Mining Publication: Multiple Type Discriminating Mine Fire Sensors
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Original creation date: December 2003
Researchers determined that a selection of different types of fire sensors could be used to discriminate mine fires from nuisance emissions produced by diesel equipment. A neural network (NN) was developed for application to coal, wood, and conveyor belt fires in the presence of diesel emissions and was evaluated with the successful prediction of 22 out of 23 mine fires based on a fire probability determination. The optimum sensor selection for the NN was composed of a carbon monoxide sensor, two types of metal oxide semiconductor sensors, and an optical-path smoke sensor.
Authors: JC Edwards, RA Franks, GF Friel, CP Lazzara, JJ Opferman
Peer Reviewed Journal Article - December 2003
NIOSHTIC2 Number: 20024717
Trans Soc Min Metall Explor 2003 Dec 314:166-171
See Also
- Impact of Air Velocity on the Detection of Fires in Conveyor Belt Haulageways
- In-Mine Evaluation of Smart Mine Fire Sensor
- In-Mine Evaluation of Smoke Detectors
- Mine Fire Detection in the Presence of Diesel Emissions
- 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
- Rapid Detection and Suppression of Mining Equipment Cab Fires
- Real-time Neural Network Application to Mine Fire - Nuisance Emissions Discrimination
- Technology News 498 - Multiple Fire Sensors for Mine Fire Detection and Nuisance Discrimination
- Content source: National Institute for Occupational Safety and Health, Mining Program