Artificial neural network models to predict density, dynamic viscosity, and cetane number of biodiesel

• We develop ANN models to predict the density, dynamic viscosity, and cetane number of biodiesel.
• Temperature, composition of methyl esters, number of carbon atoms, and number of hydrogen atoms were used as input variables in models.
• We identify the most influential variables for each case.
• Unsaturated methyl esters have the greatest influence on the prediction of density.
• Methyl esters of higher molecular weight and temperature have the greatest influence on the prediction of dynamic viscosity.

Biodiesel is considered as an alternative source of energy obtained from renewable materials. This paper presents models based on artificial neural networks (ANNs) to predict the density, dynamic viscosity, and cetane number of methyl esters and biodiesel. An experimental database was used for the developing of models, where the input variables in the network were the temperature, number of carbon atoms and hydrogen atoms, as well as the composition of methyl esters. The learning task was done through hyperbolic and linear functions, while the Levenberg–Marquardt algorithm was used for the optimization process. Correlation coefficients of 0.91946–0.99401 were obtained by comparing the experimental and calculated values, while a mean squared error (MSE) of 1.842 × 10−3 was obtained in the validation stage. All models met the slope-intercept test with a confidence level of 99%. The ANN models developed here can be attractive for their incorporation in simulators.