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

Chang, Haoqian; Meng, Xiangrui; Wang, Xiangqian; Hu, Zuxiang

Authors

Haoqian Chang

Xiangrui Meng

Xiangqian Wang

Zuxiang Hu



Abstract

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.

Citation

Chang, H., Meng, X., Wang, X., & Hu, Z. (2024). Research on coal mine longwall face gas state analysis and safety warning strategy based on multi-sensor forecasting models. Scientific Reports, 14(1), Article 13795. https://doi.org/10.1038/s41598-024-64181-7

Journal Article Type Article
Acceptance Date Jun 5, 2024
Online Publication Date Jun 14, 2024
Publication Date Jun 14, 2024
Deposit Date Jul 10, 2024
Journal Scientific Reports
Publisher Nature Research
Peer Reviewed Peer Reviewed
Volume 14
Issue 1
Article Number 13795
DOI https://doi.org/10.1038/s41598-024-64181-7
Keywords Time series analysis, Mining management engineering, Mining safety, Warning strategy
Public URL https://durham-repository.worktribe.com/output/2493202


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