A machine learning NOxemission model for SCR system considering mechanism knowledge and catalyst deactivation

Abstract

In this work, an adaptive NOx emission model is proposed for a SCR system of a 660 MW utility boiler. First, 3-years operating data was collected from the plant SIS system as raw data, which was then filtered using the R-statistic method and clustered by the condensed nearest neighbor (CNN) rule to form a classified steady-state database. In addition, a sliding window approach was used to deal with the continuous data stream. As the newest steady state sample was introduced into the database, the most similar old sample in the same data class was replaced. The crowding distance (CD) operator was also used to eliminate the redundant samples. This new method RCNN-CD is proven to be a good tool to improve the representatives of the samples. Based on the selected samples, a fusion monotony support vector regression (FM-SVR) was used to establish the NOx emission model. The results show that, this model can reasonably reflect SCR mechanism and follow the degradation of SCR performance.