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Sustainable biofuel operating space: multi-criteria assessment and multi-objective optimization

Sustainable biofuel operating space: multi-criteria assessment and multi-objective optimization. Bioenergy Life-Cycle Analysis (4.6.2). Primary drivers for bioenergy include reducing demand for petroleum and emissions of greenhouse gases.

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Sustainable biofuel operating space: multi-criteria assessment and multi-objective optimization

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  1. Sustainable biofuel operating space: multi-criteria assessment and multi-objective optimization

  2. Bioenergy Life-Cycle Analysis (4.6.2) • Primary drivers for bioenergy include reducing demand for petroleum and emissions of greenhouse gases. • Life-cycle analysis (LCA) estimates the overall contribution of bioenergy system towards meeting these objectives. • LCA evaluates the entire process (i.e., field to wheels) including all upstream and downstream energy and material inputs and associated greenhouse gas emissions • Evaluate bioenergy pathways that are cost-effective and sustainable relative to net greenhouse gas impact, long-term soil quality, and ecosystem impacts.

  3. Great Lakes Bio-Energy Research Center: Sustainability Thrust 4. Modeling Bio-Energy Systems

  4. Establish minima • or ideals w1 w2 w3 w4 . . . wi Si • Sustainability • Index Claudio Gratton www.glbrc.org

  5. Safe operating space Johan RockstrOm (2009, Nature) www.glbrc.org

  6. Landscape “services” Claudio Gratton

  7. An example of watershed planning Handbook for Developing Watershed Plans to Restore and Protect Our Waters (USEPA, 2008)

  8. Multi-objective Optimization • Solutions (Itineraries) • Price Feasible Region Pareto front • Time www.glbrc.org

  9. Multi-objective Optimization A Multi ALgorithm Genetically Adaptive Method for multiobjective optimization (Vrugt et al. 2007, PNAS) www.glbrc.org

  10. SEIMF: Spatially-explict Integrative Modeling Framework Zhang et al., (2010, GCB Bioenergy) www.glbrc.org County Watershed Land use Soils

  11. Spatially-explicit simulations of N2O flux in Michigan RIMA

  12. Optimizing ecosystem services of bioenergy crop configurations • Optimization the configuration of the 54 scenarios on 39 10-digit watershed in Michigan RIMA • 54^39 (3.65895E+67) • Different multi-objectives www.glbrc.org

  13. SEIMF: Spatially-explict Integrative Modeling Framework www.glbrc.org

  14. Conclusions • Multi-objective optimization can provide promising candidate biofuel crop landscape configurations for multi-criteria assessment of sustainability. • Spatially-explicit variables (e.g. carbon and yield) related to LCA assessment • Spatially-explicit modeling map can be used to calculate the distance from crop fields to biorefinery and transportation cost for LCA. www.glbrc.org

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