Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network
Autor: | M. Soleimani, S. Salahi, Ahmad KhazaiePoul |
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Rok vydání: | 2016 |
Předmět: |
Work (thermodynamics)
Environmental Engineering Supercritical carbon dioxide Artificial neural network Correlation coefficient Chemistry General Chemical Engineering Thermodynamics 02 engineering and technology General Chemistry 010502 geochemistry & geophysics Perceptron 01 natural sciences Biochemistry 020401 chemical engineering Acentric factor Organic chemistry Sensitivity (control systems) 0204 chemical engineering Solubility 0105 earth and related environmental sciences |
Zdroj: | Chinese Journal of Chemical Engineering. 24:491-498 |
ISSN: | 1004-9541 |
DOI: | 10.1016/j.cjche.2015.11.027 |
Popis: | 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. |
Databáze: | OpenAIRE |
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