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Title: Integrating biomass quality variability in stochastic supply chain modeling and optimization for large-scale biofuel production

The production of biofuels using second-generation feedstocks has been recognized as an important alternative source of sustainable energy and its demand is expected to increase due to regulations such as the Renewable Fuel Standard. However, the pathway to biofuel industry maturity faces unique, unaddressed challenges. Here, to address this challenges, this article presents an optimization model which quantifies and controls the impact of biomass quality variability on supply chain related decisions and technology selection. We propose a two-stage stochastic programming model and associated efficient solution procedures for solving large-scale problems to (1) better represent the random nature of the biomass quality (defined by moisture and ash contents) in supply chain modeling, and (2) assess the impact of these uncertainties on the supply chain design and planning. The proposed model is then applied to a case study in the state of Tennessee. Results show that high moisture and ash contents negatively impact the unit delivery cost since poor biomass quality requires the addition of quality control activities. Experimental results indicate that supply chain cost could increase as much as 27%–31% when biomass quality is poor. We assess the impact of the biomass quality on the topological supply chain. Our case studymore » indicates that biomass quality impacts supply chain costs; thus, it is important to consider the impact of biomass quality in supply chain design and management decisions.« less
 [1] ;  [2] ;  [1]
  1. Univ. of Texas, San Antonio, TX (United States)
  2. Clemson Univ., SC (United States)
Publication Date:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Journal of Cleaner Production
Additional Journal Information:
Journal Volume: 149; Journal Issue: C; Journal ID: ISSN 0959-6526
Research Org:
Univ. of Texas, San Antonio, TX (United States)
Sponsoring Org:
National Science Foundation (NSF); USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Country of Publication:
United States
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1474204