Conveners
AI and Machine Learning in Process Optimization II
- Cosmo Di Cecca (Feralpi Siderurgica)
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Poomalai Paramasivam (EMSTEEL Group)12/05/2026, 16:30EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimizationOral Presentation
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...
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Prof. Valentina Colla (Scuola Superiore Sant'Anna)12/05/2026, 16:50EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimizationOral Presentation
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...
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148. Machine Learning-based Scrap Characterization for Process Optimization in Electric Arc FurnacesSiddharth Nachankar (IOB RWTH Aachen)12/05/2026, 17:10EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimizationOral Presentation
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...
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Pratibha Biswal (ULB)12/05/2026, 17:30EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimizationOral presentation (paper for Ironmaking & Steelmaking special issue)
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...
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Mr Julian Naveira (John Cockerill Industry)12/05/2026, 17:50EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimizationOral Presentation
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....
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Maoqiang Gu (University of science and technology Liaoning)12/05/2026, 18:10EEC 2.C Process control and quality improvement techniquesOral Presentation
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.
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To address these challenges, a data-driven...