BFI VDEh-Betriebsforschungsinstitut GmbH

Initial situation:

Aging equipment and the harsh operating environment in steel plants contribute to frequent machinery failures, leading to unplanned production stops. The intense heat, dust, vibrations, and corrosive conditions accelerate wear, making equipment more prone to breakdowns. Additionally, many steel plants still rely on reactive and preventive maintenance strategies, which are often insufficient to avoid unexpected failures. Limited maintenance windows due to tight production schedules further complicate efforts to carry out thorough inspections and repairs without disrupting operations. Human factors such as operational errors and insufficient training also play a role in maintenance challenges, while supply chain disruptions for spare parts can delay critical repairs.

Project targets:

The project aims to enhance the performance, reliability, and sustainability of the Electric Arc Furnace (EAF) process by integrating advanced monitoring technologies with artificial intelligence (AI) tools. Through four targeted use cases, it will address key challenges such as

• EAF scrap melting management,
• optimization of electrical parameters of the EAF,
• EAF component and event management (BFI contribution),
• overall EAF performance monitoring.

For each case, AI models will be developed based on real industrial data and multimodal inputs (acoustic, volumetric, visual, and thermal) and integrated into an advanced Decision Support System (DSS) capable of providing real-time recommendations to operators.

Innovative approaches:

The project introduces major innovations in both Computer Vision—through robust image analysis techniques designed for extreme EAF environments—and in time-series analysis, by applying advanced AI methods to acoustic and vibration signals for the early detection of inefficiencies and failures. By combining these solutions, the project will deliver a holistic and predictive approach to EAF management not currently available on the market.

Benefits for the industry:

This approach will enable more accurate process control, reduced unplanned maintenance, longer equipment lifetime, and significant energy and cost savings. It will strengthen both decarbonization and competitiveness of the European steel industry, directly supporting the objectives of the RFCS program and the European Green Deal through improved resource efficiency, higher metallic yield, and reduced CO₂ emissions.

About the project:

This project has received funding from the European Union, through the Research Fund for Coal and Steel (RFCS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.