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

International Journal of Industrial Chemistry

دوره 4، شماره 1، اسفند 2013، صفحه 101-108

نوع مقاله: research article

شناسه دیجیتال (DOI): https://doi.org/10.1186/2228-5547-4-14

نویسندگان

Abdolreza Moghadassi^{* 1}؛ Mahmoodreza Nikkholgh²؛ Sayedmohsen Hosseini²؛ Fahime Parvizian²

¹Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran.

²Department of Chemical Engineering, Faculty of Engineering, Arak University, 38156-8-8349, Arak, Iran

چکیده

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 PρT 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.

کلیدواژه‌ها

Neural network؛ Multilayer perceptron؛ Sulfur dioxide؛ Density؛ Equation of state