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Coal-gas outburst is a violent phenomenon of energy release and strong dynamics is generated in underground coal mining. Coal-gas outbursts are accompanied by strong coal pulverization, and the gas desorbed by pulverized coal will in turn promote the development and spread of outbursts. Quantitative study on the role desorption gas played can improve the disaster-causing mechanism of coal-gas outburst and guide the work of outburst prevention and disaster reduction. Based on established gas emission model, the gas desorption is combined with the outburst process and its dynamic characteristics in this paper. The energy contribution and outburst dynamics with desorption gas are quantitatively studied under various desorption conditions, verified by outburst experiments. The results indicate that the energy contribution of desorption gas is 15.49–58.59% for JG71 coal, 4.49–57.26% for JG82 coal. The coal pulverization in the JG82 coal seam has a great influence on the energy contribution of desorption gas. After correction of gas desorption, the energy contribution increases by ∼20%. The maximum energy release rate (MERR) of the outburst with 0.075–0.15 mm JG71 coal sample reaches is 2.87 times that of the 1–2.36 mm coal sample at 3 MPa, and even 5.72 times that of the JG82 coal sample. For the outburst experiments, the energy contribution of desorption gas is 31.97–40.06% with 1–2.36 mm coal, 50.29–52.76% with < 0.25 mm coal. This paper presents a novel quantitative study defining the role of gas desorption in outburst energy and dynamics, applicable to disaster risk assessment and an accurate method to derive the energy contribution of desorption gas in experiments is proposed. [ABSTRACT FROM AUTHOR]

Analyzing the causes of accidents, excavating accident paths, and applying accident prevention are important tasks in safety management. Focusing on coal and gas outburst accidents, this study examined the primary accident path and conducted applied research on the reasoning of the accident case. First, combined with the obtained accident causes, a coupling analysis of the causes of coal and gas outburst accidents was conducted. Second, using the method of data mining coupled with Apriori algorithm, the coupling relationship between each cause module of the coal and gas outburst accident was obtained, and consequently, a path map of the coal and gas outburst accident was drawn. Third, a Bayesian network model for the causes of coal and gas outburst accidents was established based on the accident path map and the probability of occurrence of each cause. Finally, considering the safety concept element (SC1) as an example, the Bayesian network model was used to conduct a sensitivity analysis of accident causes. Thereafter, considering the coal and gas outburst accident of the Sanjia Coal Mine in Guizhou Province as an example, probabilistic reasoning research on the cause of the accident was conducted. The application results showed that (1) under normal conditions, there are approximately 797,280 accident paths for coal and gas outbursts. Following data mining, 188 main accident paths were found. (2) Sensitivity analysis determined 19 factors that were sensitive to safety concept elements (SC1), of which the three most sensitive factors were (i) resource management system procedures (SM7), (ii) safety policy (SM1), and (iii) safety training system procedure (SM8). 13 paths exhibited a sensitivity ≥0.5%, of which 7 exhibited strong sensitivity. (3) The absolute accuracy rate of accident cause reasoning in the Sanjia Coal Mine in Guizhou Province was 71.43%, while the relative accuracy rate was close to 100%. Thus, it was concluded that: (1) the accident path mining method proposed in this paper is feasible for main accident path mining. (2) The Bayesian network model for the causes of coal and gas outburst accidents established in this study can be practically applied for the sensitivity analysis of accident causes and exhibits high reliability in the probabilistic reasoning of accident causes. The results of this study is expected to aid in the prevention of coal and gas outburst accidents, and provide reference and help for the path mining of other accident causes and the probabilistic reasoning of accident causes. [ABSTRACT FROM AUTHOR]

Mining protective coal seams is an effective approach for coal and gas outburst control, but a large amount of gangue is usually produced when mining a thin protective seam and dealing with the gangue can be a challenging task. In the paper, a novel integrated mining system by extracting two panels simultaneously was demonstrated in the 12th coal mine of the Pingdingshan coalfield: one panel was mined in the thin protective coal seam, and the other one was mined in the protected coal seam. The thickness of the protective seam was only 0.8 m, but the mining height reached 1.8 m, indicating that there was about 1 m thick roof stone being mined. An underground coal preparation plant was built, and the mixture of coal and gangue from the protective panel was sorted underground. The sorted coal was delivered to the surface stockpile, while the waste rock was directly backfilled into the gob of the panel in the protected coal seam. Thus, waste rock did not need to be lifted up to the surface, which can reduce material handing cost and minimise mining footprint on the surface. In order to prevent the migration of pressure relief gas into the working areas of the protective coal seam, the main reasons causing gas exceedance in the upper corner of U-shaped ventilation system were investigated, and then the Y-shaped ventilation system and various gas extraction boreholes were implemented to effectively control the pressure relief gas. Numerical models were developed to assess the effect of applying gob backfilling during the extraction of two panels in parallel. The results show that the gob backfilling can reduce the surface subsidence and horizontal movement, which is very helpful for the protection of ground water and surface infrastructures. [ABSTRACT FROM AUTHOR]

For the characteristics of high frequency and strong suddenness of dynamic disasters in deep coal mines, the traditional detection and evaluation techniques applied to shallow coal mine failed to accurately judge the risk degree of disasters. Therefore, it is of great significance to use advanced detection technologies and appropriate evaluation methods to improve the accuracy and efficiency of risk assessment in the process of coal mining. The present paper applies the rapid detection and multi-source dynamic detection technologies used in the field of mining with the purpose of improving the reliability of detection technologies for typical dynamic disaster. In this study, the data fusion technology was used to analyze data obtained from laboratory experiments, engineering survey, detection and historical data, so as to form the final dynamic and static indicators. Then, the new combined evaluation models with time series of coal and gas outburst as well as rock burst were established respectively to carry out the comprehensive risk evaluation using the least-squares method and the time-varying weight method. After the comprehensive analysis on the results of the above two evaluation models, the risk areas of the typical dynamic disasters were judged and classified. Finally, the evaluation models were coded to build an early-warning software platform that could achieve automatic evaluation and the actual 3D visualization of coal mining areas. The early-warning software platform was applied to risk assessment of dynamic disasters in Gengcun Coal Mine in Yima City, Henan Province, China. The results of the 6-month experiment showed that the risk assessment accuracy and reliability of the proposed evaluation models was 100% and 90% respectively, which indicates that the newly developed approach is reliable and can be recommended for applying in more coal mines to improve the process safety risk control. [ABSTRACT FROM AUTHOR]