Artificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engine

Abstract

ABSTRACTThe present paper investigates the prediction of vibration, noise level, and emission characteristics of a four-stroke, four-cylinder diesel engine fueled with sunflower, canola, and corn biodiesel blends while H2 injected through inlet manifold using two different artificial intelligence methods: artificial neural network (ANN) and support vector machines (SVM). The aim of using these methods is to predict vibration, noise, carbon monoxide (CO), CO2, and NOx based on the initial experimental study by varying engine speed, blends of biodiesel, and H2 energy substitution ratio. Experimental data were gathered from the literature. For the ANN method, LevenbergMarquardt backpropagation training algorithm with logarithmic sigmoid and linear transfer function for hidden and output layers, respectively, gives the best results for prediction of vibration, noise, and emission characteristics. For SVM, a regression model is implemented with Gaussian kernel function. Results show that the ANN performs better than SVM, and the best mean average percent error and R2 for the models developed are 2.03 and 0.988 for vibration acceleration, 0.39 and 0.9615 for noise, 7.27 and 0.8549 for CO, 5.09 and 0.9398 for NOx, and 2.21 and 0.993 for CO2 values, respectively. Eventually, it is found that the ANN method is a good choice for simulation and prediction of dual fueled hydrogen sunflower, canola, and corn biodiesel blends.