Skip to main content
SpringerLink
Log in

Artificial intelligence and expert systems in the steel industry

  • Steel
  • Overview
  • Published:
JOM Aims and scope Submit manuscript

Abstract

This article was prepared in an effort to determine the state of the art with respect to the use of artificial intelligence and expert system technologies within the steel industry. A number of important developments have been reported and most of them resulted in significant savings. Mathematical modeling is quite important both for understanding and for controlling a process. However, most steelmaking operations are extremely complex and cannot be described mathematically. They are, however, adequately controlled by human operators on the basis of their knowledge and expertise. Because of this, artificial intelligence is an ideal technology for the automation of many steelmaking-related processes.

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. T. Kawaguchi and T. Ueyama, Steel Industry II: Control System, Japanese Technology Reviews, vol. 7 (New York: Gordon and Breach Science Publishers, 1989).

    Google Scholar 

  2. J. Heidepriem, “Trends in Process Control of Metal Rolling Mills,” Proc. 11th Triennial World Congress of IFAC, vol. VI, ed. Jaaksoo and Utkin (Oxford, U.K.: Pergamon Press, 1990).

    Google Scholar 

  3. J. Miettunen, “Expert System Type Support for the Process Operator,” Automation in Mining, Mineral and Metal Processing 1989 (Oxford, U.K.: Pergamon Press, 1989), pp. 109–114.

    Google Scholar 

  4. D.K. Kahaner, “Nippon Steel Kimitsu Works,” Computer Science Department, University of Arizona (April 1991). The reports of D.K. Kahaner are available via Internet through anonymous FTP from host cs.arizona.edu (directory japan/kahaner.reports.)

    Google Scholar 

  5. J. Giarratano and G. Riley, Expert Systems Principles and Programming (Boston, MA: PWS-Kent Publishing Co., 1989).

    Google Scholar 

  6. T. Saito, “Application of Artificial Intelligence in the Japanese Steel Industry,” in Ref. 3, pp. 31-38.

  7. T. Atsumi, “Artificial Intelligence in the Japanese Steel Industry,” Use and Application of Expert Systems and Artificial Intelligence, (Brussels, Belgium: IISI, 1989), pp. 31–43.

    Google Scholar 

  8. H.N.G. Wadley and E.W. Eckhart, Jr., eds., Intelligent Processing of Materials (Warrendale, PA: TMS, 1990)

    Google Scholar 

  9. S. Yue, ed., Mathematical Modelling of Hot Rolling of Steel (Montreal, Canada: CIM, 1990)

    Google Scholar 

  10. K. Otsuka et al., “A Hybrid Expert System Combined with a Mathematical Model for Blast Furnace Operation,” ISIJ International, 30(2) (February 1990), pp. 118–127.

    Google Scholar 

  11. T. Yoshida et al., “A Hybrid Expert System Combined with a Mathematical Model for BOF Process Control,” Expert Systems in Mineral and Metal Processing, (Oxford, U.K.: Pergamon Press, 1991), pp. 51–56.

    Google Scholar 

  12. I. Okamura et al., “Development of Automatic Knowledge-Acquisition Expert System,” Proc. IECON ’91 Conference (New York: IEEE, 1991), pp. 37–41.

    Google Scholar 

  13. H. Narazaki et al., “The AI Tool, and Its Applications to Diagnosis Problems,” ISIJ International, 30(2) (February 1990), pp. 98–104.

    Google Scholar 

  14. E. Minami and O. Dairiki, “ESTO: Environment for Building a Diagnostic Expert System,” Nippon Steel Technical Report, 51 (October 1991), pp. 24–28.

    Google Scholar 

  15. L.G. Lock Lee et al., “Rapid Prototyping Tools for Real-Time Expert Systems in the Steel Industry,” ISIJ Intemational, 30(2) (February 1990), pp. 90–97.

    Google Scholar 

  16. R.I.L. Guthrie and J.J. Jonas, “Steel Processing Technology,” Metals Handbook, vol. 1, Properties and Selection, 10th ed. (Materials Park, OH: ASM, 1989).

    Google Scholar 

  17. L. Maeda and C. Osaka, “Knowledge Engineering Applying Reheating Furnace Control,” JEEJ (1988), pp. 21–24.

    Google Scholar 

  18. Y. Lu et al., “An Integrated Computer Control System for a Steel Plate Mill,” in Ref. 3, pp. 293-297.

  19. K. Noderer and H. Henein, “A Survey on the Use of Expert Systems in the Iron and Steel Industry,” Ironmaking Conf. Proceedings (Warrendale, PA: ISS, 1990), pp. 497–501.

    Google Scholar 

  20. S. Watanabe et al., “Application of Expert System to Blast Furnace Operation,” in Ref. 3, pp. 255-260.

  21. H. Zhao and M. Ma, “The Application of Fuzzy and Artificial Intelligence Methods in the Building of a Blast Furnace Smelting Process Model,” Industrial Applications of Fuzzy Control, ed. M. Sugeno (Amsterdam, the Netherlands: Elsevier Science Publishers, 1985), pp. 241–247.

    Google Scholar 

  22. D. Partington, “Expert Systems Developments Within British Steel,” in Ref. 7, pp. 15-29.

  23. T.H. Choi et al., “An Expert System to Aid Operation of a Blast Furnace,” in Ref. 11, pp. 45-50.

  24. B. Shepherd, Y. Cho, and J. Jang, “An Expert System for Raw Materials Scheduling in a Steel Mill,” Proc. of the 1992 IEEE International Symposium on Intelligent Control (New York: IEEE, 1992), pp. 128–131.

    Chapter  Google Scholar 

  25. K. Yui et al., “Application of an Expert System to Blast Furnace Operation,” Future Generations Computer Systems, 5(1) (August 1989), pp. 137–142.

    Google Scholar 

  26. S. Amano et al., “Expert System for Blast Furnace Operation at Kimitsu Works,” ISIJ International, 30(2) (February 1990), pp. 105–110.

    Google Scholar 

  27. K. Shiota et al., “Planning Expert System for Optimum Operation of Coke Plant and Sintering and Ore Treating Plant,” Nippon Steel Technical Report, 49 (April 1991), pp. 1–8.

    Google Scholar 

  28. N. Nagai, A. Arai, and K. Matsuda, “Application of Expert System to Forecasting Heat Level at Kobe No. 3 Blast Furnace,” CAMP-ISIJ, 2 (1989), pp. 10–13.

    Google Scholar 

  29. K. Matsuda et al., “Forecasting System for Decreasing Heat Level in Blast Furnace,” Trans. ISIJ, 28 (1988), pp. 900–906.

    Google Scholar 

  30. Y. Niwa et al., “Application of a Self-Learning Function to an Expert System for Blast Furnace Control,” ISIJ International, 30(2) (February 1990), pp. 111–117.

    Google Scholar 

  31. R. Nakajima et al., “Operation Control of a Blast Furnace by Artificial Intelligence,” Proc. 46th AIME Ironmaking Conference (Warrendale, PA: ISS, 1987), pp. 209–216.

    Google Scholar 

  32. Nippon Kokan Co., “Artificial Intelligence in Blast Furnace Control,” Trans. ISIJ, 27(11) (November 1987), p. 911.

    Google Scholar 

  33. Nippon Kokan Co., “Application of Fuzzy Control for Iron Ore Sintering Process,” Trans. ISIJ, 28(8) (August 1988), p. 685.

    Google Scholar 

  34. M. Iwamoto et al., “Application of Fuzzy Control for Iron Ore Sintering Process,” Trans. ISIJ, 28(5) (May 1988), pp. 341–345.

    CAS  Google Scholar 

  35. M. Yamazaki, M. Sato, and M. Kiguti, “Development of Advanced GO-STOP System,” CAMP-ISIJ, 2 (1989), pp. 6–9.

    Google Scholar 

  36. Y. Maki et al., “Application of Fuzzy Theory for Automatic Control of Hot Stove Combustion Gas Flow Rate,” in Ref. 3, pp. 247-253.

  37. E. Nakata, H. Fujimoto, and K. Terazono, “Expert System Coal Blending,” in Ref. 11, pp. 109-14.

  38. M. Kanemoto et al., “An Application of Expert System to LD Converter Processes,” ISIJ International, 30(2) (February 1990), pp. 128–135.

    Google Scholar 

  39. T. Hatanaka et al., “Development of Blowing Control System in BOF Applying AI Technology,” Proc. of the Int. Symposium on Artificial Intelligence in Materials Processing (Montreal, Canada: CIM, 1992), pp. 97–106.

    Google Scholar 

  40. M. Hadijiski, K. Spassov, and D. Filev, “Knowledge Based Model of Thermal State of Metallurgical Ladle,” in Ref. 11, pp. pp57-62.

  41. G. Bartolini et al., “Development of Performance Adaptive Fuzzy Controllers with Application to Continuous Casting Plants,” in Ref. 21, pp. 73-86.

  42. G. Bartolini et al., “Development and Application of Adaptive Control Methods in Continuous Casting Plants,” Proc. 4th Int. IEEE Conf. on Trends in On-Line Computer Control Systems (New York: IEEE, 1988), pp. 34–37.

    Google Scholar 

  43. Y. Sasabe et al., “Real-Time Expert System Applied to Mold Bath Level Control of Continuous Caster,” ISIJ International, 30(2) (February 1990), pp. 136–141.

    Google Scholar 

  44. A.B. Bulsari, M. Sillanpaa, and H. Saxéen, “An Expert System for Continuous Steel Casting Using Neural Networks,” in Ref. 11, pp. 155-160.

  45. F. Becker, “Overview of the Artificial Intelligence Applications at ARVED,” in Ref. 7, pp. 45-53.

  46. H. Kominami et al., “Neural NetworkSystem for Breakout Prediction in Continuous Casting Process,” Nippon Steel Technical Report (49) (April 1991), pp. 34–39.

    Google Scholar 

  47. S. Kumar et al., “Knowledge Engineering an Expert System to Trouble-Shoot Quality Problems in the Continuous Casting of Steel Billets,” in Ref. 11, pp. 95-102.

  48. S. Kumar et al., “Inferencing Strategies to Troubleshoot Quality Problems in Continuous Casting of Steel Billets,” in Ref. 39, pp. 97-106.

  49. O.M. Puhringer and F. Wallner, “Computer-Aided Quality Control System of Voest Alpine,” Metallurgia International, 1(3) (August 1988), pp. 192–197.

    Google Scholar 

  50. J. Suni and H. Henein, “A Frame-Based System for Use in Dispositioning of Continuous Cast Steel Slabs,” in Ref. 39, pp. 155-164.

  51. G. Cermile, “Five Stand Tandem Mill Expert System,” in Ref. 39, pp. 39–52.

  52. D. Sbararo-Hofer, D. Neumerkel, and K. Hunt, “Neural Control of a Steel Rolling Mill,” in Ref. 24, pp. 122-127.

  53. H. Ogai et al., “Free Tension Set-Up of Rod Bar Rolling by Fuzzy Inference Methods,” Proc. IECON ’91 Conference (New York,: IEEE, 1991), pp. 60–64.

    Google Scholar 

  54. R. Martinez et al., “TANDEM: An Expert System in Troubleshooting Electrical Faults on a Cold Rolling Mill,” in Ref. 3, pp. 305-310.

  55. O. Lassila et al., “Applying Knowledge-Based Techniques to the Scheduling of Steel Rolling,” in Ref. 11, pp. 103-108.

  56. H. Fujimoto et al., “Expert System for Manufacturing Order Determination in Hot Rolling Processes,” in Ref. 11, pp. 71-78.

  57. T.C. Fogarty, “An Expert System for Optimizing and Controlling Combustion in Multiple Burner Furnaces and Boiler Plants,” Proc. of the 27th Conference on Decision and Control (New York: IEEE, 1988), pp. 2249–2250.

    Chapter  Google Scholar 

  58. T.C. Fogarty, “The Machine Learning of Rules for Combustion in Multiple Burner Installations,” Proc. of the 5th IEEE Conf. on Artificial Intelligence Applications (New York: IEEE, 1989), pp. 215–221.

    Google Scholar 

  59. W.H. Verduin, “Optimizing Combustion with Integrated Neural Networks and Al Technologies,” Control Engineering (July 1992), pp. 38–40.

    Google Scholar 

  60. M. Yonekura, “The Application of Fuzzy Sets Theory to the Temperature Control of Box Annealing Furnaces Using Simulation Techniques,” Proc. of the 8th Triennial World Congress of IF AC (Oxford, U.K.: Pergamon Press, 1981), pp. 773–778.

    Google Scholar 

  61. Y. Mishima et al., “A New Scheduling System with Assumption-Based Inference,” Proc. of the IECON ’90 Conference (New York: IEEE, 1990), pp. 1298–1302.

    Google Scholar 

  62. F. Hirao et al., “An Expert System for Efficient Coil Transfer in Finishing Line of Hot Strip Mill,” ISIJ International, 30(2) (February 1990), pp. 167–172.

    Google Scholar 

  63. M. Hosoda et al., “Coil Transfer Expert System for a Hot Strip Mill Finishing Line,” in Ref. 11, pp. 89-94.

  64. Y. Anabuki, R. Owaki, and H. Sakiyama, “Application of Expert System to Real Time Cold Coil Transportation Control in Finishing Line,” in Ref. 11, pp. 63-70.

  65. M. Shah, R. Damian, and J. Silverman, “Knowledge Based Dynamic Scheduling in a Steel Plant,” Proc. IEEE 6th Conf. on Artificial Intelligence Applications (New York: IEEE, 1990), pp. 151–154.

    Google Scholar 

  66. T.H. Stohl et al., “Development of a Scheduling Expert System for a Steel Plant,” in Ref. 11, pp. 39-44.

  67. M. Numao and S. Morishita, “Scheplan—A Scheduling Environment for Steel Making Processes,” Proc. of the IEEE International Workshop on Artificial Intelligence for Industrial Applications (New York: IEEE, 1988), pp. 467–472.

    Chapter  Google Scholar 

  68. M. Numao and S. Morishita, “A Scheduling Environment for Steel Making Processes,” Proc. of the 5th IEEE Conf. on Artificial Intelligence Application (New York: IEEE, 1989), pp. 279–286.

    Google Scholar 

  69. H. Ishikawa et al., “Development of Quality Design Expert System for Steel Plate,” NKK Technical Journal (142) (1993).

    Google Scholar 

  70. M. Watanabe et al., “Development of Shape Steel Quality Expert System,” NKK Steel Technical Report (53) (April 1992), pp. 29–34.

    Google Scholar 

  71. E. Veijola et al., “An Expert System for Designing Production Parameters to Achieve Given Steel Plate Properties,” in Ref. 7, pp. 59-62.

  72. R. Haataja et al., “Expert System for the Automatic Surface Inspection of Steel Strip,” in Ref. 11, pp. 79-88.

  73. K. Asano et al., “Neural Network Model for Recognition of Characters Stenciled on Slabs,” in Ref. 11, pp. 147-154.

  74. K. Tashiro et al., “Automation Expert System for Air Separation Plant,” in Ref. 11, pp. 19-24.

  75. M. Hattori et al., “On-Line AI System for Benzol Recovery Plant,” NKK Technical Journal (142) (1993).

    Google Scholar 

  76. M. Dubas, “Expert Systems in Industrial Practice: Advantages and Drawbacks,” Expert Systems, 7(3) (August 1990), pp. 150–156.

    Google Scholar 

  77. J. Cullen and A. Bryman, “The Knowledge Acquisition Bottleneck: Time for Reassessment?” Expert Systems, 5(3) (1988), pp. 216–225.

    Google Scholar 

  78. J.A. Bernard, “Use of a Rule-Based System for Process Control,” IEEE Control Systems Magazine (October 1988)., pp.3–13.

    Google Scholar 

  79. M.J. Willis, G.A. Montague, and A.J. Morris, “Modelling of Industrial Processes Using Artificial Neural Networks,” Computing & Control Engineering Joumal (May 1992), pp. 113–117.

    Google Scholar 

  80. B. Kosko, Neural Networks and Fuzzy Systems—A Dynamical Systems Approach to Machine Intelligence (New York: Prentice-Hall, 1992).

    Google Scholar 

  81. L. Zadeh, “Fuzzy Logic and the Calculus of Fuzzy If-Then-Else Rules,” in Ref. 39, pp. 65-76.

  82. K.S. Leung and W. Lam, “Fuzzy Concepts in Expert Systems,” IEEE Computer, 21(9) (September 1988), pp. 43–56.

    Google Scholar 

  83. C.C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller—Part II,” IEEE Transactions on Systems, Man and Cybernetics, 20(2) (March/April 1990), pp. 419–435.

    Google Scholar 

  84. C.C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller—Part I,” IEEE Transactions on Systems Man and Cybernetics, 20(2) (March/April 1990), pp. 404–418.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. McGill University, Canada

    Gregory Carayannis Ph.D. (research professor) & Gregory Carayannis Ph.D. (research professor)

Authors
  1. Gregory Carayannis Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Carayannis, G. Artificial intelligence and expert systems in the steel industry. JOM 45, 43–51 (1993). https://doi.org/10.1007/BF03222461

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03222461

Keywords

Search

Navigation