Login

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

11–13 May 2026
Milan
Europe/Rome timezone

Organising secretariat

Session

AI and Machine Learning in Process Optimization I

11 May 2026, 16:10
Parini room

Parini room

Conveners

AI and Machine Learning in Process Optimization I

  • Giovanni Bavestrelli (Tenova)

Presentation materials

There are no materials yet.

  1. 178. Mapping AI in Metallurgy: From Current State to What the Industry Needs
    Prof. Marcello Urgo (Politecnico di Milano), Dr Raffaella Poggio (President of AIM AI Technical Committee)
    11/05/2026, 16:10
    Oral Presentation
  2. 46. Closed-Loop Optimization for Energy Efficiency and Scrap Utilization in Steelmaking at Marcegaglia Sheffield
    Andreas Rohrhofer (Primetals Technologies Austria)
    11/05/2026, 16:30
    EEC 2.A Energy efficiency and consumption reduction strategies
    Oral Presentation

    This paper presents the deployment and benefits of a comprehensive closed-loop process optimization system at Marcegaglia Sheffield, UK, covering Scrap Yard, Electric Arc Furnace (EAF), Argon Oxygen Decarburization (AOD), and Ladle Furnace (LF) operations.
    The paper focusses on Scrap Yard and EAF as key stages, and discusses how advanced sensor systems in combination with closed loop control...

    Go to contribution page
  3. 81. Automated Noise Source Identification in Steel Plants Using Deep Learning
    Prof. Valentina Colla (Scuola Superiore Sant'Anna)
    11/05/2026, 16:50
    EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimization
    Oral 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...

    Go to contribution page
  4. 35. Multi-Tiered AI Implementation for Slag Property and Alloy Prediction in EAF Operations: Operational Validation Access Three Steel Producers
    Tim Eschert (Fero Labs GmbH)
    11/05/2026, 17:10
    EEC 1.F Use of artificial intelligence (AI) and machine learning in process optimization
    Oral Presentation

    EAF operations face critical variability challenges in scrap mix composition and quality that directly impact slag basicity control and alloy chemistry prediction. Traditional multivariate statistical approaches rely on reactive empirical adjustments where conventional analytical tools reach computational limits for real-time process optimization.
    This study presents operational validation of...

    Go to contribution page
Building timetable...