Artificial Neural Network Modelling of a Large-Scale Wastewater Treatment Plant Operation

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dc.contributor.authorGüçlü, Dünyamin
dc.contributor.authorDursun, Şükrü
dc.date.accessioned2020-03-26T17:47:08Z
dc.date.available2020-03-26T17:47:08Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractArtificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk University [2005-101018]en_US
dc.description.sponsorshipThis paper has been derived from a part of PhD thesis of Dunyamin Guclcu. Authors would like to thank Ankara Water and Sewerage Administration (ASKI) General Directorate (Turkey) for their help during the experimental study and in providing WWTP process data. This study was supported by the Selcuk University Research Fund (BAP) with Project No: 2005-101018.en_US
dc.identifier.citationGüçlü, D., Dursun, Ş., (2010). Artificial Neural Network Modelling of a Large-Scale Wastewater Treatment Plant Operation. Bioprocess and Biosystems Engineering, (33), 1051-1058. Doi: 10.1007/s00449-010-0430-x
dc.identifier.doi10.1007/s00449-010-0430-xen_US
dc.identifier.endpage1058en_US
dc.identifier.issn1615-7591en_US
dc.identifier.pmid20445993en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1051en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00449-010-0430-x
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24630
dc.identifier.volume33en_US
dc.identifier.wosWOS:000283080500005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorGüçlü, Dünyamin
dc.institutionauthorDursun, Şükrü
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofBioprocess and Biosystems Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectActivated sludge processen_US
dc.subjectModellingen_US
dc.subjectArtificial neural networken_US
dc.subjectWastewater treatment planten_US
dc.titleArtificial Neural Network Modelling of a Large-Scale Wastewater Treatment Plant Operationen_US
dc.typeArticleen_US

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