A Bayesian Network Based Approach for Risk Modeling to Aid in Development of Sustainable Biomass Supply Chains

J. Amundson, W. Faulkner, S. Sukumara, J. Seay, F. Badurdeen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

15 Scopus citations

Abstract

If lignocellulosic biomass is to become a viable competitor with fossil based resources for the production of energy and chemical products, sustainable sources of this material must be established. To address this issue an understanding about the risks affecting biomass sources and the biorefineries which they supply is crucial. In this paper, a quantitative approach using Bayesian Belief Networks (BBN) to model and analyze risks in a biorefinery's biomass supply chain is presented. Centralized aggregated corn stover supply locations and an integrated biorefinery located in Kentucky's Jackson Purchase Region are considered as a case study for demonstrating the approach.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
Pages152-156
Number of pages5
DOIs
StatePublished - 2012

Publication series

NameComputer Aided Chemical Engineering
Volume30
ISSN (Print)1570-7946

Keywords

  • Bayesian Belief Network
  • Biomass
  • Risk Modeling
  • Supply Chain

ASJC Scopus subject areas

  • Chemical Engineering (all)
  • Computer Science Applications

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