Graphite electrodes contribute about 10–20% of the conversion cost in the electric arc furnace (EAF). Although few empirical relations between electrode consumption and EAF operational factors were presented in research in the past, factors such as arc stability, efficiency of the foamy slag, water-cooling, etc., are not taken into consideration. In this study, the variation in electrode consumption and its pattern within the furnace and between various other furnaces for different operating conditions is thoroughly researched to achieve best practices to optimize electrode consumption. This will include discovery of the “severity factor” of the oxidizing environment of the furnace, representing its influence on electrode consumption.
Slag samples are typically collected at the end of the steelmaking process, and the slag compositions are then often used as a reference for process control and refractory compatibility. While these samples are important and essential, they don’t reflect the transient slags that were responsible for the refining reactions such as dephosphorization, desulfurization and inclusion absorption during the process. This two-part paper will focus on the role of these transient slags and how they can be engineered to benefit the basic oxygen furnace, electric arc furnace and ladle refining processes. This paper will also provide some insights on the physical-chemical aspects of slags in steelmaking.
Bottom skull formation is a common problem in electric arc furnaces for high-alloy steel production. Skull formation creates a number of process problems: reduced furnace volume capacity, lower tapping weight hit ratio and lower steel yield. The formation mechanism of bottom skull in an 70-ton eccentric bottom tapping furnace for tool and stainless steel production in a steel plant in Korea has been investigated. The effect of bottom gas stirring and electromagnetic stirring (EMS) on the reduction of skull thickness has been compared. The results show that EMS is a more efficient way to reduce skull formation compared to gas stirring.
In recent years, there has been a strong effort to integrate artificial intelligence (AI) into manufacturing operations. In heav-ily automated operations with repetitive operating cycles, this may be possible. However, in a process such as electric arc furnace (EAF) steelmaking, the process variations make it difficult to apply AI effectively. As an alternative, the authors propose real intelligence — the use of effective tools based on a sound understanding of process fundamentals to allow the operator to make better decisions in real time. As greater EAF automation is implemented, the operator role must change from equipment operation to frontline process optimization.
The DC free-burning electric arc configuration was studied using computational fluid dynamics (CFD) in the present work. The simulation results obtained by the CFD model for both two-dimensional and three-dimensional cases are in good agreement with former experimental data. Investigation of the electric arc behavior was conducted through corresponding flow variables including temperature field, velocity field, etc. The electrode movement was further considered in the model to evaluate the impact of the moving electrode on the arc performance. The simulation reveals that shortening the arc length will have the arc plasma gradually lose its bell shape. The ambient temperature may increase significantly due to the arc column compression, which may result in the electrode wall overheating problem.