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

Wu, Robert M. X. and Yan, Wanjun and Zhang, Zhongwu and Gou, Jinmen and Fan, Jianfen and Liu, Bao and Shi, Yong and Shen, Bo and Zhao, Haijun and Ma, Yanyun and Soar, Jeffrey ORCID: https://orcid.org/0000-0002-4964-7556 and Sun, Xiangyu and Gide, Ergun and Sun, Zhigang and Wang, Peilin and Cui, Xinxin and Wang, Ya (2021) A correlational research on developing an innovative integrated gas warning system: a case study in ZhongXing, China. Geomatics, Natural Hazards and Risk, 12 (1). pp. 3175-3204. ISSN 1947-5705

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Abstract

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: � 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 15 Dec 2021 01:20
Last Modified: 15 Dec 2021 03:45
Uncontrolled Keywords: Case study; correlational research; gas monitoring system; machine learning; warning system
Fields of Research (2008): 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460999 Information systems not elsewhere classified
Socio-Economic Objectives (2008): B Economic Development > 84 Mineral Resources (excl. Energy Resources) > 8499 Other Mineral Resources (excl. Energy Resources) > 849999 Mineral Resources (excl. Energy Resources) not elsewhere classified
Identification Number or DOI: https://doi.org/10.1080/19475705.2021.2002953
URI: http://eprints.usq.edu.au/id/eprint/44769

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