Multiple Type Discriminating Mine Fire Sensors

Details

• Personal Author:
  Edwards JC ; Franks, Robert A. ; Friel, Gene F. ; Lazzara, Charles P. ; Opferman JJ
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Coal Coal Mining Mining Occupational Health Safety Vehicle Emissions
• Keywords:
  Coal-mining Diesel-emissions Hazards Mine-fires Safety-research Sensors Underground-mining
• Series:
  Mining Publications
• ISSN:
  1075-8623
• Publisher:
  Society for Mining, Metallurgy, and Exploration, Inc.
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  PRL - Pittsburgh Research Laboratory
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  166-171
• Volume:
  314
• NIOSHTIC Number:
  nn:20024717
• Citation:
  Trans Soc Min Metall Explor 2003 Dec; 314:166-171
• Contact Point Address:
  NIOSH Pittsburgh Research Laboratory, P.O. Box 18070, Pittsburgh, PA 15236
• CAS Registry Number:
  Carbon monoxide (CAS RN 630-08-0)
• Editor(s):
  Yernberg WR
• Federal Fiscal Year:
  2004
• Peer Reviewed:
  True
• Source Full Name:
  Transactions of the Society for Mining, Metallurgy, and Exploration
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0a8c63ab77885f7f67a732af5f60a7498052c57d8a5fe16b47a11514e3641f711ae3aab78b08193f2ef65afef1d66ffc4fee2df137e1ad6359332ea377d5a33a
• Download URL:
  https://stacks.cdc.gov/view/cdc/220823/cdc_220823_DS1.pdf
• File Type:
  [PDF - 1.32 MB ]