Determination of Critical Properties and Acentric Factors of Pure Compounds Using the Artificial Neural Network Group Contribution Algorithm
datasetposted on 12.05.2011 by Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi, Dominique Richon
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
In this article, artificial neural network group contribution (ANN-GC) method is applied to calculate and estimate critical properties including the critical pressure, temperature, and volume and acentric factors of pure compounds. About 1700 chemical compounds from various chemical families have been investigated to propose a comprehensive and predictive model. Using this dedicated model, we obtain satisfactory results quantified by the following absolute average deviations of the calculated and estimated properties from existing experimental values: 1.1 % for critical pressure, 0.9 % for critical temperature, 1.4 % for critical volume, and 3.7 % for acentric factor.