A Cyber-Physical Systems Framework for Tracking Post Thermal-Runaway Temperature and Smoke Dynamics in Underground Mines
Yukta Pareek, Khadija Omar Said, Satadru Dey, Ashish Ranjan Kumar
TL;DR
This paper tackles the safety-critical problem of tracking post-thermal runaway temperature and smoke in underground mines, where high-fidelity CFD models are too slow for real-time use. It introduces a cyber-physical system framework that leverages CFD-trained reduced-order models and moving horizon estimation to infer full spatio-temporal temperature and smoke fields from sparse sensor data. Key contributions include identifying temperature and smoke ROMs with N4SID, formulating a robust MHE-based estimator, and demonstrating improved tracking accuracy over open-loop ROMs across realistic sensor configurations. The results highlight the approach's potential for real-time decision support in ventilation control, fire suppression, and mine-rescue operations during LIB TR events.
Abstract
Underground mining operations are actively exploring the use of large-format lithium-ion batteries (LIBs) to power their equipment. LIBs have high energy density, long cycle life, and favorable safety record. They also have low noise, heat, and emission footprints. This fosters a conducive workplace environment for underground mining personnel. However, many occurrences of LIB failure have resulted in dangerous situations in underground mines. The combustion products, including toxic emissions, can rapidly travel throughout the mine using the ventilation network. Therefore, it is critical to monitor the temperature and smoke concentration underground at all times to ensure the safety of the miners. High-fidelity models can be developed for specific scenarios of LIB failure, but are computationally prohibitive for large underground mine volumes, complex geometries, and long duration combustion events. To mitigate computation-related issues associated with high-fidelity models, we developed cyber-physical systems (CPS) models to examine temperature and smoke dynamics. The mine supervisory control center, acting as the cyber framework, operates in conjunction with the physical underground mine. The CPS models, trained on high-fidelity computational fluid dynamics (CFD) model data sets, present an exceptional estimate of the evolution of temperature and smoke concentration in the underground mine tunnel. Once implemented, the research results can help mine operators make informed decisions during emergencies.