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A Machine Learning approach for enhancing permittivity mixing rules of binary liquids with a Gaussian modification and a new interaction factor estimation

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IIT-24-067R (1.530Mb)
Date
2024-04-01
Author
Monteagudo Honrubia, Miguel
Herraiz Martínez, Francisco Javier
Matanza Domingo, Javier
Estado
info:eu-repo/semantics/publishedVersion
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Abstract
 
 
The microstructure and solvation mechanics of binary liquids are key for predicting mixture permittivity. However, since traditional mixing rules do not consider this complexity, they must be modified to address the mixture characteristics through an interaction factor (Κint). This paper evaluates this parameter for several mixing rules, applying Support Vector Regressor models trained with glycerin-water reflective signals acquired with a Dielectric Resonator sensor. The regression error of these models indicates both the optimal interaction factor and the mixing rule that fits the most with experimental permittivity values. Kraszewski and Hashin-Shtrikman mixing rules achieved the best performance with an RMSE of around 1. In addition, this paper suggests that the interaction factor can be estimated through the molar volume and the dielectric contrast between liquids (Kint=2.67) without acquiring experimental data. Moreover, after analyzing the physical limitations of a linear modification formula, this paper proposes an alternative based on a Gaussian function that avoids unrealistic volume fractions. Both contributions enhance mixing rule accuracy and improve the flexibility to model mixture dielectric behavior.
 
URI
https:doi.org10.1016j.molliq.2024.124290
A Machine Learning approach for enhancing permittivity mixing rules of binary liquids with a Gaussian modification and a new interaction factor estimation
Tipo de Actividad
Artículos en revistas
ISSN
0167-7322
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave

Binary mixture; Dielectric characterization; Machine Learning; Mixing rules; Permittivity
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