Asis Giri

Back propagation artificial neural network (BPANN) based performance analysis of diesel engine using biodiesel

This work deals with the implementation of back propagation based artificial neural network (BPANN) model for prediction of the different diesel engine parameters, i.e., mean effective pressure, mechanical efficiency, fuel consumption, air-fuel ratio, and torque, to overcome the difficulties of practical experimentation and minimization of time and cost in this endeavor. The parameters provided as input to this model were engine speed, load and different biodiesel, and diesel fuel blends. The model has been trained with approximately 85% of the test results, and the rest were kept for prediction. The number of hidden layers and the number of neuron in each layer were varied to achieve the best predicting model. Once the model was ready, it had been used to predict the remaining data where the mean square error was as low as 6.51×10−4 and had a very low total R value. This work hereby shows that the BPANN based model can predict the performance of diesel engine fed with biodiesel and diesel fuel blends.