화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.34, No.2, 425-439, February, 2017
DOI10.1007/s11814-016-0271-7  Export Citation
Implementation of soft computing approaches for prediction of physicochemical properties of ionic liquid mixtures
Saeid Atashrouz, Hamed Mirshekar1, Abdolhossein Hemmati-Sarapardeh2, Mostafa Keshavarz Moraveji3, †, and Bahram Nasernejad3
  • Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Mahshahr Campus, Mahshahr, Iran
  • 1Iran Polymer and Petrochemical Institute (IPPI), Tehran, Iran
  • 2Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran
  • 3Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez 424, P. O. Box 15875-4413, Tehran, Iran
E-mail:
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 GALSSVM 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.
Keywords:Physicochemical Properties;Ionic Liquid;GMDH-PNN;LSSVM;SVM
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