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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
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.
Great Lakes Bio-Energy Research Center: Sustainability Thrust 4. Modeling Bio-Energy Systems
Establish minima • or ideals w1 w2 w3 w4 . . . wi Si • Sustainability • Index Claudio Gratton www.glbrc.org
Safe operating space Johan RockstrOm (2009, Nature) www.glbrc.org
Landscape “services” Claudio Gratton
An example of watershed planning Handbook for Developing Watershed Plans to Restore and Protect Our Waters (USEPA, 2008)
Multi-objective Optimization • Solutions (Itineraries) • Price Feasible Region Pareto front • Time www.glbrc.org
Multi-objective Optimization A Multi ALgorithm Genetically Adaptive Method for multiobjective optimization (Vrugt et al. 2007, PNAS) www.glbrc.org
SEIMF: Spatially-explict Integrative Modeling Framework Zhang et al., (2010, GCB Bioenergy) www.glbrc.org County Watershed Land use Soils
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
SEIMF: Spatially-explict Integrative Modeling Framework www.glbrc.org
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