Coal Ash Processing in Thermal Power Plants

SCADA Solutions for Coal Ash Processing in Thermal Power Plants

📌 Project Overview

Datang Ningde Power Plant operates four coal-fired units (2×600MW + 2×660MW) with an annual fly coal ash processing capacity of 360,000 tons. The automation system ensures efficient solid waste utilization, producing Grade I/II fly ash. Key objectives include:

  • 📡 Real-time monitoring for process optimization
  • 🔒 Safety interlocks to prevent equipment failures
  • 📊 Historical data traceability for quality control

🏗 Control System Architecture

The system utilizes Siemens PLCs for local control and Advantech industrial PCs with InTouch SCADA for visualization.

  • 🌡 The system displays pressure and temperature parameters both locally and on DCS screens.
  • 🔄 Redundant communication interfaces ensure seamless integration with plant-wide networks.

🎯 Core Functional Requirements

  • 🖥 Local control panels provide pressure/temperature displays and allow operators to adjust setpoints.
  • 📈 Historical temperature data is stored for quality audits and compliance.
  • 🛢 Dual oil pumps in lubrication stations enable automatic fault switching, ensuring uninterrupted operation.
  • 🚨 Differential pressure switches trigger alarms and mill shutdowns during abnormal conditions.

🛡 Safety and Interlocking Logic

  • ⚠️ Oil pressure indicators & differential pressure alarms prevent equipment damage.
  • 🔌 The PLC implements interlock trip logic, automatically halting operations during critical faults.
  • 🌍 Remote communication interfaces support centralized monitoring via Modbus/TCP protocols.

🚀 Operational Advantages

  • Real-time DCS visualization reduces manual inspections.
  • Automated pump switching minimizes downtime and enhances reliability.
  • Historical trend analysis supports predictive maintenance strategies.
  • Standardized protocols simplify third-party system integration.

🔮 Future Scalability

📡 Modular design supports additional sensors or processing lines.
☁️ Cloud connectivity could enhance remote diagnostics and data analytics.
🤖 Machine learning algorithms may be implemented to optimize grinding efficiency in later phases.

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