Nonlinear model predictive control of furnace temperature for a municipal solid waste incineration process

被引：0
作者：
Hu, Kai-Cheng [1 ,2 ]
Yan, Ai-Jun [1 ,2 ,3 ]
Wang, Dian-Hui [4 ,5 ,6 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] Engineering Research Center of Digital Community, Ministry of Education, Beijing
[3] Beijing Laboratory for Urban Mass Transit, Beijing
[4] Artificial Intelligence Research Institute, China University of Mining and Technology, Jiangsu, Xuzhou
[5] State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning, Shenyang
[6] Department of Computer Science and Information Technology, La Trobe University, Melbourne, 3086, VIC
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 11期
基金：
中国国家自然科学基金;
关键词：
furnace temperature; municipal solid waste; nonlinear model predictive control; seagull optimization algorithm; set value evaluation and learning; stochastic configuration network;
D O I：
10.7641/CTA.2023.20397
中图分类号：
学科分类号：
摘要：
To realize the stable control of furnace temperature in a municipal solid waste incineration (MSWI) process, a nonlinear model predictive control (NMPC) method for furnace temperature is proposed in this paper. First, using the grate temperature and primary air temperature as the intermediate variables, a new MSWI furnace temperature control structure is obtained by integrating the cascade control strategy into NMPC. Then, the stochastic configuration network (SCN) is used to establish the furnace temperature static nonlinear prediction model offline, and the output weights of the hidden layer neurons of the SCN are updated online through the recursive least square method, so the furnace temperature dynamic nonlinear prediction model is established. Finally, an improved rolling optimization strategy is obtained by integrating the improved seagull optimization algorithm with the set value evaluation and learning model, which is used to improve the solution accuracy and efficiency of NMPC rolling optimization.The experimental results show that the dynamic nonlinear prediction model of furnace temperature can predict the furnace temperature accurately. The proposed control method has good adaptability and robustness, and can realize the stable control of furnace temperature in the MSWI process. © 2024 South China University of Technology. All rights reserved.
引用
收藏
页码：2023 / 2032
页数：9
相关论文
共 25 条
  • [1] FU Z, ZHANG S H, LI X P, Et al., MSW oxy-enriched incineration technology applied in China: Combustion temperature, flue gas loss and economic considerations, Waste Management, 38, 4, pp. 149-156, (2015)
  • [2] CARRASCO F, LLAURO X, POCH M., A methodological approach to knowledge-based control and its application to a municipal solid waste incineration plant, Combustion Science and Technology, 178, 4, pp. 685-705, (2006)
  • [3] SHEN K, LU J D, LI Z H, Et al., An adaptive fuzzy approach for the incineration temperature control process, Fuel, 84, 9, pp. 1144-1150, (2005)
  • [4] LIU Y C, LIU Y B., Human-simulated intelligent control technique for incineration treatment of municipal solid waste, Information Technology Journal, 12, 23, pp. 7758-7761, (2013)
  • [5] WU Q, XU H., Intelligent control strategy of incineration process pollution in municipal solid waste, Advances in Intelligent Systems and Computing, 433, 6, pp. 311-319, (2016)
  • [6] DAI Wei, CHAI Tianyou, Data-driven optimal operational control of complex grinding processes, Acta Automatica Sinica, 40, 9, pp. 2005-2014, (2014)
  • [7] GAO T Y, LUO H, YIN S, Et al., A recursive modified partial least square aided data-driven predictive control with application to continuous stirred tank heater, Journal of Process Control, 89, 5, pp. 108-118, (2020)
  • [8] CARRASCO R, SANCHEZ E N, RUIZ-CRUZ R, Et al., Neural control for a solid waste incinerator, Proceedings of the International Joint Conference on Neural Networks, 9, pp. 3289-3294, (2014)
  • [9] XI Yugeng, LI Dewei, LIN Shu, Model predictive control-status and challenges, Acta Automatica Sinica, 39, 3, pp. 222-236, (2013)
  • [10] WANG Haokun, XU Zuhua, ZHAO Jun, Et al., A survey on offset-free model predictive control, Acta Automatica Sinica, 46, 5, pp. 858-877, (2020)
123