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Mining Publication: Mine Fire Source Discrimination Using Fire Sensors and Neural Network Analysis

NOTE: This page is archived for historical purposes and is no longer being maintained or updated. Contact NIOSH Mining if you need an accessible version.

Original creation date: April 2000

Image of publication Mine Fire Source Discrimination Using Fire Sensors and Neural Network Analysis

Fire experiments were conducted in the Safety Research Coal Mine (SRCM) at the National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory, with coal, diesel-fuel, electrical cable, conveyor-belt, and metal-cutting fire sources to determine the response of fire sensors to products-of- combustion (POC). Metal oxide semiconductor (MOS) and smoke fire sensors demonstrated an earlier fire detection capability than a carbon monoxide sensor. This capability was of particular significance for a conveyor-belt fire in which the optical visibility was reduced to 1.52 m with an increase in carbon monoxide of less than 2 ppm at a distance of 148 m from the fire. 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, belt, and metal-cutting combustion with a mean of 96% of the test data correctly classified.

Authors: JC Edwards, GF Friel, RA Franks, CP Lazzara, JJ Opferman

Conference Paper - April 2000

NIOSHTIC2 Number: 20020920

Combustion Fundamentals and Applications - Proceedings of the 2000 Technical Meeting of the Central States Section of the Combustion Institute (April 17-18, 2000, Indianapolis, IN); :207-211