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Article dans une revue

Use of Artificial Neural Network-Group Contribution Method to Determine Surface Tension of Pure Compounds

Abstract : This work aims at applying an artificial neural network-group contribution method to represent/predict the surface tension of pure chemical compounds at different temperatures and atmospheric pressure. To propose a comprehensive, reliable, and predictive tool, about 4700 data belonging to experimental surface tension values of 752 chemical compounds at different temperatures and atmospheric pressure have been studied. The investigated compounds belong to 78 chemical families containing 151 functional groups (group contributions), which include organic and inorganic liquids. Using this dedicated strategy, we obtain satisfactory results quantified by the following statistical parameters: absolute average deviations of the represented/predicted properties from existing experimental values, 1.7 %, and squared correlation coefficient, 0.997.
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Article dans une revue
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00614245
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : mercredi 10 août 2011 - 11:46:10
Dernière modification le : jeudi 24 septembre 2020 - 17:22:03

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Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi, Dominique Richon. Use of Artificial Neural Network-Group Contribution Method to Determine Surface Tension of Pure Compounds. Journal of Chemical and Engineering Data, American Chemical Society, 2011, 56 (5), pp.2587-2601. ⟨10.1021/je2001045⟩. ⟨hal-00614245⟩

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