Abstract: The AI intelligent regulator enables precise temperature control and monitoring of parameters such as temperature, pressure, flow, displacement, and weight loss in electric furnaces. Integrated with an industrial control system, it forms a distributed control solution, widely applied in metallurgical performance measurement and control systems. This system offers reliable operation, strong resistance to interference, and high precision in control.
Introduction: The blast furnace smelting system is a complex and costly process to analyze. By simulating the behavior of temperature, pressure, flow, displacement, and weight loss during the smelting process, the system creates a large-scale laboratory for metallurgical performance testing. Experimental results guide the smelting process, and the system uses a distributed control structure. The AI smart instruments from Xiamen Yudian provide accurate temperature control, while the PLC manages process switching, analog signals, and timing. An industrial computer centralizes management, using the AIBUS+ communication protocol and RS485 interface to monitor 19 channels of temperature, flow, pressure, and displacement through serial communication.
1. Hardware Components of the Control System
The system primarily controls 11 large electric furnaces and their auxiliary equipment, including gas treatment and detection units. In addition to precise temperature monitoring, it also measures load softening displacement, material weight, gas flow, and pressure during reactions, along with droplet count during melting. These parameters are detected by on-site AI smart meters and transmitted digitally via a bus. The system architecture is illustrated in Figure 1.
Figure 1: Hardware Block Diagram
1.1 Control Mode Selection
The system mainly focuses on precise temperature control. The AI instruments offer various modes, including position control (ON-OFF), standard PID, AI adjustment (APID or MPT). Most electric furnaces use standard PID control, which meets process requirements. Users can adjust parameters like M5, P, and t for custom settings. For special systems, self-tuning is enabled. The advanced AI algorithm features self-tuning and learning, ensuring no overshoot or undershoot, and meeting process needs effectively.
1.2 Power Limiting Mode
Most electric furnaces are resistance-based, while some high-temperature models use silicon-molybdenum rods. To prevent excessive current at low temperatures, power segmentation limits are set using the CF parameter. When temperature is below LoAL, the output upper limit is oPL; otherwise, it is oPH. This two-stage power limit prevents dangerous current surges during initial heating.
1.3 System Commissioning
For certain electric furnaces, furnace wall temperature is monitored to calculate temperature gradients during heating. This helps reduce pure hysteresis and improve control. Furnace temperature start conditions may vary, but they can be corrected using Sc. During commissioning, the AI meter's self-tuning function is used, setting parameters like M5, P, and t. Each heater section has similar characteristics, and the self-tuning performs well, achieving control accuracy within ±1°C and maximum overshoot under 2°C.
2. Software Structure and Function
The system is built on a two-layer architecture, with the upper-level industrial control machine handling centralized control and data processing. The lower-level includes PLCs and on-site AI instruments managing digital and analog signals. The system supports manual and automatic control, allowing smooth switching without disturbance. The main interface of the host computer is shown in Figure 2.
Figure 2: Main Interface Diagram
The system provides several key functions:
2.1 Human-Machine Interaction Interface
Displays the layout and online status of virtual devices, dynamically updating real-time data. It shows real-time curves for parameters like temperature and flow, allows on-site switch control, historical data queries, Excel report generation, and AI meter alarm and parameter settings.
Figure 3: Instrument Settings and Data Display
2.2 Database Management
Manages real-time and historical data, supporting storage and export to Excel.
2.3 User and System Management
The system divides roles into administrators, operators, and security officers, each with different permissions. It manages sampling frequency, real-time refresh intervals, and alarm settings based on system needs.
3. Conclusion
The system has been successfully implemented in Guangdong Shaoguan Iron and Steel Plant. It is safe, reliable, and efficient, offering strong anti-interference and high control precision. It meets all required process conditions and control performance standards.
Foshan Gruwill Hardware Products Co., Ltd. , https://www.zsgruwill.com