Performance Assessment of Advanced Biological Wastewater Treatment Plants Using Artificial Neural Networks

dc.contributor.authorTürkmenler, Harun
dc.contributor.authorPala, Murat
dc.date.accessioned2018-12-11T14:34:37Z
dc.date.available2018-12-11T14:34:37Z
dc.date.issued2017-09-26
dc.descriptionDOI: 10.19072/ijet.324091en_US
dc.description.abstractIn this study, the application of Artificial Neural Network (ANN) techniques was used to predict the performance of wastewater treatment plant. The ANN-based model for prediction of effluent biological oxygen demand (BOD) concentrations was formed using a three-layered feed forward ANN, which used a back propagation learning algorithm. Based on the mean absolute percentage error (MAPE), the sum of the squares error (SSE), the absolute fraction of variance (R2), the root-mean-square (RMS), the coefficient of variation in percent (cov) values, and ANN models predicted effluent BOD concentration. The R2 values were found to be 94.13% and 93.18% for the training and test sets of treatment plant process, respectively. It was found that the ANN model could be employed successfully in estimating the daily BOD in the effluent of wastewater biological treatment plants.en_US
dc.identifier.issn2149-0104
dc.identifier.issn2149-5262
dc.identifier.urihttps://hdl.handle.net/11363/546
dc.language.isoenen_US
dc.publisherİstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Pressen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Yayınıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectResearch Subject Categories::TECHNOLOGYen_US
dc.titlePerformance Assessment of Advanced Biological Wastewater Treatment Plants Using Artificial Neural Networksen_US
dc.typeArticleen_US

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