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A review and future directions in techno-economic modeling and optimization of upstream forest biomass to bio-oil supply chains

Abstract Recent interest in biomass supply chain management has stimulated research efforts in the industry and academic communities. Techno-economic modeling and optimization efforts targeted on the upstream segment of the forest biomass to bio-oil supply chain are reviewed. Key components of upstream supply chain decision making are then presented through an overview and classification of the existing methods and contributions. There is a need to classify and analyze the relevant methodologies and approaches identified in prior studies, and to subsequently assess their usefulness through empirical research and case-based analysis. Both narrative and systematic literature reviews are performed using qualitative analysis and classic bibliometric techniques to demonstrate the scope of current papers and the call for future needs. It is found, due to growing demands for bioenergy, future biomass-to-bioenergy supply chains should draw upon existing research toward the development of efficient and effective forest biomass supply chain networks. It is further concluded that a new generation of pretreatment technologies is needed for techno-economic optimization of upstream forest biomass value chains.
- Oregon State University United States
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).114 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
