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

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.

Keywords

• case study
• correlational research
• gas monitoring system
• machine learning
• warning system