The electric arc furnace has been the subject of extensive research due to its complex and
chaotic nature. Machine learning methods provide a powerful forensic examination of …

The steel industry has been forced to switch from the traditional blast furnace to the electric
arc furnace (EAF) process to reduce carbon emissions. However, EAF still relies entirely on …

In the steel industry, the optimization of production processes has become increasingly
important in recent years. Large amounts of historical data and various machine learning …

Machine learning (ML) is a promising modeling framework that has previously been used in
the context of optimizing steel processes. However, many of the more advanced ML models …

Abstract Electric Arc Furnaces (EAFs) account for almost half of the North American steel
production. Arc furnaces draw high and dynamic electrical power to melt scrap metal loads …

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 …

The electric arc furnace (EAF) represents the most important process route for recycling of
steel and the second most productive steelmaking process overall. Considering the large …

The article presents the results of analyses of numerical modelling of selected factors in
electric arc furnace melts. The aim of the study was to optimise the melting process in an …

The development of tools to predict energy consumption is very useful because it can be
employed to evaluate the effect of the process variables, selection of the metallic charge …

The present work aims to answer three essential research questions (RQs) that have
previously not been explicitly dealt with in the field of applied machine learning (ML) in steel …