Prediction of Bromate Removal in Drinking Water Using Artificial Neural Networks

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dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authoridGoz, Eda/0000-0002-3111-9042
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.authorwosidGoz, Eda/AAH-3388-2020
dc.contributor.authorKaradurmus, Erdal
dc.contributor.authorTaskin, Nur
dc.contributor.authorGoz, Eda
dc.contributor.authorYuceer, Mehmet
dc.date.accessioned2024-08-04T20:45:25Z
dc.date.available2024-08-04T20:45:25Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
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.en_US
dc.description.sponsorshipHitit University Scientific Research Foundation [MUH19004.13.003]en_US
dc.description.sponsorshipThis study was supported by Hitit University Scientific Research Foundation (Project No: MUH19004.13.003).en_US
dc.identifier.doi10.1080/01919512.2018.1510763
dc.identifier.endpage127en_US
dc.identifier.issn0191-9512
dc.identifier.issn1547-6545
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85053471789en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage118en_US
dc.identifier.urihttps://doi.org/10.1080/01919512.2018.1510763
dc.identifier.urihttps://hdl.handle.net/11616/98468
dc.identifier.volume41en_US
dc.identifier.wosWOS:000461398100003en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofOzone-Science & Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
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 Networksen_US
dc.typeArticleen_US

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