Dynamic NOx emission prediction based on composite models adapt to different operating conditions of coal-fired utility boilers
2022
Yin, Guihao | Li, Qinwu | Zhao, Zhongyang | Li, Lianmin | Yao, Longchao | Weng, Weiguo | Zheng, Chenghang | Lu, Jiangang | Gao, Xiang
An accurate NOₓ concentration prediction model plays an important role in low NOₓ emission control in power stations. Predicting NOₓ in advance is of great significance in satisfying stringent environmental policies. This study aims to accurately predict the NOₓ emission concentration at the outlet of boilers on different operating conditions to support the DeNOₓ procedure. Through mutual information analysis, suitable features are selected to build models. Long short-term memory (LSTM) models are utilized to predict NOₓ concentration at the boiler’s outlet from selected input features and exhibit power in fitting multivariable coupling, nonlinear, and large time-delay systems. Moreover, a composite LSTM model composed of models on different operating conditions, like steady-state and transient-state condition, is prosed. Results of one whole day of typical operating data show that the accuracy of the NOₓ concentration and fluctuation trend prediction based on this composite model is superior to that using a single LSTM model and other non-time-sequence models. The root mean square error (RMSE) and R² of the composite LSTM model are 3.53 mg/m³ and 0.89, respectively, which are better than those of a single LSTM (i.e., 5.50 mg/m³ and 0.78, respectively).
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