Implementation of soft computing approaches for prediction of physicochemical properties of ionic liquid mixtures

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• 作者：Saeid Atashrouz ; Hamed Mirshekar…
• 关键词：Physicochemical Properties ; Ionic Liquid ; GMDH ; PNN ; LSSVM ; SVM
• 刊名：Korean Journal of Chemical Engineering
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：34
• 期：2
• 页码：425-439
• 全文大小：
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Industrial Chemistry/Chemical Engineering; Catalysis; Materials Science, general; Biotechnology;
• 出版者：Springer US
• ISSN：1975-7220
• 卷排序：34

文摘

The main objective of this study was to develop soft computing approaches for prediction of physicochemical properties of IL mixtures including: density, heat capacity, thermal conductivity, and surface tension. The proposed models in this study are based on support vector machine (SVM), least square support vector machines (LSSVM), and group method of data handling type polynomial neural network (GMDH-PNN) systems. To find the LSSVM and SVM adjustable parameters, genetic algorithm (GA) as a meta-heuristic algorithm was utilized. The results showed that LSSVM is more robust and reliable for prediction of physicochemical properties of IL mixtures. The proposed GA-LSSVM model provides average absolute relative deviations of 0.38%, 0.18%, 0.77% and 1.18% for density, heat capacity, thermal conductivity, and surface tension, respectively, which demonstrates high accuracy of the model for prediction of physicochemical properties of IL mixtures.