2.1 Current Gas Monitoring System: A Case Study in China
This research focused on Mine No.1209 coal mining working-face at Shanxi Fenxi Mining ZhongXing Coal Industry Co. Ltd (ZhongXing) - a large coal company in China. The ZhongXing gas monitoring system monitors gas, temperature, wind, dust, etc. (Fig.1). If gas data are normal, the data outputs are forwarded to the monitoring system. If the data outputs of gas reach the TLVs, the alarm system will immediately alert the safety-responsive team. Other data outputs of temperature, wind, and dust are also available to be communicated to the monitoring system. However, the current gas monitoring system does not include any risk analysis potentially impacted by other outputs, such as temperature sensors, wind sensors, and dust sensors.
2.2 Literature Review
A literature review was conducted to understand the state of research into gas monitoring systems and early warning systems in underground coal mining. The literature focuses on both international research and Chinese research in English and Chinese.
This research first reviewed the literature published between 2016 and 2020 in Scopus. The search terms were “gas,” “coal mine,” “monitoring system/model/framework,” “alarming system /model/framework,” “warning system/model/framework,” “prediction system/model/ framework.” 188 papers were found. After reviewing the abstracts, most were found to focus on analyzing gas data to explore the methods and framework for providing quick responses or early warnings on any anomaly detected from gas data. A few correlational pieces of research focused on the correlation between the data obtained from different types of sensors but did not conduct any correlational analysis. Jo, Khan & Javaid (2019, p.190) conducted correlational research between temperature, humidity, gas, and CO2 in underground coal mines and found a strong correlation between temperature and humidity. Zhao et al. (2020, p.1982) discussed gas geology, mining effects, daily prediction, mining pressure, and gas emission dynamic analysis systems. Wang, Li & Li (2019, p.1722) found that coal seam depth, coal seam thickness, temperature, and gas concentration impacted historical monitoring data on gas prediction. Xie et al. (2018, p.170) focused on 45 coal and gas outburst accidents between 1984 and 2009 in Pingdingshan No.8 mine China. They explored geological factors, coal structure factors, operation factors, and gas factors, including absolute gas emission, gas concentration, and gas release initial velocity. The analysis results showed that the soft and fallen coal seam's most sensitive factors, soft layer thickness variation, change of coal thickness, coal seam thickness, and geological structure but not explore any correlation analysis of gas factors and temperature, wind, and dust. Ma & Dai (2017, p.93) developed a warning index system for coal mine safety based on safety management, facility performance, behavior Monitor, and emergency rescue.
This research also reviewed the literature published in the core journals indexed in the Chinese science citation Index (CSCI) and the Chinese social sciences Citation Index (CSSCI) scientific database from the China Academic Journals full-text database-called China National Knowledge Infrastructure (CNKI). CNKI is the largest, continuously updated Chinese journal database globally and was released in June 1999 (CNKI 2019). The search was also limited to papers published between 2016 and 2020. The search terms were the same as for Scopus. 4590 publications were found. After reviewing the abstracts, current research in China focused on alarms to avoid exceeding the limit value of gas concentration.
Overall, there appears to be a gap in research on conducting correlational analysis to provide alarms or warnings by anomaly data detected between gas and temperature, wind, and dust.