Author:
Chang Haoqian,Meng Xiangrui,Wang Xiangqian,Hu Zuxiang
Abstract
AbstractIntelligent 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.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Reference37 articles.
1. Lei, Y., Cheng, Y., Wang, L., Ren, T. & Tu, Q. Mechanisms of coal and gas outburst experiments: Implications for the energy principle of natural outbursts. Rock Mech. Rock Eng. 56, 363–377 (2023).
2. Guo, Z. et al. Prediction of coalbed methane production based on deep learning. Energy 230, 120847 (2021).
3. Xiang, W. et al. Short-term coalmine gas concentration prediction based on wavelet transform and extreme learning machine. Math. Probl. Eng. 2014, 1 (2014).
4. Ye, Z. et al. A digital twin approach for tunnel construction safety early warning and management. Comput. Ind. 144, 103783 (2023).
5. Fan, C., Li, S., Luo, M., Du, W. & Yang, Z. Coal and gas outburst dynamic system. Int. J. Mining Sci. Technol. 27, 49–55 (2017).