Smart paradigm to predict copper surface area of Cu/ZnO/Al2O3 catalyst based on synthesis parameters

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dc.authoridZendehboudi, Sohrab/0000-0001-8527-9087|Gunay, M. Erdem/0000-0003-1282-718X
dc.authorwosidZendehboudi, Sohrab/ABG-7741-2021
dc.contributor.authorSaffary, Soheil
dc.contributor.authorRafiee, Mansoureh
dc.contributor.authorSaeidi, Mohammadreza
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorZendehboudi, Sohrab
dc.date.accessioned2024-07-18T20:42:30Z
dc.date.available2024-07-18T20:42:30Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractCu/ZnO/Al2O3 catalyst is used in processes of water-gas shift, methanol steam reforming, and methanol synthesis in the industry. According to various experimental studies, the catalytic activity of this catalyst is directly proportional to its copper surface area (Cu(SA)). In this study, a machine learning approach for predicting Cu(SA) ranges in three classes (low, medium, and high) is introduced based on catalyst preparation factors. Three models of random forest (RF), support vector machine (SVM), and multilayer perceptron artificial neural network (MLP-ANN) classifiers are developed and optimized using grid search 10-fold cross-validation for a 188 sample dataset extracted from 45 experimental studies. It is found that the RF classifier with 90% cross-validation accuracy score and 94.7% test data prediction accuracy score outperforms the other two models. The SHAP (or SHapley Additive exPlanations) analysis is performed to investigate the effects of synth-esis factors, such as aging conditions, precipitant type, and pH on Cu(SA). It is concluded that Cu/Zn ratio has the greatest influence on Cu(SA). The optimum synthesis conditions yielding high Cu(SA) are also discovered, which is of great importance for synthesis of Cu/ ZnO/Al2O3 catalysts with high catalytic activity. (c) 2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cherd.2023.01.031
dc.identifier.endpage616en_US
dc.identifier.issn0263-8762
dc.identifier.issn1744-3563
dc.identifier.scopus2-s2.0-85148064186en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage604en_US
dc.identifier.urihttps://doi.org/10.1016/j.cherd.2023.01.031
dc.identifier.urihttps://hdl.handle.net/11411/7304
dc.identifier.volume191en_US
dc.identifier.wosWOS:000943829300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofChemical Engineering Research & Designen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectWater-Gas Shift Reactionen_US
dc.subjectMethanol Steam Reformingen_US
dc.subjectMethanol Synthesisen_US
dc.subjectCuen_US
dc.subjectZnoen_US
dc.subjectGas-Shift Reactionen_US
dc.subjectModified Coprecipitation Methoden_US
dc.subjectTemperature Methanol Synthesisen_US
dc.subjectFuel-Cell Applicationsen_US
dc.subjectCu-Zn Catalystsen_US
dc.subjectHydrogen-Productionen_US
dc.subjectKnowledge Extractionen_US
dc.subjectCo2 Hydrogenationen_US
dc.subjectOxide Catalysten_US
dc.subjectAl Catalysten_US
dc.titleSmart paradigm to predict copper surface area of Cu/ZnO/Al2O3 catalyst based on synthesis parametersen_US
dc.typeArticleen_US

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