Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks

Abstract

The experiment about gasoline fuel plus oxygen-enriched combustion (OEC) technology is conducted on a combined injection engine in this paper. OEC is an effective in-cylinder clean combustion technology, but it will lead to extremely high NOx emissions and even cause the NOx detector failure at high oxygen ratios. The particulate number (PN) emissions on OEC are emphatically analyzed in this paper, which are not covered in previous studies. The results show that with the increase of oxygen ratio, PN emissions first increase and then decrease. After analyzing the experimental results, convenient and efficient artificial neural network (ANN) models are developed to predict the torque output, HC, CO, NOx, and PN emissions based on Levenberg-Marquardt, Bayesian regularization and Scaled conjugate gradient algorithms. The models have good fitting and prediction performance with correlation coefficient values > 0.99 and mean square error values less than 1E-3, which effectively predict the invalid NOx emissions data. The predicted results show that the ANN model can assist the bench test study as an effective tool for predicting the emissions and power performance of gasoline engines with OEC technology.