Model predictive control of the refining stage in an electric arc furnace using soft sensors

Blažič, Aljaž (author), Škrjanc, Igor (author), Logar, Vito (author)

In this paper, a novel Model Predictive Control (MPC) based on soft sensors is developed to optimize the refining stage in Electric Arc Furnaces (EAFs). Given the significant energy consumption and environmental impact associated with EAF operations, there is a critical need for more precise and efficient control strategies. The proposed MPC approach utilizes real-time soft sensors to accurately determine critical parameters, i.e., bath temperature and dissolved oxygen content. By replacing operators’ subjective judgments with continuous, data-driven feedback, this methodology facilitates precise control adjustments throughout the refining process. The implementation of this MPC has shown that the duration of the refining stage has been reduced by approximately 80 seconds, which directly translates into shorter tap-to-tap times, lower energy consumption and improved operational efficiency. In addition, consistently maintaining optimal bath conditions ensures improved steel quality, greater reliability, and higher overall productivity of the EAF process.

• Language: English
• Keywords: model predictive control, bath temperature, electric arc furnace, dissolved oxygen
• Work type: Article
• Typology: 1.08 - Published Scientific Conference Contribution
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 8 str.
• PID: 20.500.12556/RUL-172523
• UDC: 621.365.2:681.5
• ISSN on article: 1742-6596
• DOI: 10.1088/1742-6596/3050/1/012010
• COBISS.SI-ID: 246338563
• Publication date in RUL: 08.09.2025

Record is a part of a proceedings

• Title: 6th European Academic Symposium on EAF Steelmaking 2025 (EASES 2025)
• COBISS.SI-ID: 246334211

Record is a part of a journal

• Title: Journal of physics : Conference series
• Publisher: Institute of Physics Publishing
• ISSN: 1742-6596
• COBISS.SI-ID: 2035044

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: prediktivno vodenje, temperatura kopeli, elektroobločna peč, raztopljen kisik

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0219
• Name: Modeliranje, simulacija in vodenje procesov