EEC 2026 - 14th European Electric Steelmaking Conference & EMECR 2026 - 5th International Conference on Energy and Material Efficiency and CO2 Reduction in the Steel Industry

Session

AI and Machine Learning in Process Optimization II

12 May 2026, 16:30

Washington room (ground floor)

Conveners

AI and Machine Learning in Process Optimization II

• Andrea Faraci (Pipex Italia Spa)

148. Machine Learning-based Scrap Characterization for Process Optimization in Electric Arc Furnaces

Siddharth Nachankar (IOB RWTH Aachen)

12/05/2026, 16:30

EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimization

Oral 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...

36. Physics-Informed Reduced-Order Modeling for Temperature Prediction in Industrial Reheating Furnaces

Pratibha Biswal (ULB)

12/05/2026, 16:50

EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimization

Oral 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...

8. AI Assisted Thermal Modelling for Advanced Furnace Control and Process Optimization

Mr Julian Naveira (John Cockerill Industry)

12/05/2026, 17:10

EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimization

Oral 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....

129. Temperature prediction model in an electric arc furnace based on data-driven

Maoqiang Gu (University of science and technology Liaoning)

12/05/2026, 17:30

EEC 2.C Process control and quality improvement techniques

Oral 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.
To address these challenges, a data-driven...

21. Mathematical modelling of slag foaming of EAF slags

Alberto Conejo (USTB)

12/05/2026, 17:50

EEC 1.A Innovations in electric arc furnace (EAF) technology

Oral Presentation

Slag foaming is a very important phenomena in the operation of the Electric Arc Furnace due to multiple benefits. It has been extensively investigated from an academic view point, however there are still many unresolved issues dealing with the real conditions that promote these phenomena. I many of the previous investigations the gas is artificially injected, rather than created at the...