SCI和EI收录∣中国化工学会会刊

Chin.J.Chem.Eng. ›› 2016, Vol. 24 ›› Issue (4): 491-498.DOI: 10.1016/j.cjche.2015.11.027

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Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network

Ahmad KhazaiePoul1, M. Soleimani2, S. Salahi3   

  1. 1 PhD Candidate of Faculty ofWater and Environmental Engineering, Shahid Beheshti University, Tehran, Iran;
    2 Amol University of Special Modern Technologies, Amol 46168-49767, Iran;
    3 Chemical Engineering Department, Islamic Azad University, Shahrood Branch, Shahrood, Iran
  • Received:2015-03-11 Revised:2015-10-24 Online:2016-05-27 Published:2016-04-28
  • Contact: M. Soleimani

Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network

Ahmad KhazaiePoul1, M. Soleimani2, S. Salahi3   

  1. 1 PhD Candidate of Faculty ofWater and Environmental Engineering, Shahid Beheshti University, Tehran, Iran;
    2 Amol University of Special Modern Technologies, Amol 46168-49767, Iran;
    3 Chemical Engineering Department, Islamic Azad University, Shahrood Branch, Shahrood, Iran
  • 通讯作者: M. Soleimani

Abstract: Nowadays artificial neural networks (ANNs) with strong ability have been applied widely for prediction of nonlinear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density,molecular weight and acentric factor has been used for solubility prediction of three disperse dyes in supercritical carbon dioxide (SC-CO2) and ethanol as co-solvent. Itwas shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-CO2. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposedmodel. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.

Key words: Solubility, Disperse dyes, Supercritical carbon dioxide, Neural networks, Co-solvent

摘要: Nowadays artificial neural networks (ANNs) with strong ability have been applied widely for prediction of nonlinear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density,molecular weight and acentric factor has been used for solubility prediction of three disperse dyes in supercritical carbon dioxide (SC-CO2) and ethanol as co-solvent. Itwas shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-CO2. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposedmodel. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.

关键词: Solubility, Disperse dyes, Supercritical carbon dioxide, Neural networks, Co-solvent