Estimation of BOD in wastewater treatment plant by using different ANN algorithms

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dc.authorid0000-0002-7553-9313en_US
dc.contributor.authorBaki, Osman Tugrul
dc.contributor.authorAras, Egemen
dc.date.accessioned2021-03-20T20:12:58Z
dc.date.available2021-03-20T20:12:58Z
dc.date.issued2018
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThe measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.en_US
dc.identifier.doi10.12989/mwt.2018.9.6.455en_US
dc.identifier.endpage462en_US
dc.identifier.issn2005-8624
dc.identifier.issn2092-7037
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage455en_US
dc.identifier.urihttp://doi.org/10.12989/mwt.2018.9.6.455
dc.identifier.urihttps://hdl.handle.net/20.500.12885/754
dc.identifier.volume9en_US
dc.identifier.wosWOS:000451243700007en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAras, Egemen
dc.language.isoenen_US
dc.publisherTechno-Pressen_US
dc.relation.ispartofMembrane Water Treatmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial bee colonyen_US
dc.subjectartificial neural networksen_US
dc.subjectbiochemical oxygen demanden_US
dc.subjectteaching-learning base algorithmen_US
dc.subjectwastewater treatment planten_US
dc.titleEstimation of BOD in wastewater treatment plant by using different ANN algorithmsen_US
dc.typeArticleen_US

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