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Article dans une revue

Determination of Critical Properties and Acentric Factors of Pure Compounds Using the Artificial Neural Network Group Contribution Algorithm

Abstract : 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.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00614242
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : mercredi 10 août 2011 - 11:34:58
Dernière modification le : jeudi 24 septembre 2020 - 17:22:03

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Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi, Dominique Richon. Determination of Critical Properties and Acentric Factors of Pure Compounds Using the Artificial Neural Network Group Contribution Algorithm. Journal of Chemical and Engineering Data, American Chemical Society, 2011, 56 (5), pp.2460-2476. ⟨10.1021/je200019g⟩. ⟨hal-00614242⟩

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