Electric Arc Furnace (EAF) steelmaking is an energy-intensive and highly dynamic process in which accurate bath temperature prediction is essential for stable refining, energy optimization, and minimizing tap-to-tap variability. This study develops a hybrid temperature prediction framework by integrating first-principles metallurgical modeling with machine-learning validation using historical...
Steelmaking facilities are significant sources of environmental noise pollution, with complex acoustic emissions arising from diverse operations including Electric Arc Furnace (EAF) melting, hot rolling, scrap handling, and material transport. Effectively managing and reducing industrial noise emissions requires identifying which specific processes and equipment mostly contribute to elevated...
Scrap characteristics significantly influence the performance of the melting process in the Electric Arc Furnace (EAF). Along with other charged materials, scrap accounts for significant production costs. Hence, marginal process improvements can yield significant economic gains. However, the inherent heterogeneity of charged materials and the lack of elemental composition data on scrap...
Industrial steel reheating furnaces operate at temperatures between 800°C and 1300°C and consume a significant fraction of energy for the heating process. These units are significantly energy-intensive, and accurate prediction of furnace gas and slab temperature distributions is essential. This can help to maintain slab quality, support operational decision-making, improve thermal efficiency...
John Cockerill is developing AI-assisted enhancements to its physics-based furnace control models to improve process efficiency and stability in steelmaking. Building on the Line Thermal Optimization Process (LTOP) experience, this work explores how artificial intelligence can refine transient anticipation, heat transfer estimation, and adaptive response under changing operating conditions....
Accurate prediction of molten steel temperature in electric arc furnaces plays a crucial role in optimizing steelmaking processes. However, the EAF smelting process is characterized by strong nonlinearity, complex multivariable coupling, and significant uncertainty, which pose substantial challenges to conventional mechanism-based temperature models.
To address these challenges, a data-driven...