Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012

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dc.authoridGünay, M. Erdem/0000-0003-1282-718X|Odabasi Ozer, Cagla/0000-0003-3552-6371|YILDIRIM, RAMAZAN/0000-0001-5077-5689
dc.authorwosidGünay, M. Erdem/I-1564-2019
dc.authorwosidYILDIRIM, RAMAZAN/AAQ-4867-2020
dc.authorwosidOdabasi Ozer, Cagla/C-4292-2018
dc.contributor.authorOdabasi, Cagla
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.issued2014
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, a database (containing 4360 experimental data points) on water gas shift reaction (WGS) over Pt and Au based catalysts was constructed using the data obtained from the published papers between the years 2002 and 2012. Then, the database was analyzed using three data mining tools to extract knowledge in three areas: Decision trees to determine the empirical rules and conditions that lead to high catalytic performance (high CO conversion); artificial neural networks (ANNs) to determine the relative importance of various catalyst preparation and operational variables and their effects on CO conversion; support vector machines (SVMs) to predict the outcome of unstudied experimental conditions. It was concluded that, all three models were quite successful and they complement each other to extract knowledge from the past published works and to deduce useful trends, rules and correlations, which are not easily comprehensible by the naked eyes. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipBogazici University [09A503D]en_US
dc.description.sponsorshipThe financial support provided by Bogazici University Research Fund Project 09A503D is gratefully acknowledged. We also thank to Elif Can, Esra Cagan, Meltem Baysal and Derya Ozturk for their contributions.en_US
dc.identifier.doi10.1016/j.ijhydene.2014.01.160
dc.identifier.endpage5746en_US
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-84897377213en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage5733en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2014.01.160
dc.identifier.urihttps://hdl.handle.net/11411/7386
dc.identifier.volume39en_US
dc.identifier.wosWOS:000334977900029en_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.subjectWater Gas Shift Reactionen_US
dc.subjectData Miningen_US
dc.subjectKnowledge Extractionen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectDecision Treesen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectSelective Co Oxidationen_US
dc.subjectCopper-Based Catalystsen_US
dc.subjectIn-Situ Driftsen_US
dc.subjectPt/Ceo2 Catalysten_US
dc.subjectGold Catalystsen_US
dc.subjectHeterogeneous Catalysisen_US
dc.subjectHydrogen-Productionen_US
dc.subjectMesoporous Titaniaen_US
dc.subjectOxide Catalystsen_US
dc.subjectPd-Cuen_US
dc.titleKnowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012
dc.typeArticle

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