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

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• 作者：Ahmad KhazaiePoul¹ ; M. Soleimani² ; ^{m.soleimani@ausmt.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; S. Salahi³
• 关键词：Solubility ; Disperse dyes ; Supercritical carbon dioxide ; Neural networks ; Co-solvent
• 刊名：Chinese Journal of Chemical Engineering
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：24
• 期：4
• 页码：491-498
• 全文大小：1158 K

文摘

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. It was 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 proposed model. 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.