Graduate Thesis Or Dissertation

 

A supply chain model for optimizing fixed and mobile bio-oil refineries on a regional scale Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/0g354j085

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  • The use of fossil fuels and their related impact on the environment and global warming have encouraged societies to pursue more sustainable and renewable alternatives, e.g., forest-based bio-oil. Thus, a vital need to decrease the level of greenhouse gas emissions and the tendency of nations to reduce their dependency on imported oil have created a new mission for society: To increase the robustness of the environmental and economic aspects of woody biomass to bio-oil supply chains. Prior studies have focused on developing novel methods and approaches for improving single stages of biomass supply chains. Others have focused on ameliorating biomass supply chain performance from a systems perspective for a host of different biomass types, e.g., agricultural residues and forest residues, and logistics issues, e.g., transportation distance and storage. Bio-oil can be produced from woody biomass through the fast pyrolysis process, among different methods. Mobile processing has been developed in recent years to facilitate bio-oil production from woody waste and to reduce overall bio-oil supply chain cost, however, questions surrounding the environmental and economic benefits of using mobile processing plants in combination with large-scale non-mobile (fixed) processing plants remain unanswered. The research presented develops a mathematical model capable of assisting decision makers in determining the optimal combination and location of fixed and mobile bio-refinery plants for a known woody waste supply stream and set of harvesting areas. The major cost elements in the optimization model are transportation costs and capital costs. The model is applied to hypothetical case for northwest Oregon by using historical harvesting data for state-owned and private forests in the region. Distances between locations are obtained by using a geographical information system to elucidate roadway effects. The model is optimized for cost by using an integer linear programming solver. Supply chain environmental impacts are then assessed by considering the carbon footprint (CO₂ equivalent mass) of transportation activities and the bio-refinery infrastructure. Sensitivity analysis is conducted for six major factors within the mathematical model to assess their effects on the estimated supply chain cost and carbon footprint, as well as on the number and location of the mobile and fixed bio-refineries. The application of the model indicates that the utility of a mobile processing plant aligned with a fixed processing plant is more obvious when transportation cost and distance increase. In addition, this study seems to confirm the premise that transferring bio-oil to a processing facility is often more preferable than transporting woody biomass. However, results indicate that the capital intensity (cost and environmental impact) of mobile processing plants can greatly degrade their relative utility within a mixed mode supply chain.
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