Real-time contamination monitoring platform using fixed air sensing in underground mining environments
摘要
Airborne contamination has long been acknowledged as a health hazard in underground mining. With a changing regulatory environment in the United States surrounding respirable crystalline silica and other respirable particulates, new tools are required for the assessment of contamination behaviors in underground working environments. Current methods for the simulation and modeling of ventilation systems either lack sufficient spatial resolution or require computational methods that preclude real-time assessment of airborne contamination distributions. Mine ventilation networks are commonly employed for ventilation system design and control, but the one-dimensional graph representation of the network makes contamination behavior within a discrete airway mathematically unobservable. Computational fluid dynamics methods are not suitable for real-time modeling and monitoring despite the superior spatial resolution these methods provide because of the time required for computation and computational barriers to modeling large excavations. We present a real-time contamination monitoring platform leveraging spatial statistics to provide improved spatial resolution compared to mine ventilation network representations while avoiding the computational costs of numerical simulation of the Navier–Stokes equations. Statistical Air Monitoring - Underground (SAM-UG) was developed for the assessment of particulate matter concentration distributions in underground working environments using low-cost, real-time air quality monitors to provide an improved insight into airborne contamination distributions for use in ventilation design and health risk assessment. SAM-UG yielded a total monitoring uncertainty of 30.4% and average RMSE of 0.54