Abstract：Aiming at the problem that a single prediction model for SCR inlet NOx concentration could not maintain prediction accuracy under different operating conditions, a dynamic model for predicting SCR inlet NOx concentration based on weighted fusion of Dung Beetle Optimizer (DBO) ensemble model was proposed. Firstly, a hybrid model of CatBoost and LightGBM was used to filter auxiliary variables while obtaining the delay time and order information of the auxiliary variables, and the input variables of the prediction model were determined based on the above information. Then, an integrated model consisting of LightGBM,XGBoost, and CatBoost was established, and the prediction results were weighted and fused using the dung beetle optimization algorithm. Finally, the DBO integrated weighted fusion dynamic prediction model was compared with three single models and the two weighted fusion prediction models optimized by the dung beetle algorithm. The evaluation indicators of the DBO integrated weighted fusion dynamic prediction model were superior to other models, with higher prediction accuracy, real-time performance, and adaptability, which could better meet the requirements of NOx concentration prediction under different working conditions.
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2024, Vol. 45 
