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

Fakhri Yousefi; Zeynab Amoozandeh

doi:10.1016/j.cjche.2016.05.003

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

Chemical Engineering Thermodynamics | Updated：2026-01-06

- Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids
- Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids
- 中国化学工程学报（英文版） 2016年24卷第12期页码：1761-1771
- Affiliations：
  
  Department of Chemistry, Yasouj University, Yasouj,Iran,75914-353
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.05.003
  中图分类号：
- 纸质出版：2016
- Accepted：
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Fakhri Yousefi, Zeynab Amoozandeh. Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids[J]. 中国化学工程学报（英文版）, 2016,24(12):1761-1771.

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.
Fakhri Yousefi, Zeynab Amoozandeh. Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids[J]. 中国化学工程学报（英文版）, 2016,24(12):1761-1771. DOI： 10.1016/j.cjche.2016.05.003.

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. DOI： 10.1016/j.cjche.2016.05.003.

摘要

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.

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