Mine ventilation systems face significant challenges in dynamic control due to complex network topologies and uncertain underground environments. This paper proposes an intelligent decision-making framework that synergistically integrates graph neural networks (GNN) with deep reinforcement learning (DRL) for optimal ventilation control. A multi-level hierarchical graph representation method is developed to capture topological structures and spatial dependencies of ventilation networks, while an improved Actor-Critic algorithm with prioritized experience replay enables adaptive policy learning under safety constraints. The GNN encoder extracts graph-structured features that enhance the DRL agent’s state representation, facilitating efficient exploration and decision optimization. Experimental validation on simulation platforms and six-month field deployment in an operational coal mine demonstrate substantial improvements: 34.7% higher cumulative rewards compared to conventional methods, 23.7% reduction in energy consumption, and 98.4% safety compliance rate across diverse operational scenarios. The proposed framework advances intelligent mine ventilation management by simultaneously achieving enhanced safety assurance, improved energy efficiency, and robust adaptability to complex dynamic conditions.
Zhang et al. (Fri,) studied this question.