Tapan, N. AlperGunay, M. ErdemYildirim, Ramazan2024-07-182024-07-1820160263-87621744-3563https://doi.org/10.1016/j.cherd.2015.11.018https://hdl.handle.net/11411/7303This 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.eninfo:eu-repo/semantics/closedAccessDirect Alcohol Fuel CellsData MiningKnowledge ExtractionArtificial Neural NetworksDecision TreesArtificial Neural-NetworkSelective Co OxidationNoble-Metal CatalystsPlatinum-Based AnodesKnowledge ExtractionStatistical-AnalysisSemiempirical ModelMethanol CrossoverPart IPerformanceArticle2-s2.0-8495450523710.1016/j.cherd.2015.11.018170Q2162105Q2WOS:000370104900016