Statistical review of dry reforming of methane literature using decision tree and artificial neural network analysis

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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.authorwosidLeba, Aybüke/AAA-3435-2021
dc.authorwosidYILDIRIM, RAMAZAN/AAQ-4867-2020
dc.contributor.authorSener, Ayse Neslihan
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorLeba, Aybuke
dc.contributor.authorYildirim, Ramazan
dc.date.accessioned2024-07-18T20:42:30Z
dc.date.available2024-07-18T20:42:30Z
dc.date.issued2018
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThe aim of this work was to extract knowledge for dry reforming of methane (DRM) reaction from experimental data using data mining tools such as decision trees and artificial neural networks. An extensive database containing 5521 data points depending on 63 catalyst preparation and operational variables was constructed from 101 papers published between 2005 and 2014; the output variables were CH4 conversion, CO2 conversion and H-2/CO ratio of the product stream. Then, the database, as a whole or as subsets for different base metals were analyzed using decision trees (DT) to develop heuristics for high performance and artificial neural networks (ANN) to determine relative importance of input variables and predict the performance under unstudied conditions; mostly CH4 conversion, which is the most frequently reported output variable, were used in analysis. The testing accuracy of the decision tree was about 80% leading to four heuristics (i.e. four possible courses of action) for high CH4 conversion over Ni based catalyst. The first decision point to separate these heuristics is the reaction temperature as can be expected. This is followed by the other variables such as support type, W/F and reduction temperature. ANN analysis revealed that operational variables have higher relative importance (55%) compared to catalyst preparation variables (45%). The most important operational variable was found to be the reaction temperature while the active metal and the support are the most important catalyst preparation variables. ANN model was also tested to predict the data, which was not seen by the model before, and the data in 65 papers out of 101 were predicted within 15% error while 76 papers had the error rate of less than 20%.en_US
dc.description.sponsorshipTUBITAK through National Support Program for Master Students [2211]en_US
dc.description.sponsorshipThis work was supported by TUBITAK through 2211 National Support Program for Master Students.en_US
dc.identifier.doi10.1016/j.cattod.2017.05.012
dc.identifier.endpage302en_US
dc.identifier.issn0920-5861
dc.identifier.issn1873-4308
dc.identifier.scopus2-s2.0-85020814568en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage289en_US
dc.identifier.urihttps://doi.org/10.1016/j.cattod.2017.05.012
dc.identifier.urihttps://hdl.handle.net/11411/7300
dc.identifier.volume299en_US
dc.identifier.wosWOS:000417050000033en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofCatalysis Todayen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDry Reforming Of Methaneen_US
dc.subjectData Miningen_US
dc.subjectKnowledge Extractionen_US
dc.subjectDecision Treesen_US
dc.subjectArtificial Neural Networken_US
dc.subjectPromoted Ni/Al2o3 Catalystsen_US
dc.subjectSupported Nickel-Catalystsen_US
dc.subjectSelective Co Oxidationen_US
dc.subjectNoble-Metal Catalystsen_US
dc.subjectOne-Pot Synthesisen_US
dc.subjectSol-Gel Methoden_US
dc.subjectCarbon-Dioxideen_US
dc.subjectSyngas Productionen_US
dc.subjectNi Catalystsen_US
dc.subjectKnowledge Extractionen_US
dc.titleStatistical review of dry reforming of methane literature using decision tree and artificial neural network analysis
dc.typeArticle

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