Authors : Maaz Bahauddin Naveed

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/m37pvmpx

Scribd : https://tinyurl.com/3jfuvx3z

DOI : https://doi.org/10.38124/ijisrt/25apr652

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Abstract : This study presents a methodology to analyze and model the continuous mill process of billets in steel plants. A reliable billet tracking system able to track each workpiece from the furnace entrance to the exit area of the rolling mill stands is designed based on information derived from rolling mill signals processed through advanced data science algorithms. From the stored information, two issues are solved: the data of the thermal sensors (of a thermographic camera or pyrometers) and the current linked to the absorption of the rolling mill stands; and a mathematical modelisation of the temperature of the billets along its way through the rolling mill is elaborated. Based on this data analysis, we concluded that some hardware changes were needed: the IR camera was moved to eliminate the interference of the scaly formation of the sensor with the data collected. The modelization step served as a foundation for future applications of control and/or diagnosis that will make use of a temperature decay model.

Keywords : Steel Industry; Reheating Furnace; Rolling Mill Stands; Billet; Tracking System; Data Analysis; Modelization.

References :

1. Zanoli, S.M.; Cocchioni, F.; Pepe, C. MPC-based energy efficiency improvement in a pusher type billets reheating furnace. Adv. Sci. Technol. Eng. Syst. J. 2018, 3, 74–84. [CrossRef]
2. Zanoli, S.M.; Orlietti, L.; Cocchioni, F.; Astolfi, G.; Pepe, C. Optimization of the clinker production phase in a cement plant. In CONTROLO 2020. Lecture Notes in Electrical Engineering; Gonçalves, J.A., Braz-César, M., Coelho, J.P., Eds.; Springer: Cham, Switzerland, 2021; Volume 695. [CrossRef]
3. Borel-Saladin, J.M.; Turok, I.N. The Green Economy: Incremental Change or Transformation? Environ. Policy Gov. 2013, 23, 209–220. [CrossRef]
4. Agenda 2030. Available online: https://unric.org/it/agenda-2030/ (accessed on 11 August 2022).
5. PNRR. Available online: https://www.mise.gov.it/index.php/it/pnrr (accessed on 11 August 2022).
6. Cavaliere, P. Clean Ironmaking and Steelmaking Processes: Efficient Technologies for Greenhouse Emissions Abatement; Springer: Cham, Switzerland, 2019. [CrossRef]
7. Holappa, L. Challenges and Prospects of Steelmaking towards the Year 2050. Metals 2021, 11, 1978. [CrossRef]
8. Wu, M.; Cao, W.; Chen, X.; She, J. Intelligent Optimization and Control of Complex Metallurgical Processes; Springer: Singapore, 2020. [CrossRef]
9. Steinbock, A. Model-Based Control and Optimization of a Continuous Slab Reheating Furnace; Shaker: Düren, Germany, 2011.
10. Bonci, A.; Pirani, M.; Longhi, S. A DataBase-Centric Framework for the Modeling, Simulation, and Control of Cyber-Physical Systems in the Factory of the Future. J. Intell. Syst. 2018, 27, 659–679. [CrossRef]
11. Trinks, W.; Mawhinney, M.H.; Shannon, R.A.; Reed, R.J.; Garvey, J.R. Industrial Furnaces; John Wiley & Sons: New York, NY, USA, 2004. [CrossRef]
12. Mullinger, P.; Jenkins, B. Industrial and Process Furnaces.Principles, Design and Operation; Elsevier: Amsterdam, The Netherlands, 2008. [CrossRef]
13. Zhang, Y.; Li, Q.; Zhou, H. Theory and Calculation of Heat Transfer in Furnaces; Elsevier: Amsterdam, The Netherlands, 2016.
14. NIIR Board of Consultants & Engineers. Steel Rolling Technology Handbook; Asia Pacific Business Press Inc.: New Delhi, India, 2018.
15. NIIR Board of Consultants & Engineers. The Complete Technology Book on Hot Rolling of Steel; Niir Project Consultancy Services: New Delhi, India, 2010.
16. NIIR Board of Consultants & Engineers. The Complete Technology Book on Steel and Steel Products; Asia Pacific Business Press Inc.: New Delhi, India, 2008.
17. Mui, C. Steel BilletReheating: An Expert Approach. Master of Applied Science. Ph.D. Thesis, The University of British Columbia, Vancouver, BC, Canada, 15 October 1998.
18. Development of Next Generation Heating System for Scale Free Steel Reheating. Available online: https://www.osti.gov/ servlets/purl/1004059 (accessed on 11 August 2022).
19. Sarda, K.; Yerudkar, A.; Vecchio, C.D. Missing data imputation for real time-SERIES data in a steel industry using generative adversarial networks. In Proceedings of the IECON 2021—47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada, 13–16 October 2021. [CrossRef]
20. Wu, H.; Jin, F.; Zhao, J.; Wang, W. Anomaly detection method based on multi-criteria evaluation for energy data of steel industry. In Proceedings of the 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), Suzhou, China, 14–16 May 2021. [CrossRef]
21. Zhang, L.; Zou, D. Product quality prediction of rolling mill in big data environment. In Proceedings of the 2020 International Conference on Big Data and Informatization Education (ICBDIE), Zhangjiajie, China, 23–25 April 2020. [CrossRef]
22. Hsu, C.-Y.; Kang, L.-W.; Lin, H.-Y.; Fu, R.-H.; Lin, C.-Y.; Weng, M.-F.; Chen, D.-Y. Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. In Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, Japan, 13–17 April 2018. [CrossRef]
23. Zhukov, P.; Fomin, A.; Glushchenko, A. Development of Relationship Between Steel Billet Temperature and Data on Its Heating History for Continuous Furnace of Rolling-Mill Shop. In Proceedings of the 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Lipetsk, Russia, 11–13 November 2020. [CrossRef]
24. Xu, Z.; Hua, T.; Fan, X.; Zhu, X. Research on the Strategy of Delay Rolling for Reheating Furnace based on Swarm Intelligence Algorithm. In Proceedings of the 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi’an, China, 15–17 April 2022. [CrossRef]
25. Chen, C.-J.; Chou, F.-I.; Chou, J.-H. Temperature Prediction for Reheating Furnace by Gated Recurrent Unit Approach. IEEE Access 2022, 10, 33362–33369. [CrossRef]
26. Zhukov, P.; Fomin, A.; Glushchenko, A.; Podvalnyi, E. Comparison of finite-difference and data-based models of temperature transfer process in heating furnaces for cast billet temperature prediction. In Proceedings of the 2020 2nd International Con- ference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Lipetsk, Russia, 10–12 November 2021. [CrossRef]
27. Zanoli, S.M.; Pepe, C.; Astolfi, G.; Moscoloni, E. Analysis and modeling of steel industry reheating furnace billets temperature. In Proceedings of the 2022 23rd International Carpathian Control Conference (ICCC), Sinaia, Romania, 29 May–1 June 2022; pp. 337–342. [CrossRef]
28. Moscoloni, E. Analisi e Modellazione Della Temperatura di Semilavorati di un Forno di Riscaldo di un’acciaieria. Master’s Thesis, Università Politecnica delle Marche, Ancona, Italy, 2020. Supervisor: Zanoli, S.M..
29. Åström, K.J.; Hägglund, T. PID Controllers: Theory, Design, and Tuning; ISA—The Instrumentation, Systems and Automation Society: Research Triangle Park, NC, USA, 1995.
30. Borja-Castro, L.E.; Bustamante Dominguez, A.; Valerio-Cuadros, M.I.; Valencia-Bedregal, R.A.; Cabrera-Tinoco, H.A.; Espinoza Suarez, S.M.; Kargin, J.; Moreno, N.O.; Barnes, C.H.W.; De Los Santos Valladares, L. Characterization of steel billet scales generated during the continuous casting process in SIDERPERU steel plant. Hyperfine Interact. 2021, 242, 53. [CrossRef]
31. Wang, Z.-C.; Sun, X.-D.; Yuan, W. Influence of oxide scale on continuous casting billet on thickness measurement by electro- magnetic ultrasonic transducer. In Proceedings of the 2018 5th International Conference on Information Science and Control Engineering (ICISCE), Zhengzhou, China, 20–22 July 2018. [CrossRef]
32. Kelleher, J.D.; Tierney, B. Data Science; MIT Press: Cambridge, MA, USA, 2018.
33. Archdeacon, T. Correlation and Regression Analysis: A Historian’s Guide; University of Wisconsin Press: Madison, WI, USA, 1994.
34. Navidi, W. Probabilità e Statistica per L’ingegneria e le Scienze; McGraw-Hill Education: New York, NY, USA, 2006.
35. Jaklicˇ, A.; Glogovac, B.; Kolenko, T.; Zupancˇicˇ, B.; Težak, B. A simulation of heat transfer during billet transport. Appl. Therm. Eng. 2002, 22, 873–883. [CrossRef]
36. Serajzadeh, S.; Mirbagheri, H.; Karimi Taheri, A. Modelling the temperature distribution and microstructural changes during hot rod rolling of a low carbon steel. J. Mater. Process. Technol. 2002, 125–126, 89–96. [CrossRef]
37. MathWorks. Available online: https://it.mathworks.com/ (accessed on 13 August 2022).