JACIII Vol.28 No.3 pp. 644-654
doi: 10.20965/jaciii.2024.p0644

Research Paper:

Intelligent Control of Pre-Chamber Pressure Based on Working Condition Identification for the Coke Dry Quenching Process

Yi Ren*,**,*** ORCID Icon, Xuzhi Lai*,**,***,† ORCID Icon, Jie Hu*,**,*** ORCID Icon, Sheng Du*,**,*** ORCID Icon, Luefeng Chen*,**,*** ORCID Icon, and Min Wu*,**,*** ORCID Icon

*School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

***Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
No.388 Lumo Road, Hongshan District, Wuhan 430074, China

Corresponding author

November 30, 2023
February 8, 2024
May 20, 2024
coke dry quenching, pressure control, working condition identification, fuzzy proportional-integral-derivative controller, expert controller

The pre-chamber pressure is an important control parameter that affects the coke dry quenching process. It often fluctuates violently and is detrimental for the safe operation of the coke dry quenching process. This study proposes an intelligent control method for the pre-chamber pressure based on working condition identification for the coke dry quenching process to realize stable control of the pre-chamber pressure. First, by describing the coke dry quenching process and analyzing the factors affecting the pre-chamber pressure, an intelligent control strategy was developed. Then, the K-means clustering algorithm was used to identify the working conditions of pre-chamber, and the working conditions were divided into two categories: stable and fluctuating. For stable conditions, a fuzzy proportional-integral-derivative controller was designed to improve the pressure control accuracy. For fluctuating conditions, an expert controller was designed to rapidly adjust the pressure. Finally, experiments based on actual data were performed and the results showed that the proposed method can effectively improve the control accuracy of pressure under different conditions. This satisfies the requirements for a continuous coke dry quenching process.

Pre-chamber pressure control scheme

Pre-chamber pressure control scheme

Cite this article as:
Y. Ren, X. Lai, J. Hu, S. Du, L. Chen, and M. Wu, “Intelligent Control of Pre-Chamber Pressure Based on Working Condition Identification for the Coke Dry Quenching Process,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 644-654, 2024.
Data files:
  1. [1] H. Zameer, Y. Wang, D. G. Vasbieva, and Q. Abbas, “Exploring a pathway to carbon neutrality via reinforcing environmental performance through green process innovation, environmental orientation and green competitive advantage,” J. of Environmental Management, Vol.296, Article No.113383, 2021.
  2. [2] Q. Zhang, W. Zhang, Y. J. Wang, J. Xu, and X. C. Cao, “Potential of energy saving and emission reduction and energy efficiency improvement of China’s steel industry,” Iron & Steel, Vol.54, No.2, pp. 7-14, 2019.
  3. [3] Z. W. Du, “Energy saving, emission reduction and environmental protection analysis of CDQ technology,” Metallurgy and Materials, Vol.41, No.2, pp. 77-78, 2021.
  4. [4] C. Chen, M. Wu, L. F. Chen, W. Zhang, and S. Du, “Flatness prediction method based on operating mode recognition for roller quenching process,” Control Theory & Applications, Vol.38, No.9, pp. 1407-1413, 2021.
  5. [5] S. Du, M. Wu, L. F. Chen, W. H. Cao, and W. Pedrycz, “Operating mode recognition of iron ore sintering process based on the clustering of time series data,” Control Engineering Practice, Vol.96, Article No.104297, 2020.
  6. [6] Y. Feng, M. Wu, L. F. Chen, X. Chen, W. H. Cao, S. Du, and W. Pedrycz, “Hybrid intelligent control based on condition identification for combustion process in heating furnace of compact strip production,” IEEE Trans. on Industrial Electronics, Vol.69, No.3, pp. 2790-2800, 2022.
  7. [7] F. Q. Ning, H. L. Yu, S. Z. Lu, and P. R. Zhao, “Study on Recognition of Thermal Efficiency Operating Conditions of Cement Rotary Kiln Based on K-means,” Proc. of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conf. (ITNEC), 2019.
  8. [8] X. Y. Zhu, H. B. Zhang, Q. Ren, D. L. Zhang, F. X. Zeng, X. J. Zhu, and L. Y. Zhang, “An automatic identification method of imbalanced lithology based on deep forest and K-means SMOTE,” Geoenergy Science and Engineering, Vol.224, Article No.211595, 2023.
  9. [9] S. Wu, P. Z. Hou, and H. B. Zou, “An improved constrained predictive functional control for industrial processes: A chamber pressure process study,” Measurement and Control, Vol.53, No.5, pp. 833-840, 2020.
  10. [10] Q. F. Chen, “Development and industrial application of a fuzzy PID based pressure control system for blast furnace tops,” Changsha: Central South University, 2010.
  11. [11] Y. H. Zhang, M. Hu, and Z. Liu, “Research and application of combustion air pressure control in reheating furnace,” Metallurgical Industry Automation, Vol.44, No.1, pp. 74-76, 2020.
  12. [12] A. J. Wiid, J. D. Roux, and I. K. Craig, “Pressure buffering control to reduce pollution and improve flow stability in industrial gas headers,” Control Engineering Practice, Vol.115, Article No.104904, 2021.
  13. [13] C. H. Yang, M. Wu, D. Y. Shen, and G. Deconinck, “Hybrid intelligent control of gas collectors of coke ovens,” Control Engineering Practice, Vol.9, No.7, pp. 725-733, 2001.
  14. [14] J. Wen, D. L. Chen, and Y. Xue, “Intelligent control system based on RBF neural network for CDQ pre-chamber pressure,” Metallurgical Power, Vol.6, pp. 69-71, 2019.
  15. [15] P. Wang, “Application of intelligent control system for CDQ,” China Metrology Association Metallurgical Branch 2015 Annual Conf. Proc. Chongqing, pp. 177-180, 2015.
  16. [16] J. M. Zhang, “Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace,” ISA Trans., Vol.67, pp. 208-214, 2017.
  17. [17] J. L. Tao, Z. H. Yu, and Y. Zhu, “PFC based PID design using genetic algorithm for chamber pressure in a coke furnace,” Chemometrics and Intelligent Laboratory Systems, Vol.137, pp. 155-161, 2014.
  18. [18] Y. Zhang, “Design of CDQ control system,” Anshan: University of Science and Technology Liaoning, 2018.
  19. [19] X. L. Li, D. X. Liu, C. Jia, and X. Z. Chen, “Multi-model control of blast furnace burden surface based on fuzzy SVM,” Neurocomputing, Vol.148, pp. 209-215, 2015.
  20. [20] X. Chen, X. X. Chen, M. Wu, and J. H. She, “Modeling and optimization method featuring multiple operating modes for improving carbon efficiency of iron ore sintering process,” Control Engineering Practice, Vol.54, pp. 117-128, 2016.
  21. [21] A. Urbas, J. Klosinski, and K. Augustynek, “The influence of the PID controller settings on the motion of a truck-mounted crane with a flexible boom and friction in joints,” Control Engineering Practice, Vol.103, Article No.104610, 2020.
  22. [22] S. Du, M. Wu, L. F. Chen, K. L. Zhou, J. Hu, W. H. Cao, and W. Pedrycz, “A fuzzy control strategy of burn-through point based on the feature extraction of time-series trend for iron ore sintering process,” IEEE Trans. on Industrial Informatics, Vol.16, No.4, pp. 2357-2368, 2020.
  23. [23] J. Q. An, J. Y. Yang, M. Wu, J. H. She, and T. Terano, “Decoupling control method with fuzzy theory for top pressure of blast furnace,” IEEE Trans. on Control Systems Technology, Vol.27, No.6, pp. 2735-2742, 2019.
  24. [24] Y. Feng, M. Wu, X. Chen, L. F. Chen, and S. Du, “A fuzzy PID controller with nonlinear compensation term for mold level of continuous casting process,” Information Sciences, Vol.539, pp. 487-503, 2020.
  25. [25] H. K. Yun, X. Y. Yang, and H. Y. Yin, “Application of intelligent control engineering in metallurgical production machinery,” China Steel Focus, Vol.539, pp. 114-116, 2019.
  26. [26] A. B. Belle, T. C. Lethbridge, M. Garzon, and O. O. Adesina, “Design and implementation of distributed expert systems: On a control strategy to manage the execution flow of rule activation,” Expert Systems with Applications, Vol.96, pp. 129-148, 2018.

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Last updated on Jun. 03, 2024