Process Systems Engineering
Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis
Article first published online: 28 APR 2011
DOI: 10.1002/aic.12637
Copyright © 2011 American Institute of Chemical Engineers (AIChE)
Additional Information
How to Cite
You, F., Tao, L., Graziano, D. J. and Snyder, S. W. (2012), Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis. AIChE J., 58: 1157–1180. doi: 10.1002/aic.12637
Publication History
- Issue published online: 8 MAR 2012
- Article first published online: 28 APR 2011
- Accepted manuscript online: 30 MAR 2011 08:57AM EST
- Manuscript Revised: 16 MAR 2011
- Manuscript Received: 10 FEB 2011
Funded by
- U.S. Department of Energy. Grant Number: DE-AC02-06CH11357
- Abstract
- Article
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- Cited By
Keywords:
- planning;
- biofuel supply chain;
- sustainability;
- life cycle analysis;
- input–output analysis;
- multiobjective optimization
Abstract
This article addresses the optimal design and planning of cellulosic ethanol supply chains under economic, environmental, and social objectives. The economic objective is measured by the total annualized cost, the environmental objective is measured by the life cycle greenhouse gas emissions, and the social objective is measured by the number of accrued local jobs. A multiobjective mixed-integer linear programming (mo-MILP) model is developed that accounts for major characteristics of cellulosic ethanol supply chains, including supply seasonality and geographical diversity, biomass degradation, feedstock density, diverse conversion pathways and byproducts, infrastructure compatibility, demand distribution, regional economy, and government incentives. Aspen Plus models for biorefineries with different feedstocks and conversion pathways are built to provide detailed techno-economic and emission analysis results for the mo-MILP model, which simultaneously predicts the optimal network design, facility location, technology selection, capital investment, production planning, inventory control, and logistics management decisions. The mo-MILP problem is solved with an ε-constraint method; and the resulting Pareto-optimal curves reveal the tradeoff between the economic, environmental, and social dimensions of the sustainable biofuel supply chains. The proposed approach is illustrated through two case studies for the state of Illinois. © 2011 American Institute of Chemical Engineers AIChE J, 2012

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