Machine Learning model for the prediction of self-diffusion coefficients in liquids, compressed gases and supercritical fluids

Author:

Dias Andreia F.F.ORCID,Portugal InêsORCID,Aniceto José P.S.ORCID,Silva Carlos M.

Funder

Fundação para a Ciência e a Tecnologia

Publisher

Elsevier BV

Reference48 articles.

1. Separation process principles: chemical and biochemical operations;Seader,2011

2. Modelling of Transport Properties of Hard Sphere Fluids and Related Systems, and its Applications;Silva,2008

3. J. Millat, J.H. Dymond, C.A. Nieto de Castro, eds., Transport Properties of Fluids: Their Correlation, Prediction and Estimation, Cambridge University Press, Cambridge, 1996. https://doi.org/10.1017/CBO9780511529603.

4. Unified approach to the self-diffusion coefficients of dense fluids over wide ranges of temperature and pressure—hard-sphere, square-well, Lennard-Jones and real substances;Liu;Chem. Eng. Sci.,1998

5. The mathematical theory of non-uniform gases: an account of the kinetic theory of viscosity, thermal conduction and diffusion in gases;Chapman,1970

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