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dc.creatorSekulić, Zoran
dc.creatorAntanasijević, Davor
dc.creatorStevanović, S.
dc.creatorTrivunac, Katarina
dc.date.accessioned2021-03-10T13:32:50Z
dc.date.available2021-03-10T13:32:50Z
dc.date.issued2017
dc.identifier.issn1735-1472
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/3722
dc.description.abstractComplexation-microfiltration process for removal of heavy metal ions such as lead, cadmium and zinc from water had been investigated. Two soluble derivates of cellulose was selected as complexing agents. The dependence of the removal efficiency from the operating parameters (pH value, pressure, concentration of metal ion, concentration of complexing agent and type of counter ion) was established. Two approaches of preparation of input data and two different artificial neural network architectures, general regression neural network and back-propagation neural network have been used for modeling of experimental data. The extrapolation ability of selected architectures, i.e., the prediction of rejection coefficient with inputs beyond the calibration range of original model, was also determined. The predictions were successful, and after evaluation of performances, the models that were developed gave relatively good results of mean absolute percentage error from 4 to 14% and R-squared from 0.717 to 0.852 for general regression neural network and from 0.897 to 0.955 for back-propagation neural network.en
dc.publisherSpringer, New York
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.rightsrestrictedAccess
dc.sourceInternational Journal of Environmental Science and Technology
dc.subjectBack propagationen
dc.subjectHeavy metalsen
dc.subjectMicrofiltrationen
dc.subjectModeling of rejection coefficienten
dc.titleApplication of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration processen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage1396
dc.citation.issue7
dc.citation.other14(7): 1383-1396
dc.citation.rankM22
dc.citation.spage1383
dc.citation.volume14
dc.identifier.doi10.1007/s13762-017-1248-8
dc.identifier.rcubconv_5322
dc.identifier.scopus2-s2.0-85020516875
dc.identifier.wos000403068500002
dc.type.versionpublishedVersion


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