Modeling the Effect of Scrap on the Electrical Energy Consumption of an Electric Arc Furnace

Abstract

The melting time of scrap is a factor that affects the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF) process. The EE consumption itself stands for most of the total energy consumption during the process. Three distinct representations of scrap, based partly on the apparent density and shape of scrap, were created to investigate the effect of scrap on the accuracy of a statistical model predicting the EE consumption of an EAF. Shapley Additive Explanations (SHAP) was used as a tool to investigate the effects by each scrap category on each prediction of a selected model. The scrap representation based on the shape of scrap consistently resulted in the best performing models while all models using any of the scrap representations performed better than the ones without any scrap representation. These results were consistent for all four distinct and separately used cleaning strategies on the data set governing the models. In addition, some of the main scrap categories contributed to the model prediction of EE in accordance with the expectations and experience of the plant engineers. The results provide significant evidence that a well-chosen scrap categorization is important to improve a statistical model predicting the EE and that experience on the specific EAF under study is essential to evaluate the practical usefulness of the model.

Decreasing the environmental impact of the electric steel making process through implementation of furnace retrofitting solutions

“…This also includes the influence of different charge material on the energy demand of the process (c.f. Carlsson et al [6]). The model also includes the current availability of the different charge material types into the optimisation.…”

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

“…Due to constraints in publishing the actual values of the examined EAF and confidentiality issues, all data and results in this study are presented in a normalized form. The operational behaviour of the EAF is characterized by stochastic variability, primarily because melting steel scrap necessitates varying amounts of energy due to fluctuations in chemical composition [7]. Depending on the desired steel quality, specific scrap qualities are utilized.…”

“…The operational behaviour of the EAF is characterized by stochastic variability, primarily because melting steel scrap necessitates varying amounts of energy due to fluctuations in chemical composition [7]. Depending on the desired steel quality, specific scrap qualities are utilized.…”

Special Issue on “Process Modeling in Pyrometallurgical Engineering”

“…The high temperatures and complex melting phenomena in the electrical arc furnace are reasons why many model-based studies of the process have been undertaken. Carlsson et al [25] determined the effect of scrap shape and density on the energy consumed to melt the scrap in the EAF by using a statistical model and process optimization algorithms validated through plant trials. The results provide significant evidence that a well-chosen scrap categorization is important to predict the electric energy demand.…”