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A Process Intensification Approach to Improve Productivity, Quality, and Reducing Emissions in the Iron Ore Sintering Process

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Abstract

Process Intensification (PI) is a rapidly growing field of research aiming to improve productivity, accelerate the process with higher reaction rates innovatively, improve efficiency, safety, and reduce emissions to fulfill Environmental Social, and Governance (ESG) commitments. The PI approach is also a pathfinding tool to achieve carbon neutrality and net zero. Intensification of the iron ore sintering process was approached by injecting hydrogen-rich gas into the sinter bed. The location of the injection was critical to the pyrolysis of the solid fuel, and the SOx and NOx emissions. After installing the injection system in the most appropriate zone, the process intensified, and the emissions were reduced. Oxy-hydrogen burners are suggested in the ignition furnace to lower NOx emissions. In another approach of maximizing the calcined lime addition, a maximum in the productivity was noticed at 40 kg/t of sinter; beyond which there was no further intensification. Granulation was intensified with polymer and lignin binders, but no significant increase in bed permeability was noticed. A new term process intensification index has been introduced taking into account the change in productivity, quality, emissions, and cost. An Artificial Intelligence (AI) model with an explainable optimizer is suggested to monitor the intensification.

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Data availability

The authors declare that they abide by the data availability regulations of the journal. Our manuscript contains sufficient data/information within the work and through the citations of our previous work. We have not stored our data in any online repository.

Abbreviations

AI:

Artificial intelligence

COG:

Coke oven gas

EOS:

Emission-optimized sintering

ESG:

Environmental Social and Governance

ESP:

Electrostatic precipitator

HTBF:

High-temperature bag filter

LPG:

Liquefied petroleum gas

MEROS:

Maximized emission reduction of sintering

ML:

Machine learning

MND:

Mixing and nodulizing drum

NOx :

Oxides of nitrogen

PCDD/Fs:

Polychlorinated dibenzo paradioxins/furans

PI:

Process intensification

SFCA:

Silico Ferrite of Calcium and Aluminum

SOx :

Oxides of sulfur

TI:

Tumbler index

WGR:

Waste gas recycling

WHRS:

Waste heat recovery system

References

  1. Angalakuditi VB, Karre S, Singh LR, Baral SS (2021) Rate-limiting mechanism in iron ore sintering process with waste gas recycling. Trans Indian Inst Met 74:713–723. https://doi.org/10.1007/s12666-020-02162-w

    Article  CAS  Google Scholar 

  2. Acharyulu AVB, Sudhakar K, Ramarao G et al (2021) Thermodynamic and mineralogical aspects of injecting LPG, coke oven gas, and oxygen into goethitic iron ore sintering process. J Sustain Metall. https://doi.org/10.1007/s40831-020-00327-x

    Article  Google Scholar 

  3. Angalakuditi VB, Gujare R, Anbarasu R et al (2022) Characterization and in situ abatement of SOx, NOx, and PCDD/Fs in iron ore sinter machine wind legs. J Sustain Metall. https://doi.org/10.1007/s40831-022-00524-w

    Article  Google Scholar 

  4. Ji Z, Zhou H, Fan X et al (2020) Insight into the application of hydrogen-rich energy in iron ore sintering: parameters optimization and function mechanism. Process Saf Environ Prot 135:91–100. https://doi.org/10.1016/j.psep.2019.12.016

    Article  CAS  Google Scholar 

  5. Angalakuditi VB, Bhadravathi P, Gujare R et al (2022) Mineralogical aspects of reducing lump iron ore, pellets, and sinter with hydrogen. Metall Mater Trans B. https://doi.org/10.1007/s11663-022-02442-4

    Article  Google Scholar 

  6. Williams B, Kreuzer J, Giddens T, Kucukelbir A (2020) Accelerating product chemistry refinement using machine learning. In: AISTech2020 proceedings of the iron and steel technology conference. AIST, pp 1886–1895

  7. Umadevi T, Naik DK, Sah R et al (2016) Studies on parameters affecting sinter strength and prediction through artificial neural network model. Miner Process Extr Metall 125:32–38. https://doi.org/10.1179/1743285515Y.0000000020

    Article  CAS  Google Scholar 

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Acknowledgements

The authors express their gratitude to the management of JSW Steel Ltd, Vijayanagar Works and Birla Institute of Technology and Science, Goa Campus.

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Authors and Affiliations

  1. JSW Steel Ltd, Vijayanagar Works, Toranagallu, 583275, Karnataka, India

    Veera Brahmacharyulu Angalakuditi, Ashwin Appala, Naveen Singh & Lokendra Raj Singh

  2. Birla Institute of Technology and Science, Goa Campus, Vasco-Da-Gama, 403726, India

    Veera Brahmacharyulu Angalakuditi & Saroj Sundar Baral

Authors
  1. Veera Brahmacharyulu Angalakuditi
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  2. Ashwin Appala
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  3. Naveen Singh
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  4. Lokendra Raj Singh
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  5. Saroj Sundar Baral
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Correspondence to Veera Brahmacharyulu Angalakuditi.

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The contributing editor for this article was Il Sohn.

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Angalakuditi, V.B., Appala, A., Singh, N. et al. A Process Intensification Approach to Improve Productivity, Quality, and Reducing Emissions in the Iron Ore Sintering Process. J. Sustain. Metall. 9, 73–80 (2023). https://doi.org/10.1007/s40831-023-00660-x

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  • DOI: https://doi.org/10.1007/s40831-023-00660-x

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