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In relation to this article, we declare that there is no conflict of interest.
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Received May 4, 2016
Accepted September 30, 2016
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Implementation of soft computing approaches for prediction of physicochemical properties of ionic liquid mixtures

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
moraveji@aut.ac.ir
Korean Journal of Chemical Engineering, February 2017, 34(2), 425-439(15), 10.1007/s11814-016-0271-7
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Abstract

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.

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