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Article Dans Une Revue Journal of Chemical and Engineering Data Année : 2011

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

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Ali Eslamimanesh
Amir H. Mohammadi
  • Fonction : Auteur
  • PersonId : 915863
Dominique Richon
  • Fonction : Auteur
  • PersonId : 915941

Résumé

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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Dates et versions

hal-00614245 , version 1 (10-08-2011)

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Citer

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, 2011, 56 (5), pp.2587-2601. ⟨10.1021/je2001045⟩. ⟨hal-00614245⟩
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