The number of Electric Arc Furnaces (EAF) is growing constantly as EAF-based steelmaking has several benefits compared to other steelmaking methods. However, until now the control of the EAF process has been suboptimal. Variance in the scrap mix, condition of the furnace, and other changing variables cause challenges for the typically used static control methods. Pre-set control practices do not take into account the real ongoing events in the furnace during the process.
Online information is required to control the process based on the real-time events (Dynamic EAF control). Availability of this information from inside the furnace has been typically very limited since we cannot just literally see into the furnace. It is challenging to understand what is really happening in the process. How is the scrap melting progressing and when to change voltage levels/load additional charges/start foaming/start the burners and so on?
Of course, the inefficiency in the EAF control is not the steel manufacturer’s or furnace manufacturer’s fault. There simply has not been a way to measure the process conditions reliably due to the extreme conditions inside the furnace – until now.
Static vs. Dynamic EAF control
Static control is the most common and basic approach in EAF process control. It implies that parameters such as the duration of the voltage ramps, the timing of the carbon/ lime injection, and the termination of the process are pre-programmed. This type of control is highly dependent on the operator’s knowledge and experience when choosing the suitable parameters for the process. The available volume in the furnace also varies due to the buildup of slag on the walls and wear of refractories. This can also cause some challenges for the static models. All in all, creating a perfect static model is very difficult since there are a lot of unknown and changing variables in the process.
On the other hand, dynamic control involves the use of online, real-time sensing that provides continuous feedback from the process and allows parameters to be adjusted on the fly for more precise EAF process control and operation. For example, we could measure scrap melting and determine when the scrap has melted enough to load the next scrap basket or start another process phase. A lot of effort has been made to measure, fully understand, and utilize different signals from the EAF process and how they can be used as dynamic process control parameters.
Requirements for dynamic EAF control
Four criteria must be fulfilled to achieve reliable and safe results with dynamic EAF control. These criteria are:
- Data from the process. Multiple sources of data and different measured phenomenon give the best understanding of the process status.
- The phenomenon being monitored must be relevant from the EAF process control point of view and must be able to distinguish different phases of the process. There are multiple reasons to measure the EAF during the heat in addition to the dynamic control. For example, the composition of the batch should be analyzed for ensuring quality, etc. However, only certain measurable phenomena are usable from the process control point of view.
- On-line measurements. The measurements must project the current state of the heat. Delays in the information can be vital for process efficiency or in even some cases for safety.
- Robustness and reliability of the measurement method and data. The environment at the EAF is extremely hostile which can cause distortion and even block the measurements entirely.
The most challenging part of building a dynamic control model for EAF is the availability of reliable real-time data directly from the process itself. Like said, the conventional methods to measure something are not well applicable to EAF since the environment inside the furnace is so hostile and typically the measurement devices can not tolerate the harsh conditions.
There are several different indirect ways to measure the EAF process in real-time. However, these methods have their limitations since they are not measuring the process of melting itself. For example, measuring the cooling water temperature gradient from the side panels indicates how the scrap is melting. However, there is a delay when the heat is transferred to the water and the sensors due to slag adhering to side panels. This delay decreases the effectiveness of the data to be used as a control parameter. Another often utilized online measurement method is off-gas analysis. Unfortunately, because the off-gas is gathered to a single off-gas channel, it is also impossible to determine how the scrap smelting is progressing in different parts of EAF.
Optical Emission Spectroscopy (OES) has been finally proven to be a solution for this challenge. OES is the study of light and the spectrum of light. Since the melting progression emits a lot of light, OES is capable to track the changes in the process reliably in real-time. With this scrap melting information, the dynamic control of EAF is finally possible.
To increase the awareness of different measurement methods, we are releasing a comprehensive guide about the methods in the coming weeks. Follow us on LinkedIn or subscribe to our newsletter to stay tuned!
Scrap melting information
The stage of the process and the scrap melting information can be deducted from the light that is emitted from the furnace with help of the OES. Figure 1. Illustrates the different spectrum types in an EAF during the melting.
Figure 2. Spectrum types in an EAF during melting (modified from Aula et al. 2014).
At the beginning of the heat when the arc is covered completely by the steel scrap, only a dark current is observed. When the scrap in the viewing cone has melted the thermal radiation emerges. Observation of thermal radiation in the earlier stages is also possible due to the hot fumes coming from the heat and reflections from the steel scrap. The main light sources during flat bath conditions are molten slag and the arc. Combustible material produces flames that also emit light in the EAF atmosphere. Alkali peaks are generated by the atomic emissions of alkali metals contained in the hot slag or gas. (Aula et al. 2014)
More information about the light emissions in the EAF can be read here!
Dynamic process control based on the scrap melting information can be used to control the voltage ramps more efficiently. For example, using the high-power mode as long as there is un-melted metal scrap. Or when the EAF is not the bottleneck, lower power can be used safely to save the electrodes without damaging the refractories and the side panels.
The scrap melting information can be used to time additional scrap charges exactly when there is enough room for the new charge. It can be also used for optimizing the timing of carbon injections and burners.
In our industrial tests, the dynamic process control based on scrap melting information has been achieving:
- 3-6% increase in energy efficiency
- Up to 7% faster tap-to-tap times
- 5-10% Decrease in electrode consumption
Enabling dynamic control is one of the biggest leaps in EAF development in recent years. Significant benefits can be achieved with only small investments since the optimization can be done without heavy modifications to the process. The adaptation to this method has already started and several different steel plants have already begun to use the dynamic control. We believe that in the future all the EAF’s will use the dynamic control now that it has been enabled.
We at Luxmet have done almost ten years of research and development work to solve the challenge. How to enable dynamic EAF control and reliably measure the scrap melting in real-time? Our years of work culminate in the ArcSpec system. ArcSpec measures and analyses the light from the furnace. The data is then processed into control signals which are sent to the furnace automation system.
In addition to the dynamic control that the ArcSpec system enables, it can be also used for producing data for research purposes and fault detection. Contact our experts to learn more about the ArcSpec system!
Aula, M., Leppänen, A., Roininen, J., Heikkinen, E.-P., Vallo, K., Fabritius, T. and Huttula, M., 2014. Characterization of process conditions in industrial stainless steelmaking electric arc furnace using optical emission spectrum measurements. Metallurgical and Materials Transactions B [online], 45 (3), 839-849