Advertisement

Steel in Translation

, Volume 49, Issue 9, pp 618–621 | Cite as

Control of Blast Furnace Filling Based on Cluster Analysis of Burden Surface Temperature

  • E. V. Ershov
  • O. L. SelyanichevEmail author
  • A. I. Lavrukhin
Article
  • 4 Downloads

Abstract

The cluster analysis of burden surface temperature and plotting graphs, in which nodes are predicted states and edges are a type and weight of filled materials, is a control tool of the blast furnace operation. The control of material charge aiming for a high efficiency of furnace operation (high yield of cast iron, t/day, low specific energy consumption) is maintained by predicted adjustments of weight or filling frequency on the basis of cycles in graphs with the highest ore charge. The proposed control methods make it possible to maintain efficient operation modes of furnace, to increase ore charges, and to decrease specific coke consumption by 4%.

Keywords:

blast furnace burden surface temperature cluster analysis 

Notes

REFERENCES

  1. 1.
    Bol’shakov, V.I., Lebed’, V.V., and Zherebetskii, A.A., Using modern control devices to regulate the radial distribution of the charge in the blast furnace, Fundam. Prikl. Probl. Chern. Metall., 2010, no. 21, pp. 53–66.Google Scholar
  2. 2.
    Dal’skii, A.M. and Barsukov, T.M., Tekhnologiya konstruktsionnykh materialov (Technology of Construction Materials), Moscow: Mashinostroenie, 2003, p. 31.Google Scholar
  3. 3.
    Lavrukhin, A.I. and Selyanichev, O.L., Analysis of the surface temperature of the stockline as an element of the decision support system for control of a blast furnace, Vestn. Cherepovetsk. Gos. Univ., 2018, no. 6 (87), pp. 10–18.Google Scholar
  4. 4.
    Lavrukhin, A.I. and Selyanichev, O.L., Geometric model of a thermal imaging of the surface of the stockline for the blast furnace, Vestn. Cherepovetsk. Gos. Univ., 2017, no. 1, pp. 48–55.Google Scholar
  5. 5.
    Lavrukhin, A.I. and Selyanichev, O.L., Thermogram of concentric discharge rings from a blast furnace in global practice, Materialy II Vserossiiskoi nauchno-prakticheskoi konferentsii “ICITY 2015: Informatizatsiya promyshlennogo goroda” (Proc. II All-Russ. Sci.-Pract. Conf. “ICITY 2015: Informatization of Industrial City”), Cherepovets: Cherepovetsk. Gos. Univ., 2016, pp. 202–204.Google Scholar
  6. 6.
    Lavrukhin, A.I. and Khinskii, L.D., Automated system for thermal imaging monitoring of the state of the distribution tray of a coneless loading device and the surface temperature of the stockline at the mouth of blast-furnace no. 5 of the Cherepovets Metallurgical Plant (Severstal Company), Chern. Metall., Byull. Nauchno-Tekh. Ekon. Inf., 2016, no. 4 (1396), pp. 35–37.Google Scholar
  7. 7.
    McLachlan, G., Discriminant Analysis and Statistical Pattern Recognition, New York: Willey, 2004.Google Scholar
  8. 8.
    De Amorim, R.C. and Henning, C. Recovering the number of clusters in data sets with noise features using features rescaling factors, Inf. Sci., 2015, vol. 324, pp. 126–145.CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  • E. V. Ershov
    • 1
  • O. L. Selyanichev
    • 1
    Email author
  • A. I. Lavrukhin
    • 1
  1. 1.Cherepovets State UniversityCherepovetsRussia

Personalised recommendations