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Low-Carbon Manufacturing and Optimization Strategies of Iron and Steel Industry Based on Industrial Metabolism

  • Design, Production, and Applications of Steels for a Sustainable Future
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Abstract

Energy saving and emissions reduction in the iron and steel industry is a significant challenge to achieve carbon neutrality and sustainable development. Many studies focus on the optimization of materials, energy and carbon emissions but lack of optimization strategies in the iron and steel industry. A comprehensive and effective system model is still needed to optimize the CO2 emissions to face the production planning of a company in the future. This article applies industrial metabolism to develop a practical optimization model for improving materials consumption and energy consumption in coking, sintering, pelleting, ironmaking and steelmaking, and the minimum carbon emission is set as the optimization objective. The results show that carbon emissions reduce 148.65 kg/ton crude steel compared with the original data with a yield of 8.4 Mt crude steel. In addition, the optimum material and energy consumption of each production unit under different production plans is studied. If breakthrough technologies are not applied to long-route processes, improving scrap steel ratio is the most promising approach for low-carbon manufacturing in the iron and steel industry in the future.

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References

  1. WSA, "World Steel Association2021". https://worldsteel.org/zh-hans/media-centre/press-releases/2021/2021-steel-statistical-yearbook-published/. Accessed 14 December 2021.

  2. H. Ali, A. Marlene, J.C.R. Cardenas, L. Price, and R. Triolo, Resources. Conserv. Recycl. 113, 127 (2016).

    Google Scholar 

  3. X. Wang, B. Yu, R. An, F. Sun, and S. Xu, Appl. Energy 322, 119453 (2022).

    Article  Google Scholar 

  4. R. An, B. Yu, R. Li, and Y.M. Wei, Appl. Energy 226, 862 (2018).

    Article  Google Scholar 

  5. J. Shen, Q. Zhang, L. Xu, S. Tian, and P. Wang, J. Clean. Prod. 326, 129354 (2021).

    Article  Google Scholar 

  6. L. Ye, Z. Peng, L. Wang, A. Anzulevich, I. Bychkov, and D. Kalganov, JOM 71(11), 3931 (2019).

    Article  Google Scholar 

  7. M.Q. Abdul, S. Ahmed, S.Z. Dawal, and Y. Nukman, Renew. Sustain. Energy Rev. 55, 537 (2016).

    Article  Google Scholar 

  8. J. Tang, M. Chu, F. Li, C. Feng, Z. Liu, and Y. Zhou, International Journal of Minerals. Metallurgy and Materials 27(6), 713 (2020).

    Article  Google Scholar 

  9. T. Buergler, and J. Prammer, BHM Berg- Huettenmaenn. Monatsh. 164(11), 447 (2019).

    Article  Google Scholar 

  10. WorldMetalBulletin. HYBRIT project will enter the pilot phase. http://www.worldmetals.com.cn/viscms/bianjituijianxinwen1277/20180906/245527.html. Accessed 6 September 2018.

  11. X. Zhang, K. Jiao, J. Zhang, and Z. Guo, J. Clean. Prod. 306, 127259 (2021).

    Article  Google Scholar 

  12. Q. Wang, G. Li, W. Zhang, and Y. Yang, Metall. and Mater. Trans. B. 50(4), 2006 (2019).

    Article  Google Scholar 

  13. M.A. Quader, S. Ahmed, R.A.R. Ghazilla, S. Ahmed, and M. Dahari, Renew. Sustain. Energy Rev. 50, 594 (2015).

    Article  Google Scholar 

  14. S. Watakabe, K. Miyagawa, S. Matsuzaki, T. Inada, Y. Tomita, and K. Saito, ISIJ Int. 53(12), 2065 (2013).

    Article  Google Scholar 

  15. S. Gupta, and L. Li, JOM 74(2), 414 (2022).

    Article  Google Scholar 

  16. A.G. Olabi, T. Wilberforce, K. Elsaid, E.T. Sayed, H.M. Maghrabie, and M.A. Abdelkareem, J. Clean. Prod. 362, 132300 (2022).

    Article  Google Scholar 

  17. J. Chen, H. Zhang, and G. Zhao, Fresenius Environ. Bull. 31, 8077 (2022).

    Google Scholar 

  18. W. Sun, Q. Wang, Y. Zhou, and J. Wu, Appl. Energy 268, 114946 (2020).

    Article  Google Scholar 

  19. Z. Wen, Y. Wang, H. Li, Y. Tao, and D.C. De, J Environ Manage 246, 717 (2019).

    Article  Google Scholar 

  20. Q. Zhang, Y. Li, J. Xu, and G. Jia, J. Clean. Prod. 172, 709 (2018).

    Article  Google Scholar 

  21. Q. Zhang, J. Xu, Y. Wang, A. Hasanbeigi, W. Zhang, and H. Lu, Appl. Energy 209, 251 (2018).

    Article  Google Scholar 

  22. H. Zhang, W. Sun, W. Li, and G. Ma, Appl. Energy 309, 118485 (2022).

    Article  Google Scholar 

  23. S. Zhang, B. Yi, F. Guo, and P. Zhu, J. Clean. Prod. 340, 130813 (2022).

    Article  Google Scholar 

  24. T. Dai, Resources Conserv. Recycl. 95, 183 (2015).

    Article  Google Scholar 

  25. Q. Zhang, Z. Wei, J. Ma, Z. Qiu, and T. Du, Appl. Therm. Eng. 157, 113635 (2019).

    Article  Google Scholar 

  26. Y. Wang, Z. Wen, J. Yao, and C.D. Dinga, Renew. Sustain. Energy Rev. 134, 110128 (2020).

    Article  Google Scholar 

  27. H. Na, J. Sun, Z. Qiu, Y. Yuan, and T. Du, Energy 257, 124822 (2022).

    Article  Google Scholar 

  28. B. Lu, G. Chen, D. Chen, and W. Yu, Appl. Therm. Eng. 100, 285 (2016).

    Article  Google Scholar 

  29. A. Özgür, Y. Uygun, and M.T. Hütt, Comput. Ind. Eng. 151, 106606 (2021).

    Article  Google Scholar 

  30. J. Lin, M. Liu, J. Hao, and S. Jiang, Comput. Oper. Res. 72, 189 (2016).

    Article  Google Scholar 

  31. Y. Wang, C. Chen, Y. Tao, Z. Wen, B. Chen, and H. Zhang, Appl. Energy 242, 46 (2019).

    Article  Google Scholar 

  32. D.S. Wong, A. Kvithyld, and H. Peng, JOM 72(10), 3332 (2020).

    Article  Google Scholar 

  33. S. Yu. Mathematical model of metallurgical process. (Metallurgical Industry Press, Beijing, 1996), p 23.

  34. IPCC. 2006 IPCC Guidelines forNational Greenhouse Gas Inventories. In: Simon Eggleston LBM, Todd Ngara and Kiyoto Tanabe, editor. Japan2006. https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html.

  35. X. Li, Carboniferous Flow Operation and CO2 Emission Reduction in Steel Manufacturing Process. (Metallurgical Industry Press, Beijing, 2020), p 257.

  36. F. Gao, MATLAB Smart Algorithm Super Learning Manual. (Posts & Telecom Press, Beijing, 2014), p 105.

  37. G. Zhao, X. Gao, Z. Wang, S. Yang, J. Duan, and J. Hu, ISIJ Int. 59(2), 381 (2019).

    Article  Google Scholar 

  38. K.E. Daehn, A.S. Cabrera, and J.M. Allwood, Environ. Sci. Technol. 51(11), 6599 (2017).

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Fund of National Natural Science Foundation of China (Grant NO. 52175480). Many thanks to the anonymous reviewers for their valuable and constructive comments.

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Authors and Affiliations

  1. Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan, 430081, Hubei, People’s Republic of China

    Junwen Chen & Hua Zhang

  2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, People’s Republic of China

    Gang Zhao

  3. School of Management, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, People’s Republic of China

    Shujun Yu

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  1. Junwen Chen
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  2. Hua Zhang
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  3. Gang Zhao
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Contributions

JC: Methodology, Data curation, Writing-Original draft preparation; HZ: Resources, Supervision, Funding acquisition; GZ: Methodology, Conceptualization, Project administration; SY: Conceptualization, Project administration.

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Correspondence to Gang Zhao.

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Chen, J., Zhang, H., Zhao, G. et al. Low-Carbon Manufacturing and Optimization Strategies of Iron and Steel Industry Based on Industrial Metabolism. JOM 75, 2199–2211 (2023). https://doi.org/10.1007/s11837-023-05830-6

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  • DOI: https://doi.org/10.1007/s11837-023-05830-6

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