Prediction of bromate removal in drinking water using artificial neural networks

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dc.contributor.authorKaradurmuş, Erdal
dc.contributor.authorTaşkın, Nur
dc.contributor.authorGöz, Eda
dc.contributor.authorYüceer, Mehmet
dc.date.accessioned2019-05-10T09:39:40Z
dc.date.available2019-05-10T09:39:40Z
dc.date.issued2018
dc.departmentHitit Üniversitesi, Mühendislik Fakültesi, Kimya Mühendisliği Bölümü
dc.description.abstractIn treatment of natural water resources, bromide transforms into carcinogenic bromate, especially during the ozonation process. Adsorption was used in the experimental part of this study to remove this harmful compound from drinking water. For this purpose, technically, HCl-, NaOH-, and NH3-modified activated carbons were used. Scanning Electron Microscopy (SEM) and Brunauer–Emmett–Teller (BET) analyses were carried out within the characterization study. Moreover, the effects of diameters and heights of adsorption columns, flowrate, and particle size of adsorbent were investigated on the removal amounts of bromate. Optimum conditions were obtained from the experiments, and regional/real samples were collected and analyzed. After the experiments, an artificial neural network (ANN) was used to predict bromate removal percentage by using the observed data. Within this context, a feed-forward back-propagation ANN was chosen in this study. Additionally, the transfer function was selected as tangent sigmoid and 3 neurons were used in the hidden layer. Particle size and amount of the activated carbon, height and diameter of the column, volumetric flowrate, and initial concentration were selected as the input variables. Bromate removal percentage was selected as the output. It was found that the model an R value of 0.988, RMSE value of 3.47 and mean absolute percentage error (MAPE) of 5.19% in the test phase. © 2018, © 2018 International Ozone Association.
dc.identifier.citationKaradurmuş, E., Taşkın, N., Göz, E., Yüceer, M. (2019). Prediction of Bromate Removal in Drinking Water Using Artificial Neural Networks. Ozone: Science and Engineering, 41(2), 118-127.
dc.identifier.doi10.1080/01919512.2018.1510763
dc.identifier.endpage127
dc.identifier.issn0191-9512
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage118
dc.identifier.urihttps://doi.org/10.1080/01919512.2018.1510763
dc.identifier.urihttps://hdl.handle.net/11491/749
dc.identifier.volume42en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor and Francis Inc.
dc.relation.ispartofOzone: Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAdsorptionen_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectBromate Removalen_US
dc.subjectDisinfection of Drinking Wateren_US
dc.subjectOzoneen_US
dc.titlePrediction of bromate removal in drinking water using artificial neural networks
dc.typeArticle

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