There is a delay in NO_x measurement for flexible operations in coal-fired power plants

which leads to a delayed response in ammonia injection control system of selective catalytic reduction(SCR) reactor

resulting in potential over or under-injection of ammonia and significant fluctuations in NO_x mass concentration at outlet of the SCR reactor. To enable proactive adjustment of ammonia injection and considering the interconnected factors influencing the NO_x emissions from coal combustion

a prediction model for NO_x mass concentration at the SCR reactor inlet is proposed based on convolutional neural networks(CNNs) and long short-term memory neural(LSTM) networks. By using operational parameters from a 330 MW coal-fired power plant

a Pearson coefficient method is employed to calculate the correlation between feature variables. Significant features are extracted to define the model input matrix and output matrix. The random search algorithm is used for hyper-parameters optimization to enhance predictive performance. The SHAP algorithm is then applied to interpret the model structure and explain the black-box model. Finally

the control effects of model with NO_x concentration prediction is verified through Simulink simulation. The results indicate that

the CNN-LSTM prediction model demonstrates higher predictive accuracy for the variable NO_x mass concentration at the SCR reactor inlet during the frequent load fluctuations. It can provide feedback to the ammonia injection control system of 25 seconds in advance. The optimized ammonia injection control strategy not only reduces the standard deviation between the NO_x mass concentration at the SCR reactor outlet and the set value by 28%

but also improves the response speed of NH3/NO_x regulation

reducing the maximum ammonia slip by 22%. The research findings can provide guidance for intelligent SCR denitration system and combustion optimizing operating during flexible operation of coal-fired power plants.