Machine learning for design of thin-film nanocomposite membranes

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dc.authorid0000-0002-5713-3141en_US
dc.authorscopusid57210825265en_US
dc.contributor.authorFetanat, Masoud
dc.contributor.authorKeshtiara, Mohammadali
dc.contributor.authorKeyikoğlu, Ramazan
dc.contributor.authorKhataee, Alireza
dc.contributor.authorDaiyan, Rahman
dc.contributor.authorRazmjou, Amir
dc.date.accessioned2022-04-21T06:04:20Z
dc.date.available2022-04-21T06:04:20Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractIn this study, a novel machine learning approach is proposed for estimation of the permeate flux and foulant rejection in nanocomposite filtration membranes. Nine independent variables are fed to artificial neural networks (ANNs) including support, nanoparticles concentration, concentration of organic phase trimesoyl chloride (TMC) in-n-hexane (TMC in n-hexane), operation pressure, contact angle, thin layer thickness, location of the nanoparticles (NPs), post-treatment temperature and duration, and permeate flux and foulant rejection were derived as the outputs of the ANNs. The proposed method was evaluated on two datasets across training, validation and test datasets, and an unseen dataset. 2250 different initial weights and number of the neurons in the hidden layer for the proposed ANN models were considered and compared to find the optimized ANN models. The mean squared error (MSE) and coefficient of determination (R2) were employed to select the best 20 ANN models for further analysis. The proposed ANN models resulted in accurate estimates for both permeate flux and foulant rejection with R2 of 0.9958 and 0.9412 in all data included in the training, validation and test datasets and R2 of 0.9938 and 0.9811 in unseen dataset, respectively. In addition, results of sensitivity analysis revealed that post treatment temperature and contact angle were found the most important input variables for estimation of permeate flux and foulant rejection. The proposed method can provide valuable insights for formulating permeate flux and foulant rejection and considering the effects of each experimental condition on nanocomposite filtration membranes without doing real experiments, which is time-consuming and expensive.en_US
dc.identifier.doi10.1016/j.seppur.2021.118383en_US
dc.identifier.issn13835866
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1934
dc.identifier.volume270en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKeyikoğlu, Ramazan
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofSeparation and Purification Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectMachine learningen_US
dc.subjectSensitivity analysisen_US
dc.subjectThin-film nanocomposite (TFN)en_US
dc.titleMachine learning for design of thin-film nanocomposite membranesen_US
dc.typeArticleen_US

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