Modeling Prediction of Physical Properties in Sustainable Biodiesel-Diesel-Alcohol Blends via Experimental Methods and Machine Learning

Abstract

This study investigated the production of biodiesel from canola oil, the formulation of sustainable ternary fuel blends with diesel and alcohol (ethanol or propanol), and the experimental and machine learning-based modeling of their physical properties, including density and viscosity over a temperature range of 10 degrees C to 40 degrees C. Biodiesel was synthesized via alkali-catalyzed transesterification (6:1 methanol-to-oil molar ratio, 0.5 wt % NaOH of oil) and blended with diesel and alcohols (ethanol and propanol) in varying volume ratios. The experimental results revealed that blend density decreased from 0.8622 g/cm3 at 10 degrees C to 0.8522 g/cm3 at 40 degrees C for a blend containing ethanol. Similarly, the viscosity showed a significant reduction with temperature, e.g., the blend exhibited a viscosity decline from 8.5 mPas at 10 degrees C to 7.2 mPas at 40 degrees C. Increasing the alcohol or diesel content further reduced density and viscosity due to the lower intrinsic properties of these components. The machine learning models, Gaussian process regression (GPR), support vector regression (SVR), artificial neural networks (ANN), and decision tree regression (DTR), were applied to predict the properties of these blends. GPR demonstrated the best predictive performance for both density and viscosity. These findings confirm the strong potential of GPR for the accurate and reliable prediction of fuel blend properties, supporting the formulation of alternative fuels optimized for diesel engine performance. These aspects contribute new insights into modelling strategies for sustainable fuel formulations.