ABSTRACT Fatty acid methyl esters (FAMEs) are renewable, biodegradable biofuels. This study built machine learning models (DT, AdaBoost, EL, K ‐nearest neighboring ( K NN), random forest (RF), EN, CNN, SVR, and MLP‐ANN) to predict their dynamic viscosity using 488 literature data points. Inputs: temperature, pressure, molar mass, and C/H/O fractions. Models used five‐fold cross‐validation (90% training/validation, 10% testing). Metrics: R 2 , mean squared error (MSE), AARE%. MLP‐ANN performed best ( R 2 = 0.9987, MSE = 0.0038, and AARE = 1.68%). DT and AdaBoost showed higher errors; elastic net was weakest ( R 2 = 0.8486, AARE ≈ 25.78%). Pressure was the most impactful parameter, followed by temperature. SHapley Additive exPlanations (SHAP) analysis confirmed pressure as dominant. The framework is robust, accurate, and cost‐effective across broad thermodynamic conditions.
Abu-Shareha et al. (Wed,) studied this question.