Artificial Neural Network Modeling of Solubilities of 21 Commonly Used Industrial Solid Compounds in Supercritical Carbon Dioxide - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Industrial and engineering chemistry research Année : 2011

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

(1) , (2) , (3) , (3)
1
2
3
Ali Eslamimanesh
Amir H. Mohammadi
  • Fonction : Auteur correspondant
  • PersonId : 915863

Connectez-vous pour contacter l'auteur
Dominique Richon
  • Fonction : Auteur
  • PersonId : 915941

Résumé

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.
Fichier non déposé

Dates et versions

hal-00573680 , version 1 (04-03-2011)

Identifiants

Citer

Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi, Dominique Richon. Artificial Neural Network Modeling of Solubilities of 21 Commonly Used Industrial Solid Compounds in Supercritical Carbon Dioxide. Industrial and engineering chemistry research, 2011, 50, pp.221-226. ⟨10.1021/ie101545g⟩. ⟨hal-00573680⟩
138 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More