Decreasing the Environmental Impact of the Electric Steelmaking Route Through Advanced Modelling Techniques

Although EAF-based steelworks produce steel from recycled ferrous scrap and inherently implement the concept of circularity, they are challenged to reduce their overall environmental impact, reduce CO2 emissions and maximize energy and resource efficiency. The paper exemplary shows how advanced digital technologies, including Artificial Intelligence-based techniques, can support decarbonization and sustainability improvement of electric steelmaking. The paper presents computationally efficient machine learning models estimating sterile content in different types of scrap reaching the scrap yard as well as steel chemical composition and temperature at the exit of the Ladle Furnace. The models are designed to be included in an innovative software platform based on Federated Learning helping industrial staff in decision-making by estimating energy consumption and other parameters affecting the environmental impact according to the material mix fed to the EAF. The paper describes the rationale behind models’ design, the approach for selecting their hyperparameters, and the results achieved on data gathered from two different steelworks, the first one exploited as reference for models’ first setup, the second one used to assess models’ usability in the considered Federated Learning context. The performances are satisfactory in both cases, and key issues for implementation and further improvement are discussed.