Comparison of viscosity prediction capabilities of regression models and artificial neural networks

Nowadays, biodiesel is seen as an alternative fuel to diesel fuel due to its many advantages such as higher density, cetane number and flash point. Although several methods are available for estimating fuel properties of biodiesel-diesel fuel blends, there is still the lack of works on the comparison of regression models and artificial neural networks (ANN) in predicting viscosities of the blends. Therefore, in this work, (1) optimum reaction parameters providing the lowest viscosity were determined for methanolysis of waste cooking oil, (2) waste cooking oil methyl ester was synthesized based on the determined optimum parameters, and it was mixed with diesel fuel on different volume ratios (3) viscosity measurements of the prepared blends were made at the temperature ranges between 273.15 K and 373.15 K, (4) changes in viscosity versus temperature and biodiesel fraction in blend were investigated and the rational model was proposed, finally (5) the predictive capability of rational model was compared to the three-term Vogel model, Bingham model and ANN by fitting to viscosity data measured by the authors and by Geacai et al. According to results, the measured values by the authors and Geacai et al. are the most accurately predicted by the rational model.