Anticipatory NH3 injection control for SCR system based on the prediction of the inlet NOx concentration. (February 2021)

• Record Type: Journal Article
• Authors: Liu, Guofu; Zhang, Yu; Shen, Dekui; Yuan, Bing; Li, Rui; Sun, Yu
• Is Part Of: Journal of the Energy Institute. Volume 94(2021)
• Page Start: 167
• Page End: 175
• Publication Date: 2021-02
• Subjects: Ammonia injection -- SCR system -- Anticipatory control -- Feed-forward; Power (Mechanics) -- Periodicals; Power resources -- Periodicals; Fuel -- Periodicals; 621.04205
• DOI: 10.1016/j.joei.2020.07.002
• Languages: English
• ISSNs: 1743-9671

Abstract

Abstract: The anticipatory NH3 injection control strategy with a prediction-assisted feed-forward was proposed to improve the accuracy of the outlet NOx control. The large lag of SCR system was ascribed to the lag duration by flue gas sampling for inlet NOx analysis. A 660 MW power plant equipped tangentially fired pulverized coal boiler was involved in the process of theoretical analysis and engineering application. 22 key running parameters of the studied power plant were determined to influence the inlet NOx concentration of SCR system substantially through the multiple linear regression (MLR) method. Multiple input single output (MISO) model was adopted to predict the non-lag inlet NOx concentration based on a multi layer perception (MLP) model with three hidden layers. An optimization strategy for MISO model application was proposed to improve the prediction accuracy of inlet NOx concentration. The root mean square error (RMSE) was decreased to be 5.52 mg·Nm −3 and the mean absolute percentage error (MAPE) was only 1.12%. The maximum absolute deviation was also decreased by 41.28% compared with the deviation in the direct application case. The average constant control deviation (CCD) of the outlet NOx concentration was calculated to be 2.27 mg·Nm −3 in the case analysis of engineering application. The technology can be developed as a potential strategy for improving the NH3 injection performance of SCR system. Graphical abstract: Image 1 Highlights: Key parameters to predict inlet NOx were obtained via multi-linear regression. The multi layer perception was adopted for the prediction of the inlet NOx . The predictive result was indirectly adopted for higher prediction accuracy. Accuracy of outlet NOx control was notably improved via inlet NOx prediction.