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Zidong Wang T. Edward Yu Burton C. English – Presenter James A. Larson

Optimization of Cost and Greenhouse Gas Emissions of a Dedicated Energy Crop Supply System to Biorefineries in Tennessee. Integrated Biomass Supply Systems. Zidong Wang T. Edward Yu Burton C. English – Presenter James A. Larson. July 30, 2013. Currently.

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Zidong Wang T. Edward Yu Burton C. English – Presenter James A. Larson

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  1. Optimization of Cost and Greenhouse Gas Emissions of a Dedicated Energy Crop Supply System to Biorefineries in Tennessee Integrated Biomass Supply Systems ZidongWang T. Edward Yu Burton C. English – Presenter James A. Larson July 30, 2013

  2. Currently • Producing biofuels from lignocellulosic biomass (LCB) has been suggested as a way to mitigate the dependence on fossil fuels and the production of greenhouse gas (GHG) emissions. • In the U. S., the Renewable Fuel Standard (RFS2) in the Energy Independence and Security Act (EISA) of 2007 mandated 16 billion gallons of LCB-based biofuels per year for transportation use by 2022. • Considerable amounts of feedstock will be needed to fulfill this goal. • Configuration of the feedstock supply chain for biofuels should be carefully examined since the quality and quantity of feedstock will influence the cost of biofuels production and environmental performance. • GHG emissions associated with LCB feedstock supply from changes in land use and LCB feedstock production, storage, and transportation activities can also impact the sustainability of LCB-based biofuel production.

  3. Objectives • Determine the optimal energy crop supply chain including the location of biorefinery, the layout of feedstock draw area, the harvest and storage technologies and monthly inventory management by considering both cost and GHG emissions as the objectives. • Analyze the potential trade-off between the economic and environmental performance of the energy crop supply chain and the impact factors leading to this tradeoff effect.

  4. Methods • A spatial multi-objective mixed integer programming model is developed. • The output from the multi-objective optimization is compared with single-objective optimization results.

  5. Methods – Spatial Framework 21,902 five square mile hexagons as the potential feedstock supply area 233 industrial parks eligible to build the biorefinery plant

  6. Methods – Multiple Objective Functions -- Minimize

  7. Additional Model Assumptions • Production • Non-private land excluded • 50% Hay land/pasture available • Land in Tennessee and within 50 miles state border • Storage • Field side • Tarp • Pallet • Biorefinery • 50 million gallon • 76 gallon per ton • Power, water, roads and storage area • Transportation • Semi-truck • 75 miles • Monthly delivery schedule • Harvest • Nov. – Feb. • Square bale

  8. Methods (Continued) • With cost and GHG emissions minimization as objectives, the model will optimize the following variables simultaneously: • Location of the biorefinery and associated feedstock draw area, • Amount of land converted from previous crops, and • Month of delivery and month of harvest • Input use including energy consumption, fertilizer herbicide, seed and farm machinery usage. • Subject to a set of Constraints

  9. Emission Modeling GREET used to model farm and harvest machines, energy consumption, and indirect, DAYCENT used to model land use change.

  10. Model Operations Multiple Potential Locations in Region Single Plant Location in a Region

  11. Individual Solution Points for the 233 Industrial Parks

  12. Results Min Cost Curve For Firm A Min GHG Curve For Firm B Tradeoff Curve

  13. Tradeoff Curve B0 Min Cost Curve For Firm A Min GHG Curve For Firm B A0 O0 Tradeoff Curve

  14. Costs at the Three Selected Points on the Tradeoff Curve B O A

  15. Land Use Change 79,816 80,819 82,808 acres

  16. GHG Emissions at the Three Selected Points on the Tradeoff Curve LUC Condition Point

  17. Minimize Costs

  18. Minimize GHG

  19. Somewhere in the Middle: Location Changes

  20. Discussion • Land availability, land use, and transportation play an important role in determining the location of biorefinerybased on the economics of the feedstock supply chain. • Comparing the (O) site with the cost-efficient site, its feedstock supply chain system reduced nearly GHG by nearly half at the expense of a 10% increase in cost. • Location site is impacted based on the criteria used • Policies that reduce GHG may increase conversion of land traditionally in crop production. • Livestock impacts were not incorporated in this analysis • Pasture/hay production practices need further study ( Extension recommendations vs actual practice)

  21. Funding for this project was advanced by the following: : Integrated Biomass Supply Systems

  22. GHG Emissions Change Converting Different Crops into Switchgrass from DAYCENT

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