AN ARTIFICIAL NEURAL NETWORK APPROACH TO ESTIMATE THE VISCOSITY OF BIODIESEL-DIESEL-ETHANOL BLENDS
In order to estimate the viscosity of biodiesel-diesel fuel-bioethanol blends viscosity data of 15 ternary blends - measured between 273.15 K and 343.15 K - were used. Two artificial neural networks, with one and two neurons, were built using the temperatures and the compositions of the blends as input and the corresponding viscosities as output. The neural networks had been trained using 70% of the data and were tested and validated, using 15% of remained data for each of these actions. The precisions of the resulted models were compared with the precision of a simple additive and a semilogarithmic model developed in our earlier work. We found that the artificial neural network with two neurons had the strongest correlation (R2 = 0.9982) and the lowest standard deviation (RMSE = 0.0968).
Key words: viscosity, model, artificial neural network, biodiesel, bioethanol, diesel fuel.
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