A Novel Methodology to Locate an Abnormal Airflow in Underground Mine Ventilation Networks
Public Domain
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2022/02/27
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Series: Mining Publications
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Description:Mine ventilation is one of the most important aspects of mining operations in underground mines. It is critical to maintain and deliver required fresh air to the active areas to reduce the risk of overexposure to hazardous contaminants or explosive atmospheres. An unexpected or unknown event such as roof collapse, unexpected mine door closure/opening, a fire/explosion, or fan malfunction could adversely alter the airflow distribution within the mine ventilation network that could lead to a hazardous underground environment. Knowing the occurrence and location of such an incident is of critical importance. In this study, the authors developed a novel methodology to assist mine operators in quickly identifying the location of any abnormal airflow change within the network using the airflow changes at monitored airways. The concept is based on a direct derivative method developed by the authors. This paper provides the details of the developed method as well as numerical verification examples. The application of this method can benefit mine operators and safety personnel in making better decisions during a mine emergency response operation to mitigate hazardous conditions arising from an unexpected airflow disturbance. [Description provided by NIOSH]
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ISBN:9781713845089
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Pages in Document:84-88
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NIOSHTIC Number:nn:20065268
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Citation:MineXchange: 2022 SME Annual Conference and Expo, February 27-March 2, 2022, Salt Lake City, Utah, preprint 22-018. Englewood, CO: Society for Mining, Metallurgy & Exploration, 2022 Feb; :84-88
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Contact Point Address:D. Bahrami, CDC NIOSH, Pittsburgh, PA, United States
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Federal Fiscal Year:2022
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Peer Reviewed:False
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Source Full Name:MineXchange: 2022 SME Annual Conference and Expo, February 27-March 2, 2022, Salt Lake City, Utah, preprint 22-018
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Main Document Checksum:urn:sha-512:e3e9255d629a9588c0a32f32260ba1b2d8df6f68c5dd94f82def1bd2fd6f896fee7b98fd011af608787c3d1f6c7ce19881a86752788c0cd20194c9ddc7a9d382
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