Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes

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dc.authorid0000-0002-5713-3141en_US
dc.authorscopusid57210825265en_US
dc.contributor.authorFetanat, Masoud
dc.contributor.authorKeshtiara, Mohammadali
dc.contributor.authorLow, Ze-Xian
dc.contributor.authorKeyikoğlu, Ramazan
dc.contributor.authorKhataee, Alireza
dc.date.accessioned2022-04-21T06:04:18Z
dc.date.available2022-04-21T06:04:18Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractAlthough the incorporation of nanoparticles into ultrafiltration polymeric membranes has shown promising outcomes, their commercial implementation has yet to be fulfilled due to inconsistency in data, lack of a reliable recipe for the optimum filler content, and reluctance in disrupting the production line which requires significant time and resources. There is a growing demand among membrane communities for a design platform that can accelerate the discovery of new nanocomposite membranes. In this work, a feed-forward ANN (artificial neural network) model that has one hidden layer and the Bayesian regularization training algorithm were chosen for designing a graphical user interface platform to predict the ultrafiltration nanocomposite membrane performance, that is, solute rejection, flux recovery, and pure water flux, thereby saving time and resources used in membrane design. Experimental data (735 samples from 200 reports published between 2006 and 2020) were derived from the literature for training, validation, and testing of the ANN models. The results indicated that the best 30 ANN models produce the most accurate estimation of membrane performance using the seven input variables of polymer concentration, polymer type, filler concentration, average filler size, solvent concentration (in the dope solution), solvent type, and contact angle on the unseen data set. Furthermore, a sensitivity analysis was performed on the achieved models to identify the most effective input variables for each nanocomposite membrane performance. This work has the potential to be extended to other mixed matrix membrane types that are going to be used for microfiltration, nanofiltration, reverse osmosis, and so forth.en_US
dc.identifier.doi10.1021/acs.iecr.0c05446en_US
dc.identifier.endpage5250en_US
dc.identifier.issn08885885
dc.identifier.issue14en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage5236en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1933
dc.identifier.volume60en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKeyikoğlu, Ramazan
dc.language.isoenen_US
dc.publisherAmerican Chemical Societyen_US
dc.relation.ispartofIndustrial and Engineering Chemistry Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectUltrafiltrationen_US
dc.subjectMachine learningen_US
dc.subjectFilled polymersen_US
dc.subjectGraphical user interfacesen_US
dc.subjectNanocompositesen_US
dc.titleMachine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranesen_US
dc.typeArticleen_US

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