Research on coal mine longwall face gas state analysis and safety warning strategy based on multi-sensor forecasting models

Intelligent computing is transforming safety inspection methods and response strategies in coal mines. Due to the significant safety hazards associated with mining excavation, this study proposes a multi-source data based predictive model for assessing gas risk and implementing countermeasures. By e...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 13795 - 12
Hlavní autoři: Chang, Haoqian, Meng, Xiangrui, Wang, Xiangqian, Hu, Zuxiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 14.06.2024
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1305
631/114/2164
639/166
Accident prevention
Coal
Coal mines
Coal mining
Datasets
Decision making
Excavation
Gas monitoring
Gases
Humanities and Social Sciences
Mines
Mining accidents & safety
Mining management engineering
Mining safety
Mortality
multidisciplinary
Prediction models
Safety
Science
Science (multidisciplinary)
Sensors
Time series analysis
Trends
Warning strategy
Work environment
Mining safety
Time series analysis
Mining management engineering
Warning strategy
ISSN:2045-2322, 2045-2322
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Shrnutí:Intelligent computing is transforming safety inspection methods and response strategies in coal mines. Due to the significant safety hazards associated with mining excavation, this study proposes a multi-source data based predictive model for assessing gas risk and implementing countermeasures. By examining the patterns of gas dispersion at the longwall face, utilizing both temporal and spatial correlation, a predictive model is crafted that incorporates safety thresholds for gas concentrations, four-level early warning method and response strategy are devised by integrating weighted predictive confidence with these correlations. Initially tested using a public dataset from Poland, this method was later verified in coal mine in China. This paper discusses the validity and correlation of multi-source monitoring data in temporal and spatial correlation and proposes a risk warning mechanism based on it, which can be applied not only for safety warning but also for regulatory management.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-64181-7