Feedstock (lb) Low Baselin

e High Theoretica

l

Oil 100 100 100 100

H 2 (51586 Btu/lb) 1.5 2.72 3.8 2.14 Key Products (lb) HRD (18908 Btu/lb) 83.0 84.19 86 89.28 Propane Mix Gas (18568 Btu/lb) 2.0 4.75 5 0.00 Process Energy (Btu) Electricity 5785 6942 8099

-- Natural Gas 8950 8950 8950

-- Notes: 1) Steam is assumed to be produced from natural gas at 80% efficiency 2) Energy contents are taken from GREET (2008) 3) Experimental data taken from Appendix 2 of Huo et al. (2008) with modifications per recommendations from UOP (Kalnes, 2009)

Further refinement of HRD is required for the creation of HRJ. The strategy to estimate the process requirements of HRJ was to use the experimental data for the creation of HRD and subsequently estimate the additional requirements to convert the HRD into HRJ. For this analysis, HRD and HRJ are assumed to be symmetrical distributions of straight chains

### 54 of 129 hydrocarbons centered on C 12 and C 18 respectively. As shown schematically in Figure 11, the cracking from diesel fuel to jet fuel was assumed to occur via the addition of gaseous hydrogen.

Figure 12 : Schematic showing the change in hydrocarbon composition between HRD and HRJ fuels that results from additional hydroprocessing.

The mechanism by which hydrocarbon chains crack is through smaller molecules, (e.g., pentane (C 5 H 12 ) and hexane (C 6 H 14 )) breaking off the end. The dominant effect that tak es place is the

reduction of C 18 to C 13 by cracking pentane and C 12 by cracking hexane (Kalnes, 2009). Other reactions are also occurring where molecules from the distribution about C 18 crack to those from

the distribution about C 12

. To account fully for t his effect would entail including the statistical nature by which chemical reactions are more likely to take place. In keeping with the level of detail required of this analysis, the aforementioned effects were assumed to cancel out if the two

distribution s have the same shape about their mean.

Making use of these arguments simplifies the analysis to two chemical reactions converting diesel fuel to jet fuel:



### € C 18 

### H 38 

### + H 2 

### → C 12 

### H 26 

### + C 6 

### H 14 

### C 18 

### H 38 

### + H 2 

### → C 13 

### H 28 

### + C 5 

### H 12 

### 2C 18 

### H 38 

### + 2 H 2 

### → C 12 

### H 26 

### + C 13 

### H 28 

### + C 5 

### H 12 

### + C 6 

### H 14 Equation 9

Written in mass balance form and normalized for one lb of HRD, the overall equation governing the formation of HRJ from renewable oil can be expressed as:

1.00 lb HRD + 0.0079 lb Hydrogen → 0.697 lb HRJ + 0.311 lb Naphtha

Although variations in carbon chain length are not captured in this analysis, fatty acids with carbon chain length distributions around 12 are better suited for use as feedstock for jet fuel because higher blending percentages can be used without the need for hydrocracking. Furthermore, carbon chain lengths closer to 12 would result in a higher yield of jet fuel per unit mass of oil input.

Naphtha in this case is a combination of 46 percent C 5 H 12 and 54 percent C 6 H 14 by mass. Using these ratios of HRD to HRJ, the process energies from Table 42 were modified to reflect the energy requirements to create HRJ as shown in Table 43

. Based on discussions with experts at UOP (Kalnes, 2009), it is assumed that total process energies (natural gas and electricity) will increase by 10 to 30 percent per pou nd of renewable feedstock when including the hydrocracking required for the formation of HRJ. The total hydrogen consumption is the sum of that needed to

first make HRD and then to crack it to HRJ. In all cases where renewable oils are processed into

finis hed fuel products, energy and emissions were allocated based on energy content.

### 55 of 129 Table 43 : Energy requirements for the creation of HRJ

Emissions Scenarios

Feedstock (lb) Low Baselin

e High

Oil 100 100 100

H 2 (51586 Btu/lb) 2.15 3.38 4.48 Key Products (lb) HRJ (18950 Btu/lb) 57.8 58.7 59.9 Naphtha (19215 Btu/lb) 25.8 26.2 26.8 Propane Mix Gas (18568 Btu/lb) 2.0 4.8 5.0 Process Energy (Btu) Electricity 6364 8330 10529

Natural Gas 9845 10740 11635

The hydroprocessing step of converting renewable oil into HRD has a mass yield of 84% and

results in 8.7 gCO 2 / MJ. After making the aforementioned changes, hydroprocessing renewable oil into HRJ has a mass yield below 60% and results in 10.3 gCO 2 /MJ. The emissions associated with hydrogen production in this work are representative of steam reforming natural gas and are consistent with default GREET assumptions. While these results provide a first approximation of mass and energy inputs, they do no t reflect the impact of oil composition on process inputs or differences in real world production scenarios.

The decrease in yield is accompanied by an increase in naphtha production. Although naphtha is used as a blending stock in gasoline, it has a low er economic value than HRD, which is a high performance diesel fuel. Because petroleum refineries have on

- site hydrogen production to meet internal demands that could be expanded to supply the hydrogen for hydroprocessing renewable oils, hydroprocessing fa cilities would likely have lower emissions if they were integrated into existing petroleum refineries. With such a configuration, excess naphtha resulting from HRJ production can be integrated into the refinery naphtha stream and catalytically reformed to high value hydrocarbons or steam reformed to supplement the internal hydrogen needs of the process. However, if the value of the HRJ and naphtha stream is less than the HRD stream, a fuel producer would likely focus on HRD production unless they could char ge a premium for HRJ fuel. Under such a situation, the aviation community could consider using HRD as a dilute blend stock in gas turbine engines (~10%). This option would require further research to ensure viability

with the current fleet of aircraft. Fur ther research is being devoted to understand the tradeoffs among HRD, HRJ, and FAME production.



### 7.2 HRJ from Soybean Oil

The production of HRD from soybean oil is available in GREET (as documented in Huo et al., 2008). Default assumptions on farming energy, f ertilizer use, yield, etc, were updated where necessary using data from the literature (mainly the U.S. Department of Agriculture (USDA) and Hill et al., 2006). The key parameters chosen for establishing the low emissions, baseline, and high emissions scen arios were yield per hectare, N 2 O emissions, liming emissions and the processing emissions whereby soy oil is converted into HRJ (from Se ction 7.1). While one case assumed zero land use change emissions, two other independent land use change scenarios

were examined; within which each of the aforementioned key parameters were varied.

### 56 of 129 7.2.1 Cultivation of Soybeans

The farming energy data used was based on 2002 survey results provided by USDA’s Economic

Research Service. 39 Th ese data are shown in Table 44

.

Table 44 : Farming energy required for average US soybean production

Fuel type Farming

F u els ( gal/ha) Farming

energy (Btu/ ha) 2 Fuel share (%)

Diesel 11.9 1 1523500 70.1

Gasoline 3.2 372900 17.1

LPG 1.0 83960 3.9
Electricity 19.3 (kWh/ha) 65780 3.0

NG 3.7 (m 3 /ha) 127500 5.9

Total 2174000 100

Notes: 1) Diesel use included custom work of 1.7 gal/acre (Hill et al., 2006; Sheehan et al., 1998a). 2) LHV values of fuels (except electricity) were obtained from GREET (GREET, 2008).

Soybean production and yield 40 data were based on historical and projected U.S. data. The baseline case assumes a projected soybean yield in 2015 of 110 bu/ha 41 (FAPRI, 2008). The low and high emissions scenarios were based on analysis of historical soybean yield da ta from 1987 through 2007 (USDA, 2008a). The variation between the lowest yield and the mean was

- 28% while that between the highest yield and the mean was +17%. 42 Based on these historical data, it was assumed that the yield in some future drought year cou ld be 28% lower than in the baseline case, corresponding to the high emissions case of 79.5 bu/ha. Similarly, a yield in some future ideal growing year could be 17% higher than in the baseline case, corresponding to the low

emissions case of 129 bu/ha.

U sing the farming energy per hectare and the soybean yield per hectare, the farming energy per bushel of soybean was determined as shown in Table 45 . The high emissions case includes the energy used to grow and process the soybean seed (i.e. seeding energy) as estimated by Hill et al. (2006) to be an additional 4.5% of total energy inputs (including farming and fertilizer energy).

The data on fertilizer (nitrogen, phosphorus and potassium), herbicide and insecticide use in soybean cultivation from recent ye ars was obtained from the USDA (USDA, 2008b). Glyphosate was the most commonly applied herbicide in soybean cultivation. The energy data for the manufacture of glyphosate are not available in GREET, so the energy requirements provided by Hill et al. (2006) (475 MJ/kg) were used. Default GREET assumptions for herbicide energy use were applied for the remaining proportion of non

- glyphosate herbicide use. Insecticide and fungicide are also applied in soybean cultivation, but in negligible amounts (e.g. 0.4 g/b u of insecticide and 0.1 g/bu of fungicide were applied in 2006) compared to fertilizer and herbicide application. Hence, these were not considered here. The average agrichemical use from 2002,

2004 and 2006 was ad opted in all cases (see Table 46 ).

39 Via e

- mail from Kathleen Kassel to Hsin Min Wong on February 28, 2008.

40 Yield is defined as total production divided by total planted area.

41 Based on average of projections in the years 2014/15 and 2015/16. One bushel of soybeans is defined as 60lbs.

42 The average yield between 1987 and 2007 was 90.2 bu/ha; the lowest and hi ghest yields within this time period were 65.0 bu/ha and 105.0 bu/ha, respectively.

### 57 of 129 Table 45 : Farming energy for US soybean production for low emissions, baseline and high emissions cases

Low Baseline High 1

Soybean yield (bu/ha) 129 110 79.5

Diesel 11,789 13,793 20,009

Gasoline 2,886 3,376 4,898

LPG 650 760 1,103 Electricity 509 596 864

NG 987 1154 1,675Farming fuel energy(Btu/bu)

Total 16,820 19,680 28,549

Notes: 1) Includes an additional 4.5% energy to grow and process seeds.

Table 46 : US soybeans agrichemical use

Year Agrichemical use (g/bu) 2002 2004 2006 Average Nitrogen 52 64 31 49 Phosphorus 154 196 114 155

Potash 311 310 214 278

Herbicide (Percentage of glyphosate in herbicide use) 15 (73.6%) 13 (83.1%) 15 (88.8%) 14 (81.8%)

Emissions from N 2 O were estimated using a combination of GREET and IPCC Tier 1 methodology (IPCC, 2006) in the low and baseline scenarios and from Hill et al. (2006) in the high emissions scenario. Specifically, GREET calculated the total amount of nitrogen (in the form of soybean biomass) left on the field as 200.7 g/bushel of soybeans harvested. The IPCC Tier 1 methodology estimates the combined direct and indirect conversion rate for nitrogen from synthetic fertilizers as 1.325% and nitrogen from crop residues as 1.225%. These rates incl ude the atmospheric deposition of nitrogen volatilized from managed soils as well as nitrogen from leaching and runoff. The formula for calculating N 2 O emissions from soybean cultivation is given by:

€ N 2 O Emissions g N 2 O
bu "

# $ %

& '

= g nitrogen fertilizer bu "

# $ %

& '

⋅ 0.01325 + 200.7 g N

- crop residue bu "

# $ %

& '

⋅ 0.01225 "

# $ %

& '

⋅ 44 g N 2 O
28 g N "

# $ %

& ' Equation 10

Assuming a nitrogen fertilizer application rate of 49 g/bu, total N 2 O emissions are 4.9 g/bu. Hill et al. (2006) assumed N 2 O emissions to be an all

- encompassing number of 1760 g/ha based on conventional tillage and rotation of corn, soybean and wheat. Under a soybe an yield of 110 bu/ha, emissions from N 2 O would be 16 g/bu, roughly 3 times higher than those estimated by

GREET.

The scenario analysis adopts zero nitrogen fertilizer use in the low emissions case, 43 the

estimates of GREET in the baseline case and those f rom Hill et al. in the high emissions case. Hence, N 2 O emissions were 3.9 g/bu in the low case, 4.9 g/bu in the baseline case and 22 g/bu in the high emissions case (the high emissions case assumes a soybean yield of 79.5 bu/ha).

Lime (composed primarily of calcium carbonate) can be added to soil to reduce acidity and increase nutrient uptake. However, the use of lime must be accounted for in the overall life cycle

43 Growing condition for soybeans around the US vary substantially. While the average nitrogen usage across the US is 49g/bu, it is uncommon for soybeans to respond to pre

- p lant fertilizer N applications in the central US soybean belt (Franzen, 1999). In many cases, soybeans fix nitrogen into the soil and are planted prior to corn to increase soil nitrogen in soy/corn/wheat rotation farms.

### 58 of 129 analysis of soybean farming emissions. GREET does not account for lime use in soybean

cultiv ation. Hill et al. (2006) estimate 230 kg/ha of CO 2 from liming. The energy and emissions associated with the manufacture of lime were assumed negligible. 44

The low emission case assumes zero liming and the high emissions case uses the full CO 2 emissions from Hill et al. (230 kg/ha); the baseline case uses the average of these extremes (115 kg/ha). The soybean yields from Table 45 result in 1045 gCO 2 /bu (using a yield of 110 bu/ha) in the baseline case and 2893 gCO 2 /bu (using a yield of 79.5 bu/ha) in the high emissions case. A summary of the parameters used in the cultivation of soybeans is gi ven in Table 47

.

Table 47 : Input assumptions for the cultivation of soybeans for the low emissions, baseline and high

emissions cases

Input pa rameter Low Baseline High Soybean yield (bu/ha) 129 110 79.5 Farming energy (Btu/bu) 16 ,820 19,680 28,549 Fertilizer inputs (g/bu) Nitrogen Phosphorus

Potash 0

155

278 49

155

278 n/a

155

278 Herbicide (g/bu) 14 14 14 Percentage of glyphosate in herbicide use (%) 81.8 81.8 81.8

N 2 O emissions (g N 2 O/bu) 3.9 4.9 22 Liming emissions (g/bu) 0 1,045 2,893

7.2.2 Extraction of Oil from Soybeans

The energy needed for the crushing and extraction of oil from soybeans was based on GREET default assumptions (~5,800 Btu/lb soy oil produced). GREET data is based on the study by Sheehan et al. (1998a), but adjusted to reflect potential technological improvements (about 10% less total process energy than the value estimated by Sheehan et al., 1998a). The inputs, outputs and process energy needed per lb of processed soy oil are summarized in Table 48

.

Table 48 : Input, output and process energy for extraction of oil from soybeans

Input (lb) Soybean 5.70 Output (lb) Soy oil 1 Soy bean meal 4.48 Process energy (Btu) Electricity 551

Natural Gas 5134

N

- hexane 182

Total 5867 1

Note: 1) GREET default value of 5867 Btu for simulation year 2010 was adjusted to 5800 Btu for the simulation year 2015 and

used here.

44 The errors introduced by this assu mption are likely small because the energy and emissions that arise from the manufacture of lime are small compared to those of other fertilizers and herbicides, (e.g., nitrogen fertilizer manufacture energy and CO 2 emissions are 42 mmBtu/ton and 2.5 Mg/to n, respectively, compared to 7 mmBtu/ton and 0.5 Mg/ton for lime).

### 59 of 129 7.2.3 Co

- Product Usage and Allocation Methodology
In the extraction of soy oil from soybeans, soybean meal is produced in large quantities (by mass relative to soy oil) as a co

- product. Four methods were explored to allocate the energy and emissions of soybean farming and soy oil extraction between soy oil and soybean meal. These were allocation by mass, energy, market value, and the displacement (system expansion) method. The resultant allocation of GHG emissions between soy oil and soybean meal based on

the four methods a re given in Table 49

.

Soybean meal is primarily used as an animal feed and could potentially displace barley, corn, and soybean. The amount of product that soybean meal would displace was calculated on a protein equivalence basis (i.e. matching of protei n content). Soybean meal was assumed to have protein content of ~48% (Ahmed et al., 1994). Avoidance of land use change emissions associated with the cultivation of the displaced crops was taken into account in the analysis for illustrative

purposes.

7.2.3.1 Displacement of Barley The protein content of barley was assumed to be 12% (USDA, 2008c; OMAFRA, 2003; Kennelly et al., 1995). Hence, 1 lb of soy meal displaces 4 lbs of barley on a protein equivalence basis. As 0.38 lbs of soy meal is produced per MJ of f uel, about 1.5 lbs of barley is displaced per MJ of fuel. From the life cycle assessment of GHG emissions of barley cultivation conducted by Lechón et al. (2005), the CO 2 credit is 71.5 g/lb of barley displaced (71.3 gCO 2 /MJ fuel) and N 2 O credit is 0.36 g/ lb of barley displaced (107.0 gCO 2 e/MJ fuel). Lechón et al. did not include land use change emissions associated with the farming of barley. This work assumed the land use change credit associated with the displaced barley was the avoidance of the conversi on of Brazilian Cerrado grassland to cropland (~2,833,333 gCO 2 /ha when emissions were amortized over 30 years, Fargione et al, 2008). Assuming a barley yield of 5940 lb/ha (Lechón et al., 2005), the resultant land use change credit was 477 gCO 2 /lb of barle y displaced (476g CO 2 /MJ).

7.2.3.2 Displacement of Corn Corn is a widely used animal feed that could be displaced by soy meal. The protein content of corn is about 9% (OMAFRA, 2003; Parish, 2007; Kennelly et al., 1995). Hence, 1 lb of soybean meal displaces 5.3 l bs of corn on a protein equivalence basis. As 0.38 lbs of soybean meal is produced per MJ of fuel, about 2.0 lbs of corn is displaced per MJ of fuel. The life cycle GHG emissions for corn farming were analyzed in GREET using default GREET values (version 1 .8b, GREET, 2008). Specifically, the CO 2 , CH 4 and N 2 O credits per lb of corn displaced were 100.6 g/lb (133.3 gCO 2 /MJ fuel), 0.15 g/lb (5.0 gCO 2 e/MJ fuel) and 0.22 g/lb (86.9 gCO 2 e/MJ fuel), respectively. In the case of corn displacement, land use change c redit was assumed to result

from the avoidance of worldwide conversion of non

- cropland to cropland for corn cultivation as simulated by Searchinger et al. (2008) (11.7 Mg CO 2 /ha when emissions were spread over 30 years). Assuming a corn yield of 150 bu/acr e (average yield from 2003

- 2007, USDA, 2008a), the land use change credit was estimated as 1393 g/lb of corn displaced (1846.0 gCO 2 /MJ fuel).

7.2.3.3 Displacement of Soybeans Soybean is the default product assumed to be displaced by soybean meal in GREET. The pro tein content of soybeans was assumed to be 40% (GREET, 2008). Hence, 1 lb of soybean meal displaces 1.2 lbs of soybean on a protein equivalence basis. As 0.38 lbs of soybean meal is produced per MJ of fuel, 0.46 lbs of soybean is displaced per MJ of fuel p roduced. Based on the assumptions of the baseline scenario, the CO 2 , CH 4 and N 2 O credits per lb of soybeans displaced were 71.0 g/lb (21.3 gCO 2 /MJ fuel), 0.08 g/lb (0.57 gCO 2 e/MJ fuel) and 0.09 g/lb (7.9 gCO 2 e/MJ fuel), respectively. In considering land us e change emissions, worldwide conversion of non

cropland to cropland for corn cultivation as simulated by Searchinger et al. (2008) was used (11.7 Mg CO 2 /ha when emissions were spread over 30 years). This scenario was assumed to broadly apply to the case o f soybean farming. Assuming a soybean yield of 110 bu/ha (as in the baseline case described above), the land use change credit was estimated to be 1773 g/lb of soybean displaced (530 gCO 2 /MJ fuel).

### 60 of 129 7.2.3.4 Results Table 48 shows six different approaches to allocat ion between soy oil and soy meal. The first three rows show allocation by mass, market value and energy while the rest shows the displacement calculation described above.

Table 49 : Allocation of GHG emissions between soy oil and soy meal using various methodologies

Allocation Approach Soy Oil (%) Soy Meal (%)

Mass 18.2 81.8

Market Value 1 44.7 55.3 Energy 2 33.9 66.1

LUC included for soy meal and barley - 5.7 105.7

LUC included for soy meal but not barley 71.2 2 8 .8 Displacement of barley

no LUC

- 205.8 305.8

LU C included for soy meal

and corn - 234.5 334.5

LUC included for soy meal

but not corn 63.6 3 6 .4 Displacement of corn

no LUC

- 286.2 386.2
LUC included for both soy

meal and displaced soybeans 9.6 90.4

LUC included for soy meal but not displaced soybeans 95.2 4 .8 Displacement of soybean

no LUC 48.8 51.1

Notes: 1) Market value of soy oil and soy meal are $1.05/kg and $0.29/kg (FAPRI, 2009) 2) Energy content of soy oil and soy meal are 37.2 MJ/kg and 16.2 MJ/kg (GREET, 2008) 3) Life cycle GHG emissions from soybean production, soy oil extraction and land use change are 31.5 gCO 2 e/MJ, 26.9 gCO 2 e/MJ, 599.0 gCO 2 e/MJ of fuel produced 4) Conversion ratio used is 0.587 lb HRJ /lb oil and energy allocation to HRJ after hydroprocessing i s 65.3% 5) LUC refers to land use change

7.2.3.5 Discussion

The use of different co

- product allocation approaches can result in substantially different results, particularly when significant amounts of co

- product are produced and different allocation approaches are appropriate for different scenarios and pathways. The displacement method, as applied here, is not appropriate because of the large variation in life cycle GHG emissions that would result from the choice of feedstock (i.e., barley, corn, and soybean) displaced by soy meal and how land use change are addre ssed. A more appropriate use of the displacement method would be achieved by modeling worldwide agriculture; however, that is beyond the scope of the analysis presented here.

In this case, the mass or energy allocation method may not be most appropriate a s soy meal is not valued based on its mass, or sold as a commercial energy product. Further, allocation by mass would benefit a fuel producer, as it will result in most of the emissions being assigned to the meal instead of the oil. As discussed with regar d to the product slate from a Fischer Tropsch facility, a general shortcoming in the displacement approach arises when a large quantity of co

product is generated relative to the primary product. This causes an overestimation of credits for

the co

- product and may even lead to net negative emissions. Because the high protein content of soy meal leads to commercial value as an animal feed, market value allocation was adopted here

### 61 of 129 and in all cases where oil extraction from an oilseed results in meal leaving th e system and an independent product. Market value allocation is further explored in the next section.

7.2.3.6 Market Value Allocation with Soy Oil to HRJ In using the market value allocation approach, temporal fluctuations in the market prices of co

products cou ld cause temporal changes in the allocation values. The sensitivity to market forces is apt in this case as it allocates shares of the energy and emissions from production based on the current utility (of which price is the most reasonable measure) for eac h product. If a market

becomes saturated with a co

- product then the utility and market value will both approach zero. Market fluctuations make getting a consistent result challenging. Figure 13 shows the temporal

variation in the allocation fraction betwee n soy oil and soy meal from 2005 through 2015. Price projections are based on the database maintained by FAPRI (FAPRI, 2009) and physical quantities are based on Table 48. The dashed lines correspond to the constant values used within GREET (GREE T, 2008).

Figure 13 : Temporal variation in the soy oil and soy meal allocation fractions when using market value

allocation. Constant dashed lines correspond to default GREET prices

Although the GREET default values turn out to be a reasonable mean of actual prices over time, this study used the 2015 FAPRI projects prices of $1.05/kg and $0.29/kg for soy oil and so ybean meal, respectively (FAPRI, 2009).

7.2.4 Transportation of Soy Oil to HRJ Production Facilities

The e stimation of GHG emiss ions arising from transportation (of feedstock and fuel) is based on

de f ault GREET assumptions.

7.2.5 Land Use Change Emissions from Soy Oil Production

The use of food crops as a source of renewable oils for fuel production may lead to emissions

from land use ch ange. The magnitude of land use change emissions can depend on the type of converted land, the type of crops grown and the farming practices employed. In addition to a scenario where no land use change emissions were incurred, two scenarios of land use cha nge were chosen to explore the range of magnitudes of GHG emissions due to land use change. The goal of this section is to provide the reader an understanding of how land use change emissions compare to the emissions from the other five life cycle stages. It is not intended to explicitly quantify the specific land use change emissions that would result from expanded soy oil production.

The first scenario considers the conversion of Cerrado grassland in Brazil to soybean fields and the second scenario considers converting tropical rainforests in Brazil to soybean fields (Fargione !"#$ %"#$ &"#$ '"#$ ("#$

)""&$ )""($ )""*$ )"++$ )"+!$ )"+&$!""#$%&#'()*%$&#'(

,-.$/01$2345678$ ,-.$9:;1$2345678$ ,-.$/01$2<6==>8$ ,-.$9:;1$2<6==>8$

### 62 of 129 et al., 2008). Using these Brazilian estimates with the pre sent analysis of soybean production implies there is little difference between soybean cultivation in Brazil and soybean cultivation in the US. It further implies a transportation profile for the transportation of soy oil from Brazil to the US; however, th e magnitude of the GHG emissions resulting from this additional transportation were assumed to be negligible relative to the GHG emissions from either LUC scenario. In all scenarios, the impact of varying soybean yield was explored on land use change emiss ions amortized over 30 years. The impacts per bushel of soybeans are shown in Tabl e 50

.

Table 50 : Land use change emissio ns arising from soybean production

Scenario Land use change

emissions (Mg CO 2 /ha) Soybean yi e ld ( bu/ha) Land use change

emissions ( g CO 2 /bu) 3

Grassland

conversion 1 85 2 79.5

110

129 35,600 25,800 22,000

Tropical rainforest

conversion 1 737 2 79.5

110

129 309,000 223,300 190,400

Notes: 1) Soybean yields in Brazil were assumed to be similar to soybean yields in the U.S. (FAPRI, 2008) 2) From Fargione et al., 2008 3) Amortized over 30 years (no discounting)

7.2.6 Results
The life cycle GHG emissions from the production and use of HRJ from soy oil for e ach land use change option are summarized Tables 51

- 53

. Without land use change emissions, the life cycle GHG emissions of the production of HRJ from soy oil range from 0.31 to 0.68 times those of conventional jet fuel. However, as illustrated in the vario us land use change scenarios, land use change emissions can dramatically increase the overall life cycle GHG emissions of the fuel to several times that of conventional jet fuel. Specifically, the land use change scenarios investigated in this work result in life cycle GHG emissions ranging from 0.9 to nearly 9 times those of conventional jet fuel.

Table 51 : Life cycle emissions for the soy oil to HRJ assuming zero land use change emissions

Land Use Change Scenario S0 Low Baseline High

Key Assumptions Soybean yield (bu/ha) 129 110 79.5
Life Cycle CO 2 Emissions by Stage
Biomass Credit (gCO 2 /MJ)

- 73.7

- 70.5

- 68.9 Recovery of feedstock (gCO 2 /MJ) 17.5 20.1 25.9 Transportation of feedstock (gCO 2 /MJ) 1.2 1.2 1.1 Processing of feedstock to fuel (gCO 2 /MJ) 7.1 10.3 13.2 Transportation of jet fuel (gCO 2 /MJ) 0.6 0.6 0.6

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 47.3

- 38.4

- 28.1

WTT CH 4 emissions (gCO 2 e/MJ) 1.2 1.3 1.5

WTT N 2 O emissions (gCO 2 e/MJ) 3.0 3.6 15.4 Total WTW GHG emissions (gCO 2 e/MJ) 27.3 37.0 59.2 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.31 0.42 0.68

### 63 of 129 Table 52 : Life cycle emissions for the soy oil to HRJ assuming conversion of Cerrado grassland to soybean

field

Land Use Change Scenario S1 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ) 54.4 60.8 82.5

WTW CO 2 emissions (gCO 2 /MJ) 77.4 92.8 124.9 Total WTW GHG emissions (gCO 2 e/MJ) 81.7 97.8 141.7 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.93 1.12 1.62

Notes: 1) All other input assumptions (cultivation of soybeans, extraction of soy oil, processing of soy oil to HRJ) are based on those in the S0 emissions case of the corresponding scenario.

Table 53 : Life cycle emissions for the soy oil to HRJ assuming conversion of tropical rainforest

Land Use Change Scenario S2 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ) 471.5 527.2 715.5

WTW CO 2 emissions (gCO 2 /MJ) 494.5 559.2 757.8 Total WTW GHG emissions (gCO 2 e/MJ) 498.8 564.2 774.7 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 5.70 6.45 8.85

Notes: 1) All other input assumptions (cultivation of soybeans, extraction of soy oil, processing of soy oil to HRJ) are based on those in the S0 emissions case of the corresponding scenario.



### 7.3 HRJ from Palm Oil

A palm oil to HRJ pathway was created within the GREET framework using information on farming energy, fertil izer use, yield, oil extraction energy, etc., obtained from the literature, mainly from the Malaysian Palm Oil Board and Wicke et al. (2007). 45 The extraction of oil from palm fresh fruit bunches (FFB) yields two types of oil: palm oil and palm kernel oil. Both oil types were assumed to be main products in the FFB extraction process and used in the production of HRJ. 46 The only co

- product from the process is palm kernel expeller, which can be used as an animal feed. The key parameters chosen for establishing the low emissions, baseline, and high emissions cases were palm fresh fruit bunch yield per acre, farming energy, methane emissions from palm oil mill effluent treatment and processing of palm oil into HRJ 47 (from Section 7.1). In

addition to a scenario whe re land use change emissions were assumed zero, three land use change scenarios were examined where the aforementioned key parameters were varied.

7.3.1 Cultivation of Palm Fresh Fruit Bunches (FFB)

Using a process similar to that of soybean

- to

- HRJ, the yields o f palm FFB were derived from historical data spanning 1987 through 2007 in Malaysia (MPOB, 2008a). The data was extrapolated using a linear fit to estimate the yield in 2015. 48 The resultant yield of 21.3 ton/ha (all

45 Palm oil is not a preexisting pathway within GREET. As such, a new pathway was built within the GREET framework using the soy oil to renewable diesel pathway as a guide.

46 This is in cont rast to other studies where diesel is the desired product from renewable oil processing and palm kernel
oil, whose carbon number distribution centered on C 12 is outside the diesel range, is considered a by

- product instead of a main product in the FFB extra ction process.

47 Although palm kernel oil has a shorter carbon length (mostly 12 carbons in length) than palm oil (mostly 16 and 18 in length), for the purposes of this study, they are both assumed to be 18 carbon atoms in length. This was discussed briefl y in Section 7.1.

48 From the linear fit, the yield was estimated to increase at a rate of about 0.0435 tonnes per year from 18.08 tonnes in

1987.

### 64 of 129 references in this section to tons refer to short tons) was adopted in the baseline case. The percentage variations in the data between the lowest yield and mean, and the highest yield and

mean were found to be

- 14% and +9%, respectively. The high emissions case assumed a yield
14% lower than th e baseline case, corresponding to 18.2 ton/ha. The low emissions case assumed a yield 9% higher than the baseline case, corresponding to 23.1 ton/ha.

In Southeast Asian oil plantations, most of the cultivation and harvest of the palm FFB are done manually with the help of animals. The main fossil energy used is diesel fuel for powering farm machinery and equipment. This work assumed that 3.0 mmBtu/ha of diesel was consumed in the low emissions and baseline cases, while 4.9 mmBtu/ha was used in the high emi ssions case (Wicke et al., 2007). The farming energy per ton of FFB harvested was estimated by combining the farming energy per hectare with the yield of palm FFB (see Table 54 ).

Only nitrogen fertilizer was considered in this study as the GHG emissions contributions from the production of phosphate and potash fertilizers, as well as herbicides, were negligible compared to those of nitrogen fertilizers (Wicke et al., 2007). The application of organic fertilizers (e.g. empty fruit bunches) was not consider ed because their emissions are carbon

- neutral. The nitrogen fertilizer application rate adopted in all three cases (low, baseline and high) was 139 kg/ha (Wicke et al., 2007). The corresponding nitrogen fertilizer application in terms of grams of nitrogen per ton of FFB was calculated based on the yields in each scenario.

Emissions from N 2 O were estimated using IPCC Tier 1 methodology (IPCC, 2006). Due to the lack of data, N 2 O emissions from nitrogen in above and below ground crop residues were not

account ed for in this analysis. The IPCC Tier 1 methodology estimates the combined direct and indirect conversion rate for nitrogen from synthetic fertilizers as 1.325%. This rate includes the atmospheric deposition of nitrogen volatilized from managed soils as w ell as nitrogen from leaching and runoff. The formula for calculating N 2 O emissions from the cult ivation of palm fresh fruit bunches is given by:



### € N 2 

### O Emissions g N 2 O



### ton FFB 

### "

# $ %

& '

= g nitrogen fertilizer



### ton FFB 

### "

# $ %

& '

⋅ 0.01325 "

# $ %

& '

⋅ 44 g N 2 O


### 28 g N 

### "

# $ %

& ' Equation 11

A summary of the parameters assumed in the cultivation of palm FFB discussed above is given in

Table 54.

Table 54 : Assumptions in the cultivation of palm fresh fruit bunches for the low emissions, baseline and high

emissions cases

Input parameter Low Baseline High FFB yield (ton/ha) 23.1 21.2 18.2 Farming energy (Btu/ton FFB) 131,300 143,100 270,800

Nitrogen fertilizer inputs (g/ton FFB) 6,020 6,560 7,640

N 2 O emissi o ns ( g N 2 O/ton FFB) 125.3 136.6 159.1

7.3.2 Extraction of Oil from Palm FFB

Palm oil and palm kernel oil are produced from palm FFB at a processing mill. During the extraction process, palm kernel shells (PKS), fiber, empty fruit bunches (EFB) and kernels are also produced. Palm Kernel Effluent and fiber are burnt to generate electricity to fuel mill processes, while EFB are used as o rganic fertilizers. Palm kernels can be further processed to produce palm kernel oil and palm kernel expeller, which can be used as an animal feed. A waste stream called palm oil mill effluent (POME) is also produced from the extraction of oil from FFB.

PO ME treatment (through anaerobic digestion) results in the emission of CH 4

.

### 65 of 129 The palm oil extraction rate, kernel recovery rate, palm kernel oil extraction rate, palm kernel expeller extraction rate and the energy inputs needed for the crushing and extract ion of oil from palm FFB and palm kernels were derived from data from the Malaysian Palm Oil Board (MPOB, 2008b) and Wicke et al. (2007). The input parameters, energy consumption and product yield are shown in Table 55. The total process energy of 199.5 Bt u/lb oil was used in all the three scenarios (low emissions, baseline and high emissions cases).

The treatment of palm oil mill effluent results in methane production. For every ton of FFB processed, between 1.5 to 2.2 kg of CH 4 is emitted from POME treat ed in an open digesting tank (Reijnders and Huijbregts, 2008). This work assumed methane emissions of 1.5 kg CH 4 /ton FFB in the low emissions case, 1.85kg CH 4 /ton FFB (average value) in the baseline case, and 2.2 kg

CH 4 /ton FFB in the high emissions case. The resultant CH 4 emissions per pound of oil are given

in Table 5 6

.

Table 55 : Inputs, outputs and process energy in the extraction of palm o il and palm kernel oil

Extraction of palm oil from FFB Palm oil extraction rate (ton palm oi l /ton FFB) 1 0.20 Amount of kernels produced (ton kernels/ton palm oil) 1 0.25

Energy input Diesel (litre/t o n FFB ) 2,3 1.36 Extraction of palm kernel oil from kernels Palm kernel oil extraction rate (ton PK O /ton kernel) 1 0.45 Palm kernel expeller extraction rate (ton PK E /ton kernel) 1 0.51

Energy input 2
Electricity (kWh/ton kernel) Diesel (litres/ton kernel) 77.3

17.3 Overall energy use and product yields Input FFB (lb) 4.5 Output Palm oil and palm kernel oil (lb) Palm kernel expeller (lb) 1

0.11 Process energy Electricity (Btu/lb oil) Diesel (Btu/lb oil) Total (Btu/lb oil) 29.7

169.8

199.5

Notes: 1) MPOB, 2008b. 2) Wicke et al., 2007. 3) The amount of diesel energy needed for electricity generation is reduced by the use of palm kernel shell and fiber (by

- products of palm oil extraction) for electricity production.

Table 56 : Methane emissions from POME treatment

Low Baseline High

CH 4 emissions from POME treatment (kg

CH 4 /ton FFB) 1.5 1.85 2.2

CH 4 emissions from POME treatment (g

CH 4 /lb oil) 3.32 4.15 4.98

### 66 of 129 7.3.3 Co

- Product Usage and Allocation Methodology

The market allocation method was used to apportion emissions among palm oil, palm kernel oil and palm kernel expeller 49 to be consistent with the approach taken in the soy oil pathway. The market prices assumed were $0.98/kg for palm oil, $1.22/kg for palm kernel oil and $0.14/kg for PKE, based on average export prices between January and October 2008 (MPOB, 2008b).

7.3.4 Transp ortation of Palm Oil and Palm Kernel Oil to HRJ Production Facilities

It is assumed that palm oil and palm kernel oil are produced in facilities near palm plantations in Southeast Asia and then transported to the United States for hydroprocessing to HRJ. This transportation pathway is not available in GREET. The key assumptions used in this work are

summarized in Table 57.

Table 57 : Assumptions in the transportation of palm oil and palm kernel oil from Southeast Asia to the US

Trans portation of palm oil and PKO from mill to harbor in Southeast Asia Mode (%)

Truck 100 Distance (miles) 63 1

Transportation of palm oil and PKO from Southeast Asia to U.S. ports 2

Mode (%)

Ocean tanker 100 Distance (miles) Gulf Coast (50%)

East Coast (25%) West Coast (25%) Average 13500

12300

7700

11750 Transportation of palm oil and PKO from U.S. ports to HRJ production

facilities 3

Mode (%)

Truck

Rail 50

50 Distance (miles) By truck By rail 100

500

Notes: 1) Wicke et al., 2007. 2) Shipping distances from http://www.asaim.org.tw/Ocean%20Shipping%20Distances.pdf 3) Authors’ own estimates.

7.3.5 Land Use Change Emissions from Palm Oil Production

Like soy oil, palm oil requires the use of arable land for growth and its use in fuel production may lead to land use change emissions. In addition to a scenario where no land use change emissions were incurred, three scenarios of land use change were cons idered using available data in the literature to illustrate the possible range of land use change emissions. The scenarios are regionally appropriate for Southeast Asia and are thus consistent with all the present analysis of palm oil production . The goal of this section is to provide an understanding for the reader of how land use change emissions compare the emissions from the other five life cycle stages. It is

49 In the extraction of palm oil from FFB and palm kernel oil from kernels, co

- products such as palm kernel she ll, fiber, empty fruit bunches and palm kernel expeller are formed. As palm kernel shell and fiber are used for electricity production in the mill and empty fruit bunches as fertilizers in palm FFB cultivation to reduce energy requirement and fertilizer in put, respectively, they are recycled streams within the process rather than co

- products. In addition, the emissions generated by the use of these products are considered carbon

- neutral as they were previously absorbed from the atmosphere during growth. Hen ce, palm kernel expeller is the only co

- product considered in the allocation of process energy and emissions.

### 67 of 129 not intended to explicitly quantify the specific land use change emissions that would result f rom expanded palm oil production.

The first case assumed direct land use change emissions from the conversion of previously logged forest to palm plantations (Wicke et al., 2007). The second and third cases assumed land use change emissions resulting fro m the conversion of tropical rainforest and peat land rainforest in Southeast Asia, respectively, to palm plantations (Fargione et al., 2008). In all scenarios, the total land use change emissions were uniformly allocated over 30 years with no discounting, roughly the life

- span of a palm plantation. In addition, the impact of varying palm FFB yield was explored in each land use change scenario. The resultant land use change emissions per ton of

FFB are shown in Table 58

.

Table 58 : L and use change emissions in the cultivation of palm fresh fruit bunches

Scenario Land use change

Emissions (Mg CO 2 /ha) Assumed palm FFB

y i eld ( ton/ha) Land use change

emissions ( g CO 2 /ton FFB) 1

Conversion of previously logged over

forest -- -- 78,400 2

Conversion of tropical

rainforest 702 3 18.2

21.2

23.1 1,285,700 1,103,800 1,013,000

Conversion of peat

land rainforest 3,452 3 18.2

21.2

23.1 6,322,300 5,427,700 4,981,200

Notes: 1) Assumed to be amortized over 30 years 2) Derived from data in Wicke et al., 2007, assuming crude palm oil energy content of 36 MJ/kg and that 1 lb of FFB produces 0.2 lbs of crude palm oil. 3) Fargione et al., 2008

7.3.6 Results
The key assumptions and corresponding life cycle GHG emissions in the production and use of HRJ from palm oil with and without land use change emissions are outlined in Tables 59

- 62. Excluding land use change emissions, the life cycle GHG emissions of the production of HRJ from palm oil and palm kernel oil range from 0.2 to 0.4 times those of conventional jet fuel. However, when including land use change emissions, they can dominate GHG emissions of the entire pathway. Specifically, the land use change scen arios investigated in this work result in life cycle GHG emissions ranging from 0.4 to over 9 times those of conventional jet fuel.

### 68 of 129 Table 59 : Life cycle emissions for the palm oil to HRJ assuming zero land use change emissions

Land Use Change Scenario P0 Low Baseline High

Key Assumptions Palm FFB yield (ton/ha) 23.1 21.2 18.2
Life Cycle CO 2 Emissions by Stage
Biomass Credit (gCO 2 /MJ)

- 73.7

- 70.5

- 68.9 Recovery of feedstock (gCO 2 /MJ) 4.8 4.9 6.6 Transportation of feedstock (gCO 2 /MJ) 3.3 3.1 3.1 Processing of feedstock to fuel (gCO 2 /MJ) 7.1 10.3 13.2 Transportation of jet fuel (gCO 2 /MJ) 0.6 0.6 0.6

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 58.0

- 51.6

- 45.5

WTT CH 4 emissions (gCO 2 e/MJ) 5.2 6.3 7.4

WTT N 2 O emissions (gCO 2 e/MJ) 4.9 5.1 5.8 Total WTW GHG emissions (gCO 2 e/MJ) 22.5 30.1 38.1 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.26 0.34 0.44

Table 60 : Life cycle emissions for the palm oil to HRJ assuming conversion of logged over forest

Land Use Change Scenario P1 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ) 10.1 9.6 9.5

WTW CO 2 emissions (gCO 2 /MJ) 22.5 28.5 34.4

Total WTW GHG emissions (gCO 2 e/MJ) 32.6 39.8 47.6 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.37 0.45 0.54

Notes: 1) All other input assumptions (cultivation of palm FFB, extraction of palm oils, processing of palm oils to HRJ) are based on those in P0 emissions case of corresponding scenario.

Table 61 : Life cycle emissions for the palm oil to HRJ assuming conversion of tropical rainforest

Land Use Change Scenario P2 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ) 130.7 135.8 155.2

WTW CO 2 emissions (gCO 2 /MJ) 143.1 154.6 180.1 Total WTW GHG emissions (gCO 2 e/MJ) 153.2 166.0 193.3 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 1.75 1.90 2.21

Notes: 1) All other input assumptions (cultivation of palm FFB, extraction of palm oils, processing of palm oils to HRJ) are based on those in P0 emissions case of corresponding scenario.

### 69 of 129 Table 62 : Life cycle emissions for the palm oil to HRJ assuming conversion of peatland rainforest

Land Use Change Scenario P3 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ) 642.8 667.9 763.1

WTW CO 2 emissions (gCO 2 /MJ) 655.2 686.7 788.0 Total WTW GHG emissions (gCO 2 e/MJ) 665.3 698.0 801.2 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 7.60 7.98 9.16

Notes: 1) All other input assumptions (cultivation of palm FFB, extraction of palm oils, processing of palm oils to HRJ) are based on those in P0 emissions case of corresponding scenario.



### 7.4 HRJ from Rapeseed Oil

The production of HRJ from rapeseed oil was analyzed with the GREET framework using cultivation and processing data from the literature. 50 Rapeseed has been grown for the production of animal feed and vegetable oils for both human consumption a nd biofuel production. The leading producers of rapeseed are currently China, Canada, India and the European Union (FAO, 2010). The use of rapeseed oil as a feedstock for biofuels is of particular interest in Europe

, where Rapeseed Methyl Ester is one of t he two main biofuel s under consideration (CONCAWE, 2002). This analysis assumes that ra peseed oil is produced in Europe and subsequentl y imported to the United States to be hydroprocessed into HRJ. The key parameters used to for m the low, baseline and high emissions scenario were the rapeseed yield, oil content, farming energy, fertilizer application, transportation distance and oilseed drying energy requirements. In addition to a scenario where land use change emissions were assumed zero, a scenario where rapeseed is g r own on set

- aside land 51 was examined; the aforementioned key parameters were varied

within each land use change scenario

.

7.4.1 Cultivation of Rapeseed

Rapeseed cultivation was assumed to take place predominantly in the United Kingdom (UK) and

Franc e based on data from Mortimer and Elsayed (2006), Edwards et al. (2007 ), Richards (2000) and Prieur et al. (2008). The analysis was supplemented by additional data from Sweden and Denmark from Bernesson et al. (2004) and Schmidt (2007). This was deemed app ropriate due to the relative similarity in climate among southern Sweden, Denmark, France and the UK.

Rapeseed yield was estimated using data for the UK and France from 1999 through 2009. The baseline scenario adopted a projected rapeseed yield in 2015 o f 3.35 Mg/ha (Eurostat, 2010) using linear regression on the historical data. The low and high emissions scenarios were developed using the same method employed to estimate soybean and palm yields. Specifically, based on historical rapeseed yield data from 1999 through 2009 (Eurostat, 2010), the variation between the lowest yield and the line of best fit was

- 16.8% (UK in 2001) while that between the highest yield and the line of best fit was +15.7% (France in 2009). Based on these historical data,

it was a ssumed that yield fluctuations in some future year could be 16.8% lower than in the baseline case, corresponding to the high emissions case of 2.79 Mg/ha. Similarly, a yield in some

50 Rapeseed oil is not a preexisting pathway within GREET. As such, a new pathway was built within the GREET framework using the soy oil to renewa ble diesel pathway as a guide.

51 From 1988 through 2009, the EU government compensated farmers to remove 10%

- 15% of their land from production to deliver some environmental benefits following considerable damage to agricultural ecosystems and wildlife as a result

of the intensification o f agriculture. The program has since changed such that the set aside system is on a voluntary basis with no compensation. Land that was set aside by farmers could be available for increased rapeseed cultivation (The Telegraph, 2009).

### 70 of 129 future ideal growing year could be 15.7% higher than in the baseline case, corresponding to the low emissions case of 3.89 Mg/acre.

Yearly data from both France and the UK is shown in Figure 14. The weighted average corresponds to the ratio of total harvested weight to total planted area from both countries. Although there is substantial fluctuation in yield from year to year, the underlying trend is increasing over time at a rate of 23.7 kg/ha/year.

Figure 14 : Yearly rapeseed yield for France and the UK between 1999 and 2009

The oil fraction of rapeseed ranges from 40% to 45% by mass based on the sources used to model cultivation. The oil yield per kilogram of rapeseed is higher than any other renewable oil feedstock considered in this work. In this analysis, oil fractions of 45% (Prieur et al., 2008; Bernesson et al., 2004), 44% (Schmidt, 2007) and 41% (Richards, 2000; Mortimer and Elsayed, 2006) were assumed in the low, baseline and high emissions scenarios.

Inputs to rapeseed cultivation are fuels for farming operations, nitrogen fertilizers, phosphate, potash and herbicides. The energy consumption of seed crop growth and processing of seeds is less than 1% of all energy used in cultivation and harvesting and was considered negligible for the purposes of this analysis (Richards, 2000). The usag e per hectare of each of these resources in the low, baseline and high emissions scenario is given in Table 63. Nitrogen fertilizer application was assumed to occur in the form of 50% ammonia and nitrogen solutions and 50%

ammonium nitrate and ammonium sul fate. Defualt GREET assumptions for soybeans were adopted regarding the types of herbicides applied during rapeseed cultivation. The largest

variation was found in the use of diesel fuel on the farm. The low emissions scenario was based

on data from Mortim er and Elsayed (2006) whose estimate for North East England was by far the most optimistic. The baseline scenario was based on French data from Prieur et al. (2008), although Bernesson (2004) gave a similar estimate in his analysis of rapeseed production i n Sweden. The high emissions scenario employs data from Richards (2000) for arable lands in England, which is confirmed by Schmidt (2007) with his farming energy estimates of rapeseed production in Denmark.

The corresponding production inputs per Mg of rapeseed for each scenario were calculated by combining the production inputs and yields per hectare. The production of rapeseed oil results in straw biomass production. The average ratio of oilseed production to straw is approximately 0.96:1. 52 This analys is assumed that the straw was ploughed back into the fields after harvest. This leads to minimized depletion of soil nutrients and fertilizer

52 The straw and oilsee d production from Richards (2000) were 4 Mg/ha and 4.08 Mg/ha, respectively (ratio of 0.98:1). The straw and oilseed production from Schmidt (2007) were 2.93 Mg/ha and 3.13 Mg/ha, respectively (ratio of 0.94:1). !"#$ !"%$ &"#$ &"%$ '"#$ '"%$ ("#$

!))*$ &###$ &##&$ &##($ &##+$ &##*$ &#!#$!"#$%$$&'()$*&'+,-./"0'

,-./01$ 23$ 45/1.-$-167-16658/$

### 71 of 129 savings, which were accounted for in the cultivation inputs (Prieur et al., 2008). Where there is a nearby heat or power generation facility that is outfitted to accommodate biomass feedstocks, the straw represents a potential energy source (Richards, 2000); however, straw from rapeseed is rarely harvested because of burning prob lems with the newer varieties and lower yields than grasses or wheat. Lower yield makes rapeseed straw more expensive to harvest so it is simply tilled back into the soil by most farmers (Bernesson et al., 2004).

Table 63 : Farming energy, fertilizer and herbicide usage f or the production of rapeseed in the low emissions, baseline and high emissions scenarios

Low Baseline High Rapeseed Yield (Mg/ha) 2.79 3.35 3.89 Rapeseed Oil Fraction (mass) 45% 44% 41% Fuel Usage

Diese l (MJ/ ha) 1 1857 2310 3934 Fertilizer Usag e Nitrogen(kg

- N/ha) 2 140 164 180 Phosphate (kg

- P 2 O 5 / ha) 3 34 47 56 Potash (k g

- K 2 O/ ha) 4 35 43 82

Herbicides 5 1.8 2.3 2.8

Notes: 1) Mortimer and Elsayed (2006)

– Low Case; Prieur et al. (2008)

– Baseline Case; Richards (2000)

– High Case 2) Bernesson et al. (2004) and Schmidt (2007)

– Low Case; Prieur et al. (2008)

– Baseline Case; Edwards et al. (2007 )

– High Case 3) Bernesson et al. (2004)

– Low Case; Prieur et al. (2008)

– Baseline Case; Mortimer and Elsayed (2006) and Schmidt (2007)

– High Case 4) Prieur et al. (2008)

– Low Case; Richards (2000) and Bernesson et al. (2004)

– Baseline Case; Mortimer and Elsayed (2006) and Schmidt (2007)

– High

Case 5) Richards (2000)

– Low Case; Prieur et al. (2008)

– Baseline Case; Mortimer and Elsayed (2006)

– Hi gh Case

Emissions from N 2 O were estimated using IPCC Tier 1 methodology (IPCC, 2006). Nitrogen in above and below ground crop residues was estimated by applying the aforementioned crop residue production ratio to estimate the amount of straw tilled back into the soil. Rapeseed straw has been characterized as 0.75% nitrogen by mass (Karaosmanoglu et al., 1999) leading to 7125 g of nitrogen reapplied to the field in the form of straw biomass per megagram of oilseed production

. The IPCC Tier 1 methodology es timates the combined direct and indirect conversion rate for nitrogen from synthetic fertilizers as 1.325% and nitrogen from crop residues as 1.225%. These rates include the atmospheric deposition of nitrogen volatilized from managed soils as well as

nitro gen from leaching and runoff. The formula for calculating N 2 O emissions from rapeseed cultivation is given by:

€ N 2 O Emissions g N 2 O Mg "

# $ %

& '

= g nitrogen fertilizer Mg "

# $ %

& '

⋅ 0.01325 + 7125 g N

- crop residue Mg "

# $ %

& '

⋅ 0.01225 "

# $ %

& '

⋅ 44 g N 2 O 22g N Equation 12

The least defined aspects of rapeseed production are the drying and storage practices. In practice, th ere is little consensus on the oilseed moisture content at harvest, which has implications for energy consumption during drying. A recent survey of current harvesting, drying and storage practices of oilseed rape in the UK found that most farmers harvest a bove 12% moisture content, which is the threshold for Ochratoxin B production (Armitage et al., 2005). 53

53 Ochratoxin B is the most abundant food co ntaminating mycotoxin in the world. Human exposure occurs primarily through consumption of improperly stored food products (Armitage et al., 2005)

### 72 of 129 After harvest the rapeseed must be dried to a moisture content of 9% for storage (Prieur et al., 2008; Richards, 2000; Schmidt, 2007). Most rapeseed is stored for about 3 months before being sold. This tendency is driven mainly by market strategy and cash flow. Longer storage periods may require lower moisture contents to minimize mite infestation and deterioration through rancidity. Mites are the greates t problem faced by rapeseed famers and were observed on more than 25% of sites. From the perspective of seed crushers, moisture content (high or low) and admixture of stores were the most common reason for rejection or price reductions (Armitage et al., 20 05). This analysis assumed moisture contents of 13%, 14% and 15% at harvest and 9% after drying in the low emissions, baseline and high emissions scenario, respectively. Losses due to mites, rancidity and admixture were neglected due to lack of quantitativ e data but these could become important for ill

- maintained rapeseed stored over long periods of time.

Table 64 : Rapeseed drying and storage assumptions in the low emissions, baseline and high emissions

scenario

Low Baseline High

Moisture Content

Harvest 1 13% 14% 15% Storage 2 9% 9% 9% Drying Energy Consumption 3

Diesel (MJ/L evapor a ted ) n/a 4.7652 6 Grid Electricity (MJ/L evapor a ted ) n/a 0 3.6 Diesel (MJ/Mg) 165.0 277.0 423.5 Grid Electricity (MJ/Mg) 0.0 0.0 254.1

Notes: 1) Mortimer and Elsayed (2006)

– Low Case; Average of low and high

cases

– Baseline case; Bernesson et al. (2004)

– High Case 2) Prieur et al. (2008), Richards (2000), Mortimer and Elsayed (2006), Schmidt (2007) 3) Mortimer and Elsayed (2006)

– Low Case; Ber nesson et al. (2004)

– Baseline case; Schmidt (2007)

– High Case

Approximately one third of farmers surveyed in the UK use ambient air drying to reduce the moisture content of their seed from the value at harvest to that required for storage. Drying using ambient air can take from 2 to 4 weeks, although 2 weeks is the most common. The other two thirds of farmers use hot air dryers to reduce drying times at the expense of increased energy consumption (Armitage et al., 2005). This work assumed the use o f hot air drying according to energy consumption estimates from Mortimer and Elsayed (2006), Bernesson et al. (2004) and Schmidt (2007) in the low, baseline and high emissions scenarios, respectively. The assumptions regarding the drying and storage of rap eseed used in this work are summarized in Table 64.

7.4.2 Extraction of Oil from Rapeseed

A modified version of the process for oil extraction from soybeans established by Sheehan et al. (1998a), including only the processes relevant to rapeseed in an N

- hexane e xtraction facility, was used to model the process inputs to extracting oil from rapeseed. Ozata et al. (2009) also used this approach in their analysis of biodiesel from rapeseed. The changes to the data from Sheehan et al. (1998a) were limited to removing the energy demands for drying as this has been explicitly quantified for rapeseed in Table 64. The process energies were converted from energy per unit mass of oilseed to energy per unit mass of oil using the oil fractions from the low emissions,

baseline and high emissions scenarios. The outputs and energy consumption assumed in the extraction of oil from rapeseed are shown in Table 65.

### 7 3 of 129 Table 65 : Process inputs for extracting oil from rapeseeds

Low 1 Baseline 1 High 1

Receiving and Storage Electricity 18.60 19.02 20.41 Rapeseed Preparation Electricity 68.76 70.32 75.46

Steam 153.35 156.83 168.31

Oil Extraction Electricity 11.46 11.73 12.58

N

- hexane 94.15 96.29 103.34 Meal Processing Electricity 63.56 65.01 69.77

Steam 492.54 503.74 540.59 Oil Recovery Electricity 1.21 1.24 1.33

Steam 77.04 78.79 84.55 Solvent Recovery Electricity 1.66 1.69 1.82

Steam 0.00 0.00 0.00 Oil Degumming Electricity 5.38 5.50 5.91

Steam 60.53 61.91 66.44

Waste Treatment Electricity 1.82 1.86 1.99

Steam 32.56 33.30 35.74

Totals Electricity 172.45 176.37 189.27

Natural Gas 2 1020.03 1043.21 1119.54

N

- hexane 3 94.15 96.29 103.34

Notes: 1) All values are in Btu per pound of oil 2) Steam is generated from natural gas with an efficiency of 80%. 3) GREET uses Liquefied Petroleum Gas (LPG) as a surrogate for N

hexane when calculating emissions

7.4.3 Co

- Product Usage and Allocation Methodology
In the extraction of oil from rapeseed, rapeseed meal is produced in large quantities as a co

product (1.22

- 1.44 kg of meal per kg of oil). This is similar to the extraction of oil from soybeans; hence, the same challenges with respect to allocation arise. Rapeseed meal is primarily used as an animal feed and could potentially displace barley, c orn, and soybean meal. One kg of rapeseed meal is equivalent to 0.87 kg of soybean meal on a protein basis (Prieur et al., 2008).

Based on the results from section 7.2.3, the displacement method, as applied here, is not appropriate because of the large va riation in life cycle GHG emissions that will result from the choice of feedstocks (i.e., barley, corn, and soybean meal) displaced by rapeseed meal. The mass or energy allocation method may not be most appropriate as rapeseed meal is not valued

based on i ts mass, or sold as a commercial energy product. The commercial value of the protein in rapeseed meal for animal feed resulted in market value allocation being adopted in this work. This is internally consistent with the other pathways considered in this w ork as well as with Prieur et al. (2008), Mortimer and Elsayed (2006) and Ozata et al. (2009).

7.4.4 Transportation of Rapeseed Oil to HRJ Production Facilities

Rapeseed cultivation, harvesting and oil extraction were assumed to occur primarily in France

and the UK. The one

- way distance from the farm to the oil extraction facility was assumed to be

### 74 of 129 115 km (Prieur et al., 2008). 54 It was then assumed that the United States imports this oil to a domestic hydroprocessing facility where it is converted to jet fuel. Th is transportation profile is not available in GREET and was created using the data from Table 66. Default GREET assumptions were used for the details of each transportation mode.

Table 66 : Transportation profile of Rapeseed Oil from Europe to the United States

Transportation of Rapeseed Oil to European Shipping Ports 1

Mode (%)

Truck 100 Distance (kilometers) 150 Transportation of Rapeseed Oil from European Ports to U.S. ports 2

Mode (%)

Ocean tanker 100 Distance (kilometers) Western UK to Eastern US (50%) Western France to Eastern US (25%) Southern France to Western US (25%) Average 5520

5780

7170

6000 Transportation of Rapeseed Oil from U.S. ports to HRJ production facilities 1

Mode (%)

Truck

Rail 50

50

Distance (miles) By truck By rail 160

800

Notes: 1) Author’s own estimates 2) Shipping distances from http://www.searates.com/reference/portdistance/

7.4.5 Land Use Change Emissions from Rapeseed Oil Production
Two scenarios of land usage were considered for the cultivation of rapeseed in France and the UK. Rapeseed is an established crop in these areas where over 2 million hectares were harvested in 2008 (Eurostat, 2010). The first scenario represents established rapeseed production with an assumption that land use change emissions were zero. A second scenario resulting in positive GHG emissions was envisioned where rapeseed production is expanded for biofuel production on set aside land thus resulting in land use change emissions. Set aside land is land that was removed from agricultural production as a result of government mandates to re

establish some environmental benefits to agricultural ecosystems and wildlife. This is similar to

the Conservation Reserve Program (CRP) in the United States. While removed from production, the natural carbon and nitrogen stocks of the land are replenished. Participation by farmers in the set aside system has recently changed to voluntary and these lands could be avail able for rapeseed cultivation (The Telegraph, 2009). A return to crop production would cause the accumulated carbon and nitrogen in the soils to be depleted over time. The goal of this section is to provide an understanding for the reader of how land use c hange emissions compare to emissions from the other five life cycle stages. It is not intended to explicitly quantify the specific land use change emissions that would result from expanded palm oil production.

The estimate of GHG emissions resulting from the conversion of set aside land to rapeseed cultivation was based on Schmidt (2007). The total land use change emissions were amortized over 30 years with no discounting. The corresponding land use change emissions per Mg of rapeseed were calculated by c ombining the GHG estimate with the yield per hectare; this is presented in Table 67.

54 Richards (2000) and Mortimer and Elsayed (2006) estimated one

- way transport distances of 90 km and 130 km, respectively.

### 75 of 129 Table 67 : Land use change emissions from rapeseed cultivation on set aside lands in Europe

Scenari o Land use change

Emissions (Mg CO 2 e/ha) Assumed Rapeseed yield (Mg/ha) Land use change

emiss i ons ( g CO 2 e/Mg) 1

Conversion of

set

- aside land 2 94.6 2.79

3.35

3.89 1,129,700 940,800 810,200

Notes: 1) Assumed to be amortized over 30 years with no discounting 2) Estimate from Schmidt (2007)

7.4.6 Results
The life cycle GHG emissions from the production and use of HRJ fuel from rapeseed oil are given in Table s 68 and 69

. When no land use change emissions are present, the life cycle GHG emissions range from 0.45 to 0.87 times those of conventional jet fuel; however, nitrous oxide emissions represent between 39% and 44% of the total. Edwards et al. (2007 ) found nitrous oxide emissions from rapeseed production to be of similar magnitude using an independent, well

validated soil chemistry model (DNDC, version 82 N) . When rapeseed is grown on set aside land, the life cycle GHG emissions range from 0.87 to 1.47 times those of conventional jet fuel. The variation in the biomass credit is due to minor changes in the allocation scheme through the

p athway. The transport ation of oil across the Atlantic is responsible for only 0.6 g CO 2 e/MJ; these emissions would not have been incurred had the fuel been processed and used within Europe.

Nitrous oxide emissions represent more than approximately 40% of the total life cycle GHG emissions from the rapeseed to HRJ pathway. As such, the consequences of the uncertainty associated with IPCC correlations are more important for this pathway. Although the magnitude of

N 2 O emissions in this work compare favorably with the detailed mod el used by Edwards et al. (2007), the reader should be aware of these inherent uncertainties when comparing different pathways for GHG reduction potential in sections 8.1 and 8.2.

Table 68 : Summary of results from renewable jet fue l production and use from rapeseed

Land Use Change Scenario R0 Low Baseline High Key Assumptions Total Biomass Yield (Mg/ha/yr) 2.79 3.35 3.89

Seed Oil Fraction 45% 44% 41% Life Cycle CO 2 Emissions by Stage Biomass Credit (gCO 2 /MJ)

- 73.7

- 70.5

- 68.9 Recovery of feedstock (gCO 2 /MJ) 13.6 17.2 26.4 Transportation of feedstock (gCO 2 /MJ) 3.2 3.1 3.1 Processing of feedstock to fuel (gCO 2 /MJ) 7.1 10.3 13.2 Transportation of jet fuel (gCO 2 /MJ) 0.6 0.6 0.6

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 49.2

- 39.2

- 25.7

WTT CH 4 emissions (gCO 2 e/MJ) 1.0 1.3 1.7

WTT N 2 O emissions (gCO 2 e/MJ) 17.6 22.4 29.5 Total WTW GHG emissions (gCO 2 e/MJ) 39.8 54.9 75.9 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.45 0.63 0.87

### 76 of 129 Table 69 : Life cycle GHG emissions for production and use of renewable jet fuel from rapeseed assuming

cultivation on set

- aside land

Land Use Change Scenario R1 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ) 38.4 43.0 52.6

WTW CO 2 emissions (gCO 2 /MJ) 35.8 40.0 48.9

WTW N 2 O emissions (gCO 2 e/MJ) 2.7 3.0 3.6

Total WTW GHG emissions (gCO 2 e/MJ) 78.2 97.9 128.5

Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.89 1.12 1.47

Notes: 1) All other input assumptions (rapeseed cultivation, extraction of oil, processing of oil to HRJ) are based on those in the R0 emissions case of the corresponding scenario.



### 7.5 HRJ from Jatropha Oil

The creation of HRJ from jatropha oil is not a pathway available in GREET; hence, supporting information was obtained from the literature and a pathway was constructed within the GREET framework. The jatropha fruit is composed of an outer capsule containing two or three seeds.

Eac h seed has a shell and a kernel, which contains oil. This structure differs from other oil seed crops discussed thus far because additional co

- products beyond meal result from the oil extraction proces s.

There are also concerns with jatropha cultivation f or biofuels that do not apply to soy, palm, or rapeseed. An overarching concern of jatropha cultivation for fuel production is that the biomass

co

- products are toxic to both humans and animals. Further, there are questions regarding the

introduction of a n on

- native invasive species to the North American ecosystem. The toxicology of jatropha oil is discussed later in this section while more details on the impacts of invasive species within the North American ecosystem are provided in Section 8.4.

7.5.1 Yield and P lant Characterization

Jatropha plants are well adapted to semi

- arid conditions, although more humid environments are shown to result in higher crop yields. The plant can tolerate high temperatures but it does not tolerate frost, which causes immediate damage. Upon removal of the outer capsule, the primary products are seeds and the co

- products are husks; at this stage, the seeds can be processed directly or the shells can be removed through decortication. 55 If the seeds are left intact, oil is

extracted leaving de

- oiled cake as the co

- product. If the shells are removed before oil extraction, oil and meal are created from the kernel and the shells are considered a separate co

- product. The parts of the jatropha fruit are characterized in Table 70.

Upon exa mination of 28 growth sites worldwide, a correlation of 0.22 was found between the quantity of precipitation and seed yield (Achten et al., 2008). This means that although more precipitation is moderately connected to higher seed yields, there are many cas es where excellent yields have been realized in dry conditions and poor yields realized in wet conditions. The majority of data used in this work came from cultivation details and physical characterization of jatropha by Reinhardt et al. (2008) and Achten et al (2008).

Based on these data, it was concluded that an average yield of 2500kg/ha/yr of dry seeds is a representative estimate while 5000kg/ha/yr could be realized under optimal management practices; 1000kg/ha/yr appears to be a reasonable lower bou nd (Achten et al., 2008; Reinhardt et al., 2008). These values provide the bounds on yield for the low and high emissions cases.

55 Decortication is a procedure involving the removal of a surface layer, membrane or fibrous cover. In the case of jatropha, this refers to the removal of the shells from the kernels.

### 77 of 129 Note that yields are quoted in terms of seed weight. To fully characterize the fruit, mass ratios of oil to seed, husk to seed and kernel to seed were developed for the low emissions, baseline and high emissions cases. These are shown Table 71 in conjunction with yield assumptions.

Table 70 : Parts of the jatropha fruit including processed states

Product Energy

Content (MJ/kg) Sub

fractions Description

Capsule

-- Husk +

seeds Entire fruit

Husk 15.5

-- Outer core of fruit, surrounding seeds; green and ‘fleshy’ in fresh state, later brown and dry

Seed

-- Shell +

kernel Compact unit inside fruit, consists of shell and kernel, usually 2

- 3 per capsule

Shell 19.0

-- Brown or black shell surrounding the

kernel

Kernel

-- -- White compact nucleus of seed, actual

oil

- containing part of the fruit

Oily cake 19.5 Seeds (processed) Leftovers from the mechanical oil extraction from seeds (incl. shells),

contains residual oils

De

- oiled

cake 17.5 Seeds (processed) Leftovers from the mechanical and

solvent

- aided oil extraction from seeds

Meal 18.0 Kernels (processed) Leftovers from the mechanical and

solvent

- aided oil extraction from seeds

Notes: 1) Reinhardt et al (2007 and 2008)

Table 71 : Yields and mass fractions characterizing the jatropha fruit

Low Baseline High Jatropha Yield (kg seed /ha/yr) 5000 2500 1000

Oil Ratio (kg oil /kg seed ) 0.37 0.35 0.34 Husk Ratio (kg husk /kg seed ) 0.48 0.60 0.60 Kernel Ratio (kg kernel /kg seed ) 0.67 0.63 0.63

7.5.2 Cultivation of Jatropha Fruit

The input assumptions surrounding jatropha cultivation are based on a 30

- hectare test plot in India documented by Reinhardt et al. (2008). The inputs required for growth are seedlings, irrigation water (first three years only), diesel fuel (for tractor and irrigation pump) and mineral fertilizers in the form of nitrogen, phosphorous pentoxide (P 2 O 5 ) and potassium oxide (K 2 O).

S ince irrigation water is needed for only the first three years of growth, this analysis does not include the diesel fuel required for pumping irrigation water in calculating life cycle GHG emissions of HRJ from jatropha. The cultivation assumptions adopted in this work are listed in

Table 72.

### 78 of 129 Table 72 : Cultivation inputs for the growth of jatropha

Low Baseline High

Cultivation Diesel (Btu/kg seed ) 1163 1320 1419

Pesticides (g/kg seed ) 0 0 0 Nitrogen (g/kg seed ) 31.8 34.0 35.3

P 2 O 5 (g/kg seed ) 12.6 13.0 13.4

K 2 O (g/kg seed ) 31.3 37.4 37.4

Nitrous oxide emissions from jatropha cultivation were estimated using the IPCC Tier 1 methodology (IPCC, 2006). Due to the lack of data, N 2 O emissions from nitrogen in above and below ground crop residues were not accounted for in this analysis. The IPCC Tier 1 methodology estimates the combined direct and indirect conversion rate of nitrogen from synthetic fertilizers to N 2 O emissions as 1.1%. These rates include the atmospheric deposition of nitrogen volatilized from managed soils; however, nitrogen from leaching and runoff was assumed to be negligible, in terms of N 2 O conversion, as jatropha is ideally grown in well

- drained or gravelly soils 56 (Achten et al., 2008). The formula for calculatin g N 2 O emissions from jatropha cultivation is given by:



### € N 2 

### O Emissions g N 2 O 

### kg seed 

### "

# $ %

& '

= g nitrogen fertilizer 

### kg seed 

### "

# $ %

& '

⋅ 0.011 "

# $ %

& '

⋅ 44 g N 2 O 

### 28g N 

### "

# $ %

& ' E quation 13

7.5.3 Toxicity of Jatropha Fruit and Oil

Due to the toxicity of jatropha fruit and oils, attention is warranted to the impacts on human health and work environment. The fruits contain irritants affecting pickers and manual dehuskers. In addition, accidental consumption of the seeds or oils can le ad to severe digestion problems. For these reasons, intercropping edible crops with jatropha is only recommended during the period before any fruit is borne (Achten et al., 2008). Gandhi et al. (1995) evaluated the crude oil in vivo

and in vitro for toxici ty, skin irritation and haemolytic activity. The toxic fraction containing phorbol esters was subsequently isolated from the oil. Locals use the oil as a cathartic purgative and for treating skin ailments. The seeds are also used for the treatment of drops y, gout, paralysis and rheumatism. Upon administering tests on mice, rats and rabbits, jatropha oil was found to be acutely toxic after oral administration, leading to diarrhea, bloodshot eyes and inflammation of the gastro

- intestinal tract. Topical applic ation not only had an irritant effect but also caused diarrhea and mortality in the animals. Gandhi et al. (1995) summarize the impacts of ingestion and contact with jatropha oil as a severe health hazard to livestock and humans. The phorbol esters in the oil

are irritants of skin and mucous membranes produce haemolysis of red blood cells and are reported to be tumor promoters (Gandhi et al., 1995). These results lead to the conclusion that complete removal of toxins is essential before any industrial appli cations are considered for jatropha oil. The removal of toxins was not modeled as a source of GHG emissions within this analysis

.

7.5.4 Transportation of Jatropha Fruit to Production Facilities

Even though there are questions regarding the wisdom of introducing potentially invasive species such as jatropha to non

- native environments, the production of jatropha in this analysis is assumed to take place in the southwestern United States. If the oil were produced overseas, then

there would be an increase in the GHG emissions from transportation, comparable to that discussed in regards to rapeseed oil (see Section 7.4.3). After harvesting, assumptions of the transportation mechanisms and distances are consistent with GREET defaults for the soybeans to HRJ pathway. Spe cifically, the transportation of capsules is by truck to a local storage area and

56 Nitrous oxide emissions from leaching and run off only apply to soils where the soil water

- holding capacity is exceeded (IPCC, 2006)

### 79 of 129 subsequently to a local oil extraction facility. The capsule processing, oil extraction and hydroprocessing of oil to HRJ are assumed to occur at the same location with on si te capabilities for power generation using biomass co

- products. As was the case in other HRJ pathways, transportation elements of the life cycle GHG emissions are sufficiently small to be within the margin of error; therefore, assumptions in this area carr y little consequence.

7.5.5 Extraction of Oil from Jatropha Fruit

Extracting oil from the jatropha fruit has a large influence on the life cycle emissions. Before beginning oil extraction, a dehusker must remove the husks and obtain the seeds. In small

- scale

pro duction facilities, the seeds are then crushed in a screw press to extract the oil. This method can only obtain up to 80% of the oil so larger production facilities mill the seeds into small particles and N

- hexane chemical solvent is used to obtain up to 9 9% of the oils (Achten et al., 2008). The deterministic factor of this life cycle is how the co

- products are used (husks, shells, meal). It is not necessary to remove the shells from the kernels before the solvent treatment, but it is more energy efficient to do so because the additional burden of processing the added material from the shells through the chemical solvent is more than the burden of removing the

shells beforehand. If the shells are removed prior to milling, they are obtained independently of

the meal and the oil. If the shells were not removed, the seeds could be split into a de

- oiled cake and the oil itself. Regardless of whether the shells are removed, the resultant product is not suitable for animal and human consumption because of its toxi city (see Section 7.5.3).

The co

- products from oil extraction can be used in a multitude of capacities. Figure 15 shows the products resulting from this process and potential uses for each.

Figure 15 : Utilization of by

- products from jatropha cultivation and oil extraction (adapted from Reinhardt, 2007)

When using the co

- products from jatropha, caution must be maintained due to its toxicity. For example, the use of shells and meal as fertilizer in edible crop production raises bi o

- safety questions and warnings have been issued to the serious lack of information surrounding the

he alth effects of burning the oil in closed quarters (Achten et al., 2008).

To estimate the emissions from oil extraction, this work combined the process in puts from Reinhardt et al. (2008) with modified processes for oil extraction from soybeans established by Sheehan et al. (1998a) to model oil extraction from jatropha fruit in an N

- hexane extraction facility. The assumed process energies are summarized Tab le 73

. !"#$%&$'()

*+&(,-'+.)

/+'01,,2(3)

4 516",72(3))

&(58'+)
-+'01,,2(39)

510'+$0&$'()) &(5):2##2(39)

+1;(2(3<)

=&.+'-6& )>2#) ?",7,)

@1+$#2A1+) /'B1+)

@'551+)

C2'3&,) /'B1+) @1+$#2A1+)

D1&#) >2#E)!&71)

F1G'2#15)!&71)

H61##,)

F1.'I2;0&$'() C+2J"1K(3)

/'B1+)

### 80 of 129 Table 73 : Process inputs for extracting oil from jatropha fruit

Low 1 ,3 Baseline 1 High 1

Receiving and Storage Electricity 33.5 38.3 39.4 Jatropha Preparation Electricity (briquetting) 93.2 122.8 126.6 Electricity (decorticator) 23.2 24.6 25.3

Electricity (dehusking) 51.6 59.0 60.7

Electricity (cleaning) 3.0 2.9 3.0 Steam (cleaning) 125.9 123.2 127.0

Oil Extraction Electricity 9.4 9.2 9.5

N

- hexane 4 77.3 75.7 77.9 Meal Processing Electricity 38.1 37.3 38.4

Steam 368.7 361.0 371.9 Oil Recovery Electricity 1.0 1.0 1.0

Steam 63.2 61.9 63.8 Solvent Recovery Electricity 1.4 1.3 1.4

Steam 0.0 0.0 0.0 Oil Degumming Electricity 4.4 4.3 4.5

Steam 49.7 48.6 50.1

Waste Treatment Electricity 1.5 1.5 1.5

Steam 26.7 26.2 27.0

Totals Electricity 260.3 302.1 311.2

Natural Gas 2 792.8 776.3 799.6

N

- hexane 4 77.3 75.7 77.9

Notes: 1) All values are in Btu per pound of oil 2) Steam is generated from natural gas with an efficiency of 80%. 3) The low emissions case appears to have higher process energy than the baseline; however, this arises due a larger fraction of the seed weight going through the extraction processes (see Table 71) and is counteracted in the fuel production stage. 4) GREET uses Liquefied Petroleum Gas (LPG) as a surrogate for N

hexane when calculating emissions

7.5.6 Co

- Product Usage and Allocation Methodology

To understand the influence of co

- product type and usage on the life cycle emissions of the fuel, four scenarios, outlined in Table 74, were considered. These scenarios examined how the life cycle GHG emissions of HRJ from jatropha oil change depending on the use of co

- products for animal feed, fertilizer or electricity production. The electricity production was further broken d own to examine displacement of US average grid electricity and energy allocation between oil and electricity. The life cycle GHG emissions resulting from each scenario are summarized in Figure

16.

As explained below, Scenario 1 was chosen for this analysi s. Using the products for power generation seems a more logical choice for the biomass co

- product because the displacement of

### 81 of 129 fertilizer on large scales with a product that is toxic to humans was deemed undesirable due to the potential for ground water con tamination (this eliminates Scenario 2). Energy allocation was chosen over displacement due to the large quantities of co

- product generated per unit of oil production (this eliminates Scenarios 3 and 4). As shown in Figure 16, the displacement of fossil

ba sed electricity results in the unrealistic result of negative life cycle GHG emissions for the fuel. It is important to understand that the allocation method used in scenario 4 attributes energy and emissions based on the electricity produced from the biom ass co

- products and the energy

contained in the oil. This is equivalent to expanding the system boundary of the oil extraction stage to include the power generation unit. Assumptions regarding the generation of renewable electricity from biomass are outlin ed in Table 75

.

Table 74 : Co

- product creation and allocation scenarios from the oil extraction process for jatropha capsules. The corresponding life cycle emissions for each scenario are shown in Figure 1 6

Scenario Co

- Product Use Emissions Allocation Method

1 Husks, shells

and meal Burned for electricity Energy allocation via energy of electricity produced and energy

contained in the oil

2 Husks, shells

and meal Fertilizer Displacement method (system expansion) where fertilizers are displaced in subsequent cultivation practices

Husks and

shells Burned for electricity Displacement method (system expansion) where electricity from US grid is displaced 3

Meal Detoxified and sold

for animal feed Market value allocation between oil and

meal.

4 Husks, shells

and meal Burned for electricity Displacement method (system expansion) where electricity from US grid is displaced

Figure 16 : Sensitivity of life cycle emissions of HRJ from jatropha to co

- product utilization and allocation
scheme. Scenarios are described in Table 74

Table 75 : Assumptions used in establishing average US biomass conversion efficiency to ele ctricity !"#$ !%#$ !&#$ !'#$ !(#$ #$ (#$ '#$ &#$ %#$ "#$ %$ &$ '$ ($

!"# $%&' ()$%*+*%,-".."/0.1%2& 3 4 $567% 

### !"#$%&'()*)

!"#$%&'()+))

!"#$%&'(),))

!"#$%&'()-)) .%/#0'$#)1 %02#)

Generation Method Production
Share Generation Efficiency Biomass Utility Boiler 99% 32.1%

Biomass IGCC 1% 43.0%

Note: 1) Transmission losses of 8% were also included in the overall efficiency of generation

### 82 of 129 7.5.7 Results
The life cycle emissions from the production and use of HRJ fuel from jatropha oil are given in

Table 76. The most noticeable difference in life cycle GHG emissions of jatropha HRJ in comparison to other renewable oil plants is the larger nitrous oxide emissions arising from nitrogen f ertilizer requirements for plant growth. The life cycle GHG emissions resulting from the production and use of jatropha HRJ range from 0.36 to 0.52 times those from conventional jet fuel. The variation in the biomass credit is due to minor changes in the a llocation scheme through the pathway. Because marginal land was assumed and no estimates of root carbon sequestration from jatropha were available, the GHG emissions from land use change were assumed to be zero and no other land use change scenarios were c reated.

Table 76 : Life cycle emissions from the jatropha oil to HRJ pathway

Low Baseline High

Key Assumptions Jatropha Seed Yield (kg/ha/yr) 1000 2500 5000 Jatropha Seed Oil Fraction 0.34 0.35 0.37 Life Cycle CO 2 Emissions by Stage Biomass Credit (gCO 2 /MJ)

- 73.7

- 70.5

- 68.9 Recovery of feedstock (gCO 2 /MJ) 16.1 16.7 17.6 Transportation of feedstock (gCO 2 /MJ) 1.5 1.5 1.5 Processing of feedstock to fuel (gCO 2 /MJ) 7.1 10.3 13.2 Transportation of jet fuel (gCO 2 /MJ) 0.6 0.6 0.6

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 48.5

- 41.3

- 36.1

WTT CH 4 emissions (gCO 2 e/MJ) 1.0 1.2 1.4

WTT N 2 O emissions (gCO 2 e/MJ) 8.9 9.1 9.4 Total WTW GHG emissions (gCO 2 e/MJ) 31.8 39.4 45.1 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.36 0.45 0.52

Nitrous oxide emissions represent more than 20% of the total life cycle GHG emissions from the jatropha to HRJ pathway. As such, the consequences of the uncertainty associated with IPCC correlations are more pronounced for this pathway. The reader should b e aware of these inherent uncertainties when comparing different pathways for GHG reduction potential in sections 8.1 and

8.2.



### 7.6 HRJ from Algae Oil

The creation of HRJ from algae oil is not a pathway available in GREET; hence, supporting

information was obt ained from the literature. 57 Algae can be grown in an open pond, a controlled bioreactor, or a combination of both. A typical open pond approach consists of a raceway (oval) shaped pond using a paddlewheel to circulate the water and mix the algae for even l ight exposure and growth. In bioreactors, the algae are grown in sheets or tubes, allowing for much higher growth rates per unit area than open ponds. Bioreactors shield the algae from weather variations and facilitate growth in vertical geometries thereby reducing land requirements; however, these designs are cost intensive relative to open ponds. Only open pond technologies were examined in this analysis because of their reduced capital costs, the relative abundance of experimental documentation, and thei r increased technological readiness (relative to bioreactors). Given time, the capital costs of bioreactors could decrease as technological advances are made;

57 Algae oil is not a preexisting pathway within GREET. As such, a new pathway was built within the GREET framework using the soy oil to renewable diesel pathway as a guide.

### 83 of 129 these concepts will be examined further as a part of this continuing research effort. Furthermore

, only conventional means of oil extraction using chemical solvents were considered in this report. As was discussed in the introduction of Chapter 7, there is currently much research devoted to other methods of oil extraction including genetic modificatio n of the algae such that they naturally

excrete oil and using electric shock s to open the alga l cells. Such methods were not considered

here.

7.6.1 Algal Yield and Lipid Content

The two defining characteristics for algae as a biofuel are its growth rate (general ly given in grams/m 2 /day) and lipid content (generally given as a weight percent of total biomass). Both quantities vary greatly within the literature as they depend on variables including algae type and weather conditions, among many others. Generally higher algal growth rates reported in the literature represent bioreactor technology and not open ponds. Some recent presentations have employed yields equivalent to 140g/m 2 /day at 25% lipids content; an estimate over 550% of the yields adopted in this stu dy (Daggett, 2007). The current analysis is based on the engineering judgment of the authors gained from their literature review of open ponds. During peak periods of growth, 50g/m 2 /day could be achieved, but a yearly average of 20 g/m 2 /day appears to be m ore reasonable (Ben

- Amotz, 2008; Sheehan et al., 1998b). A survey of algal strains also returned a range of lipid contents up to 40% (Becker, 2007). Assuming that technology will improve by the simulation year of 2015, 50 g/m 2 /day and 40% lipids by weight was adopted for the low emissions case, 25 g/m 2 /day and 25% lipids by weight was adopted for the baseline case and 20 g/m 2 /day at 15% lipids by weight was adopted for the high emissions case.

7.6.2 System Expansion (Displacement) for Electricity Emissions

While much of the methodology for the analysis of algal HRJ is similar to that discussed for other HRJ pathways, the life cycle is complicated by the need to feed CO 2 to the algae to sustain acceptable growth rates. As schematically shown in Figure 17, system ex pansion (displacement method) was used instead of energy allocation when apportioning emissions between the fuel and any electricity generated in providing the CO 2 required for growth. Justification for this choice is rooted in the argument that electricit y used for CO 2 production operates independently of the algae cultivation and hence, should be treated differently than co

- products physically created from the cultivation itself. The system boundary was expanded to include both the electricity and

emissio ns from a power plant producing equivalent electricity to that within the original system boundary. The expanded system had both HRJ and biomass co

- products leaving the system boundary, but zero net electricity exiting the expanded system. Thus, the expand ed system could be treated in a similar fashion as the other HRJ pathways.

The life cycle GHG emissions from the type of electricity used to supply CO 2 for algal growth and the type of electricity being displaced both impact the life cycle emissions of the algal fuel. This is due to the variation that could exist in the emissions per kilowatt

- hour of different power generation technologies (e.g. a coal u tility boiler has higher emissions than an integrated gasification combined cycle which, in turn, has higher emissions than a nuclear power plant). The options in Table 77 outline the effects on the life cycle emissions that these choices could cause.

When CO 2 created from ‘dirty’ electricity is used to feed algal growth, but ‘clean’ electricity is included in the expanded system then the CO 2 emissions credit is less than the CO 2 used to grow the algae; this results in a fuel that appears to be ‘dirty’ in t erms of higher CO 2 emissions. When

CO 2 created from ‘clean’ electricity is used to feed the algae but ‘dirty’ electricity is included in the expanded system then the CO 2 emissions credit is greater than the CO 2 used to grow the algae;

this results in a fue l that appears to be ’clean’ in terms of lower CO 2 emissions. In this analysis, the electricity used to provide the CO 2 was assumed to be the same as that in the expanded system. For this configuration, the CO 2 emissions credit was approximately equal to the biofuel combustion emissions, which is the assumption used for biofuels that have not been ‘fed’ CO 2 for enhanced growth.

### 84 of 129 Figure 17 : System boundary expansion of the algal HRJ fue l pathway

Table 77 : Impact of CO 2 source and electricity choice on the biomass credit given to algal HRJ

CO 2 Source for Algae Growth Electricity displaced in expanded s ystem boundary CO 2 “Biomass Credit” allocated to the algal fuel

Conventional Coal Biomass Credit ≈ Combustion

CO 2

US Average Grid Biomass Credit < Combustion

CO 2 Conventional Coal Electricity

Nuclear (zero CO 2 ) Biomass Credit ≈ 0

Conventional Coal Biomass Credit > Combustion

CO 2

US Average Grid Biomass Credit ≈ Combustion

CO 2 US Average Grid Electricity

Nuclear (zero CO 2 ) Biomass Credit ≈ 0

Carbon dioxide usage in algae cultivation can vary depending on the lipid content and meal
carbon content of the algae. A simpler approach was adopted in this analysis where data from the literature were used to conclude that algae as a whole is approximately 60% carbon (Weissman and Goebel, 1987; Kadam, 2001). The supporting literature indicates that CO 2 requirement s range from 2.16

- 2.2 kg per kg of algae grown. Emissions factors for power plant technologies from GREET are listed in Table 78 (these are not the full life cycle GHG emissions from power generation, rather they are just the CO 2 available to be captured a nd used from the power plant itself). Depending on the power plant, between 0.23 and 0.5 kg of algae could ideally be grown

from the CO 2 resulting from each kWh of electricity. This includes electricity generated from

biomass. Electricity

Biomass Co-Product GHG = N 2 O, CH 4 , CO 2 Raw Material

Movement Jet Fuel

Production Jet Fuel Transportation Jet Fuel

Combustion

CO 2 , PM, NO X , SO X , H 2 O Recovery &

Extraction Recovery &

Extraction Raw Material

Movement Electricity

Production

CO 2 GHG = N 2 O, CH 4 , CO 2 Electricity

Production

GHG = N 2 O, CH 4 , CO 2 

### ?

Electricity

Aviation Productivity Original System Boundary Expanded System Boundary

Other Liquid Fuel Products

### 85 of 129 Table 78 : US power generation data as it relates to algae cultivati on

Generation Technology Fraction of

US Mix

(%) Power

Plant Efficiency (%) Power Plant

Emiss i ons ( g CO2 /kWh) Plant Capacity for Algae

Cultivation ( kg alga e /kWh)

Oil

- Fired 2.6 34.8 834 0.38

NG

- Fired 22.5 43.5 495 0.23

Coal

- Fired 48.7 36.1 1027 0.47

Biomass Fired 1.3 32.2 1084 0.50

Nuclear 17.6

-- 0 0 Other (hydro, wind, etc.) 7.3

-- 0 0

Notes: 1) All data from GREET (2008) 2) Based on 2.18 kg CO 2 being required per kg of algal growth

7.6.3 Algal Carbonation Options and Technologies
Algae cultivation using an open pond configuration can be supplied CO 2 as a part of flue gas either from an adjacent power plant or from a purified CO 2 stream. Flue gas is approximately 14%

CO 2 (Kadam, 2001) but can be purified to more than 99% CO 2 using the recyclable solvent monoethanolamine (MEA). The process known as MEA extraction is specifically targeted to remove large amounts of CO 2 from flue gas

. The greatest advantage of MEA extraction is its relatively high ca rrying capacity in terms of the amount of CO 2 absorbed per unit volume of

solvent. Less solvent circulation is required for a given system performance specification, leading

to lower capital and operating costs than other solvent extraction methods (Herzog et al., 1991).

Little data exists documenting the benefits, if any, of using pure CO 2 compared to flue gas as a carbon source for algae cultivation. The assumption here is that both methods result in the same algae yield. The disadvantage of flue gas manifests from the compression and transportation of over seven times more gas volume for the same quantity of CO 2 . For this reason, flue gas usage is only considered an option for algae cultivation facilities co

- located with their CO 2 source. The disadvantage of pure CO 2 is that flue gas must be scrubbed to remove all non

- CO 2 elements and

subs equently compressed for transportation; flue gas requires only a simple blower. Scrubbing flue gas using the MEA process requires substantial quantities of steam and energy intensive compression. The compression can represent between 13 and 30 percent of t he total process energy consumption (Herzog et al., 1991). Table 79 compares the energy usage of direct flue gas injection with two studies estimating energy usage for the MEA process. In both MEA studies, 98% of the energy results from producing steam to strip CO 2 from the amine solution. The remainder of the MEA process energy, and all energy for direct inj ection, comes from electricity.

Table 79 : Comparison of energy inputs of direct flue gas injection and MEA extraction

Total En ergy (MJ/t CO2 ) Total Energy (MJ/kg algae ) Emiss i ons ( g CO2 /kg algae )

Direct Injection Kadam et al. (2001) 80 0.18 35

MEA Kadam (2001) 6650 14.63 911 Herzog et al. (1991) 6097

- 10248 13.41

- 22.55 835

- 1404

Note: 1) Steam production is assumed to occur using an 80% efficient industrial

boiler

The emissions to create a pure CO 2 stream are over 25 times higher than those from using flue gas from a co

- located source. For this reason, the low and baseline scenarios adopted the assumption that algae cultiva tion occurs adjacent to a power plant, thus using flue gas as the

### 86 of 129 CO 2 source. The high emissions scenario assumed that CO 2 must be scrubbed from the flue gas using MEA extraction prior to compression for transportation and storage, according to the

estimat e by Kadam (2001). This assumption is appropriate for facilities that are not located in the immediate vicinity of a CO 2 source. Furthermore, transportation energy is not included in the

estimates of Table 79 and would be a function of distance between the CO 2 source and algae cultivation facility.

7.6.4 Cultivation of Algae in Open Ponds

The process energy and nutrient requirements for algae cultivation were established from two sources: (1) a simulated open pond algae farm that was used to capture flue gas from an adjacent fossil power plant and (2) design specifications for an open pond system used for fuel production. The simulated open pond algae farm parameters were obtained for a 1000

- hectare algal production system based on primary bench scale data and pro cess modeling (Kadam, 2001). Weissman and Goebel generated the open pond design specifications for a 192

- hectare system in 1987 (Weissman and Goebel, 1987). While Kadam presents all energy inputs of cultivation and harvesting as a single value, Weissman an d Goebel show the inputs for the individual steps of mixing, pumping, primary harvesting and secondary harvesting. The data from both studies are in good agreement and were amalgamated to form the inputs to this analysis.

Since both studies quote similar electrical inputs, all electricity estimates were taken from Weissman and Goebel due to their higher degree of detail. In their design specifications, electric motors were assumed to operate with a drive efficiency of 70%. Drive efficiency encompassed

the motor, an in

- line speed reducer and a two

- stage chain and sprocket reduction. This study considers a simulation year of 2015; hence, modern motors and gearing mechanisms could be incorporated. A modern three

- phase (variable speed) electric motor meeting s imilar cost and performance requirements is rated as up to 92.4% efficient (Marathon Electric, 2009). The mechanical efficiency of a chain and sprocket has been found to reach 98.6% in ideal conditions and 81% in poor conditions (Spicer et al., 2001). Assu ming both a motor and connection efficiency of 90%, the drive efficiency climbs to 81%, corresponding to an improvement of 15.7% above Weissman and Goebel’s estimates. This efficiency improvement was applied to all process inputs related to electric motors or electric pumps.

The cultivation inputs used for the low, baseline and high emissions scenarios are shown in Table 80 (normalized per kilogram of algae) while harvesting and drying are dealt with in subsequent

sections.

Table 80 : Cultivation inputs per kilogram of algae for algae growth in an open pond

Low Baseline High

Nutrients

CO 2 (kg) 2.16 2.2 2.2 Nitrogen (kg) 0.044 0.053 0.053 Superphosphate (kg) 0.019 0.029 0.039 Potassium Sulfate (kg) 0.030 0.030 0.030

Process Energy Mixing (Btu) 281.8 281.8 281.8 Water Supply (Btu) 229.8 229.8 229.8 Nutrient Supply (Btu) 16.2 16.2 16.2

Nitrogen from fertilizer use was assumed to come only from ammonia. As a well

- established fertilizer in the farming industry, ammonia has a production pathway within the GREET framework. The production pathways of superphosphate and potassium sulfate are not

established within GREET. The process energies required for the production of superphosphate were adopted from Anderi Silva and Ale xandre Kuley (2003). Specific inputs for potassium sulfate were not available in the literature. A final emissions inventory from potassium sulfate production

### 87 of 129 was taken from Kadam (2001). The emissions resulting from the production of nitrogen fertilizer, superphosphate and potassium sulfate are summarized in Table 81.

Table 81 : Life cycle GHG emissions from the production of nutrients used in algae cultivation

Nitrogen Superphosphate Potassium

Sulfate 1

CO 2 2537 235

- 370 1

CH 4 62.5 7.5 1.5

N 2 O 5.6 1.3 1.5

Total 2605 244

- 367

Notes: 1) Negative process energy flows occur due to displacement allocation being used in their life cycle assessment 2) Units are g CO 2 e/kg of nutrient

Nitrous oxide emissions from algae cultivation were estimated using IPCC Tier 1 methodology for flooded rice fields (IPCC, 2006). It was assumed that the conversion rate of nitrogen contained in a flooded rice field is similar to the conversion rate of nitrogen from an open pond. Open ponds were the only so urce of N 2 O emissions from algae considered in this work. The IPCC Tier 1 methodology for flooded rice fields estimates the direct conversion rate of nitrogen from synthetic
fertilizers as 0.3%. No mechanisms for indirect emissions were considered due to a lack of information. The formula used in this study for calculating N 2 O emissions from algae cultivation is given by:



### € N 2 

### O Emissions g N 2 O 

### kg "

# $ %

& '

= g nitrogen fertilizer 

### kg "

# $ %

& '

⋅ 0.003 "

# $ %

& '

⋅ 44 g N 2 O 

### 28g N 

### "

# $ %

& ' Equation 14

7.6.5 Nutrient Recycling via Anaerobic Digestion

Fertilizer production is a non

- negligible GHG sourc e in the algae to HRJ pathway. As can be seen from the data in Table 82, which were derived from Tables 80 and 81, the nitrogen fertilizer emissions are comparable to those from the baseline soybean to HRJ pathway. High quantities of fertilizer in any alga l effluent leaving the system can also lead to eutrophication of natural waters.

The additional fertilizer can over

- stimulate the production of organic compounds leading to negative environmental effects such as hypoxia and reductions in water quality, fis h, and other animal populations. If not properly contained, the environmental impacts of this process include decreased biodiversity, changes in species composition and dominance, and toxicity. These considerations warrant an investigation into nutrient re cycling systems to reduce fertilizer demand. The analysis that follows assumes that all nitrogen and phosphorous is contained in the

meal after oil extraction.

Table 82 : GHG emissions resulting from fertilizer production within the algae to HRJ pathway for the three

emissions cases

Low Baseline High Nitrogen (gCO 2 e/kg algae ) 114.6 130.3 138.1 Superphosphate (gCO 2 e/kg algae ) 4.6 7.1 9.5 Potassium Sulfate (gCO 2 e/kg algae )

- 11.0

- 11.0

- 11.0

One means of nutrient recycling is anaerobic digestion, wherein microorganisms break down

carbon

- based matter in the absence of oxygen. It is widely used as a renewable energy source because the process produces a methane

- rich biogas. Other products of anaerobic digestion are a liquid efflu ent and a solid digestate. Of interest here is the nutrient

- rich liquid effluent that can be used to supplement fertilizers. A conventional reactor is maintained at an operating temperature of approximately 35°C. Retention times for conversion range from 2 0 to 30 days where about 60% of organic carbon is converted to biogas. The biogas composition is typically
60% methane and 40% CO 2 with traces of hydrogen sulfide and water vapor (Chynoweth et al,

### 88 of 129 2001). By using the algal meal co

- products to feed a digest er, a substantial nutrient fraction could be recovered. Such systems would also allow for the recycle of carbon contained in the meal, thus reducing the demand on an external CO 2 source.

Several factors affect the rate of digestion and biogas production. The most important is temperature. Anaerobic bacteria communities can endure temperatures ranging from below freezing to above 57°C, but they thrive best at temperatures between 37°C and 54°C. Bacteria activity, and thus biogas production, falls off gradua lly from 35°C to 0°C (DOE, 2008). In some cases, the conventional design discussed above is being replaced by more innovative designs influenced primarily by the suspended solids content of the feed. Designs for feed with intermediate solids contents (such as sewage sludge or aquatic plants) involve recycling the solids following settling within the digester. Such designs have increased loading rates 20

- fold and improved process stability. Furthermore, the biodegradability of certain feedstocks has been

see n to exceed 90% (Chynoweth et al., 2001).

The process flows for an algae system that includes an anaerobic digester are shown in Figure 18. It is assumed that the algae are being grown in areas with sufficient heat and sunshine to sustain the required tem perature of 35°C and the energy required to maintain a suitable temperature is negligible from the life cycle analysis standpoint. Furthermore, any additional heating requirements would be low

- grade and could be met using flue gas before injection into

the pond or exhau st gas from the drying facility.

Figure 18 : Process flows for algae oil HRJ using anaerobic digestion to recover nutrients from algae meal

Weissman and Goebel explored the potential for nutrient recovery from algae meal using anaerobic digestion. They projected that 50% of carbon not converted to biogas, 75% of all nitrogen and 50% of all phosphorous could be recovered in the liquid effluent. The remainders would be contained in the digestate, which is considered a waste of zero value.

The carbon content of algae meal can vary depending on the total carbon content and lipid faction of the algae. It is also directly proportional to the methan e production potential from anaerobic digestion. Algae was assumed to have a constant carbon content of 60% while lipids were assumed to have a constant carbon content of 76.1% (based on their molecular formula). !"#$%&

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### 89 of 129 Under these assumptions, the carbon content of meal, and thus the methane production potential, is a function of lipid fraction. 58

When digested, the resulting biogas can be used directly to meet internal heat demands or to fuel any other heat engine. Methane within the biogas can be upgraded to th e same standards as fossil natural gas. If allowed, the biogas may be utilized within the local gas distribution networks. Gas must be cleaned to reach pipeline quality, and be of the correct composition for the local distribution network to accept it. The se restrictions on raw biogas usage outside the system lead

to the conclusion that on

- site usage is the most practical use for biogas produced from anaerobic digestion.

7.6.6 Dewatering and Drying
After cultivation, algae can represent as little as 1 part in 20 00 in water (Kadam, 2001; Weissman and Goebel, 1987). Conventional chemical oil extraction technologies require a feedstock that is approximately 90% dry. The task of extracting and drying the solid algae is the most energy intensive step of the cultivatio n process. As shown schematically in Figure 19, the algae must first be harvested, then dewatered and finally dried to reach the desired concentration. The applicability of algae as an environmentally beneficial biofuel when using conventional oil

extracti on facilities can be highly dependent on the extent of dewatering and the method of drying.

Figure 19 : Flow chart showing the steps involved in dewatering and drying of algae

Generally, harvesting and initial dewatering is perfo rmed by mechanical or gravitational force while drying relies on direct heating to evaporate the remaining water. Due to the high latent heat of water, dewatering and drying have considerable impact on the life cycle GHG emissions of the pathway. Additiona l dewatering and less drying generally lead to reduced emissions. Solar, natural gas, algae meal and biogas from the anaerobic digestion of algae meal were considered as options for providing the drying energy. Common types of dewatering procedures are

flo cculation 59 or settling, filtering with a vacuum or press and centrifugation. The performance of each method is characterized by the total suspended solids (TSS) of the initial feed (given as a percentage by mass), the concentration factor, 60 energy usage pe r m 3 of throughput and energy usage per kg of processed solids. The initial TSS relates the energy usage per kg of processed solids to the energy usage per m 3 of throughout.

Weissman and Goebel consider three different two

- stage harvesting and dewatering configurations. Each begins with a solids concentration of 0.075% and is comprised of primary harvesting using a micro strainer, vacuum belt filter or settling pond followed by secondary dewatering using a centrifuge to achieve 10% solids. The centrifuge dominates energy consumption from this step; hence, a higher concentration factor in the primary harvesting step reduces energy consumption in the centrifuge. Specifications for a belt

- filter, micro

- strainer and settling pond are given in Table 83 (Shelef et al., 1984). Notice that the concentration factor of the

58 Thi s assumption is most likely only valid for lipid contents between 0 and 50%; however, that is irrelevant because algae strains considered in this work do not exceed 50% lipids.

59 Flocculation is a process where particles come out of suspension in the form of floc or flakes. The action differs from precipitation in that, prior to flocculation, the particles are merely suspended in a liquid and not actually dissolved in a

solution.

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### 90 of 129 belt filter and settling pond are higher than that of the micro

- strainer. In all cases, flocculent must be used to aid in the harvesting process; however, the environmental impacts of the flocculen t were assumed to be negligible in terms of the life cycle GHG emissions.

Table 83 : Energy consumption and performance specifications for primary algae harvesting mechanisms

Micro

- strainer Vacuum Belt Filter Settling pond Electricity (Btu/kg) Concentration

Factor Electricity (Btu/kg) Concentration

Factor Electricity (Btu/kg) Concentration

Factor

23.8 10 47.6 70 46.9 50

It is desirable to extend the centrifuge energy consumption estimates to explore the impacts of varying the %TSS after dewatering. The result of combining the energy consumed per m 3 of throughput with the initial TSS is the energy consumed per kg of processed solids. The relationship between the energy consumed per kg of processed solids and concentration f actor is plotted in Figure 18 and was subsequently used to define a continuous function for the centrifuge energy consumption. Data points originate from the three configurations described by Weissman and Goebel and independent average data for a solids ej ecting disc centrifuge from Shelef et al. (1984)

. The advantage of this type of centrifuge for algae harvesting is its ability to reliably

dewater to 12

- 25% solids, although, solids finer than algae may be retained in the overflow

stream. Based on a review of the technology survey conducted by Molina Grima et al. (2003) and the conclusions of Shelef et al. (1984), even the best centrifuges appear to have a concentration limit of 25% TSS. More common results place the final TSS level around 15 percent. While following the energy consumption mapped in Figure 20, this analysis assumed that dewatering
can occur to a maximum of 25% TSS.

Figure 20 : Energy consumption of a centrifuge secondary harvesting mechanism. Line is a fit through four data points

The values in Figure 20 are based on historical studies and may be pessimistic for a simulation year of 2015. As such, the efficiency improvement of 15.7% discussed with regards to cultivation was applied to all electric al inputs of harvesting and dewatering.

Each method must be considered within the context of reliability and scalability. The advantages

of micro

- strainers are their operation, low energy consumption, and high filtration ratios. Their problems include in complete solids removal and the buildup of bacterial and algae slime on the micro fabric (Shelef et al., 1984). Regardless of their merits, micro

- strainers result in high

- energy consumption at the centrifugation stage due to their low concentration factor, making them an undesirable choice. Belt filters and settling ponds are both promising from the perspective of energy consumption and they have been assessed as a reliable method for harvesting (given the !" #!!" $!!!" $#!!" %!!!" %#!!" &!!!" &#!!" '!!!"

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### 91 of 129 use of a flocculent). Furthermore, the concentratio n factors estimated by Weissman and Goebel are reasonable when compared to data from Shelef at al. (1984). Because some experiments have indicated that belt filters may be less successful at harvesting small algae such as Chlorella (Weissman and Goebel, 1987), settling ponds were chosen for the low, baseline and high emissions scenarios.

Figure 21 : An examination of the post

- dewatering moisture content impact on energy consumption of drying algae to 90% tot al suspended solids

The impacts of dewatering on drying energy requirements are demonstrated in Figure 21. As the extent of dewatering increases, there is a sharp reduction in the energy required for drying to 90% TSS. Drying energy must be in the form of direct heat in order to evaporate water from the algae slurry. Solar drying is considered to be an optimistic choice but is not out of the realm of possibility, with some reports estimating a drying area only 12% of that required for growing (Shelef et al ., 1984). The low emissions scenario therefore adopted solar drying while the baseline and high emissions scenarios require the combustion of a process fuel for heat. The process fuel may be natural gas, algae meal or biogas from the anaerobic digestion of algae

meal.

As discussed in the previous section, an opportunity for system integration is available by using energy contained in the algae meal to supply internal energy demands. This energy can be obtained by burning the meal directly in a boiler or by combusting the biogas produced from anaerobic digestion. Depending on the quantity of meal produced per kg of algae, some or all of the drying energy can be supplied by these methods. This threshold point, where all drying energy is supplied by the meal, is fundamentally related to the lipid content and the post

dewatering %TSS content. Designing a sustainable drying technique that does not rely on the

combustion of non

- renewable fuels is essential in creating a sustainable fuel.

The threshold lipid cont ent is also dependent on whether the meal is burned directly or whether anaerobic digestion is used to obtain biogas. These relationships are explored in Figure 22 where two different biomass conversion factors from anaerobic digestion are compared to dire ctly burning the meal in a boiler. While both methods result in zero net GHG emissions from the drying process, nutrient recovery is not possible with meal combustion. The opportunity to recover nutrients leads to the conclusion that an algae cultivation f acility concerned with minimizing GHG emissions would be equipped with an anaerobic digester. Therefore, the low and baseline emissions scenario assumed the use of an anaerobic digestion system with biomass conversion factors of 80% and 70% respectively. T he high emissions scenario assumed the meal

is sold as animal feed. !" #!!!!" $!!!!" %!!!!" &!!!!" '!!!!" (!!!!"

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### 92 of 129 Figure 22 : Algal lipid content and post

- dewatering moisture content that result in a sustainable drying process. Points above each line require additional energy to be added (e.g. from natural gas)

For an algae strain comprised of 25% oil by mass, sufficient meal is produced to supply the energy to dry other algae from a post

- dewatering %TSS content of 19% via direct burning and from 17% to 21% via anaerobic digestion (depending on anaerobic digester efficiency). If the post

- dewatering %TSS content were above these t hreshold values then the algae meal would need to be supplemented by natural gas to supply sufficient energy for drying to 90% TSS.

The decision to use algae meal or na t ural gas as a drying fuel must be made in the context of the economic, technological and energy usage limitations of dewatering. The trade

- offs between these factors and other system parameters can only be understood by considering the entire algal production system.

7.6.7 Transportation of Algae to Production Facilities

The production of algae in this analysis is assumed to take place in the southwestern United

States. After harvesting, assumptions about the transportation mechanisms and distances have been modified from GREET defaults for the soybeans to HRJ pathway. When meal is used as an

en ergy source, oil extraction must be collocated with the cultivation facility to enable the recycling and integration of energy flows. In these configurations, only the oil is transported from the cultivation site to a hydroprocessing facility. Specifically , the transportation is by tanker truck in

25

- ton shipments; raw algae transportation would occur in truckloads of only 15 tons. In cases where meal is sold as a supplement to animal feed, standard GREET transportation assumptions were adopted. As was the case in every other pathway considered, the contribution from transportation elements in the life cycle analysis are sufficiently small to be within the margin of error; therefore, the importance of these assumptions is minimal. As such, the geographic

loc ation of the facility is of secondary importance to the overall results. However, as was discussed in Section 7.6.3, the relative location of the algae facility to its CO 2 source is very important to the life cycle GHG emissions.

7.6.8 Extraction of Oil from Alg ae The extraction of oil from algae is currently an area of considerable research. This analysis used a modified version of the process for oil extraction from soybeans established by Sheehan et al. (1998a) that includes only the processes relevant to alga e in an N

- hexane extraction facility. The process energies from Sheehan et al. were converted from energy per unit mass of biomass to energy per unit mass of oil using the oil fractions from the low emissions, baseline and high

emissions scenarios. The out puts and energy consumption assumed in the extraction of oil from algae are shown in Table 84

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### 93 of 129 Table 84 : Outputs and process energy for N

- hexane oil extraction from algae

Low 1 Baseline 1 High 1

Algae Lipid Content 40% 25% 15%

Meal Produced (lb meal /lb oil ) 1.5 3 5.67 Receiving and Storage Electricity 20.9 33.5 55.8 Algae Preparation Electricity 4.0 6.5 10.8

Steam 172.5 276.0 460.0

Oil Extraction Electricity 12.9 20.6 34.4

N

- hexane 3 105.9 169.5 282.5 Meal Processing Electricity 52.2 83.5 139.1

Steam 505.4 808.6 1347.7 Oil Recovery Electricity 1.4 2.2 3.6

Steam 86.7 138.7 231.1 Solvent Recovery Electricity 1.9 3.0 5.0

Steam 0.0 0.0 0.0 Oil Degumming Electricity 6.1 9.7 16.1

Steam 68.1 109.0 181.6

Waste Treatment Electricity 2.0 3.3 5.4

Steam 36.6 58.6 97.7

Total Electricity 101.4 162.2 270.3

Natural Gas 2 1086. 6 1738.6 2897.6

N

- hexane 3 105.9 169.5 282.5

Notes: 1) All values are in Btu per pound of oil 2) S team is assumed to be generated from natural gas with an efficiency of 80%. 3) GREET uses Liquefied Petroleum Gas (LPG) as a surrogate for N

hexane when calculating emissions

As discussed previously, algae meal resulting from oil extraction can be burned in a utility boiler,
used as a fe ed to an anaerobic digester or sold as a substitute product to soy meal consumed as animal feed. When burned in a boiler, the direct heat can be used to fuel the drying process. If there is excess meal after drying is completed, the rest is sold as animal feed; if meal is unable to provide all the drying energy then natural gas could be used to supply the difference. When digested, the resulting biogas can be used directly to fuel the drying process or any other heat engine. Methane within the biogas can be upgraded to the same standards as fossil natural gas. If allowed, the biogas may be utilized within the local gas distribution networks. Gas must be cleaned to reach pipeline quality, and be of the correct composition for the local distribution

network to accept it. These restrictions on raw biogas usage outside the system lead to the

conclusion that on

- site electricity generation is the most practical use for biogas not consumed for drying. Any electricity generated over and above the internal demand of c ultivation is considered exportable to the grid and emissions from cultivation and oil extraction were allocated between oil and exported electricity based on energy. If insufficient biogas is produced to provide all the drying energy then natural gas coul d be used to supply the difference.

### 94 of 129 When sold as an independent product, market value allocation was used in partitioning emissions between algae oil and meal. As discussed with regards to soy oil to HRJ, using market value allocation captures temporal c hanges in the allocation fractions between products. Since no established market price exists for algae oil, or algae meal, they were assumed related to the prices of soy oil and soy meal. Prices assumed for soy oil and soybean meal are the 2015 projection s made by FAPRI (FAPRI, 2009). Algae oil was assumed to have the same market price as soy oil, $1.05/kg, while algae meal was assumed to have a price related to that of soy meal based on protein equivalency. Protein content serves as a common denominator b ecause both meals would be used as animal feed. Soybean meal was assumed to have a protein content of 48% (Ahmed et al., 1994) and algae meal to have an average protein content of 52% (Becker, 2007). 61 Based on these protein contents, a price of $0.31/kg wa s calculated for algae meal.

7.6.9 Sensitivity Analysis and System Design

For algal facilities yielding a fixed lipid content, the life cycle GHG emissions of fuel production are most sensitive to the extent of dewatering. Using the relationships and assumptions discussed in the previous section, life cycle GHG emissions were explored for a range of post

dewatering %TSS contents. Using assumptions consistent with the baseline case, the results are plotted in Figure 23 for three different system configurations. It can be seen that maximum dewatering is always optimum for minimizing life cycle GHG emissions, regardless of the source of drying energy and use of meal. If the post

- dewatering %TSS content is less than 15%, algal HRJ results in higher GHG emissions on a life cycle basis than jet fuel from conventional petroleum. Recall that dewatering to 25% TSS content was assumed as the maximum dewatering achievable with current technology. By comparing the two natural gas drying cases,

one can see the benefit of nutrie nt recycling as cases using fertilizer to meet all nutrient demands resulted in approximately twice the GHG emissions, for higher post

- dewatering %TSS contents, as those where nutrients were recycled. The lowest life cycle GHG emissions were achieved by

us ing biogas from the anaerobic digester and recycled nutrients.

The low emissions, baseline and high emissions scenarios assumed post

- dewatering %TSS contents of 25%, 20% and 15% respectively. While the high emissions scenario assumed natural gas as the fu el used for drying, the baseline scenario was constructed with an anaerobic digester producing biogas configured to supply the drying energy. The low scenario assumed solar drying with an anaerobic digester producing biogas, which would then be used for on

- site electricity generation to meet internal demands with excess power being exported to the gird.

Local sensitivity analysis was conducted on the anaerobic digestion biomass conversion efficiency, lipid content and CO 2 injection method. Each parameter was varied with all others held at their baseline values with the impacts quantified as a percent change from the baseline value. Figure 24 presents this information in a manner that allows the magnitude of each change to be seen in comparison to the other s. Section 7.6.3 discussed the use of MEA extraction to create a pure CO 2 stream from flue gas. The importance of collocating the cultivation facility to its CO 2 source is further emphasized by these results. The unexpected result that life cycle GHG

emiss ions are reduced by using algae with lower lipid contents can be understood by considering that additional meal, and hence more biogas and by extension more energy, is available per kg of oil if the algae has lower lipid contents. Finally, the conversion e fficiency is directly proportional to the quantity of energy available from the meal, making it the second most sensitive operational specification of the system (second to post

- dewatering %TSS content).

61 The meal is assumed to be the only oil

- containing portion of algae. This hypothesis is verified by considering soybeans and soy meal. Soybeans have a protein content of 40% (GREET, 2008) and an oil fraction of 18.25% (She ehan et al., 1998a) leading to a soy meal protein content of 48.9%. The average protein and lipid contents of the strains documented by Becker (2007) were 45.1% and 13.2%, respectively.

### 95 of 129 Figure 23 : Life cycle GHG of HRJ production from algae as a function of the extent of dewatering. Three different system configurations were explored which are described more fully in the figure

Figure 24 : Sensitivity analysis of operational specific ations and configurations of HRJ production from algae

7.6.10 Results
The key assumptions and corresponding life cycle GHG emissions in the production and use of HRJ from algae oil are outlined in Table 85. It should be noted that although water usage was not

qua ntified in this section, it is a part of ongoing research efforts. The most notable differences between the cases lie in the recovery step and the WTT CH 4

. The recovery step includes

emissions from CO 2 injection, dewatering and drying. The dominant factors of each life cycle stage were described in previous sections and are the driving sources of variation between

scenarios. The increased WTT CH 4 in the high emissions scenario is a result of natural gas usage for MEA extraction and drying. 

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### 96 of 129 The life cycle GHG emissions resulting from the production and use of HRJ from renewable algae oil range from 0.16 to 2.2 times those from conventional jet fuel. Variation in the biomass credit is due to minor changes in the allocation schemes used throughout the pathway

.

Table 85 : Life cycle emissions from the algae oil to HRJ pathway

Low Baseline High Key Assumptions Algae Y ield (g/m 2 /day) 40 25 20 Algal Lipid Content 40% 25% 15% Anaerobic Conversion Efficiency 80 % 70% n/a Life Cycle CO 2 Emissions by Stage Biomass Credit (gCO 2 /MJ)

- 73.7

- 70.5

- 68.9 Recovery of feedstock (gCO 2 /MJ) 5.4 29.6 143.1 Transportation of feedstock (gCO 2 /MJ) 0.3 0.3 1.2 Processing of feedstock to fuel (gCO 2 /MJ) 7.1 10.3 13.2 Transportation of jet fuel (gCO 2 /MJ) 0.6 0.6 0.6

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 60.3

- 29.7 89.2

WTT CH 4 emissions (gCO 2 e/MJ) 0.7 1.8 27.7

WTT N 2 O emissions (gCO 2 e/MJ) 3.3 8.1 5.8 Total WTW GHG emissions (gCO 2 e/MJ) 14.1 50.7 193.2 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.16 0.58 2.21

The uncertainty surrounding N 2 O emissions from algae cultivation is larger that other pathways due to the assumption that algae ponds have the same emission factor as flooded rice fields. While there is little existing information on N 2 O formation from algae ponds, the results from

Table 85 indicate that N 2 O represents less than 16% of the total life cycle GHG emissions. The

reader should be aware of the additional uncertainty with respect to N 2 O emissions from algae cultivation when comparing different pathways for GHG reduction potential in sections 8.1 and

8.2.



### 7.7 HRJ from Salicornia Oil

The creation of HRJ from salicornia oil is not a pathway avail able in GREET; hence, supporting information was obtained from the literature and a pathway was created within the GREET framework. Salicornia is both a wild and cultivated annual shrub that germinates, grows and reproduces in areas of high salinity such a s coastal shorelines, marshes or inland lakes. The plant itself is leafless with green jointed and succulent stems that form terminal fruiting spikes in

which seeds are borne. In subtropical regions, salicornia can grow up to 50cm in height with most

of th e seed spikes on the upper one third of the plant (Anwar et al., 2002). Since oil containing seeds represent only a small fraction of the total plant, the deterministic factor in the life cycle analysis of jet fuel from Salicornia is the usage of non

- oil c ontaining biomass. The cultivation of halophytes on arid or semi

- arid land, where there is little or no carbon stock naturally present in the soil, can lead to substantial net long

- term carbon storage. Salicornia falls into the category of coastal halophyt es because of its ability to grow in saltwater

.

This work considers the production of HRJ from Salicornia oil while varying the crop productivity, oil yield, cultivation inputs and nutrient usage to establish the low emissions, baseline and high

emissions scenarios. Due to the nature of Salicornia growth, the combination of either electricity generation or Fischer

- Tropsch synthesis with an HRJ facility were explored. Long

- term carbon sequestration potential is quantified through scenarios that vary the ben eficial GHG credit from land use change

.

### 97 of 129 7.7.1 Biomass, Seed and Oil Yield In a similar manner to jatropha, additional products are created while growing seeds for oil production. When considering the yield of salicornia, three yield parameters define productivi ty: total biomass yield per hectare, seed yield per kilogram of total biomass and seed oil fraction. Total biomass is defined as seeds and straw biomass (similar to other forms of herbaceous biomass considered for BTL facilities). Data used to establish gr owth and oil yields was taken from field trials conducted at Puerto Penasco, Sonora, Mexico in an extreme coastal desert environment at the northern Gulf of California (Glenn et al., 1991). The ranges for the emissions scenarios were established using data from multiple plots taken from 1982 through 1988. The baseline total biomass yield and seed yield were defined by taking a weighted average (weighted by the number of plots) of the data from Glenn et al. (1991). The low emissions and high

emissions data w ere defined as plus and minus one standard deviation from the weighted mean, respectively (standard deviation also weighted by the number of plots). The oil fractions adopted were taken directly from the range quoted by Glenn et al. (1991). The assumptions regarding total biomass yield, seed yield and oil fractions for Salicornia are given in Table 86.

The total biomass yield is almost an order of magnitude higher than the seed yield (per hectare). In the baseline case, this translates to 7.22 kg of straw biomass for every kilogram of seeds, 25.6 kg of straw biomass per kilogram of oil and 43.5 kg of straw biomass per kilogram of HRJ. The most important point highlighted by these numbers is that the production of salicornia will not be driven by the demand for its seeds, but rather the demand for its straw biomass. Were salicornia grown to use its straw biomass for either electricity production or as feedstock to a Fischer

Tropsch facility, the produc tion of hydroprocessed renewable fuels from its oil seeds would be a high value co

- product from the process.

Table 86 : Salicornia yield and oil fraction assumptions

Low Baseline High Total Biomass Yield (kg/ha/yr) 17614 16247 14880 Seed production (g seed /kg total biomass ) 142 122 101 Seed Yield (kg/ha/yr) 2506 1977 1504

Seed Oil Fraction 33% 28.2% 26%

In section 7.7.5, a system is considered where straw biomass is used for renewable power generation and seeds are used for HRJ production. The heating value of salicornia biomass varies with the species, the chief variable being the ash content, but in general salicornia biomass falls in the range of Lignite A or B coals (Glenn et al., 1992).

In section 7.7.6, a system is con sidered where straw biomass is used as a feedstock for a BTL facility and seeds are used for HRJ production. The BTL plant is similar to that discussed in section 6.4; namely, that biomass feedstock is used to meet internal process energy needs, with

littl e or no excess electricity produced for export, and commercial quality middle distillate fuels such as diesel and jet fuel are produced.

7.7.2 Cultivation of Salicornia The cultivation of salicornia is generally motivated by the opportunity to directly sequester carbon from the atmosphere. This occurs by reversing the trend towards desertification through

cultivation of salicornia on saline lands and re

- vegetation of degraded rangelands (Glenn et al., 1993). For this reason, process energy requirements for the cu ltivation of salicornia are sometimes presented in the form of fossil carbon emitted per carbon absorbed during growth. Glenn et al. (1992) calculated the carbon costs of salicornia assuming that all farm operations, from pumping water to planting, harvest ing, baling and hauling the crop were performed using diesel fuel. They found that salicornia required 225kg to 300kg of fossil carbon for each 1000kg of atmospheric carbon fixed. The low emissions and high emissions case were defined as the lower and upp er extremes, respectively, while the mean is assumed in the baseline case. All calculations assumed diesel fuel composed of 85.6% carbon (see Table 87) and salicornia composed of 24.7% carbon (Glenn et al., 1992).

### 98 of 129 The use of fertilizers in the cultivation of salicornia is a subject of debate. The test plots created by Glenn et al. in the Sonoran Desert were irrigated solely with hyper saline seawater that had first been used in a shrimp aquaculture facility (O’Leary et al., 1985), which added nitrogen and

o ther nutrients to the water. In later trials, fields irrigated with un

- enriched seawater received fertilizer additions equivalent to 200kg of nitrogen per hectare as urea, di

- ammonium phosphate or ammonium nitrate (Glenn et al., 1991). The degree to which fertilizer must be added is a function of the coupling between aquaculture farms and salicornia farms. Large

- scale coastal shrimp farms have caused algal blooms and disease problems in rivers and basins that receive

their nutrient

- rich effluent. The same p roblems can be expected to occur from the large volume of highly saline drainage water containing unused fertilizer discharging from salicornia farms (Glenn et al. 1998). When coupled together, salicornia farms could help mitigate the problem if aquacultur e effluent is recycled onto a salicornia farm instead of being discharged directly to the sea; hence, any assumptions made regarding fertilizer use carry a degree of uncertainty. The assumptions adopted in this work to establish a reasonable range deal wit h varying the fraction of irrigation water that comes from aquaculture facilities. The low emissions case assumed 100% of the irrigation water was pre

- enriched from an aquaculture facility; the baseline case assumed 50% was pre

- enriched, leading to 100kg o f nitrogen usage per hectare, and the high emissions case assumed the full 200kg of nitrogen usage per hectare.

Irrigation is the single most expensive production cost in growing salicornia. In general, seawater irrigation requires copious and frequent, sometimes even daily, irrigation to prevent salt from building up in the root zone. Partially offsetting this effect is that increased salinity of irrigation water can lead to higher water usage efficiencies; therefore, higher salinity leads to less water being required to produce a kilogram of dry biomass (Glenn et al., 1992). During cultivation experiments in the Sonoran Desert, it was determined that salicornia can thrive when water salinity exceeds 100ppm (three times the normal ocean level); however, t he volume of seawater required for irrigation is 35% more than the value that would be required if freshwater were being

used. The additional volume is needed to control salt levels in the soil (Glenn et al., 1998). Although more volume is required for sal icornia cultivation, seawater farms often require less water lifting than conventional farms, which may lift water from wells far deeper than 100 meters. This difference results in seawater farms using less energy for water pumping than freshwater

farms. T he life cycle GHG emissions calculated in this work include the emissions from pumping irrigation water but secondary effects of salt

- water irrigation were not quantified. Glenn et al. (1998) showed that normal farm and irrigation equipment could be modifi ed such that it is protected from salt damage from seawater.

The input assumptions discussed above are summarized in Table 87.

Table 87 : Input assumptions regarding the cultivation of salicornia

Low Baseline High

Process Fuels Diesel (kg/kg C Fixed ) 225 262.5 300

Diesel (Btu/ kg
s eed ) 1 18557 25316 34831

Fertilizer Use Nitrogen (kg/ha/yr) 0 100 200 Nitrog e n (g/ kg
s eed ) 1 0 50.6 133.0

Notes: 1) Calculated using the yield assumptions from Table 86

Emissions from N 2 O were estimated using IPCC Tier 1 methodology (IPCC, 2006). Due to the lack of data, N 2 O emissions from nitrogen in above and below ground crop residues were not accounted for in this analysis. The IPCC Tier 1 methodology estimates the combined direct and

indire ct conversion rate for nitrogen from synthetic fertilizers as 1.325%. These rates include the atmospheric deposition of nitrogen volatilized from managed soils as well as nitrogen from leaching and runoff. The formula for calculating N 2 O emissions from sal icornia cultivation is given by:

### 99 of 129 € N 2 

### O Emissions g N 2 O 

### kg seed 

### "

# $ %

& '

= g nitrogen fertilizer 

### kg seed 

### "

# $ %

& '

⋅ 0.01325 "

# $ %

& '

⋅ 44 g N 2 O 

### 28g N 

### "

# $ %

& ' Equation 15

7.7.3 Transportation of Salicornia Biomass and Seeds to Production Facilities The production of salicornia biomass and seeds was assumed to take place on the southwestern

coast of the Un ited States. After harvesting, GREET default values from the soybeans to HRJ pathway were assumed in regards to transportation mechanisms and distances travelled for the seeds. The assumptions of transportation mechanisms and distances for straw biomass are consistent with GREET defaults for herbaceous biomass in the BTL pathway. Specifically, the transportation of seeds is by truck to a local storage area and subsequently to a local oil extraction facility. The transportation of straw biomass is also by truck directly to an F

- T or renewable power generation facility assumed to be the same distance as the HRJ facility. The difference between seed and straw biomass transportation is that seeds are loose and moved in

loads of 15 short tons while straw biomas s is baled and moved in loads of 24 short tons. Even after accounting for transportation of both seeds and straw biomass, the transportation elements of the life cycle GHG emissions are sufficiently small to be within the margin of error; therefore,

assump tions in this area carry little consequence.

7.7.4 Oil Extraction from Salicornia Seeds As there is no commercially available process for extracting oil from salicornia seeds, the extraction process was modeled with the soybean oil extraction process described b y Sheehan et al. (1998a). Glenn et al. (1998) classify salicornia oil as highly poly

- unsaturated and similar to

s af flower oil in fatty

- acid composition (as presented in Table 41) . They also conclude that it can be extracted from the seed and refined using conventional oilseed equipment. This same method was applied to both algae and jatropha oil extraction. Salicornia seeds more closely resemble soybeans than either algae or jatropha; hence, less modification to the soybean process was required. Specificall y, pre

- drying of seeds was eliminated and seed shell removal energy was

included. Field drying of the seeds, prior to harvest was assumed and the energy for removing shells from jatropha seeds was applied to salicornia seeds. The process inputs assumed in this analysis are summarized Table 88.

7.7.5 Co

- Product Usage and Allocation Methodology
Based on the yields adopted in the baseline case, 7.22 kg of straw biomass is produced for each kilogram of seed. Although salicornia could be grown for its seeds with the s traw biomass tilled back into the field to facilitate higher rates of long

- term carbon sequestration in the soil, it is doubtful that this would be done in practice. In an economy where 85% of all energy is derived from fossil carbon sources, the waste bio mass could be used as a renewable energy source thus displacing fossil

- based electricity or heat generation. Assuming the energy content of straw biomass is 16.3 MJ/kg, within the range of lignite A and B coals, the energy contained in straw

biomass create d per pound of oil is 10.5 times that contained in the oil itself.

7.7.5.1 Electricity Production from Biomass Co

- products

The use of straw biomass co

- product for electricity production was modeled with the assumption that to the electricity production occurred a t the same location as the oil extraction and HRJ production. Three allocation methodologies were envisaged for this configuration. A summary of each is given in Table 89. The first methodology, which is the most straightforward, would displace average US grid electricity with the result being the lowest life cycle emissions for HRJ. The second methodology, system level energy allocation of recovery and feedstock transportation emissions, is the most complicated and is shown schematically in Figure 25. The unallocated cultivation and transportation emissions are first divided between HRJ production and electricity production based on the energy of the final products (39.9% and 60.1% respectively); the emissions associated to HRJ production are then allocated between oil and meal based on market value (61.5% and 39.5% respectively); both are then summed back together and allocated based on energy between HRJ and all other energy products from the system (15.4%

### 100 of 129 and 84.6% respectively). The other energy products from the system are comprised of renewable naphtha, mixed propane gas and electricity. The third allocation methodology involves initially allocating only the cultivation emissions between the seeds and straw biomass based on market valuation (67.2% and 3 2.8% respectively). 62 The life cycle GHG emissions resulting from each of the aforementioned systems are compared in Figure 26.

Table 88 : Process inputs for extracting oil from salicornia seeds. All values are in Btu/lb of oil

Low 1 Baseline 1 High 1

Receiving and Storage Electricity 20.9 33.5 55.8 Salicornia Preparation Electricity 4.0 6.5 10.8

Steam 172.5 276.0 460.0

Oil Extraction Electricity 12.9 20.6 34.4

N

- hexane 105.9 169.5 282.5 Meal Processing Electricity 52.2 83.5 139.1

Steam 3 505.4 808.6 1347.7 Oil Recovery Electricity 1.4 2.2 3.6

Steam 86.7 138.7 231.1 Solvent Recovery Electricity 1.9 3.0 5.0

Steam 0.0 0.0 0.0 Oil Degumming Electricity 6.1 9.7 16.1

Steam 68.1 109.0 181.6

Waste Treatment Electricity 2.0 3.3 5.4

Steam 36.6 58.6 97.7

Totals Electricity 101.4 162.2 270.3

Natural Gas 2 1086 .6 1738.6 2897.6

N

- hexane 3 105.9 169.5 282.5

Notes: 1) All values are in Btu per pound of oil 2) S team is assumed to be generated from natural gas with an efficiency of 80%. 3) GREET uses Liquefied Petroleum Gas (LPG) as a surrogate

for N

- hexane when calculating emissions

62 Market value of seeds is the sum of market value of oil and meal ind ividually. Value of salicornia oil was taken as equivalent to soy oil while the value of salicornia meal was found by scaling the value of soy meal by the ratio of protein contents. Soy meal has an average protein content of 48% (Ahmed et al., 1994) while salicornia meal has a protein content of 42% (Glenn et al., 1992). The market value of straw biomass was assumed to be proportional to the cost of coal based on energy value. The average cost of coal for electric utilities in 2008 was $2.07/mmBtu (EIA, 200 9a).

### 101 of 129 Figure 25 : System boundary definitions for system level energy allocation between HRJ production from salicornia oilseeds and electricity generation from salicornia straw biomass

Table 89 : Allocation methodologies examined for the production of HRJ from salicornia oil when using the

straw biomass co

- product for electricity generation

Scenario Co

Product Use Emissions Allocation Method

1 Straw

biomass Burned for electricity production Displacement method (system expansion) where electricity from US grid is displaced . The corresponding emissions credits are given to the fuel production

process.

2 Straw

biomass Burned for electricity production Electricity production considered a parallel process to HRJ production. Cultivation and feedstock transportation emissions are allocated using a system level energy allocation scheme between all energy products from the system.

3 Straw

biomass Burned for electricity production Cultivation and feedstock transportation emissions are

allocated prior to any electricity production based on

market valuation of the unprocessed seeds and straw

biomass.

Figure 26 : Comparison of life cycle GHG emissions from the allocation methodologies of Table 74 based on the production of HRJ from salicornia oil when using the straw biomass co

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### 102 of 129 As was observed for other fuel pathways, different allocation schemes result in varied emissions within the same pathway. The most important point to realize from this configuration is that there

are 5 MJ of electricity generated fo r each MJ of HRJ . Hence, giving a liquid fuel credit for this electricity will reduce its emissions considerably. Additionally, when only the oilseeds are used for fuel production, the yield of HRJ per hectare is small, being less than even soybeans. This underscores the importance of the straw biomass as the driving renewable energy re source from

salicornia cultivation.

7.7.5.2 Fischer

- Tropsch Fuel Production from Biomass Co

- products

If the straw biomass co

- product from salicornia oilseeds is used for electricit y production then jet fuel represents only 15% of the total energy products created. This is a smaller fraction than any other oilseed crops considered in this work and is not conducive to being a part of a large

- scale biofuel production network. In sectio n 6.4, jet fuel from solid terrestrial biomass via the Fischer

Tropsch process was discussed with switchgrass as the feedstock. Any carbon containing matter could theoretically be used to create syngas, which contains primarily H 2 , CO and CO 2 . This

section discusses the implementation of a fuel production configuration where salicornia oilseeds are processed through an HRJ facility and all of the straw biomass is used as feedstock to a BTL facility.

By incorporating a BTL facility producing 25% jet fuel, the total yield of jet fuel grows to be three times larger than that from HRJ alone for the same quantity of feedstock consumed. Furthermore,

F

- T diesel and naphtha are also created which contribute to GHG mitigation efforts in other energy consuming secto rs. For these reasons, this work focused on a coupled HRJ and F

- T facility. All assumptions discussed thus far were applied to the HRJ section, while input assumptions pertinent specifically to the BTL section are summarized in Table 90.

Table 90 : Input assumptions relevant to a BTL facility using salicornia straw biomass as feedstock

Low Baseline High BTL Plant Efficiency 52% 45% 42%

F

- T Jet Fuel Yield (fraction of all F

- T fuel products) 30% 25% 20% Straw Biomass Energy Content (MJ/kg) 17.4 16.3 15.1

Figure 27 : System boundary definitions for system level energy allocation of coupled HRJ and F

- T fuel production from salicornia oilseeds and straw biomass

As was the case when electricity was produced, the allocation methodology is an important choice in determining the final life cycle GHG emissions of the jet fuel. Figure 27 outlines how the coupling of an HRJ facility with an F

- T facility was arranged for this analysis. Straight black lines correspond to emissions flows following one or more products. This methodology allocates !"#$%& ' $%()*')+*

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### 103 of 129 energy and emissions common to both processes on a pseudo

- system wide level by energy content and attributes energy and emissions coming from a specific process to the products from the process. The unallocated cultivation and transportation emissions are first divided between HRJ production and F

- T fuel production based on the final energy products (allocation ratio 1); those associated to HRJ production are then allocated betwe en oil and meal based on market value (allocation ratio 2); the HRJ and F

- T emissions are then summed back together and allocated based on energy between jet fuel and all other energy products from the system (allocation ratio 3). The other energy products from the system are comprised of F

- T diesel, F

- T naphtha, hydroprocessed renewable naphtha and mixed propane gas. The allocation ratios and combined facility product slate resulting in the low emissions, baseline and high emissions scenarios are presented in Table 91.

Table 91 : Allocation ratios and product slates describing a coupled HRJ and F

- T facility processing

salicornia oilseeds and straw biomass

Low Baseline High Product Slate (MJ/MJ Jet )

F

- T Diesel 1.2 1.5 1.8

F

- T Naphtha 0.30 0.49 0.75 Renewable Naphtha 0.16 0.16 0.16 Mixed Propane Gas 0.012 0.027 0.028

Salicornia Meal 0.52 0.64 0.71

Allocation Ratios

Allocation Ratio 1 32.1% HRJ
67.9% F

- T 30.9% HRJ
69.1% F

- T 27.8% HRJ
72.2% F

- T

Allocation Ratio 2 66.7% Oil
33.3% Meal 61.5% Oil
38.5% Meal 58.8% Oil
41.2% Meal

Allocation Ratio 3 37.1% Jet Fuel
62.9% Other Fuels 31.9% Jet Fuel
68.1% Other Fuels 26.6% Jet Fuel
73.4% Other Fuels

7.7.6 Land Use Change Emissions from Salicornia Cultivation The degree to which carbon can be sequestered through salicornia cultivation depends on the initial state of the soil in which they are grown and the penetration of the roots in this soil. Dry land soils are typically low in organic carbon and could concei vably hold greater carbon under restored conditions (Glenn et al., 1993).

Estimates of soil organic carbon, root carbon and charcoal carbon storage of semi

- permanent cultures conducted by Sommer et al. (1999) have been previously used to estimate potenti al carbon sequestration from halophyte cultivation (Hendricks, 2008; Hendricks and Bushnell, 2008). These estimates are for deep soils under small farmer land use systems in the Eastern Amazon region that contain conventional vegetation and crops, not sali cornia. They were intended to quantify current carbon stock; hence, using the carbon storage values from Sommer et al. as a sequestration potential implicitly assumes similar soi l carbon storage capacity and zero soil carbon prior to any halophyte cultivat ion. Soil carbon store d for semi

- permanent secondary vegetation range in the Eastern Amazon region range from 146 t o 167 tC ha - 1 , where approximately 90% is soil organic carbon, 5% is root carbon and the remaining 5% is charcoal in the soil. Furthermore, a lthough this range applies to the top 6m of soil, an average of 67% of the carbon is stored in the top 1.2 meters (Sommer et al., 1999). A second independent analysis of carbon sequestration from salicornia was carried out for a joint project between the United States and Mexico. The project is called “Project Salicornia: Halophyte Cultivation in Sonora” and is o perated under the United Nation Framework convention for Climate Change. Project developers have a p reliminary estimate that the cultivation area would reach a steady state soil carbon content o f 49 tC ha - 1 after 100 years (UNFCCC, 1998). This value happens to correspond to half the carbon stock in the top 1.2 meters of soil as estimated by Sommer et al. (1999), namely 48.7 to 55.7 tC ha - 1 . The UNFC CC estimate was adopted for this study as a land use change scenario.

### 104 of 129 Under the optimistic assumption that all of the soil carbon sequestration occurs in the first 30 years, the impact of long

- term soil carbon sequestration on life cycle GHG emissions are given in Table 92. The sequestered carbon was amortized over 30 years with no discounting. The values are on the basis of carbon sequestered per MJ of jet fuel created from s alicornia oil and straw

biomass

Table 92 : Long term soil, root and charcoal carbon sequestration from the cultivation of salicornia for fuel production

Long Term Carbon Sequestration (gCO 2 /MJ) Long Term Carbon Sequestration (tC ha - 1 ) Low Baseline High

49 49.7 41.9 33.9

Note: 1) Since values represent carbon sequestration, larger values correspond to lower emissions.

Soil carbon sequestration estimates lead to emissions credits ranging from 0.39 to 0.57 times the life cycle GHG emissions from the production and use of conventional jet fuel. Recall that these estimates assume that the carbon content of the soil used for cultivation is depleted prior to cultivation occurring. Were salicornia grown on land with non

- depleted carbon stocks then it is unlikely that any long

- term soil carbon sequestration would resu lt.

7.7.7 Results The life cycle GHG emissions resulting from the production and use of HRJ and F

- T jet fuel from salicornia seeds and straw biomass are summarized in Table 93. Results incorporating long

- term carbon sequestration are given in Table 94.

These re sults reflect the sum of emissions from HRJ production and F

- T jet production for one total megajoule of jet fuel. Based on the input assumption adopted in this work and the allocation methodologies discussed above, the life cycle emissions of the salicorn ia to HRJ and F

- T jet pathway range from 0.35 to 0.76 times those from the production and use of conventional jet fuel. If long

- term carbon sequestration occurs because of the salicornia growth, the life cycle GHG emissions range between

- 0.2 to 0.37 times those from conventional jet fuel.

Table 93 : Summary of results from renewable and F

- T jet fuel production and use from salicornia

Land Use Change Scenario H0 Low Baseline High Key Assumptions Total Biomass Yield (kg/ha/yr) 17614 16247 14880 Seed production (g seed /kg total biomass ) 142 122 101 Seed Yield (kg/ha/yr) 2506 1977 1504

Seed Oil Fraction 33% 28.2% 26% Life Cycle CO 2 Emissions by Stage Biomass Credit (gCO 2 /MJ)

- 90.9

- 105.3

- 116.9 Recovery of feedstock (gCO 2 /MJ) 26.7 36.8 47.1 Transportation of feedstock (gCO 2 /MJ) 1.0 1.1 1.2 Processing of feedstock to fuel (gCO 2 /MJ) 21.6 38.3 51.5 Transportation of jet fuel (gCO 2 /MJ) 0.5 0.5 0.5

Combustion CO 2 (gCO 2 /MJ) 70.4 70.4 70.4 WTT GHG Emissions by Species

WTT CO 2 emissions (gCO 2 /MJ)

- 41.0

- 28.6

- 16.5

WTT CH 4 emissions (gCO 2 e/MJ) 1.0 1.3 1.7

WTT N 2 O emissions (gCO 2 e/MJ) 0.2 4.6 10.5 Total WTW GHG emissions (gCO 2 e/MJ) 30.5 47.7 66.1 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel 0.35 0.55 0.76

### 105 of 129 Table 94 : Life cycle GHG emissions for production and use of renewable and F

- T jet fuel salicornia assuming long

- term carbon sequestration

Land Use Change Scenario H1 Low 1 Baseline 1 High 1

Land use change emissions (gCO 2 /MJ)

- 49.7

- 41.9

- 33.9

WTW CO 2 emissions (gCO 2 /MJ)

- 20.3

- 0.1 20.0 Total WTW GHG emissions (gCO 2 e/MJ)

- 19.2 5.8 32.2 Life Cycle GHG Emissions Relative to

Baseline Conventional Jet Fuel - 0.22 0.07 0.37

Notes: 1) All other input assumptions (salicornia cultivation, extraction of oil, processing

of oil to HRJ and straw biomass to F

- T jet) are based on those in the H0 emissions case of the corresponding scenario.

Without the opportunity to obtain nutrients from the recycled streams of aquacul ture farms, substantial increases in nitrous oxide emissions are likely to occur. An increase in N 2 O emissions translates to more pronounced consequences from the uncertainty associated with the IPCC correlations. The reader should be aware of the potentia l for these inherent uncertainties when comparing different pathways for GHG reduction potential in sections 8.1 and 8.2.

### 106 of 129 

# 8 Summary of Results

8.1 Life Cycle GHG Emissions

Table 95 summarizes the results of the life cycle GHG emissions for the baseline scenari o of the fuel pathways considered in this work. Figure 25 presents the results of the analysis in graphical form. It is important to note that this plot does not show cumulative totals, but rather it displays the contribution of emissions from each step in the fuel life cycle. The impact of the land use change scenarios, which are summarized in Table 96, is included in the form of two pathways for switchgrass fed F

- T jet, three pathways for soy oil to HRJ, four pathways for palm oil HRJ, two pathways for ra peseed to HRJ and two pathways for salicornia oil HRJ. These results highlight the need to avoid land usage changes that result in positive GHG emissions. This method of presentation displays the ‘biomass credits’ that are given to biofuels from the CO 2 ab sorbed during biomass growth; these credits are largely the reason why these fuels offer the potential for reduced GHG emissions. With the exception of BTL and CBTL, the biofuel pathways all have similar ‘biomass credits’ and the magnitude of these credits is approximately 63 the magnitude of

the combustion emissions. The ‘biomass credit’ for CBTL is smaller because the fuel is created from a combination of coal and biomass. The ‘biomass credit’ for BTL is larger because biomass is used to power the entire fu el production process.

The baseline life cycle GHG emissions values in Figure 25 were combined with the low and high emissions scenario values to create Figure 26. The uncertainty bars represent the range of emissions as given by the low and high emission s cases. Many pathways have baseline life cycle GHG emissions that are lower than conventional jet fuel but have the potential to have GHG emissions that are higher than conventional jet fuel. For this reason, it is essential not to simply

assume that biof uels are environmentally beneficial without knowing the specifics of how the fuel is produced.

A few of the key results are outlined below:

o Life cycle GHG emissions are but one of many considerations when evaluating the feasibility and sustainability of an alternative fuel option.

o The data do not include all of the feedstock

- to

- fuel pathways that could be use to create jet fuel. Some interesting options not covered include camelina oil to jet fuel, fuels created from pyrolysis oils and advanced fermentation of sugars to hydrocarbons. These will be addressed as part of the ongoing work and will appear in future revisions to this report.

o Of the fuel options considered herein, conventional petroleum has the lowest emissions of any jet fuel pathway that that relies exclusively on fossil fuel resources.

o Few biofuels have zero life cycle GHG emissions.

o There is considerable variability in the life cycle GHG emissions; emissions from land use change contribute the most to this for the biofuel pathways considered.

63 In some of the high and low emissions scenarios, the ‘biomass credit’ is not quite equal to the combustion emissions due to variations in the allocation ratios throughout the pathway. Since the biomass credit’ is given in the first life cycle stage (feedstock recovery), it is subject to all allocation ratios.

### 107 of 129 Table 95 : Baseline life cycle GHG emissions for all fuel pathways studied. Land use change scenarios ar e described in Table 96BiomassCreditRecoveryFeedstockTransportProcessingFuelTransportCombustionWTT N2OWTT CH4Land UseChangeTotal

Crude to conventional jet fuel 0.0 4.2 1.5 5.5 0.8 73.2 0.1 2.3 0.0 87.5

Crude to ULS jet fuel 0.0 4.2 1.5 7.3 0.8 72.9 0.1 2.4 0.0 89.1
Oil sands to jet fuel 0.0 19.0 1.3 5.5 0.5 73.2 0.1 3.1 0.0 102.7

Oil shale to jet fuel 0.0 41.2 0.6 3.3 0.6 73.2 0.2 2.5 0.0 121.5

Natural gas to F

- T

fuel 0.0 4.6 0.0 20.2 1.2 70.4 0.0 4.6 0.0 101.0

Coal to F

- T f u el ( no carbon capture) 0.0 0.8 0.1 117.2 0.6 70.4 0.0 5.7 0.0 194.8

Coal to F

- T f u el ( with carbon capture) 0.0 0.8 0.1 19.4 0.6 70.4 0.0 5.9 0.0 97.2

Switchgrass to F

- T
fuel (LUC

- B0) - 222.7 6.4 0.6 152.1 0.5 70.4 10.3 0.2 0.0 17.7

Switchgrass to F

- T
fuel (LUC

- B1) - 222.7 6.4 0.6 152.1 0.5 70.4 10.3 0.2

- 19.8

- 2.0

Coal and Switchgrass to F

- T
fuel with CCS (LUC

- B0) - 44.3 1.2 0.2 21.9 0.5 70.4 2.0 4.9 0.0 56.9

Coal and Switchgrass to F

- T
fuel w/o CCS (LUC

- B1) - 44.3 1.2 0.2 21.9 0.5 70.4 2.0 4.9

- 3.9 53.0

Soy oil to HRJ (LUC

- S0) - 70.5 20.1 1.2 10.3 0.6 70.4 3.6 1.3 0.0 37.0

Soy oil to HRJ (LUC

- S1) - 70.5 20.1 1.2 10.3 0.6 70.4 3.6 1.3 60.8 97.8

Soy oil to HRJ (LUC

- S2) - 70.5 20.1 1.2 10.3 0.6 70.4 3.6 1.3 527.2 564.2

Palm oils to HRJ (LUC

- P0) - 70.5 4.9 3.1 10.3 0.6 70.4 5.1 6.3 0.0 30.1

Palm oils to HRJ (LUC

- P1) - 70.5 4.9 3.1 10.3 0.6 70.4 5.1 6.3 9.6 39.8

Palm oils to HRJ (LUC

- P2) - 70.5 4.9 3.1 10.3 0.6 70.4 5.1 6.3 135.8 166.0

Palm oils to HRJ (LUC

- P3) - 70.5 4.9 3.1 10.3 0.6 70.4 5.1 6.3 667.9 698.0

Rapeseed oil to HRJ (LUC

- R0) - 70.5 17.2 3.1 10.3 0.6 70.4 22.4 1.3 0.0 54.9

Rapeseed oil to HRJ (LUC

- R1) - 70.5 17.2 3.1 10.3 0.6 70.4 22.4 1.3 43.0 97.9

Jatropha oil to HRJ

- 70.5 16.7 1.5 10.3 0.6 70.4 9.1 1.2 0.0 39.4

Algae oil to HRJ

- 70.5 29.6 0.3 10.3 0.6 70.4 8.1 1.8 0.0 50.7

Salicornia to HRJ

and

F

- T Fuel (LUC

- H0) - 105.3 36.8 1.1 38.3 0.5 70.4 4.6 1.3 0.0 47.7

Salicornia to HRJ

and

F

- T Fuel (LUC

- H1) - 105.3 36.8 1.1 38.3 0.5 70.4 4.6 1.3

- 41.9 5.8

### 108 of 129 Table 96 : Land use change scenarios considered in this work

Land use

change Scenario 0 Scenario 1 Scenario 2 Scenario 3

Switchgrass None Carbon depleted

soils converted to switchgrass

cultiv a tion n/a n/a

Soy oil None Grassland

conversion to soybean field Tropical rainforest

conversion to soybean f ield n/a

Palm oil None Logged over forest conversion to palm

plantation field Tropical rainforest

conversion to palm plantation

field Peat land
rainforest

conve rsion to palm plantation

field

Rapeseed

oil None Set

- aside land

converted to rapeseed cultiv a tion n/a n/a

Salicornia None Desert land

converted to

salicornia cultivation

f ield n/a n/a

Figure 28 : Life cycle GHG emissions for the alternative jet fuel pathways under consideration. Note: CCS denotes carbon capture and sequestration and the land use change (LUC) scenarios are defined in Table 96 

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### 109 of 129 Figure 29 : L ife cycle GHG emissions for the alternative jet fuel pathways under consideration. Uncertainty bars represent the low emissions, baseline, and high emissions scenarios. Please note the different scales for the top and bottom portions of the figure. Note: C CS denotes Carbon Capture and Storage and Land Use Change (LUC) scenarios are defined in Table 96



### 8.2 Life Cycle GHG Intensity

As was discussed in Section 3.6, the life cycle GHG emissions from Table 95 as well as Figures

28 and 29 can be combined with an airc raft energy efficiency to yield a metric of life cycle GHG intensity. With a Payload Fuel Energy Intensity (PFEI) of 0.015 MJ / kg

- km, the U.S. commercial fleet had a life cycle GHG intensity of 1.3 g CO 2e /kg

- km in 2007. 64 Because SPK fuels have increased specific energy (energy per unit mass) relative to conventional jet fuel, the fleet wide PFEI will decrease by 0.3% ( Hileman et al. , 2010). Despite the decrease in energy usage, there
would be a 3% increase in fuel volume relative to conventional jet fuel. The change in energy with the use of SPK fuels is sufficiently small relative to the change in life cycle GHG emissions that have been discussed in this document that a life cycle GHG intensity value could be obtained for any of the fuels considered in this document by multiplying the life cycle GHG emissions by the PFEI. Other fuel efficiency metrics could similarly be converted to life cycle GHG metrics using

the same method.

64 This value was calculated based on calculations performed by Hileman et al. (2008) using the mean energy content of

JP

- 8 and 2007 data from the Bureau of Transportation Statistics (1.17 x 10 11 revenue ton miles flown and 1.99 x 10 10

gallons of jet fuel consumed, http://www.transtats.bts.gov/). 

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### 110 of 129 8.3 Biofuel Yield

The focus of this work was to establish life cycle GHG emissions inventories for a variety of alternative jet fuels and to determine the scales at which these fuels would need to be implemented in order to achieve emissions reduction targets. The former is covered in the results

from S ection 8.1 while the latter requires additional analysis of biofuel yield.

Figure 30 summarize s the fuel production potential for all of the biofuel pathways presented in Figure s 2 8 and 29 . The range in yields of fuel per kilogram of oilseed feedstock aris es from the both the crop yield per acre of land and the variation in oil fraction. Rapeseed seed yields the most oil per kilogram (44%) followed by jatropha seeds (35%), algae (25%), palm kernels (22%) and finally soybeans (18%). Recall that there can be considerable variability in biomass oil yields, (e.g., potential algal yields up to 390% of those used in this work have been quoted by stakeholders in the industry). The fuel production from salicornia is the result of the combined jet fuel production fro m HRJ and BTL facilities. When configured in this manner, each megajoule of jet fuel is composed of 65% F

- T jet and 35% HRJ.

Figure 30 : Fuel production potential for various alternative jet fuels that could be derived from biomass. This

is not an all

- encompassing list of alternative jet fuel options; it merely represents those examined by the authors as part of their ongoing research

A s ubtle but important point surrounding the F

- T jet fuel results is that only 25% of the fuel output

from the F

- T fuel facility was assumed to be jet fuel. Using switchgrass CBTL as an example, there would be 12,000 (3000 × 4) liters of liquid hydrocarbons p roduced for every hectare of switchgrass, only 3000 liters of which would be jet fuel (the rest could be fuels such as diesel, gasoline, and naphtha). All 12000 liters of fuel produced carry an environmental benefit; therefore, all liquid fuel products mus t be considered to capture the total CO 2 mitigation. The switchgrass (or other biomass) used as feedstock for pure BTL plants have very low energy

densities. This causes a large quantity to be required in order to make a relatively small quantity of jet fu el (which has a high energy density). Although the same feedstock could be used for both pure BTL and CBTL, when supplemented by coal, the same fuel output can be obtained from a smaller quantity of biomass. This increase is only due to supplementing the b iomass feedstock with coal and not to an increase in biomass usage efficiency. Corn stover or forest residue can also be used as a feedstock while maintaining life cycle GHG emissions within 9% of the value found using switchgrass. !" #!!!" $!!!!" $#!!!" %!!!!" %#!!!" &!!!!" '()*"+,(-.)"/01" +23,456)788"/01" '()*"+,(-.)"'/01" +23,456)788"'/01" +(9:.7*";<=" =7,)(>57";<=" <7>.8..?";<=" +7@34()*37";<=A/01" B7@C"DD/";<=" E@67.";<="

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C::<9?7EQ&E78>796H7=&7C&/2K& G678+99&+=<&42K&E7+;&!G,& 8+99.&
1. R,<-7A-:=:F+G;:&L:?&CD:;9&

>-7<DE:&-:=:F+G;:&=+>*?*+& +=<&86S&>-7>+=:&(+9&+9&G,&
>-7<DE?9$&&T=;,&-:=:F+G;:& =+>*?*+&69&E7D=?:<&+9&+&;6ID6<&
CD:;&GD?&>-7<DEH7=&7C&86S&
>-7>+=:&(+9&69&86=68+;&G,&
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### 111 of 129 The life cycle GHG emi ssions from Figure 2 9 and production potentials from Figure 30 can be combined to select fuel pathways that hold the most potential for reducing aviation’s GHG

emissions. This combination is needed to reduce aviation’s GHG emissions because fuel pathways h aving both low life cycle GHG emissions as well as large fuel production potential are required. A graphical representation of the land requirements to supply the entire 2009 US jet fuel market with 100% SPK and a 50/50 blend of SPK and conventional jet fu el is shown in Figure

31. 65 Three representative fuel yields were chosen to span the relevant range from Figur e 30

.

Figure 31 : Land area requirements to replace conventional jet fuel use within the US with 100% SPK and

50/50 blend of SPK with conventional jet fuel. Average US conventional jet fuel consumption in 2009 is 1.4 million bbl/day

Fossil

- to

- jet fuel pathways have large production potential, but they have comparable or higher emissions than conventional jet fuel; ther efore, their use will not reduce GHG emissions. BTL fuels have low GHG emissions, but they also have limited fuel production potential due to the large capital costs for F

- T production facilities. With the use of excess rapeseed, palm or soy (available aft er food needs are met) for HRJ production, rapeseed to HRJ, soy to HRJ and palm to HRJ have low life cycle GHG emissions; however, there is little excess available and new cropland is required for additional production. Current global production of soy, pa lm and rapeseed oil translate to only 34%, 43% and 18% of US jet fuel demand, respectively (FAPRI, 2009). As such, expanded production of soy oil and palm oil for large

- scale HRJ production could result in significant GHG emissions from land use change. Be cause of its toxic characteristics and low yield, jatropha is likely limited to small regional applications making it inappropriate to replace considerable quantities of conventional jet fuel. Hence, BTL fuels as well as HRJ fuels from soy, palm, and jatro pha have limited potential for reducing GHG emissions. The production potential of CBTL is largely dependent on the biomass weight fraction of the feedstock. Switchgrass was found to yield three times more volume of jet per hectare than corn stover when fe d with a weight fraction of 25%. Salicornia holds promise if it is used to make both HRJ and BTL (or CBTL) fuels as it could reduce life cycle GHG emissions by 25% compared to conventional jet fuel and has a production potential equivalent to one third of palm. The most obvious opportunity for large

- scale production is using algae to make HRJ, which explains the recent wave of investments in the algae industry.

65 Average US consumption of jet fuel thus far in 2009 has been roughly 1.4 million barrels per day (EIA, 2009b) ! 15000 L

ha

" yr ! 3000 L

ha

" yr ! 600 L

ha

" yr

!""#$% &""'%()*+,-)$)./%01% -0.2).30.,+%4)/%15)+% !"'%()*+,-)$)./%01% -0.2).30.,+%4)/%15)+%

### 112 of 129 8.4 Biofuel Impact on Domestic Water Resources

Water plays an essential role in developing and utili zing energy resources. It is used in energy

resource extraction, refining and processing, and transportation. The dependence of biofuels on water extends even further to include water used for feedstock growth. This work has thus far only focused on life c ycle GHG emissions and yield as the bounding factors to large

- scale biofuel production. Water requirements and regional availability serve as a third bound to the trade space of biofuel development.

It is important to distinguish between water withdrawal and water consumption. Water withdrawal involves withdrawing water from its source and subsequently returning it after use. The quality of the returned water may not be the same as when it was originally removed. Electricity generation is the best example where water use is dominated by withdrawn water used for cooling (some cooling water is lost to evaporation and this fraction is considered consumed). Water consumption involves withdrawing water from its source and not returning it after use. Crop irrigat ion represents the largest fraction of water consumption within the US. Figure 32 shows water withdrawals and water consumption by sector for the US in 2000 and 1995 respectively. Notice that the volume of consumed water is only 30% of that withdrawn.

Figure 32 : Water consumption and water withdrawals in the US by sector (data adapted from DOE, 2006)

The water required to refine one liter of petroleum fuels was found to be between 1 and 2.5 liters (King and Webber, 2008). Jet fu el consumption in the US is currently at 1.4 million barrels per day, or 222 million liters per day (EIA, 2009b). This means that the aviation industry alone is responsible for consuming between 222 and 556 million liters of water each day. Most alternativ e fuels will require at least the quantity of water that refineries currently consume and have the potential to consume several orders of magnitude more (DOE, 2006). This fact leads us to define the life cycle water consumption in fuel production using a s imilar metric to that used to quantify life cycle GHG emissions. The units of liters consumed per MJ of fuel energy delivered to the tank are used to encompass the water consumed during the recovery and transportation of the feedstock from the well, field, or mine to the production facility, processing of these materials into fuels, transportation and distribution of the fuel to the aircraft tank, and combustion of the fuel in the aircraft. Without considering any specific examples, water consumption from f eedstock recovery and processing will dominate the life cycle water consumption while water consumption from feedstock and fuel transportation will be indirect through consuming fuels that required water for their production. Water consumption from the com bustion stage is zero.

The primary focus of this work was on life cycle GHG emissions; hence no new data has been generated concerning water usage within fuel production pathways. While this is an active part of our ongoing research, this work makes use of a study concerning energy demand on water resources from the US Department of Energy (2006) and a study on the water intensity of transportation from King and Webber (2008). The relevant data from both papers is presented in Figure 33. Notice that corn and soybeans requiring irrigation consume 3 orders of magnitude more water than refining of conventional petroleum based fuels. The irrigation needed for crop !"#$%&'()*+,'&$-./(0123./4$567$89::9./$:9,'&($2'&$;+<$ !"#$%&'()*+,'&$=9,);&+*+:(4$>577$89::9./$:9,'&($2'&$;+<$

!"#$%&'()*+,-+

.//"012'3*+45-+

.367%&/"18*+9-+

:"3"30*+,-+

;'<$%2(*+,-+ =>$/<'$8$(&/"(*+45-+ ?7@8"(+A7BB8C*+,4-+ DE71(78&7/$*+,-+

!"#$%&'()*+,-+ .//"012'3*+45-+ .367%&/"18*+9-+

:"3"30*+5-+ ;'<$%2(*+

=-+ >?$/<'$8$(&/"(*+9-+

@'<<$/("18*+5-+

### 113 of 129 production varies greatly depending on the region. For example, water use for irrigated soy produ ction varies from 600,000 liters per hectare for Pennsylvania to about 4.3 million liters per hectare for Colorado, with a national average of 2.4 million liters per hectare (DOE, 2006). The

o verall average is deceiving because many areas use no irrigation while others have much higher

demands.

Few new reservoirs have been built since 1980, and fresh surface

- water withdrawals have peaked at about just over 1 trillion liters per day. Many regio ns depend on groundwater to meet increasing water demands, but declining groundwater tables could severely limit future water availability. Some regions have seen groundwater levels drop as much as 300 to 900 feet over the past 50 years from pumping water from aquifers at a rate faster than the natural rate of recharge (DOE, 2006). The expansion of biofuel production could exacerbate non

- uniform regional water stresses between states; hence, the impact of biofuels on water consumption will be regionally dep endent and cannot be summarized by a single value.

If feedstocks can be found that require no additional water above the status quo, their processing to jet fuel will still place additional strain on the current system. Converting natural gas and coal to

F

- T diesel are 5 and 7 times more water intensive than refining of conventional petroleum, respectively. Similarly, water consumption from steam reforming of natural gas to make hydrogen for hydroprocessing renewable oils to HRJ will be at least comparable to refining conventional petroleum.

Figure 33 : Water consumption for the production of various fuels (data adapted from DOE, 2006 and King and Webber, 2008)

Depending on the water quality needs for particular applications, fre shwater supplies can be augmented with degraded or brackish water. This applies to the algae and salicornia pathways discussed in sections 7.6 and 7.7. Algae grown in open ponds have the additional complication of evaporation from the pond surface. Algae a re most effectively grown in shallow, high surface area ponds. Assuming an evaporative rate of 1.5 cm/day (Weissman and Goebel, 1987), 100 liters of water per hectare need to be replenished each minute. This constraint will limit the location of algae farm s to being adjacent to a plentiful source of degraded or brackish water.

As previously discussed, biofuel production would aggravate regional strains on freshwater !"!!#$ !"!#!$ !"#!!$ #"!!!$ #!"!!!$ #!!"!!!$ %&'(&)*)+$, ) -&.&./$ 0' 1$233&/ 4 5&'.$ 6 574.'+$83 '9)**&./$ :'3.$233&/ 4 5&'.$ ;1 (3 '/ ).$6+)953 '+1 *&*$ ;1 (3 '/ ).$, ) - '3<&./$ =&+$05 '3 4/ )$&.$04+5$:4 > )3.$ =&+$04.(*$ =&+$074+)$2.?0&5@$ =&+$074+)$0@3- 49)$, ) 5 '35$ , ) -&.&./$ 6.74.9)($=&+$, )9 ' > )3 1$

8 ) 53 '+)@<$6A 53 495&'.$ B4*$05 '3 4/ )$&.$04+5$:4 > )3.$ C4 5@3 4+$B4*$8&D)+&.)$=D)3 4 5&'.*$ C4 5@3 4+$B4*$6A 53 495&'.$4.($83 '9)**&./$ :'. > )35&./$:'4+$5 '$E?F$G&)*)+$ :'4+$0+@33 1$ :'4+$H&I@&- 495&'.$ :'4+$J 4*7&./$

:'4+$K&.&./$ :'. > )35&./$CB$5 '$E?F$G&)*)+$

! " # $%&'()*+,- #.()&/01234&

### 114 of 129 supply and local infrastructure. Further insight as to where these strains will be felt can be gained by considering the data from DOE (2006) in conjunction with the regions within the US where biofuel feedstock cultivation expansion is likely. In their 2006 report on the interdependency of energy and water , the US Department of E nergy (DOE) showed that freshwater withdrawal in most regions of the US exceeds the available precipitation (precipitation minus evapotranspiration 66 ) . While the rat io of freshwater withdrawals to available precipitation is less
than 30 for most of the nort h e ast, midwest and southeast, values between 100 and 500 are not

uncommon for western states . The magnitude of this ratio indicates the degree to which current water demands are being met with groundwater pumping or transport of surface water from other

re gions (DOE, 2006).

Locations most conducive to next generation biofuel feedstock cultivation are the Southwest and Southeast for algae, Southwest for salicornia, and Midwest and Southeast for switchgrass. With the exception of Midwest and part of the sout heast, these are regions where groundwater withdrawal is well in excess of the available precipitation each year. The southwest should receive particular attention when discussing the expansion of domestically produced biofuels because of its large water c onstraints.

These data demonstrate that water availability could be a limiting factor for biofuel production in certain regions of the US. Water usage is part of this ongoing work and will be addressed further in subsequent reports.



### 8.5 Invasive Species with Respect to Biofuels

Crops ideal for large

- scale biofuel production are those with high yield that do not require fresh water irrigation and can maintain high yields when grown on marginal lands. These are also general traits that describe invasive specie s. More specifically, the National Invasive Species Information Center (NISIC) defines the following characteristics as some of the traits of species likely to be invaders (NISIC, 2006).

• Rapid growth and short life cycle: go from seed to producing seed very rapidly

– sometimes within a few weeks.

• Able to grow in a wide range of habitats

• High number of seeds produced

• Long seed dormancy and staggered germination

• Efficient method of seed dispersal

• Benefit fro m allelopathy, which is the release of chemicals into the surrounding soil that prohibit the growth of other plants.

As defined by most government organizations, invasive species are non

- indigenous and adversely affect the habitats they invade economically, environmentally or ecologically. There are approximately 50,000 non

- indigenous species in the United States. Some of these are noninvasive and beneficial to society, such as corn, w heat and rice. Others have caused major economic losses in agriculture and forestry and have irreversibly damaged the ecosystems to which they were introduced (Pimentel et al., 2000).

Historically most plant introductions, with the exception of agricultu ral weeds, have been intentional while microbe introductions have been accidental. Regardless of initial rationale, an additional 700,000 hectares of US wildlife habitat are invaded each year by non

- indigenous weeds. As an example, 4 million hectares of gr assland in northern California have been lost to yellow star thistle (Campbell, 1994). These are lands that may otherwise have been used for energy crop cultivation such as switchgrass. Invaded lands can also become predisposed to fires. Cheatgrass growth in the Great Basin in Idaho has increased the frequency of fires by more than an order of magnitude (Whisenant, 1990).

66 Describes the sum of evaporation and plant transpiration from the Earth’s land surface to the atmosphere.

### 115 of 129 The accidental introduction of weeds and microbes often occurs via crop seeds and other parts of host plants. The agriculture industry suffers an overall reduction of 12% in crop yields due to weeds with each 1% decrease in crop yield being accompanied by a 4.5% increase in crop cost to the farmer. Ecologists agree that non

- indigenous weeds are a greater risk than native ones. Plant patho gens carried by foreign microbes result in crop losses of approximately 65% those of weeds. Similarly, pathogens of forest plants cause the loss of 9% of forest products each year (Pimentel et al., 1997).

The majority of the aforementioned impacts occurr ed because of intentional introduction of foreign plants to the North American ecosystem. Many of the feedstocks considered in this work have never before been grown at large scale and/or require deliberate introduction to an ecosystem. The financial burde n of losses and control due to invasive plants and microbes has been estimated as several tens of billions of dollars each year (Pimentel et al., 2000). While the authors are not attempting to imply that the expansion of biofuels could result in such signi ficant economic losses, it is important to understand that controlling invasive plants is not a negligible consideration in the decision making process. T he awareness that invasive species can be a

direct financial burden highlights the potentially negativ e economic consequences of introducing new species to the environment. Maintaining environmentally sustainable feedstock production that meets food and energy demands is essential to the economic success of meeting large

- scale

biofuel demands.

The expans ion of biofuel consumption within the U.S. will require a significant increase in crop and feedstock production and this presents the possibility that non

- indigenous species will be introduced into an unprepared ecosystem. Ironically, the impact of these i nvasives could be to inhibit crop production, which could hurt the industry that was responsible for their introduction.

### 116 of 129 

# 9 Conclusions

As part of continuing research on alternative jet fuels, a screening level life cycle assessment of a wide range of poten tial drop

- in alternative jet fuels was conducted. Three scenarios were developed for each pathway corresponding to optimistic, nominal and pessimistic assumptions regarding specific process inputs and production characteristics. In most fuel pathways, the choice of allocation methodology and potential for GHG emissions from land use change were found to have the largest impact on the results.

Consistent allocation methodologies were implemented across multiple pathways to facilitate equitable comparisons o f different alterative fuels. In all analyses, the use of displacement or system expansion was minimized to reduce the variability of the results to subjective assumptions. The displacement method was only implemented where assumptions could be

made that m inimize the impact of allocation on the result (e.g. algae electricity). Energy allocation was used for any process that resulted in a product slate of liquid hydrocarbons (e.g., jet, diesel and naphtha from F

- T synthesis). Market value allocation was used for processes resulting in a combination of oil and meal (e.g. separation of soybeans into soy oil and soy meal). In all such cases, the oil price was assumed equal to that of soy oil and the price of meal assumed relative to soy meal based on relative pr otein content. The use of mass or v olume allocation was avoided.

The treatment of land use change emissions (both positive and negative) in this work was developed to provide an understanding for the reader of how land use change emissions compare with th e emissions from the other five life cycle stages. It was not intended to explicitly quantify the specific land use change emissions that would result from expanded production of any given feedstock. The scope of this work was limited to only quantifying t he impacts of direct land use change; emissions from indirect land use change were not considered. Multiple

scenarios were use d to explore the range of magnitudes of GHG emissions due to land use change; these included a scenario where no land use change e missions were incurred. This approach allowed the impacts of different land use change scenarios to be isolated from the other emissions from fuel production.

As part of ongoing research, a more complete assessment of land use change emissions that

includ es indirect effects is being developed. Proper evaluation of the indirect effects of alternative fuels within aviation requires modeling of the demand for renewable energy resources within the transportation sector, including aviation, as well as the deman d for renewable energy resources from the energy sector as a whole. Most indirect effects are expected to occur on an international scale; hence, domestic analyses, such as those in this work, need to be done in the context of the global market.

Given the ir reduced life cycle GHG emissions relative to conventional jet fuel, some alternative fuels could play a central role in mitigating aviation's contribution to climate change, including helping aviation to achieve carbon

- neutral growth when combined with improved technology and operations that are more efficient. If appropriate renewable feedstocks were used, both Fischer

Tropsch (F

- T) fuels and Hydroprocessed Renewable Jet (HRJ) fuel could provide aviation with modest (~10%) to large (~50%) reductions in emissions that contribute to global climate change. If projections of soil carbon sequestration prove valid, a salicornia

- based biofuel could have a 100% reduction in life cycle GHG emissions.

Aviation is not the only potential user of renewable biomass resources, and it will have to compete for these limited resources. Furthermore, large land area requirements indicate that it is unlikely that a single region could create sufficient biomass to supply the entire planet with biofuels. Hence, it is probable that large

- scale implementation of biofuels would arise as a superposition of regionally appropriate feedstocks. It is critical to continue to examine feedstocks that could be used to create transportation fuels, such as jet fuel, without the need for ara ble land, with a minimum of fresh water, and with large yields per hectare.

### 117 of 129 Although this analysis has identified several shortcomings that could prevent biofuels from being a complete environmental solution for the aviation industry, they could still be an important part of the aviation industry’s strategy for reducing life cycle GHG emissions. Current actions with regard to biofuel expansions are important in realizing the potential of this industry. Not all feedstocks need to have the potential to disp lace large volumes of petroleum fuel. Any feedstock produced today can lead to valuable experience through benefitting the local economy and providing essential lessons in production and processing techniques. This experience would be invaluable

should a h igher yield crop, such as algae, become commercially viable.

The most significant challenge is not in developing viable alternative fuels that could reduce

aviation's GHG emissions

-- the technology exists; rather, the challenge lies in developing and

com mercializing the large scale production of next generation of biomass feedstocks that could be grown in a sustainable manner.

### 118 of 129 

# Appendix A

– General Feedstock and Fuel Properties

The properties of the main feedstocks and fuels used in this analysis are giv en in Table 97. In most cases, these properties represent only a single value within the typical range of each characteristic. While these data are appropriate for the level of detail of this work, sample specific data should be used (where available) in c onducting analyses of individual production configurations.

Table 97 : Feedstock and Fuel Properties

Feedstock or Fuel LHV (MJ/kg) Densit

y (kg/L) Carbon content (wt %) Sulfur

con t ent ( wt ppm) Source(s)

Crude oil or syncrude 41.3 1 0.878 2 84.6 1 16,900 2 EIA, 2008a

Conventional Jet A 43.2 0.802 86.2 600 Hileman et al.

, 2010

ULS Jet A 43.3 0.791 85.9 15 Hileman et al.

, 2010

F

- T Jet Fuel / HRJ 44.1 0.76 84.7 0 Hileman et al.

, 2010

F

- T Diesel / HRD 44.0 0.78 87.1 0 GREET, 2008

F

- T Naphtha / HR Naphtha 44.4 0.70 84.2 0 GREET, 2008

Coal (U.S. a v erage ) 4 22.7

- 59.0 11,100 GREET 2007; EIA, 2006

Bituminous coal 4 26.4

- 64.8 29,400 SSEB, 2006

Sub

- bituminous coal 4 18.4

- 49.2 3,500 SSEB, 2006

Petroleum coke 5 33.2

- 92.3 68,000 EIA, 2006; GREET, 2008 Biomass (f o rest residue) 6 15.4

- 51.7 0 GREET, 2008

Biomass ( corn stover) 6 16.3

- 44.5 0 GREET, 2008

Bio m ass ( switchgrass) 6 17.6

- 47.0 900 GREET, 2008

Natural gas 47.1 0.0007

8 72.4 6 GREET, 2007

Hydrogen 120 0.0000

9 0 0 GREET, 2007

Notes: 1) Energy content of crude oil assumed to be 5.8 million Btu per barrel (HHV); carbon content calculated from formula: percent carbon = 76.99 + (10.19 × Specific Gravity) + (

- 0.76 × Sulfur Content). (EIA, 1999) 2) Density and sulfur content derived using his torical data (1995

- 2006) provided in EIA, 2008a 3) As source of process energy (e.g. electricity generation). LHV and sulfur content from GREET, 2007; carbon content derived from coal HHV and U.S. average coal carbon emission factor of

26.0 million metric ton s per quadrillion Btu for the electric power sector in 2004. (EIA, 2006) 4) For Coal

- To

- Liquids (CTL) process. 5) Used as a source of process energy in the refining of jet fuel. LHV and carbon content from EIA, 2006, sulfur content from GREET, 2008. 6) For Biomass

- To

- Liquids (BTL) process.

### 119 of 129 

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