ARTIFICIAL NEURAL NETWORK OPTIMIZATION MODELING ON ENGINE PERFORMANCE OF DIESEL ENGINE USING BIODIESEL FUEL

This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling. To acquire data for training and testing of the proposed ANN, a four-cylinder, four-stroke diesel engine was fuelled with different palm oil methyl ester blends as biodiesel, operated at different engine loads. The properties of biodiesel produced from waste vegetable oil were measured based on ASTM standards. The experimental results revealed that blends of palm oil methyl ester with diesel fuel provided better engine performance. An ANN model was developed based on the Levenberg-Marquardt algorithm for the engine. Logistic activation was used for mapping between the input and output parameters. It was observed that the ANN model could predict the engine performance quite well with correlation coefficients (R) of 0.996684, 0.999, 0.98964 and 0.998923 for the in-cylinder pressure, heat release, thermal efficiency, and volume, respectively. The predicted MSE (mean square error) error was between the desired outputs, as the measured and simulated values were obtained as 0.0001 by the model. Long-term effects on engine performance when running on biodiesel fuel can be further studied and improved.