Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation...

Area of Science:

• Ventilation Engineering
• Network Analysis
• Sensor Networks

Background:

• Accurate monitoring of ventilation parameters is crucial for intelligent ventilation systems.
• Visualizing airflow necessitates solving the airflow reconstruction problem with limited sensors.
• Existing methods may require extensive sensor deployment for comprehensive monitoring.

Purpose of the Study:

• To present a novel concept, the independent cut set, for determining optimal sensor placement.
• To minimize the number and identify strategic locations for sensors in ventilation networks.
• To enable effective airflow reconstruction using a reduced sensor network.

Main Methods:

• Developed the 'independent cut set' concept based on graph theory and network structure.
• Applied the method to determine sensor requirements for airflow reconstruction in a coal mine.
• Validated the feasibility and accuracy of the proposed sensor placement strategy.

Main Results:

• Identified that fewer than 30% of tunnels require wind speed sensors for full airflow reconstruction.
• Demonstrated the algorithm's feasibility in a real-world coal mine environment (>200 tunnels).
• Achieved reconstructed air volume matching actual air volume in the ventilation network.

Conclusions:

• The independent cut set method provides a theoretically sound approach for airflow reconstruction.
• This method significantly reduces the number of sensors needed for effective ventilation monitoring.
• The findings offer practical guidance for optimizing sensor deployment in intelligent ventilation systems.