1. Cost Assessment of Cellulosic Ethanol Production and Distribution in the US William R Morrow W. Michael Griffin H. Scott Matthews

2. 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

3. Part I – Optimization Modeling (Modeling) 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 Modeling Goals

4. Part I – Optimization Modeling (Modeling) 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 Our Model

5. Part I – Optimization Modeling (Parameter Estimation) Gasoline Consumption Top 271 Consuming MSA’s (76% of US Gasoline Consumption)

6. Part I – Optimization Modeling (Parameter Estimation) Gasoline To Ethanol Consumption Current (1997 Modeled year) Gasoline Consumption: →130 Billion Gallons per yr Fuel Energy Content: Gasoline: → 120,000 BTU/Gal Ethanol: → 86,100 BTU/Gal

7. Part I – Optimization Modeling (Parameter Estimation) Expanded Corn Ethanol Plants Current Corn Ethanol Production: → 3 Billion Gallons Expanded Corn Ethanol Production → 5 Billion Gallons

8. Part I – Optimization Modeling (Parameter Estimation) Ethanol by Feedstock

9. Part I – Optimization Modeling (Parameter Estimation) Based on ORECCL – Oak Ridge Energy Crop County Level Database  Energy Crop Availability & Yield  Production Costs & Land Rents  Projects Energy Crop Farmgate Prices Comprised of 305 “Regions” (Similar to ASD’s)  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.) Switchgrass Availability Modeling using ORNL POLYSIS Model (published Data)

10. Part I – Optimization Modeling (Parameter Estimation) 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

11. Part I – Optimization Modeling (Parameter Estimation) Transforming Switchgrass into Ethanol Gallons Minimum plant size of 2,200 Ton SWG/day  based on the work of Wooley et al. (1999, 1999a) 85 Gallons / Ton SWG (from range of 68 ~ 100 Gallons / Ton SWG)  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)?

12. 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

13. Part I – Optimization Modeling (Parameter Estimation) % Usable Switchgrass (as a function of $/ton)

14. Part I – Optimization Modeling (Parameter Estimation) Switchgrass Availability (50 $/Ton SWG)

15. 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)

16. Part II – Optimization Solutions (Scenarios) Forecasted E20 Scenario (50 $/ Ton SWG)

17. Part I – Optimization Modeling (Modeling) Linear Optimization Equations Variables: Constraints: Economic Eq.:

18. Part II – Optimization Solutions (Scenarios) Optimization Solutions Scatter Plot E20 Scenario

19. Part II – Optimization Solutions (Trends) Optimization Solutions Histograms Trend toward shorter shipments as production expands

20. 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

21. Part III – Monetizing the Solutions (Freight Rate Estimation) Freight Rate Estimation Method EIO Accounts:  Use of Commodities by Industry 1997 – Total Commodity Output. IO Code 482000 – Truck Transportation IO Code 244000 – Rail Transportation CFS Database:  Shipment by Destination and Mode of Transport 1997 Truck Rail US State to State Distance matrix

22. Part III – Monetizing the Solutions (Freight Rate Estimation) Freight Rate Equations & Data Let: i = Origin State; j = Destination State

23. Part III – Monetizing the Solutions (Freight Rate Estimation) Freight Rate: f (Distance) Average Freight Rate per Ton-Mile: US DOT ME Truck – 26.6 ¢/Ton-Mile (2001)21.5 ¢/Ton-Mile Class I Rail – 02.2 ¢/Ton-Mile (2001)07.2 ¢/Ton-Mile

24. Part III – Monetizing the Solutions (Trans. Cost Estimations) Monetized Optimization Solutions Legend Truck Freight Rates Rail Freight Rates

25. Part IV – Quick Comparison to Gasoline Economics Source: Aden et. al. 2002 Based on Energy Equivalency

26. Part IV – Quick Comparison to Gasoline Transportation By Mode Petroleum Ethanol Truck Rail

27. Part IV – Quick Comparison to Gasoline Petroleum Plant Locations Geographical Dispersion

28. Part IV – Quick Comparison to Gasoline Petroleum Pipeline Locations Geographical Dispersion

29. Part IV – Quick Comparison to Gasoline Petroleum & E20 Ethanol Locations Geographical Dispersion

30. Part IV – Quick Comparison to Gasoline 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? Ethanol Pipeline Challenges

31. Part V – Global Biomass Production Raw Biomass Energy Potential  Year 2050 → 440 joules 18 per year  Year 2100 → 310 joules 18 per year Converted to Liquid biofuels (@ 35% efficiency – EIA)  Year 2050 → 154 joules 18 per year  Year 2100 → 109 joules 18 per year Converted to Gallons of Gasoline Equivilent  Year 2050 → 785 Gallons 9 per year  Year 2100 → 555 Gallons 9 per year Gasoline Consumption (OECD Countries) - EIA  ~ 300 Gallons 9 per year Estimates from IPCC 3 rd Assessment Report

32. 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