A review study on ladle slag detection technologies in continuous casting process
Abstract
In steel production, continuous casting method using BOF or EAF is increasing day by day. However, the complex nature of the continuous casting process poses many challenges for steelmakers. During the general steel production process of liquid steel, the state of ladle slag penetration into liquid steel is one of the most influential factors in steel quality. If the ingress of ladle slag into liquid steel cannot be controlled, undesirable results in terms of poor quality, safety and castability can occur. Generally, ladle slag consists of oxides such as CaO, SiO 2, Al 2 O 3, MgO. In conventional methods, the operator prevents the slag entry by manually controlling it. This method is performed directly by the operator, so the error rate is high. For this reason, it is not desired to be used by steel producers who want high quality products. In this context, steel mills carry out various activities to separate slag from liquid steel. The development of sensor technologies has accelerated the slag detection process. Acceleration and magnetic sensors are among the most widely used methods in this field. In this study, the systems used worldwide for the determination of slag in the continuous casting process were investigated and presented. The advantages and disadvantages of these systems are discussed. The detection methods by considering investment cost, detection time, accuracy were compared and presented. In the scope of this study, it is seen that every method has own advantages and disadvantages over other.
Keywords
References
- 1. Oberbach M. High Grade Refractories for Clean Steel Technology. In: Steel Technology International, 1991. 26: pp 169–176.
- 2. Ozturk M., U. Korkut, Investigation of mechanical and microstructural performance of alkali activated electrical arc furnace slag mortars. International Advanced Research and Engineering journal, 2019. 03: p. 55–59.
- 3. Madias J., Electric Furnace Steelmaking. 2014, Elsevier Ltd.
- 4. Li P.Y., D.P. Tan., X.H. Pan, B.Y. Lin, Steel water continuous casting slag detection system based on wavelet. Key Engineering Materials, 2007. 353: p 3067–3071.
- 5. Yoshitani Y., Contribution of Control System to Energy Savings in Steel Works. IFAC Proceedings Volumes, 1983. 16: p 25–38.
- 6. Takács G., K. Ondrejkovič, G. Hulkó, A low-cost non-invasive slag detection system for continuous casting. IFAC-PapersOnLine, 2017. 50:438–445.
- 7. Zhang L., B.G. Thomas, Inclusions in continuous casting of steel. XXIV National Steelmaking Symposium2, 2013. 26:138–183.
- 8. Louhenkilpi S., Continuous Casting of Steel. In: Treatise on Process Metallurgy. 2014, Elsevier Ltd., pp 373–434.
Details
Primary Language
English
Subjects
Engineering
Journal Section
Review
Authors
Hakan Kapusuz
This is me
0000-0001-5938-5759
Türkiye
Mehmet Ali Güvenç
0000-0002-4652-3048
Türkiye
Publication Date
December 15, 2019
Submission Date
May 7, 2018
Acceptance Date
October 23, 2019
Published in Issue
Year 2019 Volume: 3 Number: 3
Cited By
Development and Testing of Operator Vision Assistance System for Foundry Applications
steel research international
https://doi.org/10.1002/srin.202300271Oxidation behavior of Cr–Mn–Si alloyed steel by mold flux melt in high temperatures
Journal of Materials Research and Technology
https://doi.org/10.1016/j.jmrt.2024.02.124Optimization of Vortex Slag Entrainment during Ladle Teeming Process in the Continuous Casting of Automobile Outer Panel
steel research international
https://doi.org/10.1002/srin.202400322Criteria for early detection of slag in steel casting
Metallurgist
https://doi.org/10.1007/s11015-024-01803-5Potential Reuse of Ladle Furnace Slag as Cementitious Material: A Literature Review of Generation, Characterization, and Processing Methods
Minerals
https://doi.org/10.3390/min14121204Industrial steel slag flow data loading method for deep learning applications
Expert Systems with Applications
https://doi.org/10.1016/j.eswa.2025.130457The Influence of Different Factors on the Thermal Stress of Ladle Lining Under Typical Working Conditions
Concurrency and Computation: Practice and Experience
https://doi.org/10.1002/cpe.70373
