Odabasi, CaglaGunay, M. ErdemYildirim, Ramazan2024-07-182024-07-1820140360-31991879-3487https://doi.org/10.1016/j.ijhydene.2014.01.160https://hdl.handle.net/11411/7386In 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.eninfo:eu-repo/semantics/closedAccessWater Gas Shift ReactionData MiningKnowledge ExtractionArtificial Neural NetworksDecision TreesSupport Vector MachinesSelective Co OxidationCopper-Based CatalystsIn-Situ DriftsPt/Ceo2 CatalystGold CatalystsHeterogeneous CatalysisHydrogen-ProductionMesoporous TitaniaOxide CatalystsPd-CuArticle2-s2.0-8489737721310.1016/j.ijhydene.2014.01.160574611Q1573339Q1WOS:000334977900029