Skip to main content

An Optimization Regression Model for Predicting Average Temperature of Core Dead Stock Column

  • Conference paper
  • First Online:
Intelligent Computing Methodologies (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11645))

Included in the following conference series:

  • 1912 Accesses

Abstract

Hearth activity is one of the most important factors which affect the smooth progress of production and even the life of blast furnace. However, the calculation of hearth activity depends on the empirical model entirely, and the model parameter acquisition is difficult. To overcome this deficiency, this paper presents a novel method based on an improved multiple linear regression model to predict average temperature of core dead stock column for evaluating it. In the algorithm, the Pearson correlation analysis, metallurgical formulas and the Akaike Information Criterion based on least square method are used to establish a multiple linear regression model. The method makes the estimation of hearth activity out of the empirical formula. And it is easy for the evaluated model to obtain parameters. Meanwhile, experimental results show our proposed method can achieve 0.69% average relative error on the test data set and average relative error of 0.57% on the training data set. Moreover, the function of low average temperature of core dead stock column warning can be realized.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhou, D., Cheng, S., Zhang, R., Li, Y., Chen, T.: Uniformity and activity of blast furnace hearth by monitoring flame temperature of raceway zone. ISIJ Int. 57, 1509–1516 (2017)

    Article  Google Scholar 

  2. Luomala, M.J., Mattila, O.J., Härkki, J.J.: Physical modelling of hot metal flow in a blast furnace hearth. Scand. J. Metall. 30, 225–231 (2010)

    Article  Google Scholar 

  3. Shibata, K., Kimura, Y., Shimizu, M., Inaba, S.I.: Dynamics of dead-man coke and hot metal flow in a blast furnace hearth. Revue De Métallurgie 87, 333–340 (2007)

    Article  Google Scholar 

  4. Jiao, K., Zhang, J.L., Liu, Y.X., Li, S.F., Liu, F.: Analysis on the stamping coke dissolution of hot metal in the blast furnace hearth. Can. Metall. Q. 56, 1–7 (2017)

    Article  Google Scholar 

  5. Gao, C., Jian, L., Luo, S.: Modeling of the thermal state change of blast furnace hearth with support vector machines. IEEE Trans. Industr. Electron. 59, 1134–1145 (2011)

    Article  Google Scholar 

  6. Zhang, Y., Deshpande, R., Huang, D., Chaubal, P., Zhou, C.Q.: Numerical analysis of blast furnace hearth inner profile by using CFD and heat transfer model for different time periods. Int. J. Heat Mass Transf. 51, 186–197 (2014)

    Article  Google Scholar 

  7. Zolotykh, M.O., Dmitriev, A.N., Vitkina, G.Y.: The association of various approaches to the monitoring of lining condition in the blast furnace hearth. Defect Diffus. Forum 380, 186–190 (2017)

    Article  Google Scholar 

  8. Gomes, F.S.V., Coco, K.F., Salles, J.L.F.: Multistep forecasting models of the liquid level in a blast furnace hearth. IEEE Trans. Autom. Sci. Eng. 14, 1286–1296 (2017)

    Article  Google Scholar 

  9. Komiyama, K.M., Guo, B.Y., Zughbi, H., Zulli, P., Yu, A.B.: Improved CFD model to predict flow and temperature distributions in a blast furnace hearth. Metall. Mater. Trans. B 45, 1895–1914 (2014)

    Article  Google Scholar 

  10. Agrawal, A., Kor, S.C., Nandy, U., Choudhary, A.R., Tripathi, V.R.: Real-time blast furnace hearth liquid level monitoring system. Ironmaking Steelmaking 43, 160128032747001 (2016)

    Google Scholar 

  11. Dmitriev, A.N., Zolotykh, M.O., Chen, K., Vitkina, G.Y.: The thermophysical bases of monitoring of the fireproof lining wear in the blast furnace hearth. Defect Diffus. Forum 370, 113–119 (2017)

    Article  Google Scholar 

  12. Raipala, K.: Deadman and hearth phenomena in the blast furnace. Scand. J. Metall. 29, 39–46 (2010)

    Article  Google Scholar 

  13. Chen, H., Wu, S.L., Yu, X.B.: New index of evaluating activity of blast furnace hearth. Iron Steel 42, 12–15 (2007)

    Google Scholar 

  14. Dai, B., Liang, K., Wang, X.J., Xin, L.I., Guo, Y.W., Works, I.: Development and practice of quantitative calculation models of blast furnace hearth activity. China Metallurgy (2015)

    Google Scholar 

  15. Chen, C., Gang, A.N., Plant, I.: Activity index and improvement measure of BF hearth. Iron Steel 29–33 (2018)

    Google Scholar 

  16. Jin, J.: Practice of active hearth condition during long-term production with High PCR. Bao Steel Technol. 13–16 (2002)

    Google Scholar 

  17. Neto, A.M., Rittner, L., Leite, N., Zampieri, D.E.: Pearson’s correlation coefficient for discarding redundant information in real time autonomous navigation system. In: IEEE International Conference on Control Applications, pp. 50–19 (2007)

    Google Scholar 

  18. Liu, C., Jin, R., Gong, E., Liu, Y., Yue, M.: Prediction for the performance of gas turbine units using multiple linear regression. Proc. CSEE 37, 4731–4738 (2017)

    Google Scholar 

  19. Zhou, H.Y.: On oxygen enrichment blast in front of blower. Enterprise Sci. Technol. Dev. (2012)

    Google Scholar 

  20. Arnold, T.W.: Uninformative parameters and model selection using Akaike’s information criterion. J. Wildlife Manag. 74, 1175–1178 (2011)

    Article  Google Scholar 

  21. Verhagen, S., Teunissen, P.J.G.: Least-squares estimation and Kalman filtering. In: Teunissen, P.J.G., Montenbruck, O. (eds.) Springer Handbook of Global Navigation Satellite Systems. SH, pp. 639–660. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-42928-1_22

    Chapter  Google Scholar 

  22. Mudgal, A., Baffaut, C., Anderson, S.H., Sadler, E.J., Thompson, A.L.: APEX model assessment of variable landscapes on runoff and dissolved herbicides. Trans. ASABE 53, 1047–1058 (2010)

    Article  Google Scholar 

  23. Weakley, A., Williams, J.A., Schmitteredgecombe, M., Cook, D.J.: Neuropsychological test selection for cognitive impairment classification: a machine learning approach. J. Clin. Exp. Neuropsychol. 37, 899–916 (2015)

    Article  Google Scholar 

Download references

Acknowledgement

This work is supported by the National Natural Science Foundation of China (Nos. 61472282, 61672035, and 61872004), Key Laboratory of Metallurgical Emission Reduction & Resources Recycling in AHUT (KF 17-02), Anhui Province Funds for Excellent Youth Scholars in Colleges (gxyqZD2016068), Co-Innovation Center for Information Supply & Assurance Technology in AHU (ADXXBZ201705), and Anhui Scientific Research Foundation for Returness.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bing Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, G. et al. (2019). An Optimization Regression Model for Predicting Average Temperature of Core Dead Stock Column. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26766-7_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26765-0

  • Online ISBN: 978-3-030-26766-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics