New Approach for Monitoring the Underground Coal Mines Atmosphere Using IoT Technology

Because the atmosphere in underground coal mines contains toxic and flammable gasses, assessing the well-being of miners at all times while working in underground coal mines is an important task. The hazardous environment in underground coal mines reduces the miners' performance, which negative...

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Bibliographic Details
Published inInstrumentation, Mesure, Metrologie Vol. 22; no. 1; p. 29
Main Authors Tripathi, Abhishek Kumar, Mangalpady Aruna, Prasad, Sandeep, Jonnalagadda Pavan, Kant, Ramesh, Choubey, Chandan Kumar
Format Journal Article
LanguageFrench
Published Edmonton International Information and Engineering Technology Association (IIETA) 01.02.2023
Subjects
Air temperature
Artificial neural networks
Carbon dioxide
Carbon monoxide
Coal mines
Coal mining
Connectivity
Gas detectors
Gas monitoring
Gases
Hazardous areas
Humidity
Internet of Things
Machine learning
Measurement techniques
Miners
Mines
Monitoring
Monitoring systems
Safety measures
Transmitters
Underground mines
Ventilation
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Summary:Because the atmosphere in underground coal mines contains toxic and flammable gasses, assessing the well-being of miners at all times while working in underground coal mines is an important task. The hazardous environment in underground coal mines reduces the miners' performance, which negatively affects the overall productivity of the mines. Therefore, it is necessary to regularly monitor the environment of underground mines so that appropriate safety measures can be taken. In this work, an IoT-based system was proposed using sensors to detect the concentration of mine gasses, air temperature, and humidity in the environment of underground mines. The developed wireless monitoring system was tested under laboratory conditions for measuring carbon dioxide, carbon monoxide, methane gas, air temperature, and humidity. The proposed monitoring system allows to store the measurement data that will help in predicting future hazardous conditions through artificial neural network and machine learning. The results of this research will help to introduce an innovative monitoring technology in underground coal mines so that miners' safety can be improved by changing safety measures from preventive to predictive.
ISSN:1631-4670
2269-8485
DOI:10.18280/i2m.220104