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dc.contributor.authorCrespo, Maria ( )
dc.contributor.authorIvorra, Benjamin ( )
dc.contributor.authorRamos, Angel M. ( Orcid Icon 0000-0002-7882-0725 )
dc.contributor.authorRapaport, Alain ( Orcid Icon 0000-0002-8515-0838 )
dc.date.accessioned2021-11-29T19:45:51Z
dc.date.available2021-11-29T19:45:51Z
dc.date.issued2019-07-11
dc.identifier.citationCrespo, M., Ivorra, B., Ramos, A. M., & Rapaport, A. (2019). Shape optimization of spatial chemostat models. Electronic Journal of Differential Equations, 2019(84), pp. 1-26.en_US
dc.identifier.issn1072-6691
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/14972
dc.description.abstractIn this work, we study the shape optimization of a continuous bioreactor in which a substrate is degraded by a microbial ecosystem in a nonhomogeneous environment. The bioreactor considered here is a three-dimensional vertically oriented cylindrical tank. The behavior of reactants is described with a spatial chemostat model based on an Advection-Diffusion-Reaction system while the fluid flow is modeled using incompressible Navier-Stokes equations. We consider that the reaction rate between biomass and nutrient shows either monotonic or non-monotonic behavior. We tackle an optimization problem which aims to minimize the considered total reactor volume, with an output concentration (at stationary state) maintained below a desired threshold, by choosing a suitable bioreactor shape. We propose a methodology to create three different discrete parametrizations of the bioreactor geometry and obtain the optimized shapes with the help of a Hybrid Genetic Algorithm. We show that the optimized reactors exhibit height much larger than width and their exterior wall is concavely curved (the concavity at the upper part of the exterior wall being more pronounced for non-monotonic functions).en_US
dc.formatText
dc.format.extent26 pages
dc.format.medium1 file (.pdf)
dc.language.isoenen_US
dc.publisherTexas State University, Department of Mathematicsen_US
dc.sourceElectronic Journal of Differential Equations, 2019, San Marcos, Texas: Texas State University and University of North Texas.
dc.subjectShape optimizationen_US
dc.subjectOptimal designen_US
dc.subjectContinuous bioreactoren_US
dc.subjectSpatial chemostat modelen_US
dc.subjectAdvection-diffusion-reactionen_US
dc.subjectHybrid genetic algorithmen_US
dc.titleShape optimization of spatial chemostat modelsen_US
dc.typepublishedVersion
txstate.documenttypeArticle
dc.rights.licenseCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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