Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning

Srijith Mohanan; Prajna Mohapatra; Arun Kumar C.; Rama Krishna Adepu; Vipul Mohan Koranne; Y. G. S. Prasad; A. S. Reddy; R. V. Ramna

doi:doi:10.11648/j.am.20211003.12

Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning

Srijith Mohanan, Prajna Mohapatra, Arun Kumar C., Rama Krishna Adepu, Vipul Mohan Koranne, Y. G. S. Prasad, A. S. Reddy, R. V. Ramna

Published in Advances in Materials (Volume 10, Issue 3)

Received: 3 August 2021 Accepted: 13 August 2021 Published: 28 October 2021

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Abstract

Prior to dispatch of sinter to the blast furnace for hot metal production, the sinter product from the sinter cooler is screened to remove smaller/finer particles. The undersize so generated is called internal return fines, which are generally recirculated into the sintering machine. A very high level of internal return fines generation limits the use of virgin ore for sintering which may hamper sinter productivity. Recently, the sinter plant at Tata Steel’s Kalinganagar works has faced issues of high internal return fines generation. As the sinter plant begins to increase its productivity levels, it becomes critical to control the generation of internal return fines to allow fresh material consumption. Limited literature is available on factors affecting the internal return fines generation in sinter plant. Given the current computational capabilities, a machine learning model was developed to ascertain the factors affecting the internal return fines generation. The development of the machine learning model and the optimization carried out based on model output is described in this work. The key parameters affecting the internal return fines generation were the sintering rate, sinter basicity, charge density and temperature in the ignition hood. In Kalinganagar, the increase in ignition hood temperature was limited by the furnace refractory condition. Further, the sinter basicity is determined by the percentage of sinter in blast furnace burden. Incorporating these constraints, the model was used to optimize the process parameters to generate the lowest possible return fines. The understanding generated from this machine learning framework has resulted in a reduction of 2-3% in internal return fines generation, which implied higher net sinter production.

Published in	Advances in Materials (Volume 10, Issue 3)
DOI	10.11648/j.am.20211003.12
Page(s)	42-47
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Internal Return Fines, Sintering Rate, Prediction

References

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[3]	Ram Pravesh Bhagat, Factors Affecting Return Sinter Fines Regimes and Strand Productivity in Iron Ore Sintering, ISIJ International, 39, 9, 889-895 (1999).
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[6]	The perfect sinter plant control, Technical Document by Primetals.
[7]	Shengli Wu, Juan Zhu, Jiaxin Fan, Guoliang Zhang and Shaoguo Chen, Sintering behavior of Return Fines and their effective utilization method, ISIJ International, 53, 9, 1561-1570, 2013.
[8]	Himanshu Khandelwal, Shweta Srivastava, Adity Ganguly and Abhijit Roy, Prediction and Control of Coke Plant Wastewater Quality using Machine Learning Techniques, Coke and Chemistry, 63, 1, 47-56, 2020.
[9]	Li-Heng Hsieh, Effect of Iron Ore Concentrate on Sintering Properties, ISIJ International, 57, 11, 1937-1946, 2017.
[10]	Satendra Kumar, Arvind Kumar Jaiswal, Rameshwar Sah, Marutiram Kaza, Manjini Sambandam and Prabhat Kumar Ghorui, An Innovative Approach for Utilization of Iron Ore Microfines (-150 microns) in Sintering, AISTech 2019, pp 495-499.
[11]	Ziming Wang, Takayudi Maeda, Ko-ichiro Ohno and Kazuya Kunitomo, Effect of Magnetite on Mineral Phase Formation in Sintering Process, ISIJ International, 60, 2, 233-237, 2020.
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[13]	Hui Guo and Xing-Min Guo, Effect of Aluminum Occurrence State on the Formation of Calcium Ferrites in the Sintering Process of Iron Ore Fines, AISTech 2018, pp 595 – 606.
[14]	M. Sinha, S. H. Nistala, S. Chandra, T. R. Mankhand and A. K. Ghose, Correlating mechanical properties of sinter phases with their chemistry and its effect on sinter quality, Ironmaking and Steelmaking, 44, 2, 100-107, 2017.
[15]	C. E. Loo and R. D. Dukino, Laboratory iron ore sintering studies. 2 Quantifying flame front properties, Mineral Processing and Extractive Metallurgy, 123, 4, 197-203, 2014.

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APA Style

Srijith Mohanan, Prajna Mohapatra, Arun Kumar C., Rama Krishna Adepu, Vipul Mohan Koranne, et al. (2021). Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning. Advances in Materials, 10(3), 42-47. https://doi.org/10.11648/j.am.20211003.12

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ACS Style

Srijith Mohanan; Prajna Mohapatra; Arun Kumar C.; Rama Krishna Adepu; Vipul Mohan Koranne, et al. Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning. Adv. Mater. 2021, 10(3), 42-47. doi: 10.11648/j.am.20211003.12

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AMA Style

Srijith Mohanan, Prajna Mohapatra, Arun Kumar C., Rama Krishna Adepu, Vipul Mohan Koranne, et al. Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning. Adv Mater. 2021;10(3):42-47. doi: 10.11648/j.am.20211003.12

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@article{10.11648/j.am.20211003.12,
  author = {Srijith Mohanan and Prajna Mohapatra and Arun Kumar C. and Rama Krishna Adepu and Vipul Mohan Koranne and Y. G. S. Prasad and A. S. Reddy and R. V. Ramna},
  title = {Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning},
  journal = {Advances in Materials},
  volume = {10},
  number = {3},
  pages = {42-47},
  doi = {10.11648/j.am.20211003.12},
  url = {https://doi.org/10.11648/j.am.20211003.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.am.20211003.12},
  abstract = {Prior to dispatch of sinter to the blast furnace for hot metal production, the sinter product from the sinter cooler is screened to remove smaller/finer particles. The undersize so generated is called internal return fines, which are generally recirculated into the sintering machine. A very high level of internal return fines generation limits the use of virgin ore for sintering which may hamper sinter productivity. Recently, the sinter plant at Tata Steel’s Kalinganagar works has faced issues of high internal return fines generation. As the sinter plant begins to increase its productivity levels, it becomes critical to control the generation of internal return fines to allow fresh material consumption. Limited literature is available on factors affecting the internal return fines generation in sinter plant. Given the current computational capabilities, a machine learning model was developed to ascertain the factors affecting the internal return fines generation. The development of the machine learning model and the optimization carried out based on model output is described in this work. The key parameters affecting the internal return fines generation were the sintering rate, sinter basicity, charge density and temperature in the ignition hood. In Kalinganagar, the increase in ignition hood temperature was limited by the furnace refractory condition. Further, the sinter basicity is determined by the percentage of sinter in blast furnace burden. Incorporating these constraints, the model was used to optimize the process parameters to generate the lowest possible return fines. The understanding generated from this machine learning framework has resulted in a reduction of 2-3% in internal return fines generation, which implied higher net sinter production.},
 year = {2021}
}

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TY  - JOUR
T1  - Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning
AU  - Srijith Mohanan
AU  - Prajna Mohapatra
AU  - Arun Kumar C.
AU  - Rama Krishna Adepu
AU  - Vipul Mohan Koranne
AU  - Y. G. S. Prasad
AU  - A. S. Reddy
AU  - R. V. Ramna
Y1  - 2021/10/28
PY  - 2021
N1  - https://doi.org/10.11648/j.am.20211003.12
DO  - 10.11648/j.am.20211003.12
T2  - Advances in Materials
JF  - Advances in Materials
JO  - Advances in Materials
SP  - 42
EP  - 47
PB  - Science Publishing Group
SN  - 2327-252X
UR  - https://doi.org/10.11648/j.am.20211003.12
AB  - Prior to dispatch of sinter to the blast furnace for hot metal production, the sinter product from the sinter cooler is screened to remove smaller/finer particles. The undersize so generated is called internal return fines, which are generally recirculated into the sintering machine. A very high level of internal return fines generation limits the use of virgin ore for sintering which may hamper sinter productivity. Recently, the sinter plant at Tata Steel’s Kalinganagar works has faced issues of high internal return fines generation. As the sinter plant begins to increase its productivity levels, it becomes critical to control the generation of internal return fines to allow fresh material consumption. Limited literature is available on factors affecting the internal return fines generation in sinter plant. Given the current computational capabilities, a machine learning model was developed to ascertain the factors affecting the internal return fines generation. The development of the machine learning model and the optimization carried out based on model output is described in this work. The key parameters affecting the internal return fines generation were the sintering rate, sinter basicity, charge density and temperature in the ignition hood. In Kalinganagar, the increase in ignition hood temperature was limited by the furnace refractory condition. Further, the sinter basicity is determined by the percentage of sinter in blast furnace burden. Incorporating these constraints, the model was used to optimize the process parameters to generate the lowest possible return fines. The understanding generated from this machine learning framework has resulted in a reduction of 2-3% in internal return fines generation, which implied higher net sinter production.
VL  - 10
IS  - 3
ER  -

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Author Information

Srijith Mohanan

Tata Steel Kalinganagar, Duburi, India
Prajna Mohapatra

Tata Steel Kalinganagar, Duburi, India
Arun Kumar C.

Tata Steel Jamshedpur, Jamshedpur, India
Rama Krishna Adepu

Tata Steel Kalinganagar, Duburi, India
Vipul Mohan Koranne

Tata Steel Jamshedpur, Jamshedpur, India
Y. G. S. Prasad

Tata Steel Kalinganagar, Duburi, India
A. S. Reddy

Tata Steel Jamshedpur, Jamshedpur, India
R. V. Ramna

Tata Steel Jamshedpur, Jamshedpur, India

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APA Style

Srijith Mohanan, Prajna Mohapatra, Arun Kumar C., Rama Krishna Adepu, Vipul Mohan Koranne, et al. (2021). Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning. Advances in Materials, 10(3), 42-47. https://doi.org/10.11648/j.am.20211003.12

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ACS Style

Srijith Mohanan; Prajna Mohapatra; Arun Kumar C.; Rama Krishna Adepu; Vipul Mohan Koranne, et al. Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning. Adv. Mater. 2021, 10(3), 42-47. doi: 10.11648/j.am.20211003.12

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AMA Style

Srijith Mohanan, Prajna Mohapatra, Arun Kumar C., Rama Krishna Adepu, Vipul Mohan Koranne, et al. Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning. Adv Mater. 2021;10(3):42-47. doi: 10.11648/j.am.20211003.12

Copy | Download

@article{10.11648/j.am.20211003.12,
  author = {Srijith Mohanan and Prajna Mohapatra and Arun Kumar C. and Rama Krishna Adepu and Vipul Mohan Koranne and Y. G. S. Prasad and A. S. Reddy and R. V. Ramna},
  title = {Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning},
  journal = {Advances in Materials},
  volume = {10},
  number = {3},
  pages = {42-47},
  doi = {10.11648/j.am.20211003.12},
  url = {https://doi.org/10.11648/j.am.20211003.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.am.20211003.12},
  abstract = {Prior to dispatch of sinter to the blast furnace for hot metal production, the sinter product from the sinter cooler is screened to remove smaller/finer particles. The undersize so generated is called internal return fines, which are generally recirculated into the sintering machine. A very high level of internal return fines generation limits the use of virgin ore for sintering which may hamper sinter productivity. Recently, the sinter plant at Tata Steel’s Kalinganagar works has faced issues of high internal return fines generation. As the sinter plant begins to increase its productivity levels, it becomes critical to control the generation of internal return fines to allow fresh material consumption. Limited literature is available on factors affecting the internal return fines generation in sinter plant. Given the current computational capabilities, a machine learning model was developed to ascertain the factors affecting the internal return fines generation. The development of the machine learning model and the optimization carried out based on model output is described in this work. The key parameters affecting the internal return fines generation were the sintering rate, sinter basicity, charge density and temperature in the ignition hood. In Kalinganagar, the increase in ignition hood temperature was limited by the furnace refractory condition. Further, the sinter basicity is determined by the percentage of sinter in blast furnace burden. Incorporating these constraints, the model was used to optimize the process parameters to generate the lowest possible return fines. The understanding generated from this machine learning framework has resulted in a reduction of 2-3% in internal return fines generation, which implied higher net sinter production.},
 year = {2021}
}

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TY  - JOUR
T1  - Prediction and Optimization of Internal Return Fines Generation in Iron Ore Sintering Using Machine Learning
AU  - Srijith Mohanan
AU  - Prajna Mohapatra
AU  - Arun Kumar C.
AU  - Rama Krishna Adepu
AU  - Vipul Mohan Koranne
AU  - Y. G. S. Prasad
AU  - A. S. Reddy
AU  - R. V. Ramna
Y1  - 2021/10/28
PY  - 2021
N1  - https://doi.org/10.11648/j.am.20211003.12
DO  - 10.11648/j.am.20211003.12
T2  - Advances in Materials
JF  - Advances in Materials
JO  - Advances in Materials
SP  - 42
EP  - 47
PB  - Science Publishing Group
SN  - 2327-252X
UR  - https://doi.org/10.11648/j.am.20211003.12
AB  - Prior to dispatch of sinter to the blast furnace for hot metal production, the sinter product from the sinter cooler is screened to remove smaller/finer particles. The undersize so generated is called internal return fines, which are generally recirculated into the sintering machine. A very high level of internal return fines generation limits the use of virgin ore for sintering which may hamper sinter productivity. Recently, the sinter plant at Tata Steel’s Kalinganagar works has faced issues of high internal return fines generation. As the sinter plant begins to increase its productivity levels, it becomes critical to control the generation of internal return fines to allow fresh material consumption. Limited literature is available on factors affecting the internal return fines generation in sinter plant. Given the current computational capabilities, a machine learning model was developed to ascertain the factors affecting the internal return fines generation. The development of the machine learning model and the optimization carried out based on model output is described in this work. The key parameters affecting the internal return fines generation were the sintering rate, sinter basicity, charge density and temperature in the ignition hood. In Kalinganagar, the increase in ignition hood temperature was limited by the furnace refractory condition. Further, the sinter basicity is determined by the percentage of sinter in blast furnace burden. Incorporating these constraints, the model was used to optimize the process parameters to generate the lowest possible return fines. The understanding generated from this machine learning framework has resulted in a reduction of 2-3% in internal return fines generation, which implied higher net sinter production.
VL  - 10
IS  - 3
ER  -

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