Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids

doi:10.1016/j.cjche.2016.05.003

Abstract

Abstract: In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The temperature dependent parameters of the equation of state have been calculated using corresponding state correlation based on only the density at 298.15 K as scaling constants. The obtained mean of deviations of modified equation of state for density of all pure ionic liquids for 1662 data points was 0.25%. In addition, the performance of the artificial neural network (ANN) with principle component analysis (PCA) based on back propagation training with 28 neurons in hidden layer for predicting of behavior of binary mixtures of ionic liquids was investigated. The AADs of a collection of 568 data points for all binary systems using the EOS and the ANN at various temperatures and mole fractions are 1.03% and 0.68%, respectively. Moreover, the excess molar volume of all binary mixtures is predicted using obtained densities of EOS and ANN, and the results show that these properties have good agreement with literature.

Key words: Ionic liquids, Thermodynamic properties, Equation of state, Artificial neural network

摘要： In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The temperature dependent parameters of the equation of state have been calculated using corresponding state correlation based on only the density at 298.15 K as scaling constants. The obtained mean of deviations of modified equation of state for density of all pure ionic liquids for 1662 data points was 0.25%. In addition, the performance of the artificial neural network (ANN) with principle component analysis (PCA) based on back propagation training with 28 neurons in hidden layer for predicting of behavior of binary mixtures of ionic liquids was investigated. The AADs of a collection of 568 data points for all binary systems using the EOS and the ANN at various temperatures and mole fractions are 1.03% and 0.68%, respectively. Moreover, the excess molar volume of all binary mixtures is predicted using obtained densities of EOS and ANN, and the results show that these properties have good agreement with literature.

关键词: Ionic liquids, Thermodynamic properties, Equation of state, Artificial neural network

Fakhri Yousefi, Zeynab Amoozandeh. Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids[J]. , 2016, 24(12): 1761-1771.

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