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Neural network application to mine-fire diesel-exhaust discrimination.

Authors
Friel-GF; Edwards-JC
Source
Mine Ventilation. Lisse, Netherlands: A. A. Balkema, 2002 Oct; :533-538
Link
NIOSHTIC No.
20023130
Abstract
A series of seven underground-coal-mine fire experiments was conducted in the Safety Research Coal Mine at the National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory. Coal and styrene-butadiene-rubber conveyor belting were burned upwind of two sensor stations, 18 m and 148 m from the fire source. Exhaust from a diesel locomotive flowed over the fire sources in six of the tests. MetaI-oxide-semiconductor (MOS), CO, and optical-path-smoke sensors were positioned at both stations and found to be an optimum set of sensors for the fire discriminations. A representative set of 7,679 samples of CO data and data from the smoke and diesel-exhaust MOS sensors were used as inputs to train a neural network (NN). By testing 42,538 data samples from the seven experiments, all fires were detected by the NN within 9.67 min from the onset of significant changes in the MOS voltages without any false alarms.
Keywords
Underground-mining; Underground-miners; Fire-hazards; Fire-protection; Fire-safety; Mine-fires; Diesel-exhausts; Diesel-emissions; Mining-industry; Coal-mining
Publication Date
20021001
Document Type
Book or book chapter; Conference/Symposia Proceedings
Editors
De Souza-E
Fiscal Year
2003
NTIS Accession No.
NTIS Price
ISBN No.
9058093875
NIOSH Division
PRL
Priority Area
Work Environment And Workforce: Emerging Technologies
Source Name
Mine Ventilation: Proceedings of the North American/Ninth US Mine Ventilation Symposium, Kingston, Ontario, Canada June 8-12 2002
State
PA
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