Analysis of PEM and AEM electrolysis by neural network pattern recognition, association rule mining and LIME

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dc.WoS.categoriesComputer Science, Artificial IntelligenceEnergy & Fuelsen_US
dc.authorid0000-0003-1282-718Xen_US
dc.contributor.authorGünay, M.Erdem
dc.contributor.authorTapan, N.Alper
dc.date.accessioned2024-04-03T12:27:27Z
dc.date.available2024-04-03T12:27:27Z
dc.date.issued2023-03
dc.description.abstractIn this work, as an extension of previous machine learning studies, three novel techniques, namely local interpretable model-agnostic explanations (LIME), neural network pattern recognition and association rule mining (ARM) were utilized for proton exchange membrane (PEM) and anion exchange membrane (AEM) electrolyzer database for hydrogen production. The main goal of LIME was to determine the positive or negative effects of a variety of descriptor variables on current density, power density and polarization. Using this technique, it was possible to uncover rules or paths that lead to high current density, low power density and low polarization. ARM provided the dominant rules leading to high current density such as using ELAT as the cathode gas diffusion layer, using pure Pt on the cathode surface and using pure carbon as the cathode support. In addition, LIME and neural network pattern recognition successfully uncovered the importance of catalytic materials such as cathode/anode support/surface elements, operational variables like K 2 CO 3 or KOH concentration in the electrolyte, certain membrane types, gas diffusion layers, and applied potential on current density. It was then concluded that machine learning can help determine the ideal conditions for developing a PEM and AEM electrolyzer to maximize hydrogen generation, which can also guide future research.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.1016/j.egyai.2023.100254
dc.identifier.issn2666-5468
dc.identifier.scopus2-s2.0-85150303338en_US
dc.identifier.urihttps://hdl.handle.net/11411/5242
dc.identifier.urihttps://doi.org/10.1016/j.egyai.2023.100254
dc.identifier.wosWOS:001146225600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors2en_US
dc.publisherELSEVIERen_US
dc.relation.ispartofEnergy and AIen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learningen_US
dc.subjectData miningen_US
dc.subjectHydrogen productionen_US
dc.subjectCurrent densityen_US
dc.titleAnalysis of PEM and AEM electrolysis by neural network pattern recognition, association rule mining and LIME
dc.typeArticle
dc.volume13en_US

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