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.
This project has 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.
- To conduct real-time mine fire simulations using MFIRE and the information generated from AMS measurements to develop mine fire intervention and control methods.
- To assess the impact of combustion products of mine fire on mine fire sensors to improve early-warning and detection capability and reliability of AMS.
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 are required to be installed in underground coal mines in the U.S. AMS 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 the fire heat release rate, location data, and AMS ventilation data are fed into MFIRE 3.0, the 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 AMS. To ensure reliable monitoring for AMS and accurate simulation of these fires, it is imperative to develop appropriate strategies for deployment of these sensors, as well predictive models for assessing the fire hazards of the combustible mine materials.
Under this project research, full-scale mine fire experiments will be conducted in the Safety Research Coal Mine (SRCM) at the NIOSH Pittsburgh site using different fire sources. The AMS will collect the data on ventilation airflow velocity, smoke spread and rollback, and levels of carbon monoxide, carbon dioxide, and oxygen, and these data will be used as input to the MFIRE 3.0 mine fire simulation program. Laboratory-scale experiments will also be conducted using a variety of numerical and analytical techniques to quantify and refine the fire hazard parameters for combustible mine materials, and to simplify the laboratory procedures for obtaining these parameters. Theoretical models will be developed to relate these hazard parameters to the physical and chemical properties of such materials. The experimental data and models will be utilized to develop improved and reliable mine fire sensors and deployment strategies for using AMS.
The software, predictive model, and peer-reviewed publications generated by this project research will result in a new method to locate and characterize the fire based on input from a mine’s AMS on a real-time basis, and improved deployment strategies of AMS sensors in mines. The outcome of this project research will also allow mine operators to simulate mine fires in order to develop ventilation control methods in the event of a real fire.
- Analysis of Post-Explosion Residues for Estimating Flame Travel During Coal Dust Deflagrations
- Fire Detection
- Fire Detection for Conveyor Belt Entries
- Impact of Air Velocity on the Detection of Fires in Conveyor Belt Haulageways
- In-Mine Evaluation of Smart Mine Fire Sensor
- Methods to Determine the Status of Mine Atmospheres - an Overview
- Mine Fire Detection Under Zero Airflow Conditions
- Mine Fire Source Discrimination Using Fire Sensors and Neural Network Analysis
- Neural Network Application to Mine-Fire Diesel-Exhaust Discrimination
- Technology News 549 - MFIRE 3.0 - NIOSH Brings MFIRE into 21st Century
- Page last reviewed: 10/22/2016
- Page last updated: 10/22/2016
- Content source: National Institute for Occupational Safety and Health, Mining Program