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Keywords

Scrap management; Electric Arc Furnace; Dynamic Monitoring; Decision support; Parameter optimisation; Federated Learning

Abstract

The Electric Arc Furnace (EAF) for scrap-based steelmaking plays an important role in realizing the transition towards Green Steel production, due to its more efficient use of resources, lower carbon emissions and inherent circularity compared to the iron-ore-based steelmaking. This work presents an approach to enhance the performance and reliability of a model-based Decision Support System for EAF online monitoring and control by means of Federated Learning, which has been developed within the EU-funded project ALCHIMIA and applied at three EAF plants within the Celsa Group. The system consists of a web application for the characterisation of scrap type properties and a charge mix optimization, which considers multiple optimization criteria like purchase costs and environmental impact factors of the different scrap types. Furthermore, the system includes a dynamic EAF process model based on energy and mass balances and thermodynamic calculations for the online monitoring of the process behaviour and for decision support in real-time control. The paper will present the model-based EAF decision support system, its performance at the Celsa plants and the benefits of the federated learning approach.