Decision tree analysis of past publications on catalytic steam reforming to develop heuristics for high performance: A statistical review

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dc.authoridGünay, M. Erdem/0000-0003-1282-718X|YILDIRIM, RAMAZAN/0000-0001-5077-5689
dc.authorwosidGünay, M. Erdem/I-1564-2019
dc.authorwosidYILDIRIM, RAMAZAN/AAQ-4867-2020
dc.contributor.authorBaysal, Meltem
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorYildirim, Ramazan
dc.date.accessioned2024-07-18T20:42:43Z
dc.date.available2024-07-18T20:42:43Z
dc.date.issued2017
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this study, a database containing 5508 experimental data points was constructed for the steam reforming of methane using 81 papers (out of 453 initially screened) published between 2004 and 2014. The database was reviewed and analyzed with the help of decision trees to extract trends, heuristics and correlations, which are not visible to the naked eyes, through the vast experimental works accumulated in the literature over the years. The performance variable was selected as CH4 conversion while 21 variables related to catalyst preparation and operational conditions were used as input variables. It was found from a simple analysis of the literature that Ni, Rh, Ru and Pt are the most frequently used active metals, and they are generally applied over the supports of Al(2)0(3), CeO2 and ZrO2 usually using impregnation methods. A decision tree analysis was also applied to the database to determine the ranges of the catalyst preparation and operational conditions leading to high CH4 conversion. It was found for the Ni based catalysts that, even though the reaction temperature higher than 970 K is always required to achieve high CH4 conversion, some additional set of conditions are also needed; the combination of other variables especially support type and the feed composition seems to determine the catalytic performance. (C) 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.ijhydene.2016.10.003
dc.identifier.endpage254en_US
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85009724464en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage243en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2016.10.003
dc.identifier.urihttps://hdl.handle.net/11411/7388
dc.identifier.volume42en_US
dc.identifier.wosWOS:000394634900024en_US
dc.identifier.wosqualityQ1en_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.subjectSteam Reforming Of Methaneen_US
dc.subjectStatistical Reviewen_US
dc.subjectData Miningen_US
dc.subjectKnowledge Extractionen_US
dc.subjectDecision Treesen_US
dc.subjectArtificial Neural-Networksen_US
dc.subjectNoble-Metal Catalystsen_US
dc.subjectNi-Based Catalystsen_US
dc.subjectLow-Temperatureen_US
dc.subjectHydrogen-Productionen_US
dc.subjectSyngas Productionen_US
dc.subjectMembrane Reactoren_US
dc.subjectMicrochannel Reactoren_US
dc.subjectCexzr1-Xo2 Promoteren_US
dc.subjectNickel-Catalystsen_US
dc.titleDecision tree analysis of past publications on catalytic steam reforming to develop heuristics for high performance: A statistical reviewen_US
dc.typeReview Articleen_US

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