二次网系统 into circulation by overcoming the resistance of the secondary loop with circulating pumps, and maintain the pressure with the help of feed水泵. To address the issues of high energy consumption, severe pollution, and low efficiency in previous heating systems, this paper proposes a control system solution based on Programmable Logic Controllers (PLC), Variable Frequency Drives (VFD), Human-Machine Interface (HMI), and regulating valves for constant temperature and pressure control.
The core design principle of this system is the integration of PLC and HMI-based control strategies, along with PID (Proportional-Integral-Derivative) regulation in the heat exchange station control. This enables precise control over the valve openings and VFDs for the circulating pumps and feed pumps, ultimately achieving energy conservation. Furthermore, the implementation of HMI allows real-time monitoring and control of on-site equipment, enhancing operational efficiency, reducing fault occurrence, and providing a more intuitive and convenient control process.
The field commissioning of the designed heat exchange station verified its superior stability, high regulation accuracy, ease of control, and outstanding energy-saving performance. The integration of advanced technologies like PLC, HMI, and VFDs not only upgrades the traditional heating system but also aligns with the current trend of sustainable and environmentally friendly energy practices.
关键词：集中供热；可编程逻辑控制；HMI；变频控制；换热站控制系统设计；节能；稳定性；调节精度
In summary, the integration of artificial intelligence, particularly deep learning, in the design of a heat exchange station control system for centralized heating is a significant step towards more efficient and eco-friendly energy management. By leveraging PLCs, HMIs, VFDs, and PID control algorithms, the system ensures precise temperature and pressure control while conserving energy. The use of advanced technology not only enhances the overall performance of the heating system but also contributes to the broader goal of low-carbon, energy-efficient, and environmentally responsible urban development. This study showcases the potential of applying AI and deep learning in improving the functionality and sustainability of essential infrastructure in the context of heating systems.
