Prediction of transient NOX emission from a non-road diesel engine using a model combining Bayesian search and Population-based training

Abstract

Due to the rising environmental concerns, particularly air quality, the emission regulations for non-road mobile machinery are becoming increasingly strict. Real-time emission prediction from diesel engines is significant for emission control and regional pollution estimation. This study aims to develop a machine learning model and optimize its hyperparameters by using a hyperparameter optimization method to NOX emission. Firstly, we collected NOX emission data from test under the non-road transient test cycle (NRTC) and built a significant dataset to choose a best model. Then, the model was trained by dataset and the hyperparameters were automatically optimized by combining Bayesian and Population based training. The accuracy of the optimized was indicated by an R2 value of 0.9784 with the 8 input features. The relative error in the cycle level was 1.3%. Lastly, the quality of NOX emissions during the cycle and the effect of each parameter on NOX emissions were analyzed. The results show that the model is able to predict the real-time concentration changes of NOX more accurately. It can provide a reference for the research and development of emission control technology for non-road mobile machinery.