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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| Published in: | Scientific reports Vol. 14; no. 1; pp. 13795 - 12 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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London
Nature Publishing Group UK
14.06.2024
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| 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. |
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| AbstractList | 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. 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.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. 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. |
| ArticleNumber | 13795 |
| Author | Hu, Zuxiang Chang, Haoqian Meng, Xiangrui Wang, Xiangqian |
| Author_xml | – sequence: 1 givenname: Haoqian surname: Chang fullname: Chang, Haoqian email: haoqian.chang@durham.ac.uk organization: School of Economics and Management, Anhui University of Science & Technology – sequence: 2 givenname: Xiangrui surname: Meng fullname: Meng, Xiangrui organization: School of Economics and Management, Anhui University of Science & Technology – sequence: 3 givenname: Xiangqian surname: Wang fullname: Wang, Xiangqian email: xiqwang@aust.edu.cn organization: School of Economics and Management, Anhui University of Science & Technology – sequence: 4 givenname: Zuxiang surname: Hu fullname: Hu, Zuxiang organization: School of Economics and Management, Anhui University of Science & Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38877166$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.4018/JDM.2019040104 10.1109/LCOMM.2021.3081593 10.1016/j.eswa.2019.113082 10.2991/ijcis.d.201214.001 10.1016/j.resourpol.2023.103802 10.1016/j.compind.2022.103783 10.1016/j.psep.2019.10.002 10.3390/ijerph16081406 10.1016/j.engappai.2017.06.002 10.1080/17480930.2015.1123599 10.1016/j.ijmst.2016.11.003 10.1016/j.psep.2017.02.023 10.3390/s21175730 10.3390/app132011369 10.1162/neco.1997.9.8.1735 10.1016/j.psep.2020.05.037 10.1016/j.energy.2021.120847 10.1007/s12613-019-1956-9 10.1007/s40789-022-00491-3 10.1016/j.psep.2022.12.077 10.1016/j.ress.2021.107433 10.1016/j.aei.2023.102275 10.1016/j.ins.2018.04.026 10.1007/s00603-022-03093-2 10.1007/s00477-023-02382-8 10.1016/j.psep.2022.08.065 10.2478/amns.2021.2.00299 10.1016/j.dib.2021.107457 10.1109/CAC48633.2019.8996314 |
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| Keywords | Mining safety Time series analysis Mining management engineering Warning strategy |
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