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Representation/Prediction of Solubilities of Pure Compounds in Water Using Artificial Neural Network-Group Contribution Method

Abstract : In this work, the artificial neural network-group contribution (ANN-GC) method has been applied to represent/ predict the solubilities of pure chemical compounds in water over the (293 to 298) K temperature range at atmospheric pressure. A set of 3585 pure compounds from various chemical families has been investigated to propose a comprehensive and predictive method. The obtained results show a squared correlation coefficient (R2) value of 0.96 and a root-mean-square error of 0.4 for the calculated/predicted properties with respect to existing experimental values, demonstrating the reliability of the proposed model.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00595551
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : mercredi 25 mai 2011 - 10:08:46
Dernière modification le : jeudi 24 septembre 2020 - 17:22:04

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Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi, Dominique Richon. Representation/Prediction of Solubilities of Pure Compounds in Water Using Artificial Neural Network-Group Contribution Method. Journal of Chemical and Engineering Data, American Chemical Society, 2011, 56 (4), pp.720-726. ⟨10.1021/je101061t⟩. ⟨hal-00595551⟩

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