Predictive Models for the Binary Diffusion Coefficient at Infinite Dilution in Polar and Nonpolar Fluids
authors Aniceto, JPS; Zezere, B; Silva, CM
nationality International
journal MATERIALS
author keywords diffusion coefficient; machine learning; modeling; nonpolar; polar; prediction
abstract Experimental diffusivities are scarcely available, though their knowledge is essential to model rate-controlled processes. In this work various machine learning models to estimate diffusivities in polar and nonpolar solvents (except water and supercritical CO2) were developed. Such models were trained on a database of 90 polar systems (1431 points) and 154 nonpolar systems (1129 points) with data on 20 properties. Five machine learning algorithms were evaluated: multilinear regression, k-nearest neighbors, decision tree, and two ensemble methods (random forest and gradient boosted). For both polar and nonpolar data, the best results were found using the gradient boosted algorithm. The model for polar systems contains 6 variables/parameters (temperature, solvent viscosity, solute molar mass, solute critical pressure, solvent molar mass, and solvent Lennard-Jones energy constant) and showed an average deviation (AARD) of 5.07%. The nonpolar model requires five variables/parameters (the same of polar systems except the Lennard-Jones constant) and presents AARD = 5.86%. These results were compared with four classic models, including the 2-parameter correlation of Magalhaes et al. (AARD = 5.19/6.19% for polar/nonpolar) and the predictive Wilke-Chang equation (AARD = 40.92/29.19%). Nonetheless Magalhaes et al. requires two parameters per system that must be previously fitted to data. The developed models are coded and provided as command line program.
publisher MDPI
isbn 1996-1944
year published 2021
volume 14
issue 3
digital object identifier (doi) 10.3390/ma14030542
web of science category 33
subject category Chemistry, Physical; Materials Science, Multidisciplinary; Metallurgy & Metallurgical Engineering; Physics, Applied; Physics, Condensed Matter
unique article identifier WOS:000615389500001

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