Constructing global models from past publications to improve design and operating conditions for direct alcohol fuel cells

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dc.authoridGünay, M. Erdem/0000-0003-1282-718X|TAPAN, N. Alper/0000-0001-8599-0450|YILDIRIM, RAMAZAN/0000-0001-5077-5689
dc.authorwosidGünay, M. Erdem/I-1564-2019
dc.authorwosidYILDIRIM, RAMAZAN/AAQ-4867-2020
dc.authorwosidTAPAN, Niyazi A/H-6416-2013
dc.contributor.authorTapan, N. Alper
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorYildirim, Ramazan
dc.date.accessioned2024-07-18T20:42:30Z
dc.date.available2024-07-18T20:42:30Z
dc.date.issued2016
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThis work aims to analyze past publications on direct alcohol fuel cells (DAFC) in the literature using two data mining tools (artificial neural networks and decision trees) and to develop global models to predict the conditions leading to high performance of DAFC. The database constructed for this purpose contains 4682 data points over 271 polarization (IV) curves obtained from 36 publications in the literature. Decision tree classification models were used to develop heuristics to select the suitable fuel cell design and operational conditions to improve the maximum power density while artificial neural network models (ANN) were developed to test the predictability of IV curves at the conditions where experimental results were not available. The same ANN models were also used to determine the relative importance of design and operational variables to provide some insight to determine the variable to be manipulated. All these analyses were quite successful deducing some useful heuristics and models for the future studies from the continuously growing experience accumulated in the literature. (C) 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.cherd.2015.11.018
dc.identifier.endpage170en_US
dc.identifier.issn0263-8762
dc.identifier.issn1744-3563
dc.identifier.scopus2-s2.0-84954505237en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage162en_US
dc.identifier.urihttps://doi.org/10.1016/j.cherd.2015.11.018
dc.identifier.urihttps://hdl.handle.net/11411/7303
dc.identifier.volume105en_US
dc.identifier.wosWOS:000370104900016en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInst Chemical Engineersen_US
dc.relation.ispartofChemical Engineering Research & Designen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDirect Alcohol Fuel Cellsen_US
dc.subjectData Miningen_US
dc.subjectKnowledge Extractionen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectDecision Treesen_US
dc.subjectArtificial Neural-Networken_US
dc.subjectSelective Co Oxidationen_US
dc.subjectNoble-Metal Catalystsen_US
dc.subjectPlatinum-Based Anodesen_US
dc.subjectKnowledge Extractionen_US
dc.subjectStatistical-Analysisen_US
dc.subjectSemiempirical Modelen_US
dc.subjectMethanol Crossoveren_US
dc.subjectPart Ien_US
dc.subjectPerformanceen_US
dc.titleConstructing global models from past publications to improve design and operating conditions for direct alcohol fuel cellsen_US
dc.typeArticleen_US

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