The optimization control of the selective catalytic reduction (SCR) denitrification system holds significant importance in the production of the power generation industry. This paper presents an active disturbance rejection stair-like generalized predictive control with time delay weakening (WADRC-SGPC) to enhance the control performance in SCR denitrification system. Compared with classical generalized predictive control (GPC), active disturbance rejection generalized predictive control (ADRC-GPC) combines the advantages of ADRC, uses Extended State Observer (ESO) to compensate the uncertainty of the system, and regards the compensated system as a series integrator. Therefore, it has the advantages of no system identification and online calculation of Diophantine equation. In addition, a time delay weakening link is incorporated for the known part of the system model, which effectively improves the performance of the controller. The parameter selection issue of the weight function in time delay weakening is addressed using a hybrid strategy whale optimization algorithm. Finally, the superior performance of the proposed control strategy in terms of set-point tracking and disturbance rejection is verified through simulation experiments.
AhmedFChoHJKimJK, et al. (2015) A real-time model based on least squares support vector machines and output bias update for the prediction of NO x emission from coal-fired power plant. Korean Journal of Chemical Engineering32: 1029–1036.
2.
ChenZCuiJLeiZ, et al. (2020) Design of an improved implicit generalized predictive controller for temperature control systems. IEEE Access8: 13924–13936.
ClarkeDWMohtadiCTuffsPS (1987a) Generalized predictive control—Part I. The basic algorithm. Automatica23(2): 137–148.
5.
ClarkeDWMohtadiCTuffsPS (1987b) Generalized predictive control—Part II extensions and interpretations. Automatica23(2): 149–160.
6.
FuCTanW (2020) Linear active disturbance rejection control for processes with time delays: IMC interpretation. IEEE Access8: 16606–16617.
7.
GaoZ (2006) Active disturbance rejection control: A paradigm shift in feedback control system design. In: 2006 American control conference, Minneapolis, MN, 14–16 June.
8.
GuoWChenXQiuX (2008) Application of improved PID model algorithmic control algorithm. In: 2008 international conference on intelligent computation technology and automation (ICICTA), Changsha, China, 20–22 October, pp. 309–312. New York: IEEE.
9.
HanJQ (2002) From PID technique to active disturbances rejection control technique. Control Engineering of China9(3): 13–18.
10.
HoggBWEl-RabaieNM (1991) Multivariable generalized predictive control of a boiler system. IEEE Transactions on Energy Conversion6(2): 282–288.
11.
HouGGongLHuangC, et al. (2019) Novel fuzzy modeling and energy-saving predictive control of coordinated control system in 1000 MW ultra-supercritical unit. ISA Transactions86: 48–61.
12.
HuangYZhangWJinX (2018) Linear active disturbance rejection cascade control of SCR denitrification system. Proceedings of the CSEE38(18): 5518–5526.
13.
KaurGAroraS (2018) Chaotic whale optimization algorithm. Journal of Computational Design and Engineering5(3): 275–284.
14.
LiGHuSWuBH (2014) Fuzzy self-adaptive Smith cascade control system for spraying ammonia in SCR reactors. Thermal Power Generation43(8): 147–150.
15.
LiJGuoH (2021) A hybrid whale optimization algorithm for plane block parallel blocking flowline scheduling optimization with deterioration effect in lean shipbuilding. IEEE Access9: 131893– 131905.
16.
LiXYGaoZTianSP, et al. (2024) Time delay weakening and active disturbance rejection control for uncertain systems with large time delay. Control Theory and Applications41(2): 249–260.
17.
LiuT (2016) Analysis on measurement and control strategy for denitration system of thermal power generating units and its optimization. Zhejiang Electric Power35: 42–44.
18.
LiuYWanMXiaoQB, et al. (2021) Combined predictive and feedback contour error control with dynamic contour error estimation for industrial five-axis machine tools. IEEE Transactions on Industrial Electronics69(7): 6668–6677.
19.
LuZLiWLiaoY (2021) Influential factors-based optimization simulation of ammonia injection in SCR DeNOx system. Zhongguo Dianji Gongcheng Xuebao41: 4923–4930.
20.
MengXBGaoXZLuL, et al. (2016) A new bio-inspired optimisation algorithm: Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence28(4): 673–687.
21.
MirjaliliSLewisA (2016) The whale optimization algorithm. Advances in Engineering Software95: 51–67.
22.
QiuXFXueMSSunDM, et al. (2000) The stair-like generalized predictive control for main-steam pressure of boiler in steam-power plant. In: Proceedings of the 3rd world congress on intelligent control and automation, Hefei, China, 26 June–2 July, pp. 3165–3167. New York: IEEE.
23.
RenJChenZSunM, et al. (2020) Design and implementation of the PI-type active disturbance rejection generalized predictive control. In: 2020 IEEE 9th data driven control and learning systems conference (DDCLS), Liuzhou, China, 20–22 November, pp. 12–17. New York: IEEE.
24.
TanCChenZShaoY, et al. (2023) Research on multi-model predictive control of denitrification system of CFB unit based on feedforward modification. Proceedings of the Chinese Society for Electrical Engineering43: 1867–1874.
25.
WuXChenZZhaoY, et al. (2019) A comprehensive decoupling control strategy for a gas flow facility based on active disturbance rejection generalized predictive control. The Canadian Journal of Chemical Engineering97(3): 762–776.
26.
YangXS (2009) Firefly algorithms for multimodal optimization. In: 5th International Symposium on Stochastic Algorithms—Foundations and Applications, Hokkaido University, Sapporo, Japan, 26–28 October, pp. 169–178.
27.
YuLFeiSZhuH (2011) Design and application of switching control platform for a power plant’s superheated steam temperature system. In: Proceedings of the 30th Chinese control conference, Yantai, People’s Republic China, 22–24 July, pp. 3323–3326.
28.
YuSFengYYangX (2021) Extended state observer–based fractional order sliding-mode control of piezoelectric actuators. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering235(1): 39–51.
29.
ZhangCLiMZhouL, et al. (2022) A variable self-tuning horizon mechanism for generalized dynamic predictive control on DC/DC boost converters feeding CPLs. IEEE Journal of Emerging and Selected Topics in Power Electronics11(2): 1650–1660.
30.
ZhangJZhangQLvJ, et al. (2016) Reheated steam temperature control based on the γ incremental SGPC-PID. In: 2016 12th world congress on intelligent control and automation (WCICA), Guilin, China, 12–15 June, pp. 682–685. New York: IEEE.
31.
ZhangMXuYWilliamsBL, et al. (2021) Catalytic materials for direct synthesis of dimethyl carbonate (DMC) from CO2. Journal of Cleaner Production279: 123344.
