Skip to main content
Log in

Development of a method for discrete-continuous inverting control of pressure control circuit inside a heating furnace

  • Published:
Metallurgist Aims and scope

Abstract

The article addresses the problem of designing an inverting regulator for controlling the pressure in a furnace for metal heating prior to rolling. Based on actual engineering data, a regression model is constructed with the elements of differential equations describing the in-furnace pressure with high accuracy, taking into account various technological factors (the position of the gate, the total gas flow into the furnace, and the operation of the metal unloading gates). Based on the obtained model and considering the features of the pressure circuits, a new discrete-continuous inverting pressure control method was designed and modeled, taking the operation of unloading machines into account.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Ginkul SI, Biryukov AB, Ivanova AA, Gnitiev PA (2018) Predictive mathematical model of metal heating in walking-beam furnace. Metallurg 1:24–28

    Google Scholar 

  2. Biryukov AB, Ivanova AA (2018) Diagnostics of the thermal state of metal during heat treatment in continuous furnaces. Metallurg 4:33–37

    Google Scholar 

  3. Andreev SM (2017) Forecasting heating time of blanks in non-stationary continuous furnaces. Élektrotekh Sist Kompleks 3(36):35–40

    Google Scholar 

  4. Zhukov PI, Glushchenko AI, Fomin AV (2020) Prediction model of temperature of cast billet based on its heating retrospection using boosting ‘random forest’ structure. Vest Nsu Ser Inform Tekhnol 18(4):11–27

    Google Scholar 

  5. Biryukov AB, Ivanova AA (2019) Process control of metal heat treatment in a furnace using a diagnostic system of batch heat capacity. Metallurg 8:54–58

    Google Scholar 

  6. Vokhmyakov AM, Kazyaev MD, Kazyaev DM (2011) Investigation of convective heat transfer in a through furnace equipped with high-speed burners. Tzsvet Met 12:89–93

    Google Scholar 

  7. Biryukov AB (2013) Analysis of measures to increase the value of fuel utilization factor when heating metal in furnaces. Énergosberezheniye Énergetika Énergoaudit 10(116):31–37

    Google Scholar 

  8. Biryukov AB (2021) Investigating the dependence of design parameters of heat exchange nozzles of regenerative burners on the thermotechnical conditions of a process. Stal 4:65–69

    Google Scholar 

  9. Parsunkin BN, Samarina IG (2017) System of automatic energy-saving control based on a mathematical model of the gas-dynamic behavior of a continuous furnace. Élektrotekh Sist Kompleks 2(35):55–60

    Google Scholar 

  10. Eremenko YI, Poleshchenko DA, Glushchenko AI (2015) Using a neural network optimizer for PI-controller parameters to control heating furnaces in various operating modes. Upravl Bol’sh Sist 56:143–175

    Google Scholar 

  11. Muravyeva EA, Solovyov KA, Semibratchenko AV (2017) Flow regulation using a fuzzy controller with a double rule base. Nefte Delo 15(1):210–215

    Google Scholar 

  12. Fomin AV (2023) Mathematical model for the dependence of gas consumption in the furnace zones on the performance of the rolling mill. Metallurg 2:111–116. https://doi.org/10.52351/00260827_2023_02_111.—EDN VWRXSZ

    Article  Google Scholar 

  13. Parsunkin BN, Vasiliev MI, Sibileva NS (2018) Energy-saving automatic fuzzy pressure control in working space of heating furnaces. Élektrotekh Sist Kompleks 2:63–69

    Google Scholar 

  14. Widrow B (1987) Adaptive inverse control. Ifac Proc Vol 20:1–5 (Pergamon)

    Article  Google Scholar 

  15. Plett GL (2003) Adaptive inverse control of linear and nonlinear systems using dynamic neural networks. IEEE Trans Neural Netw 14(2):360–376

    Article  CAS  PubMed  Google Scholar 

  16. Chernov KA, Fomin AV, Glushchenko AI (2022) Adaptive control of technological units of JSC OEMK named after A.A. Ugarov based on neural network setting of regulator parameters. Metallurg 1:70–78. https://doi.org/10.52351/00260827_2022_01_70

    Article  Google Scholar 

  17. Fomin AV, Glushchenko AI (2019) Improving control quality of heating furnaces at JSC ‘OEMK’ by using open-loop PI controllers. Metallurg 3:37–42

    Google Scholar 

  18. Fomin AV, Zhukov PI (2022) Mathematical model of pressure in the chamber of a multizone furnace. Promish Asu Kontrol 1:19–25. https://doi.org/10.25791/asu.1.2022.1339

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. V. Fomin.

Additional information

Translated from Metallurg, No. 11, pp 118–123, 2023. https://doi.org/10.52351/00260827_2023_11_118

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

Fomin, A.V. Development of a method for discrete-continuous inverting control of pressure control circuit inside a heating furnace. Metallurgist (2024). https://doi.org/10.1007/s11015-024-01669-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11015-024-01669-7

Keywords

UDC

Navigation