| Issue |
Korean Journal of Chemical Engineering,
Vol.31, No.8, 1496-1504, 2014
Vol.31, No.8, 1496-1504, 2014
Development of artificial neural network models for supercritical fluid solvency in presence of co-solvents
This paper presents the application of artificial neural networks (ANN) to develop new models of liquid solvent dissolution of supercritical fluids with solutes in the presence of cosolvents. The neural network model of the liquid solvent dissolution of CO2 was built as a function of pressure, temperature, and concentrations of the solutes and cosolvents. Different experimental measurements of liquid solvent dissolution of supercritical fluids (CO2) with solutes in the presence of cosolvents were collected. The collected data are divided into two parts. The first part was used in building the models, and the second part was used to test and validate the developed models against the Peng-Robinson equation of state. The developed ANN models showed high accuracy, within the studied variables range, in predicting the solubility of the 2-naphthol, anthracene, and aspirin in the supercritical fluid in the presence and absence of co-solvents compared to (EoS). Therefore, the developed ANN models could be considered as a good tool in predicting the solubility of tested solutes in supercritical fluid.
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[Cited By]
참고문헌정보(참고문헌, 인용문헌)는 화학공학소재연구정보센터에 의해 제공되고 있습니다.