Comparative Analysis of Machine Learning and UNIFAC Dortmund Models for Liquid-Liquid Equilibrium Prediction in Biodiesel Production Systems

Increasing greenhouse gas emissions have intensified efforts to replace fossil fuels with renewable alternatives. Biodiesel offers reduced emissions compared to fossil diesel. However, the biodiesel production feasibility, design, and optimization show a strong influence of the liquid-liquid equilibrium (LLE). This work compared three machine learning models, an artificial neural network (ANN), a Random Forest, and an XGBoost, against a correlated UNIFAC Dortmund thermodynamic model for predicting LLE in biodiesel systems. For the model’s correlation, 1,119 experimental tie lines compiled from 54 peer-reviewed papers were employed. All machine learning models significantly outperformed the baseline, with XGBoost achieving the highest accuracy (RMSE of 0.0093), representing an 84% improvement over UNIFAC Dortmund predictions. Additionally, a sensitivity analysis was implemented to understand the temperature and pseudomolecule molecular weight effects on the LLE. The XGBoost model revealed strong temperature and molecular weight influence across all scenarios. Low values for these variables promote the component of interest recovery. While machine learning models showed superior predictive accuracy, their black-box nature precludes a mechanistic understanding of molecular interactions and thermodynamic property estimation. Traditional phenomenological models, despite lower accuracy, provide the best interpretability for the LLE.