Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network

This study investigates the use of ANN (artificial neural networks) modelling to predict BSFC (break specific fuel consumption), exhaust emissions that are CO (carbon monoxide) and HC (unburned hydrocarbon), and AFR (air–fuel ratio) of a spark ignition engine which operates with methanol and gasoline. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. The experimental results reveal that the methanol improved the emission characteristics compared with the gasoline. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, four different learning algorithms were used such as BFGS (Quasi-Newton back propagation), LM (Levenberg–Marquardt learning algorithm). It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.998621, 0.977654, 0.998382 and 0.996075 for the BSFC, CO, HC and AFR for testing data, respectively. It was obvious that the developed ANN model is fairly powerful for predicting the brake specific fuel consumption and exhaust emissions of internal combustion engines.