Artificial Neural Network Modeling of Solubilities of 21 Commonly Used Industrial Solid Compounds in Supercritical Carbon Dioxide

作者：Gharagheizi Farhad; Eslamimanesh Ali; Mohammadi Amir H^*; Richon Dominique

来源：Industrial & Engineering Chemistry Research, 2011, 50(1): 221-226.

DOI：10.1021/ie101545g

In this communication, a feed-forward artificial neural network algorithm has been applied to calculate/predict the solubilities of 21 of the commonly used industrial solid compounds in supercritical carbon dioxide. An optimized three-layer feed-forward neural network using critical properties of solute and operating temperature and pressure is presented. Application of the model for 795 data points of 21 compounds gives a squared correlation coefficient of 0.9533 and an average absolute deviation of about 14% from the experimental values.

• 出版日期2011-1-6