Estimation of vapor pressures, compressed liquid, and supercritical densities for sulfur dioxide using artificial neural networks

Background: Artificial neural networks (ANNs) as a solution for semi-structural or non-structural problems have widespread applications in engineering and science with acceptable results. In this research, the ability of multilayer perceptron artificial neural networks based on back-propagation algorithm was investigated to estimate sulfur dioxide densities. Results: The best network configuration for this case was determined as a three-layer network including 15, 10, and 1 neurons in its layers, respectively, using Levenberg-Marquardt training algorithm. The uncertainties in the presented network for prediction of unseen data including P rho T and saturated liquid densities are less than 0.5% and 1%, respectively. Another network for estimation of vapor pressure has trained with uncertainty less than 0.67%. Comparisons among the artificial neural network predictions, several equations of state, and experimental data sets show that the ANN results are in good agreement with the experimental data better than the equations of states. Conclusion: Artificial neural network can be a successful tool to represent thermophysical properties effectively, if developed efficiently.