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The diesel premixed low-temperature combustion mode avoids the generation of thick mixture and the high temperature region in which a great amount of NOx and PM generates. It makes a significant reduction in the emissions of both NOx and PM available at the same time. However, with the quantity of pre-injection increases and the injection time advances, the emission of HC increases significantly, which causes a decrease in the combustion efficiency. Studies have shown that the flame quench caused by too thick or too lean mixture and the oil film on the chamber is the main source for the emission of HC. As a result, understanding the mechanism of atomization and evaporation of the fuel and the formation of the mixture makes significant sense. This paper focuses on the mixture formation process. And the methods of testing the distribution of the mixture, the influential factors and control methods are studied.

In order to meet increasingly stringent emission regulations, it is significance to establish a control- oriented transient NOx and PM emission prediction model and improve the accuracy and real-time performance. In this study, the prediction model of transient PM emissions based on Transformer is established. In terms of model accuracy and real-time performance, Transformer emission prediction model is compared with Multilayer perceptron (MLP) neural network and Long-Short Term Memory (LSTM) emission prediction model. The results show that the performance of Transformer transient emission prediction model is superior to other model structures, it can be used for real-time prediction.