Optimal Design of Sustainable Cellulosic Biofuel Supply Chains: Multiobjective Optimization Coupled with Life Cycle Assessment and Input-Output Analysis

Ling Tao, Fengqi You, Diane Graziano, Seth Snyder

Research output: Contribution to journalArticlepeer-review

590 Scopus Citations

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.

Original languageAmerican English
Pages (from-to)1157-1180
Number of pages24
JournalAIChE Journal
Volume58
Issue number4
DOIs
StatePublished - 2012

NREL Publication Number

  • NREL/JA-5100-72007

Keywords

  • Biofuel supply chain
  • Input-output analysis
  • Life cycle analysis
  • Multiobjective optimization
  • Planning
  • Sustainability

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