# Life-Cycle Assessment of Energy and Greenhouse Gas Effects of Soybean-Derived Biodiesel and Renewable Fuels ANL/ESD/08-2



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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. Putsche 3

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 Base d 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 Assumpti ons 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 N 2 

### O 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 Di esel 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 Refere nces ................................................................................................................... ...... 44

Appendix 1: ASPEN Simulati on Process of Renewable Di esel I (Super Cetane) ................... 49

Appendix 2: ASPEN Simulation Pro cess of Renewable Diesel II (Hydrogenation-Derive d 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 Re newable Diesel Production ............................................ 9

2-3 UOP-Proposed Renewabl e 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 Reduc tions for Soybean-Derived Fuels Compared with Petroleum Gasoline or Diesel............................................................... 40



## Tables

2-1 Current and Planned Rene wable Diesel Facilities ......................................................... 5

2-2 Feedstock Availability for Renewable Diesel Production in theUnited States ......................................................................................................... 6

2-3 NREL-Simulated Renewable Fuel s 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 Da ta 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-Pr oducts 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 Materi al 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 Sec ond Subsystem from Using the Hybrid Approach...................................................................................................................... .. 33

### vii 

## Notation

Acronyms and Abbreviations

BFW boiler feed water BP British Petroleum CETC CANMET Energy Technology Centre CH 4 

### methane CIDI compression-ignition, direct-injection CO carbon monoxide CO 2 

### carbon dioxide CSO clarified slurry oil DeCO 2 

### 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 N 2 

### O nitrous oxide NaOH sodium hydroxide NG natural gas NO x 

### nitrogen oxide NRCan Natural Re sources Canada NREL National Renewable Energy Laboratory P phosphorus PM 10 

### particulate matter with a diamet er of 10 micrometers or less PM 2.5 

### particulate matter with a diamet er of 2.5 micrometers or less PTW pump-to-wheels RFG reformulated gasoline SI spark-ignition SMR steam methane reforming SO x 

### 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 ft 3 

### 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 laborato ry managed by UChicago Argonne, LLC, under Contract No. DE-AC02-06CH11357.

We are grateful to our DOE sponsor, Linda Bluestei n, 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 repo rt. 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 G reenhouse Gases, R egulated E missions, and
E nergy Use in T ransportation (GREET) model: (1) biodiesel produced from soy oil transesterification, (2) renewable dies el produced from hydrogenation of soy oil by using two processes (renewable dies el 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 mark et 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 environm ental benefits and because it is made from rene wable biomass resources. Biodiesel can be derived from various biological sources such as seed oils (e.g., so ybeans, 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 engi ne modifications (Keller et al. 2007). In recent years, the sales volume for biodiesel in the Unite d States has increased dr amatically: 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 fa ts has been the major technology for biodiesel production to date. New pr ocess technologies based on hydrogen ation to convert seed oils and animal fats to diesel fuel and gasoline have recently emerged. The CANMET Energy Technology Centre (CETC) of Natural Resources Ca nada (NRCan) has de veloped a technology to convert seed oils and animal fats into a high -cetane, low-sulfur dies el fuel blending stock called “SuperCetane” [(S&T) 2 

### Consultants Inc. 2004]. UOP de veloped conversion processes based on conventional hydroproce ssing technologies that are already widely deployed in petroleum refineries. The hydro-generation tec hnologies utilize seed oils or animal fats to produce an isoparaffin-rich diesel substitute referr ed 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 t hose of petroleum diesel and gasoline. In the United States, soybeans are th e major feedstock for biodiesel production now and, potentially, for renewable diesel and gasolin e production in the future. In our study, we evaluated production of biodies el, renewable diesel, and rene wable gasoline from soybeans.

For this study, we expanded and updated the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model (s ee 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 Tr ansportation 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 transporta tion technologies. The latest version — GREET 1.8a — is capable of analyzing more than 100 transportation fuel pathways.

For a given vehicle and fuel syst em, GREET evaluates total energy use, fossil fuels, natural gas (NG) use, coal use, and petroleum use; emissions of carbon dioxide (CO 2 

### )-equivalent greenhouse gases (GHGs) including CO 2 

### , methane (CH 4 

### ), and nitrous oxide (N 2 

### O); and emissions of six criteria pollutants — volatile organic compound s (VOCs), carbon monoxide (CO), nitrogen oxides (NO x 

### ), particulate matter with a diameter of 10 micrometers or less (PM 10 

### ) and 2.5 micrometers or less (PM 2.5 

### ), and sulfur oxides (SO x 

### ). 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 petrol eum-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 gaso line”) produced by using catalytic cracking.

We used petroleum gasoline and diesel as the ba seline fuels; our analysis was conducted for year 2010. We estimated consumption of total energy, fo ssil energy, and petroleum oil and emissions of GHGs (CO 2 

### , N 2 

### O, and CH 4 

### ) for each of the six pathways. Figure 1-1 illustrates the system boundary for the six fuel pathways. The four so ybean-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 rene wable 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-bas ed fuel pathways have three common stages: soybean farming, soybean transportation, and soy o il 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 N 2 

### O emission simulations for soybean fields by using newly released data from the Intergovernmental Panel on Climate Change (IPCC 2006). Moreover, we expanded GR EET 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 CO 2 

### 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 th at have been proposed for renewable diesel and gasoline production: two for renewable diesel fu el and one for renewable gasoline. It also presents the results of the process modeling wo rk 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-barre l-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 petroleu m-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 producti on 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, par ticularly 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 bi odiesel 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 re moved prior to their processing with other petroleum-based feedstocks. Th e two standard processes to remove oxygen from hydrocarbon feeds are hydrodeoxygenation (HDO) and decarboxylation (DeCO 2 

### ). 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.

C n 

### COOH + 3H 2 

### → C n+1 

### + 2H 2 

### O (2-1)

In the DeCO 2 

### reaction, shown in Equation 2-2, the oxygen in the feed is removed as simple CO 2 

### in a lead/hydrogen catalytic reaction.

Pb/H C n 

### COOH → C n 

### + CO 2 

### (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 wh ich 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 resu lt 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 DeCO 2 

### -process (Equation 2-2) is CO 2 

### — a GHG that is of concern in life-cycle modeling. However, the CO 2 

### from this process is the CO 2 

### uptaken during soybean growth.

### 7 

### 2.1 Renewable Diesel Production Based on SuperCetane

The first renewable diesel production pathway, re newable diesel I, was modeled after a process called SuperCetane that was orig inally developed in the 1980s at the Saskatchewan Research Council and is now being de veloped by NRCan’s CETC.

The SupereCetane process is ba sed on adapting a conventional hy drotreating process so it can operate under proprietary operating conditions. Figure 2-1 show s a general proce ss schematic for the SuperCetane process. A number of reactions occur in the process, including hydrocracking, hydrotreating, and hydrogenation. The hydrocrack ing 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 dis tillate, 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 oi l (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 pathwa y, renewable diesel II, was modeled on a hydrogenation process developed by UOP, a lead ing supplier and licensor of process technology, catalysts, adsorbents, process plants, and consulting services to the petroleum refining, petrochemical, and gas pro cessing industries. UOP, located in Des Plaines, Illinois, is a wholly owned subsidiary of Honeywell Internationa l. 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 En ergy 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 pr ocess soy, rapeseed, palm, and other oils, is expected to come online in 2009. Facility opera tions will be based on a newly branded UOP process called Ecofining TM 

### . 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 modele d for renewable diesel I, the UOP standalone process scheme was characterized for this proj ect 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 opera ting 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 not es 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 Rene wable 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 ar e basically chains of carbon and hydrogen with added oxygen, standard refinery vessels coul d 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 di esel, 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 greas es. 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 stan dalone 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 proce sses is that additional hydrogen is not required for the gasoline process. Another difference is th at 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 in clude light ends, light-cycle oil (LCO), and clarified slurry oil (CSO). Diesel Hydrotreater Vegetable oil H 2 

### , Steam El ect ri c i ty

Renewable Di ese l

### 10 Vegetable Oi l FCC VGO Stea m 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 b een 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 developedinitial mass and energy balances by using the ASPEN process simulation model. The NRELmodeled mass and energy balances for the three fu els are listed in Table 2-3. Details of NREL’s ASPEN simulations are presented in Appendice s 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 th ree 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 an y 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 exampl e, when renewable diesel I and renewable diesel II are compared, differences in hydrogen requirements, as well as the resultant CO 2 

### emissions, demonstrate the exte nt to which the HDO or DeCO 2 

### 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 CO 2 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 O 2 0.0201 0.0593 N 2 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 CO 2 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-genera ted fuel gas in the renewable diesel I scheme. This process characteristic increases facility emissions and re duces facility energy by-products. These types of tradeoffs are central to the use of GREET in linki ng 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 H 2 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 harves ted 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 de cades 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 (10 6 acres) Yield (bu/acre) 3-Year Moving Average Yield (bu/acre)

Year Planted Harvested Total Production

(10 6 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 2010Soybean 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 electrici ty (energy loss for electricity generation is simulated separately in GREET); and 983 Btu/ft 3 

### 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 Patz ek (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 Farmin g in the United States (USDA 2007b)

State Diesel (gal/acre) Gasoline (gal/acre) LPG (gal/acre) Electricity (kWh/acre) Natural Gas (ft 3 /acre)

Arkansas 9.9 1.3 L a 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 23,474/34,625 a 20,447 b 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 fa rming 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 fe rtilizer 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 NA a 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 2004Phosphorus (g/bu), Potassium (g/bu).

0 10 20 30 40 50 60 70Nitrogen (g/bu). Phosphorus

Potassium Nitrogen Left axis

Right axis



### Figure 3-2 Fertilizer Use for Soybea n Farming in the United States



### 3.1.4 N 2 

### O Emissions

N 2 

### O, a potent GHG, is produced from nitrogen in th e soil through nitrification and denitrification processes (direct N 2 

### O emissions). N 2 

### O can also be produced thro ugh volatilization of nitrate from the soil to the air and through leaching and runoff of nitrate into water streams (indirect
N 2 

### O emissions).

Estimation of direct and indirect N 2 

### O emissions from crop farmin g requires two important parameters: (1) the amount of nitrogen applied to soil and (2) rates for converting nitrogen into
N 2 

### O. The application of nitrogen fertilizer is the key to crop farming. For legume crops, such as soybeans, nitrogen fixation is another major n itrogen input. In 1996, IPCC considered nitrogen input to soil from biological nitrogen fixation by legume crops in estimating N 2 

### O 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 re sults 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 bi omass left in the field after harvest and in the belowground

### 18 

### biomass (i.e., roots). For crops such as cor n, 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 N 2 

### O 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 co ntent of 0.6%. Belowground biomass is about 22% of aboveground biomass, with a nitrogen co ntent of 0.7%. The total amount of nitrogen in corn biomass that is left in co rn fields per bushel of corn harv ested 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 N 2 

### O 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 n itrogen content of 0.8%. Belowground biomass is about 19% of aboveground biomass, with a n itrogen 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 N 2 

### O 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 N 2 

### O 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
N 2 

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

Indirect N 2 

### O emissions include those from volatilization of nitrate from the soil to the air and leaching and runoff of nitrate into water streams where N 2 

### O 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 N 2 

### O 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 N 2 

### O emissions is 0.75%, with a range of 0.05–2.5%.

### 19 

### Thus, the conversion rate for direct and indirect N 2 

### O 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 N 2 

### O 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 N 2 

### O conversion factors. In particular, Crutzen et al. adopted the global N 2 

### O emission balance from a 2001 study but adopted the nitrogen inputs from a separate 2004 study for deriving N 2 

### O conversion factors. Furthermore, Crutzen et al. did not get into agricultural s ubsystems (such as crop farming, animal waste management, and crop residual burning), which are required for generating N 2 

### O conversion rates for the nitrogen inputs into crop farmin g. Their allocation of aggregate N 2 

### O emissions (even after subtracting N 2 

### O emissions from industrial sources) to the aggregate agricultural system could result in overestimation of N 2 

### O conversion rates from nitrog en inputs into crop farming systems. Nonetheless, N 2 

### O 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 c ontain 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. Th e 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. Tabl e 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. Comme rcial hexane is manufactured by distillation of straight-run gasoline produced fro m crude oil or natural gas liquids. In GREET, hexane is assumed to be produced from crude oi l, and its upstream production energy use and emissions are adopted from energy use and emi ssions 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 Value a

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 proce sses 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 tran sesterification 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 produc tion simulations on the ba sis of data in Haas et al. (2006). Table 3-6 presents the inputs and ou tputs of biodiesel plants per pound of biodiesel produced. To apply the values specified in Tabl e 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 ha lf 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 Biodie sel 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 reacti ons, 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 simu lations conducted by NR EL assumed that the required steam would be generated with the fuel ga s and/or heavy oils that are co-produced fromthe 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 amount s (i.e., after steam generation for internal use). In GREET, hydrogen used in rene wable 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 o il 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 ou tputs 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 pl ace 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 pr ocess. The process also generates extra steam for export. The outputs are renewabl e diesel, product gas, LCO, and CSO. Table 3-9 presents the inputs and outputs from renewable gasoline plan ts 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 produc t and co-product that can be produced from 1 ton of soybeans. According to Table 3-10, the tr ansesterification process can generate a much larger amount of diesel product and co-products fro m 1 ton of soybeans than the other processes; however, it requires a lot more energy and chem ical 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 va lue of the co-products of these processes is an important factor in evaluating the energy and em ission 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 pr oduction 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 hydr ogen 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 biodi esel, renewable diesel fuels ha ve 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 renewabl e diesel as a diesel blending component and a cetane enhancer.

3.5 Fuel Use in Vehicles



### For our life-cycle analysis, we assumed that soyb ean-derived diesel fuels are used in 100% pure form in compression-ignition, dir ect-injection (CIDI) engine vehi cles, and renewable gasoline is used in 100% pure form in spark-ignition (SI) en gine vehicles. Since there were no testing data, we assumed that the fuel economy and CH 4 

### and N 2 

### O emissions for CIDI vehicles are the same for all three diesel types. Likewise, we assumed that the fuel economy and CH 4 

### and N 2 

### O 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 gasoline a 113,602 6.23 84.0 NA f NA Petroleum diesel a 129,488 7.06 87.1 0.0 40 Biodiesel a 119,550 7.40 77.6 11.0 50–65 Renewable diesel I b 117,059 6.24 87.1 0.0 100 Renewable diesel II c 122,887 6.49 87.1 0.0 70–90 Renewable gasoline d 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 pr imary 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 pr oduct is assumed to be displaced by a new product. The life-cycle energy that would have been used and the emi ssions that would have been generated during production of the displa ced product are counted as credits for the new product that is co-produced from the fuel pathwa y under evaluation. These credits are subtracted from the total energy use and emissions associat ed 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 compar ed with the amount of primary product from a given process (as is the case for renewable dies el 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 di storted results for the primary product.

The allocation method allocates the feedstock us e, 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-cy cle analyses than th e 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; solven ts 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 renewa ble 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 approa ches were employed to address the co-product issues: (1) the displacement approach, (2) an en ergy-based allocation method, (3) an allocation method based on the market values of the primar y 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 gl ycerin, 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 a ssumed to replace equivale nt-Btu residual oil. Table 4-2 lists the products that are to be displa ced 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 pr oduction of one million Bt u 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. Petroleu m-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 theo retical 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 Input a Industry Input b

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 (Associat ion of European Plastic Industry 2005). The Eco-Profile reports average industr y 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, natu ral gas, electricity, and other fu els used as process fuels. We use the GREET model to generate the upstrea m energy use and emissions for the fuel (e.g., petroleum, natural gas, an d 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 ener gy (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 a llocation method is easier to im plement than the displacement method in terms of data require ments. With the energy-value-based allocation method, the

### 30 

### energy contents of the primary product and co-p roducts are used to split the burden of energy input, feedstock input, and pollutant emissions. W ith 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: syst em level and subsystem level. As Figure 4-1 shows, systemlevel allocation takes soy oil ex traction and fuel production proce sses as a whole system, with soybeans and the required energy and chemic als 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. Becau se the allocation ratio is determined by the energy value or market value of the primary product and co-products, th e variation in market

### 31 

### value of soy oil could obviously affect the allocat ion results of the first subsystem level but not affect the result of the second s ubsystem level, which means that it could affect the final results. However, in the soybean-to-biodiesel/renewable fu els 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 th is 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 me al 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 releas ed when it is digested. The energy content of renewable fuels and their co-products were obt ained 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 so y 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 worl dwide (Malveda et al. 2005), so the price for glycerin is not expected to rise in the near fu ture; 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 produc tion cost of biodiesel is higher than that of conventional petroleum diesel. A w ealth of research has been conducted to examine the cost for producing biodiesel at different industry s cales (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 comp etitive 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 biodies el fuel (on a per-million-Btu basi s). Because the co-products of

### 32 

### renewable diesel and gasoline produc tion 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 th e 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 ener gy 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 re newable 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 resu lt in negative energy input and emissions. On the other hand, in the energy-based allocation met hod, 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, wh ich 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 addre ssed 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 a nd GHG Emission Results for Soybean-Derived Fuels

On the basis of the data and key assumptions pr esented 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 — CO 2 

### , CH 4, 

### and N 2 

### O — weighted by their global warming potentials. According to IPCC, the global warming potentials of CO 2 

### , CH 4, 

### and N 2 

### O 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 (W TP) and pump-to-wheels (PTW). Well-to-pumpstages 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 com bustion of gasoline in vehicles.

Feedstock

- Related Stages:

Recovery, pro cessing, storage, and transportation of feedstocks Fuel

- Related Stages:

Production, transportation, storage, and distribution of fuel Vehicle:

Refueling and operation

Well

- to

- Pump Pump

- to

- Wheel s

Well

- to

- Wheel s



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



### In the following sections, petroleum-based RF G is the baseline for soybean-based renewable gasoline, and petroleum-based LSD is the baseli ne 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 GasolineWTW 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 displacem ent 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 si milar 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 c ounterparts. 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 alloca tion 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 w hole life cycle. The higher energy use of the hybrid approach compared with the displacement a pproach 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 us e of the six fuel options on the basis of 1 million Btu of fuel produced and used. Fossil en ergy use includes petroleum, natural gas, and coal.

Figure 5-4 reveals that all soybean-derived fu els 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 us ed as process fuels during soybean farming and fuel production processes, can still achieve substan tial 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 displacem ent 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-p roducts 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%, a nd 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 GasolineWTW 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 an d 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 pe troleum diesel. Like fossil energy use, the petroleum use associated with renewable ga soline is low because its production process generates large quantities of co-products (product ga s, LCO, and CSO) in terms of Btu, and the co-products (LCO and CSO) are assumed to re place petroleum fuels (die sel 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 th at 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 GasolineWTW 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 rene wable 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 CO 2 

### -equivalent grams of GHGs (including CO 2 

### , CH 4 

### , and N 2 

### O) for the six fuel pathways studied. To clearly show the GHG reduction benefit of different soybeanbased fuels, Figure 5-7 presents the change s 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 displace ment 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 reduc tion 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 GasolineWTW 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% -74% -68% -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 GasolineRelative 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 an d 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 fuel s achieve a modest reduction in GHG emissions (57–74%). The results from using the hybrid appr oach 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 em issions for the four di esel 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 emi ssion credits for co-products is a key issue in life-cycle analysis. The production processes of th e 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 met hods). The four allocatio n approaches generate considerably different results.

For WTW total energy use, the displacement appro ach 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 petrol eum fuels. The two allocation approaches show good agreement with each other, providing very si milar results. The hybrid approach gives the highest total energy use results. Both the a llocation 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 reducti on ratios are around 63–71%. The hybrid approach shows a 52–61% reduction in fossil energy use fo r 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%) compar ed with petroleum-based fuels. While with the allocation approach, soybean-based fu els achieve a modest re duction in GHG emissions (57–74%).

### 44 

## 7 References

Ahmed, I., J. Decker, and D. Morris, 1994, How Much Energy Does It Take to Make a Gallon of 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.e du/usda/ers/OCS//2000s/2007/OC S-10-15-2007.pdf, accessed Nov. 2007.

Association of European Plastic Industry, 2005, Eco-Profiles of the Euro pean 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 Fa rmer Cooperatives,” Bioresource Technology 

### , 70: 81–87.

Britzman, D.G., 2000, Soybean Meal — An Excellent Protein Source for Poultry Feeds


### , American Soybean Association Technical Bulle tin, available at http://www.asaim-europe.org/ pdf/Britzman.pdf, accessed Nov. 2007.

CETC (CANMET Energy Technol ogy 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, “N 2 

### O 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 Consultin g, Menlo Park, Calif.

EIA (Energy Information Administration), 2007a, Annual Energy Outlook 2007


### , U.S. Department of Energy, available at h ttp://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/cont ents.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, “E nvironmental, 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 C limate Change (IPCC), 2006, “N 2 

### O Emissions from Managed Soils, and CO 2 

### 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_V olume4/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 Ch emical 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 Fo rce, 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 R apeseed Oil and Biodiesel (RME) to Replace Petroleum Dies el 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 Consu lting, Menlo Park, Calif.

National Biodiesel Board, 2007, FAQs: How Much Biodiesel Has Been Sold in the U.S.? 

### available at http://www.biodiesel.org/r esources/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 Ecofining TM 

### Technology to Produce Green Diesel Fuel 

### , available at http://www.reuters.co m/article/pressRel ease/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/Li quid_biofuels_Newsletter-07_e .pdf, accessed Nov. 2007.

(S&T) 2 

### Consultants Inc., 2004, The Addition of NRCan’s Supercetane and ROBYS TM 

### 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.

USDA (U.S. Department of Agriculture), 2007a, Quick Stats: Agricultural Statistics Data Base


### , 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/Fuelbys tate.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 Em ission Impacts of EthanolDiesel Blends in Urban Buses and Farming Tractors 

### , prepared for Illinois Department of Commerce and Economic Opportun ities, 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 Laboratory 1



### A1-1 Introduction



### A preliminary analysis was conducted for a hy drogenation-derived rene wable diesel (HDRD) facility on the basis of the Natural Resources Canada (NRCan) process [(S&T) 2 

### Consultants 2004]. NRCan has named its renewable diesel “S uperCetane.” 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 in cluding hydrocracking, hydrotreating, and hydrogenation [(S&T) 2 

### Consultants 2004]. A commercial refine ry 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 opera tions were modeled except hydrog en 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 pr oduct 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 CO 2 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 select ed 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 contai ns 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, strippi ng, and pressure swing adsorption (PSA)/gas recycle. Each of these areas is briefly disc ussed, and the flow diagram from ASPEN Plus ® 

### is presented. The flow diagram shows only t hose 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 si mulation; all were m odeled as conventional (e.g., water) components in the mixed substream. Th e following is a list of the components in the simulation:

• Hydrogen – H 2



### • Linolenic acid – C 18 

### H 30 

### O 2



### • Palmitic acid – C 16 

### H 32 

### O 2

### 52 

### • Stearic acid – C 18 

### H 36 

### O 2



### • Linoleic acid – C 18 

### H 32 

### O 2



### • Arachidic acid – C 20 

### H 40 

### O 2



### • Oleic acid – C 18 

### H 34 

### O 2



### • Green Diesel – C 18 

### H 38



### • Water – H 2 

### O

• Hydrogen Sulfide – H 2 

### S

• Ammonia – NH 3



### • Propane – C 3 

### H 8



### • Naphtha

• Oxygen – O 2



### • Nitrogen – N 2



### • Wax – C 26 

### H 54



### • Carbon dioxide – CO 2



### Green diesel is not a specific compound but is a complex mixture of hydrocarbons; however, for simplicity, it was modeled as a single component, C 18 

### H 38 

### , 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, linolei c acid, arachidic acid, and olei c 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 C 18 H 30 O 2 LINOL3 0.075 Palmitic C 16 H 32 O 2 PALM 0.11 Stearic C 18 H 36 O 2 STEARIC 0.041 Oleic C 18 H 34 O 2 OLEIC 0.22 Linoleic C 18 H 32 O 2 LINOL2 0.54 Arachidic C 20 H 40 O 2 ARACHID 0.014



### One Henry component, CO 2 

### , 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: (A 1000). 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 incl uded 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 nomi nal 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

P107 CWS2 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 hydrog en (Stream 120) and recycl e 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., CO 2 

### , H 2 

### , 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 react or 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 ga ses (Stream 115) are flashed off a nd 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 distilla tion column where the SuperCetane (Stream 111) with a small amount of naphtha is separate d 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, a nd the heavies are taken off on stage 8.

Gas Recycle 

### As noted earlier, the off-gas from the water sepa rator, S101, is sent to a splitter where it is separated for gas recycle (124), combustion (1 17), 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 th e 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 H 2 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 proces s generates three other products: fu el gas, naphtha, and heavies. The amount of naphtha is very small and is included in the S uperCetane 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 por tion 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
NO x 32 1.17E-03
PM 7.6 2.78E-04
VOCs 5.5 2.01E-04



### The NRCan process uses hydrogenation to conve rt 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=1 02 QHX101 OUT =104 QHX1 01XS

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 QC ONDXS

### 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-Derive d Renewable Diesel)

Victoria Putsche Center for Transportation Technologies and Systems National Renewable Energy Laboratory 2



### A2-1 Introduction



### A preliminary analysis was conducted for a hy drogenation-derived rene wable 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-cy cle 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:

C n 

### COOH (bio-oil) + 3 H 2 

### → C n+1 

### (HDRD) + 2 H 2 

### O

Decarboxylation:

C n 

### COOH (bio-oil) → C n 

### (HDRD) + CO 2



### The selectivity of the reactions de pends on the processing conditions.

For this analysis, the production of HDRD is ba sed 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. Figu re 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 appor tion 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 select ed 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 t horough 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. Ther efore, 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 CO 2 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 th e 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, strippi ng, and pressure swing adsorption (PSA)/gas recycle. Each of these areas is briefly disc ussed, and the flow diagram from ASPEN Plus ® 

### is presented. The flow diagram shows only t hose 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 si mulation; all were m odeled as conventional (e.g., water) components in the mixed substream. Th e following is a list of the components in the simulation:

• Hydrogen – H 2



### • Linolenic acid – C 18 

### H 30 

### O 2



### • Palmitic acid – C 16 

### H 32 

### O 2



### • Stearic acid – C 18 

### H 36 

### O 2



### • Linoleic acid – C 18 

### H 32 

### O 2



### • Arachidic acid – C 20 

### H 40 

### O 2



### • Oleic acid – C 18 

### H 34 

### O 2



### • Green Diesel – C 18 

### H 38



### • Water – H 2 

### O

• Hydrogen Sulfide – H 2 

### S

• Ammonia – NH 3



### • Propane – C 3 

### H 8



### • Naptha

• Carbon dioxide – CO 2



### Green diesel is not a specific compound but is a complex mixture of hydrocarbons; however, for simplicity, it was modeled as a single component, C 18 

### H 38 

### , 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, linolei c acid, arachidic acid, and olei c 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, CO 2 

### , 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 C 18 H 30 O 2 LINOL3 0.075 Palmitic C 16 H 32 O 2 PALM 0.11 Stearic C 18 H 36 O 2 STEARIC 0.041 Oleic C 18 H 34 O 2 OLEIC 0.22 Linoleic C 18 H 32 O 2 LINOL2 0.54 Arachidic C 20 H 40 O 2 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: (A 1000). 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 incl uded 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 pro cess is the hydrogenator, where the oil stream is combined with the inlet hydr ogen (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 exotherm ic, 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) ar e 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 strippi ng column where LP (50 psig) steam (Stream 114) is used to remove the light ends from the gree n diesel product (112). The stripping column is modeled as a RADFRAC column with eight st ages 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 ope ration has two other outlet streams: CO 2 

### and Stream 125. The CO 2 

### stream contains all of the carbon diox ide 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: PROPAN E and Streams 122 and 123. Stream 111 is the overheads from the stripping column, ST-101. PROP ANE is a fuel gas, composed primarily of propane (93%) with small amounts of green diesel and CO 2


### .

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 Ma rkel (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, in cluding 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 H 2 2.72 2.72 LP steam 2.72 2.80

Products Propane mix gas 4.75 5.02 HDRD 84.19 85.23 CO 2 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 compos ed of 93.3% propane, 5.7% CO 2 

### , 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 ga s 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 conve rt 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 resu lts 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. Departme nt of Energy, NREL/TP-580-24772.

UOP, 2006, Opportunities for Biorenewables in Oil Refineries 

### , Final Technical Report, DOE Award Number DE-FG36-05GO15085, contributors we re 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 Filena me 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=1 03 QHX101 OUT =104 QHX1 01XS

BLOCK HX102- IN=1 04 QHX102 OUT =105 QHX1 02XS

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

### 84PROPERTY-REP NOPARAM-PLUS101 102 103 104 105 106 107 108 109 110P101 MX101HX101-HX102-RX101 4 ; S101 P105 SP-101P101MX101HX101-HX102-RX1014 4 S101S101LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED LIQUID LIQUIDSubstream: MIXEDMass Flow lb/hrH2 000001.084999 1.084999 1.084999 02.04E-07LINOL3 7.5 7.5 7.5 7.5 7.5 00000PALM 11 11 11 11 11 00000STEARIC 4.1 4.1 4.1 4.1 4.1 00000OLEIC 22 22 22 22 22 00000LINOL2 54 54 54 54 54 00000ARACHID 1.4 1.4 1.4 1.4 1.4 00000GDSL 0000085.05216 85.05216 85.05216 085.052H2O 000005.54688 5.54688 5.54688 5.445815 0.0218078H2S 0000000000NH3 0000000000PROPANE 000004.82784 4.82784 4.82784 02.468692NAPTHA 0000000000CO2 000007.29312 7.29312 7.29312 00.2876933Total Flow lbmol/hr 0.3591571 0.3591571 0.3591571 0.3591571 0.3591571 1.456175 1.456175 1.456175 0.3022887 0.3985819Total Flow lb/hr 100 100 100 100 100 103.805 103.805 103.805 5.445815 87.8302Total Flow cuft/hr 1.786671 1.795957 1.795957 1.975812 2.061152 29.28627 25.1053 10.49892 0.0877934 1.907063Temperature F 68 80.58947 80.58948 290 370 617 507.6096 100 100 100Pressure psi 14.7 500 500 500 500 500 500 500 175 175Vapor Frac 000000.8408124 0.7704222 0.4824463 00Liquid Frac 111110.1591876 0.2295778 0.5175537 11

### 85PROPERTY-REP NOPARAM-PLUS (Cont.)101 102 103 104 105 106 107 108 109 110P101 MX101HX101-HX102-RX101 4 ; S101 P105 SP-101P101MX101HX101-HX102-RX1014 4 S101S101LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED LIQUID LIQUIDSolid Frac 0000000000Enthalpy Btu/lbmol -3.14E+05 -3.12E+05 -3.12E+05 -2.83E+05 -2.71E+05 -70801.07 -77885.8 -1.00E+05 -1.22E+05 -1.99E+05Enthalpy Btu/lb -1126.343 -1120.916 -1120.916 -1017.751 -971.6202 -993.1961 -1092.581 -1406.734 -6797.624 -903.7778Enthalpy 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 -37018.6 -79378.98Entropy Btu/lbmol-R -415.036 -412.2374 -412.2374 -367.7857 -351.4557 -80.66001 -87.58823 -118.2502 -38.21506 -382.6785Entropy Btu/lb-R -1.490631 -1.48058 -1.48058 -1.320929 -1.262278 -1.131497 -1.228686 -1.658812 -2.121258 -1.736632Density lbmol/gal 0.0268725 0.0267335 0.0267335 0.0243 0.0232939 6.65E-03 7.75E-03 0.0185411 0.4602862 0.0279396Density lb/gal 7.482102 7.443416 7.443416 6.765855 6.485721 0.4738298 0.5527402 1.321727 8.292184 6.156685Average MW 278.4297 278.4297 278.4297 278.4297 278.4297 71.28609 71.28609 71.28609 18.01528 220.3567Liq Vol 60F cuft/hr 1.803603 1.803603 1.803603 1.803603 1.803603 2.596039 2.596039 2.596039 0.0874017 1.834368110A 110B 110C 111 112 113 114 115 117 118ST-101P106MX101FL-101P102ST-101PSACP102MX102SP-101 SP-101 P106 ST-101 ST-101 P102 S101 PSA CP102LIQUID MISSING MISSING VAPOR LIQUID LIQUID VAPOR VAPOR VAPOR VAPORSubstream: MIXEDMass Flow lb/hrH2 2.04E-07 002.04E-07 4.05E-35 4.05E-35 01.084998 1.084999 1.084999LINOL3 0000000000PALM 0000000000STEARIC 0000000000OLEIC 0000000000LINOL2 0000000000

### 86PROPERTY-REP NOPARAM-PLUS (Cont.)101 102 103 104 105 106 107 108 109 110P101 MX101HX101-HX102-RX101 4 ; S101 P105 SP-101P101 MX101 HX101- HX102- RX101 4 4 S101 S101LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED LIQUID LIQUIDARACHID 0000000000GDSL 85.052 001.60E-04 85.05184 85.05184 01.58E-04 00H2O 0.0218078 000.4588025 2.363005 0.029885 2.8 0.0792568 00H2S 0000000000NH3 0000000000PROPANE 2.468692 002.321827 0.1468655 0.1468655 02.359148 00NAPTHA 0000000000CO2 0.2876933 000.2876933 4.45E-12 4.45E-12 07.005427 00Total Flow lbmol/hr 0.3985819 000.0846584 0.469347 0.3398392 0.1554236 0.7553041 0.5382258 0.5382258Total Flow lb/hr 87.8302 003.068483 87.56171 85.22859 2.8 10.52899 1.084999 1.084999Total Flow cuft/hr 1.907065 0034.10666 1.85258 1.832826 19.0754 25.87814 18.60182 8.231991Temperature F 100.00298.90795 143.0657 149.9646 297.7949 100 99.97435 240.933Pressure psi 175500 14.7 21.7 31.7 64.7 175 175 500Vapor Frac 01001111Liquid Frac 10110000Solid Frac 00000000Enthalpy Btu/lbmol -1.99E+05-72054.6 -1.90E+05 -2.16E+05 -1.02E+05 -39284.66 163.5656 1154.758Enthalpy Btu/lb -903.7778-1987.963 -1018.401 -861.4131 -5676.514 -2818.112 81.13857 572.831Enthalpy Btu/hr -79378.98-6100.031 -89172.93 -73417.02 -15894.24 -29671.86 88.03524 621.5208Entropy Btu/lbmol-R -382.6781-40.91684 -319.5699 -425.8229 -10.82218 -7.527309 -4.635141 -5.164754Entropy Btu/lb-R -1.73663-1.128883 -1.712954 -1.69792 -0.6007222 -0.5399766 -2.299314 -2.562034Density lbmol/gal 0.02793963.32E-04 0.0338676 0.0247868 1.09E-03 3.90E-03 3.87E-03 8.74E-03Density lb/gal 6.1566790.0120268 6.318376 6.216305 0.0196224 0.0543903 7.80E-03 0.0176194

### 87PROPERTY-REP NOPARAM-PLUS (Cont.)101 102 103 104 105 106 107 108 109 110P101 MX101HX101-HX102-RX101 4 ; S101 P105 SP-101P101 MX101 HX101- HX102- RX101 4 4 S101 S101LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED LIQUID LIQUIDAverage MW 220.356736.24543 186.5607 250.7909 18.01528 13.94006 2.01588 2.01588Liq Vol 60F cuft/hr 1.834368 000.0864746 1.792832 1.755387 0.0449381 0.6742691 0.4617512 0.4617512119 120 121 122 123 124 125 CO2 CWR CWR1RX101 CP101 MX102 P105 FL-101 P104MX102 CP101 FL-101 FL-101 P102 PSA PSA HX103- P104VAPOR VAPOR VAPOR LIQUID LIQUID LIQUID MIXED VAPOR LIQUID LIQUIDSubstream: MIXEDMass Flow lb/hrH2 3.804999 2.72 2.72 0000000LINOL3 0000000000PALM 0000000000STEARIC 0000000000OLEIC 0000000000LINOL2 0000000000ARACHID 0000000000GDSL 0002.84E-04 001.58E-04 000H2O 0004.63E-08 0.4894927 2.33312 0.0792568 02309.68 2309.68H2S 0000000000NH3 0000000000PROPANE 0001.48E-05 002.359148 000NAPTHA 0000000000

### 88PROPERTY-REP NOPARAM-PLUS (Cont.)101 102 103 104 105 106 107 108 109 110P101 MX101HX101-HX102-RX101 4 ; S101 P105 SP-101P101 MX101 HX101- HX102- RX101 4 4 S101 S101LIQUID LIQUID LIQUID LIQUID LIQUID MIXED MIXED MIXED LIQUID LIQUIDCO2 0003.64E-08 0007.005427 00Total Flow lbmol/hr 1.887512 1.349287 1.349287 1.46E-06 0.0271709 0.1295079 0.0578996 0.1591788 128.2067 128.2067Total Flow lb/hr 3.804999 2.72 2.72 2.99E-04 0.4894927 2.33312 2.438563 7.005427 2309.68 2309.68Total Flow cuft/hr 42.90768 520.0834 34.66854 6.53E-06 7.85E-03 0.0381251 1.507645 5.167461 37.93036 37.93266Temperature F 586.7733 68 724.6138 68 68 149.9646 99.97435 99.97435 110.4626 110.571Pressure psi 500 14.7 500 14.7 14.7 31.7 175 175 500 510Vapor Frac 1110000.9295689 100Liquid Frac 0001110.070431 011Solid Frac 0000000000Enthalpy Btu/lbmol 3577.495 -61.40031 4543.916 -1.88E+05 -1.23E+05 -1.22E+05 -50942.12 -1.69E+05 -1.22E+05 -1.22E+05Enthalpy Btu/lb 1774.657 -30.45832 2254.061 -914.9968 -6829.944 -6748.178 -1209.537 -3843.429 -6786.474 -6786.371Enthalpy Btu/hr 6752.566 -82.84663 6131.045 -0.2738398 -3343.207 -15744.31 -2949.533 -26924.86 -1.57E+07 -1.57E+07Entropy Btu/lbmol-R -2.354373-0.1171418 -1.486812 -363.1769 -39.26957 -36.67663 -66.63749 -4.062922 -37.84993 -37.8467Entropy Btu/lb-R -1.167913-0.0581095 -0.7375498 -1.771269 -2.179792 -2.035862 -1.582198 -0.0923185 -2.10099 -2.100811Density lbmol/gal 5.88E-03 3.47E-04 5.20E-03 0.02986 0.4624672 0.4541016 5.13E-03 4.12E-03 0.4518477 0.4518203Density lb/gal 0.0118546 6.99E-04 0.0104882 6.122437 8.331475 8.180767 0.2162236 0.1812281 8.140163 8.139668Average MW 2.01588 2.01588 2.01588 205.0377 18.01528 18.01528 42.11703 44.0098 18.01528 18.01528Liq Vol 60F cuft/hr 1.619323 1.157571 1.157571 6.32E-06 7.86E-03 0.037445 0.0759562 0.1365617 37.06883 37.06883

### 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 0.1248467A U.S. Department of Energy laboratory managed by UChicago Argonne, LLC 

## Energy Systems Division Argonne National Laboratory 9700 South Cass Avenue, Bldg. 362 Argonne, IL 60439-4815

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