Discriminatory Mine Fire Source Detection
Public Domain
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2001/06/17
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Series: Mining Publications
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Description:Product-of combustion sensors were used to discriminate mine fire sources of coal, diesel fuel, electrical cable insulation, conveyor belt, and nuisance emissions from acetylene torch cutting operations in experiments conducted in the National Institute for Occupational Safety and Health (NIOSH), Pittsburgh Research Laboratory (PRL) Safety Research Coal Mine (SRCM). The sensors consisted of CO, ionization and optical smoke, and metal oxide semiconductor (MOS) sensors. Metal oxide semiconductor and smoke sensors demonstrated an earlier fire detection capability than a CO sensor. This capability was of particular significance for a smoldering conveyor-belt fire in which the optical visibility was reduced to 1.52 m with an increase in CO of less than 2 ppm at a distance of 148 m from the fire. An application of a neural-network program to the sensor responses from each type of fire source resulted in correct classifications of coal, diesel-fuel, cable insulation, belt, and metal-cutting combustion with a mean of 96% of the in-mine test data correctly classified. In a battery charging building, a fire sensor configuration consisting of an ionization type smoke sensor and a MOS, NOx - sensitive sensor was demonstrated to be capable of discriminating a coal fire from diesel equipment when Hz from a battery charging operation saturated the CO chemical sensor cell. [Description provided by NIOSH]
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ISBN:8391310914
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NIOSHTIC Number:nn:20021298
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Citation:Proceedings of the Seventh International Mine Ventilation Congress, June 17-22, 2001, Crakow, Poland. S Wasilewski, ed., Crakow, Poland: Research & Development Center for Electrical Engineering and Automation in Mining (EMAG), 2001 Jun; :649-655
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Federal Fiscal Year:2001
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Peer Reviewed:False
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Source Full Name:Proceedings of the Seventh International Mine Ventilation Congress, June 17-22, 2001, Crakow, Poland
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Main Document Checksum:urn:sha-512:56b2d16b39d6597811b800b42b21134a747db984a62b490a4601581f91753c8155cedb73d45bd2f2707acb13a3755532dedc49c2f3c5f96ea26892dc0b2ce58d
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