Stochastic Programming Models to Design Biomass Supply Chains for Co-firing in Coal-Fired Power Plants

dc.contributor.advisorCastillo-Villar, Krystel
dc.contributor.authorAranguren, Maria F.
dc.contributor.committeeMemberAlaeddini, Adel
dc.contributor.committeeMemberMillwater, Harry R.
dc.contributor.committeeMemberWan, Hung-Da
dc.contributor.committeeMemberGiacomoni, Macio H.
dc.creator.orcidhttps://orcid.org/0000-0003-1617-0509
dc.date.accessioned2024-01-26T16:48:22Z
dc.date.available2024-01-26T16:48:22Z
dc.date.issued2021
dc.description.abstractA sustainable alternative to fossil fuels in the energy production sector is biomass. The production of cost-efficient biomass networks is necessary in order to compete with non-renewable resources. The creation of integrated biomass supply chain (BSC) network design models and solution procedures can contribute to achieving this goal. In particular, the emerging bioenergy industry requires taking advantage of the economies of scale in transportation to minimize the final product's cost. Hub-and-spoke networks have been proposed as a modeling approach to design large-scale BSCs. The majority of these models are deterministic and do not consider the inherent variability in the biomass feedstock such as physical and chemical properties of the biomass that affect transportation, the effects of future climate on the agricultural supply, initial distribution, and production operations. Levels of ash and moisture are directly related to the quality of the feedstock, which negatively affects the production of biofuels increasing transportation and handling costs and putting a burden on the BSC's efficiency. Varying weather affects biomass yield, which creates a fluctuation in the incoming supply into the network, creating a complex large-scale Newsvendors Problem. In this research, stochastic Hub-and-spoke networks, mathematical models, and optimization algorithms are proposed to minimize logistic costs and biomass quality costs by finding an optimal production, distribution, and transportation network while reducing computational burden when solving large NP instances. Case studies with several scenarios based on varying weather conditions are created using realistic data from the south-central and northeast regions of the U.S. Corresponding results presented.
dc.description.departmentMechanical Engineering
dc.format.extent109 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9798505540114
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2539
dc.languageen
dc.subjectBiomass
dc.subjectCo-firing
dc.subjectEnergy
dc.subjectMetaheuristic
dc.subjectOptimization
dc.subjectStochastic
dc.subject.classificationOperations research
dc.subject.classificationIndustrial engineering
dc.subject.classificationMechanical engineering
dc.titleStochastic Programming Models to Design Biomass Supply Chains for Co-firing in Coal-Fired Power Plants
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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