Fuels made from renewable resources, such as cellulosic plant materials, have been recognized as important sources of energy that will reduce the nation's dependence on petroleum and have a positive impact on the economy, environment, and society . In final rule-making of the Renewable Fuel Standard (RFS) mandated by Congress, the EPA has set a renewable fuels requirement of 36 billion gallons by 2022 of which 16 billion gallons must be cellulosic biofuels (defined as yielding a minimum 60% reduction in greenhouse gas emissions relative to a 2005 baseline gasoline or diesel) . Current cellulosic biofuels capacity is less than 0.1 billion gallons . The rapid expansion of biofuels production needed to meet the RFS will require optimized design and management of new, sustainable, and regionally dependent supply chains . Novel decision tools and systems analysis approaches are needed to facilitate supply chain development that minimizes the cost and negative environmental impact while maximizing the benefits to regional economies .
In this work, we consider the optimal design and operations of cellulosic ethanol supply chains under economic, environmental, and social criteria. A mixed-integer linear programming (MILP) model is developed that takes into account the main characteristics of cellulosic ethanol supply chains, such as seasonality of feedstock supply, biomass deterioration with time, geographical diversity and availability of biomass resources, feedstock density, conversion technology and performance, fuel price, infrastructure compatibility, spatial distribution of fuel stations, regional economic condition, tax subsidies, and policy. Techno-economic models based on ASPEN Plus for the conversion processes of potential feedstocks with possible biochemical and thermochemical pathways are linked to the MILP optimization model for detailed process economics of biorefineries. The MILP optimization model integrates decision-making across multiple temporal and spatial scales and simultaneously predicts the optimal network design, facility location, capital investment, production operations, inventory control, and logistics management decisions.
In addition to the economic objective of minimizing the annualized net present cost, the MILP model also integrates with life cycle assessment (LCA) and economic input-output (EIO) analysis through the multi-objective optimization scheme to include another two objectives: the environmental objective measured by life cycle greenhouse gas emissions and the social objective measured by regional economic impact. This allows the model to establish trade-offs between the economics, environmental impact, and social performance of the cellulosic ethanol supply chains in a systematic way. The multi-objective optimization problem is solved with ε-constraint method and produces a Pareto-optimal surface, which reveals the tradeoffs between the three objectives.
We apply the modeling and optimization framework to study cellulosic ethanol production in the state of Illinois and the Midwest area. Three major cellulosic biomass feedstock sources (corn stover, switchgrass, and woodchips) with biochemical and thermochemical conversions are considered. County-level results will be presented that provide regionally-based insight into transition pathways and consequent energy, economic, environmental, and social impacts of biomass production and conversion. Armed with this insight, we identify economically viable strategies for expanding biofuel production to benefit regional economies while minimizing negative impacts on the environment including land, water, and energy use.
 Growing America's Fuel - An Innovation Approach to Achieving the President's Biofuels Target; Biofuels Working Group, U.S. Whitehouse, February 2010
 National Renewable Fuel Standard Program for 2010 and Beyond; U.S. EPA, February 2010
 Biomass Program Multi-Year Program Plan 2010; EERE, U.S. DOE, March 2010.
 National Biofuels Action Plan; Biomass Research and Development Board: U.S. DOE & USDA, October 2008.
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