Tapan, N. AlperYildirim, RamazanGunay, M. Erdem2024-07-182024-07-1820161932-104X1932-1031https://doi.org/10.1002/bbb.1650https://hdl.handle.net/11411/6929In this study, published experimental works on catalytic transesterification were analyzed to determine the most important variables affecting fatty acid conversion and the most suitable ranges of these variables for high performance. A database of 1324 data points was constructed from the experimental results in 31 representative papers published between 2008 and 2014, and this database was analyzed using artificial neural network (ANN) and decision tree (DT) techniques. It was found from ANN analysis that the most important variable for high fatty acid conversion was reaction time (with about 40% relative importance) followed by catalyst loading, alcohol:oil molar ratio, operating temperature, and support type with similar relative importance (about 10% each). DT analysis revealed 14 combinations of conditions leading to high performance, and some of these seemed to be generalizable for the use for the future studies; some heuristics were also derived from these generalizable conditions. (c) 2016 Society of Chemical Industry and John Wiley & Sons, Ltdeninfo:eu-repo/semantics/closedAccessData MiningKnowledge ExtractionBiodieselDecision TreesArtificial Neural NetworksEsterificationArtificial Neural-NetworksSelective Co OxidationSolid Base CatalystSoybean OilCalcium-OxideKnowledge ExtractionVegetable-OilsAcid CatalystRapeseed OilTransesterificationArticle2-s2.0-8496467744010.1002/bbb.16504344Q242210Q1WOS:000379919400009