Modeling of Trivalent Chromium Sorption onto Commercial Resins by Artificial Neural Network

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dc.contributor.authorTumer, Abdullah Erdal
dc.contributor.authorEdebali, Serpil
dc.date.accessioned2024-02-23T14:20:16Z
dc.date.available2024-02-23T14:20:16Z
dc.date.issued2019
dc.departmentNEÜen_US
dc.description.abstractIn this research, artificial neural network (ANN) model having three layers was developed for precise estimation of Cr(III) sorption rate varying from 17% to 99% by commercial resins as a result of obtaining 38 experimental data. ANN was trained by using the data of sorption process obtained at different pH (2-7) values with Amberjet 1200H and Diaion CR11 amount (0.01-0.1 g) dosage, initial metal concentration (4.6-31.7 ppm), contact time (5-240 min), and a temperature of 25 degrees C. A feed-forward back propagation network type with one hidden layer, different algorithm (transcg, trainlm, traingdm, traincgp, and trainrp), different transfer function (logsig, tansig, and purelin) for hidden layer and purelin transfer function for output layer were used, respectively. Each model trained for cross-validation was compared with the data that were not used. The trainlm algorithm and purelin transfer functions with five neurons were well fitted to training data and cross-validation. After the best suitable coefficient of determination and mean squared error values were found in the current network, optimal result was searched by changing the number of neurons range from 1 to 20 in the current network hidden layer.en_US
dc.identifier.doi10.1080/08839514.2019.1577015
dc.identifier.endpage360en_US
dc.identifier.issn0883-9514
dc.identifier.issn1087-6545
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85061314760en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage349en_US
dc.identifier.urihttps://doi.org/10.1080/08839514.2019.1577015
dc.identifier.urihttps://hdl.handle.net/20.500.12452/13095
dc.identifier.volume33en_US
dc.identifier.wosWOS:000463824200004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofApplied Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[Keyword Not Available]en_US
dc.titleModeling of Trivalent Chromium Sorption onto Commercial Resins by Artificial Neural Networken_US
dc.typeArticleen_US

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