ABSTRACT
Underground coal mine fires pose significant risks due to toxic gases and smoke, complicating safe evacuation. This study addresses the need for a comprehensive solution to aid personnel in finding safe evacuation routes during fire emergencies. A laboratory-scale simulation rig is developed to model the room-and-pillar mining methods and simulate smoke propagation in underground coal mines. Given the limitations of numerical simulations like Ventsim in integrating real-time sensor data for continuous monitoring of gas concentrations and temperature for decision-making during fire emergencies, this rig offers a more accurate representation of underground mining environments. Carbon monoxide data collected from the rig is used in conjunction with a pathfinding algorithm to optimize escape routes. The simulation successfully replicated smoke propagation in underground environments while the algorithm efficiently determined the optimal escape route. The research demonstrates the potential of utilizing fire sensor data and pathfinding algorithms to enhance self-evacuation during fire emergencies.
