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estimating onset of precipitation of dissolved asphaltene in the solution of solvent+precipitant using artificial neural network technique

Amir H. Mohammadi 1 Dominique Richon 1
1 CEP/Fontainebleau
CEP - Centre Énergétique et Procédés
Abstract : Asphaltene precipitation is traditionally modeled using polymer solution theories or cubic equations of state. We propose another approach based on artificial neural network technique to model onset of precipitation of dissolved asphaltene in the solution of solvent + precipitant. A mathematical model based on feed-forward artificial neural network technique, which takes advantage of a modified Levenberg-Marquardt optimization algorithm, has been used to model onset of precipitation of dissolved asphaltene in the solvent + precipitant solution. The experimental data reported in the literature have been used to develop this model. The acceptable agreement between the results of this model and experimental data demonstrates the capability of the neural network technique for estimating onset of precipitation of dissolved asphaltene in the solution of solvent + precipitant.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00797802
Contributeur : Jordane Raisin-Dadre <>
Soumis le : jeudi 7 mars 2013 - 11:25:39
Dernière modification le : mercredi 14 octobre 2020 - 04:02:45

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  • HAL Id : hal-00797802, version 1

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Amir H. Mohammadi, Dominique Richon. estimating onset of precipitation of dissolved asphaltene in the solution of solvent+precipitant using artificial neural network technique. Open Thermodynamics Journal, Bentham Open, 2008, 2, pp.82-88. ⟨hal-00797802⟩

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