Predicting the methane number of gaseous fuels using an artificial neural network

Abstract

Methane number (MN) is a critical gas quality parameter for gaseous-fueled engines. It is a measure of knock resistance for gaseous fuels, as is the octane number for gasoline. Therefore, a priori knowledge of the MN of gaseous fuel is important to avoid any structural damage to the engine due to knock. In the present study, a model was developed to predict the MN of gaseous fuels using an artificial neural network (ANN). The model utilized measured MNs of 1202 different gaseous fuel compositions, out of which 90% of the data (randomly) was used to train the ANN model using the Levenberg–Marquardt algorithm. In order to obtain the best performance, the number of neurons in the hidden layer and the transfer function of the hidden and output layers were changed. The ANN model incorporating hyperbolic tangent sigmoid function in the hidden layer with 53 neurons, and linear function in the output layer, showed the best performance – with mean square error and correlation coefficient of 0.055 and 1, respectively. The MNs of the remaining 10% of the data were determined using the ANN model, and were compared with those determined by the AVL (Anstalt für Verbrennungskraftmaschinen List) model. The model was able to predict MN accurately (R = 0.999).