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Prediction of engine performance and exhaust emissions with different proportions of ethanol–gasoline blends using artificial neural networks

ABSTRACT The main purpose of this study is to experimentally investigate the use of ANNs (artificial neural networks) modelling to predict engine power, torque and exhaust emissions of a spark ignition engine which operates with gasoline and methanol blends. For the ANN modelling, the standard back-propagation algorithm was found to be the optimal choice for training the model. Afterwards, the performance of the ANN predictions was evaluated with the experimental results by comparing the predictions. Fuel type and engine speed have been used as the input layer, while engine torque, power, exhaust emissions, Tex and BSFC have also been used separately as the output layer. It was found that the ANN model is able to predict the engine performance, exhaust emissions, Tex and BSFC with a correlation coefficient of 0.9991887425, 0.9990868573, 0.9986749623, 0.9988624137, 0.9976761492, 0.9992943894 and 0.9978899033 for the Power, Torque, CO, CO2, HC, Tex and BSFC for testing data, respectively.