Skip to Main content Skip to Navigation
Journal articles

Representation and Prediction of Molecular Diffusivity of Nonelectrolyte Organic Compounds in Water at Infinite Dilution Using the Artificial Neural Network-Group Contribution Method

Abstract : The determination of diffusion coefficients of pure compounds in water at infinite dilution is of utmost interest in chemical and environmental engineering, especially wastewater treatment processes. In this work, the artificial neural network-group contribution (ANN-GC) method is applied to represent and predict the molecular diffusivity of nonelectrolyte organic compounds in water at infinite dilution and 298.15 K. A total of 4852 pure compounds from various chemical families has been investigated to propose a predictive model. The obtained results show the squared correlation coefficient of 0.996, root-mean-square error of about 0.02, and average absolute deviation lower than 1.5 % for the calculated or predicted property from existing experimental values.
Document type :
Journal articles
Complete list of metadata

https://hal-mines-paristech.archives-ouvertes.fr/hal-00595545
Contributor : Bibliothèque Mines Paristech <>
Submitted on : Wednesday, May 25, 2011 - 10:01:40 AM
Last modification on : Friday, April 9, 2021 - 12:02:04 PM

Identifiers

Citation

Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi, Dominique Richon. Representation and Prediction of Molecular Diffusivity of Nonelectrolyte Organic Compounds in Water at Infinite Dilution Using the Artificial Neural Network-Group Contribution Method. Journal of Chemical and Engineering Data, American Chemical Society, 2011, 56 (5), pp.1741-1750. ⟨10.1021/je101190p⟩. ⟨hal-00595545⟩

Share

Metrics

Record views

196