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mm .1 LIBRARY
9 Michigan State
2 00? University

This is to certify that the
dissertation entitled

MODELING AND ANALYSIS OF THE BIOREFINERY
INTEGRATED WITH THE AGRICULTURAL LANDSCAPE

presented by
ELIZABETH DIANE SENDICH

has been accepted towards fulfillment
of the requirements for the

Doctoral degree in Chemical Engineering

 

 

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MODELING AND ANALYSIS OF THE BIOREFINERY INTEGRATED WITH
THE AGRICULTURAL LANDSCAPE

By

Elizabeth Diane Sendich

A DISSERTATION

Submitted to
Michigan State University
In partial fuifillment of the requirements
For the degree of

DOCTOR OF PHILOSOPHY
Chemical Engineering

2008

ABSTRACT

MODELING AND ANALYSIS OF THE BIOREFINERY INTEGRATED WITH
THE AGRICULTURAL LANDSCAPE

By
Elizabeth Diane Sendich

The current energy crisis has drawn much attention to cellulosic ethanol,
but the chemical engineering system that produces this alternative fuel, called
the biorefinery, has not yet been modeled with one of its primary feedstock
suppliers, the agricultural system. Combining cropping and animal systems with
the biorefinery in a single integrated model will allow environmental and
economic analysis of biomass, bioenergy, co-product, and fertilizer production.

This study focuses on the integration of the NREL biorefinery model with
the Integrated Farm System Model (IFSM), which is shown here to be the best
choice for work with this biorefinery model. With these selected models, the
biorefinery system is simulated within realistic agricultural landscapes, which
include animal and crop production, across various US regions using the new
research tool Biorefinery and Farm Integration Tool (BFIT). This combined
modeling approach allows analysis of regional van'ability, economic profitability,
and development pathways with little environmental impact. Preliminary results
from this model development study indicate that the Midwest, already a center for
grain ethanol production, is ideal for the cellulosic ethanol industry. This study
also underscores the need for Continued research on the use of biorefinery co-

products, specifically pretreated grasses, as animal feed.

Although validation of the research tool developed in this work can only
occur when commercial scale biorefineries and biomass markets are operational,
a sensitivity analysis and verification are presented at this time. The sensitivity
analyses reveal three variables having notable effects on overall system
outcomes: biorefinery size, biomass farm gate price, and switchgrass yields.
These analyses stress the need for care with model input assumptions and
continued research on these variables.

The verification tests performed at the conclusion of this study highlight
this model’s potential for “expert users” as a decision-making tool. The outcomes
of these tests lay the ground work for future studies using this research tool,
while also pointing to areas that would benefit from further expansion and
validation. Specifically, future research should investigate the effects of
combined changes in precipitation and temperature, biomass choice, and land

use, as defined by the farm management distribution in the landscape.

Copyright by
ELIZABETH DIANE SENDICH
2008

DEDICATION

Dedicated to my parents, Dick and Deborah Newton, and to my husband,

Marc Sendich, your love and support kept me going.

ACKNOWLEDGEMENTS

I would like to thank my advisor, Bruce Dale, for his guidance and support.
Special thanks also go to my guidance committee Dr. Seungdo Kim, Dr. Dennis
Miller, Dr. Jon Sticklen, Dr. Timothy Harrigan, and Dr. William Northcott.

I am grateful to my many collaborators, especially Dr. C Alan Rotz, Dr.
Mark 8 Laser, Bryan Bals, and Pragnya Eranki.

I wish to acknowledge financial support from the Great Lakes Bioenergy
Research Center, Grant #DE-FCOZ-O7ER64494, and the Office of Biobased
Technology at Michigan State University. I also wish to thank the helpful staff in
the Department of Chemical Engineering and Material Science and my co-

workers in the Biomass Conversion Research Laboratory.

TABLE OF CONTENTS

 

Page

LIST OF TABLES ............................................................................................... IX
LIST OF FIGURES ............................................ XIV
KEY TO SYMBOLS AND ABBREVIATIONS ................................................. XVIII
CHAPTER 1: INTRODUCTION ............................................................................ 1
1.1 OBJECTIVES .................................................................................................. 4

1 .2 LIMITATIONS .................................................................................................. 5
CHAPTER 2: REVIEW OF LITERATURE ............................................................ 7
2.1 THE BIOREFINERY .......................................................................................... 7
2.1.1 Pretreatment .................................................................................................................. 8
2.1.2 Biological Processing ................................................................................................ 10

2.2 BIOMASS FEEDSTOCKS ................................................................................ 11
2.3 CROP MODELING ......................................................................................... 11
2.4 ANIMAL MODELING ...................................................................................... 12
2.5 SUSTAINABLE DEVELOPMENT ....................................................................... 13
CHAPTER 3: SELECTION OF A BIOREFINERY MODEL ................................ 15
3.1 BACKGROUND ............................................................................................. 15
3.2 MATERIALS AND METHODS ........................................................................... 17
3.3 RESULTS AND DISCUSSION ........................................................................... 20
3.4 CONCLUSIONS ............................................................................................. 25
CHAPTER 4: SELECTION OF AN AGRICULTURAL MODEL .......................... 27
4.1 BACKGROUND ............................................................................................. 27
4.1.1 Crop and Animal Modeling ........................................................................................ 27
4.1.1.1 Nitrogen Sub-models .......................................................................................... 29

4.1.1.2 DenitrificationlNitrification ................................................................................. 29

4.1.2 CENTURY/DA YCENT .................................................................................................. 30
4.1.3 IFSM ............................................................................................................................. 31
4.1.4 l-FARM ......................................................................................................................... 32

4.2 MATERIALS AND METHODS ........................................................................... 33
4.3 RESULTS AND DISCUSSION ........................................................................... 36
4.4 CONCLUSION ............................................................................................... 38

CHAPTER 5: INTEGRATION OF THE BIOREFINERY AND AGRICULTURAL
SYSTEM WITH THE BIOREFINERY AND FARM INTEGRATION TOOL (BFIT)

 

................................................................................................ =...40
5.1 BACKGROUND ............................................................................................. 40
5.2 MATERIALS AND METHODS ........................................................................... 41
5.3 RESULTS AND DISCUSSION ........................................................................... 51

5.3.1 Development Simulations .......................................................................................... 51

5.3.2 Sensitivity Analysis ................... 69

 

5.3.3 Scenario Tests ............................................................................................................ 73
5.4 CONCLUSION ............................................................................................... 97
CHAPTER 6: CONCLUSIONS AND SUGGESTIONS FOR FUTURE
RESEARCH ..................................... - __ -- ....... 100
6.1 CONCLUSIONS ........................................................................................... 100
6.2 SUGGESTIONS FOR FUTURE RESEARCH ....................................................... 102
APPENDICES ................................................................................................... 105
APPENDIX A ................................................................................................ 106
APPENDIX B ................................................................................................ 1 14
APPENDIX C ................................................................................................ 123
6.1 Sample Landscape Calculation ................................................................................. 123
0.2 Development Simulations .......................................................................................... 124
G3 Scenario Tests ............................................................................................................. 151
APPENDIX D ................................................................................................ 175
REFERENCES ................................................................................................. 179

viii

LIST OF TABLES

Page
Table 3.1: Financial parameters used in simulations described in this chapter. .18
Table 3.2: Abbreviations for simulation scenarios. .............................................. 21

Table 3.3: Ethanol production for various simulations presented in this chapter.24

Tables 4.1: Grading system used for evaluation of all the three models and the
available field data for Simulation of corn grain farms. Plus signs (+) indicate
agreement with other models and available field data in either individual variables
or separate states. Minus signs (-) indicate less agreement with other models
and available field data in either individual variables or separate states. ............ 35

Table 4.2: Evaluation system used in addition to Table 4.1 for comparison of
models to the available field data for simulations of switchgrass only ................. 36

Table 4.3: Assessment of model performance in simulations of corn grain
production for individual states over all outcomes for that state using the
evaluation system in Table 4.1 ............................................................................ 36

Table 4.4: Assessment of model performance in simulations of corn grain
production for each output variable across all geographic areas using the
evaluation system in Table 4.1 ............................................................................ 36

Table 4.5: Evaluation of model performance in simulations of switchgrass
production for individual states over all output for that state. The second row
uses the grading system in Table 4.1, and the third and fourth rows were
evaluated using the grading system in Table 4.2. ............................................... 37

Table 4.6: Evaluation of model performance in simulations of switchgrass
production for each output variable across all geographic areas. The second row
was evaluated using the grading system in Table 4.1, and the third and fourth
rows were evaluated using the grading system in Table 4.2 ............................... 37

Table 5.1: Outline of management strategies used in model development,
indicating whether animals are included and which crops are farmed. ............... 42

Table 5.2: Outline of locations used in model development, indicating
corresponding average farm size, dominant cattle type, and average herd size
based on acreage. States are standard two letter postal abbreviations. ............. 43

Table 5.3: Products sold by each farm management type for farm income by
location. .............................................................................................................. 44

ix

Table 5.4: Annual com grain yields by location obtained from USDA 11 year

averages upto and including 2006 [62]. .............................................................. 45
Table 5.5: Economics assumptions used for farm development simulations using

sources for the year 2006. .................................................................................. 47
Table 5.6: Economics assumptions used for biorefinery simulations. ................. 50

Table 5.7: Net return on a per acre basis for each farm type, as well as an overall
landscape average, for each location for development simulations. Values in
grey are farm types that were calculated as not occurring in the landscape

distribution ........................................................................................................... 54
Table 5.8: Net return on a per acre basis for control test of Table 5.7. ............... 65
Table 5.9: Sensitivity variables tested and the values used for each test. .......... 70

Table 5.10: Change in simulated outcome indicators as a ratio of percentage
outcome change to percentage sensitivity input change. Sensitivity tests are
outlined in Table 5.9 ............................................................................................ 71

Table 5.11: Outline of scenarios used to test BF IT for verification. ..................... 74

Table 5.12: Net return on a peracre basis for all scenario tests. Values in grey
are farm types that were calculated as not occurring in the landscape distribution.
............................................................................................................................ 80

Table A1: Biorefinery economic summary output for SSCF-COMP-OLD. ....... 106
Table A2: Biorefinery economic summary output for SSCF—COMP-UPD. ....... 107

Table A3: Biorefinery economic summary output for SSCF-NEW-OLD. .......... 108
Table A4: Biorefinery economic summary output for SSCF-NEW-UPD ........... 109
Table A5: Biorefinery economic summary output for CBP-COMP-OLD ........... 110
Table A6: Biorefinery economic summary output for CBP-COMP-UPD. ......... 111
Table A7: Biorefinery economic summary output for CBP—NEW-OLD. ............ 112
Table A8: Biorefinery economic summary output for CBP-NEW-UPD. ............ 113
Table 8.1: Field data sources with location and variable for which they were

used. ................................................................................................................. 120
Table 0.2.1: Parameter inputs for farm management A (Table 5.1). ................ 124

Table C.2.2: Parameters in addition to those listed in C.2.1 for farm management
B (Table 5.1). .................................................................................................... 128

Table 0.2.3: Parameters in addition to those listed in C.2.1 for farm management
C (Table 5.1). .................................................................................................... 130

Table C.2.4: Parameters in addition to those listed in C.2.1 for farm management
Y (Table 5.1). .................................................................................................... 131

Table 0.2.5: Parameters in addition to those listed in C.2.4 (management type Y)
for locations with beef rather than dairy. ........................................................... 135

Table C.2.6: Parameters in addition to those listed in C.2.4 for farm management
X (Table 5.1). .................................................................................................... 136

Table 0.2.7: Parameters in addition to those listed in C.2.6 (management type X)
for locations with beef rather than dairy. ........................................................... 139

Table C.2.8: Parameter in addition to those listed in 0.2.2 and C.2.6 for farm
management 2 (Table 5.1). .............................................................................. 140

Table 0.2.9: Parameter in addition to those listed in 0.2.8 (management type 2)
for locations with beef rather than dairy ............................................................ 141

Table 0210: Biorefinery economic summary output for development simulation
in IA at the 75% land use transportation radius ................................................. 142

Table C.2.11: Biorefinery economic summary output for development simulation
in MI at the 75% land use transportation radius. ............................................... 143

Table C.2.12: Biorefinery economic summary output for development simulation
in MN at the 75% land use transportation radius. ............................................. 144

Table C.2.13: Bioreflnery economic summary output for development Simulation
in NY at the 75% land use transportation radius. .............................................. 145

Table C.2.14: Biorefinery economic summary output for development Simulation
in OH at the 75% land use transportation radius ............................................... 146

Table C.2.15: Biorefinery economic summary output for development Simulation
in PA at the 75% land use transportation radius. .............................................. 147

Table C.2.16: Biorefinery economic summary output for development Simulation
in SD at the 75% land use transportation radius. .............................................. 148

Table C.2.17: Biorefinery economic summary output for development simulation
in TX at the 75% land use transportation radius. .............................................. 149

xi

Table C.2.18: Biorefinery economic summary output for development Simulation
in WI at the 75% land use transportation radius ................................................ 150

Table C.3.1: Parameter inputs for farm management A (Table 5.1). ................ 151

Table 0.3.2: Parameters in addition to those listed in C31 for farm management
B (Table 5.1). .................................................................................................... 154

Table C.3.3: Parameters in addition to those listed in 0.3.1 for farm management
C (Table 5.1). .................................................................................................... 156

Table C.3.4: Parameters in addition to those listed in C31 for farm management
Y (Table 5.1). .................................................................................................... 157

Table 0.3.5: Parameters in addition to those listed in 0.3.4 (management type Y)
for locations with beef rather than dairy. ........................................................... 160

Table 0.3.6: Parameters in addition to those listed in 0.3.4 for farm management
X (Table 5.1). .................................................................................................... 161

Table 0.3.7: Parameters in addition to those listed in C36 (management type X)
for locations with beef rather than dairy. ........................................................... 164

Table 0.3.8: Parameter in addition to those listed in 0.3.2 and C36 for farm - ~
management 2 (Table 5.1). .............................................................................. 165

Table C.3.9: Parameter in addition to those listed in C38 (management type 2)
for locations with beef rather than dairy. ........................................................... 166

Table C.3.10: Biorefinery economic summary output for the Decreased
Precipitation Scenario at the 75% land use transportation radius. .................... 167

Table C.3.11: Biorefinery economic summary output for the Increased
Switchgrass Productivity Scenario at the 75% land use transportation radius. .168

Table C.3.12: Biorefinery economic summary output for the Decreased Meat
Production Scenario at the 75% land use transportation radius. ...................... 169

Table C.3.13: Biorefinery economic summary output for the Decreased Winter
Temperature and Precipitation Scenario at the 75% land use transportation
radius. ............................................................................................................... 1 70

Table C.3.14: Biorefinery economic summary output for the Increased Meat
Production Scenario at the 75% land use transportation radius. ...................... 171

Table 0315: Biorefinery economic summary output for the Increased
Temperature and Precipitation Scenario at the 75% land use transportation
radius. ............................................................................................................... 172

xii

Table C.3.16: Biorefinery economic summary output for the Increased Corn
Stover Production Scenario at the 75% land use transportation radius. ........... 173

Table C.3.17: Biorefinery economic summary output for the Decreased
Biorefinery Productivity Scenario at the 75% land use transportation radius. 174

xiii

LIST OF FIGURES

Page
Figure 1.1: System boundary for integrated biorefinery system concept. ............. 3
Figure 2.1: Stages of the cellulosic ethanol biorefinery [14]. ................................. 7

Figure 2.2: Process diagram of the pretreatment stage of the biorefinery with the
older NREL recompression ammonia recovery approach ..................................... 9

Figure 2.3: Process diagram of the pretreatment stage of the biorefinery using
new quench ammonia recovery approach. ......................................................... 10

Figure 3.1: Economic comparison of various pretreatments made by Eggeman
and Elander in 2003 [20]. .................................................................................... 16

Figure 3.2: Simulation economic results as indicated by MESP. Abbreviations
are outlined in Table 3.2. .................................................................................... 21

Figure 3.3: TIC per annual gallon of ethanol produced for simulations described
in Table 3.2. ........................................................................................................ 23

Figure 3.4: Break down of operating costs for simulations listed in Table 3.2.....24

Figure 5.1: Conceptual framework and data flow for the biorefinery system
outlined in Figure 1.1. ......................................................................................... 41

Figure 5.2: Landscape area required to produce the biomass required for a 2,000
TPD biorefinery for development simulations. Participation of each farm type
contributing to the total area, also called “landscape distribution”, is indicated by
colors and patterns. ............................................................................................ 52

Figure 5.3: Annual income combined for all participating farms for development
Simulations by product source, animal agriculture or crop agriculture. Products
for each location are outlined in Table 5.3. ......................................................... 54

Figure 5.4: Total annual net return combined for all participating farms for
development simulations. Contribution of each farm type to the total profit is
indicated by colors and patterns. ........................................................................ 55

Figure 5.5: Landscape average annual nitrogen loss per acre by ecological
process from which they are lost for development simulations. Species produced
are N02, NO, N20, and N2 by denitrification, NH3 by volatilization, and N03 by
leaching. ............................................................................................................. 58

xiv

Figure 5.6: Greenhouse gas (GHG) emissions for all participating farms in the
landscape, all corresponding biorefinery operations, and biomass transportation
for development simulations. Emissions are broken out by source and given as
the sum of all GHG emissions in C02 equivalents. ............................................ 59

Figure 5.7: Total project investment (TPI) for each biorefinery development
simulation, which includes account for total installed equipment cost, facility
costs, and a 3% project contingency ................................................................... 61

Figure 5.8: Minimum ethanol selling price (MESP) calculated for each biorefinery
development simulation, which includes account for capital and operating costs

as well as a fixed 12% return to management. ................................................... 62
Figure 5.9: Redistribution of the landscape, which was fixed to development
simulation size, for control test of Figure 5.2 ....................................................... 64
Figure 5.10: Annual income for control test of Figure 5.4. Crop products do not
include biomass sales (switchgrass hay or corn stover). .................................... 65
Figure 5.11: Total annual net return for control test of Figure 5.3. ...................... 66

Figure 5.12: Average annual nitrogen loss per acre for control test of Figure 5.5.
............................................................................................................................ 68

Figure 5.13: Landscape Size and distribution for scenario tests having weather
adjustments ......................................................................................................... 75

Figure 5.14: Landscape size and distribution for scenario tests having
productivity adjustments. .................................................................................... 75

Figure 5.15: Landscape size and distribution for scenario tests having meat
production adjustments. ...................................................................................... 76

Figure 5.16: Annual income by product source for scenario tests having weather
adjustments. Products are the same as those in Figure 5.4. ............................. 78

Figure 5.17: Annual income by product source for scenario tests having
productivity adjustments. Products are the same as those in Figure 5.4. .......... 79

Figure 5.18: Annual income by product source for scenario tests having meat
, production adjustments. Products are the same as those in Figure 5.4 ............. 79

Figure 5.19: Total annual net return for scenario tests having weather
adjustments ......................................................................................................... 81

Figure 5.20: Total annual net return for scenario tests having productivity
adjustments ......................................................................................................... 81

XV

Figure 5.21: Total annual net return for scenario tests having meat production
adjustments ......................................................................................................... 82

Figure 5.22: Nitrogen loss, as described for Figure 5.5, for scenario tests having
weather adjustments. .......................................................................................... 86

Figure 5.23: Nitrogen loss, as described for Figure 5.5, for scenario tests having
productivity adjustments. .................................................................................... 87

Figure 5.24: Nitrogen loss, as described for Figure 5.5, for scenario tests having
meat production adjustments. ............................................................................. 88

Figure 5.25: Greenhouse gas (GHG) emissions for scenario tests having weather
adjustments ......................................................................................................... 90

Figure 5.26: Greenhouse gas (GHG) emissions for scenario tests having
productivity adjustments. .................................................................................... 90

Figure 5.27: Greenhouse gas (GHG) emissions for scenario tests having meat
production adjustments. ...................................................................................... 91

Figure 5.28: Total project investment (TPI) for scenario tests having weather
adjustments ......................................................................................................... 93

Figure 5.29: Total project investment (TPI) for scenario tests having productivity
adjustments ......................................................................................................... 93

Figure 5.30: Total project investment (TPI) for scenario tests having meat
production adjustments. ...................................................................................... 94

Figure 5.31: Minimum ethanol selling price (MESP) for scenario tests having
weather adjustments. .......................................................................................... 94

Figure 5.32: Minimum ethanol selling price (MESP) for scenario tests having
productivity adjustments. .................................................................................... 95

Figure 5.33: Minimum ethanol selling price (MESP) for scenario tests having
meat production adjustments. ............................................................................. 95

Figure 8.1: Com grain yield data and available published data for each state
included in this comparison. .............................................................................. 114

Figure 3.2: Corn grain nitrogen removal data and available published data for
each of the five states included in this comparison. .......................................... 114

Figure 8.3: Corn grain nitrogen leaching data and available published data for
each of the five states included in this comparison. .......................................... 115

xvi

Figure 3.4: Com grain denitrification data and available published data for each if
the five states included in this comparison ........................................................ 115

Figure 3.5: Corn grain evapotranspiration data and available published data for
each of the five states included in this comparison. .......................................... 116

Figure 8.6: Corn grain soil erosion data and available published data for each of
the five states included in this comparison ........................................................ 116

Figure 8.7: Switchgrass yield data and available published data for each of the
five states included in this comparison .............................................................. 117

Figure 8.8: Switchgrass nitrogen removal data and available published data for
each of the five states included in this comparison. .......................................... 117

Figure 8.9: Switchgrass nitrogen leaching data and available published data for
each of the five states included in this comparison. .......................................... 118

Figure 8.10: Switchgrass denitrification data and available published data for
each of the five states included in this comparison. .......................................... 118

Figure 3.11: Switchgrass evapotranspiration data and available published data
for each of the five states included in this comparison. ..................................... 119

Figure 8.12: Switchgrass soil erosion data and available published data for each
of the five states included in this comparison .................................................... 119

xvii

KEY TO SYMBOLS AND ABBREVIATIONS
AFEX = Ammonia Fiber Expansion
BFIT = Biorefinery and Farm Integration Tool
CBP = Consolidated Bio-Processing
DM = DryMatter
DML = Dry Matter Loss
GHG = Greenhouse Gas
IFSM = Integrated Farm System Model
IRR = lntemal Rate of Return
ISBAL = Integrated Biomass Supply Analysis and Logistics
LCA = Life Cycle Assessment/Analysis
MACRS = Modified Accelerated Cost Recovery System
MESP = Minimum Ethanol Selling Price
NREL = National Renewable Energy Laboratory
SHF = Separate Hydrolysis and Fermentation
SSCF = Simultaneous Sacharification and Co-Ferrnentation
TIC = Total Investment Capital
TPD = US Ton Per Day
TPl = Total Project Investment

USD ($) = United States Dollar

xviii

CHAPTER 1: INTRODUCTION

The world has recently been gripped by an energy crisis, and rapidly rising
oil prices have encouraged the need for alternative fuels. A shift to alternative
energy sources has also been hastened by the increasing awareness of global
environmental issues [1,2]. The development of a diversified and
environmentally sound energy portfolio requires a full understanding of the many
energy options currently available and those still in development.

Biological-based alternative fuels are often referred to as “biofuels”. The
production of select liquid biofuels, including ethanol, is performed in a facility
similar to an oil refinery and has thus become known as the “biorefinery‘. The
biorefinery is the combination of a number of chemical engineering unit
operations, many of which have recently become the center of heated debate.
As a result, there has been more research and development attention for all
elements of this industrial process concept, including its feedstock supply chain,
A which involves crop and forest sources [3,4,5].

The use of ethanol, traditionally made from corn grain, as an alternative
liquid transportation fuel is well accepted [6]. The use of cellulosic biomass
feedstocks for the biorefinery, however, is still in the stages of research and
development. The need to study a system for which we do not have a full-scale
commercial example or market requires the use of modeling tools to move us
forward.

According to Carolan et al., the risk for the biorefinery lies with both the

farmer and the biorefinery investOr, which creates a “Chicken and egg” situation

for the development of the biorefinery [7]. Modeling tools allow projection of
economic and environmental expectations for this system and can determine the
affect of changes for potential designs and commercialization.

The growing life cycle assessment (LCA) and sustainable development
disciplines corroborate the need for both environmental and economic impact
assessments to understand the feasibility and practicality of the biorefinery
system [8,9]. Researchers in these areas have emphasized the need for new
analyses to fill previous data gaps for the study of cellulosic ethanol [10].

Tools like the one developed here and the simulation results produced can
provide baseline expectations for the cellulosic ethanol biorefinery and the
surrounding farm system allowing further understanding of biorefinery system
feasibility. In addition, a research tool like the one presented here has great
potential for future modification and validation as real systems come online. By
de-risking investment in biorefinery technology and the feedstock supply chain,
investors and farmers alike can confidently become part of the US alternative
energy future.

Because of interaction between the biorefinery and its feedstock suppliers.
particularly the agricultural system, the assessment tool here must account for
the biorefinery, the crop production unit, and the animal production unit. The
interaction between the crop and animal units occurs by way of feed and manure
fertilizer exchange, and between these two systems and the biorefinery by way of
the biomass feedstock and pretreated biomass, which is used as a ruminant

feed. This study is a first attempt to directly simulate the biorefinery set into a

realistic landscape with both these agricultural units included. The proposed

integrated system boundary can be seen in Figure 1.1.

Fue_ls, Chemicals,
& Materials

 

 

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: Module :
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: Anlmal Energy & Protein , :
: MOdUIe Animal Feed Cogeneration :
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Food Steam
v Other? Electricity

 

Figure 1.1: System boundary for integrated biorefinery system concept.

Using an integration scheme to combine a previously developed
biorefinery model with a whole farm agricultural model, a new research tool
called Biorefinery and Farm Integration Tool (BFIT) was developed. BFIT will
allow expert users to analyze the environmentally relevant mass flows and
economics of a fully integrated US biorefinery producing cellulosic ethanol and
other secondary products over varying locations and farm types, which can direct

the future of renewable energy.

The use of pretreated grasses as an animal feed is currently under
investigation at Michigan State University, and a study of its feasibility is not
included in the research presented here. Instead this research uses historical
data about ammoniation of forage feeds, preliminary feedstock pretreatment
analysis, and related information on ruminant digestibility. Deeper investigation
into the viability of the feeds used in the animal production strategies included
here are already under way by others [11].

For this initial study used for BF IT development, a biorefinery receiving
2000 TPD of biomass is simulated in nine agriculturally relevant states within the
continental US under various farm production practices. Analysis of BFIT
biorefinery and landscape results will allow, for the first time, a baseline
projection of the economic feasibility and environmental impacts of cellulosic
ethanol. Moreover, the simulations presented here will allow side-by-Side
comparison of similar biorefineries across the US, which can project regional
variance trends. The study here is concluded with a series of basic scenario
tests, which verify this tool and reveal potential future uses for estimating the
consequences of biorefinery system changes.

1.1 Objectives

The primary goal of this work is to develop a research modeling tool to study the
biorefinery and its surrounding landscape. This first study of the system will
identify trends and build a foundation for future decision-making research, which

will identify winners and losers in the system. To achieve this objective and

address some of the issues described in this chapter, the specific research
phase objectives are as follows:
a Alter the leading biorefinery model for integration and confirm model
simulation results are consistent with previous publications
. Test leading agricultural models through Simulation and select a model for
use with the biorefinery model
. Design a research tool around the two previous model selections with long-
term viability by using programs that are readily available to expert users
0 Integrate the pieces of the system and fully develop a tool that enables
cradle-to-gate assessment/analysis of the “whole biorefinery system”
. Perform basic simulations and a sensitivity analysis, and analyze
independent verification results to assess the research tool function
0 Evaluate model results trends and their significance
0 Suggest way to elaborate on the findings presented here
' 0 Write a trained users’ guide to aid future users in installation and operation
of the research tool
0 Automate this unique tool to limit required user engagement and thereby
improve “user-friendliness”
1.2 Limitations
The limitations for the scope of this research are as follows:
. Geographical locations are limited to the nine locations within the

contiguous US selected for this initial study

Agricultural management limitations are defined by the existing model
selection’s limits

The landscape design and calculation are based on the user selected
feedstock intake rate of the biorefinery, making the biorefinery intake the
functional unit

Biomass sources are limited to agricultural sources, specifically corn stover
and switchgrass

The temporal setting chosen for final development is the year 2006 to give

- full and reliable input data sets, which maintains consistency in

development

CHAPTER 2: REVIEW OF LITERATURE
2.1 The Biorefinery
The combination of unit operations used to derive the liquid transportation
fuel ethanol is commonly referred to as a biorefinery [12,13]. A diagram outlining

the stages of the biorefinery can be seen in Figure 2.1 [14].

   

Enzyme
Production

 

 

     

 

        

 

 

 

 

 

 

 

 

 

Solid/Liquid ‘- f Enzymatic .
EPretreatment 1 Separation a. Hydrolysis f» (3:33:36

 

  
    

 

 

. Residue
Processing ‘

 

A Hemlcellulose
Sugars

 

E Fermentation of . l E

 

 

 

 

 

 

Products

 

Figure 2.1: Stages of the cellulosic ethanol biorefinery (adapted from DOE diagram) [14].

Many of the stages in the biorefinery follow well developed industrial
processes, but some are still the subject of research. The first stage,
pretreatment, and the steps boxed together, biological processing, are of
particular interest in research and have thus been highlighted in sections 2.1.1
and 2.1.2.

The remaining stages in the biorefinery are ethanol (product) recovery,

wastewater treatment, and on-site utilities, which includes residue processing.

Ethanol recovery aims to use as little energy as possible while still maintaining
high ethanol purity. This is achieved through distillation followed by molecular
sieves [15]. Water treatment and utilities refers to the cleanup of water to be re-
used in the system and its subsequent heating or cooling for use in the
biorefinery unit operations. Wastewater treatment systems may include a
suspended sludge system or an immobilized film anaerobic digester [16].
Heating of the returned water occurs as part of a steam and electricity production
system, which involves the combustion of biomass residues using methane
collected during anaerobic wastewater treatment to produce steam, used for heat
in the biorefinery, and the subsequent use of that steam in a Rankine cycle to
produce electricity, all equal to or in excess of the requirements of the biorefinery
[4]-
2.1.1 Pretreatment

AFEX is mild pretreatment that uses concentrated ammonia, heat, and
rapid pressure release to increase digestibility of biomass feedstocks. It is fairly
unique because it is a dry-to-dry process and requires no detoxification steps
following pretreatment, both of which tend to make it a highly desirable
pretreatment. AFEX has been studied for some time and continues to improve
with development [17,18,19].

The approach for recovering and recycling ammonia for use in
pretreatment has seen improvement as well. The biorefinery model designed by
NREL in 2003 used evaporation, distillation, and vapor compression to recover

and recycle ammonia, whereas current work uses an innovative quench system

[19, 20,21]. In this quench system, the pretreated slurry is flashed, stripped with
steam, and then resulting ammonia vapor from these two steps is condensed by
a combination of direct water quenching and indirect cooling with both cooling
and chilled water. This system is envisioned as using processing equipment that
is similar to that used for direct steam drying of solids [22,23]. The process flow

diagram for each of these two recover systems can be seen in Figures 2.2 and

 

  

 

   
 

 

 

 

 

 

 

 

2.3.
A NH3 2%} NH3
M
Condenser .
(J gyms:
H20 ' AFEX Reactor D_ryg NH3 Column
Biomass \___J 7L

 

 

V A

Figure 2.2: Process diagram of the pretreatment stage of the biorefinery with the older NREL
recompression ammonia recovery approach.

H20

Pump Condenser 2 Condenser 1
@ NH3 Vapor
NH4OH NH4OH/NH NH4OH/NH Q
3 3

Heater
Steam /—_—l NH3 Column
7 , AFEX Reactor t

Pretreated Slurry
Biomass
S

Figure 2.3: Process diagram of the pretreatment stage of the biorefinery using new quench
ammonia recovery approach.
2.1.2 Biological Processing

 

 

 

 

.2..

Pretreatcd Slurry

7

Biological processing refers to a system that uses saccharolytic enzymes
to hydrolyze structural carbohydrates (cellulose and hemicellulose) to oligomers.
These oligomers are then further hydrolyzed to monomers and dimers. Finally
these five and six carbon simple sugars are fermented to ethanol or other
products such as lactic acid [24]. SHF is a biological processing system that
performs all the steps mentioned above separately. SSCF performs the four
biologically-mediated events mentioned above in two separate process steps,
first enzyme production and then hydrolysis and simultaneous 5 and 6 carbon
sugar fermentation. CBP performs all four steps of enzymatic hydrolysis and
fermentation in a single vessel and is currently under study in various
laboratories, though it has not yet been perfected. For CBP to become viable,
microorganisms capable of utilizing all the appropriate components of biomass to
produce ethanol at high yields and concentrations must be developed. Such a

development would be a breakthrough that would Ultimately reduce the cost of

10

ethanol biorefining well below the current biological processing method, SSCF
[25].
2.2 Biomass Feedstocks

The Department of Energy defines biomass as “all plants and plant-
derived material” [26]. Examples of current biomass usage include oil crops for
biodiesel, starch crops for fuel alcohols, and forest and agricultural residues for
combustion or, as presented in this work, alcohol production [3]. For the work in
question, the biomass used is a combination of corn stover and switchgrass.
Corn stover refers to the portion of the corn plant Zea mays that is currently
unused (residue), which includes the cob, stalk, leaves, and husks [26].
Switchgrass refers to the prairie grass Panicum virgatum, which IS a US native,
perennial grass [4].

The benefits of using corn stover as a feedstock come primarily from its
availability, due to the large amount of corn cropping currently practiced in the
US, and its low cost, because it is a residue [26]. Switchgrass provides value to
the agro-fuel system because it is highly productive (tons per acre), requires little
input (fertilizer and irrigation) to achieve these yields, and is native to the US
posing little threat to the natural ecosystem, in fact potentially providing increased
habitat for some species [4].

2.3 Crop Modeling

The area of crop modeling has produced a limited number of crop farming

models, which simulate multiple farming activities by combining a number of

submodels. IFSM, for example, combines plant growth submodels, such as

11

ALSIM, an alfalfa only growth model, and CERES-Maize, a corn growth model,
with hydrological and soil submodels, and management related submodels,
simulating items such as machinery and farm economics [27]. Unfortunately, a
number of model focus only on farm economics, such as the IBSAL model and
BIOCOST [28,29,30].

Development of user-friendly interfaces is another hurdle facing crop
model progress. Farm models that use languages like FORTRAN and C++
without a user-interface can hinder users from making changes easily and allow
those changes made to introduce error into the code [31]. Having a program
environment that is not easy to use may limit the audience of a model,
particulariy the rural farmer.

Another limitation in current crop models is simulation of interaction with
animal systems, such as grazing by animals or application of animal manure as
fertilizer [32]. Some models allow Simulation of effects of animal systems, such
as DAYCENT that allows grazing, but only account in a limited way for these
interactions between plant/soil and animal systems [33].

2.4 Animal Modeling

As with crop modeling, modeling of livestock typically focuses on
management and economics. It is now desirable to simulate gaseous emissions,
as a result of the environmental movement, or empirical production, because it is
directly related to farm economics [34,35,36]. Those models that do Simulate
livestock systems focus mostly on cattle because they are the predominant

livestock in the US [37].

12

As pointed out in section 2.3 limited work has been dedicated to animals
in a whole farm system, but researchers have begun to recognize the importance
of integration of livestock systems with cropping systems [32]. For livestock
models that do currently simulate some crop activities, such as DairyWise dairy
farm model, there remain limitations, such as Simulation of only popular feed
crops like grazed grasses or com [34]. Whole farm simulation models having
both crop and animal management flexibility are uncommon, which helps to
highlight leading models for simulation of crop and animal combinations along
with associated soils, emissions, and economics, as described later in this work
[27,38,39,40].

2.5 Sustainable Development

The continually growing area of sustainable development calls for
assessment of both environmental and economic impacts, which are critical to
understand the feasibility of the system or process, such as the biorefinery [8,9].
Growing concern for human effects on the planet drives a movement to improve
the environmental characteristics of existing systems and design new ones with
the potential environmental impact in mind. More over, sustainable development
also calls for this environmental awareness to be combined with a mind for the
maintenance of human standards of living, thus encouraging clean, socially
conscious behaviors that do not cripple economies with costly technologies or
programs [9].

One method for assessment of these impacts is LCA. LCA has recently

been accepted by many organizations and governments, and now has agreed

l3

upon standards for assessment and analysis, which have been published by the
lntemational Standards Organization [41]. Adherence to these standards is
difficult for the study of the biorefinery system due to the lack of measured data
and limited availability of reliable and comparable emissions data for the
surrounding landscape. Following a framework for assessment Similar to LCA, it
is possible to collect and report individual outputs that indicate environmental
performance instead of reporting LCA impact categories that would encompass

that output, for example reporting nitrogen leached rather than eutrophication.

14

CHAPTER 3: SELECTION OF A BIOREFINERY MODEL
3.1 Background

In an effort to demonstrate the biorefinery model as it has been updated
for use at Michigan State University and to reveal economic advantages of
improvements in the AFEX process, simulation work was done using the latest
iteration of the biorefinery model developed at NREL.

The biorefinery model developed at the NREL was used to produce cost
estimates of pretreatment options as embedded in the overall biorefinery using
the Chemical engineering modeling software ASPEN PLUS, along with an
economics workbook in Microsoft Excel [21]. This model has since been
updated to allow for possible technology developments by researchers at
Dartmouth College with input from collaborators at Michigan State University
[20]. These alterations include eliminating feedstock washing, including an
innovative ammonia recovery approach, and raising the feedstock feed rate to
5,000 TPD.

The initial economic analysis at NREL compared dilute acid, hot water,
ammonia recycle percolation, AFEX, and lime pretreatments on corn stover [20].
This study found that the MESP of ethanol fuel produced using AFEX
pretreatment was approximately $1.41/gallon using data available in late 2003.
The results of this economic comparison of pretreatments are summarized in
Figure 3.1. The MESP is the lowest price at which ethanol produced in the
biorefinery can be sold to maintain a set IRR, while accounting for feedstock

costs, capital and operating costs, and secondary products sold at market value.

15

 

 

MESP
($/gal)

 

 

 

2001 Dilute Hot AFEX ARP Lime Corn
NREL Acid Water Dry
Design Mill

 

I Net Stover D Other Variable I Fixed w/o Depreciation
Depreciation Ell Income Tax Return on Capital

 

 

 

Figure 3.1: Economic comparison of various pretreatments made by Eggeman and Elander In 2003

Important advances in the AFEX pretreatment, and other parts of the
system, have occurred since NREL’s initial economic comparison [20]. The work
presented here reveals the economic impacts of these AFEX process advances
in the context of SSCF to produce and ferment sugars from AFEX treated corn
stover. Furthermore, this work reveals the reductions in MESP that can be
realized by pairing this improved AFEX pretreatment system with CBP. These
biological processing options have already been outlined in Section 2.1.2.

It can be seen in Figure 3.1 that feedstock cost is a relatively small fraction
of the cost for most of the pretreatment options, while processing costs are the
largest portion. This is characteristic of an immature process. Reduction of

these processing costs is necessary to make cellulosic ethanol more competitive

with petroleum-derived fuels. The AFEX process and the entire cellulosic
ethanol production system are Clearly not mature. A “mature” process
technology can be described as having raw material costs of approximately 70%
of the total manufacturing cost. By this definition, petroleum refining and corn
wet milling industries are both mature [42].
3.2 Materials and Methods

The specific changes to AF EX process were reduction of ammonia
loading and recycle concentration (using concentrated aqueous ammonia in
AFEX rather than anhydrous), updating the ammonia recovery approach, and
reduction of enzyme loading in hydrolysis. Ammonia loading refers to the ratio of
ammonia to dry biomass fed to the AFEX reactor, and in model simulations
ammonia loading was varied from 0.8 to 0.2 g NH3: 9 dry biomass. Ammonia
recycle concentration refers to the concentration of ammonia by mass in the
recycle stream, which is combined with the fresh ammonia make-up stream and
then fed to the AFEX reactor. In simulations, this parameter was varied between
70-99% by mass (ammonia in water). Enzyme loading refers to the ratio of
cellulase enzyme to glucan fed to the AFEX reactor, where glucan fed is
calculated as the dry biomass feed rate multiplied by its glucan content. This
parameter was modeled at 7, 15, and 60 FPU/g glucan, where FPU, filter paper
units, is a measure of the enzyme activity. An enzyme loading of 15 FPU was
found to be the economic optimum, and was thus fixed at this value for all

subsequent Simulations [43].

17

For calculation of MESP, assumptions use for the current work vary
somewhat from the previous model work at NREL, but the calculations are
performed using the same equations [20,21]. Assumptions for the previous work
at NREL include a corn stover feedstock cost of $35/dry ton, an IRR of 10%, and
additional feedstock costs for cellulase and com steep liquor [20]. Assumptions
for the current work include a corn stover feedstock cost of $40/dry ton, an IRR of
12%, and no requirement of corn steep liquor or cellulase as feedstock when
implemented with CBP. As a result of increased feedstock cost and IRR
assumptions, the current results require a higher MESP to meet performance
objectives and thus represent a more stringent test of profitability than the
previous Simulations. An outline of all key financial parameters used in the

economic analysis of the simulations for this work can be seen in Table 3.1.

Table 3.1: Financial parameters used in simulations described in this chapter.

 

Parameter NREL 2004 All Other Scenarios
Debt/Equity ratio 0/100 40/60
Loan rate (APR) Not Applicable 7.5%
IRR 10% 12%
Federal & state tax rate 39% 39%
Economic life 20 years 25 years
Depreciation period 0 7 years for general plant 0 7 years for general plant
. 20 years for power & steam - 20 years for power & steam
production production
Depreciation method MACRS MACRS
Capital Charge rate 18% ~17%
Indirect costs 48% of total installed capital 48% of total installed capital

In addition to the differences already described, the present work differs
from the previous work done at NREL in many of the stages depicted in Figure

2.1. Careful consideration of these differences is necessary to provide

18

appropriate comparisons of the systems modeled. The initial NREL model and
the updated model used here differ in the approach for biological conversion,
plant size, feedstock handling, product recovery, wastewater treatment, and on-
site utilities.

The biological processing step differs by Changing from SSCF in the
NREL model to CBP or SSCF in the current model. The difference between
these two biological processing options has already been outlined in Section
2.1.2. It is important to note that in the current model conversion of all sugars to
ethanol is assumed to be 95% when CBP is used. Although the AFEX
pretreatment meets this test for glucose with SSCF, it has not yet demonstrated
this level of conversion with CBP; this assumption represents future technology
and performance. SSCF is modeled to use a separate conversion for 5 carbon
sugars than 6 carbon sugars, and both types of biological conversion follow the
same process scheme in simulation as Eggeman and Elander [21].

Another Change made for the current work was a decrease in the
feedstock flow rate, from 5,000 TPD in the newer Dartmouth model back to the
original NREL feed rate of 2,205 TPD to allow better comparison between current
results and those from the NREL modeling exercise [20].

Other process Changes are as follows. Ethanol recovery in the current
model was updated from the NREL approach to reduce energy consumption, but
still maintain high ethanol purity. The evaporative concentration of ethanol
distillation bottoms liquid was eliminated, and replaced by a single distillation

column with direct steam injection and an intermediate heat pump with optimal

19

Side-stream return followed by molecular sieving. For wastewater treatment, an
immobilized film anaerobic digester system replaced a suspended sludge system
in the NREL model. For the current model a chilled water system was added to
the utilities section to enable full condensation of recycled ammonia in the new
ammonia recovery system.

The final major change in the current model is the ammonia recovery
approach used with AFEX in the pretreatment stage. Previous versions of the
biorefinery model used the traditional distillation and compression ammonia
recovery system outline in Figure 2.2 in Section 2.1.1, where as the updated
model includes the innovative ammonia quench recovery system in Figure 2.3 of
Section 2.1.1.

AS part of the design of the new quench ammonia recovery approach, the
AFEX process must still effectively treat biomass using ammonium hydroxide
rather than pure anhydrous ammonia, due to the mixing of water and ammonia in
the recycle stream. In contrast, the previous or “Classical” approach to AFEX
involved adding anhydrous ammonia to biomass containing various moisture
levels [43]. Experiments verifying concentrated ammonium hydroxide use in
AFEX with acceptable resulting enzymatic hydrolysis yields can be found in
Sendich et al. [19].

3.3 Results and Discussion

The effects on MESP of Changing both AFEX process parameters

(ammonia loading and concentration of ammonia in the recycle stream) and the

configuration of the ammonia recovery process can be seen in Figure 3.2. The

20

abbreviations for each simulation in Figure 3.2 are spelled out in Table 3.2,

highlighting the changes being made, individually or in combination [44].

 

Fl Stover Feedstock Cost I Other Processing CostsI

MESP ($lgal)

 

 

NREL- SSCF- SSCF- SSCF- SSCF- CBP- CBP- CBP- CBP-
2004 COMP— COMP— NEW- NEW- COMP- COMP- NEW- NEW-
OLD UPD OLD UPD OLD UPD OLD UPD

Figure 3.2: Simulation economic results as indicated by MESP. Abbreviations are outlined in Table
3.2.

Table 3.2: Abbreviations for simulation scenarios.
Abbreviation Meaning
SSCF-COMP—OLD SSCF, NH3 Recompression, Old AFEX parameters
SSCF-COMP-UPD SSCF, NH3 Recompression, Updated AFEX parameters
SSCF-NEW-OLD SSCF, New NH3 Recovery approach, Old AFEX parameters
SSCF-NEW-UPD SSCF, New NH3 Recovery approach, Updated AFEX parameters
CBP-COMP-OLD CBP, NH3 Recompression, Old AFEX parameters
CBP-COMP-UPD CBP, NH3 Recompression, Updated AFEX parameters
CBP-NEW-OLD CBP, New NH3 Recovery approach. Old AFEX parameters
CBP-NEW-UPD CBP, New NH3 Recovery approach, Updated AFEX parameters

The results in Figure 3.2 show a reduction in MESP with the new AFEX

process parameters compared to the previous result of $1 .41/gallon (Figure 3.1),

21

regardless of the ammonia recovery configuration with which they are Simulated.
The new ammonia recovery approach also shows reduced MESP over the
previous recovery approach, regardless of which AFEX process parameters are
used. A biological process Change of CBP rather than SSCF also exhibits
considerable cost savings. Combining the new recovery approach with the
updated process parameters, an advanced technology scenario, yields further
enhanced economics (lower MESP). A full summary of economics for each
Simulation can be found in Appendix A.

At the scale in Figure 3.2, 2,205 TPD, the lowest MESP projection has a
feedstock cost that is roughly 50% of total production cost. Hence, even after
inclusion of these process enhancements, the advanced system still falls short of
a “mature” cellulosic ethanol industry, where costs would be 70% feedstock to
30% processing cost. Increasing plant scale to 5,000-10,000 TPD would
increase the ratio of feedstock to processing costs and bringing the system
closer to “maturity”, but further reductions in processing costs can and should be
anticipated as technologies improve.

A comparison of the TIC per annual gallon of ethanol produced for all of
the simulations summarized in Table 3.2 can be seen in Figure 3.3. TIC per
gallon of annual capacity for the most advanced of these cases is comparable to
current TIC per gallon of annual capacity for the corn ethanol industry, which is

estimated at $1.25 per annual gallon for similar plants at this scale [45].

22

$1.81 -, W - W3

 

$1.61

 

$1.41
$1 .21
$1.01 a
$0.81
$0.61
$0.41

$0.21

 

 

$0.01

TIC/Annual Gal. EtOH (USD/gallyr)

NREL- SSCF- SSCF- SSCF- SSCF- CBP- CBP- CBP- CBP-
2004 COMP- COMP- NEW- NEW— COMP- COMP- NEW- NEW-
OLD UPD OLD UPD OLD UPD OLD UPD

Figure 3.3: TIC per annual gallon of ethanol produced for simulations described in Table 3.2.

A summary of the operating costs for the Simulations listed in Table 3.2
can be found in Figure 3.4. This figure shows the gradual reduction of operating

cost as process improvements are made.

23

 

Ll Feedstock I Other raw materials CI Waste disposal rm Labor 8. related Power credfl

 

$1.00

Operating Costs ($lgd)

 

$0.80 -

$0.60 ~

$0.40 -

$0.20 -

 

 

     

 

 

 

 

   
      

 

   

 

         

 

     

 

 

so.oo - . 7/
/ VA
%
-so.20 , . . . e - . .
NREL SSCF- SSCF- SSCF- SSCF- CBP- CBP- CBP- CBP-
2004 COM P- COM P- NEW- NEW- COM P- COM P- NEW- NEW-
OLD UPD OLD UPD OLD UPD OLD UPD

Figure 3.4: Break down of operating costs for simulations lIsted in Table 3.2.

The ethanol yields that were assumed for each simulation of the

biorefinery are given in Table 3.3. These yields are reasonably conservative and

will increase with improved technologies.

Table 3.3: Ethanol production for various simulations presented in this Chapter.

 

Scenario Ethanol Production
(MM gal/yr)

NREL 2004 70-5
SSCF-COMP-OLD 59-7
SSCF-COMP—UPD 59-7
SSCF-NEW—OLD 59-5
SSCF-NEW—UPD 69-5
CBP-COMP-OLD 78-1
CBP-COMP-UPD 73-1
CBP-NEW—OLD 78-0
CBP-NEW—UPD 77-9

24

3.4 Conclusions

This work has shown that the updated biorefinery model is capable of
reproducing the NREL results using Similar technologies, and it is therefore
effective for economic comparisons, here and in future work.

This work points to elements that are critical to biorefinery design Including
pretreatment, ammonia recovery, biological process Choice, and biorefinery size.
Historically the processing industries use improved technologies and techniques
to decrease processing costs thus increasing the ratio of feedstock cost to
processing cost. By minimizing the amount of water and ammonia used in the
AFEX process, and efficiently recovering and recycling ammonia, processing
costs are Shown here to be greatly reduced over estimates that are only a few
years old. Decreasing ammonia loading and lowering ammonia recycle
concentrations results in less total ammonia that must be recovered and
concentrated for each gallon of ethanol produced, ultimately driving down capital
and operating costs. The new ammonia recovery process significantly reduces
operating costs by using cool water rather than mechanical energy to recover
aqueous ammonia. The capital cost for pretreatment, however, is not greatly
affected by this new ammonia recovery approach.

This work Shows that while SSCF provides attractive ethanol prices, even
better economic performance can be expected if CBP can be realized. It is
important to note that the feedstock cost per gallon of ethanol produced in all the
cases is approximately equal. Any cost reductions seen in the simulations

studied here are a direct result of processing developments. As mentioned

25

previously, using petroleum refining as a classical example of a mature
processing industry, process maturity is achieved when raw material costs are
approximately 70% of the manufacturing costs, and the remaining 30% is
processing costs. Given the present feedstock cost assumption of $40/ton,
process maturity for cellulosic ethanol will be achieved at an MESP of about
$0.56/gallon. Therefore even with the progress described here, process maturity

is still some distance in the future.

26

CHAPTER 4: SELECTION OF AN AGRICULTURAL MODEL
4.1 Background

In consideration of the biorefinery system as described in Section 2.1, an
analysis of available simulation programs was completed to evaluate which
cropping and/or animal (agricultural) production model would be best suited for
integration with the biorefinery simulation program selected for future Simulations
in Chapter 3.

In modeling it is often difficult to select the most appropriate model for a
specific application, thus the model that is most familiar to the user is often
chosen [31]. In an effort to make the best and most rational model selection, this
evaluation was performed with leading applicable crop and/or animal system
models, including DAYCENT, IFSM, and l-FARM. The task of evaluating
Simulation programs is not a simple one. Each model is a combinationof several
sub-models, which contain complex algorithms. Each model considered in this
evaluation is described in detail in later sections.

4.1.1 Crop and Animal Modeling

The difficulty in assessing the many varied approaches for modeling crop
or animal systems is that there is no right or wrong approach, and often times
“model validity is in the eyes of the user" [46]. Many users find that their needs
would be met best if they could pick and Chose capabilities from a variety of
existing models to achieve their purposes. Using a model without any alteration
is particularly difficult given that a single improperly estimated sub-process can

result in general error, which may produce a result that fits within experimental

27

deviation, but is nonetheless wrong in a specific detail [31]. Evidence of this can
be seen in the work of Marchetti, et al. [47]; using a simple sensitivity analysis
they showed comparable algorithms can still lead to dissimilar results.

Ideally a user would select the most desirable pieces of different models
and combine them, but many sub-models are difficult or impossible to extract
from the model in which they are imbedded. This occurs because the method of
handling carbon and nitrogen often interacts with other processes in the
agricultural system, such as water movement, chemical transport, plant growth,
and management practices, and this coupling makes sub-model division difficult.
Modeling of organic matter and surface residues also creates integration
incompatibilities. Separating organic matter and residue into “pools”, and
assigning conversion rates to these “pools”, is intricate and varies among
programs [46]. Recently, two of the models being studied in this work were used
in combination, but it should be noted that the models were used for two very
different purposes, IFSM for farm machinery and DAYCENT for biogeochemical
cycling [48].

A final challenge in selecting and implementing a crop and/or animal
model is the interface. As described in section 2.3, older models using
programming languages without user-friendly interfaces hinder the addition of
new elements, or require substantial alteration to the source code to make
changes, which can introduce error in the program [31]. This is certainly the

case for DAYCENT.

28

4.1 .1 .1 Nitrogen Sub-models

In any plant growth model with a soil sub-model the simulation of nitrogen
species is the most difficult because of the complexity of interactions and the lack
of accurate field data [31]. Nitrogen sub-models typically simulate a nitrogen
budget using four major processes: crop nitrogen uptake, soil nitrogen
mineralization, denitriflcation, and leaching, and are therefore easily influenced
by nitrogen application rates and soil Characteristics entered by the user [47]. To
simulate these four processes nitrogen sub-models consists of either empirical
models, also called functional models, or mechanistic models.

Empirical models are effective, but they are not as robust or easily
adjusted to accommodate new conditions, locations, and crops. In contrast,
mechanistic models are more robust because they use algorithms to simulate the
actual physical and biological process that occur in natural systems. The
disadvantage of mechanistic models-is their complexity, which makes them more
difficult to operate because of additional required inputs and validation can be
difficult. Unfortunately, even if the model outputs agree with field measurements
it does not necessarily indicate that the processes in the model are properly
simulated. For these reasons Simpler functional models may perform better than
their complex mechanistic counterparts simply because they do not require as
many input parameters that are difficult to measure [31].

4.1 .1 .2 DenitrificationlNitrification
Of the four parts of the nitrogen sub-model, nitrification and denitrification

are the most difficult to measure in the field and to simulate. The direct

29

measurement of nitrification and denitrification is difficult because of the spatial
and temporal variability of anaerobic and aerobic zones in the soil profile. The
difficulty in simulating these processes is a result of the lack of complete
understanding of nitrification and denitrification. Because of these difficulties,
many nitrification and denitrification models are empirical in design [46].

In agricultural soils, the aerobic process of nitrification and the anaerobic
process of denitrification are constantly and simultaneously occurring. The
conditions that affect these processes in the soil include the concentration of
ammonium and nitrate ion, for nitrification and denitrification respectively, soil
water content, water filled pore space (VVFPS), temperature, carbon availability
for denitrification, and soil physical properties [49].

4.1.2 CENTURY/DAYCENT

The CENTURY model is one of the leading models for estimating long-
term environmental impact of crop systems and managed forests. The need for
short-term calculations, for which this long-term model is not appropriate, was
recently adjusted to Simulate daily time steps and renamed DAYCENT.

The DAYCENT model is of intermediate complexity, using a mechanistic
approach for important processes and empirically derived equations for other
processes. It contains sub-models for plant productivity, decomposition of dead
plant material and soil organic matter, soil water and temperature dynamics, and
trace gas fluxes. While the plant production sub-model of DAYCENT can
simulate a variety of crops, trees, and grasses, it is limited to simulation of only

one plant type (crop or grass) and one tree type at one time [33]. In a study by

30

Del Grosso, et al., DAYCENT was shown to simulate the outputs for gaseous
nitrogen emissions calculation well, but under high Spatial or temporal variation
the output values do not match well to observed data [33].

One feature of DAYCENT that was convenient for this work is its ability to
simulate both air and soil emissions and losses of nitrogen, carbon, and
phosphorus, which are included because it was designed as a biogeochemical
cycle model. A drawback to working with DAYCENT is the extensive
requirements for input data and the undeveloped, DOS based data entry format.
A publication by the authors of the software indicates that input parameters
required by DAYCENT are “Often available for many regions” [33].

4.1.3 IFSM

The whole farm Simulation model IFSM is a USDA program that was
developed from expansion of the dairy forage system model, DAFOSYM. The
major sub-models included in IFSM are crop growth, harvest, storage, beef or
dairy cattle feed utilization, manure handling, nutrient flows, crop establishment,
and economic analysis. Each individual crop growth sub-model is based on a
specific species using previously published models, such as CERES-Maize for
corn or ALSIM1 for alfalfa. All the various sub-models combine empirical and
mechanistic modeling to produce model results [27,40].

One advantage IFSM has over models like DAYCENT is the ability to
simulate a whole farm system with a variety of crop and cattle options, including
simulation of crop storage and nutrient losses locally and on the field [27,40].

Also, the well developed, user-friendly interface of IFSM allows easier use and

31

reduces the likelihood of user-introduced error in the system. One limitation of
this interface, however, is that for some items the user is confined to the
selections available in a drop-down list of options.
4.1.4 I-FARM

l-FARM is an online. database-driven whole farm simulation model
developed at Iowa State University. It allows simulation of a variety of crops and
crop rotations, including associated practices such as tillage, fertilization,
planting, weed control, harvesting, and removal of residue. The crop yields in l-
FARM are location, thus soil type, dependent and representative yields are
internally referenced for simulation from the integrated SSURGO soil database.
The model carbon balance is estimated using the integrated SCI-index, and soil
erosion is calculated by the integrated RUSLE soil erosion model [38]. The
livestock production model uses feed intake, growth rate, grazing, and
confinement options, and contains a manure management system. Economic
analysis is performed on all systems after initial Simulation. Output tables from
the model include: livestock import/export and carcasses processed; crop,
forage, and biomass import/export; manure and fertilizer import/export; nutrient
balance of N, P, and K at the field and farm scale; soil water erosion and soil
conditioning index; energy requirements for field operations at the farm scale;
labor requirements at the farm scale; and economic impacts at the
farm/enterprise scale [39].

As with IFSM, l-FARM has the advantage of simulating the whole farm,

with the added benefit of offering multiple livestock types, not just cattle. The

32

user-friendly, online interface of l-FARM makes it easy to use and accessible to
anyone with an intemet connection, while preventing users from introducing
error, just as with IFSM. A major disadvantage of I-FARM is the limitation of
simulation locations. l-FARM only allows simulation of a portion of states,
because of the limited weather and soils database [38].
4.2 Materials and Methods

The work presented here began with an investigation of the outputs
modeled by DAYCENT and IFSM, and which of the comparable variables of the
two programs were of most interest for potential future work. It was then
determined which locations simulated by both models had available soil and
weather information. The five locations chosen were: Lancaster, PA; Roanoke,
VA; Black Hawk County, IA; Sangamon County, IL; Branch County, MI. The Six
variables chosen were: crop yield, crop nitrogen removal, nitrate leaching,
denitrified nitrogen, evapotranspiration, and soil erosion. AS a way to further
assess the flexibility and usefulness of these models, simulations were run for
two different crop systems with different management systems. The first crop
arrangement was a 1000 acre corn farm using no-till management with a
nitrogen fertilizer application according to model suggestions. The second crop
system was a 1000 acre switchgrass farm using no-till management with two
harvest dates, May 21 and July 10, and late fall grazing (Sept-Oct). The l-
FARM model is unable to simulate grazing on switchgrass at present, and was

therefore only included in the simulations done for com production.

33

To ensure that each model was used properly according to design, the
creators of each model were involved in preparing the simulations for this
comparison of model performance. For preparing DAYCENT location, weather,
and field condition data, and debugging run code errors, Cindy Keough, the point
‘of contact programmer, was contacted [50]. For preparing run conditions for
IFSM, Dr. Al Rotz, program author, was contacted [51]. For preparing run
conditions for l-FARM and deciding to exclude this model from switchgrass
Simulations, Ed van Ouwerkerk, program author, was contacted [52].

Using the conditions and locations described, simulations were run with
each model accordingly. The Simulation results are also compared with available
published field data for conditions as close to the simulated scenarios as possible
when field data were available. It should be noted that much of the available field
data are published data from research and demonstration plots with the inherent
limitations of such data. The raw output comparison graphs can be seen in
Appendix B Figures B.1—B.12, and a listing of field data sources by application
can be seen in Appendix B Table 8.1. Values in these graphs are annual
averages taken over multiple years and error bars represent standard deviation.
Deviations are a result of differences that result primarily from changes in
weather patterns from year to year.

To Clarify the results seen in Appendix B, a “grading” system was
developed to evaluate each model’s performance across the different conditions,
locations, and outputs. Those Simulations performing within the standard

deviation of the other models and the field data (when available) received higher

34

scores than those that did not. The two parts of the grading system are outlined
in Tables 4.1 and 4.2. The need for two grading systems is the result of
excluding the l-FARM model in the switchgrass simulations and the lack of field
data available for strict comparison.

The first grading system, Table 4.1, uses plus Signs (+) to indicate positive
performance, which corresponds to more agreement with other models and
available field data, and minus Signs H to indicate negative performance, which
corresponds to less agreement with other models and available field data.

The second grading system, Table 4.2, is used for switchgrass simulations
only and accounts for the lack of a third model by scoring the two remaining
models based on agreement with each other and agreement with the field data
available. The first metric, agreement with each other, is assessed using the
same scale In Table 4.1. The second metric, agreement with available field data,
is found in the third and fourth rows of Tables 4.5 and 4.6, is assessed using the
grading system in Table 4.2, which credits the models for matching the available

field data without penalizing for any gaps in these data.

Tables 4.1: Grading system used for evaluation of all the three models and the available field data
for simulation of corn grain farms. Plus Signs (+) indicate agreement with other models and
available field data in either individual variables or separate states. Minus signs (-) indicate less
agreement with other models and available field data in either individual variables or separate

 

states.
Grade - - + H
Variables Matched 0-1 2-3 4-5 6
States Matched 0 1-2 3 4-5

35

Table 4.2: Evaluation system used in addition to Table 4.1 for companson of models to the
available field data for simulations of switchgrass only.

 

Grade - - + +1-
% of Available
Field Data 0-25 26-50 51-75 76-100
Matched

4.3 Results and Discussion

The overall performance results for each individual state for com grain
farms are found in Table 4.3. Single variable (sub-model) performance over all
geographic areas for corn grain farms can be seen in Table 4.4. Both of these
evaluations were done using the grading system given in Table 4.1.

Table 4.3: Assessment of model perfonnance in simulations of corn grain production for individual
states over all outcomes for that state using me evaluation system in Table 4.1.

 

Model PA VA IA IL MI
DAYCENT ++ + + + -
IFSM + + + +
I-FARM - N /A + - -

Table 4.4: Assessment of model performance in simulations of corn grain production for each
output variable across all geographic areas using the evaluation system in Table 4.1.

Grain

 

Grain . Nitrogen Denitrified Evapotrans Soil
Model . Nitrogen . . . . .
Yield Leaching Nitrogen -piration ErOSion
Removal
DAYCENT -I—l- 4—l- ++ - -l—l- -
IFSM + 4+ ++ - ++ +
I-FARM + - - - N/ A -

It is notable in Table 4.3 that both IFSM and DAYCENT have positive
scores across multiple states, indicating that they were able to accurately
simulate conditions in a variety of ecosystems, not just for the location in which

they were developed.

36

The scores in Table 4.4 indicate that the sub-models for grain yield in all
three models were quite consistent with each other, while the soil erosion and
nitrogen sub-models contained more deviation. Also, there were Significant
negative marks for the l-FARM model, which points to multiple sub-model
deficiencies.

The overall performance results for each individual state for switchgrass
farms are found in Table 4.5. Single variable (sub-model) performance over all
geographic areas for switchgrass farms are found in Table 4.6. Both of these
evaluations were done using the altered rating system in Table 4.2.

Table 4.5: Evaluation of model performance in simulations of switchgrass production for individual

states over all output for that state. The second row uses the grading system in Table 4.1, and the
third and fourth rows were evaluated using the grading system in Table 4.2.

 

Comparison PA VA IA IL MI
DAYCENT-IFSM - - .. - -
Field Data-DAYCENT - - + -- --

Field Data-IFSM + + + - -l-l-

Table 4.6: Evaluation of model performance in simulations of switchgrass production for each
output variable across all geographic areas. The second row was evaluated using the grading
system in Table 4.1, and the third and fourth rows were evaluated using the grading system in

 

Table 4.2.
. Biomass Biomass Nitrogen Denitrified Evapotrans Soil
Comparison . Nitrogen . . . . .
Yield Leaching Nitrogen -piration Erosmn
Removal
DAYCENT-

IFSM ' ' " " H "
Field Data- _ _ _ __ + __
DAYCENT
Field Data-

IFSM + ' ‘ " H H

37

The results in Table 4.5 indicate that IFSM is in agreement with the
published field data for switchgrass in the various states, while DAYCENT is not,
which in turn causes the two models to disagree with each other. It is also
shown that IFSM has some difficulty for switchgrass simulations in Illinois, due to
its low score for this state. For situations where the two models are not in
agreement with each other, but are both in agreement with field data, the two
models are on opposite extremes (high and low) of field data standard deviation.

The scores in Table 4.6 indicate that IFSM has better agreement with
available field data for switchgrass farms than DAYCENT. However, these
results are not particularly favorable for IFSM, because, although the IFSM score
is relatively higher, its absolute score is nonetheless low. The negative marks for
the three nitrogen output variables may be a sign of a serious deficiency in the
nitrogen sub-model in IFSM aS related to switchgrass. This table does Show that
both models have well performing water sub-models.

It is important to note, for the results presented here, a number of small
modifications or adjustments can be made to these models on a case-by-case
basis to improve performance. Each model presented here has a number of
parameters and specifications that could not be fully explored for this study, and
a more experienced user could likely produce even more accurate outcomes.
4.4 Conclusion

Integration of the biorefinery model with agricultural systems will allow full
economic and environmental analysis of ethanol fuel production along with other

products of the biorefinery, crop production for biomass and feed, and fertilizer

38

production from animals for use on crops. To allow selection of the most
appropriate agricultural model to integrate with the biorefinery model the crop
and animal simulation models DAYCENT, IFSM, and IFARM are compared here.
This study Shows that IFSM has the most consistent, positive performance and
therefore is best suited for future integrated biorefinery simulations.
Unfortunately, all three models have Short comings in either specific sub-
models or for specific geographic areas. Improvements are constantly being
made on these three models and with time these models will improve their ability
to model more locations and variables, more accurately [48,53]. The results here
highlight the need for further research on whole farm modeling and, more
Specifically, on nitrogen sub-models. It is important to note, however, that
models of this type often perform best in the state in which they were designed,
under the conditions most common in those areas. Ultimately, the Choice of
simulation model is left to the individual user, while giving strong consideration to

the location and intended use of the model.

39

CHAPTER 5: INTEGRATION OF THE BIOREFINERY AND AGRICULTURAL
SYSTEM WITH THE BIOREFINERY AND FARM INTEGRATION TOOL (BFIT)

5.1 Background

BFIT is a new research tool designed for expert users to simulate the
biorefinery integrated with a realistic agricultural landscape, which provides
biomass feedstock and receives AFEX treated grass as an animal feed. For the
first time in research, this program simulates all the elements highlighted in
Figure 1.1 directly for combined analysis.

The leading model for direct simulation of US ethanol production from
cellulosic biomass is the biorefinery model developed at NREL, and
subsequently updated at Dartmouth College and Michigan State University,
which is described in detail, including the state of the version, in Chapter 3.

The analysis in Chapter 4 evaluated Simulation programs to determine
which cropping and/or animal production model should be integrated with the
biorefinery simulation program described in Chapter 3. As a result of the
outcomes in Chapter 4, the work presented in this Chapter uses IFSM for all
agricultural simulation.

The model framework and conceptual design diagram can be seen in
Figure 5.1. This diagram displays the flow of data within the system and all
major underlying components. The elements that are described in Chapter 3 and
4 are outlined in dark gray, while elements that are new and part of the BFIT

development are outlined in light grey.

40

Whole Farm System

 

 

 

MasESl Energy Aggregate Math I
IFSM l O u e

 

 

 

 

 

 

 
    
      
 
    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Output
E L—> Organization 1—4 L d ,. Storage
conomIc Module an scape .
Output Distribution Biomass
Properties
Biorefinery
Summation Outputs ASPEN
_ M999“ Excel
Biorefinery
Total Single Location Economics 8. System

Environmental Balances

Figure 5.1: Conceptual framework and data flow for the biorefinery system outlined in Figure 1.1.

5.2 Materials and Methods
For landscape calculation development, the Aggregate Math Module in

Figure 5.1, six farm management strategies were Chosen for analysis. These
management strategies are outlined in Table 5.1. These management strategies
are chosen because they are the primary farm types that produce the two largest
anticipated sources of biomass, corn stover and switchgrass [3,4,54]. Strategies
X, A, and C are more conventional methods of farming, while Y, Z, and B are
designed to project future farm management better suited to a market that
includes the biorefinery. All farms were simulated with no-till management

because this more environmentally friendly farming method is growing in

41

popularity, particulariy with rapidly rising fuel prices making it a more economical
Choice [54,55,56,57].

Table 5.1: Outline of management strategies used in model development, indicating whether
animals are included and which crops are farmed.

Rye
Cover

Management Strategy
(alpha-identifier)

Cattle-Corn-Alfalfa (X)
Cattle-Corn (Y)
Cattle-Com-Switchgrass
(Z)

Corn Only (A)

Switchgrass (B) o
Corn-Rye Cover (C)

Corn Switchgrass Alfalfa Cattle

 

For the BFIT development study, the six management strategies outlined
in Table 5.1 are simulated in nine locations within the contiguous US. These
locations were chosen because of their significance to national agriculture and
the readily available and validated soil and weather data for each site [58,59,60].
These locations are outlined in Table 5.2, along with the farm Size, cattle type,
and herd size Simulated in each location. The farm acreage reported in Table
5.2 was chosen for each state using the USDA reported averages for the year
2006 [61]. There are no consistent data available for the average number of
cattle (head) per farm by individual states, so a “rule of thumb” of two acres per

head was used for all farms.

42

Table 5.2: Outline of locations used in model development, indicating corresponding average farm
size, dominant cattle type, and average herd size based on acreage. States are standard two letter
postal abbreviations.

 

Weather Farm Cattle Type (when Herd Size
Station City C°““‘V 3"“ (ac) applicable) (head)
Waterloo Black Hawk IA 355 Dairy 178
East Lansing lngham MI 191 Dairy 96

St. Cloud Stearns MN 345 Dairy 1 73
Cooperstown Otsego NY 214 Dairy 107
Akron Summit OH 187 Dairy 94
State College Centre PA 131 Dairy 66
Huron Beadle SD 1392 Beef Finishing 696
San Angelo Tom Green TX 564 Beef Finishing 282
Madison Dane WI 201 Dairy 101

The distribution of the farm management strategies in the landscape
surrounding the biorefinery, the Aggregate Math Module calculation, is

determined using the following equation:

Total Biomass = x (X) + y (Y) + 2 (Z) + a (A) + b (B) + c (C)

Simulated average annual biomass production (tons/yr/farm)...
X = from cattle + com grain, stover, & alfalfa farm type

Y = from cattle + com grain & stover farm type

2 = from cattle + corn grain, stover, & switchgrass farm type

A = from com grain & stover farm type

B = from switchgrass farm type

C = from corn grain & stover + mulched cover crop farm type

In area surrounding biorefinery, number of farms of...

x = cattle + corn grain, stover, & alfalfa farm type

y = cattle + corn grain & stover farm type

2 = cattle + corn grain, stover, & switchgrass farm type
a = corn grain & stover farm type

b = switchgrass farm type

c = corn grain & stover + mulched cover crop farm type

This equation is constrained by biorefinery size and statistical data specific
to the state being simulated [62]. To facilitate analysis, the biorefinery feedstock
input, after storage, was fixed at 2000 TPD for 350 operating days each year

(700,000 US ton DM/yr). This size was used rather than metrics such as annual

43

ethanol production or fixed equipment size because it is a common unit for both
the biorefinery and agricultural components. The statistical data constrains the
landscape distribution of alfalfa acreage grown for animal feed, the fraction of
farms with animal operations, the fraction of farms using cover cropping
methods, and the portion of farms converting land to switchgrass. For
development, each statistical constraint was set to the 2006 USDA reported
average except “acreage converted to switchgrass”, which is assumed to be 30%
based on a study by Jensen et al. [63]. A sample of this calculation can be found
in Appendix C1.

The farm type specific items produced to be sold for income are presented
in Table 5.3. Not all farm types are represented in each landscape, but each
strategy must be simulated to allow the landscape calculation in the Aggregate
Math Module.

Table 5.3: Products sold by each farm management type for farm income by location.

 

 

Management Strategy
X Y Z A B C
Locations
IA, MI, MN, NY, Mm" .C°"' Mm" .°°"‘ Mm" C°"‘ G'a'“ Corn Grain Switchgrass Corn Grain
OH PA, WI 6'3"" 3‘ G'a'" 8‘ .8‘ SIM" & Stover Hay & Stover
’ Stover Stover SWItchgrass Hay
Finished Finished Finished Beef,
SD TX Beef, Corn Beef, Corn Corn Grain & Corn Grain Switchgrass Corn Grain
’ Grain & Grain & Stover, & Stover Hay & Stover
Stover Stover Switchgrass Hay

The com grain yields used for development of all farm Simulations are
outlined in Table 5.4. For all locations the annual switchgrass combined pre-
harvest yield of approximately 6 ton/ac/yr was used due to a lack of reliable data

for individual location yields. This value is a conservative yield that is consistent

with those studies that are available and is achieved using low levels of fertilizer

and no irrigation [28,29,64,65,66].

Table 5.4: Annual corn grain yields by location obtained from USDA 11 year averages upto and
including 2006 [62].

Location (State) Corn Grain Yield (ton DMIacre)

 

lA 3.79
Ml 3.06
MN 3.09
NY 2.45
OH 2.85
PA 2.56
SD 2.29
TX* 2.82
WI 3.49
*This location required 3.2 inches of annual irrigation to
achieve proper yield

The com stover biomass removal rate was is 40% (leaving 60% on the
field) because it is a conservative value for this base-case study, which ensures
proper soil protection and prevents over drawing from soils [67,68,69]. The corn
stover yield, per acre, is thus a result of mathematical computation performed by
the model based on its internal biomass production calculations. The stover
yields are consistent with other publications [70]. When using a cover crop as
natural fertilizer (farm management strategy C) an additional 20% of corn stover
is collected raising collection to 60%, leaving 40% on the field. The winter rye
cover crop is used as a “green manure” and is not harvested. Rye is the cover
crop used for all locations due to its relatively low cost and suitability for soil
protection [71].

Although work is currently being done on the pretreatment and processing

of wet, or un-dried, biomass, in these simulations all biomass is assumed to be

45

dried to 20-25% [70]. To achieve and maintain this moisture content, biomass
Simulation includes wide-swath windrows, baling in round bales, and
transportation to the biorefinery by truck, with storage inside on a concrete slab.
This operation is a reasonably low priced and historically used practice in farming
[28]. Storage losses are calculated using the same time-rate storage equation in
IFSM, also known as the Rotz and Buckmaster Equation [27]. This loss rate is
calculated for the maximum moisture content (25% dry basis) and maximum
storage length (12 months) for all biomass to account for the worst case
scenario.

The economic assumptions used for development simulation of all
agricultural activities are outlined in Table 5.5. All values are conservative 2006
values based on published studies or expert opinion referenced in the last
column of Table 5.5. These historical values maintain internal model consistency
but can and should be updated in any future work given the rapid change of
these economic variables, particularly in the time since this model development

and preparation.

46

Table 5.5: Economics assumptions used for farm development simulations using sources for the

 

year2006.

Economic Variable Unit Value Source
Diesel Purchase Price $lgallon 2.00 72
Electricity Purchase Price $lkWh 0.08 4
Labor Wage $lhr 10.00 72

Forage Planting-Seed and Chemical Cost $lacre 50.00 29,73,74
Corn Planting-Seed and Chemical Cost $lacre 63.31 62
Rye Planting-Seed and Chemical Cost $lacre 17.00 71
Nitrogen Fertilizer Cost $llb N 0.43 72
Phosphorous Fertilizer Cost $/lb P205 0.34 72
Potassium Fertilizer Cost $llb K20 0.16 72
Soybean Meal, 44% Purchase Price $lton DM 230.00 72
Meat and Bone Meal Purchase Price $lton DM 272.00 72
Hay Feed Purchase Price $lton DM 125.19 72
Corn Purchase and Sales Price $lton DM 260.00 37
Corn Grain Silage Sales Price $lton DM 75.00 72

AFEX Treated Grass Feed Purchase Price $lton DM 104.28 11
Biomass (Corn Stover or Switchgrass) Sales Price $lton DM 80.00 28.29.75

Milk Sales Price $ch 15.00 72
Bred Heifers Sales Price $lanimal 1200.00 72
Finished Cattle Sales Price $/th 89.99 72

A biomass transportation radius is calculated for each landscape by
assuming the land area of the landscape is approximately circular and the
biorefinery is located in the center. Each development simulation includes a
transportation radius calculation, increasing linearly, for three distances, which
assume 100%, 75%, and 50% use of land area surrounding the biorefinery.
Because of land use for schools, homes, roads and other non-participating
farms, the 100% land utilization transportation radius is very optimistic and
represents the extreme minimum transportation for biomass in the system. The
location of biorefinelies, however, can be assumed to be in rural, agricultural
landscapes, thus reducing the use of land for other purposes. In addition,
conservation reserve program (CRP) lands are viable for cropping switchgrass
and can thus be attributed to grass production, making the 50% land ‘use radius

also less likely [64]. All of these considerations suggest that the transport radius

47

for 75% land use is a reasonable scenario, while 100% and 50% participation
radii are the best and worst case scenarios, respectively. With these
considerations in mind, all three radius calculations are provided for the
development simulation outputs showing the influence of transportation distance
on economics and the environment. Simulations for the sensitivity analysis and
scenario tests, however, were calculated for the 75% land use radius only.

All farm preparations in IFSM were reviewed by agricultural expert and
author of IFSM, Dr. 0. Alan Rotz of the USDA-ARS-Pasture Systems and
Watershed Management Research Unit in Pennsylvania [72]. These inputs
include many of the conditions outlined in Tables 5.1-5.5. Detailed tables of all
inputs for each farm preparation in both Section 5.3.1 and 5.3.3 can be seen in
Appendix 02 and 0.3, respectively.

The parameters chosen for development of biorefinery simulation include
the biological conversion process, pretreatment conditions, and animal feed
separation. The SSCF was selected for the biological conversion process with a
purchased enzyme loading of 15 FPU per gram of glucan, at a cost of $0.2374/lb
of enzyme cocktail ($0.5235/kg), which is representative of near-temi technology
expectations [4,7,18,54,76]. Pretreatment conditions were simulated at 0.3‘kg of
ammonia per kg of dry biomass, 60% biomass moisture, treated by the AFEX
process at 100 C for 5 minutes residence time. These conditions have been
demonstrated to give a conversion of 95% for cellulose and 85% for hemi-
cellulose (patent applied for). The separation of animal feed directly after

pretreatment required the addition of solid separating equipment, which was

48

designed, sized, and priced following precedence for the existing biorefinery
model [77].

The ash produced in the cogeneration facility of the biorefinery can be
sold and used as a product similar to fly ash [78]. The current market for fly ash
products includes building materials, such as concrete, cement, and asphalt, and
soil stabilization or back filling materials [79,80,81,82]. This vast market gives
the co-generation ash in the biorefinery a fair market value of $20/ton, and it is
assumed to be sold at this price [83]. The residue composition is based on
calculations already built into the biorefinery model [20].

The post-treatment wastewater bled from the biorefinery contains only
trace amounts of any one component, none of which are harmful. Because of
the small amount of any item present, the bleed water is assumed to be pH
neutral and to have no effect on plant growth, other than that provided by regular
irrigation water [21]. The waste water was therefore assumed to be sold back to
nearby specialty consumers, such as small farms or greenhouses, for irrigation at
half the purchase price, or $0.127/kL.

The economic assumptions for development simulations of the biorefinery
are summarized in Table 5.6. All values are based on 2006 sources or near-term
biorefinery technology expectations from other studies [4,7,19]. The raw
economic “summary" outputs for key biorefinery simulations in Section 5.3.1 and

5.3.3 can be seen in Appendix 0.2 and 03, respectively.

49

Table 5.6: Economics assumptions used for biorefinery simulations.

 

Parameter Assumed Value
Debt/Equity Ratio 40/60
Loan Rate (APR) 7.5%
IRR 1 2%
Federal and State Tax Rate 39%
Economic Life 25 years
a 7 years general plant
Depreciation Period 0 20 years steam and
power generation
Depreciation Method -MACRS
Capital Charge Rate ~17%
Indirect Costs 48% of installed cost

All economic variables are independent inputs defined by the user and are
simulated as an equilibrium value at a static period. These parameters do not
change or compensate automatically when other changes are made within the
model and are assumed to be an annual average. This design was selected to
reduce the potential for compound effects in the model, therefore increasing
transparency and maintaining flexibility for this tool.

Gaseous emissions for the agricultural system and the biorefinery are
directly simulated while biomass transportation GHG emissions are calculated
using the GREET model [84]. All of the emissions are combined in BFIT, yielding
the total 002 equivalent GHG emissions for each whole biorefinery system, what
could be called the “GHG footprint”. Biorefinery GHG emissions include both
biological ethanol production and electricity co-generation. Negative GHG values

reported for crop agriculture are carbon sequestration credits from plant growth.

50

5.3 Results and Discussion
5.3.1 Development Simulations

Using the conditions and locations described in the Materials and Methods
section, nine biorefineries were simulated with a surrounding landscape including
Six management strategies distributed according to historical data. The outputs
of the BFIT development simulations include the following: design of the
landscape, including size and farm distribution (Figure 5.2); farm economics,
including farm income by product source, net return per acre, and total landscape
net return to farmers (Figures 5.3, 5.4, and Table 5.7); environmental emissions,
including nitrogen loss by ecological process and GHG emissions (Figures 5.5
and 5.6); and biorefinery economics, as characterized by MESP and TPI (Figure
5.7 and 5.8). To test the agricultural landscape portion of the model, a control
was calculated for the “no biorefinery” condition, meaning the landscape is
calculated using only conventional farming strategies (X, A, and C) and biomass
is not sold as a product. The outputs of these control tests are given in Figures

5.9-5.12 and Table 5.8.

51

3.5E+05

3.0E+05

2.5E+05

Farm Area (Ac)

1.0E+05 -

5.0E+04 .

0.0E+00 -

 

 

 

 

 

2.0E+05 .

1.5E+05 -

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

/
_ i,

’/
g /
/

 

 

 

 

 

I

 

I Cattle-Corn
Converntional (X)

El Cattle-Corn
Alternative (Y)

I Cattle-Corn-SG

Alternative (Z)
Corn Only (A)
I SG Only (B)

Com-Rye Cover (0)

 

 

 

T I I T T

IA MI MN NY OH PA

I

l

I

SDTXWI

Figure 5.2: Landscape area required to produce the biomass required for a 2,000 TPD biorefinery
for development simulations. Participation of each farm type contributing to the total area, also

called “landscape distribution”, is indicated by colors and patterns.

The results in Figure 5.2 highlight those locations that use the least land

area to produce the 2000 TPD required biorefinery biomass and visually

distinguish the farm management distribution in the landscape. In Figure 5.2,

stripes indicate farm management strategies that are crop only, and solid bars

indicate strategies that include animal operations, while the overall total draws

attention to locations with the largest participation area. Using patterns and

colors to Show the landscape distribution results underscores for the user the

farming strategies that are most important in each landscape, which can also be

called the “agricultural emphasis”.

52

 

The locations with the highest overall land requirements in Figure 5.2 have
more farms that include animal operations, such as Texas, or have less farm
type variety, also know as monoculture type farming, as in Iowa. Landscapes
with the greatest animal agriculture have larger participation area because the
use of land for feed growth and animal production overlaps the acreage used for
biomass production. Monoculture type farming often raises land area because
the management strategies that dominate the landscape are more conventional
strategies that do not reach the full productivity potential of the land. Locations
simulated in BFIT that have large land requirements, but have moderate animal
agriculture and well mixed distributions, are experiencing the effect of low
productivity. In Figure 5.2 this effect is seen for the location selected in Ohio.
Having greater land area has the benefit of more soil surface area for
sequestration, but has the draw back of greater transportation distance, which

ultimately leads to higher emissions and cost associated with transportation.

53

3.5E+08

5 Animal Crop

 

 

3.0E+08

2.5E+08

 

 

Income ($/yr)
:" .N
U! 0
I11 rn
+ +
O O
on CD

 

 

 

Figure 5.3: Annual income combined for all participating farms for development simulations by
product source, animal agriculture or crop agriculture. Products for each location are outlined in

Table 5.3.

Table 5.7: Net return on a per acre basis for each farm type, as well as an overall landscape
average, for each location for development simulations. Values in grey are farm types that were

calculated as not occurring in the landscape distribution.

 

 

Management Strategy

Location X Y Z A B C Average
lA $386.09 $567.51 $526.03 $602.77 $339.47 $534.11 $522.23
Ml $159.25 $378.19 $392 .C 1 $366.90 $645.01 $375.69 $447.62
MN $328.37 $540.56 $550 .99 $413.02 $561 .30 $359.30 $466.05
NY $159.52 $371.94 $384.17 $271.47 $395.13 $218.43 $334.43
OH $204.31 $404.40 $434 .88 $454.60 $296.51 $393.28 $388.35
PA $34.58 $233.84 $240 . 23 $268.20 $333.73 $211.09 $262.17
SD $155.59 $202.04 $143.66 $292.27 $502.83 $282.72 $324.53
TX $155.60 $236.30 $163.56 $44 7 4 9 $356.61 $249.68 $238.41
WI $361.48 $566.53 3:388 .20 $493.76 $383.06 $412.43 $466.38

54

1 .2E+08

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.0E+08 -
ICattle-Corn
£8 0E+07 1 "' / F— Converntional (X)
\ DCattle-Corn
2’} — ¢ F g Alternative (Y)
E + _ 7 /. ICattle-Corn-SG
/
2.6-05 07 / y 5 Alternative (2)
(at, / / / ' Co 0 l A
‘5 é % % rn ny( )
z4.0E+07 — é / use Only (B)
/ 2
Corn-Rye Cover (C)
2.0E+07 ~
o.oe+oo - . . . . r / .

IA MI MN NY OH PA SD TX WI
Figure 5.4: Total annual net return combined for all participating farms for development

simulations. Contribution of each farm type to the total profit is indicated by colors and patterns.

For the results in Figure 5.3, stripes again illuminate each location’s
agricultural emphasis, with overall values showing productivity. States with
greater animal production have greater income in Figure 5.3, but, after
accounting for production costs, the net return results in Table 5.7 and Figure 5.4
show true profitability. Net return includes costs for the following: planting,
production, harvesting, temporary storage for biomass, purchased feed, feed
storage, and animal housing; and income from the following: animals and/or
animal products, feed, bedding, and biomass sales. The details of the farm
parameters are outlined in the Materials and Methods section.

The net return per acre in Table 5.7 is highest for the switchgrass only

management strategy (B) for all locations, except for the com only management

55

strategy (A) for Iowa and Ohio, and the dairy growing corn based feeds
supplemented with AFEX feeds (Y) for Wisconsin. These results reiterate the
reduced productivity for Ohio, showing clearly that the problem is particularly
important for switchgrass, and reinforces the commonly accepted identity of Iowa
as a “corn state” and Wisconsin as a “dairy state”.

It can be easily seen in Table 5.7 that the com-switchgrass animal farm
using AFEX feed (2) type farm is not included in any landscape calculation
except for Texas. This result suggests that switchgrass conversion to larger
continuous acreage (whole farms) fits with current farming distributions better,
except in locations like Texas that have a strong agricultural emphasis on animal
production.

Average landscape values in Table 5.7 suggest that some traditional farm
strategy distributions do not achieve the highest potential return per acre. These
averages, which are weighted for the distribution of farm types in the landscape,
indicate that with historic farm distribution, the Iowa landscape has the highest
overall average net return per acre. The landscapes with the next highest
average returns on a per acre basis are grouped closely, being Michigan,
Minnesota, and Wisconsin. These four locations with the highest landscape
average return per acre are known for their agricultural significance and have
some of the highest returns per acre for each individual farm type as well.

Total landscape net return and the contribution of each farm type to this
return can be seen in Figure 5.4. These results show the influence of land

requirement and distribution, from Figure 5.2, combined with the individual farm

56

type returns, given in Table 5.7. Iowa and Wisconsin have high returns and high
land requirements, while Michigan and Minnesota have high returns but fewer
farms (lower land area) giving a lower total landscape return. An opposite
combination yields a moderate total net return for the Texas landscape, where
the high income seen in Figure 5.3 is offset by costs giving lower net return in
Table 5.7. Also, Table 5.7 indicates that little acreage is dedicated to the farm
type with the highest calculated returns.

The economic results described above demonstrate BFIT’s ability to
highlight potential increased landscape return by conversion to underutilized
management strategies having greater return per acre. This change from
conventional farming is not easy, but may be more readily accepted when
farming communities are shown their potential for increased revenue. The
current results already reinforce the generally accepted idea that the Midwest

region is a prime location for agricultural industries.

57

 

Denitrification I Volatilization Cl Leaching J

 

\l
O

 

 

8

 

 

8

 

 

h
C

 

 

(a)
O

 

 

N
O
1

 

Nitrogenous Species Release (lb/ac/yr)
8

 

 

 

 

 

 

 

 

 

I

OH PA SD TX WI

 

 

Figure 5.5: Landscape average annual nitrogen loss per acre by ecological process from which
they are lost for development simulations. Species produced are N02, NO, N20, and N2 by
denitrification, NH3 by volatilization, and N03 by leaching.

58

4.0E+09

 

 

 

 

 

 

 

 

 

 

 

3.0E+09
I50% Land Use

E 2.0E+09 ~ “8"st
B 7I5% Land Use
8 Transport
0 1 0E+09 . I100% Land Use
9 Transport
2 0.0E+00 _ IFarm Machinery
.2
8 DBiorefinery
E -1.0E+09 - Operations
3 IAnimal Production
I
(D ’2'0E+09 ‘ ICrop Production

-3.0E+09 -

-4.0E+09

IA MI MN NY OH PA SD TX wr
Figure 5.6: Greenhouse gas (GHG) emissions for all participating farms in the landscape, all

corresponding biorefinery operations, and biomass transportation for development simulations.
Emissions are broken out by source and given as the sum of all GHG emissions in C02
equivalents.

Two of the three processes represented in Figure 5.5 are directly affected
by water in the agricultural system. Denitrification losses increase with
increasing soil moisture and drainage controls leaching. Drainage depends on
water flow through the soils, rainfall and/or irrigation, and the amount and timing
of manure and fertilizer application. The patterns for denitrification follow
reported soil moistures with the exception of Texas, which is the only location
with irrigation [85]. Leaching trends are similar but are influenced by how much
and when water reaches the soil, as well as fertilization parameters.

Volatilization depends on the expulsion of waste products by animals and

therefore follows trends for animal production in each landscape distribution. Soil

59

type and farming intensity requirements can also affect the trends observed in
Figure 5.5.

The calculations and assumptions for the GHG emissions presented in
Figure 5.6 are further described in the Materials and Methods section of this
chapter. The three “land use” values in Figure 5.6 correspond to biomass
transportation radii, which are also described in Section 5.2, Materials and
Methods.

The GHG results in Figure 5.6 are heavily influenced by the land
requirement results in Figure 5.2 because of the relationship of surface area and
carbon sequestration by crops. It can also be seen in Figure 5.6 that landscapes
with greater animal production have increased GHGs, mostly from methane.
Biorefinery emissions are directly proportional to overall biorefinery operation
(total size), which is discussed in the paragraphs following Figure 5.7 and 5.8. It
is also clear that farm machinery and biomass transportation emissions are
similar for all the locations included in this study. These results also indicate that
changes for different land use or transportation distances are small, and may not
be significant for many studies. This limited influence may decrease concern for
previously emphasized environmental impacts from biomass transportation

[1 ,7,28].

60

 

2.05E+08

 

2.00E+08 4

 

1 .95E+08 -

1 .85E+08 -

 

 

 

1 .80E+O8 -

IA MI MN NY OH PA SD TX Wl

Figure 5.7: Total project investment (T PI) for each biorefinery development simulation, which
includes account for total installed equipment cost, facility costs, and a 3% project contingency.

61

 

@00% Land Use I75°/o Land Use I50% Land Use?

 

 

 

 

2.00
1.80 «
1.60 - 3.
1 40 - ,3 1;;
‘ {3'1 i?
if? 511‘.
9 - .3
33 1.20 33 33
a 1.00 — 3 ..
m 11'
0.80 -
2 $.11
353:.
0.60 — g
[511.
0.40 — $5
0.20 - $
0.00 - 7%

 

 

 

 

 

 

 

 

 

 

 

 

|A MI MN TX

Figure 5.8: Minimum ethanol selling price (MESP) calculated for each biorefinery development
simulation, which includes account for capital and operating costs as well as a fixed 12% return to
management.

The total project investment (TPI) results in Figure 5.7 depend directly on
the biorefinery equipment cost, which directly corresponds to equipment size. In
this system, equipment is sized based on the amount of biomass passing
through each unit operation, which fluctuates for steps before and after
pretreatment. Before pretreatment, equipment used for handling com stover
biomass is more costly than for switchgrass because corn stover harvest has a
greater potential for undesirable inclusions, such as dirt, rocks or metal, which
require additional washing and removal equipment. Pretreated grass feed
removal after pretreatment varies by location and defines the amount of biomass

continuing through the biorefinery for ethanol production. Locations with greater

62

animal production have greater feed return that leads to a smaller biorefinery for
that location, which in turn has a lower TPI. Based on these correlations, the two
distinct groups seen in Figure 5.7 reflect the agricultural emphasis of each
location for either animal production (lower TPI) or crop production (higher TPI).

The minimum ethanol selling price (MESP) results in Figure 5.8 are
calculated using the same method previously described in Section 3.1. The
relationship between MESP and TPI yields the repeated two group trend in
Figure 5.8. The variations amongst those within the two groups in Figure 5.8 are
muted by differences in biomass transportation costs and AFEX feed sales,
which essentially subsidize ethanol production. It is reasonable to expect that
the differences between these two groups would decrease long-term, where

biorefinery size is 5,000-10,000 TPD, due to economies of scale [4,19,20].

63

3.5E+05

 

 

 

 

   

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

3.0E+05
2.5E+05
EM... 5 'gzfigfnzwna..x,
Q % ICom Only (A)
< 9 ¢
g 1'SE+05 ' T% g ZCom-Rye Cover (C)
u. / ¢ /
/ / /
¢ / / 2 2 /
1.0E+05~ % a Q é é / ,
5.05+04 - ——V / ——- ‘ / /
0.0E+00 . . . 4 . I , z

 

 

 

 

 

 

 

 

 

 

IA MI MN NY OH PA SD TX WI
Figure 5.9: Redistribution of the landscape, which was fixed to development simulation size, for

control test of Figure 5.2.

The results in Figure 5.9 are the landscape distribution calculation for the
same area corresponding to each location in Figure 5.2. These distribution
calculations use only management strategies that are “conventional”, as
previously described. This figure shows BFIT simulations for each landscape
before the introduction of the biorefinery and its corresponding biomass supply
chain. When limited to conventional management strategies, one can see even
more clearly locations where animal production is emphasized. It is also clear
that distributions continue to follow what is expected for the locations included in
these development simulations. These results suggest that alternative

management strategies are beneficial to the farmer, because they are included

64

by the landscape optimization algorithm to replace some conventional

management acreage.

 

[I Animal I Crop I

 

 

3.5E+08

 

3.0E+08

 

2.5E+08

 

 

2.0E+08

 

1 .5E+08 -

Income ($lyr)

  

1 .0E+08 1

5.0E+07 1

 

0.0E+00 1
I

  

 

l jg,
NY

 

p

 

 

IA M MN OH SD TX

A

  
   

  

 

.3. .37
, 1
3.
1
.
.33 w
1,, .
A v

Figure 5.10: Annual income for control test of Figure 5.4. Crop products do not include biomass
sales (switchgrass hay or corn stover).

Table 5.8: Net return on a per acre basis for control test of Table 5.7.

 

Management Strategy
Location X A C Averagi

lA $310.83 $493.93 $371 .41 $488.44
Ml $85.95 $261 .76 $220.72 $230.59
MN $254.40 $310.99 $207.42 $304.69
NY $89.99 $173.52 $76.37 $133.81
OH $129.44 $349.36 $243.98 $306.44
PA -$48.47 $158.89 $54.76 $83.71
SD $58.81 $188.82 $138.35 $142.41
TX $52.76 $338.41 $136.91 $108.01
Wl $274.32 $385.90 $258.00 $350.69

 

65

 

1.2E+08

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.0E+08 1
8.0E+07 1
E>~ % ICattle-Corn
2’; 6.0E+07 1 / Converntional (X)
g 7 g ICom Only (A)
'55 /
E 40807 “ é / CIom-Rye Cover (C)
a) / a
2 g g
2.05+07 . / r /
g /
/ /
0.0E+00 - 1 1 1 I 1
IA MI MN NY OH PA SD TX WI
-2.0E+07

Figure 5.11: Total annual net return for control test of Figure 5.3.

For the income results in Figure 5.10, the trends and patterns are similar
to those reported in Figure 5.4, but absolute values are lower for all locations
except Iowa and Ohio. These results show the influence of increased crop
income for switchgrass and reduced animal feed costs for AFEX feed in the BFIT
income calculations. This influence suggests that the existence of the
biorefinery, and thus an additional market for crop sales, is of value to farmers.

For the net return calculations in Table 5.8, the corn only management
strategy (A) is always the most profitable farm type, as would be expected given
the high corn price in recent years. The highest landscape averages are the
same as those in Table 5.7 except the landscape calculated for Ohio increases

net returns and Michigan drops below that of the top four. The exchange of

66

these most profitable landscapes again emphasizes the influence of switchgrass
in the landscape, because Iowa and Ohio maintain higher returns in this non-
switchgrass (conventional) landscape. This occurs because Iowa was already
highly profitable, with limited switchgrass effects for the development landscape,
and the Ohio development net return was reduced by the influence of
switchgrass, which has been removed in Table 5.8. The reduced returns in
Table 5.8 combined with fixed land requirements in Figure 5.9 reduce all total net
returns.

The animal income in Figure 5.10 appears to be a larger fraction of farm
economics than for net return results in Figure 5.11, which underscores the high
costs that are offsetting the income from animal products. In one case,
Pennsylvania, the costs are actually greater than the income, once again pointing
to a landscape that could greatly benefits from the inclusion of new alternative

farm management strategies.

67

 

Denitrification I Volitilization E] Leaching I

 

 

80

 

 

 

 

 

 

 

Nitrogenous Species Release (lb/ac/yr)

 

 

 

 

 

 

 

 

 

IA 3 MI MN NY OH PA SD TX wr
Figure 5.12: Average annual nitrogen loss per acre for control test of Figure 5.5.

In Figure 5.12 nitrogen loss is greater for denitrification and leaching for all
landscapes because the same area is simulated with more “conventional
farming”, which require more intensive crop management. Nitrogen loss through
volatilization is nearly unchanged in dairy states (IA, MI, MN, NY, OH, PA, WI)
because conventional strategies do not differ as much from alternative strategies
for animal rationing. Nitrogen losses for beef cattle, on the other hand, are
actually reduced using conventional farming methods because the finishing
ration uses a number of feed stuffs (high moisture and dry grain, grain silage,
soybean meal, and meat and bone meal). This ration more carefully meets
animal protein needs, keeping nitrogen excretions low. For the alternative

feeding simulations (Y and Z) the finishing ration is calculated to be entirely

68

comprised of the high energy protein feed, which is AFEX treated grass from the
biorefinery. This ration is simulated using NRC recommendations, but allows
over feeding of nitrogenous protein, which then leads to excretion of unused
nitrogen.

This control test of the BFIT development simulations reveals valuable
insights that can be obtained through running simulations on a fixed landscape
area in future studies. The outputs of these types of simulations reveal the
underlying structure of BFIT and its landscape calculations. This type of pre-
bioreflnery simulation, used as a control, can reveal agricultural changes that can
enhance farmer and community profits, reduce emissions over conventional
farming practices, and direct land use for both biorefinery feedstocks and animal
feed production.

It is important to note that all simulations presented here, both the control
and basic simulations, do not include any “new land” for agriculture. All area
simulated in this model is assumed to be existing farms or previously farmed
acreage converted to the selected management.

5.3.2 Sensitivity Analysis

To assess which input variables have the strongest effect on model

outcomes, a sensitivity analysis was performed. The variable changes made are

outlined in Table 5.9.

69

Table 5.9: Sensitivity variables tested and the values used for each test.

 

Original
. Increase Decrease
Sensitivity Vanable (Development)
Test Value Value Test Value
Diesel Purchase Price ($lgal) 4 2 1

Corn Purchase 8. Selling Price

($lton DM,$/ac seeded) 311.75.97 260.63.31 207, 50.64

Electricity Purchase & Selling Price
($lkWh) 0.16 0.08 0.04
Grass Seed Purchase Price
($lac seeded) 60 50 40
Biomass Selling Price ($lton DM) 160 80 40
AFEX Feed Purchase Price
($ Ito n DM) 208 104 52
AFEX Feed Crude Protein Content
(CP % DM) 26.04 21.7 17.36
Switchgrass Yield (ton DM/ac) 12 6 3
Biorefinery Feed Size
(ton DM fed / d ay) 5000 2000 1000
Biorefinery Internal Rate of Return 15 12 9
1%)
Biomass Storage Dry Matter Loss 0 14 0 069 0 035

We)

Sensitivity analysis results using three system indicators are in Table 5.10.
Values in this table are the ratio of the percentage change in outcomes to the
percentage change in the sensitivity input value. The outcome indicators
reported are total landscape net return as a farm economic indicator, MESP as a
biorefinery economic indicator, and total landscape GHG emissions as an
environmental indicator. Negative values represent changes opposing sensitivity
variable adjustment (eg. an indicator increases when the sensitivity variable is

decreased).

70

Table 5.10: Change in simulated outcome indicators as a ratio of percentage outcome change to
percentage sensitivity input change. Sensitivity tests are outlined in Table 5.9.

 

Farm Economics Biorefinery Environment
Variable Change (Net Landscape Economics (Landscape
Farm Return) (MESP) GHG Emissions)

Hi Diesel Price 01 0 0
Lo Diesel Price 01 0 0
Hi Corn Price 0.9 0 0
Lo Corn Price 1 3 0 0
Hi Electricity Price 0 0 0
Lo Electricity Price 0 0 0
Hi Grass Seed Price 0 0 0
Lo Grass Seed Price 0 0 0
Hi Biomass Price 0 3 0.4 0
Lo Biomass Price 0 4 0.4 0
Hi AFEX Feed Price 0 0 0
Lo AFEX Feed Price 0 0 0
Hi AFEX Feed Protein 0 0 0
Lo AFEX Feed Protein 0 0 0

Hi SG Yield —0.4 0 0.3

L0 SG Yield -1.3 0 1.1

Hi Bioref. Size 1.0 -0.1 -0.9

L0 Bioref. Size 1.0 -0.2 -1.0
Hi IRR 0 0.3 0
Lo IRR 0 0.3 0

Hi DML 0.1 0 0.2

L0 DML 0.1 0 0.1

Values in Tables 5.10 increase and decrease equally with sensitivity
variable change follow linear trends. All results in Table 5.10 that are less than
one have a smaller system influence, and those greater than one have a stronger
influence on BFIT results. Those values reported as zero have either no or very
small influence on outcomes (<5%), meaning that input variable change is
insignificant to outcomes.

The unequal change in the farm economic indicator seen for corn price
changes occurs because farms in the landscape have differing or no corn
acreage and are thus not affected equally by com price changes. The non-linear

farm economic results for biomass price change occur because grain price

71

overwhelms stover price changes for farms having primarily grain profit (farm
types Y, A, & C), while biomass prices have a distinct effect on farms having
income from hay (farm types X, Z, & B). The uneven change in both farm
economics and environmental indicators for switchgrass yield findings is the
result of an adjustment in the distribution. This distribution change occurs
because switchgrass acreage contributes differing amounts of biomass to the
landscape total, while switchgrass acreage is a fixed fraction of the landscape
area (30%). The non-linear nature of economies of scale in the range studied
here causes the non-linear biorefinery economic and environmental indicator
changes when biorefinery size is adjusted. These same results have also been
shown in previous publications for these same biorefinery sizes, using NREL
model versions similar to that used as the BFIT sub-model [4,7,20,42,44]. The
uneven environmental indicator change with DML adjustments occurs because
biomass requirement differences cause a land requirement change, thus a
transportation distance change, and produce small distribution changes as well.

The results of this sensitivity analysis highlight the compound effect of
some variables on multiple parts of the system, particularly switchgrass yields,
biorefinery design (sizing), biomass pricing, and biomass storage and losses.
Unfortunately, these areas are still under study and developing, and they are
sources of disagreement amongst scientists [1,3, 7,70,74,75,86].

These sensitivity results also underline the non-linear effect of some input
parameters as a result of the landscape algorithm. These non-linear effects

highlight the need for great care in selecting all model input assumptions and the

72

need for transparency reporting them. The non-linear effects explained here are
also important for future studies using this research tool because they can guide
user input adjustments made for system design and/or decision making.
5.3.3 Scenario Tests

Because this research tool estimates a system that is not currently in
existence at an equivalent (commercial) scale, it is unable to be validated, but it
can be verified [87,88]. The reliability of the two model pieces that are combined
by this tool has already been proven [4,19,20,27]. To verify the combination of
these pieces in BFIT, l devised a series of scenario tests that were carried out by
Pragnya Eranki. This researcher was trained on the model, and is educated in
the related issues making her an “expert user” [87,88,89]. The contribution from
this verifier was limited to execution of the model and a description of challenges
facing new users, no intellectual content was contributed. The scenarios are

outlined in Table 5.11.

73

Table 5.11: Outline of scenarios used to test BFIT for verification.

 

Scenario Name Location Change
Decreased Precipitation lA Precipitation decreased 42 cm/yr
. Switchgrass yield increased to 12 ton/ac and
Increased Swrtchgrass (86) IA fraction of farms growing switchgrass increases
Productrvrty 0
to 70 /o
Decreased Meat Production TX Fraction of farms with animals decreased to 1%

Decreased Winter Temperature

Winter (Nov.-Apr.) temperature decreased by
. . . MI
and Precrprtatron

11°F and precipitation decreased 19 cm/yr

. Fraction of farms with animals increased to 90%,
Increased Meat Production TX cover cropping requirement was reduced to 10%

Increased Temperature and SD Temperature increased by 13°F and precipitation

Precipitation increased by 50 cm/yr
Increased Corn Stover Corn stover collection increased by 20% on all
. . IA
Productrvrty farms

. Biorefinery operation reduced to 283 days/yr,
Decregrzzclljgrisirgfinery PA biomass storage loss increased 1% making
annual required biomass production 614,248

The results of the changes made for the eight scenarios in Table 5.11 can
be seen in Figures 5.13-5.33 and Table 5.12. The outputs, labeled by scenario
name, are the same metrics as those presented in Section 5.3.1 for development
simulations. Each scenario figure also contains the corresponding development
value for that location, labeled as “original” with the two letter state abbreviation.

Results for these scenario tests are grouped by similar variable changes for

weather, crop productivity (including reduced usage from the biorefinery), and

meat production.

74

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

4.0E+05
3.5E+05 T—'
ICattle-Corn
3.0E+05 Converntional (X)
’5 EICattIe-Com
g 2.5E+05 ' Alternative (Y)
8 I ‘ ’22; I Cattle-Corn-SG
<7: 2'0E+05 ‘_7 % Alternative (2)
,E, 1.5E+05 % % _ V I-_ ICom Only (A)
11 7
1.0E+05 é % / 61 use Only (B)
5.0E+04 - ICom-Rye Cover (C)
0.0E+00 1
§ 8 3.? 53 ”2 § 2 6‘
§ 1%. .2 g 9 333 2 § 5 1'3 a
g g -°’ § 3 '— C» 8 m “a 5
h 5 S .0. ”C 5 h ._ .25
m g 8. S O 5 8. § 0
eém E‘

Figure 5.13: Landscape size and distribution for scenario tests having weather adjustments.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

 

 

 

 

   
 

 

 

 

 

 

 

 

 

 

 

 

 

2.5E+05
2 05-105 V -_ ICattle-Corn
' % Converntional (X)
/ ElCattle-Corn
:(o‘ E % Alternative (Y)
v1-5 +05 V A —‘I
m / ICattle-Com-SG
g % Alternative (2)
g 1.0E+05 / ICom On|y (A)
% ISG Only (B)
3 A
5.0E+04 — 5 I T 1 1 ‘
3. . . 3 1} CIorn-Rye Cover
- ' ' . j (C)
0.0E+00 - ‘ . 7 . ’

 

Increased Increased Original (IA) Decreased Original (PA)
86 Corn Stover Biorefinery
Productivity Production Productivity

Figure 5.14: Landscape size and distribution for scenario tests having productivity adjustments.

75

 

6.0E+05

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

     

 

 

 

5.0E+05 3
I Cattle-Corn
Converntional (X)
El Cattle-Corn
3:" 4.0805 Alternative (Y)
7; ucatue-com-se
0) Alternative (Z)
'- 3.0E+05
E Corn Only (A)
to
“- 2.0505 — //     3 use Only (B)
//- I Com-Rye Cover (C)
1.0E+05 -
7 /
0.0E+00 . , ,
Decreased Meat Increased Meat Original (TX)
Production Production

Figure 5.15: Landscape size and distribution for scenario tests having meat production
adjustments.

The greatest factor for the changes seen in Figure 5.13 is variation in crop
productivity as a result Of climatological adjustments. Because corn yields
decreased slightly and switchgrass yields increase with drier conditions in the
Decreased Precipitation scenario, tested in Iowa, the distribution and overall land
requirement did not change much. Changes to the winter weather in Michigan
showed very minor decreases in all farm type yields increasing land requirement
slightly, while the distribution remains unchanged. The increase in precipitation
for the third weather scenario had potential corn yield benefits, but the year round
temperature increase caused corn yields to decrease instead. The increase in
precipitation also drastically reduced switchgrass yields, as might be expected in

opposition to the results for the Decreased Precipitation scenario. This reduction

76

Of all yields, particularly switchgrass yields, in this final weather scenario caused
the calculation of a significantly higher land requirement to reach the same level
of biomass production.

For Figure 5.14, as expected from the previous scenario results, when
biomass productivities increase, as it does for the first two scenarios in this
figure, the overall land requirement decreases. The Increased Switchgrass
Productivity scenario distribution changes are prescribed by the scenario test
design, switchgrass acreage increases to 70% Of the landscape. This
distribution thus requires some switchgrass acreage on what little animal farming
that does occur (farm type 2). The third scenario in Figure 5.14, Decreased
Biorefinery Production, shows the effect of a reduction in biomass needs for this
scenario, reducing overall land requirement.

In Figure 5.15 the near elimination of animal productiOn for the Decreased
Meat Production scenario is clearly shown in the distribution, having the stripe
pattern for almost every acre. The opposite is seen for the Increased Meat
Production scenario, having solid bars for much of the landscape acreage. The
lack of land required for animal feed production in the Decreased Meat
Production scenario decreases overall land requirement, and again the Opposite

is seen for the Increased Meat Production scenario.

77

5 Animal I Crop

 

 

 

 

 

2.0E+08
1.8E+08
1.6E+08
A 1 4E+08 ~
I—
5 1 2E+08 —
ID 1 0E+08 ~
E
8 8.0E+07 «
E
6.0E+07 <
4.0E+07 —
2.0E+07 1
0.0E+00 ~
Decreased Decreased Original (Ml) Increased Original (SD)
Precipitation anter Temperature
Temperature and
and Precipitation
Precipitation

Figure 5.16: Annual income by product source for scenario tests having weather adjustments.
Products are the same as those in Figure 5.4.

78

 

I Animal I Crop

O

2.0E+08
1.8E+08
1.6E+08
1.4E+08
1.2E+08
1.0E+08
8.0E+07

Income ($lyr)

6.0E+07
4.0E+07
2.0E+07

 

0.0E+00

Increased SG Increased Corn Original (IA) Decreased Original (PA)
Productivity Stover Production Biorefinery
Productivity

Figure 5.17: Annual income by product source for scenario tests having productivity adjustments.
Products are the same as those in Figure 5.4.

EAnimal I Crop

7.0E+08
6.0E+08
5.0E+08
4.0E+08

3.0E+08

lncome($/yr)

2.0E+08

1.0E+08

 

0.0E+00

Decreased Meat Production Increased Meal Production Original (TX)

Figure 5.18: Annual income by product source for scenario tests having meat production
adjustments. Products are the same as those in Figure 5.4.

79

Table 5.12: Net return on a per acre basis for all scenario tests. Values in grey are farm types that
were calculated as not occurring in the landscape distribution.

 

Management Strategy
Scenario X Y Z A B C Averag_3_
Decreased .
Precipitation $193.82 $323.69 $396.56 $301.99 $346.11 $280.56 $315.52
Decreased Winter
Temperature and $100.93 $335.90 $367.80 $324.43 $516.61 $340.95 $380.13
Precipitation
Increased
Temperature and -$153.58 -$51.32 $97.57 $179.43 $72.13 $164.33 $70.12
Precipitation
'“°'°‘“" 36 $386 09 1567 51 $680 86 $602 77 $565 60 $534 11 $579 74
Produdivity ‘ - Vs; l . . . u I
Increased Corn -5 Q,
Decreased
Biorefinery $34.58 $233.84 $240.23 $268.20 $333.73 $211.09 $262.17
Productivity
Decreased Meat , , 3
Production 8155.60 $236.30 $163.56 $447.49 $356.61 $249.68 $358.92
Increased Meat , , -2
Production $155.60 $236.30 $163.56 $447.49 $356.61 $249.68 $215.40

 

80

1 .4E+08

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

1.2E+08
ICattle-Corn
1.0E+08 / Converntional (X)
E, / DCattle-Com
5 8.0E+07 / Alternative (Y)
E % _ '— ICattIe-Com-SG
3 6-OETO7 1 / V Alternative (2)
° / 7 /
E 4.0E+07 - / / 'C°"‘ my”)
2
2.0E+07 1 ISO Only (3)
O-OE+00 ‘ ' 1 ' H 1 ICom-Rye Cover (C)
-2.0E+07
§ .3 if. :2- E E ‘8 e g
a g g a 2 é a 8 e a 1,;
‘- -- -- B .53 E 3 3 :53 c
8 8 Fa” § m 'a. .21 2 S .9- '6:
O i 0 “1 2'8 0 8 8 3 5
g E ct - 0% CL
8 .9 1—
Figure 5.19: Total annual net return for scenario tests having weather adjustments.
1.4E+08
1.2E+08
I ICattle-Corn
_ / Converntional (X)
A 1.0908 / ' DCattle-Corn
S. / ~ / Alternative (Y)
8 8.0E‘I'07 / / I Cattle-Corn-SG
E / / Altematlve (2)
13 / / ICom Only (A)
rx 6.0E+07 / / ,_
g / / use Only (B)
4.0E+07 « / /
% / Com-Rye Cover (C)
2.0E+07 1
0.0E+00 -
Increased Increased Original (IA) Decreased Original (PA)
SC Corn Stover Biorefinery
Productivity Production Productivity

Figure 5.20: Total annual net return for scenario tests having productivity adjustments.

8]

1.2E+08

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.0E+08
I Cattle-Corn
Converntional (X)
1: 3 0E+07 El Cattle-Corn
3. 1 Alternative (Y)
ee
V I Cattle-Corn-SG
E 6.0E+07 / Alternative (2)
3:) Corn Only (A)
‘65
Z 4.0E+07 953.. .86 Only (B)
f: Com-Rye Cover (C)
2.0E+O7 - /
0.0E+00 . . fl
Decreased Meat Increased Meat Original (TX)
Production Production

Figure 5.21: Total annual net return for scenario tests having meat production adjustments.

Income for the Decreased Precipitation scenario is reduced in Figure 5.16.
This reduction is a result of compounding distribution and land requirement
changes, shown in Figure 5.13, that lower overall corn acreage, thus reducing
income from both corn stover and corn grain. In Figure 5.16 it is evident, again,
that only minor changes occur for the winter weather scenario. Because
landscape distribution is calculated by fractions of the total landscape, not
biomass contributions, and all biomass yields per acre decrease in the Increased
Temperature and Precipitation scenario, the acreage of all farm types increases.
This increase in acreage includes those farms with animal production, thereby
increasing animal income and showing very little change in crop product income

in Figure 5.16.

82

The Switchgrass Productivity scenario income reduction seen in Figure
5.17 is due to the almost exclusive sale of switchgrass hay, nearly eliminating
both corn stover and corn grain as sources of additional crop income. This same
landscape size constraint yields less total income for the Increased Corn Stover
Productivity scenario as well. In other words, if a biorefinery can get all the
biomass they need from 200 farms, and the year before they needed 300, the
100 farms no longer needed are “left out”. This does not mean that these farms
cannot participate in other landscapes/markets, but the BFIT scale is a fixed
bioreflnery intake size, a limitation of the model. This explains the third
productivity scenario in Figure 5.17, where diminished biorefinery requirements
means reduced biomass demand, which ultimately leads to less income from
biomass over the whole landscape.

The income by product for the Meat Production scenarios can be seen in
Figure 5.18. The greater switchgrass hay contribution to the landscape biomass
total and a corn acreage reduction lowers crop income for the Decreased Meat
Production scenario, which lacks any significant animal income by design. This
income result is in contrast to the Increased Meat Production scenario, where
animal income increases by design, but, as a side effect, the greater acreage
overall lends itself to greater crop sales as well.

As with the development simulations, individual farm type and landscape
average net return per acre is calculated and displayed in Table 5.12. The
reduction in farm return for the Decreased Precipitation scenario emphasizes the

cost to maintain corn yields under dry conditions. The limited effect of winter

83

weather changes are reflected in only minor reductions in net return for the
second scenario. The greatest change from this scenario test occurs for farm
type B, which highlights the effects of these weather changes on switchgrass.
The effect of weather, as noted for Figures 5.13 and 5.16, is most obvious in the
third weather scenario, where the year round increase in temperature and
precipitation raises costs and, in some cases, reduces income per acre.
Although there is an increase in overall income in Figure 5.16, the drastic
increase in acreage and costs far outweighs these gains.

For the productivity scenarios in Table 5.12, greater biomass production
raises returns for both the switchgrass and com stover productivity scenarios for
farms producing these biomass sources respectively. As expected given the
scenario design, the returns for the Decreased Biorefinery Production scenario
landscape are unchanged individually and for the landscape average, because
the farms and their distribution drop proportionally with reduced need at the
biorefinery. For the Meat Production scenario results in Table 5.12, the individual
farm returns are maintained, while the landscape average return per acre
changes with the appropriate distribution adjustments. Because crop only farms
are more profitable per acre in this design, the average net return per acre is
higher for the Decreased Meat Production scenario, and the opposite occurs for
the Increased Meat Production Scenario.

The net return results in Table 5.12 when combined with land requirement

results in Figures 5.13-15, as before, yield the total landscape net returns seen in

84

Figures 5.19-21. Reduced income and increased costs over a similar sized
landscape reduces farm returns for the Decreased Precipitation scenario.

The minor changes seen in all variables for the Winter Weather scenario
carry through to display very little change in Figure 5.19. In contrast, the severity
of previous changes for the Increased Temperature and Precipitation scenario
are reflected in this scenario’s overall returns. This last weather scenario
suggests that the landscape distribution and/or farm management should change
in response to weather initiated productivity declines. It is not realistic for a farm
to continue operating with negative net return. This type of scenario result gives
the beginning indicators of where changes might be made when using BFIT as a
decision making tool in the future.

For the two Increased Productivity scenarios in Figure 5.20, again it can
be seen that the reduction in the number of participating farms reduces overall
landscape size and thus overall landscape return. It is also apparent for the
Increased Switchgrass Productivity scenario that a limitation in the number of
products that can be sold also deceases total landscape income, but switchgrass
has a higher per acre return than corn. These results show great switchgrass
expansion potential for non-ideal land, such as CRP lands. This is better than
replacing high yielding arable lands currently producing corn, which is a valuable
livestock feedstuff. For the final scenario in Figure 5.20, reduced biorefinery
productivity decreases biomass purchases, lowering the overall landscape

return.

85

The overall landscape returns seen in Figure 5.21 reflect the income
results seen in Figure 5.18 for both meat production scenarios. Costs for both
these scenarios were balanced by income changes yielding the landscape net
return results. These results show that a shift away from beef agriculture in
Texas, a beef dominated state, does not reduce farm income much. However,
the Increased Meat Production scenario shows that further expansion of the beef

industry in Texas offers even greater potential revenue.

 

‘55] Denitrification I Volatilization III Leaching j

 

(Oh->0!
010010

(A)
O

A—lN
OU'IO

Nitrogenous Species Release (Ib/ac/yr)
N
01 01

 

O

Decreased Original (IA) Decreased Original (Ml) Increased Original (SD)

Precipitation Winter Temperature
Temperature and
and Precipitation
Precipitation
Figure 5.22: Nitrogen loss, as described for Figure 5.5, for scenario tests having weather
adjustments.

86

 

Denitrification I Volatilization El Leaching

 

00
UI

 

 

o:
O

N
01

 

 

20—

 

 

 

 

Nitrogenous Species Release (lb/ac/yr)

 

 

 

 

 

 

Increased 36 Increased Corn Original (IA) Decreased Original (PA)
Productivity Stover Production Biorefinery
Productivity

Figure 5.23: Nitrogen loss, as described for Figure 5.5, for scenario tests having productivity
adjustments. ‘

87

 

LEI Denitrification l Volatilization El Leaching

 

Nitrogenous Species Release (lb/ac/yr)
N 0) #- UI O) N on
O O O O O O O

._|
O

 

Decreased Meat Production Increased Meat Production Original (TX)

Figure 5.24: Nitrogen loss, as described for Figure 5.5, for scenario tests having meat production
adjustments.

For the Decreased Precipitation scenario in Figure 5.22, leaching
decreases while denitrification increases because fewer rain events are
simulated with each being less intense. These conditions allow water to wet the
soil but not pass through it, thereby removing less nitrate, which is the first
electron acceptor for the denitriflcation process, and creating anoxic zones ideal
for this process. The same can be said for the winter weather scenario, but the
effects are not as great due to the limited time of decreased precipitation. The
yield reduction for the final weather scenario in Figure 5.22 increases farming
intensity, which is, in part, responsible for nitrogen loss increases seen in this
figure. The remaining loss is from the combination of increased rainfall intensity,

which increases leaching, and aftenrvard prolonged soil wetting with warmer

88

ambient temperatures, which, when combined, also increase denitrification [90].
The only volatilization change in Figure 5.22 is for the Increased Temperature
and Precipitation scenario, because elevated temperatures evaporate additional
ammonia from animal excretions.

For the first scenario in Figure 5.23, the switchgrass farm management
severity reduction drives down nitrogen loss because of reduced fertilizer
application and greater root structure uptake. For the remaining two scenarios in
Figure 5.23 very little change is seen on a per acre basis because farming
practices remain mostly unchanged.

The results in Figure 5.24 show an increase in intensively managed farm
strategies in the Decreased Meat Production scenario that drives up both
denitrification and leaching, while the opposite shift is seen for the Increased
Meat Production scenario. The opposing changes seen for volatilization
correspond directly to the increase or decrease in the number of animals in the

scenanolandscape.

89

GHG Emissions (lb COZe/yr)

 

3.0E+09

  
 
 
 
  

2.0E+09 -

1.0E+09 -

0.0E+00 —

 

 

 

 

 

I Biomass

I Farm Machinery

 

Transport

[3 Biorefinery
Operations

IAnimal Production

I Crop Production

 

 

 

 

-4.0E+09

Decreased

Precipitation

Original (IA)

Decreased Winter

Temperature and

Precipitation

Original (Ml)

Increased

Temperature and

Precipitation

Original (30)

Figure 5.25: Greenhouse gas (GHG) emissions for scenario tests having weather adjustments.

GHG Emissions (lb COZe/yr)

 

3.0E+09

 

2.0E+09

1.0E+09

 

0.0E+OO ~

-1.0E+09 n

-2.0E+09 a

 

 

-3.0E+09

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I Biomass Transport

I Farm Machinery

El Biorefinery
Operations

I Animal Production

I Crop Production

 

 

 

 

-4.0E+09

Increased
86

Increased Original (IA) Decreased Original (PA)
Corn Stover
Productivity Production

Biorefinery
Productivity

Figure 5.26: Greenhouse gas (GHG) emissions for scenario tests having productivity adjustments.

90

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3.0E+09
2.0E+09 ' .
IBlomass
A Trans art
5 10E+09 — p
3 I Farm Machinery
N
o 0.0E+00 - .1, f
0 Q ._ :1 ElBiorefinery
a -1 0E+09 — 383‘s? Operations
g EQ‘SE‘SQ‘ IAnimal Production
'35 -2.0E+09 - “six
-9 :K‘ {1:1 ICrop Production
E fi\{‘\\'\
Lu -3.0E+09 - )3- \‘x
o t 3% “it:
I fiQxO‘gfi.
(9 -4.0E+09 ”$3.131“;
«24‘s
-5.or5+09 " "
-6.0E+09
Decreased Meat Increased Meat Original (TX)
Production Production

Figure 5.27: Greenhouse gas (GHG) emissions for scenario tests having meat production
adjustments.

The GHG emissions results for the Decreased Precipitation scenario in
Figure 5.25 show a small transportation emission decrease, which is a direct
result of decreased area. This area decrease combined with lowered corn
productivity reduces total carbon sequestration such that emissions reductions
are insignificant relative to the additional carbon that remains unfixed. As with
the other results for the winter weather scenario, very little change is seen in
Figure 5.25 for this scenario as well. For the Increased Temperature and
Precipitation scenario, just as with volatilization, animal emissions of GHG’s
increase from warming. This negative impact is coupled with a decrease in
carbon sequestration that occurs from the decrease in switchgrass yield, which

includes reduced root biomass that helps maintain soil carbon and nitrogen.

91

The reduction in area requirement for the Switchgrass Productivity
scenario causes the reduced transportation emissions seen in Figure 5.26, but
also leads to less carbon sequestration as well. Once again, it is important to
note that the monoculture farming limits the system boundary to the small area
required for biomass growth, which is the functional unit of the model. This same
landscape area influence affects the other two production scenarios at varying
degrees of severity, reducing sequestration with shrinking area.

In Figure 5.27 land requirement again affects transport emissions and
sequestration, and landscape distribution changes cause shifts in emissions
sources. The increase or decrease in animal agriculture is reflected in the
“animal production” emissions results, indicating once again that the production
of animals involves certain trade-offs for economic benefits and environmental
impacts.

It is important to note, in many of these scenario tests, GHG emissions
reductions are often offset by diminished carbon sequestration, and vice versa.
This suggests that one very important use for this tool is quantification, pound for
pound, of the trade-offs associated with potential environmentally or

economically beneficial landscapes/farm management arrangement.

92

 

2.06E+08

 

2.04E+08

 

 

2.02E+08

 

 

 

1 96E+08 -
1 94E+08 -
1.92E+08 -
1.90E+08 -
Decreased Original (IA) Decreased Original (MI) Increased Original (SD)
Precipitation Vlfinter Temperature
Temperature and
and Precipitation
Precipitation

Figure 5.28: Total project investment (T PI) for scenario tests having weather adjustments.

 

2.06E+08

 

2.04E+08

 

 

 

2.02E+08

 

 

 

2.00E+08

 

 

 

1 .98E+08

 

 

 

1 .96E+08

TPI ($)

 

 

 

1.94E+08 -

 

1.92E+08 -

 

 

 

 

1 .86E+08 -

Increased SG Increased Corn Original (IA) Decreased Original (PA)
Productivity Stover Production Biorefinery
Productivity

Figure 5.29: Total project investment (T PI) for scenario tests having productivity adjustments.

93

TPI ($)

2.5E+08

 

2.0E+08 -

1 .5E+08 -

1 .0E+08 -

5.0E+07 -

 

 

0.0E+00 -

Decreased Meat Production

Increased Meat Production

 

 

Original (TX)

Figure 5.30: Total project investment (T PI) for scenario tests having meat production adjustments.

$1.95

 

 

 

Decreased Original (IA)
Preclpitation

 

 

 

 

 

 

 

 

Decreased
Winter

Temperature

and

Precipitation

Original (Ml)

 

It

Increased Original (SD)
Temperature
and
Precipitation

Figure 5.31: Minimum ethanol selling price (MESP) for scenario tests having weather adjustments.

94

 

$1.90

 

 

 

$1.85

 

 

$1.80

 

 

 

$1.75

9d)

 

g $1.70 —

 

$1.65 -

MESP(

 

 

$1.60 -

 

 

$1.55-

 

 

$1.50 -

 

     

 

 

 

$1.45 —
Increased SG Increased Corn Original (IA) Original (PA)
Productivity Stover Production Biorefinery
Productivity

Figure 5.32: Minimum ethanol selling price (MESP) for scenario tests having productivity
adjustments.

$2.00
$1.80
$1.60
$1.40

7‘; $1.20

2’

‘3

0. $1.00

$

2 $0.80
$0.60
$0.40

$0.20

 

$0.00

Decreased Meat Production Increased Meat Production Original (TX)

Figure 5.33: Minimum ethanol selling price (MESP) for scenario tests having meat production
adjustments.

95

The increased biomass switchgrass composition for the Decreased
Precipitation and Increased Switchgrass Productivity scenarios reduces TPI in
Figure 5.28 and 5.29, respectively, by lowering corn stover handling equipment
costs. The opposite effect is seen for the Increased Temperature and
Precipitation and Increased Com Stover Production scenarios in these same
figures. Little change is seen for the Decreased Winter Temperature and
Precipitation, and Reduced Biorefinery Productivity scenarios because the type
and rate of biomass passing through the system is unchanged. Meat Production
scenario TPI changes, seen in Figure 5.30, are a result of varied pretreated
grass feed sales, as expected from previous results.

The limited MESP change for the Decreased Precipitation scenario in
Figure 5.31 occurs because TPI is the only biorefinery parameter that changed
from the development scenario. The Increased Temperature and Precipitation,
and Increased Switchgrass Productivity scenarios experience additional effects
on MESP because biomass transportation costs also change. On top of these
influences already described, both meat productivity scenarios have even further
MESP changes from feed sales variations, and the Decreased Biorefinery
Productivity scenario from reduced ethanol product revenue.

The research tool developed here yields results that meet expectations for
scenarios that include multiple variable changes and extreme values, which is a
positive outcome for this model verification. Some scenario tests reveal
limitations of the model and the necessity of the user to carefully select inputs

and review outputs for irregularities. An example of such irregularities would be

96

farm management strategies that have negative returns, as was the case for the
Increased Temperature and Precipitation scenario.

These scenario tests not only verify the function of BFIT, but also verify its
value as a tool for inexperienced, expert users in the future. These tests also
verify that the trained users’ guide offered to aid in the simulation of the scenario
tests was helpful and has value for future use. This BFIT trained users’ guide is
presented in Appendix D.

Although the outcomes here indicate some important trends and patterns
for the biorefinery, the scenarios tested here should not be used for projection of
realistic scenarios and/or decision making. Elaboration on the work presented
here will require careful economic consideration. Future input selection should
include updated or projected future values to provide meaningful projections for
decision making.

5.4 Conclusion

The simulations here are the first biorefinery simulations to directly include
a realistic landscape and cover a variety of US locations. Research tool
development aimed to allow side-by—side comparison of similar biorefineries,
over different regional landscapes following real-world statistical distributions.
The assumptions, inputs, and biorefinery technologies used in this model
development are all subject to the same temporal limitations and follow recent to
near-term expectations for the included elements.

When designing future biorefinery projects, choice of location will be

important. The results of the development simulations presented here suggest

97

the Midwest, also referred to as the Corn Belt, is ideal for the cellulosic ethanol
industry. Higher simulated investment results in the Midwest indicate that
biorefinery construction may be more difficult to fund, but economies of scale
may put these same locations in a strategic position in a more developed market.
Ultimately, for a combination of agricultural and industrial interests, while also
reducing environmental impact, the preliminary results presented here point to
the Midwest, which is already a center for bioethanol production [6]. These
results also point to locations where farm management adjustments would
provide greater farm return and potential emissions reduction, particularly when
compared to “conventional” farming distributions.

The model sensitivity analyses presented here showed that BFIT outputs
change in various ways for the input variables tested. This test revealed four
parameters which, when adjusted, show greater impact on the whole system,
including both the biorefinery and associated landscape, and in some cases non-
linear effects on outcomes. These important inputs are biorefinery size,
switchgrass yield, biomass selling price, and biomass storage losses. This result
highlights the need for careful assumption selection, as well as a need for further
research on specific biorefinery system elements. Many of these areas already
receive attention because the lack of realistic expectations and data for actual
systems has been identified.

The scenario-based verification test presented here confirms the flexibility
of BFIT and also underscores the potential for future scenario work with this tool.

Using properly developed conditions, this modeling tool could allow comparison

98

of a variety of circumstances with changes to the biorefinery and its surrounding
landscape. The verification process also draws attention to some shortcomings
of this new research tool, which are addressed in Section 6.2.

Using tools like the one developed here, biorefinery project designs can
be tested for trade-offs and define scenarios for industrial projects where there
are winners on all sides. The interests of investors, farmers, government, and
our greater society can all be balanced if proper care is taken to address

concerns for each, which can be evaluated using systems such as BFIT.

99

CHAPTER 6: CONCLUSIONS AND SUGGESTIONS FOR FUTURE
RESEARCH

6.1 Conclusions

The biorefinery system, which includes the landscape that provides
biomass feedstock and uses pretreated grass feeds, has the potential to provide
cellulosic ethanol as a liquid transportation fuel to our national energy portfolio.
The research presented here realizes the desire to use modeling tools to help
project the potential economic and environmental impacts of producing this
alternative fuel.

The first research stages, presented in Chapters 3 and 4, prepared
existing biorefinery and agricultural models for integration. The NREL chemical
engineering and economic model of the biorefinery, updated at Dartmouth
College and Michigan State University, simulates the industrial portion of this
work. This model, written in ASPEN PLUS and Microsoft Excel, was adjusted to
simulate a 2000 TPD biorefinery with AFEX pretreatment and SSCF biological
processing for the BFIT sub-model. Results presented in Chapter 3 verify that
this modified NREL model version still produced the same outcomes as previous
studies.

The whole farm agricultural model IFSM, developed at the USDA, is
shown in Chapter 4 to be the best choice for farm simulations, which are
aggregated for the biorefinery landscape. This FORTRAN model, which has its
own user-interface, is used here to simulate six farm management strategies that
would customarily produce the two most common biomass feedstocks, corn

stover and switchgrass. This model also includes animal operations, which were

100

limited to cattle, deemed acceptable because they are the dominant US
livestock.

The combination of the two sub-models described above into a new
research tool, called BF IT, is presented in Chapter 5. This tool allows expert
users to project baseline expectations for the fully integrated biorefinery with a
realistic landscape across various US regions for the first time. The BFIT
development simulations, sensitivity analysis, and scenario-based verification are
all presented in Section 5.3. These projection results show the model to be
functional and working properly, which meets the primary goal of basic model
development.

The development simulation results in Section 5.3.1 illustrate the strong
potential for the cellulosic ethanol industry in the Midwest region of the US.
Sensitivity results in Section 5.3.2 indicate that great care should be used for
selection of biorefinery size, switchgrass yield, and economic assumptions in
future BFIT simulations.

Verification of this tool in Section 5.3.3 demonstrates the future value of
BF IT as a research tool for others. Verification also highlighted the ability of a
trained user to operate the model and confirms the usefulness of the current
trained users’ guide. These scenario-based tests emphasize areas where
improvements can be made and open the door for future scenario simulations
using this model. The current inability to validate this model urges further

research to update and upgrade this tool and its components.

101

Analysis of simulation trends and suggestions for future research are
presented throughout Chapter 5. The research and outcomes in Chapter 5
directly address the application portion of the primary goal stated in Chapter 1, to
“identify trends and build a foundation for future decision-making research”.

The only objective of this work not accomplished was automation of the
model. After deeper investigation of the two pre-existing sub-models, it was
found that this goal was more difficult than expected and would require an expert
programmer for this task. The development of user-friendly interfaces that
reduce user input will be more fully developed and useful if designed by a
specialist in this area.

The objectives and outcomes achieved here are not just important to
engineers or scientists, but also investors, farmers, politicians, and indeed all of
society, because the search for environmental, economic, and socially beneficial
transportation fuels affects all inhabitants of the world.

6.2 Suggestions for Future Research

Advances in modeling techniques, process technology, and farm
management practices are constantly occurring, and this model will continue to
improve as it is updated to reflect these changes. Future steps for the
enhancement of this tool are described in this section.

First, validation of the model for commercial scale bioreflneries and full
biomass supply chains is needed. There are currently several commercial
biorefinery scale-up projects taking place, which will allow full validation of this

system as real-world data for a full-scale biorefinery will be available for the first

102

time [91]. As this data becomes available, some model parts will need updating
for improved understanding of underlying processes, at which time validation can
commence. In addition to industrial process advances, a biomass market supply
chain will develop for the first time. The observations for this supply chain can
also be incorporated into the model and validated, yielding an even fuller view of
the near-term biorefinery, its agricultural landscape, and the biomass delivery
system.

As an elaboration on the validation of the model and the sensitivity
analysis, a deeper study of biorefinery size and its influence on the overall
system should be performed. Future BFIT studies on biorefinery size and annual
operation should included comparisons with previous work on economies of
scale.

Second, further system automation and development of a user interface,
as suggested by the objectives, are important. One current limitation of BFIT is
the number of user mediated steps, or lack of automation. It will also be valuable
to upgrade the BFIT user interface, reduce user steps, and improve model speed
with sub-model program-version updates (Excel, ASPEN, etc.).

Along with the aforementioned improvements, it will be necessary to
broaden the scope and overcome many of the limitations described in Section
1.2. This will require new biorefinery simulations with a variety of biorefinery
sizes and locations. Other simulation changes should incorporate new
landscape designs with different farm management strategies, which could allow

additional biomass sources.

103

Future work should also feature commonly accepted LCA methodologies,
specifically emissions allocations to the various product outputs. Also, animal
excretion collection for biogas production might be added to the model, because
it is a growing “environmental farm practice”. This process would integrate well
with the environmental goals of many who are interested in “green engineering”
[92.93.994.95].

Finally, the adjustment of the temporal setting for the model will require
regular updates to current prices. Simulations showing the effect of updating
model development assumptions to more recent values will be particularly
relevant given recent drastic changes in many costs, particularly petroleum
products. It is important, however, that any economic assumption change should
be followed by either update of all other economic assumptions, maintaining
lntemal consistency, or fully transparent reporting of which changes were made
independenfly.

In combination, all the changes described above will enhance BFIT
biorefinery simulations and yield results that can bring to light prospective

winners and losers in the altemative fuels outlook.

104

APPENDICES

105

APPENDIX A

 

 

Minimum Ethanol Selling Price $1.4176
Feedstock Rate (dry ton/day) 2,205
Ethanol Production (MM Gal. lYear) 69.7
Ethanol Yield (Gal / Dry US Ton

Feedstock) 90.3

Feedstock Cost $lDry US Ton $40

lntemal Rate of Return (After-Tax) 12%

Equity Percent of Total lnvestrnent 60%

Capital Costs
Feed Handling $4,500,000
Pretreatment $21,600,000
Biological conversion $4,700,000
Distillation and Solids Recovery $15,400,000
Wastewater Treatment $13,600,000
Storage $1,400,000
Residue Processing $38,400,000
Utilities $4,900,000
Total Installed Equipment Cost $104,500,000
Added Costs $74,800,000
(% of TPI) 42%

Total Project Investment $179,300,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.2906

106

%of

total
4.3%
20.7%
4.5%
14.7%
13.0%
1.3%
36.7%

4.7%

Table A.1: Biorefinery economic summary output for SSCF-COMP-OLD.

Operating Costs (cents/gal ethanol)

 

 

Feedstock 44.3
Other Raw Materials 15.3
Waste Disposal 1.1
Electricity 86
Fixed Costs 15.0
Capital Depreciation 12.9
Average Income Tax 1.9
Average Return on

Investment 59.9

Operating Costs ($lyr)

Feedstock $30,900,000
Other Raw Matl. Costs $10,600,000
Waste Disposal $800,000
Electricity $6,000,000
Fixed Costs $10,400,000
Capital Depreciation $9,000,000
Average Income Tax $1,300,000
Average Return on

Investment $41 ,800,000

 

 

Minimum Ethanol Selling Price $13676
Feedstock Rate (dry ton/day) 2,205
Ethanol Production (MM Gal. lYear) 69.7
Ethanol Yield (Gal / Dry US Ton

Feedstock) 90.3

Feedstock Cost $lDry US Ton $40

lntemal Rate of Return (After-Tax) 12%

Equity Percent of Total Investment 60%

Capital Costs
Feed Handling $4,500,000
Pretreatment $18,600,000
Biological conversion $3,700,000
Distillation and Solids Recovery $15,100,000
Wastewater Treatment $13,500,000
Storage $1 ,300,000
Residue Processing $38,400,000
Utilities $4,200,000
Total Installed Equipment Cost $99,300,000
Added Costs $70,900,000
(% of TPI) 42%

Total Project Investment $170,200,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.2979

107

%of

total
4.5%
18.7%
3.7%
15.2%
13.6%
1.3%
38.7%

4.2%

Table A2: Biorefinery economic summary output for SSCF-COMP-UPD.

Operatqu Costs (cents/gal etharg)

 

 

Feedstock 44.3
Other Raw Materials 15.1
Waste Disposal 1.1
Elechicity -11.4
Fixed Costs 15.0
Capital Depreciation 12.2
Average Income Tax 1.8
Average Return on

Investment 58.7

Operating Costs ($lyr)

Feedstock $30,900,000
Other Raw Mall. Costs $10,500,000
Waste Disposal $800,000
Electricity $8,000,000
Fixed Costs $10,400,000
Capital Depreciation $8,500,000
Average Income Tax $1,300,000
Average Return on

Investment $40,900,000

Table A3: Biorefinery economic summary output for SSCF-NEW—OLD.

 

 

Minimum Ethanol Selling Price $10682
Feedstock Rate (dry ton/day) 2,205
Ethanol Production (MM Gal. lYear) 69.6
Ethanol Yield (Gal I Dry US Ton

Feedstock) 90.3

Feedstock Cost $/Dry US Ton $40

lntemal Rate of Return (After-Tax) 12%

Equity Percent of Total Investment 60%

Capital Costs
Feed Handling $7,100,000
Pretreatment $12,800,000
Biological conversion $5,100,000
Distillation and Solids Recovery $15,500,000
Wastewater Treatment $19,300,000
Storage $1,300,000
Residue Processing $39,200,000
Utilities $5,000,000
Total Installed Equipment Cost $105,300,000
Added Costs $74,600,000
(% of TPI) 41%

Total Project Investment $179,900,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.1656

108

%of

total
6.7%
12.2%
4.8%
14.7%
18.3%
1.2%
37.2%

4.7%

Operating Costs (cents/gal ethanol)

 

 

Feedstock 44.3
Other Raw Materials 20.7
Waste Disposal 1.1
Electricity -13.5
Fixed Costs 11.4
Capital Depreciation 12.9
Average Income Tax 1.9
Average Return on

Investment 28.0

Operating Costs ($lyr)

Feedstock $30,800,000
Other Raw Mall. Costs $14,400,000
Waste Disposal $800,000
Electricity $9,400,000
Fixed Costs $7,900,000
Capital Depreciation $9,000,000
Average Income Tax $1,300,000
Average Return on

Inveshnent $19,500,000

Table A4: Biorefinery economic summary output for SSCF-NEW-UPD.

Minimum Ethanol Selling Price $1.0314
Feedstock Rate (dry ton/day) 2,205

Ethanol Production (MM Gal. lYear) 69.6
Ethanol Yield (Gall Dry US Ton
Feedstock) 90.3

Feedstock Cost $lDry US Ton $40
lntemal Rate of Return (Alter-Tax) 12%
Equity Percent of Total Investment 60%

 

 

 

 

%of
Capital Costs total Operating Costs (cents/giethanol)
Feed Handling $7,100,000 7.2% Feedstock 44.3
Pretreatment $10,300,000 10.4% Other Raw Materials 20.7
Biological conversion $2,600,000 2.6% Waste Disposal 1.1
Distillation and Solids Recovery $15,100,000 15.2% Electricity -14.4
Wastewater Treatment $19,300,000 19.5% Fixed Costs 11.0
Storage $1,300,000 1.3% Capital Depreciation 12.2
Residue Processing $39,200,000 39.6% Average Income Tax 1.8
Average Return on
Utilities $4,200,000 4.2% Investment 26.4
Total Installed Equipment Cost $99,100,000
Operating Cosg$lyr)
Added Costs $70,000,000 Feedstock $30,800,000
(% of TPI) 41% Other Raw Mafl. Costs $14,400,000
Waste Disposal $800,000
Total Project Investment $169,100,000 Electricity $10,000,000
Fixed Costs $7,700,000
Loan Rate 7.5% Capital Depreciah'on $8,500,000
Term (years) 25 Average Income Tax $1,300,000
Average Return on
Capital Charge Factor 0.1662 Investment $18,300,000

109

 

 

Minimum Ethanol Selling Price $09307
Feedstock Rate (dry ton/day) 2,205
Ethanol Production (MM Gal. lYear) 78.1
Ethanol Yield (Gal / Dry US Ton
Feedstock) 101.2

Feedstock Cost $lDry US Ton $40

lntemal Rate of Return (Alter-Tax) 12%

Equity Percent of Total Investment 60%

Capital Costs
Feed Handling $4,500,000
Pretreatment $21,600,000
Biological conversion $4,300,000
Distillation and Solids Recovery $15,500,000
Wastewater Treatment $13,800,000
Storage $1,400,000
Residue Processing $38,400,000
Utilities $4,900,000
Total Installed Equipment Cost $104,400,000
Added Costs $74,900,000
(% of TPI) 42%

Total Project lnveshnent $179,300,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.1651

110

%of

total
4.3%
20.7%
4.1%
14.8%
13.2%
1.3%
36.8%

4.7%

Table A5: Biorefinery economic summary output for CBP-COMP-OLD.

 

 

Operating Costs Jgenggal ethanol)
Feedstock 39.5
Other Raw Materials 9.0
Waste Disposal 1.0
Electricity -7.8
Fixed Costs 13.4
Capital Depreciation 1 1 .5
Average Income Tax 1.7
Average Return on
Investment 24.8

Operating CosflS/yr)

Feedstock $30,900,000
Other Raw Matl. Costs $7,100,000
Waste Disposal $800,000
Electricity $6,100,000
Fixed Costs $10,400,000
Capital Depreciation $9,000,000
Average Income Tax $1,300,000
Average Return on

lnvestrnent $19,300,000

Table A6: Biorefinery economic summary output for CBP-COMP-UPD.

Minimum Ethanol Selling Price $08773

Feedstock Rate (dry ton/day) 2,205

Ethanol Production (MM Gal. lYear) 78.1
Ethanol Yield (Gall Dry US Ton
Feedstock) 101.2

Feedstock Cost $lDry US Ton $40
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%

 

 

 

 

%of
Capital Costs total Operathg Costs (centqugl ethanol)
Feed Handling $4,500,000 4.5% Feedstock 39.5
Pretreatment $18,600,000 18.5% Other Raw Materials 7.4
Biological conversion $4,300,000 4.3% Waste Disposal 1.0
Distillation and Solids Recovery $15,200,000 15.1% Electricity -10.0
Wastewater Treatment $13,700,000 13.6% Fixed Costs 13.4
Storage $1,400,000 1.4% Capital Depreciation 1 1 .0
Residue Processing $38,400,000 38.2% Average Income Tax 1.6
Average Return on
Utilities $4,300,000 4.3% lnvestrnent 23.9
Total Installed Equipment Cost $100,400,000
OperLting Costs ($lyr)
Added Costs $71,700,000 Feedstock $30,900,000
(% of TPI) 42% Other Raw Matl. Costs $5,800,000
Waste Disposal $800,000
Total Project Investment $172,100,000 Electricity $7,800,000
Fixed Costs $10,400,000
Loan Rate 7.5% Capital Depreciation $8,600,000
Term (years) 25 Average Income Tax $1,300,000
Average Return on
Capital Charge Factor 0.1656 Investment $18,600,000

11]

Table A7: Biorefinery economic summary output for CBP-NEW-OLD.

 

 

Minimum Ethanol Selling Price $08314
Feedstock Rate (dry ton/day) 2,205
Ethanol Production (MM Gal. lYear) 78.0
Ethanol Yield (Gal / Dry US Ton
Feedstock) 101.2

Feedstock Cost $lDry US Ton $40

lntemal Rate of Retum (After-Tax) 12%

Equity Percent of Total Investment 60%

Capital Costs
Feed Handling $4,500,000
Pretreatment $12,800,000
Biological conversion $800,000
Distillation and Solids Recovery $15,300,000
Wastewater Treatment $18,100,000
Storage $1 ,400,000
Residue Processing $39,300,000
Utilities $5,100,000
Total Installed Equipment Cost $97,300,000
Added Costs $68,300,000
(% of TPI) 41%

Total Project Investment $165,600,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.1661

112

%of

total
4.6%
13.2%
0.8%
15.7%
18.6%
1.4%
40.4%

5.2%

Operating Costs (cents/gal ethanol)

 

 

Feedstock 39.5
Other Raw Materials 5.8
Waste Disposal 1.0
Electricity -11.8
Fixed Costs 13.4
Capital Depreciation 10.6
Average Income Tax 1.6
Average Return on

Investment 23.0

Operating Costs ($lyr)

Feedstock $30,800,000
Other Raw Matl. Costs $4,500,000
Waste Disposal $800,000
Electricity $9,200,000
Fixed Costs $10,400,000
Capital Depreciation $8,300,000
Average Income Tax $1,200,000
Average Return on

Investment $18,000,000

Table A8: Biorefinery economic summary output for CBP-NEW-UPD.

Minimum Ethanol Selling Price $08055

Feedstock Rate (dry ton/day) 2,205

Ethanol Production (MM Gal. lYear) 77.9
Ethanol Yield (Gall Dry US Ton
Feedstock) 101.2

Feedstock Cost $lDry US Ton $40
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%

 

 

 

 

%of
Capital Costs total OperaMosts (cents/gal ethanoD
Feed Handling $4,500,000 4.9% Feedstock 39.5
Pretreatment $9,400,000 10.1% Other Raw Materials 5.8
Biological conversion $800,000 0.9% Waste Disposal 1.0
Distillation and Solids Recovery $15,300,000 16.5% Electricity -12.8
Wastewater Treatment $18,100,000 19.5% Fixed Costs 13.4
Storage $1,400,000 1.5% Capital Depreciation 10.1
Residue Processing $39,300,000 42.4% Average Income Tax 1.5
Average Return on
Utilities $3,900,000 4.2% lnvestrnent 22.0
Total Installed Equipment Cost $92,700,000
Operating Costs ($lyr)
Added Costs $64,800,000 Feedstock $30,800,000
(% of TPI) 41% Other Raw Matl. Costs $4,500,000
Waste Disposal $800,000
Total Project lnvestrnent $157,500,000 Electricity $10,000,000
Fixed Costs $10,400,000
Loan Rate 7.5% Capital Depreciation $7,900,000
Term (years) 25 Average Income Tax $1,200,000
Average Return on
Capital Charge Factor 0.1663 Investment $17,100,000

113

APPENDIX B
IDAYCENT IIFSM Il-FARM ElFieId Data]

 

 

 

1600

 

1 400

 

 

 

 

 

 

_l —|
e g N
e e

   
  

Grain Yield (glm‘2)
§

 

 

 

 

 

 

 

PA VA IA
State

Figure 3.1: Corn grain yield data and available published data for each state included in this
comparison.
[a DAYCENT I IFSM I l-FARM EIField Data)

 

 

 

 

 

 

 

 

 

Nitrogen Removal in Grain (g/m‘2)

 

 

 

 

 

Figure 3.2: Corn grain nitrogen removal data and available published data for each of the five
states included in this comparison.

114

 

[IDAYCENT I IFSM II-FARM CIField Dataj

 

14

 

.3
N

 

.5
o

 

(D

 

 

 

 

 

 

Nitrogen Leeched (g/m42)

 

 

 

 

 

 

 

 

 

 

 

   

VA IA
State

Figure B.3: Com grain nitrogen leaching data and available published data for each of the five
states included in this comparison.

[IDAYCENT I IFSM Il-FARM a Field Data]

 

 

 

 

.5

 

 

 

 

 

 

 

Denitrification (g/m42)
00
—-—r

N

 

 

 

 

   

 

 

 

 

 

VA IA IL Ml
State

Figure 8.4: Corn grain denitliflcation data and available published data for each if the five states
included in this comparison.

115

 

IIDAYCENT I IFSM EIField Dataj

 

 

90

 

80

 

 

 

 

 

 

 

 

 

 

Water Evaporated/Transplred (cm)

 

 

 

 

 

 

   

VA IA
State

MI

Figure B.5: Corn grain evapotranspiration data and available published data for each of the five
states included in this comparison.

(IDAYCENT IIFSM Il-FARM L'IField Data]

 

 

3000

 

 

2500

B
8

 

 

1 500

 

Soil Erosion (g/m"2)

 

1 000

 

 

 

 

 

 

State

Figure B.6: Corn grain soil erosion data and available published data for each of the five states
included in this comparison.

116

 

IIDAYCENT IIFSM EIField natal

 

 

1600

1 400

 

1200

 

 

 

 

 

Biomass Yleld (g/m‘2)
cc
8

 

 

 

 

 

 

 

IA
State

Figure B]: Switchgrass yield data and available published data for each of the five states included
in this comparison.

[EIDAYCENT I IFSM El Field Data)

 

 

30

 

N
OI

 

 

Nitrogen Removed in Biomass (glm‘2)

 

 

 

 

Figure B.8: Switchgrass nitrogen removal data and available published data for each of the five
states included in this comparison.

117

 

]- DAYCENT IIFSM CIField Data]

 

 

 

 

 

 

 

 

 

 

 

Nitrogen Leached (g/m"2)
A

 

 

 

 

 

 

 

PA VA IA IL MI
State

Figure 39: Switchgrass nitrogen leaching data and available published data for each of the five
states included in this comparison.

[IDAYCENT I IFSM Ell-“reld Data]

 

 

 

12

 

10

 

 

 

a:

 

 

 

 

 

Denitrification (glm‘2)
O)

.h

 

 

 

 

MI

State

Figure 8.10: Switchgrass denitriflcation data and available published data for each of the five
states included in this comparison.

118

 

 

[ta DAYCENT I IFSM ElFleld Data]

 

 

 

 

 

Water Evaporated/Transpired (cm)

 

 

 

 

 

   

IA IL MI
State

Figure B.11: Switchgrass evapotranspiration data and available published data for each of the five
states included in this comparison.

[IDAYCENT IIFSM CIField Dgi]

 

 

45

8888

Soil Erosion (g/m42)
N
O

 

PA VA IA IL MI
State

Figure 812: Switchgrass soil erosion data and available published data for each of the five states
included in this comparison.

119

Table 81: Field data sources with location and variable for which they were used.

 

Location Variable Reference Primary Author Title Year
PA Corn Grain 96 BL. Dillehay Performance of Bt Com Hybrids, 2004
Yield their Near lsolines, and Leading Corn
Hybrids in Pennsylvania and
Maryland
PA Corn Grain 97 Z. Dou Managing Nitrogen on Dairy Farms: 1996
N Removal An Integrated Approach I. Model
Description
PA Corn Grain 98 Y. Zhu Corn—Soybean Rotation Effects on 2003
N Leaching Nitrate Leaching
PA Corn Grain 99 Y. Zhongbo Evaluating the spatial distribution of 2000
ET water balance in a small watershed,
Pennsylvania
VA Corn Grain 100 J.K.F. Roygard No—Till Corn Yields and Water 2002
Yield Balance in the Mid-Atlantic Coastal
Plain
IA Corn Grain 101 J.M. Bordoli Deep and Shallow Banding of 1998
Yield Phosphorus and Potassium as
Alternatives to Broadcast Fertilization
for No—Till Corn
IA Corn Grain 102 D.L. Karlen Field-Scale Nitrogen Balances 1998
N Removal Associated with Long-Tenn
Continuous Corn Production
IA Corn Grain 103 DL. Dinnes Nitrogen Management Strategies to 2002
N Leaching Reduce Nitrate Leaching in Tile-
Drained Midwestern Soils
IA Corn Grain 104 R.F. Dale The climatology of soil moisture, 1965
ET atmospheric evaporative demand,
and resulting moisture stress days
for corn at Ames, Iowa
IA Corn Grain 105 J.E. Gilley Runoff, erosion, and soil quality 1997
Soil Erosion characteristics of a former
conservation reserve program site
IL Corn Grain 106 J.A. Lory Yield Goal versus Delta Yield for 2003
Yield Predicting Fertilizer Nitrogen Need in
Corn '
IL Corn Grain 107 D.B. Jaynes Nitrate Loss in Subsurface Drainage 2001
N Removal as Affected by Nitrogen Fertilizer
Rate
IL Corn Grain 107 DE. Jaynes Nitrate Loss in Subsurface Drainage 2001
N Leaching as Affected by Nitrogen Fertilizer
Rate
IL Corn Grain 108 MB. David Estimated Historical and Current 2001
Denitrified N Nitrogen Balances for Illinois
IL Corn Grain 109 J.A. Bowman Impacts of Irrigation and Drought on 1987
ET Illinois ground-water resources
IL Corn Grain 110 l. Hussain Long-term effects on physical 1998
Soil Erosion properties of eroded soil

120

Table 81 (cont’d)

Ml

Ml

Ml

Ml

PA

PA

PA

PA

VA

VA

VA

VA

VA

Corn Grain
Yield

Corn Grain
N Removal

Corn Grain
Denitrified N

Corn Grain
ET

Switchgrass
Yield

Switchgrass
N Removal

Switchgrass
ET

Switchgrass
Soil Erosion

Switchgrass
Yield

Switchgrass
N Removal

Switchgrass
N Leaching

Switchgrass
ET
Switchgrass
Soil Erosion

Switchgrass
Yield

111

112

113

114

114

115

116

117

118

119

120

121

65

AS. Grandy

S. Jose

A.S. Grandy

G.A. Jung

G.A. Jung

M.H. Ehlke

E.R. Beaty

W.L. Stout

E.A. Johnson

E.A.

Christopher, Jr.

R. Lemus

121

Long-Term Trends in Nitrous Oxide
Emissions, Soil Nitrogen, and Crop
Yields of Till and No-Till Cropping
Systems

Defining competition vectors in a
temperate alley cropping system in
the Midwestern USA: 3. Competition
for nitrogen and litter decomposition
dynamics
Long-Term Trends in Nitrous Oxide
Emissions, Soil Nitrogen, and Crop
Yields of Till and No-Till Cropping
Systems

G.A. Unterreiner Spatial and Temporal Distribution of

Herbicides and Herbicide
Degradates in a Shallow Glacial Drift
Aquifer/Surface Water System,
South-Central Michigan

Warrn-Season grass diversity in
yield, plant morphology, and nitrogen
concentration and removal in
northeastern USA

Wann-Season grass diversity in
yield, plant morphology, and nitrogen
concentration and removal in
northeastern USA
Comparison of methods for

computing streamflow statistics for
Pennsylvania streams

P.J.A. Kleinman Evaluation of Phosphorus Transport

in Surface Runoff from Packed Soil
Boxes

D.P. Belesky Wann-season grass production and

growth rate as influenced by canopy
management

Root-herbage production and
nutrient uptake retention by
Bermudagrass and Bahiagrass
Water quality implications of dairy
slurry applied to cut pastures in the
northeast USA

Effect on streamflow of cutting a
forest understory

Post Harvest Evaluation of Best
Management Practices for the
Prevention of Soil Erosion in Virginia
Biomass yield and quality of 20

switchgrass populations in southern
Iowa, USA

2006

2000

2006

2005

1 990

1 990

1 999

2004

1995

1 975

2000

1 956

2002

2002

Table 81 (cont’d)

IA

IA

Ml

Switchgrass
N Removal

Switchgrass
N Leaching

Switchgrass
ET

Switchgrass
Soil Erosion

Switchgrass
Yield

Switchgrass
N Removal
Switchgrass
N Leaching

Switchgrass
Denitrified N

Switchgrass
Yield

122

123

124

125

126

127

128

128

129

KP. Vogel

J.H. Ehrenreich

D.A. Frank

K.H. Lee

J.J. Faix

S.M. Old

W.J. Mitsch

W.J. Mitsch

W.I. Graham

122

Switchgrass Biomass Production in
the Midwest USA: Harvest and
Nitrogen Management

An Ecological Study of the Effect of
Certain Management Practices on
Native Prairie in Iowa

Temporal variation in actual
evapotranspiration of terrestrial
ecosystems: patterns and ecological
implications
Multispecies Riparian Buffers Trap
Sediment and Nutrients during
Rainfall Simulations

Quality, Yield, and Survival of Asiatic
Bluestems and an Eastern
Gamagrass in Southern Illinois

Microclimate, Fire, and Plant
Production in an Illinois Prairie

Reducing Nutrient Loads, Especially
Nitrate—Nitrogen, to Surface Water,
Ground Water, and the Gulf of
Mexico

Reducing Nutrient Loads, Especially
Nitrate—Nitrogen, to Surface Water,
Ground Water, and the Gulf of
Mexico
A National Assessment of Promising
Areas for Switchgrass, Hybrid
Poplar, or Willow Energy Crop
Production

2002

1 963

1 994

2000

1 980

1969

1 999

1 999

1 999

APPENDIX C

C.1 Sample Landscape Calculation

Example values for Iowa

ST1 = 0.0287, ST2 = 0.0862, ST3 = 0.0018, ST4 = 0.30

X= 334, Y= 315,2 =714, A=483, B = 2859, C = 722

x = 1, y = 17, z = 0, a = 423, b = 189, c = 1 (numbers are rounded to whole farms, no fractions)

T = x+y+z+a+b+c = 631

ST1 = (x+y+z)fT = (1+17+0)l631 ; 0.0287
ST2 = x/y = 1/17 a 0.0862

ST3 = CH = 1/631 3 0.0018

ST4 = (z+b)/T = (0+189)/631 s 0.30

BMO = X-x+Y-y+Z-z+A-a+B-b+C-c
= (334)(1 )+(31 5)(17)+(714)(0)+(483)(423)+(2859)(189)+(722)(1 ) = 752332

BMR = BMo°(1-DML) = 752332'(1-0.0695) = 700000

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Table C.2.10: Biorefinery economic summary output for development simulation in IA at the 75%

 

 

Minimum Ethanol Selling Price $1.8738
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 72.4
Ethanol Yield (Gall Dry US Ton
Feedstock) 103.4
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $6,800,000
Pretreatment $15,200,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,300,000
Wastewater Treatment $21,500,000
Storage $1,700,000
Residue Processing $44,400,000
Utilities $8,000,000
Total Installed Equipment Cost $119,200,000
Added Costs $84,200,000
(% of TPI) 41 %

Total Project Investment $203,400,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3510

142

%of

total
5.7%
12.8%
2.8%
15.4%
18.0%
1.4%
37.2%

6.7%

land use transportation radius.

Switchgrass Fraction

0.717

Operflg Costs (cents/gal ethanol)

 

 

Feedstock 78.1
Other Raw Materials 6.2
Waste Disposal 0.0
Co-Product Sales -7.8
Fixed Costs 12.1
Capital Depreciation 14.1
Average Income Tax 2.1
Average Return on

Investment 82.5

Operating Costs ($lyr)

Feedstock $56,600,000
Other Raw Matl. Costs $4,500,000
Waste Disposal $600,000
Co-Product Sales $5,600,000
Fixed Costs $8,800,000
Capital Depreciation $10,200,000
Average Income Tax $1,500,000
Average Return on

Investment $59,700,000

Table C.2.11: Biorefinery economic summary output for development simulation in MI at the 75%
land use transportation radius.

Minimum Ethanol Selling Price
Feedstock Rate (dry ton/day)

Ethanol Production (MM Gal. lYear)
Ethanol Yield (Gal / Dry US Ton
Feedstock)

Feedstock Cost $lDry US Ton
lntemal Rate of Return (After-Tax)
Equity Percent of Total Investment

Capital Costs

$1.8610
2,000
71.2

101.7
$81
12%
60%

 

Feed Handling

Pretreatment

Biological conversion
Distillation and Solids Recovery
Wastewater Treatment

Storage

Residue Processing

Utilities

$6,500,000
$15,100,000
$3,300,000
$18,100,000
$20,600,000
$1,700,000
$44,400,000

$7,900,000

 

Total Installed Equipment Cost

Added Costs
(% of TPI)

Total Project Investment

Loan Rate
Term (years)

Capital Charge Factor

$117,600,000

$83,400,000
41 %

$201,000,000

7.5%
25

0.3498

143

%of

total
5.5%
12.8%
2.8%
15.4%
17.5%
1.4%
37.8%

6.7%

Switchgrass Fraction
0. 796

OperatinLCosts (cents/gal ethanol)

 

 

Feedstock 79.4
Other Raw Materials 7.0
Waste Disposal 0.0
Co-Product Sales ~11.3
Fixed Costs 12.3
Capital Depreciation 14.2
Average Income Tax 2.1
Average Return on

Investment 82.4

Operating Costs ($lyr)

Feedstock $56,500,000
Other Raw Matl. Costs $5,000,000
Waste Disposal $600,000
Co-Product Sales $8,100,000
Fixed Costs $8,700,000
Capital Depreciation $10,100,000
Average Income Tax $1,500,000
Average Return on

Investment $58,700,000

Table C.2.12: Biorefinery economic summary output for development simulation in MN at the 75%
land use transportation radius.

 

 

Minimum Ethanol Selling Price $18620
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 71.6
Ethanol Yield (Gall Dry US Ton
Feedstock) 102.3
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total lnvestrnent 60%
Capital Costs
Feed Handling $6,600,000
Pretreatment $15,100,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,200,000
Wastewater Treatment $20,600,000
Storage $1,700,000
Residue Processing $44,400,000
Utilities $8,000,000
Total Installed Equipment Cost $117,900,000
Added Costs $83,300,000
(% of TPI) 41 %

Total Project lnvestrnent $201,200,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3509

144

%of

total
5.6%
12.8%
2.8%
15.4%
17.5%
1.4%
37.7%

6.8%

Switchgrass Fraction
0. 793

OperatinLCosts(cents/gal ethanol)

 

 

Feedstock 78.9
Other Raw Materials 6.7
Waste Disposal 0.0
Co-Product Sales -10.1
Fixed Costs 12.2
Capital Depreciation 14.1
Average Income Tax 2.1
Average Return on

Investment 82.3

Ograting Costs ($lyr)

Feedstock $56,500,000
Other Raw Mafl. Costs $4,800,000
Waste Disposal $600,000
Co-Product Sales $7,200,000
Fixed Costs $8,700,000
Capital Depreciation $10,100,000
Average Income Tax $1,500,000
Average Return on

Investment $59,000,000

Table C.2.13: Biorefinery economic summary output for development simulation in NY at the 75%
land use transportation radius.

 

 

Minimum Ethanol Selling Price $16262
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 64.4
Ethanol Yield (Gal / Dry US Ton
Feedstock) 92.0
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total lnvestrnent 60%
Capital Costs
Feed Handling $6,700,000
Pretreatment $14,900,000
Biological conversion $3,200,000
Distillation and Solids Recovery $17,100,000
Wastewater Treatment $20,200,000
Storage $1,600,000
Residue Processing $44,400,000
Utilities $4,500,000
Total Installed Equipment Cost $112,600,000
Added Costs $79,500,000
(% of TPI) 41 %

Total Project Investment $192,100,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3389

145

%of

total
6.0%
13.2%
2.8%
15.2%
17.9%
1.4%
39.4%

4.0%

Switchgrass Fraction
0. 757

Operating Costs (cents/gal ethanol)

 

 

Feedstock 87.9
Other Raw Materials 11.2
Waste Disposal 0.0
Co-Product Sales -50.7
Fixed Costs 13.2
Capital Depreciation 14.9
Average Income Tax 2.3
Average Return on

Investment 83.9

Operatiry Costs ($lyr)

Feedstock $56,600,000
Other Raw Matl. Costs $7,200,000
Waste Disposal $500,000
Co-Product Sales -$32,700,000
Fixed Costs $8,500,000
Capital Depreciafion $9,600,000
Average Income Tax $1,500,000
Average Return on

Investment $54,000,000

Table C.2.14: Biorefinery economic summary output for development simulation in OH at the 75%
land use transportation radius.

 

 

Minimum Ethanol Selling Price $18735
Feedstock Rate (dry ton/day) 2,000
EflIanoI Production (MM Gal. lYear) 70.7
Ethanol Yield (Gall Dry US Ton
Feedstock) 101.0
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total lnvestrnent 60%
Capital Costs
Feed Handling $6,900,000
Pretreatment $15,100,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,100,000
Wastewater Treatment $21,500,000
Storage $1 ,700,000
Residue Processing $44,400,000
Utilities $7,900,000
Total Installed Equipment Cost $118,900,000
Added Costs $84,000,000
(% of TPI) 41 %

Total Project lnvestrnent $202,900,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3470

146

%of

total
5.8%
12.7%
2.8%
15.2%
18.1%
1 .4%
37.3%

6.6%

Switchgrass Fraction
0.688

Operating Costs (cents/gal ethanol

 

 

Feedstock 80.1
Other Raw Materials 7.3
Waste Disposal 0.0
Co-Product Sales -12.1
Fixed Costs 12.4
Capital Depreciation 14.3
Average Income Tax 2.2
Average Return on

lnvestrnent 83.2

OperatinLCosts ($lyr)

Feedstock $56,600,000
Other Raw Matl. Costs $5,200,000
Waste Disposal $600,000
Co-Product Sales $8,600,000
Fixed Costs $8,800,000
Capital Depreciation $10,100,000
Average Income Tax $1,500,000
Average Return on

Investment $58,800,000

Table C.2.15: Biorefinery economic summary output for development simulation in PA at the 75%
land use transportation radius.

 

 

Minimum Ethanol Selling Price $16353
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 66.3
Ethanol Yield (Gal / Dry US Ton
Feedstock) 94.7
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $6,800,000
Pretreatment $14,900,000
Biological conversion $3,200,000
Distillation and Solids Recovery $17,400,000
Wastewater Treatment $20,300,000
Storage $1,700,000
Residue Processing $44,400,000
Utilities $4,500,000
Total Installed Equipment Cost $113,200,000
Added Costs $80,000,000
(% of TPI) 41 %

Total Project Investment $193,200,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3437

147

%of

total
6.0%
13.2%
2.8%
15.4%
17.9%
1.5%
39.2%

4.0%

Switchgrass Fraction
0. 724

Operating Costs (cents/gal ethanol)

 

 

Feedstock 85.3
Other Raw Materials 9.7
Waste Disposal 0.0
Co-Product Sales -44.6
Fixed Costs 12.9
Capital Depreciation 14.6
Average Income Tax 2.2
Average Return on

Investment 83.3

OperatinLCosts ($lyr)

Feedstock $56,600,000
Other Raw Matl. Costs $6,500,000
Waste Disposal $500,000
Co-Product Sales $29,500,000
Fixed Costs $8,500,000
Capital Depreciation $9,700,000
Average Income Tax $1,500,000
Average Return on

lnvestrnent $55,200,000

Table C.2.16: Biorefinery economic summary output for development simulation in SD at the 75%
land use transportation radius.

 

 

 

 

Minimum Ethanol Selling Price $16432
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 69.4
Ethanol Yield (Gal I Dry US Ton
Feedstock) 99.2 Switchgrass Fraction
Feedstock Cost $lDry US Ton $81 0.763
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
%of
Capital Costs total Operatirg Costs (cents/gal ethanol)
Feed Handling $6,600,000 5.8% Feedstock 81.4
Pretreatment $15,000,000 13.1% Other Raw Materials 7.5
Biological conversion $3,300,000 2.9% Waste Disposal 0.0
Distillation and Solids Recovery $17,800,000 15.6% Co-Product Sales 356
Wastewater Treatment $20,400,000 17.8% Fixed Costs 12.4
Storage $1,700,000 1.5% Capital Depreciation 14.1
Residue Processing $44,400,000 38.8% Average Income Tax 2.1
Average Return on
Utilities $5,100,000 4.5% Investment 82.4
Total Installed Equipment Cost $114,300,000
Operating Costs ($lyr)
Added Costs $81,100,000 Feedstock $56,500,000
(% of TPI) 42% Other Raw Matl. Costs $5,200,000
Waste Disposal $600,000
Total Project Investment $195,400,000 Co-Product Sales -$24,700,000
Fixed Costs $8,600,000
Loan Rate 7.5% Capital Depreciation $9,800,000
Term (years) 25 Average Income Tax $1,500,000
Average Return on
Capital Charge Factor 0.3506 Investment $57,200,000

148

Table C.2.17: Biorefinery economic summary output for development simulation in TX at the 75%
land use transportation radius.

 

 

Minimum Ethanol Selling Price $16288
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 56.1
Ethanol Yield (Gal I Dry US Ton
Feedstock) 80.1
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $7,500,000
Pretreabnent $14,600,000
Biological conversion $3,000,000
Distillation and Solids Recovery $15,800,000
Wastewater Treatment $19,900,000
Storage $1,600,000
Residue Processing $44,400,000
Utilities $4,300,000
Total Installed Equipment Cost $111,100,000
Added Costs $78,400,000
(% of TPI) 41 %

Total Project Investment $189,500,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3187

149

%of

total
6.8%
13.1%
2.7%
14.2%
17.9%
1.4%
40.0%

3.9%

Switchgrass Fraction
0.434

Operating Costs (cents/gal ethanol)

 

 

Feedstock 101.1
Other Raw Materials 18.6
Waste Disposal 0.0
Co-Product Sales -79.5
Fixed Costs 15.0
Capital Depreciation 16.9
Average Income Tax 2.6
Average Return on

Investment 88.1

Operating Costs ($lyr)

Feedstock $56,700,000
Other Raw Matl. Costs $10,500,000
Waste Disposal $500,000
Co-Product Sales $44,600,000
Fixed Costs $8,400,000
Capital Depreciation $9,500,000
Average Income Tax $1,500,000
Average Return on

Investment $49,400,000

Table C.2.18: Biorefinery economic summary output for development simulation in WI at the 75%
land use transportation radius.

 

 

Minimum Ethanol Selling Price $16410
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 68.3
Ethanol Yield (Gall Dry US Ton
Feedstock) 97.6
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $6,700,000
Pretreatment $15,000,000
Biological conversion $3,300,000
Distillation and Solids Recovery $17,700,000
Wastewater Treatment $20,400,000
Storage $1,700,000
Residue Processing $44,400,000
Utilities $4,600,000
Total Installed Equipment Cost $113,800,000
Added Costs $80,400,000
(% of TPI) 41 %

Total Project Investment $194,200,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3486

150

%of

total
5.9%
13.2%
2.9%
15.6%
17.9%
1.5%
39.0%

4.0%

Switchgrass Fraction
0. 732

OperatinLCosts (cents/gal ethanol)

 

 

Feedstock 82.8
Other Raw Materials 8.2
Waste Disposal 0.0
Co-Product Sales -38.5
Fixed Costs 12.5
Capital Depreciation 14.2
Average Income Tax 2.2
Average Retum on

Investment 82.7

OperatinLCosts ($lyr)

Feedstock $56,600,000
Other Raw Matl. Costs $5,600,000
Waste Disposal $600,000
Co-Product Sales $26,300,000
Fixed Costs $8,500,000
Capital Depreciation $9,700,000
Average Income Tax $1,500,000
Average Return on

Investment $56,500,000

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N $2 $5.622 9.36.2 .o .8532
.. m. .2915... am .22 8.. 9.28.2 8.262
5353.00... can 2:: o.nuta>

0.382.th 63.3.0...

5.8 8.. 8...... .8. 5... 22.82 .o. .N a... .8888... 8.0 c. 8... $2.. 0. 8...... c. .88.... 5.8 28.

166

Table C.3.10: Biorefinery economic summary output for the Decreased Precipitation Scenario at
the 75% land use transportation radius.

 

 

Minimum Ethanol Selling Price 31.8625
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 72.6
Ethanol Yield (Gal I Dry US Ton
Feedstock) 103.8
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $6,500,000
Pretreatment $15,200,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,300,000
Wastewater Treatment $20,600,000
Storage $1 ,700,000
Residue Processing $44,400,000
Utilities $8,000,000
Total Installed Equipment Cost $118,000,000
Added Costs $83,500,000
(% of TPI) 41 %

Total Project Investment $201,500,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3529

167

%of

total
5.5%
12.9%
2.8%
15.5%
17.5%
1.4%
37.6%

6.8%

 

 

Switchgrass Fraction
0.8194

OperatinLCosts (cents/gal ethang
Feedstock 77.9
Other Raw Materials 6.1
Waste Disposal 0.0
Co-Product Sales -7.7
Fixed Costs 12.0
Capital Depreciation 13.9
Average Income Tax 2.1
Average Return on
Investment 82.0

Operating Costs ($lyr)

Feedstock $56,600,000
Other Raw Matl. Costs $4,400,000
Waste Disposal $600,000
Co-Product Sales -$5,600,000
Fixed Costs $8,700,000
Capital Depreciation $10,100,000
Average Income Tax $1,500,000
Average Retum on
Investment $59,500,000

Table 0.3.11: Biorefinery economic summary output for the Increased Switchgrass Productivity
Scenario at the 75% land use transportation radius.

 

 

Minimum Ethanol Selling Price $16251
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 69.4
Ethanol Yield (Gal I Dry US Ton
Feedstock) 99.2
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $5,900,000
Pretreatment $15,000,000
Biological conversion $3,300,000
Distillation and Solids Recovery $17,900,000
Wastewater Treatment $20,400,000
Storage $1 ,700,000
Residue Processing $44,400,000
Utilities $5,100,000
Total Installed Equipment Cost $113,700,000
Added Costs $80,400,000
(% of TPI) 41 %

Total Project Investment $194,100,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3503

168

%of

total
5.2%
13.2%
2.9%
15.7%
17.9%
1.5%
39.1%

4.5%

Switchgrass Fraction
0.9526

Ope_rating Costs (cents/gal ethanol)

 

 

Feedstock 81.2
Other Raw Materials 7.6
Waste Disposal 0.0
Co-Product Sales -36.6
Fixed Costs 12.3
Capital Depreciation 14.0
Average Income Tax 2.1
Average Return on

Investment 81.9

OperatinLCosts ($lyr)

Feedstock $56,400,000
Other Raw Matl. Costs $5,300,000
Waste Disposal $500,000
Co-Product Sales $25,400,000
Fixed Costs $8,500,000
Capital Depreciation $9,700,000
Average Income Tax $1,500,000
Average Return on

Investment $56,800,000

Table C.3.12: Biorefinery economic summary output for the Decreased Meat Production Scenario
at the 75% land use transportation radius.

 

 

Minimum Ethanol Selling Price $18766
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 72.8
Ethanol Yield (Gal / Dry US Ton
Feedstock) 104.0
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $6,800,000
Pretreatment $15,200,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,300,000
Wastewater Treatment $21,600,000
Storage $1,700,000
Residue Processing $44,400,000
Utilities $8,000,000
Total Installed Equipment Cost $119,300,000
Added Costs $84,300,000
(% of TPI) 41 %

Total Project Investment $203,600,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3522

169

%of

total
5.7%
12.7%
2.8%
15.3%
18.1%
1.4%
37.2%

6.7%

Switchgrass Fraction

0. 717

Operam Costs (cefi/gal ethanol)

 

 

Feedstock 77.9
Other Raw Materials 5.9
Waste Disposal 0.0
Co-Product Sales -6.7
Fixed Costs 12.1
Capital Depreciation 14.0
Average Income Tax 2.1
Average Return on

Investment 82.4

Operating Costs ($lyr)

Feedstock $56,700,000
Other Raw Matl. Costs $4,300,000
Waste Disposal $600,000
Co-Product Sales $4,900,000
Fixed Costs $8,800,000
Capital Depreciation $10,200,000
Average Income Tax $1,500,000
Average Return on

Investment $60,000,000

Table 0313: Biorefinery economic summary output for the Decreased Winter Temperature and

Precipitation Scenario at the 75% land use transportation radius.

 

 

Minimum Ethanol Selling Price $1.8671
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 72.7
Ethanol Yield (Gal / Dry US Ton
Feedstock) 103.8
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $6,500,000
Pretreatment $15,200,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,300,000
Wastewater Treatment $21,500,000
Storage $1 ,700,000
Residue Processing $44,400,000
Utilities $8,000,000
Total Installed Equipment Cost $118,900,000
Added Costs $84,200,000
(% of TPI) 41 %

Total Project Investment $203,100,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3516

170

%of
total

5.5%
12.8%
2.8%
15.4%
18.1%
1.4%
37.3%

6.7%

Switchgrass Fraction

0. 7985

Operating Costs (cents/gal ethanol)

 

 

Feedstock 77.8
Other Raw Materials 6.1
Waste Disposal 0.0
Co-Product Sales -7.5
Fixed Costs 12.1
Capital Depreciation 14.0
Average Income Tax 2.1
Average Return on

Investment 82.2

Operating Costs ($lyr)

Feedstock $56,500,000
Other Raw Matl. Costs $4,400,000
Waste Disposal $600,000
Co-Product Sales -$5,500,000
Fixed Costs $8,800,000
Capital Depreciation $10,200,000
Average Income Tax $1,500,000
Average Return on

Investment $59,700,000

Table C.3.14: Biorefinery economic summary output for the Increased Meat Production Scenario at
the 75% land use transportation radius.

 

 

Minimum Ethanol Selling Price $1.8738
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 29.8
Ethanol Yield (Gall Dry US Ton
Feedstock) 42.5
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
. Feed Handling $8,200,000
Pretreatment $1,200,000
Biological conversion $2,300,000
Distillation and Solids Recovery $11,600,000
Wastewater Treatment $16,700,000
Storage $1 ,300,000
Residue Processing $44,400,000
Utilities $3,600,000
Total Installed Equipment Cost $89,300,000
Added Costs $61,700,000
(% of TPI) 41 %

Total Project Investment $151,000,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.2623

171

%of

total
9.2%
1.3%
2.6%
13.0%
18.7%
1.5%
49.7%

4.0%

Switchgrass Fraction
0.049

Operating Costs (cents/gal ethanol)

 

 

Feedstock 191.5
Other Raw Materials 70.4
Waste Disposal 0.0
Co-Product Sales -232.7
Fixed Costs 25.2
Capital Depreciation 25.5
Average Income Tax 4.1
Average Return on

Investment 103.5

Operating Costs ($lyr)

Feedstock $57,000,000
Other Raw Matl. Costs $20,900,000
Waste Disposal $300,000
Co-Product Sales $69,300,000
Fixed Costs $7,500,000
Capital Depreciation $7,600,000
Average Income Tax $1 ,200,000
Average Return on

Investment $30,800,000

Table C.3.15: Biorefinery economic summary output for the Increased Temperature and
Precipitation Scenario at the 75% land use transportation radius.

 

 

Minimum Ethanol Selling Price $1.8830
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 71.9
Ethanol Yield (Gal I Dry US Ton
Feedstock) 102.7
Feedstock Cost $lDry US Ton $81
lntemal Rate of Return (After-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $7,100,000
Pretreatment $15,100,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,200,000
Wastewater Treatment $21,500,000
Storage $1 ,700,000
Residue Processing $44,400,000
Utilities $8,000,000
Total Installed Equipment Cost $119,300,000
Added Costs $84,400,000
(% of TPI) 41 %

Total Project Investment $203,700,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3500

172

%of

total
6.0%
12.7%
2.8%
15.3%
18.0%
1.4%
37.2%

6.7%

Switchgrass Fraction
0.61 1

OperafinLCostsjcentslgal ethanoQ

 

 

Feedstock 78.9
Other Raw Materials 6.4
Waste Disposal 0.0
Co—Product Sales -8.5
Fixed Costs 12.2
Capital Depreciation 14.2
Average Income Tax 2.1
Average Return on

Investment 82.9

Operating Costs ($lyr)

Feedstock $56,700,000
Other Raw Matl. Costs $4,600,000
Waste Disposal $600,000
Co-Product Sales -$6,100,000
Fixed Costs $8,800,000
Capital Depreciation $10,200,000
Average Income Tax $1,500,000
Average Return on

Investment $59,600,000

Table C.3.16: Biorefinery economic summary output for the Increased Corn Stover Production
Scenario at the 75% land use transportation radius.

 

 

Minimum Ethanol Selling Price $18795
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 72.2
Ethanol Yield (Gal / Dry US Ton
Feedstock) 103.2
Feedstock Cost $lDry US Ton $81
lntemal Rate of Retum (Alter-Tax) 12%
Equity Percent of Total Investment 60%
Capital Costs
Feed Handling $7,000,000
Pretreatment $15,200,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,200,000
Wastewater Treatment $21,500,000
Storage $1,700,000
Residue Processing $44,400,000
Utilities $8,000,000
Total Installed Equipment Cost $119,300,000
Added Costs $84,400,000
(% of TPI) 41 %

Total Project Investment $203,700,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3510

173

%of

total
5.9%
12.7%
2.8%
15.3%
18.0%
1.4%
37.2%

6.7%

Switchgrass Fraction
0.6289

OperatingCosts (cents/gal ethanol)

 

 

Feedstock 78.3
Other Raw Materials 6.3
Waste Disposal 0.0
Co-Product Sales -7.8
Fixed Costs 12.2
Capital Depreciation 14.1
Average Income Tax 2.1
Average Return on

Investment 82.8

Operating Costs ($/yr)

Feedstock $56,500,000
Other Raw Matl. Costs $4,500,000
Waste Disposal $600,000
Co-Product Sales -$5,600,000
Fixed Costs $8,800,000
Capital Depreciation $10,200,000
Average Income Tax $1,500,000
Average Return on

Investment $59,800,000

Table C.3.17: Biorefinery economic summary output for the Decreased Biorefinery Productivity

Scenario at the 75% land use transportation radius.

 

 

Minimum Ethanol Selling Price $20060
Feedstock Rate (dry ton/day) 2,000
Ethanol Production (MM Gal. lYear) 58.5
Ethanol Yield (Gal / Dry US Ton
Feedstock) 103.4

Feedstock Cost $lDry US Ton $80

lntemal Rate of Return (After-Tax) 12%

Equity Percent of Total Investment 60%

Capital Costs
Feed Handling $6,800,000
Pretreatment $15,200,000
Biological conversion $3,300,000
Distillation and Solids Recovery $18,300,000
Wastewater Treatment $21,500,000
Storage $1 ,700,000
Residue Processing $44,400,000
Utilities $8,000,000
Total Installed Equipment Cost $119,200,000
Added Costs $84,200,000
(% of TPI) 41 %

Total Project Investment $203,400,000
Loan Rate 7.5%
Term (years) 25
Capital Charge Factor 0.3147

174

%of

total
5.7%
12.8%
2.8%
15.4%
18.0%
1 .4%
37.2%

6.7%

Switchgrass Fraction

0.717

Operating Costs (cents/gal ethanol)

 

 

Feedstock 77.8
Other Raw Materials 6.2
Waste Disposal 0.0
Co-Product Sales -7.8
Fixed Costs 15.0
Capital Depreciation 17.4
Average Income Tax 2.6
Average Return on

Investment 89.4

Operating Costs ($lyr)

Feedstock $45,500,000
Odier Raw Matl. Costs $3,600,000
Waste Disposal $500,000
Co-Product Sales $4,600,000
Fixed Costs $8,800,000
Capital Depreciation $10,200,000
Average Income Tax $1,500,000
Average Return on

Investment $52,300,000

APPENDIX D

BFIT Instructions (Trained Users Guide)
System requirements:

This system is designed to run in a Microsoft Windows XP environment using MS Excel
2003, ASPEN PLUS 2006 w/ Intel Visual FORTRAN 9.0 and custom database (included), and
Python 2.5 or higher (available for download at www.cvthonorq).

Installation:

Included in the install package is the Integrated Farm System Model (IFSM), the model
itself as well as the needed input files, which includes the biorefinery sub-model file for ASPEN
(.bkp) and Excel (.xls), the farm set-up examples (.frm), the text files for the model to read (.txt),
and the Python sub—model file (.py).

To begin installation, the user will need the NREL Model Files INHSPCDDAT, which is
the file that allows the GUI to directly access the in-house database pure components, and
BIODFMS3.INP, which is the new file to create the NREL in-house pure component database.
User should place INHSPCDDAT in C:\Program Files\AspenTech\APRSYSTEM
2006\GUl\custom and place BIODFMSBJNP in C:\Program Files\AspenTech\Working
Folders\Aspen Plus 2006 (on some systems may be C:\Documents and Settings\All
Users\AspenTech\Aspen Plus 2006).

Next the user should open C:\Program Files\AspenTech\APRSYSTEM
2006\GUl\custom\tbprop.dat using Notepad (or another text editor) and add the following line at
the end of the list of ‘INCLUDE’ statements in the file: INCLUDE inhspcd.dat (this adds the
custom database to the list of useable databases).

Next the user should open the Aspen Plus Simulation Engine DOS window (go to:
Start\AlI Programs\AspenTech\Aspen Engineering Suite\Aspen Plus\) and type the following
command: dfms biodfms3.

The user should now open the Aspen Plus User Interface Customization DOS window
(go to: Start\AlI Programs\AspenTech\Aspen Engineering Suite\Aspen Plus\) and type the
following command: mmcustom mmtbs (this rebuilds the record definition files); then hit Return.
Note: this takes a few minutes to execute.

Finally, type the following command: custinst (this copies the customized user interface
definition files).

Next, the language installation for Python must be completed using the Python Software
Foundation website, wwwpvthonorq. Follow the instructions on the website for download and
install. Do this in the same directory as the model.

Introduction:

The biorefinery using Ammonia Fiber Expansion (AFEX) pretreatment has been modeled
for the production of fuel ethanol from cellulosic biomass in ASPEN Plus, but this model does not
integrate cropping and animal production systems. Combining these three subsystems in a
single integrated model allows environmental and economic modeling of biomass production,
possible secondary products, fertilizer production, and bioenergy production. Using the
Integrated Farm System Model (IFSM) and the NREL/Dartmouth Biorefinery model in APSEN
and Excel, the biorefinery is concurrently simulated with the animal and crop production units in
US locations using this new research tool, BFIT: Biorefinery and Farm Integration Tool. Use of
this system is intended for users with prior knowledge of the system in question, and should not
be used as a hardened calculator for exacting design of biorefinery systems.

175

Model Components:
Figure 5.1 appears in this space for users. The colors are as described below.

All green boxes are encompassed in the BFIT model programming (BFIT.xls), the two
blue boxes are labeled with the model in which they are contained.

Model use:

For each location to be analyzed, weather and soil data must be collect and formatted for
use in IFSM. Existing weather files may be viewed in a text editor and used as templates. Data
fields on line 1 are: location abbreviation, latitude, longitude, atmospheric 002 concentration, the
remaining two characteristics should not be changed. Data fields on Lines 2 and more are: yrjda
(year and Julian day 5 digits continuously), solar radiation in MJlmz, temperature max and min (in
that order) in Celsius, precipitation in mm.

Soil data input is found in the “crop” input window during model operation. If soil
information is limited or lacking all together, predefined soil characteristics may be used.

Next farm management strategies should be selected to be aggregated around the
biorefinery. These strategies should be represented/set—up using the IFSM user-interface. For
any questions or concerns in IFSM refer to its built-in ‘help manual’.

*From the report (.RPT) and summary (.SMR) output files in IFSM complete the data
identification text files to correspond to the farm types. The data identification text files should be
developed using the template files CRN.txt, SG.txt, CC.txt, A.txt, B.txt, C.txt, DYCRN.txt,
DYALF.txt, and DYSG.txt. In these files the farm file numbers (SIM#) are listed first and can
include only one file of that type up to approximately 20 files of that type (20 is not a fixed
limitation, it is an approximate number of files at which the program will slow down). The other
changes required in these data identification files are the line and column numbers for each data
point, which are in that order at the end of each data line.

Once the data identification files are complete, they should be listed in “inputs.txt” along
with their number per the in-file description. Once this file is updated and saved, the file
“parse.py” should be run by either double clicking this file or running from a Python prompt. This
process may require some troubleshooting due to inconsistent outputs. If the output files (SIM#A,
SIM#B, SIM#C) st0p at a number before the last the script has stalled on a file with incorrect line
or column output. The problem can be identified by opening the last A, B, and C files and seeing
which data item stopped the run. By opening the SMR file for this data item the line can be
adjusted by moving it down (hitting enter) or moving it up (hitting backspace), or the column can
be adjusted using the space bar (all columns must be at least 2 spaces apart). Once this change
is saved, parse.py can be run again to complete outputs.

The SIM#A, B, and C file should now be imported into “BFIT.xIs” on the corresponding
“importX” tab/worksheet. To update already imported fields, the data should be highlighted, right-
clicking on this field will include an option for “refresh data”. From the pop-up window, choose the
location/file to be used for the new A, B, and C outputs. For blank worksheet space, new data
can be imported by going to the “data” menu and choosing “Import External Data - Import Data”.
From the pop-up window, choose the location/file to be used for the new A, B, and C outputs.*

As an alternative to the parsing function of the model described *above*, the user may
simply look up the values in the IFSM outputs and type them by hand into the corresponding cells
in “BFIT.xls”, which are labeled with the output field required.

On the “Farms” tab/worksheet in “BFIT.xIs” the farm distribution algorithm must be solved
for each case using the “Solver” function in Excel. Solver can be accessed in the “Tools” menu.
If it not available, it can be installed quickly in the “Add-ins” function in the same “Tools” menu.
The previous run of the distribution algorithm will be available when Solver is opened, so all that

176

needs to be updated are the line numbers. If a solution is not reached, it is because the values in
“x, y, z, a, b, c” are too far from the solution and should be adjusted closer to other distributions.

On the “ASPEN” tab/worksheet inputs for the biorefinery model in ASPEN are already
calculated. Using these numbers the user should open “basebkp” in APSEN and open the “data
browser” by clicking the eyeglasses icon/button. In the “Blocks” folders, sub-folder “A200”, sub-
folder “Blocks”, sub-folder “A200-1", sub-folder “Blocks", is the feedback solid splitter called
“FBSPLIT”. On the “Specifications” tab for this equipment “202FB” should be selected for the
“stream name” pull down. “MIXED-Split fraction” and “ClSOLID-Split fraction" correspond to
values given in “BFIT.xIs”. Next, in the “data browser” in the “Flowsheeting Options" folder, in the
sub-folder “Calculator” is the “FEEDPROP” folder. On the “Calculate” tab the FSWG should be
changed to the corresponding value given in “BFIT.xls”. The simulation should be run by clicking
the N-> button. To verify that the model has run without errors check the lower right corner and
save file with a unique name for the landscape being simulated if it is error free.

To import the results of the APSEN model to the corresponding economics workbook
begin on the “Stream Data” tab/worksheet in “Base.xls”. On this sheet the user should click the
“clear stream table” button, then click the “open simulation” button and choose the corresponding
ASPEN file (.bkp). To import the results the use should click the “run simulation” button and wait
for “ready” in Excel status bar (lower left comer). After data is imported and “ready” the used
should click the “close simulation” button and terminate macro if it can’t self terminate (this can be
done by selecting the “open simulation” button and then choosing “cancel”). This sheet is now
complete, and should be saved as with a unique name corresponding to the ASPEN file (.xls). To
import the remaining data the user should move to tab/worksheet “Heat Streams” and repeat the
series of buttons as above, then save. These steps will be repeated again for the tab/worksheet
“Work Streams” and the tab/worksheet “MASSFLOW Data.

The “Basexls” workbook also requires updates for biorefinery parameters. In the
“EQUIP” tab/worksheet the Biorefinery Scale should be updated in cell 33 (this is simply the six of
the biorefinery in ton/day over 5000). In this same sheet the fraction of switchgrass should be
updated (FEEDPROP). In the “OPCOST” tab/worksheet the Total Participation Radius, which
should be selected for the chosen farmer participation in the corresponding “BFIT.xls" file, should
be updated in cell BS. In the “DCFROR” tab/worksheet a variety of economic can be updated if so
desired (electricity price should be changed in “OPCOST”). After updating, in the “DCFROR”
tab/worksheet, the “solve ethanol price" macro should be run and the final results of the model
economics can be found on the tab/worksheet “SUMMARY“. At this time the file should be
saved. If an alternate participation radius is being considered it may be changed in “OPCOST”
and the “solve ethanol price” macro run again. This should then be “saved as” a different file
name.

From the “SUMMARY” tab/worksheet several values should be copied and pasted into
“BFIT.xIs”. Only the values should pasted, this can be done using “paste special” and selecting
the “values” radio button, so as to not transfer the equations. The values to be transferred are
listed in the left most column of the “Location” tab/worksheet in “BFIT.xIs" and can be found, in
order, in the following cells in “SUMMARY”: F34, F35, 05, B27, D7, and D9. The “Location”
tab/worksheet in “BFIT.xls” now contains all the agricultural and industrial emissions and
economics, individually, as well as the combined “footprint” of emissions.

177

 

REFERENCES

178

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