Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Artificial Neural Network modeling of solubility of supercritical carbon dioxide in 24 commonly used ionic liquids

Ali Eslamimanesh 1 Farhad Gharagheizi 2 Amir H. Mohammadi 1 Dominique Richon 1
1 CEP/Fontainebleau
CEP - Centre Énergétique et Procédés
2 Saman Energy Giti Co.
Saman Energy Giti Co.
Abstract : Application of supercritical CO2 for separation of ionic liquids from their organic solvents or extraction of various solutes from ionic liquid solvents have found great interest during recent years. Knowledge of phase behaviors of the mixtures of supercritical CO2+ionic liquids is therefore drastic in order to efficiently design such separation processes. In this communication, Artificial Neural Network procedure has been applied to represent the solubility of supercritical CO2 in 24 mostly used ionic liquids. An optimized Three-Layer Feed Forward Neural Network using critical properties of ionic liquids and operational temperature and pressure has been developed. Application of this model for 1128 data points of 24 ionic liquids show squared correlation coefficients of 0.993 and average absolute deviation of 3.6% from experimental values for calculated/estimated solubilities. The aforementioned deviations show the prediction capability of the presented model.
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal-mines-paristech.archives-ouvertes.fr/hal-00651416
Contributeur : Severine Dumigron <>
Soumis le : mardi 13 décembre 2011 - 15:32:48
Dernière modification le : mercredi 14 octobre 2020 - 03:45:43

Identifiants

Citation

Ali Eslamimanesh, Farhad Gharagheizi, Amir H. Mohammadi, Dominique Richon. Artificial Neural Network modeling of solubility of supercritical carbon dioxide in 24 commonly used ionic liquids. Chemical Engineering Science, Elsevier, 2011, 66 (13), pp.3039-3044. ⟨10.1016/j.ces.2011.03.016⟩. ⟨hal-00651416⟩

Partager

Métriques

Consultations de la notice

241