Life-Cycle Assessment of Energy and 

Greenhouse Gas Effects of Soybean-Derived 

Biodiesel and Renewable Fuels

ANL/ESD/08-2

Energy Systems Division
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Life-Cycle Assessment of Energy and 

Greenhouse Gas Effects of Soybean-Derived 

Biodiesel and Renewable Fuels

ANL/ESD/08-2

by

H. Huo,1

 M. Wang,1 C. Bloyd,2 and V. Putsche3

1

Energy Systems Division, Argonne National Laboratory

2

Decision and Information Science Division, Argonne National Laboratory

3

Center for Transportation Technologies and Systems, National Renewable Energy Laboratory

work sponsored by the

U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy

March 12, 2008

iii 

Contents 

Notation..................................................................................................................................... vii 

Acknowledgments..................................................................................................................... viii 

Abstract..................................................................................................................................... 1 

1 Introduction ....................................................................................................................... 2 

2 Production Processes of Soybean-Based Renewable Fuels .............................................. 5 

2.1 Renewable Diesel Production Based on SuperCetane............................................ 7 

2.2 Renewable Diesel Production Based on UOP Hydrogenation Technology ........... 8 

2.3 Renewable Gasoline Production Based on UOP FCC Technology........................ 9 

2.4 ASPEN Model Results............................................................................................ 10 

3 Data Sources and Assumptions for Greet Simulations...................................................... 13 

3.1 Soybean Farming .................................................................................................... 13 

3.1.1 Yield......................................................................................................... 13 

3.1.2 Energy Use............................................................................................... 14 

3.1.3 Fertilizer Use............................................................................................ 16 

3.1.4 N2O Emissions......................................................................................... 17 

3.2 Soy Oil Extraction................................................................................................... 19 

3.3 Production of Soybean-Derived Fuels.................................................................... 20 

3.3.1 Biodiesel .................................................................................................. 21 

3.3.2 Renewable Diesel I .................................................................................. 22 

3.3.3 Renewable Diesel II................................................................................. 22 

3.3.4 Renewable Gasoline................................................................................. 23 

3.3.5 Comparison of the Four Soybean-Derived Fuels..................................... 24 

3.4 Fuel Properties ........................................................................................................ 25 

3.5 Fuel Use in Vehicles ............................................................................................... 25 

4 Co-Product Credits for Biofuels........................................................................................ 26 

4.1 Methods for Addressing Co-Product Credits.......................................................... 26 

4.2 Displacement Approach.......................................................................................... 27 

4.2.1 Soy Meal .................................................................................................. 28 

4.2.2 Glycerin.................................................................................................... 28 

4.3 Allocation Approach............................................................................................... 29 

4.3.1 Allocation at the System Level and Subsystem Level............................. 30 

4.3.2 Energy Value and Market Value.............................................................. 31 
iv 

Contents (Cont.) 

4.3.3 Allocation Ratios ..................................................................................... 32 

4.4 Hybrid Approach .................................................................................................... 32 

5 Life-Cycle Energy and GHG Emission Results for Soybean-Derived Fuels.................... 34 

5.1 Total Energy Use .................................................................................................... 35 

5.2 Fossil Energy Use ................................................................................................... 37 

5.3 Petroleum Use......................................................................................................... 38 

5.4 GHG Emissions ...................................................................................................... 39 

6 Conclusions ....................................................................................................................... 42 

7 References ......................................................................................................................... 44 

Appendix 1: ASPEN Simulation Process of Renewable Diesel I (Super Cetane) ................... 49 

Appendix 2: ASPEN Simulation Process of Renewable Diesel II 

 (Hydrogenation-Derived Renewable Diesel)....................................................... 67 

Figures 

1-1 System Boundaries for Life-Cycle Analysis of Petroleum Gasoline 

 and Diesel Fuels and Soybean-Based Biodiesel and Renewable Fuels ......................... 4 

2-1 SuperCetane Process Flow............................................................................................. 7 

2-2 UOP-Proposed Standalone Renewable Diesel Production ............................................ 9 

2-3 UOP-Proposed Renewable Gasoline Production........................................................... 10 

3-1 Three-Year Moving Average of Soybean Yield in the United States............................ 14 

3-2 Fertilizer Use for Soybean Farming in the United States .............................................. 17 

3-3 Fuel Production Processes for the Four Soybean-Derived Fuels................................... 20 

3-4 Transesterification of Soy Oil to Biodiesel.................................................................... 21 

4-1 Two System Levels of Soybean-Based Fuel Production in the 

 Allocation Approach ...................................................................................................... 30 
v 

Figures (Cont.) 

5-1 GREET Well-to-Pump and Pump-to-Wheels Stages..................................................... 34 

5-2 Well-to-Wheels Total Energy Use of Six Fuel Types ................................................... 35 

5-3 Comparison of Total Energy Use among Three Allocation Approaches 

 for Renewable Diesel I................................................................................................... 36 

5-4 Well-to-Wheels Fossil Energy Use of the Six Fuel Types ............................................ 38 

5-5 Well-to-Wheels Petroleum Energy Use of the Six Fuel Types...................................... 39 

5-6 Well-to-Wheels GHG Emissions of the Six Fuel Types................................................ 40 

5-7 Well-to-Wheels GHG Emission Reductions for Soybean-Derived Fuels 

 Compared with Petroleum Gasoline or Diesel............................................................... 40 

Tables 

2-1 Current and Planned Renewable Diesel Facilities ......................................................... 5 

2-2 Feedstock Availability for Renewable Diesel Production 

 in theUnited States ......................................................................................................... 6 

2-3 NREL-Simulated Renewable Fuels Mass and Energy Balances................................... 11 

2-4 NREL-Provided Base Energy Values of Renewable Fuel Components ....................... 12 

3-1 U.S. Historical Soybean Acreage and Yields ................................................................ 13 

3-2 Energy Use for Soybean Farming in the United States ................................................. 15 

3-3 Comparison of Energy Use for Soybean Farming Taken 

 from Three Data Sources ............................................................................................... 15 

3-4 Fertilizer Use for Soybean Farming............................................................................... 16 

3-5 Inputs and Outputs of Soybean Oil Extraction Plants ................................................... 20 

3-6 Inputs and Outputs of Biodiesel Plants.......................................................................... 21 
vi 

Tables (Cont.) 

3-7 Inputs and Outputs of Renewable Diesel I Plants.......................................................... 22 

3-8 Inputs and Outputs of Renewable Diesel II Plants ........................................................ 23 

3-9 Inputs and Outputs of Renewable Gasoline Plants........................................................ 23 

3-10 Energy Use and Amount of Fuel Product and Co-Products 

 from One Ton of Soybeans............................................................................................ 24 

3-11 Properties of the Four Soybean-Based Fuels................................................................. 25 

4-1 Approaches to Address Co-Products of Soybean-Based Fuels ..................................... 27 

4-2 Products to Be Displaced by Co-Products ..................................................................... 28 

4-3 Raw Material Input for One Pound of Synthetic Glycerin ............................................ 29 

4-4 Total Btu in Raw Material per Pound of Glycerin......................................................... 29 

4-5 Energy Content and Market Value of Primary Products and Co-Products ................... 32 

4-6 Allocation Ratios of Total Energy and Emission Burdens between Primary 

 Products and Co-Products from Using the Allocation Approach.................................. 33 

4-7 Allocation Ratios of Total Energy and Emission Burdens between Primary 

 Products and Co-Products of the Second Subsystem from Using the Hybrid 

 Approach........................................................................................................................ 33 
vii 

Notation 

Acronyms and Abbreviations 

BFW boiler feed water 

BP British Petroleum 

CETC CANMET Energy Technology Centre 

CH4 methane 

CIDI compression-ignition, direct-injection 

CO carbon monoxide 

CO2 carbon dioxide 

CSO clarified slurry oil 

DeCO2 decarboxylation 

DOE U.S. Department of Energy 

ERS Economic Research Service (USDA) 

FCC fluidized catalytic cracker 

GHG greenhouse gas 

GREET Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation 

HDO hydrodeoxygenation 

IPCC Intergovernmental Panel on Climate Change 

K potassium 

LCA life-cycle analysis 

LCO light-cycle oil 

LHV lower heating value 

LPG liquefied petroleum gas 

LSD low-sulfur diesel 

N nitrogen 

N2O nitrous oxide 

NaOH sodium hydroxide 

NG natural gas 

NOx nitrogen oxide 

NRCan Natural Resources Canada 

NREL National Renewable Energy Laboratory 

P phosphorus 

PM10 particulate matter with a diameter of 10 micrometers or less 

PM2.5 particulate matter with a diameter of 2.5 micrometers or less 

PTW pump-to-wheels 

RFG reformulated gasoline 

SI spark-ignition 

SMR steam methane reforming 

SOx sulfur oxides 

USDA U.S. Department of Agriculture 

VGO vacuum gas oil 

VOC volatile organic compound 

WTP well-to-pump 
viii 

WTW well-to-wheels 

WWT wastewater treatment 

Units of Measure 

bpd barrel(s) per day 

Btu British thermal unit(s) 

bu bushel(s) 

°C degree(s) Celsius 

°F degree(s) Fahrenheit 

ft3 cubic foot (feet) 

g gram(s) 

gal gallon(s) 

h hour(s) 

ha hectare(s) 

kW kilowatt(s) 

kWh kilowatt-hour(s) 

L liter(s) 

lb pound(s) 

mmBTU million Btu 

ppm part(s) per million 

psia pound(s) per square inch absolute 

psig pound(s) per square inch gauge 

scf standard cubic foot (feet) 

USD U.S. dollar(s) 

yr year(s) 
ix 

Acknowledgments 

This work was sponsored by DOE’s Office of Energy Efficiency and Renewable Energy. 

Argonne National Laboratory is a DOE laboratory managed by UChicago Argonne, LLC, under 

Contract No. DE-AC02-06CH11357. 

We are grateful to our DOE sponsor, Linda Bluestein, for her support and input to this study. We 

also wish to thank Robert McCormick and Caley Johnson of the National Renewable Energy 

Laboratory for providing ASPEN simulation results and insights to this study. We thank Philip 

Heirigs of Chevron and Leland Tong of the National Biodiesel Board for providing helpful 

comments on an earlier draft version of this report. However, we are solely responsible for the 

content of this report. 
1 

Abstract 

We assessed the life-cycle energy and greenhouse gas (GHG) emission impacts of 

the following three soybean-derived fuels by expanding, updating, and using 

Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and 

Energy Use in Transportation (GREET) model: (1) biodiesel produced from soy 

oil transesterification, (2) renewable diesel produced from hydrogenation of soy 

oil by using two processes (renewable diesel I and II), and (3) renewable gasoline 

produced from catalytic cracking of soy oil. We used four allocation approaches 

to address the co-products: a displacement approach; two allocation methods, one 

based on energy value and one based on market value; and a hybrid approach that 

integrates both the displacement and allocation methods. Each of the four 

allocation approaches generates different results. The displacement method shows 

a 6–25% reduction in total energy use for the soybean-based fuels compared with 

petroleum fuels, except for renewable diesel II. The allocation and hybrid 

approaches show a 13–31% increase in total energy use. All soybean-derived 

fuels achieve a significant reduction (52–107%) in fossil energy use and in 

petroleum use (more than 85%). With the displacement approach, all four 

soybean-based fuels achieve modest to significant reductions (64–174%) in wellto-wheels GHG emissions. With the allocation and hybrid approaches, the fuels 

achieve a modest reduction in GHG emissions (57–74%). These results 

demonstrate the importance of the methods that are used in dealing with coproduct issues for these renewable fuels. 
2 

1 Introduction 

There has long been a desire to find alternative liquid fuel replacements for petroleum-based 

transportation fuels. Biodiesel, produced from seed oils or animal fats via the transesterification 

process, has been the focus of biofuel production because of its potential environmental benefits 

and because it is made from renewable biomass resources. Biodiesel can be derived from various 

biological sources such as seed oils (e.g., soybeans, rapeseeds, sunflower seeds, palm oil, 

jatropha seeds, waste cooking oil) and animal fats. In the United States, a majority of biodiesel is 

produced from soybean oil. In Europe (especially in Germany), biodiesel is produced primarily 

from rapeseeds. Biodiesel can be blended with conventional diesel fuel in any proportion and 

used in diesel engines without significant engine modifications (Keller et al. 2007). In recent 

years, the sales volume for biodiesel in the United States has increased dramatically: from about 

2 million gallons in 2000, to 75 million gallons in 2005, to 250 million gallons in 2006 (National 

Biodiesel Board 2007). 

Transesterification of seed oils and animal fats has been the major technology for biodiesel 

production to date. New process technologies based on hydrogenation to convert seed oils and 

animal fats to diesel fuel and gasoline have recently emerged. The CANMET Energy 

Technology Centre (CETC) of Natural Resources Canada (NRCan) has developed a technology 

to convert seed oils and animal fats into a high-cetane, low-sulfur diesel fuel blending stock 

called “SuperCetane” [(S&T)2

 Consultants Inc. 2004]. UOP developed conversion processes 

based on conventional hydroprocessing technologies that are already widely deployed in 

petroleum refineries. The hydro-generation technologies utilize seed oils or animal fats to 

produce an isoparaffin-rich diesel substitute referred to as “green diesel” (Kalnes et al. 2007). 

UOP also proposed a technology that can produce “green gasoline” by cracking seed oils and 

grease in a fluidized catalytic cracker (FCC) unit (UOP 2005). The diesel and gasoline produced 

from these processes are often referred to as renewable diesel and gasoline. 

In this report, we present a life-cycle analysis of the energy and GHG emission impacts of 

biodiesel, renewable diesel, and renewable gasoline relative to those of petroleum diesel and 

gasoline. In the United States, soybeans are the major feedstock for biodiesel production now 

and, potentially, for renewable diesel and gasoline production in the future. In our study, we 

evaluated production of biodiesel, renewable diesel, and renewable gasoline from soybeans. 

For this study, we expanded and updated the GREET (Greenhouse Gases, Regulated Emissions, 

and Energy Use in Transportation) model (see http://www.transportation.anl.gov/software/ 

GREET/index.html). In 1995, with funding from the U.S. Department of Energy (DOE), 

Argonne National Laboratory’s Center for Transportation Research developed the GREET 

model for use in estimating the full fuel-cycle energy and emissions impacts of alternative 
3 

transportation fuels and advanced vehicle technologies. Since that time, the model has been 

updated to include new fuels and transportation technologies. The latest version — 

GREET 1.8a — is capable of analyzing more than 100 transportation fuel pathways. 

For a given vehicle and fuel system, GREET evaluates total energy use, fossil fuels, natural gas 

(NG) use, coal use, and petroleum use; emissions of carbon dioxide (CO2)-equivalent greenhouse 

gases (GHGs) including CO2, methane (CH4), and nitrous oxide (N2O); and emissions of six 

criteria pollutants — volatile organic compounds (VOCs), carbon monoxide (CO), nitrogen 

oxides (NOx), particulate matter with a diameter of 10 micrometers or less (PM10) and 

2.5 micrometers or less (PM2.5), and sulfur oxides (SOx). These criteria pollutant emissions are 

further separated into total and urban emissions to reflect human exposure to air pollution caused 

by emissions of the six criteria pollutants. 

Our analysis in this study includes the following six fuel pathways: 

(1) Conventional petroleum-based reformulated gasoline (RFG); 

(2) Conventional petroleum-based low-sulfur diesel (LSD) with 15 parts per 

million (ppm) sulfur content; 

(3) Soybean-based biodiesel produced by using the transesterification process; 

(4) Soybean-based renewable diesel I (“SuperCetane”) produced by using the 

hydrogenation process; 

(5) Soybean-based renewable diesel II (“green diesel”) produced by using the 

hydrogenation process; and 

(6) Renewable gasoline (“green gasoline”) produced by using catalytic 

cracking. 

We used petroleum gasoline and diesel as the baseline fuels; our analysis was conducted for year 

2010. We estimated consumption of total energy, fossil energy, and petroleum oil and emissions 

of GHGs (CO2, N2O, and CH4) for each of the six pathways. Figure 1-1 illustrates the system 

boundary for the six fuel pathways. The four soybean-based pathways consist of six stages: 

(1) farming activities, including manufacture of fertilizer and other chemicals, soybean farming, 

and soybean harvest; (2) soybean transportation from farms to processing plants; (3) soy oil 

extraction in processing plants; (4) production of biodiesel or other renewable fuels in plants; 

(5) fuel transportation and distribution from plants to refueling stations; and (6) fuel use during 

vehicle operation. As shown, the four soybean-based fuel pathways have three common stages: 

soybean farming, soybean transportation, and soy oil extraction. The four paths differ in terms of 

their fuel production processes and vehicle operations. 
4 

Soybean Farming

Soybean 

Transportation

Soybean Oil 

Extraction

Soy Oil 

Transesterification

Renewable Diesel I 

Production

Renewable Diesel 

II Production

Renewable Gasoline 

Production

Biodiesel T&D Renewable Diesel I 

T&D

Renewable Diesel 

II T&D

Renewable 

Gasoline T&D

CIDI Vehicle 

Operation

CIDI Vehicle 

Operation

CIDI Vehicle 

Operation SI Vehicle Operation

Crude Oil 

Recovery

Crude Oil 

Transportation

Gasoline 

Refinery Diesel Refinery

Gasoline T&D Diesel T&D

SI Vehicle 

Operation

CIDI Vehicle 

Operation

Figure 1-1 System Boundaries for Life-Cycle Analysis of Petroleum Gasoline and Diesel Fuels and 

Soybean-Based Biodiesel and Renewable Fuels 

The pathways for petroleum gasoline, petroleum diesel, and soybean-based biodiesel had been 

incorporated into the GREET model before this study. However, for this study, we updated 

soybean farming simulations in GREET with the latest U.S. Department of Agriculture data on 

energy and fertilizer use associated with soybean farming (USDA 2007a, b). We updated N2O 

emission simulations for soybean fields by using newly released data from the Intergovernmental 

Panel on Climate Change (IPCC 2006). Moreover, we expanded GREET to include pathways for 

soybean-based renewable diesel and gasoline. 

Process energy and mass balance data for the four soybean-based fuels are from our evaluation 

of available literature and process simulations by the National Renewable Energy Laboratory 

(NREL) using the ASPEN model. The processing of energy and mass balance data is described 

in Section 2. Section 3 presents the key issues regarding life-cycle simulations, gives GREET 

input assumptions, and compares the different production processes and fuel properties of 

soybean-derived fuels. Section 4 presents the approaches used to address co-product credits. 

Section 5 provides an analysis and comparison of the life-cycle (or well-to-wheels [WTW]) 

energy and emission results for the six pathways examined in this study. Section 6 presents our 

conclusions. Finally, Appendices 1 and 2 present ASPEN simulations by NREL. 

Note that this study does not consider potential land use changes. Increased CO2 emissions from 

potential land use changes are an input option in GREET, but it was not used in the current 

analysis since reliable data on potential land use changes induced by soybean-based fuel 

production are not available. Furthermore, the main objective of this study is to concentrate on 

the process-related issues described above. 
5 

2 Production Processes of Soybean-Based Renewable Fuels 

This section describes the three basic processes that have been proposed for renewable diesel and 

gasoline production: two for renewable diesel fuel and one for renewable gasoline. It also 

presents the results of the process modeling work undertaken by NREL to characterize the mass 

and energy balances associated with the three processes. The NREL-simulated results were 

inputs to the life-cycle analysis (LCA) described in Sections 3 and 4. 

Table 2-1 provides a list of current and planned renewable energy diesel facilities. For example, 

ConocoPhillips is currently operating a 1,000-barrel-per-day (bpd) facility in Ireland using 

soybean and other vegetable oils; the company entered into a partnership with Tyson foods in 

April 2007 to produce up to 12,000 bpd from animal fat generated in the United States. 

Refinery-based biofuels have received strong support from vehicle manufacturers, both in the 

United States and abroad, because their physical and chemical properties are similar to 

conventional petroleum-based fuels. Refinery-based biofuels have also been supported by major 

international oil companies because they can be delivered by using the existing fuel delivery 

infrastructure with no modifications. 

Table 2-1 Current and Planned Renewable Diesel Facilities 

Company Size (bpd) Location Online Date 

ConocoPhillips 1,000 Ireland 2006 

ConocoPhillips 12,000 United States To be determined 

British Petroleum (BP) 1,900 Australia 2007 

Neste 3,400 Finland 2007 

Neste 3,400 Finland 2009 

Petrobras 4 × 4,000 Brazil 2007 

UOP/Eni 6,500 Italy 2009 

Feedstocks that can be used in biofuel production processes include seed oils (e.g., soy, corn, 

canola, or palm oil), recycled oils (e.g., yellow grease or brown [trap] grease), and animal fats 

(e.g., tallow, lard, or fish oil). Table 2-2 lists current estimates of these oils, which amount to 

about 100,000 bpd (UOP 2005). Vegetable oils, particularly soybean-derived oils, are of 

particular interest in this study because (1) soy oil is the principal feedstock used in the 

United States for production of biodiesel via the transesterification process and (2) soy oil is a 

currently modeled pathway in GREET. 
6 

Table 2-2 Feedstock Availability for Renewable Diesel Production 

in the United States (UOP 2005) 

Feedstock Feedstock 

Total U.S. 

Production 

(bpd) 

Available for 

Conversion to 

Fuels (bpd) 

Vegetable oils Soybeans, corn, canola, palm 194,000 33,500 

Recycled products Yellow grease, brown (trap) grease 51,700 33,800 

Animal fats Tallow, lard, fish oil 71,000 32,500 

Because crude oil and bio-feedstocks are derived from the same sources (i.e., crude oil owes its 

existence to plants and animals that have decomposed over 600 million years), the question 

arises: Why not add the bio-feedstocks directly to the feeds for conventional refineries? The 

answer is that the molecular structures of all of the bio-feedstocks listed in Table 2-2 contain 

significant amounts of oxygen that must be removed prior to their processing with other 

petroleum-based feedstocks. The two standard processes to remove oxygen from hydrocarbon 

feeds are hydrodeoxygenation (HDO) and decarboxylation (DeCO2). Under the proper 

conditions and with the addition of hydrogen, the HDO reaction, given in Equation 2-1, converts 

the oxygen in the product feed into plain water. 

 CnCOOH + 3H2 → Cn+1 + 2H2O (2-1) 

In the DeCO2 reaction, shown in Equation 2-2, the oxygen in the feed is removed as simple CO2

in a lead/hydrogen catalytic reaction. 

 Pb/H 

 CnCOOH → Cn + CO2 (2-2) 

In reality, it is difficult to have a processing vessel where only one process occurs; in all the 

current renewable diesel design schemes, both reactions take place. The particular operating 

designs and conditions determine which process is favored. A basic tradeoff is that, in order to 

optimize the HDO reaction shown in Equation 2-1, additional hydrogen is required; production 

of the hydrogen can be expensive and can result in environmental impacts. On the other hand, 

the only byproduct of the HDO process (Equation 2-1) is water, while the principal by-product of 

the DeCO2-process (Equation 2-2) is CO2 — a GHG that is of concern in life-cycle modeling. 

However, the CO2 from this process is the CO2 uptaken during soybean growth. 
7 

2.1 Renewable Diesel Production Based on SuperCetane 

The first renewable diesel production pathway, renewable diesel I, was modeled after a process 

called SuperCetane that was originally developed in the 1980s at the Saskatchewan Research 

Council and is now being developed by NRCan’s CETC. 

The SupereCetane process is based on adapting a conventional hydrotreating process so it can 

operate under proprietary operating conditions. Figure 2-1 shows a general process schematic for 

the SuperCetane process. A number of reactions occur in the process, including hydrocracking, 

hydrotreating, and hydrogenation. The hydrocracking process breaks apart large molecules; the 

hydrotreating removes oxygen. The process uses a conventional commercial refinery 

hydrotreating catalyst and hydrogen to produce a hydrocarbon liquid. This liquid can be distilled 

into three basic fractions: naphtha, middle distillate (or SuperCetane), and waxy residues. The 

principal product, the middle distillate, can be produced at yields of 70–80%. Because of the 

high cetane number (around 100), CETC believes that SuperCetane may prove most valuable as 

a blending agent for lower-quality diesels (CETC undated). 

Hydrogen Feed

Reactor

Separator

Water

Liquid product 

stream

Waxy paraffinic residue

Distillation 

column

Low sulphur high 

cetane diesel 

blending stock 

(SuperCetane)

Fuel gas byproduct

Gas recycle 

stream

Figure 2-1 SuperCetane Process Flow 

(NRCan 2003) 

The process has been used successfully in a 1-bpd pilot reactor. Feedstocks used in the pilot 

process include canola oil, soy oil, yellow grease, animal tallow, and tall oil (a by-product of the 

kraft pulping process). An important characteristic of this processing scheme is that internally 

generated fuel gas is combusted on site to meet facility steam requirements. Thus, all energy 

demands except electricity are met on site. 
8 

2.2 Renewable Diesel Production Based on UOP Hydrogenation Technology 

The second renewable diesel production pathway, renewable diesel II, was modeled on a 

hydrogenation process developed by UOP, a leading supplier and licensor of process technology, 

catalysts, adsorbents, process plants, and consulting services to the petroleum refining, 

petrochemical, and gas processing industries. UOP, located in Des Plaines, Illinois, is a wholly 

owned subsidiary of Honeywell International. In 2005, UOP conducted a study for DOE entitled 

Opportunities for Biorenewables in Oil Refineries (UOP 2005). In November 2006, UOP 

announced the formation of a new Renewable Energy and Chemicals business unit focused on 

using the company’s refinery skills to develop profitable and efficient ways to enable refineries 

to convert bio-feedstocks (e.g., vegetable oils and greases) into valuable fuels and chemicals. 

UOP took another major step in June 2007, when the company announced that it had entered into 

an agreement with Eni S.p.A, a large European refiner, to build a 6,500-bpd renewable diesel 

unit in Livorno, Italy. The facility, which will process soy, rapeseed, palm, and other oils, is 

expected to come online in 2009. Facility operations will be based on a newly branded UOP 

process called EcofiningTM. UOP has also announced that the technology that it developed in 

partnership with Eni integrates seamlessly into existing refinery operations and is currently 

available for licensing. The most recent license was granted to Galp Energia, Portugal’s largest 

refiner, to develop a 6,500-bpd facility in Sines, Portugal (Reuters News 2007). 

In its study for DOE, UOP examined two potential approaches for renewable diesel production. 

The first involved co-processing the bio-feedstock in an existing hydroprocessing unit; the 

second involved processing the bio-feedstock in a standalone processing unit. In order to design 

a process comparable to the CETC process modeled for renewable diesel I, the UOP standalone 

process scheme was characterized for this project by using ASPEN modeling. Figure 2-2 shows 

the basic production scheme for the UOP process in standalone mode. 

In the standalone process, the bio-feedstock is fed into a diesel hydrotreater, where hydrogen and 

steam are added. An advantage of the UOP operating scheme is that, although the principal 

product is renewable diesel, the by-product is a valuable propane fuel mix. UOP reports that its 

resultant renewable diesel has a cetane value in the 70–90 range, offering significant blending 

benefits for existing refinery operations. UOP notes that when the standalone process is used, 

additional pretreatment is required to remove contaminants such as water, alkali metals, 
9 

Figure 2-2 UOP-Proposed Standalone Renewable Diesel Production (UOP 2005) 

phosphorous, and ash. These would be removed by using a combination of existing equipment, 

such as hydrocyclones, desalting, acid washing, ion exchange, or fixed-guard bed catalyst 

systems (UOP 2005). 

2.3 Renewable Gasoline Production Based on UOP FCC Technology 

As mentioned earlier, because bio-feedstocks are basically chains of carbon and hydrogen with 

added oxygen, standard refinery vessels could be modified to produce gasoline from these 

feedstocks. UOP has proposed such a scheme based on the use of an FCC unit (UOP 2005). 

(It should be noted that renewable gasoline is not nearly as far along the commercialization path 

as the renewable diesel processes discussed in Sections 2.2 and 2.3.) Figure 2-3 shows the 

general flow of the system proposed by UOP. As in the case of renewable diesel, the first step is 

pretreatment of the bio-feedstock; in this case, primarily to remove metals like calcium and 

potassium that would poison the FCC catalyst. Pretreatment also prevents metallurgy issues in 

the feed system, especially when processing greases. The pretreated oil is fed into the FCC unit 

along with the vacuum gas oil (VGO) stream. It should be noted that in the ASPEN modeling 

runs used to characterize renewable gasoline in Table 2-3, the FCC unit was characterized with 

only soybean oil feedstock. Although the standalone production of green gasoline would 

probably not be as economical as dual processing with VGO, it does allow for comparable lifecycle analysis, which is the principal thrust of this study. One of the differences between the 

renewable gasoline and the renewable diesel processes is that additional hydrogen is not required 

for the gasoline process. Another difference is that a significant portion of the energy value of 

the feedstock is contained in process by-products rather than the desired end product: renewable 

gasoline. The other principal product streams include light ends, light-cycle oil (LCO), and 

clarified slurry oil (CSO). 

Diesel 

Vegetable oil Hydrotreater 

H2, Steam 

Electricity

Renewable 

Diesel
10 

Vegetable Oil

FCC VGO 

Steam

Electricity 

Light Ends

Gasoline 

LCO 

CSO 

Pretreat Pretreament

 

Figure 2-3 UOP Proposed Renewable Gasoline Production (UOP 2005) 

2.4 ASPEN Model Results 

A specific goal of the GREET WTW modeling has been to compare various transport fuels on a 

consistent basis. Consistency is achieved by basing model calculations on process mass and 

energy balances that are validated by using data from commercial operating facilities. Modeling 

of new renewable energy fuels thus presents a problem because facility mass and energy 

balances are either unavailable or available only from limited pilot plant operations that may not 

reflect mature commercial operating conditions. 

For the three new fuels characterized in this report (pathways 4 through 6), NREL developed 

initial mass and energy balances by using the ASPEN process simulation model. The NRELmodeled mass and energy balances for the three fuels are listed in Table 2-3. Details of NREL’s 

ASPEN simulations are presented in Appendices 1 and 2. Note that all data have been 

normalized to the basis of one pound of final fuel product. This adjustment allows the data to be 

incorporated into GREET on a consistent basis with existing fuel paths. The emissions presented 

in the table were estimated by using standard AP-42 emission factors. 

To conduct the GREET analysis by using the three new renewable fuel pathways, additional 

component energy data are needed. The values used in the simulation were provided by NREL 

and are listed in Table 2-4. As data from commercial facility operations become available, the 

information will need to be updated to reflect any changes that might occur as the technologies 

mature. 

The ASPEN simulations showed the mass and energy flow differences that were expected from 

proposed technology design schemes. For example, when renewable diesel I and renewable 

diesel II are compared, differences in hydrogen requirements, as well as the resultant CO2

emissions, demonstrate the extent to which the HDO or DeCO2 reaction was favored by the 
11 

Table 2-3 NREL-Simulated Renewable Fuels Mass and Energy Balances 

Fuel 

Inputs and Outputs 

Renewable Diesel I 

(SuperCetane) 

Renewable Diesel II 

(UOP-HDO) 

Renewable 

Gasoline 

 

Inputs (lb per lb of final fuel product) 

Soybean oil 1.510 1.174 2.2313 

Hydrogen 0.030 0.032 

Steam 0.0329 0.0286 

Air 0.9588 1.6782 

Boiler feed water (BFW) 1.47 

 

Outputs (lb/lb soybean oil) 

Renewable diesel 1.000 1.000 

Renewable gasoline 1.000 

Fuel gas 0.253 

Product gas 0.3447 

Heavies 0.175 

Water vapor 0.200 0.0287 

Propane fuel mix 0.059 

CO2

a

 0.049 0.082 0.4103 

LCO 0.2454 

CSO 0.2914 

Water-to-wastewater treatment (WWT) 0.0663 0.0971 0.2599 

Return BFW/steam 1.47 

O2 0.0201 0.0593 

N2 0.7355 1.2675 

 

Energy Inputs (unit per lb of final fuel product) 

Steam (Btu) 

Process is selfsufficient in energy 84.05 −1,237 

Electricity (kWh) 0.0394 0.0275 0.0544 

CW (lb/h) 65.06 27.11 50.3 

a

: This is the amount of CO2 from feedstock oil, which is eventually from the air during soybean growth 

process design. Another difference is that all facility energy demands (except electricity) are 

met by recycling process-generated fuel gas in the renewable diesel I scheme. This process 

characteristic increases facility emissions and reduces facility energy by-products. These types of 

tradeoffs are central to the use of GREET in linking the new fuels to the existing fuel pathways 

in order to assess their life-cycle energy and GHG emission impacts. 
12 

Table 2-4 NREL-Provided Base Energy Values 

of Renewable Fuel Components 

Component 

Lower Heating 

Value (Btu/lb) 

 

Soybean oil 16,000 

H2

a 52,226 

Renewable diesel I – SuperCetane 18,746 

Renewable diesel II – UOP 18,925 

Renewable gasoline 18,679 

Fuel gas 27,999 

Product gas 18,316 

Heavies 20,617 

Propane fuel mix 18,568 

LCO 19,305 

CSO 18,738 

a

 Simulation of hydrogen production is done inside 

GREET. In this analysis, we assumed that hydrogen 

would be produced from natural gas via steam 

methane reforming. 
13 

3 Data Sources and Assumptions for GREET Simulations 

3.1 Soybean Farming 

3.1.1 Yield 

Soybean yield (in bushels per acre or bu/acre) is a key factor in life-cycle analysis because it will 

affect energy use and fertilizer use per bushel of soybeans harvested. Soybeans were ranked the 

second-leading U.S. crop in terms of both harvested acreage (74.6 million acres) and revenue 

(19.7 billion U.S. dollars [USD]) in 2006 (USDA 2007a). Over the past several decades, both 

harvested acreage and soybean yield per harvested acre have experienced enormous growth, 

leading to total soybean production increases of 4% annually. Table 3-1 lists planted and 

harvested acreage and yield over the past five decades in the United States. Figure 3-1 shows the 

3-year moving average of soybean yield in the United States. The soybean yield has been 

increasing at an annual rate of 1.2%, and this trend is expected to continue in the near future. 

Table 3-1 U.S. Historical Soybean Acreage and Yields (USDA 2007a) 

Acreage 

(106

 acres)

Yield 

(bu/acre)

3-Year Moving 

Average Yield 

(bu/acre)

Year Planted Harvested

Total 

Production 

(106

 bu)

Planted 

Acres

Harvested 

Acres

Planted 

Acres

Harvested 

Acres

1950 15.0 13.8 299.2 19.9 21.7 19.5 21.8 

1960 24.4 23.7 555.1 22.7 23.5 22.9 23.7 

1970 43.1 42.2 1127.1 26.2 26.7 26.3 26.9 

1980 69.9 67.8 1797.5 25.7 26.5 28.8 29.3 

1990 57.8 56.5 1925.9 33.3 34.1 30.4 31.1 

1991 59.2 58.0 1986.5 33.6 34.2 32.8 33.5 

1992 59.2 58.2 2190.4 37.0 37.6 34.6 35.3 

1993 60.1 57.3 1869.7 31.1 32.6 33.9 34.8 

1994 61.6 60.8 2514.9 40.8 41.4 36.3 37.2 

1995 62.5 61.5 2174.3 34.8 35.3 35.6 36.4 

1996 64.2 63.3 2380.3 37.1 37.6 37.6 38.1 

1997 70.0 69.1 2688.8 38.4 38.9 36.8 37.3 

1998 72.0 70.4 2741.0 38.1 38.9 37.9 38.5 

1999 73.7 72.4 2653.8 36.0 36.6 37.5 38.2 

2000 74.3 72.4 2757.8 37.1 38.1 37.1 37.9 

2001 74.1 73.0 2890.7 39.0 39.6 37.4 38.1 

2002 74.0 72.5 2756.1 37.3 38.0 37.8 38.6 

2003 73.4 72.5 2453.7 33.4 33.9 36.6 37.2 

2004 75.2 74.0 3123.7 41.5 42.2 37.4 38.0 

2005 72.0 71.3 3063.2 42.5 43.0 39.2 39.7 

2006 75.5 74.6 3188.2 42.2 42.7 42.1 42.7 
14 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Soybean Yield (bushel/acre)

3-yr moving average yield (bu/planted acre)

3-yr moving average yield (bu/harvested acre)

Figure 3-1 Three-Year Moving Average of Soybean Yield 

in the United States (USDA 2007a) 

3.1.2 Energy Use 

The USDA’s Economic Research Service (ERS) survey data provides U.S. energy use values for 

soybean farming (on a per-acre basis) in 2002 (USDA 2007b); these values are listed in 

Table 3-2. On the basis of these energy use values and the average yields for soybeans, we 

estimated the energy use (by type) per bushel of soybeans harvested. We converted the values 

listed in Table 3-2 to Btu-based values by using the lower heating values (LHVs) of fuels in 

GREET: 128,450 Btu/gal for diesel; 116,090 Btu/gal for gasoline; 84,950 Btu/gal for liquefied 

petroleum gas (LPG); 3,412 Btu/kWh for electricity (energy loss for electricity generation is 

simulated separately in GREET); and 983 Btu/ft3

 for natural gas. The total energy use is 

estimated to be 22,084 Btu/bu: 64% diesel, 18% gasoline, 8% LPG, 7% natural gas, and 3% 

electricity. In comparison, Hill et al. (2006) reported 23,474 Btu/bu and 34,625 Btu/bu when 

custom-work-related diesel use and farm-related transportation and personal commuting energy 

use are taken into account. Pimentel and Patzek (2005) reported 20,447 Btu/bu of energy use for 

soybean production when labor, machinery, and fertilizer were taken into account. Table 3-3 

provides a detailed comparison of the energy use for soybean farming across these references. 
15 

Table 3-2 Energy Use for Soybean Farming in the United States (USDA 2007b) 

State 

Diesel 

(gal/acre) 

Gasoline 

(gal/acre) 

LPG 

(gal/acre) 

Electricity 

(kWh/acre) 

Natural Gas 

(ft3/acre) 

 

Arkansas 9.9 1.3 La 11.2 L 

Illinois 2.5 0.9 0.0 L 0.0 

Indiana 2.3 1.6 L 1.3 L 

Iowa 3.4 1.1 0.0 0.0 0.0 

Kansas 2.9 1.1 1.8 9.1 349.2 

Kentucky 2.1 1.4 L 4.5 0.0 

Louisiana 6.5 1.1 L L L 

Maryland 2.9 2.1 L 0.8 0.0 

Michigan 4.0 1.5 L L 0.0 

Minnesota 4.0 1.1 L L 0.0 

Mississippi 4.3 1.2 L 3.8 0.0 

Missouri 4.3 1.4 L L 0.0 

Nebraska 12.9 1.3 4.4 39.4 586.4 

North Carolina 2.4 1.5 L 0.6 0.0 

North Dakota 3.2 1.4 L 0.8 0.0 

Ohio 2.0 1.3 L 0.0 0.0 

South Dakota 2.8 1.4 0.0 L 0.0 

Tennessee 2.2 1.3 L 1.0 0.0 

Virginia 1.9 1.2 L L 0.0 

Wisconsin 5.2 2.4 0.0 L 0.0 

Average of all states 4.1 1.3 0.4 7.8 52.5 

Energy use (Btu/bu) 14,221.8 3,934.1 1676.9 634.7 1619.9 

Total energy use (Btu/bu) 22,087.4 a

 L = insufficient data for legal disclosure. 

Table 3-3 Comparison of Energy Use for Soybean Farming 

Taken from Three Data Sources 

Source 

Parameter USDA 2007b Hill et al. 2006 

Pimentel and 

Patzek 2005 

 

Year 2002 2002 Not available 

Energy use (Btu/bu) 22,087 23,474/34,625a 20,447b

Percentage 

Diesel 64.4 61.7 57.7 

Gasoline 17.8 17.2 35.2 

LP gas 7.6 4.1 3.3 

Electricity 2.9 11.0 3.8 

Natural gas 7.3 6.1 0 a

 The 34,625 value includes diesel use of 6.6 L/ha for custom work and 

farm-related transportation and personal commuting energy use equal 

to those values associated with corn farming. 

b

 Including energy input for labor, machinery, and fertilizer. 
16 

3.1.3 Fertilizer Use 

We updated fertilizer use values for soybean farming in GREET by using the newly released 

USDA ERS data (USDA 2007c) (see Table 3-4). We used soybean yield per planted acre to 

calculate the fertilizer use per bushel of soybeans. Figure 3-2 shows the fertilizer use for soybean 

farming over the past 15 years. The amount of fertilizer used (nitrogen [N], phosphorous [P], and 

potassium [K], in grams) per bushel of soybeans did not change significantly. In fact, the usage 

patterns for each fertilizer type follow a similar time trend. For year 2010 (as our target year for 

this study), the following amounts were used: nitrogen at 61.2 g/bu, phosphorus at 186.1 g/bu, 

and potassium at 325.5 g/bu. The energy use and emissions for fertilizer manufacturing are 

simulated separately in GREET. On the basis of GREET simulations, the total energy use values 

per gram of fertilizer produced are 45.9 Btu/g N, 13.29 Btu/g P, and 8.42 Btu/g K.

Table 3-4 Fertilizer Use for Soybean Farming (USDA 2007c) 

Year 

Percent 

Acreage 

Receiving 

Nitrogen 

Fertilizer 

Nitrogen 

Application Rate 

(lb/received acre) 

Percent 

Acreage 

Receiving 

Phosphorus

Fertilizer 

Phosphorus 

Application Rate 

(lb/received acre) 

Percent 

Acreage 

Receiving 

Potassium 

Fertilizer 

Potassium 

Application Rate 

(lb/received acre) 

 

1988 16 22 26 48 31 79 

1989 17 18 28 46 32 74 

1990 17 24 24 47 29 81 

1991 16 25 22 47 23 76 

1992 15 22 22 47 25 75 

1993 14 21 21 46 25 79 

1994 13 25 20 47 25 82 

1995 17 29 22 54 25 85 

1996 15 24 25 49 27 85 

1997 20 25 28 50 33 88 

1998 17 23 24 48 27 81 

1999 18 21 26 46 28 78 

2000 18 24 24 48 27 76 

2001 NAa

 24 NA 49 NA 84 

2002 20 21 26 49 29 89 

2003 NA NA NA NA NA NA 

2004 21 28 26 69 23 121 

2005 NA NA NA NA NA NA 

a

 NA = not available. 
17 

0

100

200

300

400

1990 1992 1994 1996 1998 2000 2002 2004

Phosphorus (g/bu), Potassium (g/bu) .

0

10

20

30

40

50

60

70

Nitrogen (g/bu) .

Phosphorus

Potassium

Nitrogen

Left axis

Right axis

Figure 3-2 Fertilizer Use for Soybean Farming in the United States 

3.1.4 N2O Emissions 

N2O, a potent GHG, is produced from nitrogen in the soil through nitrification and denitrification 

processes (direct N2O emissions). N2O can also be produced through volatilization of nitrate 

from the soil to the air and through leaching and runoff of nitrate into water streams (indirect 

N2O emissions). 

Estimation of direct and indirect N2O emissions from crop farming requires two important 

parameters: (1) the amount of nitrogen applied to soil and (2) rates for converting nitrogen into 

N2O. The application of nitrogen fertilizer is the key to crop farming. For legume crops, such as 

soybeans, nitrogen fixation is another major nitrogen input. In 1996, IPCC considered nitrogen 

input to soil from biological nitrogen fixation by legume crops in estimating N2O emissions from 

soil. However, in 2006, IPCC elected not to consider this nitrogen input because of a lack of 

evidence of significant emissions from the nitrogen fixed by legumes. 

Even without considering the nitrogen that results from the biological fixation process, two 

sources of nitrogen inputs to soil for crop farming remain: nitrogen from fertilizer application 

and nitrogen in the aboveground biomass left in the field after harvest and in the belowground 
18 

biomass (i.e., roots). For crops such as corn, nitrogen in the aboveground and belowground 

biomass is from nitrogen fertilizers. For crops such as soybeans, nitrogen in the aboveground and 

belowground biomass is eventually from nitrogen fertilizers and the biological nitrogen fixation 

process. GREET 1.8 takes into account the nitrogen in nitrogen fertilizers and the nitrogen in 

aboveground and belowground biomass in estimating N2O emissions from crop farming. 

For corn, IPCC (2006) estimates that aboveground biomass is 87% of corn yield (on a dry-matter 

basis). Aboveground biomass has a nitrogen content of 0.6%. Belowground biomass is about 

22% of aboveground biomass, with a nitrogen content of 0.7%. The total amount of nitrogen in 

corn biomass that is left in corn fields per bushel of corn harvested is calculated as shown in 

Equation 3-1: 

56 lb/bul × 85% (dry matter content of corn) × (87% × 0.6% + 87% × (3-1) 

22% × 0.7%) = 0.312 lb N/bu = 141.6 g/bu 

To estimate N2O emissions from corn farming, 141.6 g of N are added to nitrogen fertilizer 

inputs for corn farming (which are about 420 g of N per bushel). 

For soybeans, IPCC (2006) states that aboveground biomass is about 91% of soybean yield (on a 

dry-matter basis). Aboveground biomass has a nitrogen content of 0.8%. Belowground biomass 

is about 19% of aboveground biomass, with a nitrogen content of 0.8%. The total amount of 

nitrogen in soybean biomass that is left in soybean fields per bushel of soybean harvested is 

calculated as shown in Equation 3-2: 

60 lb/bu × 85% (dry matter content of soybeans) × (3-2) 

(91% × 0.8% + 91% × 19% × 0.8%) = 0.442 lb N/bu = 200.7 g/bu 

To estimate N2O emissions from soybean farming, 200.7 g of N are added to nitrogen fertilizer 

inputs for soybean farming (which are about 62 g of N per bushel). The rates for converting the 

nitrogen in soil and water streams to N2O emissions to the air are subject to great uncertainties 

(Wang et al. 2003; Crutzen et al. 2007). IPCC (2006) presents a conversion rate of 1% for direct 

N2O emissions from soil (compared with 1.25% in IPCC [1996]), with a range of 0.3–3%. 

Indirect N2O emissions include those from volatilization of nitrate from the soil to the air and 

leaching and runoff of nitrate into water streams where N2O emissions occur. IPCC (2006) 

estimates a volatilization rate for soil nitrogen of 10%, with a range of 3–30%. The conversion 

rate of volatilized nitrogen to N in N2O emissions is 1%, with a range of 0.2–5%. The leaching 

and runoff rate of soil nitrogen is estimated to be 30%, with a range of 10–80%. The conversion 

rate of leached and runoff nitrogen to N in N2O emissions is 0.75%, with a range of 0.05–2.5%. 
19 

Thus, the conversion rate for direct and indirect N2O emissions is 1.325% (1% + 10% × 1% + 

30% × 0.75%). This conversion rate was used in GREET 1.8. In contrast, Crutzen et al. (2007) 

estimated a conversion rate of 3–5% on the basis of the global N2O balance. While the top-down 

approach adopted in Crutzen et al. is a sound approach, especially for checking and verifying 

results against the bottom-up approach used by the IPCC and others, data for the top-down 

approach needs to be closely examined in order to generate reliable N2O conversion factors. In 

particular, Crutzen et al. adopted the global N2O emission balance from a 2001 study but adopted 

the nitrogen inputs from a separate 2004 study for deriving N2O conversion factors. Furthermore, 

Crutzen et al. did not get into agricultural subsystems (such as crop farming, animal waste 

management, and crop residual burning), which are required for generating N2O conversion rates 

for the nitrogen inputs into crop farming. Their allocation of aggregate N2O emissions (even 

after subtracting N2O emissions from industrial sources) to the aggregate agricultural system 

could result in overestimation of N2O conversion rates from nitrogen inputs into crop farming 

systems. Nonetheless, N2O conversion rates, which are subject to great uncertainties, need to be 

reconciled between the bottom-up and the top-down approach. 

3.2 Soy Oil Extraction 

At soybean processing plants, soybean seeds are crushed, soy oil is extracted from the crushed 

seeds, and crude soy oil is refined. Soybeans contain 18–20% oil by weight. To maximize soy oil 

production, organic solvents are used during oil extraction. The solvent extraction process is a 

widely used and well-established technology. The standard solvent extraction process uses nhexane that is produced from petroleum. Most of the n-hexane used in oil extraction is recovered 

and recycled, with some inevitable loss. Table 3-5 presents the inputs and outputs from oil 

extraction plants. In calculating emissions and energy use, we assumed that steam is generated 

from natural gas. N-hexane is a straight-chain hydrocarbon. Commercial hexane is manufactured 

by distillation of straight-run gasoline produced from crude oil or natural gas liquids. In GREET, 

hexane is assumed to be produced from crude oil, and its upstream production energy use and 

emissions are adopted from energy use and emissions calculated for production of LPG from 

crude oil. Because hexane is volatile, the amount of hexane lost during soy oil extraction is 

assumed to be in the form of VOC emissions to the atmosphere. For more details, see Wang 

(1999). 
20 

Table 3-5 Inputs and Outputs of 

Soybean Oil Extraction Plants 

Inputs and Outputs 

GREET 

Valuea

 

Input 

 Soybeans (lb) 5.7 

 Steam (Btu) 2,900 (44.5%) 

 NG (Btu) 2,800 (43.0%) 

 Electricity (Btu) 614 (9.4%) 

 N-hexane (Btu) 205 (3.1%) 

 Total energy (Btu) 6,519 (100%) 

Output 

 Soy oil (lb) 1 

 Soy meal (lb) 4.48 

a

 From previous GREET assumptions. 

We assumed in GREET that steam is 

produced from natural gas with an 

efficiency of 80%. The Btu value for 

steam is the natural gas Btu used to 

generate the needed steam. Values in 

parentheses are percentage shares of 

process fuels. 

3.3 Production of Soybean-Derived Fuels 

Figure 3-3 illustrates the fuel production processes for the four soybean-derived fuels. 

Soy oil

Feedstock

Transesterification

Production

Process

Biodiesel

Glycerin

Product

Methanol

Soy oil Hydrogenation

Renewable diesel I (SuperCetane)

Fuel gas

Heavy oils

Hydrogen

Soy oil Hydrogenation Renewable diesel II (Green diesel)

Propane fuel mix

Hydrogen

Soy oil Catalytic Cracking

Renewable gasoline (Green gasoline)

Product gas

LCO

CSO

Figure 3-3 Fuel Production Processes for the Four Soybean-Derived Fuels 
21 

3.3.1 Biodiesel 

Biodiesel is produced through the so-called transesterification process, in which soy oil is 

combined with alcohol (ethanol or methanol) in the presence of a catalyst (sodium hydroxide 

[NaOH] in this case) to form ethyl or methyl ester, as illustrated in Figure 3-4. The 

transesterification process requires steam and electricity as energy inputs and produces both 

biodiesel and glycerin. 

For this study, we updated GREET biodiesel production simulations on the basis of data in Haas 

et al. (2006). Table 3-6 presents the inputs and outputs of biodiesel plants per pound of biodiesel 

produced. To apply the values specified in Table 3-6 to GREET, we assumed that (1) steam is 

generated from natural gas with an energy conversion efficiency of 80% and (2) the energy 

embedded in the three chemical compounds is half oil and half natural gas. 

OHCH

OHCH

OHCH

'''ORCRO

''ORCRO

'ROCOR

ROH3

'''OCORCH

''OCORCH

'OCORCH

|

2

|

2

|

2

|

2

−

−

−

+ ⇔ +

−

−

−

+

+

Triglyceride Alcohol Mixture of methyl esters Glycerin 

Figure 3-4 Transesterification of Soy Oil to Biodiesel 

Table 3-6 Inputs and Outputs of Biodiesel Plants (lb or Btu/lb biodiesel) 

Inputs and Outputs Haas et al. 2006 Sheehan 1998 GREET Value 

 

Inputs 

 Soy oil (lb) 1.001 1.050 1.001 

 Methanol (lb) 0.1001 0.0900 0.1001 

 Sodium hydroxide (lb) 0.0050 0.0023 0.0050 

 Sodium methoxide (lb) 0.0125 0.0244 0.0125 

 Hydrochloric acid (lb) 0.0071 0.0077 0.0071 

 NG (Btu) 888 789 888 

 Electricity (Btu) 46 45 46 

 

Outputs 

 Biodiesel (lb) 1 1 1 

 Glycerin (lb) 0.116 0.213 0.213 
22 

3.3.2 Renewable Diesel I 

The production of renewable diesel I comprises a series of reactions, including those involved in 

hydrocracking (breaking apart of large triglyceride molecules), hydrotreating (removal of 

oxygen), and hydrogenation (saturation of double bonds). Besides soy oil, hydrogen is needed as 

input. Some steam is also needed; ASPEN simulations conducted by NREL assumed that the 

required steam would be generated with the fuel gas and/or heavy oils that are co-produced from 

the plant. The output of this process is high-cetane diesel (with fuel gas and heavy oils as coproducts). Table 3-7 lists the inputs and outputs of renewable diesel I plants. Note that the output 

values for fuel gas and heavy oils are net amounts (i.e., after steam generation for internal use). 

In GREET, hydrogen used in renewable diesel plants is assumed to be produced from natural gas 

via steam methane reforming (SMR). 

Table 3-7 Inputs and Outputs of Renewable 

Diesel I Plants (lb or Btu per lb of renewable diesel I) 

Inputs and Outputs 

ASPEN Simulation Results

as GREET Input 

 

Inputs 

 Soy oil (lb) 1.510 

 Hydrogen (lb) 0.030 

 Electricity (Btu) 134.4 

Outputs 

 Renewable diesel I (lb) 1 

 Fuel gas (Btu) 7083.7 

 Heavy oils (Btu) 3608.0 

3.3.3 Renewable Diesel II 

For the production of renewable diesel II, soy oil is combined with hydrogen in a catalytic 

reactor and then converted by a hydrogenation reaction to a high-cetane renewable diesel. This 

process requires electricity and thermal energy as inputs; the outputs are renewable diesel and a 

small amount of propane fuel mix. We assumed that thermal energy is generated from natural 

gas with an energy conversion efficiency of 80% and that hydrogen is produced from natural gas 

via SMR. Table 3-8 presents the inputs and outputs of renewable diesel plants per pound of 

renewable diesel II produced. 
23 

Table 3-8 Inputs and Outputs of Renewable Diesel II 

Plants (lb or Btu per lb of renewable diesel II) 

Inputs and Outputs 

ASPEN Simulation Results

as GREET Input 

Inputs 

 Soy oil (lb) 1.174 

 Hydrogen (lb) 0.032 

 Natural gas (Btu) 84.05 

 Electricity (Btu) 93.83 

Outputs 

 Renewable diesel II (lb) 1 

 Propane fuel mix (Btu) 1095.5 

3.3.4 Renewable Gasoline 

The production of renewable gasoline takes place in an FCC unit. This process requires 

electricity and steam. The steam is assumed to be generated by combusting the by-product and 

product gas mix that results from the cracking process. The process also generates extra steam 

for export. The outputs are renewable diesel, product gas, LCO, and CSO. Table 3-9 presents the 

inputs and outputs from renewable gasoline plants per lb of renewable gasoline produced. 

Table 3-9 Inputs and Outputs of Renewable 

Gasoline Plants (lb or Btu per lb of renewable gasoline) 

Inputs and Outputs 

Aspen Simulation Results

as GREET Input 

 

Inputs 

 Soy oil (lb) 2.231 

 Electricity (Btu) 185.6 

 

Outputs 

 Renewable gasoline (lb) 1 

 Product gas (Btu) 6313.5 

 LCO (Btu) 4737.4 

 CSO (Btu) 5460.3 
24 

3.3.5 Comparison of the Four Soybean-Derived Fuels 

On the basis of the analysis and assumptions outlined in Sections 3.3.1 through 3.3.4, Table 3-10 

summarizes the energy use and amounts of product and co-product that can be produced from 1 

ton of soybeans. According to Table 3-10, the transesterification process can generate a much 

larger amount of diesel product and co-products from 1 ton of soybeans than the other processes; 

however, it requires a lot more energy and chemical inputs than do the other processes. The 

hydrogenation process (used to produce renewable diesel II) has the best yield (in terms of 

energy content from 1 ton of soybeans) of the three new fuels, while it generate less energy 

co-product than the other processes. Because all of the processes produce other products (besides 

the target fuel), the energy value or market value of the co-products of these processes is an 

important factor in evaluating the energy and emission benefits of each soybean-based fuel. The 

co-product issue is discussed in Section 4. The production processes for the two renewable diesel 

options require hydrogen. Because hydrogen production is energy intensive, so determining 

which process is more energy intensive simply on the basis of inputs and outputs would not lead 

to a proper conclusion. The fuel cycles of hydrogen and other types of energy inputs must be 

taken into consideration, emphasizing the importance of a complete life-cycle analysis like the 

one conducted for this study. 

Table 3-10 Energy Use and Amount of Fuel Product and Co-Products 

from One Ton of Soybeans 

Fuel 

Inputs and Outputs Biodiesel 

Renewable 

Diesel I 

Renewable 

Diesel II 

Renewable 

Gasoline 

 

Outputs 

 Product 

 lb 351 232 299 157 

 mmBtu 5.66 4.36 5.66 2.94 

 Co-products 

 Soy meal (lb) 1572 1572 1572 1572 

 Glycerin (lb) 75 

 Energy co-product (mmBtu) 2.48 0.33 2.60 

 

Inputs 

 Natural gas (mmBtu) 

 I. Soy oil extraction 1.80 1.80 

 II. Fuel production 0.31 0.03 

 Electricity (mmBtu) 

 I. Soy oil extraction 0.194 0.194 0.194 0.194 

 II. Fuel production 0.016 0.031 0.028 0.029 

 Other inputs 

 Methanol (mmBtu) 0.303 

 Hydrogen (mmBtu) 0.36 0.49 
25 

3.4 Fuel Properties 

Table 3-11 presents the properties of the soybean-based fuels examined in this study. Compared 

with conventional diesel and biodiesel, renewable diesel fuels have much higher cetane numbers 

and lower density. Cetane number is one measure of the quality of a diesel fuel — a high number 

is a valuable feature for renewable diesel as a diesel blending component and a cetane enhancer. 

3.5 Fuel Use in Vehicles 

For our life-cycle analysis, we assumed that soybean-derived diesel fuels are used in 100% pure 

form in compression-ignition, direct-injection (CIDI) engine vehicles, and renewable gasoline is 

used in 100% pure form in spark-ignition (SI) engine vehicles. Since there were no testing data, 

we assumed that the fuel economy and CH4 and N2O emissions for CIDI vehicles are the same 

for all three diesel types. Likewise, we assumed that the fuel economy and CH4 and N2O 

emissions for SI vehicles are the same for the two gasoline types. 

Table 3-11 Properties of the Four Soybean-Based Fuels 

Fuel 

Lower Heating 

Value (Btu/gal) 

Density 

(lb/gal) 

Carbon Content 

(%)e

Oxygen 

Content (%) 

Cetane 

Value 

 

Petroleum gasolinea 113,602 6.23 84.0 NAf NA 

Petroleum diesela

 129,488 7.06 87.1 0.0 40 

Biodiesela

 119,550 7.40 77.6 11.0 50–65 

Renewable diesel Ib

 117,059 6.24 87.1 0.0 100 

Renewable diesel IIc

 122,887 6.49 87.1 0.0 70–90 

Renewable gasolined

 115,983 6.21 84.0 NA NA a

 From the GREET model. 

b

 From (S&T)2 Consultants Inc. (2004). c

 From Kalnes et al. (2007). 

d From UOP (2005). e

 Because of a lack of data, the carbon content of renewable diesel fuels is assumed to be the same 

as that for petroleum-based diesel; the carbon content of renewable gasoline is assumed to be the 

same as that of petroleum-based gasoline. 

F NA = not applicable. 
26 

4 Co-Product Credits for Biofuels 

4.1 Methods for Addressing Co-Product Credits 

The objective of calculating the credit allotted for co-products in life-cycle analysis is to fairly 

address the energy and emission burdens of the primary product, especially when the co-products 

have value in the marketplace. Two methods that are commonly used are the displacement 

method and the allocation method. 

With the displacement method, a conventional product is assumed to be displaced by a new 

product. The life-cycle energy that would have been used and the emissions that would have 

been generated during production of the displaced product are counted as credits for the new 

product that is co-produced from the fuel pathway under evaluation. These credits are subtracted 

from the total energy use and emissions associated with the fuel pathway under evaluation. The 

difficulties with the displacement method involve accurately determining the displaced products 

and identifying the approach to obtain their life-cycle energy use and emissions. Also, if the 

amounts of co-products are relatively large compared with the amount of primary product from a 

given process (as is the case for renewable diesel I and renewable gasoline, see Table 3-10), the 

displacement method results — which are WTW analysis results that are mathematically 

normalized to production of a unit of the primary product — can generate distorted results for the 

primary product. 

The allocation method allocates the feedstock use, energy use, and emissions between the 

primary product and co-products on the basis of mass, energy content, or economic revenue. 

This method is easier to implement in life-cycle analyses than the displacement method. 

However, it could result in inaccurate results if the values of product and co-products cannot be 

simply measured on a single basis (such as mass or energy content). 

In this study, various co-products are produced during the production of soybean-based fuels, 

including protein products such as soy meal; solvents such as glycerin; and energy products such 

as propane fuel mix and heavy oils (see Table 3-10), which makes addressing their credit very 

difficult. If the displacement method is used, it is time-consuming to identify a displaced product 

for each of the co-products and obtain the life-cycle energy use and emissions of the identified 

products. Besides, the co-products almost have Btu values equivalent to those of their primary 

products (e.g., renewable diesel I and renewable gasoline), which makes the displacement 

method not a preferable approach. On the other hand, because these co-products have different 

values (for instance, the primary products and most of the co-products have Btu values and can 

be treated as energy products; some of the co-products, however — such as soy meal and 

glycerin — have nonenergy values), the Btu-based allocation method would not be able to fairly 
27 

treat the co-products that have low energy contents but are valuable in other ways. The market 

value-based allocation method is subject to the variation in price of the co-products. 

On the basis of these considerations, four approaches were employed to address the co-product 

issues: (1) the displacement approach, (2) an energy-based allocation method, (3) an allocation 

method based on the market values of the primary products and co-products, and (4) a hybrid 

approach that employs both the displacement and the allocation methods, in which the 

displacement method is used for soy meal and glycerin, and the allocation method is used for 

other energy co-products. For biodiesel, the hybrid approach is the same as the displacement 

approach. Table 4-1 summarizes the four approaches. 

Table 4-1 Approaches to Address Co-Products of Soybean-Based Fuels 

Fuel Product Process 

Approach 1 

(Displacement) 

Approach 2 

(EnergyValue-Based 

Allocation) 

Approach 3 

(Market 

Value-Based 

Allocation) 

Approach 4 

(Hybrid) 

 

Biodiesel Soy oil extraction Displacement Allocation Allocation Displacement 

 production Transesterification Displacement Allocation Allocation Displacement 

Renewable diesel I Soy oil extraction Displacement Allocation Allocation Displacement 

 production Hydrogenation Displacement Allocation Allocation Allocation 

Renewable diesel Soy oil extraction Displacement Allocation Allocation Displacement 

 II production Hydrogenation Displacement Allocation Allocation Allocation 

Renewable gasoline Soy oil extraction Displacement Allocation Allocation Displacement 

 production Catalytic cracking Displacement Allocation Allocation Allocation 

4.2 Displacement Approach 

The first step in using the displacement method is to determine an equivalent product replaced by 

each co-product. Soy meal, which is primarily used as a livestock feed in the United States, is 

assumed in this study to replace soybeans. Soybean-based glycerin is assumed to replace 

petroleum-based glycerin. Other energy co-products are assumed to replace similar energy forms 

on the basis of their energy value; for example, fuel gas is assumed to replace equivalent-Btu 

natural gas for industrial use, heavy oil is assumed to replace equivalent-Btu residual oil. 

Table 4-2 lists the products that are to be displaced by the co-products from soybean-based fuel 

production. 
28 

Table 4-2 Products to Be Displaced 

by Co-Products 

Product Product to Be Displaced 

 

Soy meal Soybeans 

Glycerin Petroleum-based glycerin 

Fuel gas Natural gas 

Heavy oil Residual oil 

Propane fuel mix LPG 

Product gas Natural gas 

LCO Diesel fuel 

CSO Residual oil 

The energy use and emissions resulting from production of one million Btu of natural gas, 

residual oil, LPG, and diesel fuel are already simulated in GREET and can be readily used. Also, 

GREET has addressed life-cycle energy use and emissions for obtaining soybeans, including 

soybean farming and fertilizer manufacturing, and these results are also readily used. 

However, the displacement ratio between soy meal and soybeans for the purpose of feeding 

animals is yet to be determined in our study. Moreover, life-cycle analysis for petroleum-based 

glycerin is not included in GREET and thus needs further examination in this study. 

4.2.1 Soy Meal 

The displacement ratio of soy meal to soybeans is determined by protein content. Literature 

reports a protein content of 44–50% in soybean meal and 35–40% in soybeans 

(Ahmed et al. 1994; Maier et al. 1998; Britzman 2000). In this study, we assumed that soy meal 

contains 48% protein and soybeans contain 40%. On the basis of that assumption, we estimated 

that 1 lb of soy meal can replace 1.2 lb of soybeans. 

4.2.2 Glycerin 

Glycerin produced from petrochemical sources is called synthetic glycerin; natural glycerin is 

produced from plant oils and animal fats. Petroleum-based glycerin uses propylene, chlorine, and 

sodium hydroxide as raw materials. The theoretical raw material input to produce 1 lb of glycerin 

can be calculated according to the mass balance of the chemical reactions. In practice, there are 

some differences between theoretical mass balance and actual plant mass balance. Table 4-3 

shows the amount of raw material needed to produce 1 lb of synthetic glycerin. 
29 

Table 4-3 Raw Material Input for One Pound 

of Synthetic Glycerin (lb/lb glycerin) 

 Theoretical Inputa Industry Inputb

 

Propylene 0.46 0.62 

Chlorine 1.54 2.00 

Sodium compounds 0.87 0.90 

a

 Based on Chemical Economics Handbook (Greiner et al. 

2005; Malveda et al. 2005). 

b From Ahmed et al. (1994). 

Production of synthetic glycerin requires little energy, so this energy is not addressed in our 

analysis. The energy use and emissions embedded in the raw material are the key issues in 

determining the life-cycle energy use and emissions of synthetic glycerin. 

In this study, the production data for propylene, chlorine, and sodium hydroxide were taken from 

the Eco-Profile life-cycle inventory (Association of European Plastic Industry 2005). The 

Eco-Profile reports average industry data in detail for various petrochemical processes, including 

the amount of petroleum and natural gas used as feedstocks to produce each type of chemical, 

and the amount of petroleum, natural gas, electricity, and other fuels used as process fuels. We 

use the GREET model to generate the upstream energy use and emissions for the fuel 

(e.g., petroleum, natural gas, and electricity) used in producing propylene, chlorine, and sodium 

hydroxide. Table 4-4 compares the total energy embedded in raw material per pound of glycerin 

between our study and the study conducted by Ahmed et al. Some European studies report 

30,000 to 90,000 Btu of total or fossil energy (Scharmer and Gosse 1996; Malça and 

Freire 2006). 

Table 4-4 Total Btu in Raw Material per Pound of Glycerin 

Study Propylene Chlorine 

Sodium 

Hydroxide Total 

 

Our study 9,373 12,267 10,128 39,460 

Ahmed et al. (1994) 8,577 5,319 11,275 21,296 

4.3 Allocation Approach 

Two different allocation approaches are applied in this study: energy-value-based and marketvalue-based. Generally, the allocation method is easier to implement than the displacement 

method in terms of data requirements. With the energy-value-based allocation method, the 
30 

energy contents of the primary product and co-products are used to split the burden of energy 

input, feedstock input, and pollutant emissions. With the market-value-based allocation method, 

the market value of the products becomes the determining factor in splitting the burden. 

4.3.1 Allocation at the System Level and Subsystem Level 

The process of producing soybean-based fuels from soybeans involves two stages: soy oil 

extraction and fuel production. Both stages generate co-products, resulting in two different ways 

of allocating co-product credit: system level and subsystem level. As Figure 4-1 shows, systemlevel allocation takes soy oil extraction and fuel production processes as a whole system, with 

soybeans and the required energy and chemicals as inputs and fuel, soy meal, and other 

co-products as outputs. With the whole system level, the effect of soy oil is eliminated. 

Subsystem-level allocation includes two subsystems. In the first, soybeans are the inputs, and soy 

oil and soy meal are the outputs; in the second, soy oil is the input. 

Soybean

Soy Oil Extraction 

&

 Fuel Production

Soybean-based 

Fuel

Other 

Co-Products

Soybean

Soy Oil Extraction

Soy Meal

Fuel Production

Soy Meal

Soybean-based 

Fuel

Other 

Co-Products

System Level Sub-System Level

Soy Oil

Energy 

Chemicals

Energy 

Chemicals

Energy 

Figure 4-1 Two System Levels of Soybean-Based Fuel Production in the 

Allocation Approach 

The displacement method will give the same final results no matter which system level is 

considered, but the allocation method will not. Because the allocation ratio is determined by the 

energy value or market value of the primary product and co-products, the variation in market 
31 

value of soy oil could obviously affect the allocation results of the first subsystem level but not 

affect the result of the second subsystem level, which means that it could affect the final results. 

However, in the soybean-to-biodiesel/renewable fuels case, soy oil is only a transitional product, 

which is produced and then consumed, so there is no reason that its market value or other value 

could affect the final results. On the basis of this consideration, we selected the whole-system 

level for the allocation approach. 

4.3.2 Energy Value and Market Value 

As mentioned, the energy value and market value of the primary product and co-products are the 

major determining factors for splitting energy and emissions among these products by using the 

allocation method. The energy value of soy meal was obtained from the Soybean Meal Info 

Center (http://www.soymeal.org). Note that soy meal is an animal food rather than a fuel, so its 

energy value is measured as the energy released when it is digested. The energy content of 

renewable fuels and their co-products were obtained on the basis of ASPEN simulation results 

(see Section 2.4). 

Unlike the energy content value — which is stable and will not change — the market value of 

products could vary over time and by region. For soy meal, we used the average growth rate of 

the state-average market price during the last decade (1997–2007) to project market prices in 

2010 (Ash and Dohlman 2007). 

The glycerin market is heavily oversupplied worldwide (Malveda et al. 2005), so the price for 

glycerin is not expected to rise in the near future; in fact, extensive biodiesel production could 

even lower glycerin’s market price. We assumed a price of $0.15/lb for glycerin, as provided in 

the Haas et al. (2006) study. 

Because of the high cost of feedstock, the production cost of biodiesel is higher than that of 

conventional petroleum diesel. A wealth of research has been conducted to examine the cost for 

producing biodiesel at different industry scales (Haas et al. 2006; Bender 1999). These 

researchers estimate a production cost of $2.00–$2.30 per gallon of pure biodiesel, taking credits 

for soy meal and glycerin into consideration. The cost of biodiesel could vary significantly as a 

result of soybean and soy meal price variations. The United States has recently begun providing 

incentives to make biodiesel production costs competitive with those of petroleum-based diesel. 

Also, as biodiesel use increases and the infrastructure is established, the price of biodiesel could 

decrease. In this study, we used the biodiesel price before incentives. 

For renewable diesel and gasoline fuels that are not yet on the market, we assumed the same 

market value as that of biodiesel fuel (on a per-million-Btu basis). Because the co-products of 
32 

renewable diesel and gasoline production all have energy value and can be used in industry, we 

assumed the same prices per million Btu as their corresponding fuel (natural gas, residual oil, 

diesel, and LPG), determined as in Table 4-2. DOE’s Energy Information Administration (EIA) 

Annual Energy Outlook 2007 (EIA 2007a) projected the prices of natural gas, residual oil, diesel, 

and LPG in the industrial sector in 2010; these projected prices are used in our study. 

Table 4-5 summarizes the energy content and market value of all products involved in this study. 

Note that prices in Table 4-5 are normalized to 2005 U.S. dollars (2005$) on the basis of an 

implicit U.S. price deflator from 1997 to 2006, as reported in the EIA Annual Energy Review

(EIA 2007b). 

Table 4-5 Energy Content and Market Value 

of Primary Products and Co-Products 

Product or 

Co-Product 

Energy Content 

(Btu/lb) 

Market Value 

($ 2005/lb) 

 

Biodiesel 16,149 0.490 

Renewable diesel I 18,746 0.569 

Renewable diesel II 18,925 0.574 

Renewable gasoline 18,679 0.567 

Soy meal 4,246 0.274 

Glycerin 7,979 0.150 

Fuel gas 27,999 0.174 

Heavy oils 20,617 0.195 

Propane fuel mix 18,568 0.301 

Product gas 18,316 0.114 

LCO 19,305 0.248 

CSO 18,738 0.177 

4.3.3 Allocation Ratios 

Table 4-6 presents the allocation ratios for the energy and emission burdens between primary 

products and co-products for the four soybean pathways. As indicated in Table 4-6, the 

allocation ratios of primary products based on energy value are a little lower than those based on 

market value. 

4.4 Hybrid Approach 

There are some shortcomings to both the displacement and allocation approaches. First, the 

production processes for renewable diesel I and renewable gasoline generate a large amount of 
33 

Table 4-6 Allocation Ratios of Total Energy and Emission Burdens 

between Primary Products and Co-Products from 

Using the Allocation Approach (shown as %) 

Product or Co-Product Biodiesel 

Renewable 

Diesel I 

Renewable 

Diesel II 

Renewable 

Gasoline 

 

Energy-value-based allocation 

 Primary fuel (biodiesel, renewable fuels) 42.9 32.2 44.7 24.1 

 Co-products (soy meal, glycerin, and others) 57.1 67.8 55.3 75.9 

 

Market-value-based allocation 

 Primary fuel (biodiesel, renewable fuels) 45.7 39.4 47.4 29.9 

 Co-products (soy meal, glycerin, and others) 54.3 60.6 52.6 70.1 

co-products, resulting in overestimation of credits for those products if the displacement method 

is used. In fact, using this method can even result in negative energy input and emissions. On the 

other hand, in the energy-based allocation method, soy meal and glycerin have values not 

because they have energy content but for their other applications. Soy meal, particularly, has low 

energy value but high protein content and is thus valuable in the animal feed market; if soy meal 

is treated as fuel (like other energy co-products), its credit could be greatly underestimated. The 

market-value-based allocation method is subject to variations in the product prices, which may 

lead to numerous uncertainties. 

To overcome these shortcomings, we introduced a hybrid approach, in which the displacement 

method is used for soy meal and glycerin, and the energy-based allocation method is used for 

other energy co-products. For biodiesel, the hybrid approach is the same as the displacement 

approach. Unlike the allocation approach, which considers the production processes from 

soybean to fuel as a whole system, the hybrid approach separates the production system into two 

subsystems because each subsystem is addressed by using different allocation methods. 

Table 4-7 presents the allocation ratio between primary products and co-products of the second 

subsystem that results from using the hybrid approach. 

Table 4-7 Allocation Ratios of Total Energy and Emission Burdens 

between Primary Products and Co-Products of the Second Subsystem 

from Using the Hybrid Approach (%) 

Parameter 

Renewable 

Diesel I 

Renewable 

Diesel II 

Renewable 

Gasoline 

 

Primary fuel (renewable fuels) 63.7 94.5 53.1 

Co-products (heavy oil, etc) 36.3 5.5 46.9 
34 

5 Life-Cycle Energy and GHG Emission Results for 

Soybean-Derived Fuels 

On the basis of the data and key assumptions presented in Section 3 and Section 4, we used 

GREET to conduct life-cycle simulations of energy use and GHG emissions for the six pathways 

examined in this study. GHG emissions are the sum of emissions of three gases — CO2, CH4,

and N2O — weighted by their global warming potentials. According to IPCC, the global 

warming potentials of CO2, CH4, and N2O are 1, 25, and 298, respectively. 

Figure 5-1 shows the GREET WTW modeling boundary. Results of a WTW analysis are 

separated into two stages: well-to-pump (WTP) and pump-to-wheels (PTW). Well-to-pump 

stages start with fuel feedstock recovery and end with fuels available at refueling stations. Pumpto-wheels stages cover vehicle operation activities. For example, for gasoline, the simulated 

stages include crude recovery; transportation of crude oil from oil fields to central storage 

terminals; crude oil storage at terminals; crude oil transportation from terminals to petroleum 

refineries; crude oil storage at refineries; crude refining to gasoline; transportation, storage, and 

distribution of gasoline; and combustion of gasoline in vehicles. 

Feedstock-Related

Stages:

Recovery, processing,

storage, and transportation

of feedstocks

Fuel-Related Stages:

Production,

transportation,

storage, and

distribution of fuel

Vehicle:

Refueling and 

operation

Well-to-Pump Pump-to-Wheels

Well-to-Wheels

Figure 5-1 GREET Well-to-Pump and Pump-to-Wheels Stages 

In the following sections, petroleum-based RFG is the baseline for soybean-based renewable 

gasoline, and petroleum-based LSD is the baseline for soybean-based biodiesel and renewable 

diesel fuels. 
35 

5.1 Total Energy Use 

Figure 5-2 presents WTW total energy use for 1 million Btu of fuel produced and used. Total 

energy use comprises all energy sources, including fossil energy and renewable energy 

(excluding energy embedded in soybeans, which is eventually from solar energy). 

-200,000

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

Petroleum

Gasoline

Petroleum

Diesel

Biodiesel Renewable

Diesel I

Renewable

Diesel II

Renewable

Gasoline

WTW Total Energy Use (Btu/mmBtu) 

WTP

WTP-Displacement

WTP-Energy Allocation

WTP-Market Allocation

WTP-Hybrid

PTW

Figure 5-2 Well-to-Wheels Total Energy Use of Six Fuel Types 

Figure 5-2 shows that different allocation approaches provide different results. The displacement 

approach gives the lowest total energy use among the four allocation approaches except in the 

case of renewable diesel II, whose production process generates a much smaller amount of 

co-product than the others. With the displacement approach, soybean-based fuels offer 6–25% 

lower total energy use than petroleum diesel or gasoline per million Btu, again except in the case 

of renewable diesel II, for which WTW total energy increases by 29% relative to LSD. 

The two allocation approaches — energy-based allocation and market-based allocation — show 

good agreement with each other, with very similar results (1–4% difference). With the two 

allocation approaches, soybean-based fuels have 13–18% higher total energy use than petroleum 

diesel or gasoline. 
36 

Soy Oil Extraction 

&

 Fuel Production

Allocation ratio

Allocation 

(Energy-based)

Soy Oil Extraction

1.51 lbs of soy oil

Fuel Production

Soy Meal

-7,152 Btus

1 lb of fuel

Displacement

10,810 Btus

2,663 Btus

Soy Oil Extraction

1.51 lbs of soy oil

Fuel Production

Allocation ratio

Hybrid

1 lb of Fuel

1 lb of Fuel

Farming

8.6 lbs of Soybean

Farming

8.6 lbs of Soybean Farming

8.6 lbs of Soybean

Fuel gas 

heavy oils

-11,553 Btus

7,576 Btus

Energy used for per lb of fuel leaving 

plant:

2,364 Btus 

2,663 Btus

3,484 Btus 968 Btus

6,884 Btus

Fuel gas 

heavy oils

3,926 Btus 

10,810 Btus

Soy Meal

-7,152 Btus

7,576 Btus

4,824 Btus 2,752 Btus

-4,554 Btus

-2,598 Btus

Energy used for per lb of fuel leaving 

plant:

8,850 Btus 

10,810 Btus

2,663 Btus

7,576 Btus

7,326 Btus

Soy meal

Fuel gas 

heavy oils

2,442 Btus

5,134 Btus

1,696 Btus 858 Btus 1,805 Btus

Energy used for per lb of fuel leaving 

plant:

6,784 Btus 

32.2% 67.8%

63.7% 36.3%

Figure 5-3 Comparison of Total Energy Use among Three Allocation Approaches for Renewable 

Diesel I 

(Note: Red indicates energy values allocated to primary product; blue values and 

dashed lines indicate energy values allocated to co-products.) 

The hybrid approach gives the highest total energy use results for the renewable diesel and 

gasoline, 19–31% higher than their conventional counterparts. Biodiesel is an exception because 

the hybrid approach is exactly the same as the displacement approach for biodiesel. It is 

interesting that the hybrid approach provides higher energy use results than the displacement and 

allocation approaches, because the hybrid approach is derived from the integration of the both of 

the latter methods. To explore the reason, Figure 5-3 compares the allocation of energy use per 

pound of fuel leaving the plant for the three allocation approaches, taking renewable diesel I as 

an example. Note that the energy use in Figure 5-3 includes farming, transportation of feedstock, 

and production in the plant only, not over the whole life cycle. The higher energy use of the 

hybrid approach compared with the displacement approach is attributable to two factors. First, 

the farming and production energy use allocated to the final co-products (fuel gas and heavy oil) 

is much lower than their displacement credit (2,752 + 3,926 + 968 for the hybrid method versus 

11,533 for the displacement method). Second, part of the credit for soy meal (−2,598) is 

allocated to the co-product (fuel gas and heavy oil), while all soy meal credit belongs to the 

primary product with the displacement approach. The reason that energy use is higher for the 

hybrid approach than the allocation approach is because the allocation approach allocates more 

energy to the co-products (5,134 + 7,326 + 1,805 for the allocation method versus 2,752 + 3,926 
37 

+ 968 for the hybrid method) because the allocation ratio for co-products is much higher with the 

soy meal included (67.8% allocation versus 36.3% hybrid), and the difference between them 

(6,619) is larger than the soy meal credit earned in the hybrid approach (–4,554). 

Renewable diesel II has fewer co-products; thus, its co-products and the method used to address 

them have a smaller effect on the results, which is apparent from the very similar energy use 

results among the four allocation approaches for this fuel. 

5.2 Fossil Energy Use 

Figure 5-4 presents the WTW fossil energy use of the six fuel options on the basis of 

1 million Btu of fuel produced and used. Fossil energy use includes petroleum, natural gas, and 

coal. 

Figure 5-4 reveals that all soybean-derived fuels offer significant reductions (52–107%) in fossil 

energy use. These reductions result from the fact that soybeans, as the feedstock for the four 

renewable fuel options, are a nonfossil feedstock. Soybean-based fuels, even with a certain 

amount of fossil energy input when they are used as process fuels during soybean farming and 

fuel production processes, can still achieve substantial reductions in fossil energy use. 

Like the results for total energy use, the results for fossil energy use vary on the basis of the 

allocation method applied. With the displacement method, renewable gasoline can reduce WTW 

fossil energy use by 107% compared with petroleum gasoline. This large reduction in fossil 

energy use results from the large amount of co-products produced with renewable gasoline; these 

products were assumed to displace fossil energy (product gas to replace natural gas, LCO to 

replace diesel fuel, and CSO to replace residual oil), which helps renewable gasoline earn a large 

credit in fossil energy saving. Biodiesel, renewable diesel I, and renewable diesel II can achieve 

WTW fossil energy reductions of 84%, 90%, and 55%, respectively. With the allocation 

approach, the reduction ratios are around 63–71%. The hybrid approach shows a 52–61% 

reduction in fossil energy use for soybean-based renewable fuels compared with conventional 

fuels. 
38 

-200,000

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

Petroleum

Gasoline

Petroleum

Diesel

Biodiesel Renewable

Diesel I

Renewable

Diesel II

Renewable

Gasoline

WTW Fossil Energy Use (Btu/mmBtu) 

WTP

WTP-Displacement

WTP-Energy Allocation

WTP-Market Allocation

WTP-Hybrid

PTW

Figure 5-4 Well-to-Wheels Fossil Energy Use of the Six Fuel Types 

5.3 Petroleum Use 

Figure 5-5 presents the WTW petroleum energy use for the six fuel options. Soybean-derived 

fuels offer significant oil savings. Petroleum energy used in the soybean-based fuel cycle is 

entirely from the WTP stage, primarily from diesel use for farming equipment and for the trucks 

and locomotives needed to transport feedstock and fuel. For soybean-based fuels, PTW fuel use 

is zero. 

All of the four soybean-derived fuels can save more than 85% of petroleum use. With the 

displacement approach, for each million Btu of fuel produced and used, renewable gasoline 

reduces petroleum use by 148% compared with petroleum gasoline, and soybean-based diesel 

fuels reduce petroleum use by 99–106% relative to petroleum diesel. Like fossil energy use, the 

petroleum use associated with renewable gasoline is low because its production process 

generates large quantities of co-products (product gas, LCO, and CSO) in terms of Btu, and the 

co-products (LCO and CSO) are assumed to replace petroleum fuels (diesel and residual oil), 

providing large petroleum savings credits. 

With the allocation approach, petroleum use among the four soybean-based fuels is very similar; 

use by all is about 88–92% lower than that of conventional petroleum fuels. 
39 

-800,000

-600,000

-400,000

-200,000

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

Petroleum

Gasoline

Petroleum

Diesel

Biodiesel Renewable

Diesel I

Renewable

Diesel II

Renewable

Gasoline

WTW Petroleum Use (Btu/mmBtu) 

WTP

WTP-Displacement

WTP-Energy Allocation

WTP-Market Allocation

WTP-Hybrid

PTW

Figure 5-5 Well-to-Wheels Petroleum Energy Use of the Six Fuel Types 

With the hybrid approach, soybean-based fuels reduce WTW petroleum use by 97–104% relative 

to petroleum fuels. Unlike total energy use and fossil energy use results, WTW petroleum use for 

the hybrid approach is lower than that for the allocation approach for the three renewable fuels. 

This is because the production process for renewable fuels uses very little petroleum, so 

petroleum use allocated to the co-products is very small. On the other hand, farming of soybeans, 

assigned to be displaced by soy meal, consumes large amounts of diesel and gasoline, and makes 

the hybrid approach result in lower petroleum use because of the petroleum credit from soy meal. 

5.4 GHG Emissions 

Figure 5-6 presents WTW CO2-equivalent grams of GHGs (including CO2, CH4, and N2O) for 

the six fuel pathways studied. To clearly show the GHG reduction benefit of different soybeanbased fuels, Figure 5-7 presents the changes in GHG emissions of the soybean-based fuels 

relative to their petroleum counterparts. 

The emission results for the two renewable diesel fuels depend on the allocation approach used. 

Of the four allocation approaches, the displacement approach offers the best GHG reduction 

benefit, except for renewable diesel II. When this approach is used, all four soybean-based fuels 

can achieve a modest to significant reduction in WTW GHG emissions (64–174%) compared 
40 

-170,000

-140,000

-110,000

-80,000

-50,000

-20,000

10,000

40,000

70,000

100,000

Petroleum

Gasoline

Petroleum

Diesel

Biodiesel Renewable

Diesel I

Renewable

Diesel II

Renewable

Gasoline

WTW GHG Emissions (g/mmBtu) 

WTP

WTP-Displacement

WTP-Energy Allocation

WTP-Market Allocation

WTP-Hybrid

PTW

Figure 5-6 Well-to-Wheels GHG Emissions of the Six Fuel Types 

-130%

-174%

-62% -64%

-94%

-66%

-60%

-68% -74%

-57% -62%

-74%

-66%

-57%

-65%

-94%

-200%

-180%

-160%

-140%

-120%

-100%

-80%

-60%

-40%

-20%

0%

Biodiesel Renewable Diesel I Renewable Diesel II Renewable Gasoline

Relative Reduction in GHGs Emissio

Displacement Energy Allocation Market Allocation Hybrid

Figure 5-7 Well-to-Wheels GHG Emission Reductions for Soybean-Derived Fuels 

Compared with Petroleum Gasoline or Diesel 
41 

with petroleum-based fuels. The reason that renewable diesel I and renewable gasoline can 

achieve a much larger GHG emission reduction (-130% and –174%) is because they have a 

significant amount of co-products (fuel gas and heavy oil; product gas, LCO, and CSO) and 

because the production and combustion of the replaced fuels (natural gas, diesel fuel, and 

residual oil) could release lots of GHGs. 

With the allocation approach, soybean-based fuels achieve a modest reduction in GHG emissions 

(57–74%). The results from using the hybrid approach are similar to the results obtained from 

using the allocation approach. 

These results are based on 1 million Btu of fuel produced and used. While we do not expect 

significant engine efficiency differences between the two gasoline types in SI engines and among 

the four diesel types in CIDI engines, it is well known that CIDI engines are more efficient than 

SI engines. Fuel consumption in CIDI engines could be 15–20% less than that of SI engines per 

distance traveled. To compare WTW results on a per-mile basis among the six options, 

researchers could reduce energy use and GHG emissions for the four diesel fuel options as 

presented in Figure 5-6. 
42 

6 Conclusions 

We assessed the life-cycle energy and GHG emission impacts of soybean-derived biodiesel and 

soybean-derived renewable diesel and gasoline fuels by expanding, updating, and using the 

GREET model. Soybean-derived renewable diesel is produced from hydrogenation of soy oil, 

and renewable gasoline is produced from catalytic cracking of soy oil. 

The method applied to determine energy and emission credits for co-products is a key issue in 

life-cycle analysis. The production processes of the four soybean-based fuels generate various 

kinds of co-products, which could lead to very different results depending on the method that is 

used to address the co-products. We used four different allocation approaches in this study: 

displacement, energy-based allocation, market-value-based allocation, and a hybrid approach 

(integrating the displacement and allocation methods). The four allocation approaches generate 

considerably different results. 

For WTW total energy use, the displacement approach gives the lowest total energy use for the 

four bio-based fuels — showing a 6–25% reduction in total energy use for the biofuels (except 

for renewable diesel II) compared with petroleum fuels. The two allocation approaches show 

good agreement with each other, providing very similar results. The hybrid approach gives the 

highest total energy use results. Both the allocation and hybrid approaches show a 13–31% 

increase in total energy use compared with petroleum fuels. 

All soybean-derived fuels achieve a significant reduction (52–107%) in fossil energy use. The 

displacement approach offers the best benefit in fossil energy use, with a reduction of 55–107%. 

With the allocation approach, the reduction ratios are around 63–71%. The hybrid approach 

shows a 52–61% reduction in fossil energy use for soybean-based renewable fuels compared 

with conventional fuels. 

All four of the soybean-derived fuels can save more than 85% of petroleum use. With the 

displacement approach, renewable gasoline reduces petroleum use by 148% compared with 

petroleum gasoline because its production process generates a large amount of energy 

co-products. Soybean-based diesel fuels reduce petroleum use by 99–106% relative to petroleum 

diesel. With the allocation approach, the use of petroleum by the four soybean-based fuels is 

about 88–92% lower than its use by conventional petroleum fuels. With the hybrid approach, 

soybean-based fuels reduce WTW petroleum use by 97–104% relative to petroleum fuels.
43 

With the displacement approach, all four soybean-based fuels can achieve a modest to significant 

reduction in WTW GHG emissions (64–174%) compared with petroleum-based fuels. While 

with the allocation approach, soybean-based fuels achieve a modest reduction in GHG emissions 

(57–74%).
44 

7 References 

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Soydiesel? prepared for the National SoyDiesel Development Board. 

Ash, M., and Dohlman, E., 2007, Oil Crops Outlook: Prices Strengthen as Outlook for U.S. 

Soybean Supply Tightens, U.S. Department of Agriculture, Oct. 15, available at 

http://usda.mannlib.cornell.edu/usda/ers/OCS//2000s/2007/OCS-10-15-2007.pdf, accessed Nov. 

2007. 

Association of European Plastic Industry, 2005, Eco-Profiles of the European Plastic Industry: 

Propylene, Chlorine, and Sodium Hydroxide, available at http://lca.plasticseurope.org/index.htm, 

accessed Nov. 2007 

Bender, M., 1999, “Economic Feasibility Review for Community-Scale Farmer Cooperatives,” 

Bioresource Technology, 70: 81–87. 

Britzman, D.G., 2000, Soybean Meal — An Excellent Protein Source for Poultry Feeds, 

American Soybean Association Technical Bulletin, available at http://www.asaim-europe.org/ 

pdf/Britzman.pdf, accessed Nov. 2007. 

CETC (CANMET Energy Technology Centre), undated, SuperCetane Technology, available at 

http://canren.gc.ca/app/filerepository/381B2685235D4C6E924E0665F0A84344.pdf, accessed 

Nov. 2007. 

Crutzen, P.J., A.R. Mosier, K.A. Smith, and W. Miniwarter, 2007, “N2O Release from AgroBiofuel Production Negates Global Warming Reduction by Replacing Fossil Fuels,” 

Atmospheric Chemistry and Physics, 7: 11191–11205. 

Greiner, E., T. Kalin, and M. Yoneyama, 2005, “Epichlorohydrin,” in Chemical Economics 

Handbook 2004, Report #642.3000A, SRI Consulting, Menlo Park, Calif. 

EIA (Energy Information Administration), 2007a, Annual Energy Outlook 2007, 

U.S. Department of Energy, available at http://www.eia.doe.gov/oiaf/aeo/, accessed Nov. 2007. 

EIA, 2007b, Annual Energy Review 2007, U.S. Department of Energy, available at 

http://www.eia.doe.gov/emeu/aer/contents.html, accessed Nov. 2007. 
45 

Haas, M.J., A.J. McAloon, W.C. Yee, et al., 2006, “A Process Model to Estimate Biodiesel 

Production Costs,” Bioresource Technology, 97: 671–678. 

Hill, J., E. Nelson, D. Tilman, et al., 2006, “Environmental, Economic, and Energetic Costs and 

Benefits of Biodiesel and Ethanol Biofuels,” in Proceedings of the National Academy of Sciences 

of the United States of America, doi:10.1073/pnas.0604600103. 

Intergovernmental Panel on Climate Change (IPCC), 2006, “N2O Emissions from Managed Soils, 

and CO2 Emissions from Lime and Urea Application,” in 2006 IPCC Guidelines for National 

Greenhouse Gas Inventories, Volume 4, Chapter 11, available at http://www.ipccnggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf, accessed 

Nov. 2007. 

IPCC, 1996, Revised IPCC Guidelines for National Greenhouse Gas Inventories: Workbook, 

London, U.K., 2.15–2.16. 

Kalnes, T., T. Marker, and D.R. Shonnard, 2007, “Green Diesel: A Second Generation Biofuel,” 

International Journal of Chemical Reactor Engineering, 5, A48. 

Keller, G., M. Mintz, C. Saricks, M. Wang, and H. Ng, 2007, Acceptance of Biodiesel as a 

Clean-Burning Fuel: A Draft Report in Response to Section 1823 of the Energy Policy Act of 

2005, Center for Transportation Research, Argonne National Laboratory, prepared for Office of 

FreedomCAR and Vehicle Technologies, U.S. Department of Energy, Oct. 

Maier, D., J. Reising, J. Briggs, K. Day, and E.P. Christmas, 1998, High-Value Soybean 

Composition, Fact Sheet #39, Grain Quality Task Force, Purdue University, available at 

http://www.ces.purdue.edu/extmedia/GQ/GQ-39.html, accessed Nov. 2007. 

Malça, J., and F. Freire, 2006, A Comparative Assessment of Rapeseed Oil and Biodiesel (RME) 

to Replace Petroleum Diesel Use in Transportation, presented at Bioenergy I: From Concept to 

Commercial Processes, Tomar, Portugal, March 5–10, available at http://services.bepress.com/ 

cgi/viewcontent.cgi?article=1029&context=eci/bioenergy_i, accessed Nov. 2007. 

Malveda, M., M. Blagoev, R. Gubler, and K. Yagi, 2005, “Glycerin,” in Chemical Economics 

Handbook 2004, Report #662.5000A, SRI Consulting, Menlo Park, Calif. 

National Biodiesel Board, 2007, FAQs: How Much Biodiesel Has Been Sold in the U.S.?

available at http://www.biodiesel.org/resources/faqs/, accessed Nov. 2007. 
46 

NRCan (Natural Resources Canada), 2003, Technologies and Applications: The CETC 

SuperCetane Technology, last updated Aug. 11, available at http://www.canren.gc.ca/ 

tech_appl/index.asp?CaID=2&PgId=1083, accessed Nov. 2007. 

Pimentel, D., and T.W. Patzek, 2005, “Ethanol Production Using Corn, Switchgrass, and Wood; 

Biomass Production Using Soybean and Sunflower,” Natural Resources Research, 14(1): 65–75. 

Reuters News, 2007, Galp Energia Selects UOP/Eni EcofiningTM Technology to Produce Green 

Diesel Fuel, available at http://www.reuters.com/article/pressRelease/idUS44541+28-Nov2007+BW20071128, accessed Nov. 2007 

Scharmer, K., and G. Gosse, 1996, “Ecological Impact of Biodiesel Production and Use in 

Europe,” The Liquid Biofuels Newsletter-7, available at http://www.blt.bmlf.gv.at/vero/ 

liquid_biofuels_newsletter/Liquid_biofuels_Newsletter-07_e.pdf, accessed Nov. 2007. 

(S&T)2

 Consultants Inc., 2004, The Addition of NRCan’s Supercetane and ROBYSTM Processes 

to GHGenius, prepared for Natural Resources Canada. 

Sheehan, J., V. Camobreco, J. Duffield, et al., 1998, Life-Cycle Inventory of Biodiesel and 

Petroleum Diesel for Use in an Urban Bus, prepared for U.S. Department of Energy, Office of 

Fuels Development. 

UOP, 2005, Opportunities for Biorenewables in Oil Refineries, Final Technical Report, prepared 

for U.S. Department of Energy. 

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available at http://www.nass.usda.gov/QuickStats/, accessed Nov. 2007. 

USDA, 2007b, Data Sets: Commodity Costs and Returns, available at http://www.ers.usda.gov/ 

Data/CostsAndReturns/Fuelbystate.xls, accessed Nov. 2007. 

USDA, 2007c, Data Sets: U.S. Fertilizer Use and Price, available at http://www.ers.usda.gov/ 

Data/FertilizerUse/, accessed Nov. 2007. 

Wang, M.Q., 1999, GREET 1.5 — Transportation Fuel-Cycle Model, Volume 1: Methodology, 

Development, Use, and Results, Volume 1, ANL/ESD-39, Center for Transportation Research, 

Argonne National Laboratory, Argonne, Ill., Aug. 
47 

Wang, M., C. Saricks, and H. Lee, 2003, Fuel-Cycle Energy and Emission Impacts of EthanolDiesel Blends in Urban Buses and Farming Tractors, prepared for Illinois Department of 

Commerce and Economic Opportunities, by Center for Transportation Research, Argonne 

National Laboratory, Argonne, Ill., July. 
48 
49 

Appendix 1: ASPEN Simulation Process of Renewable Diesel I 

(Super Cetane) 

Victoria Putsche 

Center for Transportation Technologies and Systems 

National Renewable Energy Laboratory1

A1-1 Introduction 

A preliminary analysis was conducted for a hydrogenation-derived renewable diesel (HDRD) 

facility on the basis of the Natural Resources Canada (NRCan) process [(S&T)2

 Consultants 

2004]. NRCan has named its renewable diesel “SuperCetane.” Material and energy balances 

were developed by using ASPEN Plus® 12.1 (super_cetane2.inp). The overall goal of the study 

was to confirm the preliminary overall material and energy balances provided by NRCan 

[(S&T)2

 Consultants 2004] and to provide input for a life-cycle analysis (LCA). The following 

report summarizes the basis for the analysis and its results. 

A1-2 Design Basis and Process Description 

HDRD is made from reacting hydrogen with oil or grease in a refinery-hydrotreating process. 

Several reactions occur in the conversion including hydrocracking, hydrotreating, and 

hydrogenation [(S&T)2

 Consultants 2004]. A commercial refinery catalyst is used to facilitate 

conversion. 

For this analysis, the production of HDRD is based on the NRCan process, which involves 

hydrogen production, hydrogenation, water separation, distillation gas recycle, and steam 

generation. All of the unit operations were modeled except hydrogen production. It is assumed 

that hydrogen is supplied by an off-site hydrogen plant. Figure A1-1 is a block flow diagram of 

the NRCan process. 

One of the important characteristics of the process is that energy demands, except electricity, are 

met on site. That is, a portion of the fuel gas product is combusted on site to generate steam for 

the process. The remaining fuel gas as well as the heavy waxy fraction are sent off site and 

assumed to be used for fuel. For this process configuration, the LCA will determine the 

emissions from the off-site fuel gas and heavies combustion as well as the electricity generation 

 

1

 Contact person for further information: Paul Bergeron (Paul_Bergeron@nrel.gov) of National Renewable Energy 

Laboratory. 
50 

and will apportion it appropriately to the main process. This analysis will estimate the emissions 

from the fuel gas combusted on site. 

Hydrotreater 

370-450  ̊C 

4-15 MPa 

9.44 lb oil 

0.187 lbs. Hydrogen 

0.236 kWh electricity 

6.25 lbs SuperCetane 

1.58 lbs fuel gas 

1 lb waxy residue 

Yield – 70-80% 

Figure A1-1 HDRD (SuperCetane) Block Flow Diagram 

The renewable diesel process was modeled by using numerous assumptions and data sources. 

Table A1-1 summarizes the key design parameters and their sources. 

Table A-1 Design Basis 

Parameter Value Source 

Feedstock 

 Type 

 Throughput 

Soybean oil 

100 lb/h 

Most common oil in U.S. for biodiesel 

For LCA analysis 

Feedstock fatty acid composition 

(wt fraction) 

 Linolenic acid 

 Palmitic acid 

 Stearic acid 

 Oleic acid 

 Linoleic acid 

 Arachidic acid 

0.075 

0.11 

0.041 

0.22 

0.54 

0.014 

Hydrogenation design 

 Temperature 

 Pressure 

325 oC 

500 psia 

 

Yields (per pound inlet feed) 

 SuperCetane 

 Water 

 CO2

 Propane 

 Hydrogen 

 Naphtha 

64.9% 

5.0% 

8.2% 

8.2% 

10.4% 

0.35 

Derived from published yields [(S&T)2

 Consultants 2004] 
51 

Several of these assumptions, particularly the feedstock choice and facility size, require further 

explanation. The feedstock selected was soybean oil, even though many of the feedstocks in the 

literature were rapeseed oil or other oils, because it is the most prevalent oil in fuels production 

(i.e., biodiesel), and one of the purposes of the study was to compare the environmental impacts 

of HDRD to biodiesel, and the most thorough LCA of biodiesel (Sheehan et al. 1998) was based 

on soybean oil. The facility size of 100 lb/h was selected as an easy, round number for the LCA. 

The results of most LCAs are shown on a pound of feed or product basis since the impacts are 

directly scalable to throughput. Therefore, this simple number was selected, even though this 

would not be a typical facility size. 

A1-3 Model Description 

An ASPEN Plus® model (super_cetane) was developed for the NRCan SuperCetane process, 

based largely on the (S&T)2

 report [(S&T)2 2004]. ASPEN Plus® is a steady-state process 

simulator, and Appendix A1-6 contains the input file for the model. 

The ASPEN Plus® HDRD model has one flowsheet to model the four major process areas: 

hydrogenation, sour water separation, stripping, and pressure swing adsorption (PSA)/gas 

recycle. Each of these areas is briefly discussed, and the flow diagram from ASPEN Plus® is 

presented. The flow diagram shows only those unit operations modeled in ASPEN Plus®. 

Equipment used for operations such as conveyance, size reduction, and storage is generally not 

included in the model. The power requirements of this equipment, however, are included and are 

modeled as work streams. 

ASPEN Plus® is composed of physical property and unit operation models that are combined 

into a process model. The simulation can be broken into three major sections: components 

(i.e., chemical species), physical property option sets (e.g., what set of physical property models 

to use), and the flowsheet (i.e., the series of unit operations). Each of these sections is described 

in more detail below. 

Components 

Fourteen components were modeled in the simulation; all were modeled as conventional 

(e.g., water) components in the mixed substream. The following is a list of the components in the 

simulation: 

• Hydrogen – H2

• Linolenic acid – C18H30O2

• Palmitic acid – C16H32O2
52 

• Stearic acid – C18H36O2

• Linoleic acid – C18H32O2

• Arachidic acid – C20H40O2

• Oleic acid – C18H34O2

• Green Diesel – C18H38

• Water – H2O 

• Hydrogen Sulfide – H2S 

• Ammonia – NH3

• Propane – C3H8

• Naphtha 

• Oxygen – O2

• Nitrogen – N2

• Wax – C26H54

• Carbon dioxide – CO2

Green diesel is not a specific compound but is a complex mixture of hydrocarbons; however, for 

simplicity, it was modeled as a single component, C18H38, which is within the range of diesel 

hydrocarbons. Green diesel was specified with a specific gravity of 0.78 (Marker, T. 2007) and a 

MW of 254. Naphtha was specified with a specific gravity of 0.7 and a MW of 100. 

As noted earlier, the vegetable oil feed was modeled as a mixture of six fatty acids: linolenic acid, 

palmitic acid, stearic acid, linoleic acid, arachidic acid, and oleic acid. All of these components 

are available in the ASPEN Plus® databanks. Table A1-2 shows the molecular formula, the 

component name in the model, and the weight fraction in the feed of each fatty acid. 

Table A1-2 Organic Acid Composition of Bio-Oil 

Organic 

Fatty Acid Composition 

Component 

Name 

Weight 

Fraction 

 

Linolenic C18H30O2 LINOL3 0.075 

Palmitic C16H32O2 PALM 0.11 

Stearic C18H36O2 STEARIC 0.041 

Oleic C18H34O2 OLEIC 0.22 

Linoleic C18H32O2 LINOL2 0.54 

Arachidic C20H40O2 ARACHID 0.014 

One Henry component, CO2, was specified. The Henry’s constants were obtained from ASPEN 

Plus®. 
53 

Physical Property Option Sets 

The physical property set selected was POLYUF with properties estimated by using the 

POLYNRTL method. Physical property databanks used in the simulation were PURE13, 

AQUEOUS, SOLIDS AND INORGANIC. 

Flowsheet 

One flowsheet was developed for the process: (A1000). The flowsheet is briefly discussed, and 

flow diagrams from ASPEN Plus® are presented. The flow diagrams (Figure A1-2) show only 

those unit operations modeled in ASPEN Plus®. Equipment used for operations such as 

conveyance and storage are generally not included in the model and are thus not shown. 

Similarly, certain complex unit operations (e.g., gas turbine) require several ASPEN Plus®

models (e.g., compressors, reactors, heat exchangers). 

Bio-oil is introduced into the process in stream 101. It is assumed to be at ambient conditions 

(i.e., 68°F and 14.7 psia) with a flow rate of a nominal 100 lb/h. The 100 lb/h value was selected 

as it would be easily scaled to any other value; since the model was developed to be the basis for 

an LCA, any flow rate would be reasonable. 

101

RX101

120

106

S101

115

P101

P102

CP102 CP101

HX101+

HX101-

MX102

102 104

117

118 121

119

FUELGAS

112

111

ST-101

113

107

109

QHX101

QHX101XS

Q

HX103+

108

HX103-

CWS

CWR

QHX103

QHX103XS Q

WP-102 W

WP-101 W

110

WCP-101

W WCP-102 W

QRX101

Q

P105

P103

P104

WP-103 W

WP-104

W

WP-105

W

CWR1

CWS1

WWT

SP101

125 CB-101

122

123

QCB101

124

MX101

QREB Q

QCOND

COND

150 151

QCONDXS

Q

CWS2 P107

P108

CWR2

WP-107

W

WP-108

W

B1

126

QPROCESS

Q

Figure A1-2 ASPEN Simulation Process Flowcharts for Renewable Diesel I 

(SuperCetane) 
54 

Hydrogenation 

As shown in Figure A1-2, the soybean oil feed (Stream 101) is pumped to 500 psia (P-101) and 

then mixed with recycle oil (Stream 110C) from the splitter, SP-101, following the sour water 

separator (S101). This stream is then heated to 290°F by exchange with hydrogenator effluent 

(Stream 106) in HX101+ and HX101−. The next stage of the process is the hydrogenator, where 

the oil stream is combined with the inlet hydrogen (Stream 120) and recycle fuel gas (Stream 118) 

and reacted. 

The hydrogenator (RX101) is modeled as an RYIELD reactor. All of the incoming oil is 

converted to gas (e.g., CO2, H2, propane), water, green diesel (GDSL), waxes, and a small 

amount of naphtha. As noted in the design basis, the yield of green diesel is estimated at 64.5% 

of the total inlet feed streams on a mass basis. The hydrogenation reactions are exothermic, and 

there is excess heat (QRX101) after the reactor is brought to reaction temperature (325°C). 

After the oil feed is preheated, the hydrogenator effluent (Stream 107) is cooled with cooling 

water (Stream CWS1) to 100°F in HX103. The cooled reactor products are then sent to the sour 

water separation, S101. 

Sour Water Separation 

In sour water separation, the gases (Stream 115) are flashed off and sent to a splitter (SP101) for 

recycle, combustion, and product recovery. The aqueous stream is decanted and sent to 

wastewater treatment (Stream 109). After the separator, the organic stream (110) is sent to a 

distillation column (ST-101) for product recovery. 

Product Recovery 

The product recovery area consists of a distillation column where the SuperCetane (Stream 111) 

with a small amount of naphtha is separated from the heavies (Stream 112). The distillation 

column is modeled as a RADFRAC column with eight stages with both a condenser and a 

reboiler. The system is operated at 100 psi (stage 1). The feed is introduced on stage 5, while 

SuperCetane is recovered on stage 1, and the heavies are taken off on stage 8. 

Gas Recycle 

As noted earlier, the off-gas from the water separator, S101, is sent to a splitter where it is 

separated for gas recycle (124), combustion (117), and product (FUELGAS). The amount of 

product is controlled by overall process yields, while the amount sent to combustion is specified 

so that the system’s energy demand is satisfied. 
55 

Heat Generation 

The last major section of the flowsheet is steam generation. Here, some of the fuel gas is 

combusted (CB-101), which is operated at 1700°F. Heat is recovered from the off-gases in a 

HEATER block, B1. The amount of heat recovery is compared to the process heat demands 

[e.g., the reboiler (QREB)] to ensure that enough heat is available. A more rigorous model could 

be developed that would generate steam and meet the specific heat demands of each unit 

operation. For this analysis, this gross heat balance was deemed sufficient. 

A1-4 Results and Discussion 

This effort was aimed at confirming the material and energy balances summarized for the 

NRCan process. As shown in the table below, the ASPEN Plus® model shows good agreement 

with the published literature. All of the yields and utility requirements are similar between the 

model and the literature. Table A1-3 compares the results of this modeling effort and the values 

from the (S&T)2

 Consultants (2004). 

Table A1-3 Comparison of Overall Mass and Energy Balances 

Feedstock 

NRCan Yield 

per 100 lb oil 

Current Analysis 

per 100 lb Oil 

 

Oil 100 100 

H2 1.98 1.98 

Air 63.47 

 

Products 

 Fuel Gas 16.74 16.74 

 HDRD 66.21 66.2 

 Naphtha 0.36 

 Heavies (113) 11.60 11.6 

 Waste water 4.39 

 Flue Gas (lb/h) 66.52 

 Flue Gas (126) (scf) 24.2 

 

Utilities 

 Electricity (kWh) 2.50 2.61 

 Cooling water (lb/h) 4307 

Besides SuperCetane, this process generates three other products: fuel gas, naphtha, and heavies. 

The amount of naphtha is very small and is included in the SuperCetane product. Table A1-4 

summarizes the calculated compositions of the other products. 
56 

Table A1-4 Product Compositions 

Product Composition 

 

Fuel gas 

 Propane 

 Carbon dioxide 

 Water 

 Hydrogen 

 Naphtha 

LHV (Btu/lb) 

25.45 

27.96 

2.33 

43.68 

0.58 

27,999 

 

Heavies 

 Wax 

 Naphtha 

LHV (Btu/lb) 

80 

20 

20,617 

In addition to the material and energy balance, the analysis projected the air emissions from the 

process. As noted earlier, it is assumed that a portion of the fuel gas, which is primarily propane, 

is combusted to make steam to meet the energy demand of the process. Air emissions of criteria 

pollutants were estimated on the basis of the U.S. Environmental Protection Agency’s AP-42 

emission factors. The fuel gas is a mix of several gases, but for this analysis, the emissions were 

assumed to be equivalent to natural gas combustion. Table A1-5 summarizes the emission factors 

and the emission rate of each pollutant. 

Table A1-5 Air Emission Factors 

Pollutant 

Emission Factors 

(lb/MM scf fuel) 

Emissions 

(lb/100 lb product) 

 

CO 84 3.07E-03 

NOx 32 1.17E-03 

PM 7.6 2.78E-04 

VOCs 5.5 2.01E-04 

The NRCan process uses hydrogenation to convert bio-oils like soybean oil into a diesel 

substitute. Several companies are looking into this process. This analysis developed an ASPEN 

Plus® model of the process and compared its results with published results by (S&T)2

Consultants (2004). Good agreement was obtained between the two studies. These results will be 

used to develop an LCA for this process. 
57 

A1-5 References 

Sheehan, J., V. Camobreco, J. Duffield, M. Graboski, H. Shapouri. 1998, An Overview of Biodiesel and 

Petroleum Diesel Life Cycles, joint study sponsored by U.S. Department of Agriculture and 

U.S. Department of Energy, NREL/TP-580-24772. 

(S&T)2

 Consultants Inc., 2004. The Addition of NRCan’s SuperCetane and ROBYS TM Processes to 

GHGenius, prepared for Natural Resources Canada, March 30. 

A1-6 ASPEN Plus® Input File: Super_Cetane2.inp 

; 

;Input Summary created by Aspen Plus Rel. 20.0 at 16:58:40 Sun Oct 21, 2007 

;Directory E:\HDRD Filename E:\HDRD\super_cetane2.inp 

; 

TITLE ‘Super Cetane’ 

IN-UNITS ENG DENSITY=‘lb/gal’ POWER=kW VOLUME=gal & 

 MOLE-DENSITY=‘lbmol/gal’ MASS-DENSITY=‘lb/gal’ 

DEF-STREAMS CONVEN ALL 

DATABANKS PURE13 / AQUEOUS / SOLIDS / INORGANIC / & 

 NOASPENPCD 

PROP-SOURCES PURE13 / AQUEOUS / SOLIDS / INORGANIC 

COMPONENTS 

 H2 H2 / 

 LINOL3 C18H30O2 / 

 PALM C16H32O2 / 

 STEARIC C18H36O2 / 

 OLEIC C18H34O2 / 

 LINOL2 C18H32O2 / 

 ARACHID C20H40O2 / 

 GDSL C18H38 / 
58 

 H2O H2O / 

 H2S H2S / 

 NH3 H3N / 

 PROPANE C3H8 / 

 NAPTHA / 

 CO2 CO2 / 

 WAX C26H54 / 

 O2 O2 / 

 N2 N2 

PC-USER 

 IN-UNITS ENG 

 PC-DEF ASPEN GDSL GRAV=0.749 MW=254. 

 PC-DEF ASPEN NAPTHA GRAV=0.7 MW=72. 

ADA-SETUP 

 ADA-SETUP PROCEDURE=REL9 

HENRY-COMPS HC-1 CO2 

FLOWSHEET 

 BLOCK RX101 IN=119 104 OUT=106 QRX101 

 BLOCK S101 IN=108 OUT=115 110 109 

 BLOCK P101 IN=101 OUT=102 WP-101 

 BLOCK P102 IN=112 OUT=113 WP-102 

 BLOCK CP102 IN=125 OUT=118 WCP-102 

 BLOCK CP101 IN=120 OUT=121 WCP-101 

 BLOCK HX101+ IN=106 OUT=107 QHX101 

 BLOCK HX101- IN=102 QHX101 OUT=104 QHX101XS 

 BLOCK MX102 IN=118 121 OUT=119 

 BLOCK ST-101 IN=110 OUT=111 112 QCOND QREB 

 BLOCK HX103+ IN=107 OUT=108 QHX103 

 BLOCK HX103- IN=CWS QHX103 OUT=CWR QHX103XS 

 BLOCK P105 IN=109 OUT=WWT WP-105 

 BLOCK P103 IN=CWS1 OUT=CWS WP-103 

 BLOCK P104 IN=CWR OUT=CWR1 WP-104 

 BLOCK SP101 IN=115 OUT=117 FUELGAS 124 

 BLOCK CB-101 IN=117 123 OUT=122 QCB101 

 BLOCK MX101 IN=124 OUT=125 

 BLOCK COND IN=150 QCOND OUT=151 QCONDXS 
59 

 BLOCK P107 IN=CWS2 OUT=150 WP-107 

 BLOCK P108 IN=151 OUT=CWR2 WP-108 

 BLOCK B1 IN=122 QCB101 OUT=126 QPROCESS 

PROPERTIES POLYUF HENRY-COMPS=HC-1 

 PROPERTIES POLYNRTL 

PROP-DATA HENRY-1 

 IN-UNITS ENG 

 PROP-LIST HENRY 

 BPVAL CO2 H2O 175.2762325 -15734.78987 -21.66900000 & 

 6.12550005E-4 31.73000375 175.7300026 0.0 

STREAM 101 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=68. PRES=14.7 MASS-FLOW=100. 

 MASS-FRAC LINOL3 0.075 / PALM 0.11 / STEARIC 0.041 / & 

 OLEIC 0.22 / LINOL2 0.54 / ARACHID 0.014 

STREAM 117 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=68. PRES=14.7 MASS-FLOW=1. 

 MASS-FRAC H2 1. 

STREAM 120 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=68. PRES=14.7 MASS-FLOW=100. 

 MASS-FRAC H2 1. 

STREAM 123 

 SUBSTREAM MIXED TEMP=68. PRES=14.7 MASS-FLOW=67. 

 MOLE-FRAC O2 0.21 / N2 0.79 

STREAM 125 

 SUBSTREAM MIXED TEMP=68. PRES=14.7 MASS-FLOW=10. 

 MASS-FRAC H2 1. 

STREAM CWS 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=35. <C> PRES=500. MASS-FLOW=100. 
60 

 MASS-FRAC H2O 1. 

STREAM CWS1 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=35. <C> PRES=14.7 MASS-FLOW=100. 

 MASS-FRAC H2O 1. 

STREAM CWS2 

 SUBSTREAM MIXED TEMP=35. <C> PRES=14.7 MASS-FLOW=100. 

 MASS-FRAC H2O 1. 

DEF-STREAMS HEAT QCB101 

DEF-STREAMS HEAT QCOND 

DEF-STREAMS HEAT QCONDXS 

DEF-STREAMS HEAT QHX101 

DEF-STREAMS HEAT QHX101XS 

DEF-STREAMS HEAT QHX103 

DEF-STREAMS HEAT QHX103XS 

DEF-STREAMS HEAT QPROCESS 

DEF-STREAMS HEAT QREB 

DEF-STREAMS HEAT QRX101 

DEF-STREAMS WORK WCP-101 

DEF-STREAMS WORK WCP-102 

DEF-STREAMS WORK WP-101 

DEF-STREAMS WORK WP-102 

DEF-STREAMS WORK WP-103 
61 

DEF-STREAMS WORK WP-104 

DEF-STREAMS WORK WP-105 

DEF-STREAMS WORK WP-107 

DEF-STREAMS WORK WP-108 

BLOCK MX101 MIXER 

BLOCK MX102 MIXER 

 IN-UNITS ENG 

BLOCK SP101 FSPLIT 

 FRAC FUELGAS 0.5 

 MASS-FLOW 124 10. 

BLOCK B1 HEATER 

 PARAM TEMP=100. PRES=14.7 

BLOCK COND HEATER 

 PARAM PRES=14.7 DELT=15. 

BLOCK HX101+ HEATER 

 IN-UNITS ENG 

 PARAM TEMP=110. PRES=500. 

BLOCK HX101- HEATER 

 IN-UNITS ENG 

 PARAM TEMP=567. PRES=500. 

BLOCK HX103+ HEATER 

 IN-UNITS ENG 

 PARAM TEMP=100. PRES=500. 

BLOCK HX103- HEATER 

 IN-UNITS ENG 

 PARAM PRES=500. DELT=15. 
62 

BLOCK S101 FLASH2 

 IN-UNITS ENG 

 PARAM TEMP=100. PRES=175. 

 BLOCK-OPTION FREE-WATER=YES 

BLOCK ST-101 RADFRAC 

 IN-UNITS ENG 

 PARAM NSTAGE=8 

 COL-CONFIG CONDENSER=TOTAL REBOILER=KETTLE 

 FEEDS 110 5 

 PRODUCTS 111 1 L / 112 8 L 

 PRODUCTS QREB 8 / QCOND 1 

 P-SPEC 1 100. 

 COL-SPECS DP-STAGE=1. MASS-D=66.2 MOLE-RR=0.1 

BLOCK CB-101 RSTOIC 

 PARAM TEMP=1700. PRES=0. COMBUSTION=YES PROD-NOX=NO2 

 STOIC 1 MIXED H2 -1. / O2 -0.5 / H2O 1. 

 STOIC 2 MIXED PROPANE -1. / O2 -5. / CO2 3. / H2O 4. 

 STOIC 3 MIXED NAPTHA -1. / O2 -8. / CO2 5. / H2O 6. 

 CONV 1 MIXED H2 1. 

 CONV 2 MIXED PROPANE 1. 

 CONV 3 MIXED NAPTHA 1. 

BLOCK RX101 RYIELD 

 IN-UNITS ENG 

 PARAM TEMP=325. <C> PRES=500. 

 MASS-YIELD MIXED GDSL 0.8415 / H2O 0.02125 / CO2 & 

 0.10625 / PROPANE 0.029 / H2 0.001 / NAPTHA 0.01 / & 

 WAX 0.104 

BLOCK P101 PUMP 

 IN-UNITS ENG 

 PARAM PRES=500. 

BLOCK P102 PUMP 

 IN-UNITS ENG 

 PARAM DELP=10. 
63 

BLOCK P103 PUMP 

 IN-UNITS ENG 

 PARAM PRES=500. PUMP-TYPE=TURBINE 

BLOCK P104 PUMP 

 IN-UNITS ENG 

 PARAM DELP=10. 

BLOCK P105 PUMP 

 IN-UNITS ENG 

 PARAM DELP=10. 

BLOCK P107 PUMP 

 PARAM DELP=10. 

BLOCK P108 PUMP 

 PARAM DELP=10. 

BLOCK CP101 COMPR 

 IN-UNITS ENG 

 PARAM TYPE=ISENTROPIC PRES=500. MODEL-TYPE=TURBINE 

BLOCK CP102 COMPR 

 IN-UNITS ENG 

 PARAM TYPE=ISENTROPIC PRES=500. MODEL-TYPE=TURBINE 

DESIGN-SPEC COMBAIR 

 DEFINE O2OUT MASS-FLOW STREAM=122 SUBSTREAM=MIXED & 

 COMPONENT=O2 

 DEFINE O2IN MASS-FLOW STREAM=123 SUBSTREAM=MIXED & 

 COMPONENT=O2 

 SPEC “O2IN” TO “11*O2OUT” 

 TOL-SPEC “1” 

 VARY STREAM-VAR STREAM=123 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 LIMITS “50” “150” 

DESIGN-SPEC DS-FGAS 

 DEFINE SPLT BLOCK-VAR BLOCK=SP101 SENTENCE=FRAC & 

 VARIABLE=FRAC ID1=FUELGAS 
64 

 DEFINE FGAS STREAM-VAR STREAM=FUELGAS SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 SPEC “FGAS” TO “16.74” 

 TOL-SPEC “0.05” 

 VARY BLOCK-VAR BLOCK=SP101 SENTENCE=FRAC VARIABLE=FRAC & 

 ID1=FUELGAS 

 LIMITS “0.05” “0.95” 

DESIGN-SPEC DS-HX101 

 IN-UNITS ENG 

 DEFINE QXS INFO-VAR INFO=HEAT VARIABLE=DUTY & 

 STREAM=QHX101XS 

 SPEC “QXS” TO “0.0” 

 TOL-SPEC “0.1” 

 VARY BLOCK-VAR BLOCK=HX101+ VARIABLE=TEMP SENTENCE=PARAM 

 LIMITS “100” “617” 

DESIGN-SPEC DS-HX103 

 IN-UNITS ENG 

 DEFINE CWIN STREAM-VAR STREAM=CWS1 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE QXS INFO-VAR INFO=HEAT VARIABLE=DUTY & 

 STREAM=QHX103XS 

 SPEC “QXS” TO “0” 

 TOL-SPEC “0.1” 

 VARY STREAM-VAR STREAM=CWS1 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 LIMITS “100” “10000” 

DESIGN-SPEC DS-QCOND 

 DEFINE QXS INFO-VAR INFO=HEAT VARIABLE=DUTY STREAM=QCONDXS 

 DEFINE CWIN STREAM-VAR STREAM=CWS2 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 SPEC “QXS” TO “0” 

 TOL-SPEC “0.1” 

 VARY STREAM-VAR STREAM=CWS2 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 LIMITS “5” “5000” 

EO-CONV-OPTI 
65 

CALCULATOR H2IN 

 IN-UNITS ENG 

 DEFINE H2IN STREAM-VAR STREAM=120 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE OILIN STREAM-VAR STREAM=101 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

F H2IN = 0.0198*OILIN 

 READ-VARS OILIN 

CALCULATOR HYDCRK 

 IN-UNITS ENG 

 DEFINE FEED STREAM-VAR STREAM=101 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE GDYLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=GDSL 

 DEFINE PROYLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=PROPANE 

 DEFINE CO2YLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=CO2 

 DEFINE H2OYLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=H2O 

 DEFINE FD105 STREAM-VAR STREAM=104 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE FD119 STREAM-VAR STREAM=119 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE H2IN STREAM-VAR STREAM=120 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE H2YLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=H2 

 DEFINE NPYLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=NAPTHA 

 DEFINE FGAS STREAM-VAR STREAM=FUELGAS SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE WXYLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=WAX 

F TTLFD = FD105+FD119 

F GDYLD = 0.649*(FEED+H2IN)/TTLFD 

F PROYLD = 0.082*(FEED+H2IN)/TTLFD 

F CO2YLD = 0.082*(FEED+H2IN)/TTLFD 

F H2OYLD = 0.050*(FEED+H2IN)/TTLFD 
66 

F NPYLD = 0.0035*(FEED+H2IN)/TTLFD 

F WXYLD = 0.104*(FEED+H2IN)/TTLFD 

F SUM = GDYLD+PROYLD+CO2YLD+H2OYLD+NPYLD+WXYLD 

F DIFF = TTLFD - (SUM*TTLFD) 

F H2YLD = DIFF/TTLFD 

F WRITE(NHSTRY,*)SUM,DIFF,H2YLD 

 READ-VARS FEED FD105 FD119 H2IN FGAS 

 WRITE-VARS GDYLD PROYLD CO2YLD H2OYLD H2YLD NPYLD WXYLD 

 BLOCK-OPTION SIM-LEVEL=4 

STREAM-REPOR NOMOLEFLOW MASSFLOW 

PROPERTY-REP NOPARAM-PLUS 

; 
67 

Appendix 2: ASPEN Simulation Process of Renewable Diesel II 

(Hydrogenation-Derived Renewable Diesel) 

Victoria Putsche 

Center for Transportation Technologies and Systems 

National Renewable Energy Laboratory2

A2-1 Introduction 

A preliminary analysis was conducted for a hydrogenation-derived renewable diesel (HDRD) 

facility on the basis of the UOP process (UOP 2006). Material and energy balances were 

developed by using ASPEN Plus® 12.1 (uop_hdrd.inp). The overall goal of the study was to 

confirm the preliminary overall material and energy balances provided by UOP (UOP 2006; 

Markel 2006) and to provide input for a life-cycle analysis (LCA). The following report 

summarizes the basis for the analysis and its results. 

A2-2 Design Basis and Process Description 

HDRD is made from reacting hydrogen with oil or grease in a refinery-hydrotreating process. 

Two primary reactions occur in the conversion: hydrodeoxygenation and decarboxylation (UOP 

2006) 

Hydrodeoxygenation: 

CnCOOH (bio-oil) + 3 H2 → Cn+1 (HDRD) + 2 H2O 

Decarboxylation: 

CnCOOH (bio-oil) → Cn (HDRD) + CO2

The selectivity of the reactions depends on the processing conditions. 

For this analysis, the production of HDRD is based on the UOP process, which is composed of 

hydrogen production, hydrogenation, separation, distillation, and pressure swing adsorption 

(PSA). All of the unit operations were modeled except hydrogen production. It is assumed that 

 

2

 Contact person for further information: Paul Bergeron (Paul_Bergeron@nrel.gov) of National Renewable Energy 

Laboratory. 
68 

hydrogen is supplied by a hydrogen plant. Figure A2-1 is a block flow diagram of the HDRD 

process. 

Hydrotreater 

300-350  ̊C 

500 psia 

1 lb oil 

0.27 lbs. Hydrogen 

0.0.34 kWh electricity 

0.054 lb MP steam 

0.027 lb LP steam 

0.081 lb BFW

0.84 lbs Green Diesel 

0.48 lbs fuel gas 

0.061 lbs WW 

Yield – 70-80% 

Figure A2-1 HDRD Block Flow Diagram 

One of the important characteristics of the process is that energy demands are met off site. That 

is, the fuel gas product is not combusted on site to generate steam for the process; it is assumed 

that steam is sent to the process from an off-site source. Similarly, the process also generates a 

fuel gas, which is also sent off site and used for fuel. For this process configuration, the LCA 

will determine the emissions from the fuel gas combustion as well as the steam and electricity 

generation and will apportion it appropriately to the main process. 

The renewable diesel process was modeled by using numerous assumptions and data sources. 

Table A2-1 summarizes the key design parameters and their sources. 

Several of these assumptions, particularly the feedstock choice and facility size, require further 

explanation. The feedstock selected was soybean oil, even though many of the feedstocks in the 

literature were rapeseed oil or other oils, because it is the most prevalent oil in fuels production 

(i.e., biodiesel) and because one of the purposes of the study was to compare the environmental 

impacts of HDRD to biodiesel, and the most thorough LCA of biodiesel (Sheehan et al. 1998) 

was based on soybean oil. The facility size of 100 lb/h was selected as an easy, round number for 

the LCA. The results of most LCAs are shown on a pound of feed or product basis, since the 

impacts are directly scalable to throughput. Therefore, this simple number was selected, even 

though this would not be a typical facility size. 
69 

Table A2-1 Design Basis 

Parameter Value Source 

 

Feedstock 

 Type 

 Throughput 

Soybean oil 

100 lb/h 

Most common oil in US for biodiesel 

For LCA analysis 

 

Feedstock fatty acid composition (wt fraction) 

 Linolenic acid 

 Palmitic acid 

 Stearic acid 

 Oleic acid 

 Linoleic acid 

 Arachidic acid 

0.075 

0.11 

0.041 

0.22 

0.54 

0.014 

 

Hydrogenation design 

 Temperature 

 Pressure 

325°C 

500 psia 

UOP 2006 

UOP 2006 

 

Yields (per pound inlet feed) 

 HDRD 

 Water 

 CO2

 Propane 

 Hydrogen 

84.15% 

2.125% 

10.625% 

2.9% 

0.1% 

UOP 2006 

A2-3 Model Description 

An ASPEN Plus® model (uop_hdrd) was developed for the pyrolysis process, largely on the 

basis of the UOP report (UOP 2006). ASPEN Plus® is a steady-state process simulator. 

Appendix A2-6 contains the input file for the model. 

The ASPEN Plus® HDRD model has one flowsheet to model the four major process areas: 

hydrogenation, sour water separation, stripping, and pressure swing adsorption (PSA)/gas 

recycle. Each of these areas is briefly discussed, and the flow diagram from ASPEN Plus® is 

presented. The flow diagram shows only those unit operations modeled in ASPEN Plus®. 

Equipment used for operations such as conveyance, size reduction, and storage us generally not 

included in the model. The power requirements of this equipment, however, are included and are 

modeled as work streams. 

ASPEN Plus® is composed of physical property and unit operation models that are combined 

into a process model. The simulation can be broken into three major sections: components 

(i.e., chemical species), physical property option sets (e.g., what set of physical property models 
70 

to use), and the flowsheet (i.e., the series of unit operations). Each of these sections is described 

in more detail below. 

Components 

Fourteen components were modeled in the simulation; all were modeled as conventional 

(e.g., water) components in the mixed substream. The following is a list of the components in the 

simulation: 

• Hydrogen – H2

• Linolenic acid – C18H30O2

• Palmitic acid – C16H32O2

• Stearic acid – C18H36O2

• Linoleic acid – C18H32O2

• Arachidic acid – C20H40O2

• Oleic acid – C18H34O2

• Green Diesel – C18H38

• Water – H2O 

• Hydrogen Sulfide – H2S 

• Ammonia – NH3

• Propane – C3H8

• Naptha 

• Carbon dioxide – CO2

Green diesel is not a specific compound but is a complex mixture of hydrocarbons; however, for 

simplicity, it was modeled as a single component, C18H38, which is within the range of diesel 

hydrocarbons. Green diesel was specified with a specific gravity of 0.78 (Marker, T. 2007) and a 

MW of 254. Naphtha was specified with a specific gravity of 0.7 and a MW of 100. 

As noted earlier, the vegetable oil feed was modeled as a mixture of six fatty acids: linolenic acid, 

palmitic acid, stearic acid, linoleic acid, arachidic acid, and oleic acid. All of these components 

are available in the ASPEN Plus® databanks. Table A2-2 shows the molecular formula, the 

component name in the model, and the weight fraction in the feed of each fatty acid. 

One Henry component, CO2, was specified. The Henry’s constants were obtained from ASPEN 

Plus®. 
71 

Table A2-2 Organic Acid Composition of Bio-Oil 

Organic 

Fatty 

Acid Composition 

Component 

Name 

Weight 

Fraction 

 

Linolenic C18H30O2 LINOL3 0.075 

Palmitic C16H32O2 PALM 0.11 

Stearic C18H36O2 STEARIC 0.041 

Oleic C18H34O2 OLEIC 0.22 

Linoleic C18H32O2 LINOL2 0.54 

Arachidic C20H40O2 ARACHID 0.014 

Physical Property Option Sets 

The physical property set selected was POLYUF with properties estimated by using the 

POLYNRTL method. Physical property databanks used in the simulation were PURE13, 

AQUEOUS, SOLIDS AND INORGANIC. 

Flowsheet 

One flowsheet was developed for the process: (A1000). The flowsheet is briefly discussed, and 

flow diagrams from ASPEN Plus® are presented. The flow diagrams (Figure A2-2) show only 

those unit operations modeled in ASPEN Plus®. Equipment used for operations such as 

conveyance and storage are generally not included in the model and are thus not shown. 

Similarly, certain complex unit operations (e.g., gas turbine) require several ASPEN Plus®

models (e.g., compressors, reactors, heat exchangers). 

Bio-oil is introduced into the process in stream 101. It is assumed to be at ambient conditions 

(i.e., 68°F and 14.7 psia) with a flow rate of a nominal 100 lb/h. The 100-lb/h value was selected 

as it would be easily scaled to any other value; since the model was developed to be the basis for 

an LCA, any flow rate would be reasonable. 

Hydrogenation 

As shown in Figure A2-2, the soybean oil feed (Stream 101) is pumped to 500 psia (P-101) and 

then mixed with recycle oil (Stream 110C) from the splitter, SP-101, following the sour water 

separator (S101). This stream is then heated to 290°F by exchange with hydrogenator effluent 

(Stream 106) in HX101+ and HX101−. It is then further heated to 370°F with medium-pressure 

steam, MPSS (150 psig). The next stage of the process is the hydrogenator, where the oil stream 

is combined with the inlet hydrogen (Stream 119) and reacted. 
72 

101

RX101

120

106

S101

115

110

P101

P102

CP102

CP101

HX101+

HX101-

HX102- MX101

MX102

102

103 104 105

PSA

117

118 121

119

CO2

112

111

114

ST-101

113

107

109

QHX101

QHX101XS

Q

HX103+

108

HX103-

CWS CWR

QHX103

QHX103XS Q

HX102+

MPSS

MPSR

QHX102

QHX102XS

Q

WP102 W

WP101 W

SP-101

110A

110B

WCP-101 W

WCP-102

W

QRX101 Q

P105

P106

P103

P104

WP-103 W

WP-104

W

WP-106 W

WP-105

W

CWR1

CWS1

110C

WWT

125

PROPANE

123 FL-101

122

124

Figure A2-2 ASPEN Simulation Process Flowcharts FOR Renewable Diesel II 

The hydrogenator (RX101) is modeled as an RYIELD reactor. All of the incoming oil is 

converted to gas (e.g., CO2, H2, propane), water, and green diesel (GDSL). As noted in the 

design basis, the yield of green diesel is estimated at 84.15% of the inlet feed streams on a mass 

basis. The hydrogenation reactions are exothermic, and there is excess heat (QRX101) after the 

reactor is brought to reaction temperature (325°C). 

After the oil feed is preheated the hydrogenator effluent (Stream 107) is cooled with cooling 

water (Stream CWS1) to 100°F in HX103. The cooled reactor products are then sent to the sour 

water separation, S101. 

Sour Water Separation 

In sour water separation, the gases (Stream 115) are flashed off and sent to the PSA for recovery, 

and the aqueous stream is decanted and sent to wastewater treatment (Stream 109). After the 
73 

separator, a portion of the organic stream (110B) is recycled to the hydrogenator inlet. The 

remainder (Stream 110A) is sent to a stripping column (ST-101) for product recovery. 

Product Recovery 

The product recovery area consists of a stripping column where LP (50 psig) steam (Stream 114) 

is used to remove the light ends from the green diesel product (112). The stripping column is 

modeled as a RADFRAC column with eight stages without a condenser or reboiler under 

atmospheric pressure. 

The overheads are sent to the flash unit of the PSA system, FL-101. The product stream is taken 

from the bottom of the column (Stream 112). 

Pressure Swing Adsorption (PSA) 

The PSA system is a complex batch unit operation that was treated basically as a black box for 

this simulation. It is modeled as two unit operations in series, a separator block (PSA) followed 

by a flash block (FL-101). The separator block is assumed to remove all of the hydrogen in the 

overhead stream (111). The recovered hydrogen is then compressed (CP102) to 500 psia before 

introduction into the hydrogenator. 

In addition to hydrogen, the PSA unit operation has two other outlet streams: CO2 and 

Stream 125. The CO2 stream contains all of the carbon dioxide from the operation and is released 

to the atmosphere. Stream 125 contains a mixture of water, propane, and other organics. These 

are separated in FL-101 modeled as a FLASH2. As shown in the diagram, FL-101 has two inlets 

(Streams 111 and 125) and three outlets: PROPANE and Streams 122 and 123. Stream 111 is the 

overheads from the stripping column, ST-101. PROPANE is a fuel gas, composed primarily of 

propane (93%) with small amounts of green diesel and CO2. 

A2-4 Results and Discussion 

This effort was aimed at confirming the material and energy balances summarized for the UOP 

HDRD process as found in UOP (2006) and Markel (2006). As shown in Table A2-3, the 

ASPEN Plus® model shows good agreement with the published literature. All of the yields and 

utility requirements are similar between the model and the literature except cooling water. The 

uop_hdrd.bkp model predicts a much higher cooling water load than projected by UOP. This 

discrepancy can be due to many factors, including improved equipment design and heat 

integration in the UOP process and differing cooling water specifications (e.g., allowable 

temperature rise). The discrepancy was not explored further since cooling water is a very small 
74 

Table A2-3 Comparison of Overall Mass and Energy Balances 

Feedstock 

UOP Yield per 

100 lb of feed 

Current Analysis Yield per 

100 lb of feed 

 

Oil 100.00 100.00 

H2 2.72 2.72 

LP steam 2.72 2.80 

 

Products 

 Propane mix gas 4.75 5.02 

 HDRD 84.19 85.23 

 CO2 7.01 

 Waste water 6.11 8.27 

 

Utilities 

 Electricity (kWh) 3.39 2.34 

 LP Steam (into process) 2.72 2.80 

 MP steam 5.43 5.37 

 Cooling water 1,356 2,310 

 Boiler feed water 8.15 8.17 

 Total steam (Btu) 7,161 

contributor to the impacts in an LCA. Table 3 compares the results of this modeling effort and 

the values from the UOP report (2006). Carbon dioxide was not reported in the UOP study. 

The propane mix gas is composed of 93.3% propane, 5.7% CO2, and 1% water. The lower 

heating value (LHV) of the mix is estimated at 18,568 Btu/lb. The entire mass balance for the 

simulation is contained in Appendix A2-6. 

In addition to the material and energy balance, the analysis projected the air emissions from the 

process. As noted earlier, it is assumed that the fuel gas, which is primarily propane, is 

combusted with make-up natural gas in order to meet the energy demand of the process. Thus, it 

was assumed that there were minimal air emissions from the main process. The LCA analysis 

will provide the emissions from the combustion of the fuel gas and any other fuel needed to 

generate the necessary steam and electricity. This assessment is outside the process lines for this 

process configuration. 

The UOP HDRD process uses hydrogenation to convert bio-oils like soybean oil into a diesel 

substitute. Several companies are looking into this process. This analysis developed an ASPEN 

Plus® model of the process and compared its results with published results by UOP and NREL 

(UOP 2006). Good agreement was obtained between the two studies. These results will be used 

to develop an LCA for this process. 
75 

A2-5 References 

Marker, T., 2006, Email to V. Putsche with cc to C. Johnson of NREL, “Follow-up on Green 

Diesel and Green Gasoline LCA Requests,” Aug. 15. 

Sheehan, J., V. Camobreco, J. Duffield, M. Graboski, H. Shapouri, 1998, An Overview of 

Biodiesel and Petroleum Diesel Life Cycles, joint study sponsored by U.S. Department of 

Agriculture and U.S. Department of Energy, NREL/TP-580-24772. 

UOP, 2006, Opportunities for Biorenewables in Oil Refineries, Final Technical Report, DOE 

Award Number DE-FG36-05GO15085, contributors were Terry Marker, John Petri, Tom Kalnes, 

Micke McCall, Dave Mackowiak, Bob Jerosky, Bill Reagan, Lazlo Nemeth, Mark Krawczyk 

(UOP); Stefan Czernik (NREL); Doug Elliott (PNNL); David Shonnard (Michigan 

Technological University). 

A2-6 ASPEN Plus® Input File: UOP_HDRD.inp 

; 

;Input Summary created by Aspen Plus Rel. 13.1 at 18:15:55 Fri Sep 22, 2006 

;Directory C:\AspenTech\Aspen Plus 2004 Filename C:\AspenTech\Aspen Plus 2004\uop_hdrd.inp 

; 

TITLE ‘HDRD - UOP’ 

IN-UNITS ENG DENSITY=‘lb/gal’ POWER=kW VOLUME=gal & 

 MOLE-DENSITY=‘lbmol/gal’ MASS-DENSITY=‘lb/gal’ 

DEF-STREAMS CONVEN ALL 

DATABANKS PURE13 / AQUEOUS / SOLIDS / INORGANIC / & 

 NOASPENPCD 

PROP-SOURCES PURE13 / AQUEOUS / SOLIDS / INORGANIC 

COMPONENTS 

 H2 H2 / 

 LINOL3 C18H30O2 / 
76 

 PALM C16H32O2 / 

 STEARIC C18H36O2 / 

 OLEIC C18H34O2 / 

 LINOL2 C18H32O2 / 

 ARACHID C20H40O2 / 

 GDSL C18H38 / 

 H2O H2O / 

 H2S H2S / 

 NH3 H3N / 

 PROPANE C3H8 / 

 NAPTHA / 

 CO2 CO2 

PC-USER 

 IN-UNITS ENG 

 PC-DEF ASPEN GDSL GRAV=0.78 MW=254. 

 PC-DEF ASPEN NAPTHA GRAV=0.7 MW=100. 

ADA-SETUP 

 ADA-SETUP PROCEDURE=REL9 

HENRY-COMPS HC-1 CO2 

FLOWSHEET 

 BLOCK RX101 IN=105 119 OUT=106 QRX101 

 BLOCK S101 IN=108 OUT=115 110 109 

 BLOCK P101 IN=101 OUT=102 WP101 

 BLOCK P102 IN=112 OUT=113 19 WP102 

 BLOCK CP102 IN=117 OUT=118 WCP-102 

 BLOCK CP101 IN=120 OUT=121 WCP-101 

 BLOCK HX101+ IN=106 20 OUT=107 QHX101 

 BLOCK HX101- IN=103 QHX101 OUT=104 QHX101XS 

 BLOCK HX102- IN=104 QHX102 OUT=105 QHX102XS 

 BLOCK MX101 IN=102 OUT=103 

 BLOCK MX102 IN=118 121 OUT=119 

 BLOCK PSA IN=115 OUT=117 CO2 125 

 BLOCK ST-101 IN=114 110A OUT=111 112 

 BLOCK HX103+ IN=107 OUT=108 QHX103 

 BLOCK HX103- IN=CWS QHX103 OUT=CWR QHX103XS 

 BLOCK HX102+ IN=MPSS OUT=MPSR QHX102 
77 

 BLOCK SP-101 IN=110 OUT=110A 110B 

 BLOCK P105 IN=109 18 OUT=WWT WP-105 

 BLOCK P106 IN=110B OUT=110C WP-106 

 BLOCK P103 IN=CWS1 OUT=CWS WP-103 

 BLOCK P104 IN=CWR OUT=CWR1 WP-104 

 BLOCK FL-101 IN=125 111 OUT=PROPANE 17 16 

 BLOCK B8 IN=19 OUT=18 

 BLOCK B9 IN=QRX101 OUT=20 21 

PROPERTIES POLYUF HENRY-COMPS=HC-1 

 PROPERTIES POLYNRTL 

PROP-DATA HENRY-1 

 IN-UNITS ENG 

 PROP-LIST HENRY 

 BPVAL CO2 H2O 175.2762325 -15734.78987 -21.66900000 & 

 6.12550005E-4 31.73000375 175.7300026 0.0 

STREAM 101 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=68. PRES=14.7 MASS-FLOW=100. 

 MASS-FRAC LINOL3 0.075 / PALM 0.11 / STEARIC 0.041 / & 

 OLEIC 0.22 / LINOL2 0.54 / ARACHID 0.014 

STREAM 114 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=400. PRES=50. MASS-FLOW=2.8 

 MASS-FRAC H2O 1. 

STREAM 117 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=68. PRES=14.7 MASS-FLOW=1. 

 MASS-FRAC H2 1. 

STREAM 120 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=68. PRES=14.7 MASS-FLOW=100. 

 MASS-FRAC H2 1. 
78 

STREAM CWS 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=35. <C> PRES=500. MASS-FLOW=100. 

 MASS-FRAC H2O 1. 

STREAM CWS1 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=35. <C> PRES=14.7 MASS-FLOW=100. 

 MASS-FRAC H2O 1. 

STREAM MPSS 

 IN-UNITS ENG 

 SUBSTREAM MIXED TEMP=667. PRES=150. MASS-FLOW=100. 

 MASS-FRAC H2O 1. 

DEF-STREAMS HEAT 20 

DEF-STREAMS HEAT 21 

DEF-STREAMS HEAT QHX101 

DEF-STREAMS HEAT QHX101XS 

DEF-STREAMS HEAT QHX102 

DEF-STREAMS HEAT QHX102XS 

DEF-STREAMS HEAT QHX103 

DEF-STREAMS HEAT QHX103XS 

DEF-STREAMS HEAT QRX101 

DEF-STREAMS WORK WCP-101 

DEF-STREAMS WORK WCP-102 

DEF-STREAMS WORK WP-103 

DEF-STREAMS WORK WP-104 
79 

DEF-STREAMS WORK WP-105 

DEF-STREAMS WORK WP-106 

DEF-STREAMS WORK WP101 

DEF-STREAMS WORK WP102 

BLOCK B8 MIXER 

BLOCK MX101 MIXER 

 IN-UNITS ENG 

BLOCK MX102 MIXER 

 IN-UNITS ENG 

BLOCK B9 FSPLIT 

 FRAC 20 0.15 

BLOCK SP-101 FSPLIT 

 IN-UNITS ENG 

 FRAC 110A 0.99 

BLOCK PSA SEP 

 IN-UNITS ENG 

 PARAM 

 FRAC STREAM=117 SUBSTREAM=MIXED COMPS=H2 H2O PROPANE CO2 & 

 FRACS=1. 0. 0. 0. 

 FRAC STREAM=CO2 SUBSTREAM=MIXED COMPS=PROPANE CO2 FRACS= & 

 0. 1. 

BLOCK HX101+ HEATER 

 IN-UNITS ENG 

 PARAM TEMP=100. <C> PRES=500. 

BLOCK HX101- HEATER 

 IN-UNITS ENG 

 PARAM TEMP=290. <C> PRES=500. 
80 

BLOCK HX102+ HEATER 

 IN-UNITS ENG 

 PARAM PRES=500. VFRAC=0. 

BLOCK HX102- HEATER 

 IN-UNITS ENG 

 PARAM TEMP=325. <C> PRES=500. 

BLOCK HX103+ HEATER 

 IN-UNITS ENG 

 PARAM TEMP=100. PRES=500. 

BLOCK HX103- HEATER 

 IN-UNITS ENG 

 PARAM PRES=500. DELT=15. 

BLOCK FL-101 FLASH2 

 PARAM TEMP=68. PRES=14.7 

 BLOCK-OPTION FREE-WATER=YES 

BLOCK S101 FLASH2 

 IN-UNITS ENG 

 PARAM TEMP=100. PRES=175. 

 BLOCK-OPTION FREE-WATER=YES 

BLOCK ST-101 RADFRAC 

 IN-UNITS ENG 

 PARAM NSTAGE=8 

 COL-CONFIG CONDENSER=NONE REBOILER=NONE 

 FEEDS 114 9 / 110A 1 

 PRODUCTS 111 1 V / 112 8 L 

 P-SPEC 1 14.7 

 COL-SPECS DP-STAGE=1. 

BLOCK RX101 RYIELD 

 IN-UNITS ENG 

 PARAM TEMP=325. <C> PRES=500. 

 MASS-YIELD MIXED GDSL 0.8415 / H2O 0.02125 / CO2 & 

 0.10625 / PROPANE 0.029 / H2 0.001 
81 

BLOCK P101 PUMP 

 IN-UNITS ENG 

 PARAM PRES=500. 

BLOCK P102 PUMP 

 IN-UNITS ENG 

 PARAM DELP=10. 

 BLOCK-OPTION FREE-WATER=YES 

BLOCK P103 PUMP 

 IN-UNITS ENG 

 PARAM PRES=500. PUMP-TYPE=TURBINE 

BLOCK P104 PUMP 

 IN-UNITS ENG 

 PARAM DELP=10. 

BLOCK P105 PUMP 

 IN-UNITS ENG 

 PARAM DELP=10. 

BLOCK P106 PUMP 

 IN-UNITS ENG 

 PARAM DELP=10. PUMP-TYPE=PUMP 

BLOCK CP101 COMPR 

 IN-UNITS ENG 

 PARAM TYPE=ISENTROPIC PRES=500. MODEL-TYPE=TURBINE 

BLOCK CP102 COMPR 

 IN-UNITS ENG 

 PARAM TYPE=ISENTROPIC PRES=500. MODEL-TYPE=TURBINE 

DESIGN-SPEC DS-HX101 

 IN-UNITS ENG 

 DEFINE QXS INFO-VAR INFO=HEAT VARIABLE=DUTY & 

 STREAM=QHX101XS 

 SPEC “QXS” TO “0.0” 

 TOL-SPEC “0.1” 

 VARY BLOCK-VAR BLOCK=HX101+ VARIABLE=TEMP SENTENCE=PARAM 
82 

 LIMITS “100” “617” 

DESIGN-SPEC DS-HX102 

 IN-UNITS ENG 

 DEFINE STMIN STREAM-VAR STREAM=MPSS SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE QXS INFO-VAR INFO=HEAT VARIABLE=DUTY & 

 STREAM=QHX102XS 

 SPEC “QXS” TO “0” 

 TOL-SPEC “0.1” 

 VARY STREAM-VAR STREAM=MPSS SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 LIMITS “0” “10000” 

DESIGN-SPEC DS-HX103 

 IN-UNITS ENG 

 DEFINE CWIN STREAM-VAR STREAM=CWS1 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE QXS INFO-VAR INFO=HEAT VARIABLE=DUTY & 

 STREAM=QHX103XS 

 SPEC “QXS” TO “0” 

 TOL-SPEC “0.1” 

 VARY STREAM-VAR STREAM=CWS1 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 LIMITS “100” “10000” 

EO-CONV-OPTI 

CALCULATOR H2IN 

 IN-UNITS ENG 

 DEFINE H2IN STREAM-VAR STREAM=120 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE OILIN STREAM-VAR STREAM=101 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

F H2IN = 0.0272*OILIN 

 READ-VARS OILIN 

CALCULATOR HYDCRK 

 IN-UNITS ENG 

 DEFINE FEED STREAM-VAR STREAM=101 SUBSTREAM=MIXED & 
83 

 VARIABLE=MASS-FLOW 

 DEFINE GDYLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=GDSL 

 DEFINE PROYLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=PROPANE 

 DEFINE CO2YLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=CO2 

 DEFINE H2OYLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=H2O 

 DEFINE FD105 STREAM-VAR STREAM=105 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE FD119 STREAM-VAR STREAM=119 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE H2IN STREAM-VAR STREAM=120 SUBSTREAM=MIXED & 

 VARIABLE=MASS-FLOW 

 DEFINE H2YLD BLOCK-VAR BLOCK=RX101 VARIABLE=YIELD & 

 SENTENCE=MASS-YIELD ID1=MIXED ID2=H2 

F TTLFD = FD105+FD119 

F GDYLD = 0.828*(FEED+H2IN)/TTLFD 

F PROYLD = 0.047*(FEED+H2IN)/TTLFD 

F CO2YLD = 0.075*(FEED+H2IN)/TTLFD 

F H2OYLD = 0.050*(FEED+H2IN)/TTLFD 

F SUM = GDYLD+PROYLD+CO2YLD+H2OYLD 

F DIFF = TTLFD - (SUM*TTLFD) 

F H2YLD = DIFF/TTLFD 

F WRITE(NHSTRY,*)SUM,DIFF,H2YLD 

 READ-VARS FEED FD105 FD119 H2IN 

 WRITE-VARS GDYLD PROYLD CO2YLD H2OYLD H2YLD 

 BLOCK-OPTION SIM-LEVEL=4 

TEAR 

 TEAR 117 

STREAM-REPOR NOMOLEFLOW MASSFLOW 
84 

PROPERTY-REP NOPARAM-PLUS 

 101 102 103 104 105 106 107 108  P101 MX101 HX101- HX102- RX101 4 ; S101 

 P101 MX101 HX101- HX102- RX101 4 4 

 LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED 

Substream: MIXED 

Mass Flow lb/hr 

 H2 0 0 0 0 0 1.084999 1.084999 1.084999 

 LINOL3 7.5 7.5 7.5 7.5 7.5 0 0 0 

 PALM 11 11 11 11 11 0 0 0 

 STEARIC 4.1 4.1 4.1 4.1 4.1 0 0 0 

 OLEIC 22 22 22 22 22 0 0 0 

 LINOL2 54 54 54 54 54 0 0 0 

 ARACHID 1.4 1.4 1.4 1.4 1.4 0 0 0 

 GDSL 0 0 0 0 0 85.05216 85.05216 85.05216 

 H2O 0 0 0 0 0 5.54688 5.54688 5.54688 

 H2S 0 0 0 0 0 0 0 0 

 NH3 0 0 0 0 0 0 0 0 

 PROPANE 0 0 0 0 0 4.82784 4.82784 4.82784 

 NAPTHA 0 0 0 0 0 0 0 0 

 CO2 0 0 0 0 0 7.29312 7.29312 7.29312 

Total Flow lbmol/hr 0.3591571 0.3591571 0.3591571 0.3591571 0.3591571 1.456175 1.456175 1.456175 Total Flow lb/hr 100 100 100 100 100 103.805 103.805 103.805 Total Flow cuft/hr 1.786671 1.795957 1.795957 1.975812 2.061152 29.28627 25.1053 10.49892 Temperature F 68 80.58947 80.58948 290 370 617 507.6096 100 Pressure psi 14.7 500 500 500 500 500 500 500 Vapor Frac 0 0 0 0 0 0.8408124 0.7704222 0.4824463 

Liquid Frac 1 1 1 1 1 0.1591876 0.2295778 0.5175537 
85 

PROPERTY-REP NOPARAM-PLUS (Cont.) 

 101 102 103 104 105 106 107 108  P101 MX101 HX101- HX102- RX101 4 ; S101  P101 MX101 HX101- HX102- RX101 4 4  LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED 

Solid Frac 0 0 0 0 0 0 0 0 

Enthalpy Btu/lbmol -3.14E+05 -3.12E+05 -3.12E+05 -2.83E+05 -2.71E+05 -70801.07 -77885.8 -1.00E+05 Enthalpy Btu/lb -1126.343 -1120.916 -1120.916 -1017.751 -971.6202 -993.1961 -1092.581 -1406.734 Enthalpy Btu/hr -1.13E+05 -1.12E+05 -1.12E+05 -1.02E+05 -97162.02 -1.03E+05 -1.13E+05 -1.46E+05 Entropy Btu/lbmol-R -415.036 -412.2374 -412.2374 -367.7857 -351.4557 -80.66001 -87.58823 -118.2502 Entropy Btu/lb-R -1.490631 -1.48058 -1.48058 -1.320929 -1.262278 -1.131497 -1.228686 -1.658812 Density lbmol/gal 0.0268725 0.0267335 0.0267335 0.0243 0.0232939 6.65E-03 7.75E-03 0.0185411 Density lb/gal 7.482102 7.443416 7.443416 6.765855 6.485721 0.4738298 0.5527402 1.321727 Average MW 278.4297 278.4297 278.4297 278.4297 278.4297 71.28609 71.28609 71.28609 Liq Vol 60F cuft/hr 1.803603 1.803603 1.803603 1.803603 1.803603 2.596039 2.596039 2.596039 

110A 110B 110C 111 112 113 114 115 

 ST-101 P106 MX101 FL-101 P102 ST-101 PSA 

SP-101 SP-101 P106 ST-101 ST-101 P102 S101 LIQUID MISSING MISSING VAPOR LIQUID LIQUID VAPOR VAPOR 

Substream: MIXED 

Mass Flow lb/hr 

 H2 2.04E-07 0 0 2.04E-07 4.05E-35 4.05E-35 0 1.084998  LINOL3 0 0 0 0 0 0 0 0 

 PALM 0 0 0 0 0 0 0 0 

 STEARIC 0 0 0 0 0 0 0 0 

 OLEIC 0 0 0 0 0 0 0 0 

 LINOL2 0 0 0 0 0 0 0 0 
86 

PROPERTY-REP NOPARAM-PLUS (Cont.) 

 101 102 103 104 105 106 107 108  P101 MX101 HX101- HX102- RX101 4 ; S101 

 P101 MX101 HX101- HX102- RX101 4 4 

 LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED 

 ARACHID 0 0 0 0 0 0 0 0 

 GDSL 85.052 0 0 1.60E-04 85.05184 85.05184 0 1.58E-04  H2O 0.0218078 0 0 0.4588025 2.363005 0.029885 2.8 0.0792568 

 H2S 0 0 0 0 0 0 0 0 

 NH3 0 0 0 0 0 0 0 0 

 PROPANE 2.468692 0 0 2.321827 0.1468655 0.1468655 0 2.359148 

 NAPTHA 0 0 0 0 0 0 0 0 

 CO2 0.2876933 0 0 0.2876933 4.45E-12 4.45E-12 0 7.005427 

Total Flow lbmol/hr 0.3985819 0 0 0.0846584 0.469347 0.3398392 0.1554236 0.7553041 

Total Flow lb/hr 87.8302 0 0 3.068483 87.56171 85.22859 2.8 10.52899 Total Flow cuft/hr 1.907065 0 0 34.10666 1.85258 1.832826 19.0754 25.87814 Temperature F 100.002 98.90795 143.0657 149.9646 297.7949 100 Pressure psi 175 500 14.7 21.7 31.7 64.7 175 Vapor Frac 0 1 0 0 1 1 

Liquid Frac 1 0 1 1 0 0 Solid Frac 0 0 0 0 0 0 

Enthalpy Btu/lbmol -1.99E+05 -72054.6 -1.90E+05 -2.16E+05 -1.02E+05 -39284.66 Enthalpy Btu/lb -903.7778 -1987.963 -1018.401 -861.4131 -5676.514 -2818.112 Enthalpy Btu/hr -79378.98 -6100.031 -89172.93 -73417.02 -15894.24 -29671.86 Entropy Btu/lbmol-R -382.6781 -40.91684 -319.5699 -425.8229 -10.82218 -7.527309 Entropy Btu/lb-R -1.73663 -1.128883 -1.712954 -1.69792 -0.6007222 -0.5399766 Density lbmol/gal 0.0279396 3.32E-04 0.0338676 0.0247868 1.09E-03 3.90E-03 Density lb/gal 6.156679 0.0120268 6.318376 6.216305 0.0196224 0.0543903 
87 

PROPERTY-REP NOPARAM-PLUS (Cont.) 

 101 102 103 104 105 106 107 108  P101 MX101 HX101- HX102- RX101 4 ; S101 

 P101 MX101 HX101- HX102- RX101 4 4 

 LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED 

Average MW 220.3567 36.24543 186.5607 250.7909 18.01528 13.94006 Liq Vol 60F cuft/hr 1.834368 0 0 0.0864746 1.792832 1.755387 0.0449381 0.6742691 

119 120 121 122 123 124 125 CO2 RX101 CP101 MX102 P105 FL-101 MX102 CP101 FL-101 FL-101 P102 PSA PSA VAPOR VAPOR VAPOR LIQUID LIQUID LIQUID MIXED VAPOR 

Substream: MIXED 

Mass Flow lb/hr 

 H2 3.804999 2.72 2.72 0 0 0 0 0 

 LINOL3 0 0 0 0 0 0 0 0 

 PALM 0 0 0 0 0 0 0 0 

 STEARIC 0 0 0 0 0 0 0 0 

 OLEIC 0 0 0 0 0 0 0 0 

 LINOL2 0 0 0 0 0 0 0 0 

 ARACHID 0 0 0 0 0 0 0 0 

 GDSL 0 0 0 2.84E-04 0 0 1.58E-04 0 

 H2O 0 0 0 4.63E-08 0.4894927 2.33312 0.0792568 0  H2S 0 0 0 0 0 0 0 0 

 NH3 0 0 0 0 0 0 0 0 

 PROPANE 0 0 0 1.48E-05 0 0 2.359148 0 

 NAPTHA 0 0 0 0 0 0 0 0 
88 

PROPERTY-REP NOPARAM-PLUS (Cont.) 

 101 102 103 104 105 106 107 108  P101 MX101 HX101- HX102- RX101 4 ; S101 

 P101 MX101 HX101- HX102- RX101 4 4 

 LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED 

 CO2 0 0 0 3.64E-08 0 0 0 7.005427 

Total Flow lbmol/hr 1.887512 1.349287 1.349287 1.46E-06 0.0271709 0.1295079 0.0578996 0.1591788 Total Flow lb/hr 3.804999 2.72 2.72 2.99E-04 0.4894927 2.33312 2.438563 7.005427 Total Flow cuft/hr 42.90768 520.0834 34.66854 6.53E-06 7.85E-03 0.0381251 1.507645 5.167461 Temperature F 586.7733 68 724.6138 68 68 149.9646 99.97435 99.97435 Pressure psi 500 14.7 500 14.7 14.7 31.7 175 175 Vapor Frac 1 1 1 0 0 0 0.9295689 1 Liquid Frac 0 0 0 1 1 1 0.070431 0 

Solid Frac 0 0 0 0 0 0 0 0 

Enthalpy Btu/lbmol 3577.495 -61.40031 4543.916 -1.88E+05 -1.23E+05 -1.22E+05 -50942.12 -1.69E+05 Enthalpy Btu/lb 1774.657 -30.45832 2254.061 -914.9968 -6829.944 -6748.178 -1209.537 -3843.429 Enthalpy Btu/hr 6752.566 -82.84663 6131.045 -0.2738398 -3343.207 -15744.31 -2949.533 -26924.86 Entropy Btu/lbmol-R -2.354373 

-0.1171418 -1.486812 -363.1769 -39.26957 -36.67663 -66.63749 -4.062922 

Entropy Btu/lb-R -1.167913 

-0.0581095 -0.7375498 -1.771269 -2.179792 -2.035862 -1.582198 -0.0923185 

Density lbmol/gal 5.88E-03 3.47E-04 5.20E-03 0.02986 0.4624672 0.4541016 5.13E-03 4.12E-03 Density lb/gal 0.0118546 6.99E-04 0.0104882 6.122437 8.331475 8.180767 0.2162236 0.1812281 Average MW 2.01588 2.01588 2.01588 205.0377 18.01528 18.01528 42.11703 44.0098 Liq Vol 60F cuft/hr 1.619323 1.157571 1.157571 6.32E-06 7.86E-03 0.037445 0.0759562 0.1365617 
89 

 CWS CWS1 MPSR MPSS PROPANE WWT 

 HX103- P103 4 

 P103 4 FL-101 P105 

 LIQUID LIQUID LIQUID VAPOR VAPOR LIQUID 

 

Substream: MIXED 

Mass Flow lb/hr 

 H2 0 0 0 0 2.04E-07 0 

 LINOL3 0 0 0 0 0 0 

 PALM 0 0 0 0 0 0 

 STEARIC 0 0 0 0 0 0 

 OLEIC 0 0 0 0 0 0 

 LINOL2 0 0 0 0 0 0 

 ARACHID 0 0 0 0 0 0 

 GDSL 0 0 0 0 3.35E-05 0 

 H2O 2309.68 2309.68 5.372242 5.372242 0.0485666 7.778936 

 H2S 0 0 0 0 0 0 

 NH3 0 0 0 0 0 0 

 PROPANE 0 0 0 0 4.68096 0 

 NAPTHA 0 0 0 0 0 0 

 CO2 0 0 0 0 0.2876933 0 

Total Flow lbmol/hr 128.2067 128.2067 0.2982047 0.2982047 0.1153857 0.4317965 

Total Flow lb/hr 2309.68 2309.68 5.372242 5.372242 5.017253 7.778936 

Total Flow cuft/hr 37.61554 37.60596 0.1048887 15.27979 43.78178 0.1280346 

Temperature F 95.46259 95 366.04 366.0404 68 114.4461 

Pressure psi 500 14.7 164.7 164.7 14.7 41.7 

Vapor Frac 0 0 0 1 1 0 

Liquid Frac 1 1 1 0 0 1 

Solid Frac 0 0 0 0 0 0 

Enthalpy Btu/lbmol -1.23E+05 -1.23E+05 -1.17E+05 -1.02E+05 -53610.59 -1.22E+05 

Enthalpy Btu/lb -6800.593 -6801.025 -6510.467 -5651.786 -1232.925 -6782.69 

Enthalpy Btu/hr -1.57E+07 -1.57E+07 -34975.8 -30362.76 -6185.896 -52762.11 

Entropy Btu/lbmol-R -38.29923 -38.31317 -30.87826 -12.05098 -59.06788 -37.73145 

Entropy Btu/lb-R -2.125931 -2.126704 -1.714004 -0.6689311 -1.35843 -2.094414 

Density lbmol/gal 0.4556294 0.4557454 0.3800617 2.61E-03 3.52E-04 0.4508374 

Density lb/gal 8.208291 8.210381 6.846918 0.0470009 0.0153193 8.121961 

Average MW 18.01528 18.01528 18.01528 18.01528 43.48245 18.01528 

Liq Vol 60F cuft/hr 37.06883 37.06883 0.0862209 0.0862209 0.1545686 0.1248467 
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