Skip to main content
SpringerLink
Log in

Real-Time Non-invasive Velocity Field Measurement of Molten Iron Jet Discharged from Blast Furnace

  • Instrumentation for Process Modeling and Validation
  • Published:
JOM Aims and scope Submit manuscript

Abstract

Monitoring the internal state of a blast furnace during iron-producing operation is critical to maintaining a steady and efficient smelting process. Directly measuring the instantaneous casting rate at the blast furnace tapholes can provide process insights that would otherwise be unattainable. This research utilizes a machine vision camera with the image cross-correlation method to estimate the surface velocity field of the molten iron jet exiting from a real blast furnace during the casting process. An optimal method, the streamline-guided digital image correlation, is proposed specifically for the experiment target, the molten iron jet. The proposed method first estimates the streamline of the jet based on its edges and only applies the digital image correlation along the streamline to reduce unnecessary calculations. The proposed method is successfully integrated into a designed monitoring system for a blast furnace at United States Steel Corporation Gary Works with a refresh rate of 2.9/14.8 s with/without being boosted by graphic card. The measurement result successfully estimates the velocity field of the jet exiting the furnace and shows the proposed method could run 27 times faster than the conventional method and keep an absolute difference under \(\pm 1\) pixel for both horizontal and vertical directions.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. M. Roche, M. Helle, J. van der Stel, G. Louwerse, L. Shao, and H. Saxén, Steel Res. Int. 90(3), 1800420 (2019)

    Article  Google Scholar 

  2. A. Agrawal, S.C. Kor, U. Nandy, A.R. Choudhary, and V.R. Tripathi, Ironmak. Steelmak. 43(7), 550 (2016)

    Article  Google Scholar 

  3. R. Ricou and C. Vives, Int. J. Heat Mass Transf. 25(10), 1579 (1982)

    Article  Google Scholar 

  4. H. Hayashi, A. Becker, and J.W. Evans, Metallurgical and materials transactions. B. Process Metall. Mater. Process. Sci. 30(4), 623 (1999)

    Article  Google Scholar 

  5. M. Iguchi, H. Kosaka, H. Mizukami, M. Kawamoto, A. Hayashi, Y. Terauchi, M. Hanao, and H. Kawabata, Metallurgical and materials transactions. B. Process Metall. Mater. Process. Sci. 30(1), 53 (1999)

    Article  Google Scholar 

  6. M. Iguchi and Y. Terauchi, ISIJ Int. 42(9), 939 (2002)

    Article  Google Scholar 

  7. J. Kubota, N. Kubo, T. Ishii, M. Suzuki, N. Aramaki, and R. Nishimachi, NKK Tech. Rev. 85, 1 (2001)

    Google Scholar 

  8. R. Liu, B.G. Thomas, J. Sengupta, S.D. Chung, and M. Trinh, ISIJ Int. 54(10), 2314 (2014)

    Article  Google Scholar 

  9. N. Dubovikova, C. Karcher, and Y. Kolesnikov, IOP conference series. Mater. Sci. Eng. 143(1), 12022 (2016)

    Google Scholar 

  10. S. Eckert, G. Gerbeth, and V. Melnikov, Exp. Fluids 35(5), 381 (2003)

    Article  Google Scholar 

  11. A. Andruszkiewicz, K. Eckert, S. Eckert, and S. Odenbach, Euro. Phys. J. ST Spec. Top. 220(1), 53 (2013)

    Article  Google Scholar 

  12. Y. Saito, X. Shen, K. Mishima, and M. Matsubayashi, Nuclear instruments & methods in physics research. Section A, Accele. Spectrom. Detect. Assoc. Equip. 605(1), 192 (2009)

    Article  Google Scholar 

  13. K. Timmel, N. Shevchenko, M. Röder, M. Anderhuber, P. Gardin, S. Eckert, and G. Gerbeth, Metallurgical and materials transactions. B. Process Metall. Mater. Process. Sci. 46(2), 700 (2015)

    Article  Google Scholar 

  14. J. Priede, D. Buchenau, and G. Gerbeth, J. Appl. Phys. 110(3), 034512 (2011)

    Article  Google Scholar 

  15. T. Wondrak, V. Galindo, G. Gerbeth, T. Gundrum, F. Stefani, and K. Timmel, Meas. Sci. Technol. 21(4), 045402 (2010)

    Article  Google Scholar 

  16. P. Zhang, S.D. Peterson, M. Porfiri, Exp. Therm. Fluid Sci. 100, 207 (2019)

    Article  Google Scholar 

  17. C.H. Chien, T.H. Su, C.J. Huang, Y.J. Chao, W.L. Yeh, and P.S. Lam, Opt. Lasers Eng. 115, 42 (2019)

    Article  Google Scholar 

  18. L. Chatellier, S. Jarny, F. Gibouin, and L. David, Exp. Fluids 54(3), 1 (2013)

    Article  Google Scholar 

  19. T. Liu, L. Shen,J. Fluid Mech. 614, 253 (2008)

    Article  MathSciNet  Google Scholar 

  20. F. Tauro, F. Tosi, S. Mattoccia, E. Toth, R. Piscopia, and S. Grimaldi, Remote Sens. (Basel Switzerland) 10(12), 2010 (2018)

    Google Scholar 

  21. L. He, Z. Jiang, Z. Chen, W. Gui, and Y. Xie, IEEE Sens. J. 20(19), 11537 (2020)

    Article  Google Scholar 

  22. J. Chen and J. Katz, Meas. Sci. Technol. 16(8), 1605 (2005)

    Article  Google Scholar 

  23. M. Raffel, C.E. Willert, S.T. Wereley, and J. Kompenhans, Particle Image Velocimetry: A Practical Guide, second edition edn. Experimental Fluid Mechanics (Springer Berlin Heidelberg, Berlin, Heidelberg)

  24. N. Otsu, IEEE Transact. Syst. Man Cybernet. 9(1), 62 (1979)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to thank U.S Steel, Cleveland-Cliffs and all the members of the Steel Manufacturing Simulation and Visualization Consortium (SMSVC) for their support of this effort. Support from staff and students at the Center for Innovation through Visualization and Simulation is also appreciated. This research was supported by the US Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office Award Number DE-EE0009390. The views expressed herein do not necessarily represent the views of the US Department of Energy or the United States Government.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN, 47907, USA

    Weixiao Shang & Jun Chen

  2. Center for Innovation through Visualization and Simulation, Purdue University Northwest, 2200 169th Street, Hammond, IN, 46323, USA

    Tyamo Okosun & Chenn Q. Zhou

  3. U.S. Steel Gary Works, 1 Broadway, Gary, IN, 46402, USA

    Jason Entwistle, Brian Rodgers, Joseph Morey & Kosta Leontaras

Authors
  1. Weixiao Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tyamo Okosun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenn Q. Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jason Entwistle
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Brian Rodgers
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joseph Morey
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kosta Leontaras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Chen.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shang, W., Chen, J., Okosun, T. et al. Real-Time Non-invasive Velocity Field Measurement of Molten Iron Jet Discharged from Blast Furnace. JOM 75, 2430–2440 (2023). https://doi.org/10.1007/s11837-023-05860-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11837-023-05860-0

Search

Navigation