Research on input parameter optimization for NOx deep learning prediction model

Abstract

Optimizing the input parameters for NOx prediction model is instrumental in enhancing the precision and efficacy of the prediction model. This paper focuses on the input variables of the data-driven diesel engine NOx prediction model. According to the diesel engine NOx generation mechanism, at first relevant variables are selected, then the similarity method and parameter sensitivity method are compared to optimize the input variables. Firstly, this paper conducted a real-road emission test of a heavy-duty diesel vehicle, and proposed a data-driven deep learning model for predicting diesel engine NOx emissions based on the experimental data. The experimental data were processed using an Attention-Mechanism based Convolutional Neural Networks-Long Short-Term Memory (AM-CNN-LSTM) model. Subsequently, input parameters were selected according to the sensitivity weights assigned to each parameter in the preliminary model. This approach refined the modeling data and excluded variables not pertinent to NOx emission prediction. The AM-CNN-LSTM model prediction performance has been improved with 2% increase of model R² as well as the model training time is reduced by 23%, and the types of parameters required to train the model are reduced by 50%. It accomplished this by effectively reducing data dimensions, discarding non-essential variables for NOx prediction, and diminishing computational complexity. Six input parameters were identified as pivotal in reconstructing the modeling data for accurate NOx prediction: engine speed, torque, EGR valve position, exhaust flow rate, air mass flow rate, and oil temperature. In optimizing the NOx prediction model, it is crucial to determine an optimal number of input parameters. Excessive parameters do not enhance model accuracy, while an insufficient number impairs the model’s ability to precisely emulate the NOx generation process, leading to diminished predictive accuracy.