Artificial neural network (ANN) assisted prediction of transient NO_x emissions from a high-speed direct injection (HSDI) diesel engine

The understanding and prediction of NOx emissions formation mechanisms during engine transients are critical to the monitoring of real driving emissions. While many studies focus on the engine out NOx formation and treatment, few studies consider cyclic transient NOx emissions due to the low time resolution of conventional emission analysers. Increased computational power and substantial quantities of accessible engine testing data have made ANN a suitable tool for the prediction of transient NOx emissions. In this study, the transient predictive ability of artificial neural networks where a large number of engine testing data are available has been studied extensively. Significantly, the proposed transient model is trained from steady-state engine testing data. The trained data with 14 input features are provided with transient signals which are available from most engine testing facilities. With the help of a state-of-art high-speed NOx analyser, the predicted transient NOx emissions are compared with crank-angle resolved NOx measurements taken from a high-speed light duty diesel engine at test conditions both with and without EGR. The results show that the ANN model is capable of predicting transient NOx emissions without training from crank-angle resolved data. Significant differences are captured between the predicted transient and the slow-response NOx emissions (which are consistent with the cycle-resolved transient emissions measurements). A particular strength is found for increasing load steps where the instantaneous NOx emissions predicted by the ANN model are well matched to the fast-NOx analyser measurements. The results of this work indicate that ANN modelling could strongly contribute to the understanding of real driving emissions.