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QSPR molecular approach for representation/prediction of very large vapor pressure dataset

Abstract : Reliable estimation of vapor pressure is of great significance for chemical industry. In this communication, the capability of the Quantitative Structure-Property Relationship (QSPR) technique is studied to represent/predict the vapor pressure of pure chemical compounds from about 55 to around 3040. K. Around 45,000 vapor pressure values belonging to about 1500 chemical compounds (mostly organic ones) at different temperatures are treated in order to present a comprehensive, reliable, and predictive model. The sequential search mathematical method has been observed to be the only variable search method capable of selection of appropriate model parameters (molecular descriptors) regarding this extremely large data set. To develop the final model, a three-layer artificial neural network is optimized using the Levenberg-Marquardt (LM) optimization strategy. Through the developed QSPR model, the absolute average relative deviation of the represented/predicted properties from the applied data is about 7% and squared correlation coefficient is 0.990. In addition, the outliers of the model are identified using the Leverage Value Statistics method.
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Contributeur : Jordane Raisin-Dadre <>
Soumis le : mardi 5 mars 2013 - 12:01:22
Dernière modification le : jeudi 24 septembre 2020 - 17:22:04



Farhad Gharagheizi, Ali Eslamimanesh, Poorandokht Ilani-Kashkouli, Amir H. Mohammadi, Dominique Richon. QSPR molecular approach for representation/prediction of very large vapor pressure dataset. Chemical Engineering Science, Elsevier, 2012, 76, pp.99-107. ⟨10.1016/j.ces.2012.03.033⟩. ⟨hal-00796931⟩



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