Comparison of Modeling Approaches for Prediction of Cleaning Efficiency of the Electromagnetic Filtration Process

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dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authoridAbbasov, Tahmuraz/0000-0002-0290-8333
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.authorwosidYıldız, Zehra/AAR-4168-2020
dc.authorwosidAbbasov, Tahmuraz/ABG-8739-2020
dc.contributor.authorYildiz, Z.
dc.contributor.authorYuceer, M.
dc.contributor.authorAbbasov, T.
dc.date.accessioned2024-08-04T20:35:53Z
dc.date.available2024-08-04T20:35:53Z
dc.date.issued2011
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe present study aims at applying different methods for predicting the cleaning efficiency of the electromagnetic filtration process (psi) in the mixtures of water and corrosion particles (rust) of low concentrations. In our study, artificial neural network (ANN), multivariable least square regression (MLSR), and mechanistic modelling approaches were applied and compared for prediction of the cleaning efficiency for the electromagnetic filtration process. The results clearly show that the use of ANN led to more accurate results than the mechanistic filtration and MLSR models. Therefore, it is expected that this study can be a contribution to the cleaning efficiency.en_US
dc.identifier.endpage906en_US
dc.identifier.issn1054-4887
dc.identifier.issn1943-5711
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-84861304007en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage899en_US
dc.identifier.urihttps://hdl.handle.net/11616/95660
dc.identifier.volume26en_US
dc.identifier.wosWOS:000300753200004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherApplied Computational Electromagnetics Socen_US
dc.relation.ispartofApplied Computational Electromagnetics Society Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectelectromagnetic filtrationen_US
dc.subjectMLSRen_US
dc.titleComparison of Modeling Approaches for Prediction of Cleaning Efficiency of the Electromagnetic Filtration Processen_US
dc.typeArticleen_US

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