single-jc.php

JACIII Vol.27 No.4 pp. 638-644
doi: 10.20965/jaciii.2023.p0638
(2023)

Research Paper:

Cloud-Edge Cooperative Control System in Continuous Annealing Processes

Wenshuo Song*,** ORCID Icon, Weihua Cao*,**,***,† ORCID Icon, Wenkai Hu*,**,*** 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

Received:
November 23, 2022
Accepted:
March 26, 2023
Published:
July 20, 2023
Keywords:
annealing furnace, control system, cloud-edge collaboration
Abstract

This study proposes a cloud-edge collaboration framework for temperature regulation in continuous annealing processes. A multiobjective optimization is formulated by ensuring the control accuracy of the temperature to reduce energy consumption and increase efficiency with cloud computing. Based on process analytics, a framework for clustering operating conditions with high real-time requirements is proposed. Further, a recommendation mechanism for furnace temperatures with low real-time requirements is developed in the cloud. Compared with traditional architectures, the cloud-edge collaboration approach improves energy savings and control stability, which demonstrates its effectiveness and practicality.

Cite this article as:
W. Song, W. Cao, W. Hu, and M. Wu, “Cloud-Edge Cooperative Control System in Continuous Annealing Processes,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 638-644, 2023.
Data files:
References
  1. [1] J.-L. Ding, C.-E. Yang, Y.-D. Chen, and T.-Y. Chai, “Research Progress and Prospects of Intelligent Optimization Decision Making in Complex Industrial Process,” Acta Autom. Sin., Vol.44, No.11, pp. 1931-1943, 2018. https://doi.org/10.16383/j.aas.2018.c180550
  2. [2] S. Strommer, M. Niederer, A. Steinboeck, and A. Kugi, “Hierarchical nonlinear optimization-based controller of a continuous strip annealing furnace,” Contro. Eng. Pract., Vol.73, pp. 40-55, 2018. https://doi.org/10.1016/j.conengprac.2017.12.005
  3. [3] J. Yang, Q. Hu, T. Xiao, W. Zhang, and C. Zhang, “Energy Efficiency Modeling, Process Parameter Optimization and Sequencing Modeling Optimization of Cold Rolling Continuous Annealing Units Based on Energy Consumptions,” China Mechanical Engineering, Vol.31, No.14, pp. 1724-1732, 2020. https://doi.org/10.3969/j.issn.1004-132X.2020.14.012
  4. [4] L. Jadachowski, A. Steinboeck, and A. Kugi, “Model Averaging and Feedforward Temperature Control in an Oscillating Annealing Furnace,” IFAC PapersOnLine, Vol.51, No.21, pp. 163-168, 2018. https://doi.org/10.1016/j.ifacol.2018.09.410
  5. [5] N. Yoshitani and A. Hasegawa, “Model-based control of strip temperature for the heating furnace in continuous annealing,” IEEE. T. Contr. Syst. T., Vol.6, No.2, pp. 146-156, 1998. https://doi.org/10.1109/87.664182
  6. [6] Y. Zhang, W. Cao, and Q. Qu, “Multi-Objective Optimization for Gas Distribution in Continuous Annealing Process,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 229-235, 2019. https://doi.org/10.20965/jaciii.2019.p0229
  7. [7] M. Niederer, S. Strommer, A. Steinboeck, and A. Kugi, “Nonlinear model predictive control of the strip temperature in an annealing furnace,” J. Process. Contr., Vol.48, 2016. https://doi.org/10.1016/j.jprocont.2016.09.012
  8. [8] H. Liu, C. Yang, B. Carlsson, S. J. Qin, and C. Yoo, “Dynamic Nonlinear Partial Least Squares Modeling Using Gaussian Process Regression,” Ind. Eng. Chem., Vol.58, No.36, pp. 16676-16686, 2019. https://doi.org/10.1021/acs.iecr.9b00701
  9. [9] Y. Jin, W. Cao, M. Wu, and Y. Yuan, “Accurate fuzzy predictive models through complexity reduction based on decision of needed fuzzy rules,” Neurocomputing, Vol.323, No.5, pp. 344-351, 2019. https://doi.org/10.1016/j.neucom.2018.10.010
  10. [10] H. T. Gao, X. T. Liu, J. L. Qi, Z. R. Ai, and L. Z. Liu, “Microstructure and mechanical properties of Cu/Al/Cu clad strip processed by the powder-in-tube method,” J. Mater. Process. Tech., Vol.251, 2018. https://doi.org/10.1016/j.jmatprotec.2017.07.035
  11. [11] H. Wu, B. van Benschop, O. B. Driss, F. Frinking, and R. Speets, “Furnace combustion and control renovation to improve the productivity of a continuous annealing line,” Energy Procedia, Vol.120, pp. 454-461, 2017. https://doi.org/10.1016/j.egypro.2017.07.219
  12. [12] P. D. U. Coronado, R. Lynn, W. Louhichi, M. Parto, E. Wescoat, and T. Kurfess, “Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system,” J. Mannuf. Syst., Vol.48, pp. 25-33, 2018. https://doi.org/10.1016/j.jmsy.2018.02.002
  13. [13] N. Fujii and N. Koike, “IoT Remote Group Experiments in the Cyber Laboratory: A FPGA-Based Remote Laboratory in the Hybrid Cloud,” 2017 Int. Conf. on Cyberworlds (CW), pp. 162-165, 2017. https://doi.org/10.1109/CW.2017.29
  14. [14] Y. Y. Bai, Y. H. Huang, S. Y. Chen, J. Zhang, B. Q. Li, and F. Y. Wang, “Cloud-Edge Intelligence: Status Quo and Future Prospective of Edge Computing Approaches and Applications in Power System Operation and Control,” Acta Autom. Sin., Vol.46, No.3, pp. 397-410, 2020. https://doi.org/10.16383/j.aas.2020.y000001

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024