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Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids

  • Polymer, Industrial Chemistry
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Abstract

Knowledge of the surface tension of ionic liquids (ILs) and their related mixtures is of central importance and enables engineers to efficiently design new processes dealing with these fluids on an industrial scale. It’s obvious that experimental determination of surface tension of every conceivable IL and its mixture with other compounds would be a herculean task. Besides, experimental measurements are intrinsically laborious and expensive; therefore, accurate prediction of the property using a reliable technique would be overwhelmingly favorable. To do so, a modeling method based on artificial neural network (ANN) trained by Bayesian regulation back propagation training algorithm (trainbr) has been proposed to predict surface tension of the binary ILs mixtures. A total set of 748 data points of binary surface tension of IL systems within temperature range of 283.1-348.15 K was used to train and test the applied network. The obtained results indicated that the predictive values and experimental data are quite matching, representing reliability of the used ANN model for such purpose. Also, compared with other methods, such as SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN and ANN trained with trainlm algorithm the proposed model was better in terms of accuracy.

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Authors and Affiliations

  1. Young Researchers and Elite Club, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

    Reza Soleimani

  2. Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, 14115-143, Iran

    Amir Hossein Saeedi Dehaghani

  3. 210 15 Ave SE, Calgary, Alberta, Canada, T2G 0B5

    Navid Alavi Shoushtari

  4. Department of Physics, University of Kashan, Kashan, Iran

    Pedram Yaghoubi

  5. School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales, 2480, Australia

    Alireza Bahadori

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  1. Reza Soleimani
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  2. Amir Hossein Saeedi Dehaghani
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Correspondence to Amir Hossein Saeedi Dehaghani.

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Soleimani, R., Saeedi Dehaghani, A.H., Shoushtari, N.A. et al. Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids. Korean J. Chem. Eng. 35, 1556–1569 (2018). https://doi.org/10.1007/s11814-017-0326-4

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