Mining Project: Real-time method to characterize a mine fire using atmospheric monitoring systems and MFIRE 3.0
To provide a real-time method for determining the size and location of an underground mine fire, and the spread of smoke and toxic gases throughout the mine ventilation network, using data from atmospheric monitoring systems (AMS) and MFIRE 3.0.
Mine fires remain one of the biggest threats to miners’ health and safety. Unlike a fire on the surface, the consequence of a mine fire underground is often catastrophic because the smoke and toxic gas produced by the fire are carried throughout the entire ventilation network, exposing miners to potentially hazardous levels of toxic gases. To protect underground mine workers, mine atmospheric monitoring systems (AMSs) are required to be installed in underground coal mines in the U.S. AMSs alert miners to potential fires based on pre-defined criteria such as carbon monoxide levels. However, in order to make decisions about fire-fighting strategies, the need for underground personnel evacuation and escape, and whether and when a mine rescue and emergency team should enter the mine, more detailed information is needed. Information about the fire intensity, where in the mine the smoke and toxic gases have spread, and whether designated mine escapeways have been compromised by smoke and toxic gases from the fire would greatly increase the effectiveness of these decisions.
Typical AMS data cannot provide this needed information. However, by integrating AMS data with the mine fire simulation program, MFIRE 3.0, the fire intensity (heat release rate) can be calculated and the fire location can be determined. With a known fire heat release rate, location data, plus AMS ventilation data fed into MFIRE 3.0, this program can predict the smoke and toxic gas spread in the entire ventilation network on a real-time basis. These real-time mine fire simulations can help miners safely and promptly evacuate from the mine in the event of a fire emergency. Further, many types of combustible materials are found in underground mines for which the fire hazard potentials are unknown or ill-defined. There are no simple models existing to assess how fires from these combustible materials develop and grow, and how these fires interact with the underground ventilation system and affect the performance of the AMS. To ensure reliable monitoring and accurate simulation of these fires, it is imperative to develop appropriate strategies for deployment of these sensors, as well as predictive models for assessing the fire hazards of the combustible mine materials.
To address this need, this project had three research aims, as follows:
- To develop a method to determine a mine fire location and intensity through the analysis and interpretation of AMS data (completed).
- To conduct real-time mine fire simulations using MFIRE and, with the information generated from AMS measurements, to develop mine fire intervention and control methods (completed).
- To assess the impact of combustion products of mine fire on mine fire sensors to improve early-warning and detection capability and reliability of AMSs (completed).
Under this project research, a computer program called AMS Data Management was developed through integrating AMS and MFIRE to conduct real-time fire simulation using AMS monitoring data. Two critical modules embedded in the AMS Data Management—Fire Location Module and Heat Release Rate Calculation Module—were developed to locate a fire and calculate the real-time fire size using AMS monitoring data. The two modules were validated using results from full-scale mine fire experiments conducted in the Safety Research Coal Mine (SRCM) at the NIOSH Pittsburgh site using different fire sources. AMS sensor deployment strategies for underground battery charging stations and fuel diesel storage areas were developed to improve early warning and detection capability and reliability of AMS. Performances of various carbon monoxide sensors and smokes sensors were evaluated and strategic spacing of the sensors was determined under low air velocity conditions. The software, predictive model, and peer-reviewed publications generated by this project resulted in a new method to locate and characterize the fire based on input from a mine’s AMS on a real-time basis. In addition, this work improved deployment strategies of AMS sensors in mines. The outcome of this project research allowed mine operators to simulate mine fires in order to develop ventilation control methods in the event of a real fire.
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