In order to realize dynamic, continuous, and real-time prediction of coal and gas outburst risk in real time in blasting driving face, an outburst risk prediction method based on the characteristics of gas emission after blasting is proposed. In this study, the causes of abnormal gas concentration in blasting driving face are analyzed, and the identification method of abnormal gas concentration based on weighted K-nearest neighbor (weighted KNN) is proposed. The correlation between gas emission characteristics after blasting and K1 value is analyzed, and the prediction model of outburst risk based on convolutional neural networks (CNN) is established and applied in Jinjia coal mine in China. The results show that the causes of abnormal gas concentration mainly include ventilation stop, blasting operation, sensor adjustment, and other abnormalities. The accuracy of the identification method is 86.1%. Especially, the identification accuracy of blasting operation is 92%. There are strong correlations between the growth rate, peak value, and decay rate of gas concentration after blasting and K1 value, and the maximum correlation coefficient is 0.92. Using the prediction model, 28 times of jet holes and 1 small outburst event are predicted successfully, and the efficiency of the prediction model is 76.39%. By this technology, the utilization rate of gas information is improved, and the relationship between the change characteristics of gas concentration after blasting and the risk of coal seam outburst is established, which is of significant for improving the prediction accuracy and risk management ability of coal and gas outburst.

With the increase in coal mining depth, engineering geological conditions and the stress environment become more complex. Many rock bursts triggered by two combined faults have been observed in China, but the mechanism is not understood clearly. The focus of this research aims at investigating the influence of two combined faults on rock burst mechanisms. The six types of two combined faults were first introduced, and two cases were utilized to show the effects of two combined faults types on coal mining. The mechanical response of the numerical model with or without combined faults was compared, and a conceptual model was set up to explain the rock burst mechanism triggered by two combined faults. The influence of fault throw, dip, fault pillar width, and mining height on rock burst potential was analyzed. The main control factors of rock burst in six models that combined two faults were identified by an orthogonal experiment. Results show that six combinations of two faults can be identified, including stair-stepping fault, imbricate fault, graben fault, horst fault, back thrust fault, and ramp fault. The particular roof structure near the two combined faults mining preventing longwall face lateral abutment pressure from transferring to deep rock mass leads to stress concentration near the fault areas. Otherwise, a special roof structure causing the lower system stiffness of mining gives rise to the easier gathering of elastic energy in the coal pillars, which makes it easier to trigger a rock burst. There is a nonlinear relationship between fault parameters and static or dynamic load for graben faults mining. The longwall face has the highest rock burst risk when the fault throw is between 6 and 8 m, the fault dip is larger than 65°, the mining height is greater than 6 m, and the coal pillar width is less than 50 m. The stair-stepping, imbricate, horst, and ramp fault compared to the other fault types will produce higher dynamic load stress during longwall retreat. Fault pillar width is the most significant factor for different two combined faults, leading to the rise of static load stress and dynamic proneness.

In order to accurately predict the gas concentration, find out the gas abnormal emission in advance, and take effective measures to reduce the gas concentration in time, this paper analyzes multivariate monitoring data and proposes a new dynamic combined prediction method of gas concentration. Spearman’s rank correlation coefficient is applied for the dynamic optimization of prediction indicators. The time series and spatial topology features of the optimized indicators are extracted and input into the combined prediction model of gas concentration based on indicators dynamic optimization and Bi-LSTMs (Bi-directional Long Short-term Memory), which can predict the gas concentration for the next 30 min. The results show that the other gas concentration, temperature, and humidity indicators are strongly correlated with the gas concentration to be predicted, and Spearman’s rank correlation coefficient is up to 0.92 at most. The average R2 of predicted value and real value is 0.965, and the average prediction efficiency R for gas abnormal or normal emission is 79.9%. Compared with the other models, the proposed dynamic optimized indicators combined model is more accurate, and the missing alarm of gas abnormal emission is significantly alleviated, which greatly improves the early alarming accuracy. It can assist the safety monitoring personnel in decision making and has certain significance to improve the safety production efficiency of coal mines.

No abstract

Mining in deep coal seams is characterized by high ground stress, often accompanied by coal and rock dynamic disasters such as rock bursts. High-pressure water jet slotting technology can relieve pressure and reduce the stress concentration on the coal seam, which is one of the effective pressure relief measures in rock burst coal seams for deep mining. Reasonable pressure relief parameters are an important influence on the effectiveness of pressure relief achieved by a high-pressure water jet. This paper uses theoretical analysis and numerical simulation to analyze the principle of high-pressure water jet pressure relief and rock burst prevention, and a theoretical calculation model of six key pressure relief parameters is constructed. The optimal values of each pressure relief parameter are obtained, and good pressure relief effect is achieved in a certain rock burst risk area. The research results showed that (1) parameters such as drilling spacing–slit radius, drilling depth–slit length, and slotting cutting spacing–slotting cutting width have a great influence on the pressure relief effect, and there is a significant interaction between the parameters, while the strength of the coal seam also has a significant effect on the selection of the parameters and the pressure relief effect. (2) The displacement, vertical stress, plastic zone, elastic energy, impact risk index, and the cost of pressure relief can be used to comprehensively evaluate the quality and economy of the pressure relief effect, and the optimal pressure relief parameters of high-pressure water jet slotting under specific physical force properties of the coal seam can be obtained. (3) High-pressure water jet technology with optimal pressure relief parameters was applied to No. 3 connecting the roadway in the 730 mining area of a mine studied, and field monitoring showed that indicators such as microseismic frequency, total energy, and spatial concentration significantly decreased. Moreover, the accuracy of the theoretical model of high-pressure water jet slotting pressure relief parameter optimization is reliable in the relevant technical parameters of coal seam slotting. It is believed that the model can be used to design the high-pressure water jet slotting pressure relief parameters in deep rock burst coal seams.