Prediction of viscosity of biodiesel blends using various artificial model and comparison with empirical correlations
Résumé
From the perspective of renewability and environmental pollution, biodiesels are appropriate alternatives to conventional diesel fuels due to their proper combustion behavior and atomization characteristics, which can be influenced by the viscosity as an essential factor. Therefore, the viscosity prediction would be of importance for blend of biodiesel/diesel fuels. For biodiesel/diesel mixtures, the blended viscosity has been predicted with numerous empirical correlations available in the literature. In this work, the viscosity of fuel mixtures was evaluated through Generalized regression neural network (GRNN), Radial Basis Neural Networks (RBFNN), Multi-Layer Perceptron Neural Network (MLPNN), and Cascade Feed-forward Neural Network (CFNN), based on various experimental data. These developed models were then compared in terms of predictive accuracy and available empirical correlations to select the best model. Finally, the proposed model was compared with the most prominent biodiesel viscosity models confirming that the developed model has been superior in predicting the value of viscosity for biodiesel blends with reported values of 0.9997 and 0.87% for parameters of the coefficient of determination (R2) and absolute average relative deviation (AARD%), respectively.