Skip to main content
SpringerLink
Log in

Human-cyber-physical system for production and operation decision optimization in smart steel plants

  • Review
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Based on the core developmental demands of smart steel manufacturing plants, this review analyzes the essential characteristics of the steel manufacturing process and its implications for production and operation decision optimization (PODO). A discussion on the potential application of technologies, including the Industrial Internet, big data, cloud computing, and 5G, in the PODO of smart steel plants based on the Industry 4.0 intelligent manufacturing and human-cyber-physical system (HCPS) is also presented. An adaptive update conception for a new dual-level cyber system (for management control and unit operation) of a flat-structured HCPS for steel plants is proposed to eliminate issues such as the hierarchical structure of the cyber system, the occurrence of data islands, and weak intelligence in PODO ability. Additionally, the review provided an in-depth analysis of the closed-loop decision logic of a dual-level HCPS. The critical technologies required for developing the HCPS model include data platforms and their associated application technologies, intelligent modeling, human-machine collaboration, and platform-based integration. Finally, suggestions are proposed to develop smart steel plants, including concept renewal, technological integration, and talent training.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Industrie 4.0 Working Group, Federal Ministry of Education and Research. Recommendations for implementing the strategic initiative INDUSTRIE 4.0 (in Chinese). Mech Eng Trends, 2013, 7: 23–33

    Google Scholar 

  2. Keith B B, David B A, John D G, et al. Who will set the rules for smart factories? Issues Sci Technol, 2019, 35: 70–76

    Google Scholar 

  3. Offord A. Building a Britain Fit for the Future. Works Management, 2018, 1: 28–30

    Google Scholar 

  4. Sun Y H. New Industrial Revolution and EU New Industrial Strategy (in Chinese). Beijing: Social Science Academic Press, 2019. 151–153

    Google Scholar 

  5. Wang L Y, Li X X. Japan’s policy of intelligent manufacturing industry and its implication (in Chinese). J Northeast Asia Stud, 2019, 6: 100–110

    Google Scholar 

  6. Li H N, Chen Y T, Song W. National AI strategy of South Korea (in Chinese). Glob Sci Technol Ecol Outlook, 2020, 35: 21–26

    Google Scholar 

  7. Sun Y N, Bai X J. “Made in China 2025”—A strategy to strengthen China with Chinese characteristics (in Chinese). J Intell Manuf, 2020, 10: 43–45

    Google Scholar 

  8. Ding J L, Yang C E, Chen Y D, et al. Research progress and prospects of intelligent optimization decision making in complex industrial process (in Chinese). Acta Autom Sin, 2018, 44: 1931–1943

    Google Scholar 

  9. Yin R Y. Process engineering and manufacturing process (in Chinese). Iron Steel, 2014, 49: 15–22

    Google Scholar 

  10. Zhu J, Hu M J, Sun J J. Research on integrated mobile service platform of iron and steel enterprises (in Chinese). In: The Chinese Society for Metals. 2016 Internet+ and Steel Industry Intelligent Manufacturing Summit Proceedings. Tangshan, 2016. 37–39

  11. Zhou J, Zhou Y, Wang B, et al. Human-Cyber-Physical Systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering, 2019, 5: 624–636

    Article  Google Scholar 

  12. Yin R Y. Metallurgical Process Engineering (in Chinese). Beijing: Metallurgical Industry Press, 2009. 21–26

    Google Scholar 

  13. Yin R Y. A discussion on “smart” steel plant—View from physical system side (in Chinese). Iron Steel, 2017, 52: 1–12

    Google Scholar 

  14. Sun Y G. The development roadmap of digital, networked, and intelligent manufacturing technologies for the iron and steel industry (in Chinese). China Steel Focus, 2015, 9: 4–8

    Google Scholar 

  15. Wang C M, Zhou D D, Zu K, et al. Review of intelligent manufacturing technology in steel industry (in Chinese). China Metall, 2018, 28: 1–7

    Google Scholar 

  16. Sun Y G. Integrated optimization of intelligent manufacturing in iron and steel industry (in Chinese). Sci Technol Rev, 2018, 36: 30–37

    Google Scholar 

  17. Yin R Y. The essence, function, and future development mode of steel plant (in Chinese). Sci Sin Tech, 2008, 38: 1365–1377

    Google Scholar 

  18. Zeng J Q. Thoughts on intellectualization improvement of iron and steel production process (in Chinese). Metall Ind Autom, 2019, 43: 13–19

    Google Scholar 

  19. Zheng Z, Long J Y, Gao X Q, et al. Present situation and prospect of production control technology focusing on planning and scheduling in iron and steel enterprise (in Chinese). Comput Integr Manuf Syst, 2014, 20: 2660–2674

    Google Scholar 

  20. Schirner G, Erdogmus D, Chowdhury K, et al. The future of human-in-the-loop cyber-physical systems. Computer, 2013, 46: 36–45

    Article  Google Scholar 

  21. Krugh M, Mears L. A complementary cyber-human systems framework for Industry 4.0 cyber-physical systems. Manuf Lett, 2018, 15: 89–92

    Article  Google Scholar 

  22. Nunes D, Silva J S, Boavida F. A Practical Introduction to Human-in-the-Loop Cyber-Physical Systems. Hoboken: John Wiley & Sons, 2018. 21–24

    Google Scholar 

  23. Sowe S K, Zettsu K, Simmon E, et al. Cyber-physical-human systems: Putting people in the loop. IT Prof, 2016, 18: 10–13

    Article  Google Scholar 

  24. Fonseca F, Marcinkowski M, Davis C. Cyber-human systems of thought and understanding. J Assoc Inf Sci Tech, 2018, asi.24132

  25. Yin R Y. The energy flow behaviors and energy flow network problems in steel manufacturing process (in Chinese). J Eng Stud, 2010, 2: 1–4

    Article  Google Scholar 

  26. Yin R Y. Structural analysis on steel manufacturing process and some problems of engineering effectiveness (in Chinese). Iron Steel, 2000, 35: 1–7

    Google Scholar 

  27. Tang L, Liu G. A mathematical programming model and solution for scheduling production orders in Shanghai Baoshan Iron and Steel Complex. Eur J Operat Res, 2007, 182: 1453–1468

    Article  MATH  Google Scholar 

  28. Pacciarelli D, Pranzo M. Production scheduling in a steelmaking-continuous casting plant. Comput Chem Eng, 2004, 28: 2823–2835

    Article  Google Scholar 

  29. Tang L, Liu J, Rong A, et al. A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex. Eur J Operat Res, 2000, 124: 267–282

    Article  MATH  Google Scholar 

  30. Cowling P I, Ouelhadj D, Petrovic S. A multi-agent architecture for dynamic scheduling of steel hot rolling. J Intell Manuf, 2003, 14: 457–470

    Article  Google Scholar 

  31. Yan W L, Jiang C H, Chen Z P, et al. Narrow window control of BOF steelmaking end point based on two-mode switching (in Chinese). In: The China Automation Congress 2019, Chinese Assoc Autom. Hangzhou, 2019. 43–50

    Google Scholar 

  32. Chen C, Wang N, Yu H Y, et al. Evaluation model of converter operation process based on chi-square boxing method and logistic regression algorithm (in Chinese). J Mater Metall, 2019, 18: 87–91

    Google Scholar 

  33. Chen K, Zheng Z, Zhou C. Simulation method for multi-machine and multi-task crane scheduling in steelmaking plant (in Chinese). J Chongqing Univ, 2011, 34: 39–45

    Google Scholar 

  34. Zhang K, Liu J, Cui H, et al. Analysis of meniscus fluctuation in a continuous casting slab mold. Metall Mater Trans B, 2018, 49: 1174–1184

    Article  Google Scholar 

  35. He C W. Study on iron making technology and application of blast furnace (in Chinese). Metall Mater, 2019, 39: 110–111

    Google Scholar 

  36. Deng X X, Pan H W, Ji C X, et al. Review on high speed conventional slab continuous casting of low carbon steel (in Chinese). Iron Steel, 2019, 54: 70–81

    Google Scholar 

  37. Jiang S L, Qian W P, Gao W W. A real-time and intelligent scheduling system applied in steelmaking shop towards wire and rod (in Chinese). In: The Chinese Society for Metals, ed. Proceedings of the 9th China Iron and Steel Annual Conference. Beijing, 2013. 3334–3339

  38. PSI Metals. PSI metals brochure [OL]. PSI metals, 2017. 1–12, [2020-12-01]. https://www.psimetals.de/fileadmin/files/downloads/PSI_BT/1_Brochures/PSI_Metals_Image_Brochure.english.pdf

  39. Peter N. AIS SteelPlanner—The production planning, scheduling and control solutions for the steel industry (in Chinese). In: China Iron and Steel Association, ed. International Symposium on Steel Industry Informatization. Dalian, 2005, 7: 52–57

  40. Xie X P. Implementation and improvement of Broner production scheduling system at Tisco (in Chinese). Shanxi Metall, 2009, 32: 16–19

    Google Scholar 

  41. Zhou W, Wei Z. Steelmaking scheduling TPS solution in Baosight MES-APS (in Chinese). Inform Technol Standard, 2010, 9: 49–53

    Google Scholar 

  42. Liu Q, Qin J S. Perspectives on big data modeling of process industries (in Chinese). Acta Autom Sin, 2016, 42: 161–171

    Google Scholar 

  43. Liu M. A survey of data-based production scheduling methods (in Chinese). Acta Autom Sin, 2009, 6: 785–806

    Article  MATH  Google Scholar 

  44. Ning C, You F. Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era. Comput Chem Eng, 2018, 111: 115–133

    Article  Google Scholar 

  45. Hashem I A T, Yaqoob I, Anuar N B, et al. The rise of “big data” on cloud computing: Review and open research issues. Inf Syst, 2015, 47: 98–115

    Article  Google Scholar 

  46. Wang J N. Research on remote operation and maintenance technology based on cloud computing used in industrial scene (in Chinese). Process Autom Instrum, 2019, 40: 67–70

    MathSciNet  Google Scholar 

  47. Yang C, Huang Q, Li Z, et al. Big Data and cloud computing: Innovation opportunities and challenges. Int J Digital Earth, 2017, 10: 13–53

    Article  Google Scholar 

  48. Shen Z, An G, Yu M M. Exploration and application of 5G in industrial Internet (in Chinese). Inform Commun Technol, 2019, 13: 17–22

    Google Scholar 

  49. Volker S, Anne B, Vasco S, et al. Structural development and evaluation of profitable industrial use cases based on innovative technologies like 5G. Procedia CIRP, 2019, 81: 1119–1124

    Article  Google Scholar 

  50. Wollschlaeger M, Sauter T, Jasperneite J. The future of industrial communication: Automation networks in the Era of the Internet of Things and Industry 4.0. IEEE Ind Electron Mag, 2017, 11: 17–27

    Article  Google Scholar 

  51. Zhang H K, Cheng Y J, Yang D. State of the art of industrial network technologies: A review and outlook (in Chinese). Chin J Internet Things, 2017, 1: 13–20

    Google Scholar 

  52. Park J. Advances in future internet and the industrial internet of things. Symmetry, 2019, 11: 244–247

    Article  Google Scholar 

  53. Zhang J. The industrial intelligence network has brought about new changes in industrial production (in Chinese). Wisdom China, 2020, 4: 48–49

    Google Scholar 

  54. Guo N, Jia C. Interpretation of “Cyber-Physical Systems Whitepaper (2017)” (Part One) (in Chinese). Inform Technol Standard, 2017, 4: 36–40

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Materials Science and Engineering, Chongqing University, Chongqing, 400045, China

    Zhong Zheng & KaiTian Zhang

  2. College of Economics and Business Administration, Chongqing University, Chongqing, 400045, China

    XiaoQiang Gao

Authors
  1. Zhong Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. KaiTian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. XiaoQiang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong Zheng.

Additional information

This work was supported by the Key Program of the National Natural Science Foundation of China (Grant No. 51734004), and the National Key R&D Program of China (Grant Nos. 2017YFB0304005, and 2020YFB1712800).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, Z., Zhang, K. & Gao, X. Human-cyber-physical system for production and operation decision optimization in smart steel plants. Sci. China Technol. Sci. 65, 247–260 (2022). https://doi.org/10.1007/s11431-020-1838-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-020-1838-6

Keywords

Search

Navigation