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

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

Blog Article

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 12V Kettle 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 B-Complex 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.