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dc.creatorSekulić, Zoran
dc.creatorAntanasijević, Davor
dc.creatorStevanović, Slavica
dc.creatorTrivunac, Katarina
dc.date.accessioned2021-03-10T14:11:34Z
dc.date.available2021-03-10T14:11:34Z
dc.date.issued2019
dc.identifier.issn0049-6979
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/4323
dc.description.abstractMembrane filtration techniques are distinguished among methods for wastewater treatment and fully correspond to the requirements of the green concept of chemistry and production. The limiting factor for greater application of these methods is the phenomenon of fouling and the decline of the permeate flux. In this study, polynomial neural network based on group method data handling (GMDH) algorithm was applied to predict the performance of the complexation-microfiltration process for the removal of Pb(II), Zn(II), and Cd(II) from synthetic wastewater. The influence of working parameters such as pH, initial concentration of metal ions, type of complexing agent, and pressure on flux was experimentally determined. The data obtained were used as input parameters for the GMDH model as well as for the multiple linear regression (MLR) model. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) were used for evaluation purposes. Results showed that the developed model has excellent performance in flux prediction with R-2 of 0.9648.en
dc.publisherSpringer International Publishing Ag, Cham
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.rightsrestrictedAccess
dc.sourceWater Air and Soil Pollution
dc.subjectMicrofiltrationen
dc.subjectHeavy metalsen
dc.subjectModeling of fluxen
dc.subjectArtificial neural networken
dc.subjectGroup method data handlingen
dc.titleThe Prediction of Heavy Metal Permeate Flux in Complexation-Microfiltration Process: Polynomial Neural Network Approachen
dc.typearticle
dc.rights.licenseARR
dc.citation.issue1
dc.citation.other230(1): -
dc.citation.rankM22
dc.citation.volume230
dc.identifier.doi10.1007/s11270-018-4072-y
dc.identifier.rcubconv_5752
dc.identifier.scopus2-s2.0-85059838525
dc.identifier.wos000455532600003
dc.type.versionpublishedVersion


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