Cost Assessment of
Cellulosic Ethanol
Production and Distribution
in the US
William R Morrow
W. Michael Griffin
H. Scott Matthews
Introduction






Part I – Optimization Modeling
 Modeling
 Estimation of Parameters
Part II – Optimization Solutions
 Scenarios
 Data Trends
Part III – Monetizing the Solutions
 Freight Rate Calculation
 Transportation Cost Estimations
Part IV – A Quick Comparison to Petroleum
 Economics
 Transportation
Part V – Global Biomass resources
Part VI – Conclusions
Part I – Optimization Modeling (Modeling)
Modeling Goals




Estimate an Extended Corn Based Ethanol
Scenario
Model domestic switchgrass energy crop
(published data) as the feedstock for
cellulosic ethanol production
Estimate transportation costs as domestic
cellulosic ethanol production increases
Identify any capacity limitations for a
switchgrass based cellulosic ethanol fuel
economy
Part I – Optimization Modeling (Modeling)
Our Model





Distributes ethanol to MSAs
Capable of large blend ratios
Expands corn production as far as believable
& makes up remaining required ethanol with
switchgrass based cellulosic ethanol
Only considers truck and rail and transport
Uses freight rates derived from US Economic
Input Output data, and Commodity Flow
Survey
Part I – Optimization Modeling (Parameter Estimation)
Gasoline Consumption
Top 271 Consuming MSA’s (76% of US Gasoline Consumption)
Part I – Optimization Modeling (Parameter Estimation)
E5
E10
E20
191
151
30
E0
27
Ethanol: → 86,100 BTU/Gal
13
7
118
Gasoline: → 120,000 BTU/Gal
Gasoline
120
Fuel Energy Content:
Ethanol
123
→130 Billion Gallons per yr
200
180
160
140
120
100
80
60
40
20
0
130
Current (1997 Modeled year)
Gasoline Consumption:
Consumption (Billion Gallons)
Gasoline To Ethanol Consumption
Ethanol Blend Rate
E85
E100
Part I – Optimization Modeling (Parameter Estimation)
Expanded Corn Ethanol Plants
Current Corn Ethanol Production:
→ 3 Billion Gallons
Expanded Corn Ethanol Production
→ 5 Billion Gallons
Part I – Optimization Modeling (Parameter Estimation)
Ethanol by Feedstock
146
120
118
60
120
80
123
100
E0
E5
E10
E20
186
140
25
Gasoline
130
40
20
27
Billion Gallons
160
Corn Ethanol
8
180
Cellulosic Ethanol
2
200
0
Ethanol Blend Rate
E85
E100
Part I – Optimization Modeling (Parameter Estimation)
Switchgrass Availability Modeling
using ORNL POLYSIS Model (published Data)

Based on ORECCL – Oak Ridge Energy Crop
County Level Database




Comprised of 305 “Regions” (Similar to ASD’s)

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Energy Crop Availability & Yield
Production Costs & Land Rents
Projects Energy Crop Farmgate Prices
Several counties grouped together (Total of 2,787
Counties)
Similar Soil type, moisture, sunlight, terrain, etc.
Estimates Switchgrass:


Tons/per year for each region
Based on $/ton farmgate prices (e.g. 30$/ton, 35$/ton, etc.)
Part I – Optimization Modeling (Parameter Estimation)
Switchgrass Planted (million acres)
Switchgrass availability
(Acreage as a function of $/ton)
Estimated Range:
•Upper Bound:
•5 Tons / Acre
•85 Gallons / Ton
•Lower Bound:
•10 Tons / Acre
•100 Gallons / Ton
500
450
Total Cropland
400
350
Cropland Planted
300
E85 Estimates
250
200
150
100
50
B
B
B
30
35
40
E20 Estimates
0
20
B
25
B
B
45
50
Biomass F armgate Price ($/t on)
Part I – Optimization Modeling (Parameter Estimation)
Transforming Switchgrass into
Ethanol Gallons

Minimum plant size of 2,200 Ton SWG/day


85 Gallons / Ton SWG (from range of 68 ~
100 Gallons / Ton SWG)


based on the work of Wooley et al. (1999, 1999a)
based on the work of Wooley et al. (1999, 1999a)
Question: Can a POLYSIS Region produce
enough SWG to support the minimum plant
requirement? At what price ($ / Ton SWG)?
Part I – Optimization Modeling (Parameter Estimation)
Plant Size as a Function of Cost
(For Corn Stover)
Source: Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid
Prehydrolysis and Enzymatic Hydrolysis for Corn Stover – Aden et. al. 2002
Part I – Optimization Modeling (Parameter Estimation)
Available Switchgrass
(% of Total Produced)
% Usable Switchgrass
(as a function of $/ton)
100
90
80
70
60
50
40
10
0
25
30
35
40
45
Sw itchgrass Farmgate Price
($/ton)
50
Part I – Optimization Modeling (Parameter Estimation)
Switchgrass Availability
(50 $/Ton SWG)
Part II – Optimization Solutions (Scenarios)
Linear Optimization Scenarios



E5 Scenario – 5.2 Billion Gallon Ethanol
 Expanded corn-based ethanol production – 5.2 BGY
 No switchgrass-based cellulosic ethanol production – 0
BGY
E10 Scenario – 10.6 Billion Gallon Ethanol
 Expanded corn-based ethanol production – 5.2 BGY
 Switchgrass-based cellulosic ethanol production – 5.4
BGY (30$/ton SWG)
E20 Scenario – 22.1 Billion Gallon Ethanol
 Expanded corn-based ethanol production – 5.2 BGY
 Switchgrass-based cellulosic ethanol production – 16.9
BGY (50$/ton SWG)
Part II – Optimization Solutions (Scenarios)
Forecasted E20 Scenario (50 $/ Ton SWG)
Part I – Optimization Modeling (Modeling)
Linear Optimization Equations
n
Objective Function: Minimize:
m

i 1 j 1
Variables:
Vij  Dij
I j  Import demanded by location j  (Gallons)
Ei  Export available from location i  (Gallons)
 $ 
Rij  Freight Rate between locations i & j; f  D   

ij
 Gallons 
Dij  Distance between Locations i & j  (Miles)
Vij  Volume of ethanol transported between locations i & j  (Gallons)
n
Constraints:
V
i 1
ij
n
V
j 1
ij
 Ei
 Ij
Economic Eq.:
$(ij )  Rij Vij  Dij
Part II – Optimization Solutions (Scenarios)
Optimization Solutions Scatter Plot
E20 Scenario
350
B
Volume (million gallons)
300
250
B
B
B
200
B
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BBBB
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150
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0
200
400
600
800
1000
1200
1400
1600
1800
Distance (mi)
100
Part II – Optimization Solutions (Trends)
2000
1800
1600
1400
1200
E20
800
1000
140
120
100
80
60
40
20
0
E10
600
Trend toward shorter shipments as
production expands
140
120
100
80
60
40
20
0
400
Number of Routes
Histograms
E5
200
Optimization Solutions
140
120
100
80
60
40
20
0
Dis tances of Route (<= # of miles)
Part III – Monetizing the Solutions (Freight Rate Estimation)
Freight Rate Dilemma



Problem: Freight industry does not publishes
freight rates directly
Solution: Use US Government data sources
and extrapolate freight rates
Data sources:


US Department of Commerce; Bureau of
Economic Analysis – Input ~ Output Accounts
US Department of Transportation; Commodity
Flow Survey
Part III – Monetizing the Solutions (Freight Rate Estimation)
Freight Rate Estimation Method

EIO Accounts:

Use of Commodities by Industry 1997 – Total
Commodity Output.


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CFS Database:

Shipment by Destination and Mode of Transport
1997



IO Code 482000 – Truck Transportation
IO Code 244000 – Rail Transportation
Truck
Rail
US State to State Distance matrix
Part III – Monetizing the Solutions (Freight Rate Estimation)
Freight Rate Equations & Data
Let: i = Origin State; j = Destination State
$ij 
Ton  Mileij
i
j
1
1
Ton  Mile
 Total $
 $ / Ton ij

$ij
Tonij
ij
$800
$700
$/Ton
$600
$500
$400
Trucks
$300
Rail
$200
$100
$0
500
1000
Miles 1500
2000
2500
3000
Part III – Monetizing the Solutions (Freight Rate Estimation)
Freight Rate:
f
(Distance)
$700
Truck = 0.2146 $ / Ton-Mile
$/Ton
$600
$500
Trucks
$400
Rail
$300
Rail = 0.0721 $/ Ton-Mile
$200
$100
$0
500
1000
1500
2000
2500
3000
Miles
Average Freight Rate per Ton-Mile:
US DOT
Truck
– 26.6 ¢/Ton-Mile (2001)
Class I Rail
– 02.2 ¢/Ton-Mile (2001)
ME
21.5 ¢/Ton-Mile
07.2 ¢/Ton-Mile
Part III – Monetizing the Solutions (Trans. Cost Estimations)
Monetized Optimization Solutions
Transportation Cost ($/Gallon)
$0.7
$0.6
$0.5
$0.4
$0.3
$0.2
$0.1
$0.0
Transportation Cost ($/yr)
Billions
$12.0
$10.0
$8.0
$6.0
$4.0
$2.0
$0.0
0
5
10
15
5
10
15
Gallons per Year
20
25
Billions
25
Billions
Gallons per Year
0
20
Blended Transportation Cost ($/Gal)
$0.2
$0.2
$0.1
$0.1
Legend
Truck Freight Rates
Rail Freight Rates
$0.0
0
5
10
15
Gallons per Year
20
25
Billions
Part IV – Quick Comparison to Gasoline
Economics
$2.50
Taxes
$2.00
Retail
$1.50
Transportation (to
retail)
$1.00
Refining
$0.50
G
as
ol
in
e
E3
-L
ow
E3
-H
ig
h
E5
-L
ow
E5
-H
ig
E1
h
0
-L
ow
E1
0
-H
ig
E2
h
0
-L
ow
E2
0
-H
ig
h
$0.00
Source: Aden et. al. 2002
Based on Energy Equivalency
Transportation (to
refinery)
Feedstock
(Gasoline/Biomass)
Part IV – Quick Comparison to Gasoline
Transportation By Mode
Petroleum
Trucks
Ethanol
Truck
Rail
Water Carriers
Rail
Pipelines
Part IV – Quick Comparison to Gasoline
Petroleum Plant Locations
Geographical Dispersion
Part IV – Quick Comparison to Gasoline
Petroleum Pipeline Locations
Geographical Dispersion
Part IV – Quick Comparison to Gasoline
Petroleum & E20 Ethanol Locations
Geographical Dispersion
Part IV – Quick Comparison to Gasoline
Ethanol Pipeline Challenges

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Can not ship ethanol in petroleum pipelines
Location of ethanol production is more widely
distributed than refineries locations
Ethanol produced at an ethanol plants is small when
compared to gasoline production at refineries
CONCLUSION: Ethanol will require its own pipeline
infrastructure


Dual fuel economy
Build ethanol pipelines for E5, E10, E20, E85, E100?
Part V – Global Biomass Production
Estimates from IPCC 3rd

Raw Biomass Energy Potential




Year 2050 → 154 joules 18 per year
Year 2100 → 109 joules 18 per year
Converted to Gallons of Gasoline Equivilent



Year 2050 → 440 joules 18 per year
Year 2100 → 310 joules 18 per year
Converted to Liquid biofuels (@ 35% efficiency – EIA)


Assessment Report
Year 2050 → 785 Gallons 9 per year
Year 2100 → 555 Gallons 9 per year
Gasoline Consumption (OECD Countries) - EIA

~ 300 Gallons 9 per year
Part VI – Conclusions


Higher production – higher plant dispersion – shorter
distance – lower transport cost
Comparison to gasoline costs


Ethanol Not likely be cheaper to transport in Short Term
Domestic Switchgrass Ethanol Limitations

E20 our upper bound for modeling



Oak Ridge Data (only goes to 50$/ton)
Displaces approximately 20% of existing agricultural products
Additional Biomass is available in the US &
Internationally