화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.35, No.7, 1556-1569, July, 2018
DOI10.1007/s11814-017-0326-4  Export Citation
Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids
Reza Soleimani, Amir Hossein Saeedi Dehaghani1, †, Navid Alavi Shoushtari2, Pedram Yaghoubi3, and Alireza Bahadori4
  • Young Researchers and Elite Club, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran
  • 1Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran 14115-143, Iran
  • 2Calgary, 210 15 Ave SE, Calgary, Alberta, Canada T2G 0B5
  • 3Department of Physics, University of Kashan, Kashan, Iran
  • 4School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales 2480, Australia
E-mail:
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.
Keywords:Ionic Liquids;Surface Tension;Binary Mixtures;Prediction;Artificial Neural Network
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