The Primary Control in Electric ARC Furnace Steelmaking Process
Abstract Electric ARC Furnace (EAF) is one of the most important processes in steelmaking. In order to improve its efficiency and quality, it is necessary to increase the automation level of the whole process. The primary control of EAF steelmaking process is to maintain a stable current, voltage and power level. However, there are still some unsolved challenges, including the large current disturbance, the high power variation, the short short circuit period, and the long occupancy cycle. In this paper, a primary control system based on signal fusion (SCSF) is proposed. This system combines the traditional current control, sensor info fusion and fuzzy control methods, and uses a multi-sensor information fusion algorithm to achieve an efficient and accurate primary control. The simulation results show that the proposed system can accurately and quickly adjust the current level, reduce the power fluctuation and provide a better control environment for EAF steelmaking.
Keywords Electric ARC Furnace; Primary Control; Signal Fusion; Fuzzy Control
1. Introduction
Electric ARC Furnace (EAF) is one of the major processes for steelmaking. Its primary control is to maintain a stable current and power level during the steelmaking process. However, the current and power variables always suffer from large disturbances, such as the arc loss, voltage fluctuations, and the changing of the energy input [1]. The primary control has to face these disturbances and maintain the current and power level. As more and more steel companies are focusing on automatizing the steelmaking process, the request of the control accuracy of the primary control is increasing.
At present, most of the primary control strategies of the EAF process are based on the traditional current and voltage control [2], which cannot meet the current request of the high accuracy control. Moreover, the traditional current control strategies often suffer from large unpredictable current and voltage fluctuation, causing the power level to be unstable. Therefore, it is necessary to develop a new strategy which can effectively detect thedisturbances and accurately adjust the current and power-level.
To achieve the goal of higher accuracy, sensor information fusion technology is a potential solution [3]. In this technique, multiple sensors (such as all kinds of current transformers, voltage, temperature and relative related high sensitive sensors) are used to obtain multiple sources of information. Then, the signals from these sensors are unified and converted into a high-accurate value.
In this paper, a signal fusion-based primary control system (SCSF) is proposed. In this system, all kinds of sensors (current, voltage, voltage fluctuation, etc.) are used to get the multi-source signals. Then, the real-time parameters (current and voltage levels) are adjusted by a fuzzy controller. Finally, the signal fusion technique is used to unify and analyze the readings from multiple sensors, enabling an efficient and accurate primary control.
2. Design of the Primary Control System
To achieve the goal of higher accuracy, the signal fusion-based primary control system is proposed. The block diagram of the system is shown in Figure 1. The whole system contains three parts:
2.1 Sensor Information Collection
In this part, different types of sensors are used to obtain the related signals, including the current and voltage signals from the current transformer and voltage transformer, respectively; the voltage fluctuation signals from the voltage sensor; and the related energy input signals from the external energy source. These sensors measure the real-time parameters (current, voltage, voltage fluctuation and energy input) and generate the related sensor data.
2.2 Fuzzy Control for Current and Voltage
The fuzzy control is a non-linear control approach. In the primary control system, it is used to adjust the current and voltage levels. The fuzzy controller receives the related signals from the above sensors and the feedback signals from the EAF, and then adjusts the parameters according to the fuzzy rule base. The adjustment of the current and voltage can ensure the stability of the system and reduce the current fluctuation.
2.3 Signal Fusion
In this part, the signal fusion technique is applied to unify the readings from the above sensors, including the current and voltage signals, the voltage fluctuation signals, and the energy input signals. By combining the advantages of multiple sensors, the signal fusion can provide a more accurate and reliable result than each sensor by itself. Therefore, the signal fusion can be used to effectively reduce the current fluctuation and improve the accuracy of the primary control.
3. Simulation Results and Analysis
In order to evaluate the effectiveness of the proposed SCSF, the EAF simulation was proposed and the MATLAB/SIMULINK software was used for the control system simulation. The simulation model is built based on the steady-state analysis of the EAF process. Different current sources (such as constant current source, Gaussian disturbance current source and random noise current source, etc.) are used to simulate the current and voltage changes. The simulation results are shown in Figure 2.
Figure 2(a) shows the current variation. From the figure, it can be seen that the SCSF can reduce the current fluctuation and maintain a stable current level. In comparison with the traditional current control, the proposed method has a much better accuracy. Figure 2(b) shows the power variation. Similar to the current process, it can be seen that the proposed SCSF can effectively reduce the power fluctuation and maintain a stable power level.
Figure 2. Simulink simulation results: (a) Current variation, (b) Power variation.
4. Conclusion
In this paper, a signal fusion-based primary control system (SCSF) is proposed for the EAF process. This system combines the traditional current control, sensor info fusion and fuzzy control strategies and uses a multi-sensor information fusion algorithm to achieve an efficient and accurate primary control. The simulation results show that the proposed system can accurately and quickly adjust the current level, reduce the power fluctuation and provide a better control environment for EAF steelmaking. In the future, more advanced technologies can be introduced, such as model-based control, adaptive control, etc.
