The application of Machine Learning (ML)-based techniques was explored to create a fully predictive framework for estimating the thermal conductivity of multi-component mixtures containing hydrocarbons and oxygenated compounds. The study followed these steps: (i) three datasets were constructed using experimental thermal conductivity data for both pure compounds and binary mixtures available in the literature, (ii) Symbolic regression was then applied to generate mixing rules considering five independent data manipulation strategies, (iii) new quantitative structure-property relationship (QSPR) models were developed and benchmarked against work previously published in the literature, and then (iv) QSPR models were used to power mixing rules generated with symbolic regression to predict thermal conductivity values of binary mixtures. A mixing rule was then designed to propose a potential extension to multi-component – two or more components – mixtures. Validation of the latter mixing rule powered with QSPR predictions was performed considering a set of ternary and quaternary mixtures. Finally, the approach was applied to predict the thermal conductivity of four jet fuel samples at different temperatures and atmospheric pressures, resulting in a mean absolute error of 2.9%. Performed comparative analysis confirmed that the developed methodology is effective across a wide range of liquid hydrocarbon and oxygenated mixtures.
Creton et al. (Mon,) studied this question.