Mining Project: Improving Prevention and Suppression of Equipment Fires in Metal/Nonmetal Mines

Research Summary

Mine fires remain a serious threat to the health and safety of miners in metal and nonmetal (M/NM) operations. For example, in September 2018, three mobile equipment fires were reported with one miner dying and another one injured. The subsequent Mine Safety and Health Administration (MSHA) accident investigation report identified that the likely cause of the fire leading to the fatality was a steering hose rupture on the haul truck, causing hydraulic fluid under high pressure to spray onto the hot surface of the engine, resulting in a fast-growing fire. Most of the reportable mine equipment fires involve similar circumstances. Although this project focuses on fire safety in metal/nonmetal mines, the research results can also be applied to other mining sectors with the similar fire issues.

To reduce the number of equipment fires, it is necessary to develop effective measures to limit or prevent hot surface ignitions on mine equipment. To reduce the number of fire-related injuries, it is important to improve the equipment fire suppression techniques to protect the equipment operators. Although some mine equipment includes a fire suppression system, the efficacy of the system can be compromised by poor design, ineffective installation, and fire damage to the system if not activated in time. It is also important to provide mine operators with an effective mine ventilation diagnostic tool to make appropriate ventilation adjustments to protect underground mineworkers from smoke and toxic gases generated from an equipment fire. Currently, there are no scientific-based effective measures available to help mine operators effectively prevent and suppress equipment fires, nor to make informed decisions on underground ventilation controls, when responding to an equipment fire emergency in underground mines.

Researchers at the National Institute for Occupational Safety and Health (NIOSH) have the needed competencies in fire prevention and mine fire and ventilation simulation, and NIOSH also possesses the necessary resources to conduct such research. Under this project, NIOSH researchers will address the above needs by way of three research aims and related tasks, as summarized below.

1. Develop effective measures to prevent hot surface ignitions on mine equipment in metal and nonmetal mines. To achieve this aim, NIOSH will design and build a hot surface ignition experimental apparatus. The experimental apparatus will consist of a hot surface with temperature control system, a fluid tank with a spray or drop release representing typical modes of fluid leakage, and a release system with pressure and flow control. Devices will measure different parameters such as temperature of the hot surface, the ambient air velocity and temperature, as well as the fuel flow rate and temperature. An infrared camera will be used to record the occurrence of surface ignitions. The focus will be on the determination of effects of different parameters on the hot surface ignition.
2. Assess the efficacy of available suppression techniques for equipment fires and develop more effective engineering designs for installation of fire suppression systems. Large-scale experiments will be conducted in NIOSH’s fire test facility to evaluate the effectiveness of different fire suppression systems for fires caused by a hot surface ignition. Various fire suppression systems will be installed and tested based upon the manufacturer’s guidelines: dry chemical, wet chemical, and carbon dioxide. Based on experimental results, recommendations and guidelines to prevent hot surface ignitions and to extinguish mine equipment fires for the M/NM mining sector will be developed. Optimal locations for suppression agent discharge nozzles will also be determined so that the nozzles can be retrofitted onto existing mining equipment.
3. Develop a machine learning-based model to evaluate the response of a mine ventilation system under an equipment fire emergency. The MATLAB Machine Learning Toolbox will be used to develop a predictive/diagnostic tool to assess the response of an underground mine ventilation system to an equipment fire. The required information will include mine ventilation data such as airflow rates, pressure drops, and mine fire data such as heat release rates, carbon monoxide concentrations, and air temperatures. These data will be collected from mine visits and by way of an extensive search of the mine fire-related literature. Mine ventilation simulations will be employed to identify the responses or the sensitivities of a ventilation network to various anomalies that can impact ventilation flow and fires. The data from these sensitivity analyses will then be used as input to train the diagnostic tool to recognize the potential consequences of an underground mine fire on ventilation flow.

The recommendations and guidelines developed from this research will provide MSHA and mine safety personnel with scientific data to improve the prevention and detection of ignitions of fluids on hot surfaces commonly found on mining equipment and will provide guidelines and specifications for more effective designs of fire suppression systems on that equipment. This effort will also provide operators with machine learning tools for diagnosing and remedying ventilation issues associated with an underground mine fire by identifying areas of the ventilation network most susceptible to the effects of a fire. Such work can minimize the impact of a fire on the effectiveness of a mine’s ventilation system.