Phase equilibria prediction of solid solute in supercritical carbon dioxide with and without a cosolvent: The use of artificial neural network
In this study, a back-propagation multi-layer neural network was developed to predict the solubility of solid solute in supercritical carbon dioxide with and without cosolvent. The solubility of anthracene in CO2 with cosolvents, acetone, ethanol and cyclohexane were employed as model systems to investigate the supercritical carbon dioxide behaviour in ternary systems over a wide range of temperatures. The back-propagation neural network operated in a supervised learning mode. A number of networks were trained and tested with different network parameters using training and testing data sets. To establish the network applicability, a validating data set was used and the predictability of the network was statistically evaluated. Statistical estimations showed that the neural network predictions had an excellent agreement with experimental data. The calculated average relative deviation (ARD) and the root mean squared error (RMSD) for tested ANNs data points were 5.45% and 0.74%, respectively. A minimum number of data points have been employed to train the ANN. The predicted ARD and RMSD for the employed ternary systems were 7.83% and 0.07%, respectively. The results obtained in this work indicate that ANN is a superior technique with high level of accuracy for prediction of solubility of solid solute in ternary systems.
supercritical carbon dioxide artificial neural network solubility prediction multicomponent systems