A Machine Learning approach for the validation and optimization of permittivity mixing rules for binary liquids

Abstract

This paper presents the application of Support Vector Regressor models trained with glycerin-water mixture signals from a Dielectric Resonator sensor. Each signal is labeled with a concentration considered. The performance of these models indicates which mixing rule fits the most with experimental permittivity values. Some modifications of these formulas are validated to acquire better estimations.