Predicting the different engine parameters of a rubber seed oil-ethanol dual fuel engine using artificial neural networks

International audience ; The present study investigates the potential of artificial neural network for predicting the performance and emission characteristics of a compression ignition (CI) engine. A number of experiments are performed using diesel, rubber seed oil (RSO) and its methyl ester (RSOME) as the primary fuel, and ethanol as the secondary fuel in a dual fuel engine. The experimental data obtained is used for training and testing the neural network. The predictions are performed using feed forward-back propagation training algorithm. Engine load and ethanol energy share is used as network input parameters, and brake thermal efficiency (BTE), brake specific energy consumption (BSEC), NOx, HC, CO and smoke are the predicted parameters. The prediction performance of the network is measured by comparing it with experimental data. The measurement of statistical error shows that ANN can predict BTE, BSEC, NOx, HC, CO and smoke for a dual fuel engine with high accuracy.