Scrap Composition Estimation in EAF and BOF: State-Space Models, Hyperparameters, and Validation
For steel manufacturers, this enables increased use of recycled scrap by improving composition estimation, reducing raw ore extraction and carbon emissions.
The paper introduces state-space models for estimating scrap composition in EAF and BOF steelmaking, using Kalman and unscented Kalman filters. Validated on real BOF data from ArcelorMittal, the models outperform windowed non-negative least squares regression, providing reliable real-time estimates.
Accurate knowledge of scrap composition can increase the usage of recycled material to produce steel, reducing the need for raw ore extraction and minimizing environmental impact by conserving natural resources and lowering carbon emissions. First, we introduce two state-space models for the elemental composition of scrap in Electric Arc Furnaces (EAF) and Basic Oxygen Furnaces (BOF): a linear model for elements that transfer entirely into steel, and a non-linear model for elements that partition between steel and slag. The models are fitted with the Kalman filter and the unscented Kalman filter, respectively, using only data already collected in the standard steel production process. Crucially, the resulting scrap composition estimates can in turn be used to predict the elemental composition of future steel production. Second, we analyze how key hyperparameters affect estimation accuracy and stability, and we provide practical guidelines for tuning them from expert knowledge and historical data. Third, we validate the models on real BOF data from ArcelorMittal, using Cu and Cr as representative elements. Both filters outperform windowed non-negative least squares regression, a strong baseline method for scrap composition estimation, yielding reliable real-time estimates of scrap composition.