A Dynamic Model Incorporated with Delay Estimation and Variable Selection for Control of SCR De-NOx Process

Catalysts

Jinsong Li¹  Ze Dong²  Ling Li³  Laiqing Yan⁴  Ming Sun⁴ 

[1] Hebei Guohua Dingzhou Power Generation Co., Ltd., Dingzhou 073000, China;Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China;School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;School of Electric Power and Architecture, Shanxi University, Taiyuan 030006, China;

关键词: selective catalytic reduction nitrogen oxide denitration (SCR de-NO_x) process;    dynamic kernel partial least squares (KPLS) model;    variable selection;    delay estimation;    control strategy;   

DOI  :  10.3390/catal10111364

来源: DOAJ

【 摘 要 】

In order to control NH₃ injection for the selective catalytic reduction of nitrogen oxide (NO_x) denitration (SCR de-NO_x) process, a model that can accurately and quickly predict outlet NO_x emissions is required. This paper presents a dynamic kernel partial least squares (KPLS) model incorporated with delay estimation and variable selection for outlet NO_x emission and investigated control strategy for NH₃ injection. First, k-nearest neighbor mutual information (KNN_MI) was used for delay estimation, and the effect of historical data lengths on KNN_MI was taken into account. Bidirectional search based on the change rate of KNN_MI (KNN_MI_CR) was used for variable selection. Delay–time difference update algorithm and feedback correction strategy were proposed. Second, the NH₃ injection compensator (NIC) and the outlet NO_x emission model constituted a correction controller. Then, its output and the output of the existing controller are added up to suitable NH₃ injection. Finally, the KNN_MI_CR method was compared with different algorithms by benchmark dataset. The field data results showed that the KNN_MI_CR method could improve model accuracy for reconstructed samples. The final model can predict outlet NO_x emissions in different operating states accurately. The control result not only meets the NO_x emissions standard (50 mg/m³) but also keeps high de-NO_x efficiency (80%). NH₃ injection and NH₃ escape are reduced by 11% and 39%.

【 授权许可】

Unknown