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Issue
Korean Journal of Chemical Engineering,
Vol.34, No.2, 425-439, 2017
Implementation of soft computing approaches for prediction of physicochemical properties of ionic liquid mixtures
Atashrouz S, Mirshekar H, Hemmati-Sarapardeh A, Moraveji MK, Nasernejad B
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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[Cited By]
  1. Soleimani R, Dehaghani AHS, Shoushtari NA, Yaghoubi P, Bahadori A, Korean Journal of Chemical Engineering, 35(7), 1556, 2018
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