Artificial Neural Network Predictions of Up-Flow Anaerobic Sludge Blanket (UASB) Reactor Performance in the Treatment of Citrus Juice Wastewater

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dc.authorid0000-0002-7373-4131
dc.authorid0000-0003-2792-410X
dc.authorscopusid16635547600
dc.authorscopusid25627176000
dc.authorscopusid6701596366
dc.authorscopusid6603360127
dc.authorwosidTufekci, Nese/A-6076-2018
dc.authorwosidÇelik, Suna Özden/ABA-6170-2020
dc.authorwosidtatlier, melkon/ABB-2207-2020
dc.contributor.authorElnekave, Moiz
dc.contributor.authorÇelik, Suna Özden
dc.contributor.authorTatlier, Melkon
dc.contributor.authorTüfekçi, Neşe
dc.date.accessioned2022-05-11T14:17:12Z
dc.date.available2022-05-11T14:17:12Z
dc.date.issued2012
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Çevre Mühendisliği Bölümü
dc.description.abstractThe operation of a full-scale up-flow anaerobic sludge blanket (UASB) reactor treating citrus juice wastewater was observed for two years. The average total chemical oxygen demand (COD) removal efficiency was determined to be equal to 79% and 77%, for the first and second years of operation for this reactor, respectively. The average volumetric loading rate was equal to 8.1 and 5.7 kg COD/m(3)day, respectively, during these periods. Three artificial neural network (ANN) models, namely feed forward back propagation (FFBP), radial basis function-based neural networks (RBF), and generalized regression neural networks (GRNN) were utilized to predict the COD and total suspended solid (TSS) concentrations in the effluent leaving the UASB reactor as well as the biogas production in the reactor. In general, the FFBP model made the best predictions with an average deviation of about 6.4-15.6% from the experimental values. The predictions made for biogas production and COD concentration were more accurate, while relatively larger discrepancies existed for the TSS concentration. The utilization of the ANN models generally provided significant improvements when compared to the use of multilinear regression for the same purpose.
dc.identifier.endpage56
dc.identifier.issn1230-1485
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-84861349805
dc.identifier.scopusqualityQ3
dc.identifier.startpage49
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6250
dc.identifier.volume21
dc.identifier.wosWOS:000300035900006
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÇelik, Suna Özden
dc.language.isoen
dc.publisherHard
dc.relation.ispartofPolish Journal of Environmental Studies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial neural network models
dc.subjectchemical oxygen demand
dc.subjectcitrus juice wastewater
dc.subjectmultilinear regression
dc.subjectUASB
dc.subjectBiogas Production
dc.subjectWastewaters
dc.subjectSimulation
dc.titleArtificial Neural Network Predictions of Up-Flow Anaerobic Sludge Blanket (UASB) Reactor Performance in the Treatment of Citrus Juice Wastewater
dc.typeArticle

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