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Artificial Neural Network (ANN) Approach for Modeling Zn(II) Adsorption from Leachate Using a New Biosorbent

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dc.authorscopusid17436339900
dc.authorscopusid35237943100
dc.authorscopusid22433630600
dc.contributor.authorTuran, N.G.
dc.contributor.authorMesci, B.
dc.contributor.authorÖzgönenel, O.
dc.date.accessioned2020-06-21T14:39:38Z
dc.date.available2020-06-21T14:39:38Z
dc.date.issued2011
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Turan] Nurdan Gamze, Department of Environmental Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Mesci] Başak, Department of Materials Science and Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Özgönenel] Okan, Department of Electrical and Electronic Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkeyen_US
dc.description.abstractIn this study, an artificial neural network (ANN) based classification technique is applied for the prediction of percentage adsorption efficiency for the removal of Zn(II) ions from leachate by hazelnut shell. The effect of operational parameters-such as initial pH, adsorbent dosage, contact time, and temperature-are studied to optimize the conditions for maximum removal of Zn(II) ions. The model was first developed using a three-layer feed forward back propagation network with 4, 8 and 4 neurons in the first, second, and third layers, respectively. A comparison between the model results and experimental data gave a high correlation coefficient (R<inf>average_ANN</inf>2=0.99) and showed that the model is able to predict the removal of Zn(II) from leachate. In order to evaluate the results obtained by ANN, full factor experimental design was applied to the batch experiments. As a result, Zn(II) concentration was reduced to 321.41 ± 12.24 mgL-1 from the initial concentration of 367.25 ± 23.43 mgL-1 by using hazelnut shell. © 2011 Elsevier B.V.en_US
dc.identifier.doi10.1016/j.cej.2011.07.042
dc.identifier.endpage105en_US
dc.identifier.issn1385-8947
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-81155161907
dc.identifier.scopusqualityQ1
dc.identifier.startpage98en_US
dc.identifier.urihttps://doi.org/10.1016/j.cej.2011.07.042
dc.identifier.volume173en_US
dc.identifier.wosWOS:000295504300013
dc.identifier.wosqualityQ1
dc.language.isoenen_US
dc.publisherElsevier Science SAen_US
dc.relation.ispartofChemical Engineering Journalen_US
dc.relation.journalChemical Engineering Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networks (ANN)en_US
dc.subjectBiosorptionen_US
dc.subjectFull Factorial Experimental Designen_US
dc.subjectHazelnut Shellen_US
dc.subjectOptimizationen_US
dc.subjectZincen_US
dc.titleArtificial Neural Network (ANN) Approach for Modeling Zn(II) Adsorption from Leachate Using a New Biosorbenten_US
dc.typeArticleen_US
dspace.entity.typePublication

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