Digital twin-driven deep learning prediction and adaptive control for coal mine ventilation systems.

Area of Science:

• Mining Engineering
• Artificial Intelligence
• Cyber-Physical Systems

Background:

• Coal mine ventilation systems require real-time monitoring and control despite complex underground environments and unpredictable disturbances.
• Traditional methods struggle with dynamic conditions and achieving optimal performance.

Purpose of the Study:

• To develop an integrated framework for intelligent ventilation management in coal mines.
• To enhance safety, efficiency, and sustainability in underground mining operations.

Main Methods:

• Constructed a five-dimensional digital twin architecture for bidirectional synchronization.
• Developed an LSTM-Attention hybrid neural network for predicting ventilation parameters (MAPE 2.87%, R² 0.9612).
• Implemented an adaptive model predictive control strategy for dynamic optimization.

Main Results:

• Field validation showed 97.3% control accuracy.
• Achieved a 27% reduction in energy consumption.
• Demonstrated a 66.4% faster system response compared to conventional methods.

Conclusions:

• The proposed framework offers a practical solution for intelligent ventilation management, overcoming limitations of traditional approaches.
• This research advances cyber-physical integration in mining and validates AI for safety-critical industrial systems.