Analysis and modeling of high-performance polymer electrolyte membrane electrolyzers by machine learning

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dc.authoridTAPAN, N. Alper/0000-0001-8599-0450|Gunay, M. Erdem/0000-0003-1282-718X
dc.authorwosidTAPAN, Niyazi A/H-6416-2013
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorTapan, N. Alper
dc.contributor.authorAkkoc, Gizem
dc.date.accessioned2024-07-18T20:42:43Z
dc.date.available2024-07-18T20:42:43Z
dc.date.issued2022
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this study, box and whisker and principal component analysis, as well as classification and regression tree modeling as a part of machine learning were performed on a database constructed on PEM (polymer electrolyte membrane) electrolysis with 789 data points from 30 recent publications. Box whisker plots discovered that pure Pt at the cathode surface, Ti at the anode support, the existence of Pt, Ir, Co, Ru at the anode surface, Ti porous structures at the electrodes, pure water-electrolyte and Nafion and Aquivion type membranes in proton exchange electrolyzer provide the highest performances. Principal component analysis indicated that when cathode surface consists of mostly pure Ni, when anode electrode has no support or vanadium (10-20%) doped TiO2 support and when anode electrode surface consists of cobalt-iron alloys (0.5:0.5 and 0.333:0.666 mol ratio) or RuO2, there is a risk for low-performance. Classification trees revealed that other than current density and potential, cathode surface Ni mole fraction, anode surface Co mole fraction are the most important variables for the performance of an electrolyzer. Finally, the regression tree technique successfully modeled the polarization behavior with a RMSE (root mean square error) value of 0.18. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.ijhydene.2021.10.191
dc.identifier.endpage2151en_US
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85119213736en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2134en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2021.10.191
dc.identifier.urihttps://hdl.handle.net/11411/7389
dc.identifier.volume47en_US
dc.identifier.wosWOS:000740518900007en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofInternational Journal of Hydrogen Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPemen_US
dc.subjectClassification Treeen_US
dc.subjectMachine Learningen_US
dc.subjectBox And Whisker Plotsen_US
dc.subjectRegression Treeen_US
dc.subjectPrincipal Componenten_US
dc.subjectOxygen Evolution Reactionen_US
dc.subjectPorous Transport Layeren_US
dc.subjectHydrogen Evolutionen_US
dc.subjectWater Electrolysisen_US
dc.subjectIridium Oxideen_US
dc.subjectPast Publicationsen_US
dc.subjectAnode Catalysten_US
dc.subjectElectrocatalystsen_US
dc.subjectTemperatureen_US
dc.subjectMorphologyen_US
dc.titleAnalysis and modeling of high-performance polymer electrolyte membrane electrolyzers by machine learningen_US
dc.typeArticleen_US

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