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

doi:10.1016/j.cjche.2015.11.027

Chinese Journal of Chemical Engineering ›› 2016, Vol. 24 ›› Issue (4): 491-498.DOI: 10.1016/j.cjche.2015.11.027

• 第25届中国过程控制会议专栏 • 上一篇下一篇

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

Ahmad KhazaiePoul¹, M. Soleimani², S. Salahi³

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

收稿日期:2015-03-11 修回日期:2015-10-24 出版日期:2016-04-28 发布日期:2016-05-27
通讯作者: 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 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:2016-04-28 Published:2016-05-27
Contact: M. Soleimani

摘要/Abstract

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

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

Ahmad KhazaiePoul, M. Soleimani, S. Salahi. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. Chinese Journal of Chemical Engineering, 2016, 24(4): 491-498.

Ahmad KhazaiePoul, M. Soleimani, S. Salahi. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. Chin.J.Chem.Eng., 2016, 24(4): 491-498.

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

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

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