Analysis of the Correlation Between Ferrous Scrap Quality and Metallic Yield in EAF Steelmaking
DOI:
https://doi.org/10.24425/afe.2026.158032Abstract
This article provides a statistical assessment of the impact of the grade structure and classes of steel scrap in the metal charge on the efficiency of the steel melting process in an electric arc furnace (EAF), measured by the liquid metal yield index. The research involved a two-stage procedure: first, an analytical simulation of changes in the mass of liquid steel depending on the configuration of metallic charge materials was performed, followed by statistical verification of actual production data obtained from heat cards under industrial conditions. The analysis was conducted using technological documentation on a population of 180 randomly selected heats from a domestic steel plant employing an electric arc furnace (EAF) in the steel melting process. The conclusions and mathematical relationships formulated based on the analyses are universally applicable. They can constitute a supporting tool for engineers in any electric steel plant, allowing the determination and optimization of scrap charge selection to simultaneously maximize technological yield and minimize operating costs.
References
[1] Janiszewski, K. (2021). The influence of ferrous scrap's grade on the yield of steel smelted in an electric-arc furnace. Metalurgija. 60(3-4), 188-190.
[2] Cheng, J. Ang, Z. Zhang, P. Hang, L. Ding, Y. Zhang, L. Zhang, Ch. & Chen, L. (2026). Research progress on multi-physics field coupling and optimization control technology in the electric arc furnace steelmaking process. Progress in Natural Science: Materials International. 36(4), 701-715. DOI: https://doi.org/10.1016/j.pnsc.2026.04.001.
[3] Schwietz, J. Panic, B., Saternus, M., Pieprzyca, J. & Janiszewski, K. (2024). Improvement of the foaming agent feeding process to an electric arc furnace by analyzing the sound generated by an electric arc and the coefficient of variation of active power consumption. Materials. 17(23), 1-13. https://doi.org/10.3390/ma17235860.
[4] Abadia, M.M., Tangb, T. & Rashidia, M.M. (2024). A review of simulation and numerical modeling of electric arc furnace (EAF) and its processes. Heliyon. 10(11), e32157, 1-21. https://doi.org/10.1016/j.heliyon.2024.e32157.
[5] Manojlović, V., Kamberović, Ž., Korać, M. & Dotlić, M. (2022). Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters. Applied Energy. 307, 118209. https://doi.org/10.1016/j.apenergy.2021.118209.
[6] Marulanda-Durango, J., Escobar-Mejía, A., Alzate-Gómez, A. & Álvarez-López, M.A. (2021). A Support Vector machine-Based method for parameter estimation of an electric arc furnace model. Electric Power Systems Research. 196, 107228. https://doi.org/10.1016/j.epsr.2021.107228.
[7] Blažič, A., Škrjanc, I. & Logar, V. (2024). Prediction interval soft sensor for dissolved oxygen content estimation in an electric arc furnace. Applied Soft Computing. 167A, 112246, 1-12. https://doi.org/10.1016/j.asoc.2024.112246.
[8] Wei, R. Zheng, X. Zhu, Y. Feng, S. Long, H. & Xu, C.C. (2024). Hydrothermal bio-char as a foaming agent for electric arc furnace steelmaking: Performance and mechanism. Applied Energy. 353A, 122084. DOI: 10.1016/j.apenergy.2023.122084.
[9] Agnihotri, A., Singh, P.K., Singh, D. & Gupta, M. (2021). Foamy slag practice to enhance the energy efficiency of electric arc furnace: An industrial scale validation. Materials Today: Proceedings. 46(3), 1537-1542. DOI:10.1016/j.matpr.2021.01.770.
[10] Son, K., Lee, J., Hwang, H., Jeon, W., Yang, H., Sohn, I., Kim, Y. & Um, H. (2021). Slag foaming estimation in the electric arc furnace using machine learning based long short-term memory networks. Journal of Materials Research and Technology. 12, 555-568. https://doi.org/10.1016/j.jmrt.2021.02.085.
[11] LMM Group. (2024). Study on the melting behavior of scrap steel in electric arc furnace steelmaking process. Retrieved July 14, 2026, from https://www.lmmgroupcn.com/study-on-the-melting-behavior-of-scrap-steel-in-electric-arc-furnace-steelmaking-process/
[12] Mapelli, C. Dall’Osto, G. Scolari, S. Bazri, S. Zanaglio V. & Mombelli, D. (2025). Modelling the influence of scrap size on charge-to-melt time and metallic loss in EAFs. Ironmaking & Steelmaking. https://doi.org/10.1177/03019233251387764.
[13] Haupt, M. Vadenbo, C. Zeltner, C. & Hellweg, S. (2017). Influence of input-scrap quality on the environmental impact of secondary steel production. Journal of Industrial Ecology. 21(2), 391-401. https://doi.org/10.1111/jiec.12439.
[14] Sandberg, E. Lennox, B. & Undvall, P. (2007). Scrap management by statistical evaluation of EAF process data. Control Engineering Practice. 15(9), 1063-1075. DOI: 10.1016/j.conengprac.2007.01.001.
[15] Carlsson, L. S. Samuelsson, P. Jönsson, P. (2020). Modeling the Effect of Scrap on the Electrical Energy Consumption of an Electric Arc Furnace. MDPI Processes. 8(9), 1044, 1-35. https://doi.org/10.3390/pr8091044.
[16] Patents CN103114170B10.09.2014. (2012). Smelting process for increasing yield of electric-arc furnace (Carbon injection and slag reduction technology). Retrieved August 25, 2026, from https://patents.google.com/patent/CN103114170B/en.
[17] National Academies of Sciences, Engineering, and Medicine. (2023). Health risk considerations for the use of unencapsulated steel slag. The National Academies Press. https://doi.org/10.17226/26881.
[18] Polish Standard PN-85/H-15000 – Steel scrap.
[19] Kwiecień, J. (2009). The influence of the share of steel scrap in the metallic charge on the efficiency of the converter steel making process. Master thesis. Katowice.
[20] Kudliński, Z. (2008). The influence of the share of post-amortization steel scrap in the metallic charge of the converter process on the amount of steel yield from smelting. Hutnictwo.pl. 2(4), 15-17.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Archives of Foundry Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
