A correlational research on developing an innovative integrated gas warning system: a case study in ZhongXing, China

Geomatics, Natural Hazards and Risk

Bo Shen¹  Jeffrey Soar²  Ergun Gide³  Jianfeng Fan⁴  Zhongwu Zhang⁴  Robert M. X. Wu⁵  Xinxin Cui⁶  Jinwen Gou⁷  Bao Liu⁷  Zhigang Sun⁷  Haijun Zhao⁷  Wanjun Yan⁷  Yanyun Ma⁷  Yong Shi⁷  Peilin Wang⁸  Ya Wang⁸  Xiangyu Sun⁹ 

[1] GENEW Technologies Co. Ltd, Shenzhen, Chin;School of Business, University of Southern Queensland, Springfield, Australi;School of Engineering and Technology, Central Queensland University, Sydney, Australi;School of Geographical Sciences, Shanxi Normal University, Taiyuan, Chin;School of Geographical Sciences, Shanxi Normal University, Taiyuan, Chin;School of Engineering and Technology, Central Queensland University, Sydney, Australi;School of Medicine, Zhejiang University, Hangzhou,Chin;Shanxi Fenxi Mining Zhongxing Coal Industry Co. Ltd, Lvliang, Chin;Shanxi Kailain Technology Co. Ltd, Taiyuan, Chin;XiShan Coal Electricity Group DongQu Coal Mine, Taiyuan, Chin;

关键词: Case study;    correlational research;    gas monitoring system;    machine learning;    warning system;   

DOI  :  10.1080/19475705.2021.2002953

来源: Taylor & Francis

【 摘 要 】

Gas explosions and outbursts were the leading types of gas accidents in mining in China with gas concentration exceeding the threshold limit value (TLV) as the leading cause. Current research is focused mainly on using machine learning approaches for avoiding exceeding the TLV of the gas concentration. no published reports were found in the literature of attempts to uncover the correlation between gas data and other data to predict gas concentration. This research aimed to fill this gap and develop an innovative gas warning system for increasing coal mining safety. A mixed qualitative and quantitative research methodology was adopted, including a case study and correlational research. This research found that strong correlations exist between gas, temperature, and wind. It suggests that integrating correlation analysis of data on temperature and wind into gas would improve warning systems' sensitivity and reduce the incidence of explosions and other adverse events. A Unified Modeling Language (UML) model was developed by integrating the Correlation Analysis Theoretical Framework to the existing gas monitoring system for demonstrating an innovative gas warning system. Feasibility verification studies were conducted to verify the proposed method. This informed the development of an Innovative Integrated Gas Warning System which was deployed for user acceptance testing in 2020.

【 授权许可】

CC BY