Skip to main content
SpringerLink
Log in

Soft Sensors and Diagnostic Models Using Real-Time Data of Coke-Making at Tata Steel

  • Original Article
  • Published:
Transactions of the Indian Institute of Metals Aims and scope Submit manuscript

Abstract

Constant generation of massive data in steel industry offers huge opportunity to enable recognition of phenomena involved and identification of levers for influencing the process towards higher efficiency. Through few illustrative examples from coke-making processes, this paper attempts to show how process visualization and diagnostics through soft sensors has contributed to get to the current level of understanding and quantification. Thinking through basic phenomena and having it visualized and quantified using data has helped get a grip on understanding how raw materials and process conditions influence performance outcomes viz. productivity, efficiency and quality. Various terms have been used to describe the approach—‘derived parameters’, ‘soft sensors’ or even empirical models. Further, process performance gets diagnosed faster and sometimes anticipate helps in initiating corrective action through identified control levers. Together they represent an approach which adds on to the efforts of sensor development and analytical modelling to size up performance of coke ovens.

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

Similar content being viewed by others

References

  1. Ganguly A, Srinivas Reddy A, and Kumar A, ISIJ Int (2010). https://doi.org/10.2355/isijinternational.50.1010

    Article  Google Scholar 

  2. Ganguly A, Srinivas Reddy A, and Kumar A, ISIJ Int (2022). https://doi.org/10.2355/isijinternational.ISIJINT-2022-246

    Article  Google Scholar 

  3. Bertling H, ISIJ Int 39 (1999) 617. https://doi.org/10.2355/isijinternational.39.617

    Article  CAS  Google Scholar 

  4. Dash P S, Krishnan S H, Sharma R, Banerjee P K, and Halder S K, ISIJ Int 45 (2005) 1577. https://doi.org/10.3103/S1068364X13040030

    Article  CAS  Google Scholar 

  5. Prakash Tiwari H, Kumar Haldar S, Mishra P, Kumar A, Dutta S, and Kumar A, Metall Res Technol 114 (2017) 501. https://doi.org/10.1051/metal/2017046

    Article  CAS  Google Scholar 

  6. Anand A, and Shankar A, Coke Chem 64 (2021) 311. https://doi.org/10.3103/S1068364X21070024

    Article  Google Scholar 

  7. Patrick J W, and Wilkinson H C, Coke Oven Managers Association (COMA) Yearbook, (Mexborough), The Coke Oven Managers’ Association, London (1977), p 245.

    Google Scholar 

  8. Lumadue M, Thomas S, McKnight S J, Pisula S, and DeLibero M, Determination of ‘End of Coking’ in Byproduct Recovery Cokemaking, Proc. Association for Iron and Steel Technology (AISTech 2015), (Ohio), Iron & Steel Technology, Pennsylvania, (2015), p 126.

  9. Satomi K, Okada Y, Fujimura T, Oshima Y, Shibata Y, Sato Y, Nippon Kokan Technical Reports, Nippon Kokan, Tokyo, (1980), p 28.

  10. Bhushan A, Sonkhla S, Sen S, Reddy A S, Mishra P, and Kumar A, Development of new thermal indices in Heat-Recovery Coke making to ensure completion of the Carbonization Process, Proc. Association for Iron & Steel Technology (AISTech 2018), (Philadelphia), Iron & Steel Technology, Pennsylvania, (2018), p 18.

Download references

Acknowledgements

The above account is culled from numerous internal reports on work carried out Tata Steel over the past many years. Mr. Ashok Kumar deserves special mention under whose able guidance and motivation, most of the works were carried out. Other contributors are C. Gopi, Vinal Thool and Biswajyoti Biswas—who are the members of various technology groups. Also, the inputs of experienced and insightful operation leaders and colleagues from research both in India and Europe, have helped shape many of the thoughts—deserve gratitude.

Author information

Authors and Affiliations

  1. Tata Steel Ltd, Jamshedpur, India

    Sristy Raj, Adity Ganguly, Ashutosh Bhushan, Amitabh Shankar & Subhadra Sen

Authors
  1. Sristy Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adity Ganguly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashutosh Bhushan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amitabh Shankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Subhadra Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sristy Raj.

Ethics declarations

Conflict of interest

The authors declare 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

Raj, S., Ganguly, A., Bhushan, A. et al. Soft Sensors and Diagnostic Models Using Real-Time Data of Coke-Making at Tata Steel. Trans Indian Inst Met (2024). https://doi.org/10.1007/s12666-024-03309-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12666-024-03309-9

Keywords

Search

Navigation