Real-Time Methane Prediction in Underground Longwall Coal Mining Using AI

Details

• Personal Author:
  Bogin G ; Brune, Jürgen F. ; Demirkan DC ; Duzgun HS ; Juganda A
• Description:
  Detecting the formation of explosive methane-air mixtures in a longwall face is still a challenging task. Even though atmospheric monitoring systems and computational fluid dynamics modeling are utilized to inspect methane concentrations, they are not sufficient as a warning system in critical regions, such as near cutting drums, in real-time. The long short-term memory algorithm has been established to predict and manage explosive gas zones in longwall mining operations before explosions happen. This paper introduces a novel methodology with an artificial intelligence algorithm, namely, modified long short-term memory, to detect the formation of explosive methane-air mixtures in the longwall face and identify possible explosive gas accumulations prior to them becoming hazards. The algorithm was trained and tested based on CFD model outputs for six locations of the shearer for similar locations and operational conditions of the cutting machine. Results show that the algorithm can predict explosive gas zones in 3D with overall accuracies ranging from 87.9% to 92.4% for different settings; output predictions took two minutes after measurement data were fed into the algorithm. It was found that faster and more prominent coverage of accurate real-time explosive gas accumulation predictions are possible using the proposed algorithm compared to computational fluid dynamics and atmospheric monitoring systems. [Description provided by NIOSH]
• Subjects:
  Mining Occupational Health Safety
• Keywords:
  AI Artificial Intelligence Author Keywords: Artificial Intelligence CFD Coal Mines Computational Fluid Dynamics Explosion Prevention Explosive Gases Longwall Mining Methane Control Methane Prediction Modified Long Short-term Memory Real-time Time Series Prediction Underground Coal Mines Underground Mines Warning Systems

  More +
• ISSN:
  1996-1073
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  Colorado ; OSHA Region 8
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  15
• Issue:
  17
• NIOSHTIC Number:
  nn:20067832
• Citation:
  Energies 2022 Sep; 15(17):6486
• Contact Point Address:
  Doga Cagdas Demirkan, Mining and Earth Systems Engineering Program, Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA
• Email:
  demirkan@mines.edu
• CAS Registry Number:
  Methane (CAS RN 74-82-8)
• Federal Fiscal Year:
  2022
• Performing Organization:
  Colorado School of Mines
• Peer Reviewed:
  True
• Start Date:
  20190913
• Source Full Name:
  Energies
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f162629c5d7c57418cf5343a87eb621245d034e52749f2034c5c79f1ee055f869593e599d97959978cefd722b02d327e2a39babc571f4d649c60cdb8ce4b68db
• Download URL:
  https://stacks.cdc.gov/view/cdc/230087/cdc_230087_DS1.pdf
• File Type:
  [PDF - 7.07 MB ]