作者
Farhad Gharagheizi, Ali Eslamimanesh, Amir H Mohammadi, Dominique Richon
发表日期
2011/1/5
期刊
Industrial & engineering chemistry research
卷号
50
期号
1
页码范围
221-226
出版商
American Chemical Society
简介
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.
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F Gharagheizi, A Eslamimanesh, AH Mohammadi… - Industrial & engineering chemistry research, 2011