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This study aims to determine the composition of scrap (recycled material) used in Electric Arc Furnaces (EAF) or Basic Oxygen Furnaces (BOF) based on mass balance assumptions. Accurate knowledge of this composition can increase the usage of recycled material in steel production, reducing the need for raw ore extraction and minimizing environmental impact by conserving natural resources and lowering carbon emissions. Two models are developed to describe element behavior in EAF and BOF processes. A linear state space model is used for elements transferring completely from scrap to steel, while a non-linear state space model is applied to elements distributing between steel and slag. The Kalman filter and unscented Kalman filter are employed to solve these models, respectively. Importantly, the models leverage only data already collected during standard production, avoiding the need for additional costly measurements. This paper outlines both model formulations, the algorithms used, and discusses hyperparameter selection. Practical guidance is provided on choosing appropriate hyperparameters based on expert knowledge and historical data. The models are validated using real BOF data, with Cu and Cr serving as examples for the linear and non-linear cases, respectively. Results demonstrate that both models can reconstruct scrap composition for these elements, providing valuable insights for improving process control and ensuring product quality in steelmaking.
Keywords: Basic Oxygen Furnace, Electric Arc Furnace, scrap composition, state space model, Kalman filter© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.