Reliable predictive frameworks for thermal conductivity of ester biofuels using artificial intelligence approaches.

Ester biofuels have emerged as promising renewable alternatives to fossil fuels due to their environmental compatibility and favorable combustion characteristics. Accurate knowledge of their liquid thermal conductivity (LTC) is essential for optimizing energy systems, engine performance, and thermal modeling applications. However, existing literature lacks generalizable models capable of estimating LTC across diverse ester biofuels and operating conditions. This study addresses this gap by developing robust machine learning models using a comprehensive dataset comprising 1,641 experimental LTC measurements for 22 different ester biofuels under varied pressures and temperatures. Three advanced computational approaches, including Support Vector Machine (SVM), Decision Tree (DT), and Genetic Programming (GP), were employed to predict LTC based on temperature, pressure, critical thermodynamic properties, and molar weight of the biofuels. Among the developed models, the SVM technique exhibited superior predictive performance with a determination coefficient (R