Author:
Wu Robert M. X.,Zhang Zhongwu,Zhang Huan,Wang Yongwen,Shafiabady Niusha,Yan Wanjun,Gou Jinwen,Gide Ergun,Zhang Siqing
Abstract
AbstractAmong all the gas disasters, gas concentration exceeding the threshold limit value (TLV) has been the leading cause of accidents. However, most systems still focus on exploring the methods and framework for avoiding reaching or exceeding TLV of the gas concentration from viewpoints of impacts on geological conditions and coal mining working-face elements. The previous study developed a Trip-Correlation Analysis Theoretical Framework and found strong correlations between gas and gas, gas and temperature, and gas and wind in the gas monitoring system. However, this framework's effectiveness must be examined to determine whether it might be adopted in other coal mine cases. This research aims to explore a proposed verification analysis approach—First-round—Second-round—Verification round (FSV) analysis approach to verify the robustness of the Trip-Correlation Analysis Theoretical Framework for developing a gas warning system. A mixed qualitative and quantitative research methodology is adopted, including a case study and correlational research. The results verify the robustness of the Triple-Correlation Analysis Theoretical Framework. The outcomes imply that this framework is potentially valuable for developing other warning systems. The proposed FSV approach can also be used to explore data patterns insightfully and offer new perspectives to develop warning systems for different industry applications.
Publisher
Springer Science and Business Media LLC
Reference21 articles.
1. IEA. Coal 2020 Analysis and Forecast to 2025. Viewed 7 Jan 2021, https://www.iea.org/reports/coal-2020/supply (2020).
2. Hutzler. China’s Economic Recovery will be Powered by Coal. Viewed 08 Jan 2021, https://www.powermag.com/chinas-economic-recovery-will-be-powered-bycoal/ (2020).
3. Wu, M. X. et al. A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement. PLoS ONE 17(1), 1–26 (2022).
4. Zhang, J. Y., Ai, Z. B., Guo, L. W. & Cui, X. Research of synergy warning system for gas outburst based on Entropy-Weight Bayesian. Int. J. Comput. Intell. Syst. 14(1), 376–385 (2021).
5. Wu, M. X. et al. A correlational research on developing an innovative integrated gas warning system: A case study in ZhongXing, China. Geomat. Nat. Haz. Risk 12(1), 3175–3204 (2021).
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献