Monitoring of the refractory lining in a shielded electric arc furnace: An online multitarget regression trees approach

Two stacked time-series line charts. The top chart shows Average Mean Absolute Error and the bottom shows Average Root Mean Squared Error, both plotting a jagged blue line for the current model against a smooth orange dotted trend line across approximately 55,000 samples.
Figures 17–18: Online multitarget regression performance showing Average MAE and RMSE over time for model M0 monitoring refractory lining temperature in a shielded electric arc furnace.
Abstract
This paper presents an online multitarget regression trees approach for monitoring the refractory lining in a shielded electric arc furnace. The method enables real-time structural health monitoring to predict lining degradation and prevent costly failures.
Materials
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Authors
Jersson X. Leon-Medina
Jaiber Camacho-Olarte
Bernardo Rueda
Wilmar Vargas
Luis Bonilla
Janneth Ruiz
Jorge Sofrony
John Alexis Guerra Gomez
Felipe Restrepo-Calle
Diego A. Tibaduiza
Citation
Thumbnail image for publication titled: Monitoring of the refractory lining in a shielded electric arc furnace: An online multitarget regression trees approach
Monitoring of the refractory lining in a shielded electric arc furnace: An online multitarget regression trees approach

Jersson X. Leon-Medina, Jaiber Camacho-Olarte, Bernardo Rueda, Wilmar Vargas, Luis Bonilla, Janneth Ruiz, Jorge Sofrony, John A. Guerra-Gomez, Felipe Restrepo-Calle, and Diego A. Tibaduiza. Structural Control and Health Monitoring—STCH. 2021. DOI: 10.1002/stc.2885

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