Artificial neural network models for HFCS isomerization process

dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.contributor.authorYuceer, Mehmet
dc.date.accessioned2024-08-04T20:32:32Z
dc.date.available2024-08-04T20:32:32Z
dc.date.issued2010
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis work presents an approach to the modeling of a real industrial isomerization reactor by using artificial neural networks (ANN) pre-processed with principal component analysis (PCA). The initial model considered the output fructose concentration as the output variable, while the flow rate of substrate to the reactor as the principal input variable. Then, the ANN model was restructured and inversely trained by assuming the exit fructose concentration as the input variable and the feed flow rate as the output variable. Results indicate good performance by the application of the developed strategy to an extensive industrial data set. The results are expected to be useful in future, controlling the fructose concentration in the HFCS isomerization reactor.en_US
dc.identifier.doi10.1007/s00521-010-0437-x
dc.identifier.endpage986en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-77956886619en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage979en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-010-0437-x
dc.identifier.urihttps://hdl.handle.net/11616/95136
dc.identifier.volume19en_US
dc.identifier.wosWOS:000281908300004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectPCAen_US
dc.subjectModelingen_US
dc.subjectGlucose isomerizationen_US
dc.subjectPre-processingen_US
dc.subjectIndustrial isomerization processen_US
dc.titleArtificial neural network models for HFCS isomerization processen_US
dc.typeArticleen_US

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