Modeling and Prediction of NOx Emission of a Coal-Fired Boiler by a Learning-Based KNN Mechanism

Abstract

The production of nitrogen oxides (NOx) in coal-fired boiler combustion has been found as a significant source of environmental pollution. Flue gas denitrification is a standard NOx control technology for small- and medium-sized coal-fired boilers. Achieving steady-state control in flue gas denitrification can be challenging since coal-fired boiler systems have complexity and significant delay. A model based on a learning-based K-nearest neighbor (KNN) query mechanism created for NOx output soft prediction is proposed in this study. First, a knowledge base in the proposed model is established through spatial division in accordance with the previous combustion parameters. Moreover, the clusters are established based on the output NOx values. Next, the domain of values of combustion parameters for the respective cluster is obtained. Second, the optimal cluster is selected using the knowledge base for an input vector q with new combustion parameters ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Lastly, the K tuples in the cluster the closest to the values of the input vector q are adopted to predict the output NOx value of q. The predicted NOx value can serve as a feedforward signal to control the output of the reductant for accurate denitrification. As revealed by the experimental results, the proposed practical model, capable of conducting the prediction in a sub-second time, is highly competitive with existing techniques. Furthermore, a deep learning algorithm (DLA) is designed, whereas it underperforms the KNN model.