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

Abstract

Abstract Coal mine ventilation systems face critical challenges in real-time monitoring and dynamic control due to complex underground environments, time-varying operational conditions, and unpredictable disturbances. This study proposes an integrated framework combining digital twin technology, deep learning prediction models, and adaptive control strategies to achieve intelligent ventilation management. A five-dimensional digital twin architecture was constructed to establish bidirectional synchronization between physical ventilation networks and virtual models. An LSTM-Attention hybrid neural network was developed to capture temporal dependencies and predict ventilation parameters with mean absolute percentage error of 2.87% and coefficient of determination of 0.9612. An adaptive model predictive control strategy was designed to dynamically optimize fan operations and regulator positions based on predictive insights. Field validation at an operational coal mine over eight months demonstrated superior performance compared to conventional methods, achieving 97.3% control accuracy, 27% energy consumption reduction, and 66.4% faster system response. The proposed framework successfully addresses limitations of traditional ventilation control approaches and provides a practical solution for enhancing safety, efficiency, and sustainability in underground mining operations. This research contributes to advancing cyber-physical integration in mining engineering and demonstrates the viability of artificial intelligence technologies for complex industrial systems with safety-critical constraints.