Attention-GRU Based Intelligent Prediction of NOx Emissions for the Thermal Power Plants.

The establishment of the NO_x concentration prediction model is a prerequisite for the selective catalytic reduction (SCR) flue gas denitriflcation systems in thermal power plants to overcome measurement delays in continuous emission monitoring systems and achieve precise control. This paper proposes a NO_x emission prediction method for thermal power plants based on the attention mechanism and the gated recurrent unit (Attention-GRU) to address the issues of low accuracy and cumbersome feature selection process in NO_x emission prediction models established for the SCR system under complex working conditions. Firstly, this article utilizes the GRU to extract data features of NO_x emissions from thermal power plants at the time scale, in order to establish a long-term prediction model. Secondly, the feature dimension clustering the k-means algorithm can effectively improve the learning efficiency of temporal features and reduce the computational complexity of subsequent algorithms. Finally, the parameter attention mechanism is introduced to autonomously select favorable temporal features for predicting NO_x emissions in thermal power plants, replacing the complex and time-consuming data screening process. This paper has also collected historical datasets of the SCR system form a thermal power plant under complex and simple working conditions to verify the effectiveness of Attention-GRU. The experimental results show that compared with existing methods (see, e.g., ELM, RF, SVM, BPNN, LSTM, GRU, BiLSTM, and CNN-LSTM), our method has higher prediction accuracy of NO_x emissions from thermal power plants, which helps to improve the control performance of SCR systems to reduce atmospheric pollution. At the same time, this article has also found that the attention mechanism can significantly reduce the dependence of existing methods on the feature selection process, effectively solve the interference problem of noise data on model feature extraction, and further improve the prediction accuracy of NO_x emissions in thermal power plants. [ABSTRACT FROM AUTHOR]