Modeling Scrap Composition in Electric Arc and Basic Oxygen Furnaces

Abstract

This article aims to determine the composition of scrap (recycled material) used in an Electric Arc Furnace (EAF) or basic Oxygen Furnace (BOF) based on the assumption of mass balance. Accurate knowledge of this 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. The study develops two models to describe the behavior of elements in the EAF or BOF process. 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 moving into both steel and slag. The Kalman filter and unscented Kalman filter are employed to approximate these models, respectively. Importantly, the models leverage only data already collected as part of the standard production process, avoiding the need for additional measurements that are often costly. This article outlines the formulation of both models, the algorithms used, and discusses the hyperparameters involved. We provide practical suggestions on how to choose appropriate hyperparameters based on expert knowledge and historical data. The models are applied to real BOF data. Cu and Cr are chosen as examples for linear and non-linear models, respectively. The results show that both models can reconstruct the composition of scrap for these elements. The findings provide valuable insights for improving process control and ensuring product quality in steelmaking.

Tasks

Reproductions