Data-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubes

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dc.authorid0000-0001-6841-6457en_US
dc.contributor.authorBüyükada, Musa
dc.date.accessioned2021-06-23T19:45:22Z
dc.date.available2021-06-23T19:45:22Z
dc.date.issued2017
dc.departmentBAİBÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractData-driven modeling of removal of color index name of Acid Yellow 59 from aqueous solutions using multi-walled carbon nanotubes by multiple (non)linear regression and artificial neural networks (ANN) models based on leave-one-out cross-validation to predict the adsorbed dye amount per unit mass of adsorbent (mg g(-1)) and performance evaluation of the proposed multiple (non)linear regression and ANN models is the main novel contributor of the present study. Initial dye concentration, adsorbent concentration, reaction time, and temperature were determined as explanatory variables and input neurons for multiple (non)linear regression and ANN models, respectively. The total number of experiments was determined as 1280 statistically. The results showed that multilayer perception ANN model ( = 0.9997, = 0.9993, RMSE = 0.7678, MAE of 0.0007) predicted q (t) better than multiple (non)linear regression model ( = 0.9645, = 0.9633, SE = 9.55) and MLR (R (2) = 0.9543, SE = 10.87) models. The results justified the accuracy of ANN in prediction of q (t) , significantly.en_US
dc.identifier.doi10.1007/s13762-017-1315-1
dc.identifier.endpage2228en_US
dc.identifier.issn1735-1472
dc.identifier.issn1735-2630
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85029646528en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2215en_US
dc.identifier.urihttps://doi.org/10.1007/s13762-017-1315-1
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9142
dc.identifier.volume14en_US
dc.identifier.wosWOS:000410751800014en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBüyükada, Musa
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInternational Journal Of Environmental Science And Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectCross-Validationen_US
dc.subjectReactive Dyesen_US
dc.subjectRegressionen_US
dc.subjectSynthesis of Novel Adsorbentsen_US
dc.titleData-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubesen_US
dc.typeArticleen_US

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