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A multifunctional outburst simulation test system was independently developed to monitor the temperature of the coal seam and roadway throughout the entire outburst process. Outburst simulation experiments under different gas pressures were carried out. The results show that: ① the coal seam gas pressure quickly drops to atmospheric pressure after outburst, however, the temperature evolution of coal seam has a certain lag, which is mainly controlled by the desorption of adsorbed gas and the expansion of free gas; three stages can be seen in the coal seam temperature evolution characteristics: quick decline, rapid increase, and slow change; peak values of coal seam temperature decline are 0.56, 0.23, and 0.11 ℃, respectively, and average decline rates are 0.042, 0.015, and 0.008 ℃/s when the adsorption gas pressure is 2.0, 0.85, and 0.35 MPa; the higher the gas pressure is, the greater the coal seam temperature decline and the faster the decline rate is, which is positively correlated; ② the change of the roadway temperature is simultaneously influenced by various factors such as desorption of gas from the outburst coal, expansion and pressure relief of ejected gas, shock wave disturbance and heat exchange with the environment, showing an evolutionary trend of a brief increase followed by an immediate and significant decrease and finally rise to the ambient temperature; in the three tests, the peak values of roadway temperature drop are 3.19, 2.41 and 1.09 °C, and the average decline rates are 0.249, 0.188 and 0.094 °C/s, respectively; ③ the coal seam and roadway temperatures have overall similarity in time evolution and significant difference in the magnitude of change; the further fragmentation of the outburst coal body in the roadway and the large amount of desorption of gas are the main controlling factors for the evolution of the roadway temperature and the main reason for the large amount of decrease in the roadway temperature.

In order to systematically describe the electromagnetic radiation monitoring and early warning technology and application status of coal and gas outburst, the paper analyzes in detail coal and gas outburst electromagnetic radiation monitoring technology equipment and application from three aspects: the basic principles and methods of coal and gas outburst prediction by electromagnetic radiation, electromagnetic radiation monitoring and early warning equipment and systems, and electromagnetic radiation prediction outburst field application. Results show that electromagnetic radiation responds well to the outburst hazard of working face, gas geology and abnormal stress. It is an effective non-contact geophysical monitoring and early warning method of coal and rock dynamic disasters, which has a good development prospect in highlighting the forecast.

For the demand of automation and accuracy of coal and gas outburst early warning, a remote intelligent monitoring and warning system for coal and gas outburst was developed. The system constitution was introduced as well as key technologies including signal monitoring, interference recognition, signal transmission and early warning rules. The system takes sound acoustic emission, electromagnetic radiation and gas concentration as early warning indexes, and realizes remote monitoring and comprehensive early warning of sound acoustic emission, electromagnetic radiation and gas signals during outburst evolution process. Meanwhile, the system can automatically recognize and filter interference signals according to opening or stopping information of electromechanical equipments, so as to improve early warning accuracy of coal and gas outburst. The application result shows the system has high early warning accuracy, which can capture coal and gas outburst risk information in advance of 8-24 hours.

With the continuous increase of mining depth, coal and gas outburst poses a significant threat to mining safety. Conducting research on the mechanisms of coal and gas outbursts contributes to understanding the evolutionary process of such incidents, thus enabling accurate prediction and prevention of coal and gas outbursts during mining operations. This paper has developed a comprehensive visual experimental system that is specifically tailored to simulate diverse coal body conditions, ground stress and gas pressures. By monitoring and analyzing the real-time progression of coal fissures during the outburst process, we can obtain valuable insights into the evolution and mechanisms of coal and gas outbursts. Additionally, this study introduces a method to determine the critical threshold for predicting coal and gas outbursts, and the critical gas pressure threshold for Jiulishan Coal Mine (Jiaozuo City, Henan Province, China) is established at 0.6 MPa. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous increase in the depth and intensity of coal mining, coal and gas outburst disasters pose a severe threat to the safe production of coal mines. Thus, this experiment studied the characteristics of electromagnetic radiation, acoustic emission, and electric potential signals during gas adsorption, stress loading, and the entire outburst process. The results indicate that during the adsorption process, different parts of the coal body exhibit variations in electric potential signals, electromagnetic radiation, and acoustic emissions. During the loading process, the consistency between the acoustic–electric signals and the load change rate is good, and at the moment of outburst, the acoustic–electric signals significantly increase with the ejection of coal and gas. Outbursts generally occur during the decline in electromagnetic radiation and acoustic emission signals, with the internal electric potential signal strength first decreasing then rapidly increasing and the surface electric potential directly rising. The closer to the outburst opening, the greater the change in signal amplitude. Based on the above experimental results, the outburst can be monitored through the acoustic–magnetic–electric precursory signal changes during the adsorption and loading processes, which is of great significance to the safety production and rapid excavation of coal mines. [ABSTRACT FROM AUTHOR]

Coal and gas outburst is one of the main disasters during the production process of coal mines. Accurate recognition and advanced early warning are crucial to effectively preventing and controlling outburst. Acoustic Emission (AE) or Electromagnetic Resonance (EMR) continuous monitoring technologies have been widely used in outburst prediction due to their advantages of positive response and significant precursor characteristics of impending hazards. However, using a single signal and its one-dimensional time-domain characteristic for predicting outburst may lead to false and omissions alarms due to low credibility and lack of potential information, which may reduce the reliability and advance of early warning. To solve this problem, a new method for precursory feature recognition and early warning of outburst based on a two-dimensional CNN (Convolutional Neural Network) and heterogeneous signal fusion is proposed. Establish an outburst precursor feature recognition model based on Faster R-CNN and AE-EMR two-dimensional time-frequency signals, providing more reliable evidence sources for the fusion early warning of outburst; Then construct an outburst early warning model based on TBM (Transferable Belief Model), revealing the early warning criteria of heterogeneous signal fusion. The proposed method is applied to the Shiping Coal Mine. It is found that the outburst precursor recognition accuracy of AE and EMR achieve 98.00% and 98.57%; The heterogeneous signal fusion model usually warns to be advanced by 1–2 days, and the minimum warning time can be about 60 min in advance. The research results have practical significance in improving the reliability and advancement of outburst early warning and enhancing the ability to control safety risks in the coal mine production process. [ABSTRACT FROM AUTHOR]

In view of problem that inner mechanism of coal and gas outburst was very complex and it was difficult to establish an appropriate multivariate nonlinear prediction model, prediction method of coal and gas outburst based on catastrophe progression method was proposed. The method chooses coal and gas outburst instances of typical outburst mine as learning samples to determine catastrophe progression with different levels of impact risk, and uses them to predict the other samples. The actual application results show that the method can accurately reflect risk degree of coal and gas outburst and has high prediction accuracy.

Coal and gas outburst is one of the main disasters that seriously affect the underground safety production of coal. It is crucial to accurately assess the risk level of coal seam outbursts under different conditions and take effective prevention and control measures to avoid the occurrence of such disasters. To solve this problem, a new assessment method combining variable weight theory and unascertained theory is proposed. Based on qualitative and quantitative risk factors, eight indicators and four classification criteria are constructed. The constant weights (CW) are determined by the fuzzy analytic hierarchy process, while the variable weights (VW) of different parameters by constructing a partitioned variable weight model through the VW theory. Meanwhile, four membership functions of linear (L), parabolic (P), S, and Weibull (W) are proposed to measure the uncertainty level of risk. Based on the calculation of 45 sets of sample data, the differences between the maximum membership principle and the confidence criterion in risk identification were considered, and the optimal hybrid model for the risk evaluation of underground coal seam outburst was derived as VW-P-M. The reliability of the model was determined by further validation of the field data. Finally, the limitations of traditional identification approach are analyzed and the stability of the model under various indicators changes is examined with global sensitivity analysis (GSA). The model fully considers the uncertainty in the outburst risk assessment and the influence of index parameters change on the weight. In addition, it can well solve the risk misjudgment problem caused by low indicators parameters in the production process, providing a reasonable idea and method for the accurate assessment of outburst risk in the early stage of coal mining. [ABSTRACT FROM AUTHOR]

The section of appropriate prediction index is of great importance for coal and gas outburst prediction. The quantity of gas emission is a key factor directly relating to the outburst risk of tunneling working face in the coal roadways. Therefore, accurately predicting the quantity of gas emission is necessary and critical to prevent and control outbursts. In this paper, using the sphere diffusion equation of coal particle gas and radial unsteady flow equations of coal seam gas to analysis gas flow of fallen coal and coal wall, and a dynamic prediction model of gas emission is established including key factors, research shows that: (1) in tunneling working face, the change rule of gas emission of the new model, in which mechanical state, physical properties of coal seam, roadway tunneling parameters, and gas adsorption parameters are considered, is the same as that of the conventional index, which indicates the feasibility of the new model; (2) The new model shows that the gas emission is positively correlated with the gas pressure, driving speed and permeability coefficient of coal seam, and negatively correlated with the uniaxial compressive strength of coal mass; (3) By comparing the old prediction model of gas emission, the predicted value of the new model is closer to the measured value, fluctuating within a smaller range, and has a higher accuracy after taking the gas emission of coal particle into account. In addition, the multiple characteristics of the coal body in front of the working face are comprehensively considered. The research results offer practical significance for improving gas prevention and control of tunneling working face. [ABSTRACT FROM AUTHOR]

Accurate and advanced early warning of coal and gas outburst is an important means to ensure the process safety of coal mining. It is a difficult problem to realize the reliable early warning of coal and gas outburst because the cause of the complex causes and disaster-causing mechanism has not been clearly defined. At present, the traditional early warning models have some problems, such as low degree of information fusion, non-dynamic decision-making, and short early warning time. To optimize the early warning time and improve the process safety of tunneling, a new early warning model is proposed. This new model is a multi-source information fusion dynamic early warning model based on combining Autoregressive Integrated Moving Average (ARIMA) and the Transferable Belief Model (TBM). The ARIMA described the changes of different types of sensor data with spatial heterogeneity over time, and the TBM fused different types of sensor data and makes dynamic decisions to achieve early warning of Coal and gas outburst. Finally, Acoustic Emission (AE), Electromagnetic Resonance (EMR), and Gas concentration data of NO.12223 conveyor roadway of Jinjia coal mine from August 3, 2017, to August 7, 2017, were used as the experimental data set. The proposed model was applied to the NO.12223 conveyor roadway of Jinjia Coal Mine to verify the dynamic early warning for the risk of coal and gas outburst. The results showed that the gas gush out with tunneling is identified 1 and 38 min in advance. It was about 9 min earlier than the earliest response EMR signal obtained by the linear regression prediction model of a single indicator used in the original monitoring and early warning system. The research results are of practical significance to optimize the time of early warning of coal and gas outburst and improve process safety risk control in coal mine production. [ABSTRACT FROM AUTHOR]

Highlights • The electric characteristics of coal samples were tested in the laboratory. • Fine division was performed on outburst danger areas. • Electrical characteristics of coal samples obtained in the laboratory correspond well with the site detection. Abstract In this study, with No. II 1051 working face of Zhuxianzhuang Coal Mine taken as the research object, the electric characteristics of coal samples were tested in the laboratory, and the resistivity imaging was carried out on the working surface via direct current (DC) prospecting instrument on site. Then, fine division was performed on outburst danger areas through gas geological analysis. Finally, mining verification and analysis were conducted. The results suggest that the variation laws of electrical characteristics of samples obtained in the laboratory test correspond well to those gained through site detection. Resistance does not distribute homogeneously in the working face, demonstrating regional significant high and low resistance, which is jointly induced by gas, stress, structure and water. Based on the aforesaid analyses, five outburst areas are determined, including high- and low-resistance regions and the mixed zone. The analysis results of gas occurrence regularity agree with the on-site situation, proving that the result of DC prospecting can reflect the distribution of outburst danger areas. The finding is of great importance for realizing fine detection of gas outburst. [ABSTRACT FROM AUTHOR]

To study the influence of hydraulic slotting inclination on the mechanical behaviors of coal seam during mining process, uniaxial compression experiments on coal specimens with a single pre-existing flaw inclined at 0°, 15°, 30°, 45°, 60°, 75°, 90° and intact specimens were conducted. Acoustic emission (AE) signals in the loading process were monitored, and fractal analysis method was introduced to investigate the AE characteristics. Additionally, the laboratory experiments were simulated by a finite element code. Both the experimental and numerical results show that the existence of a flaw reduces the mechanical properties of coal. The uniaxial compressive strength and modulus of elasticity increase polynomially and linearly with the increase of inclination angle, respectively. When the coal specimen ruptures finally, the fewer the surface secondary cracks or the more the sudden drops of stress, the smaller the peak value of AE count. According to the stress–strain curve, the loading process is divided into five stages: (I) compaction stage; (II) linear elastic stage; (III) stable crack propagation stage; (IV) accelerating crack propagation stage; (V) post peak and residual stage. AE fractal characteristics in various stages of each specimen were determined by Grassberger and Procaccia algorithm based on phase space reconstruction theory. AE count show fractal characteristics from stage III. The fractal dimension declines rapidly in stage IV, and continues to decline further or rise slightly in stage V, but both are lower than that in stage III. Therefore, the changing rule of AE fractal dimension in different loading stages can be used as a precursor to coal and rock dynamic disasters. [ABSTRACT FROM AUTHOR]

Coal and gas outbursts are common mining hazards encountered worldwide. As we mine deeper, the complexity of these outbursts demands smarter, more precise risk identification methods. This is not just a pressing concern but also a growing area of research. However, a noticeable gap exists between current research and the actual implementation of measures to prevent these outbursts at mining sites. This gap spans various areas, from indicator development and the choice of mathematical and machine learning tools to model creation and the use of detection, monitoring, and early-warning systems. This article seeks to review the latest research, evaluating the advantages and drawbacks of various risk identification methods. This work pays special attention to the real-world practices of China's outburst prevention strategies and the need for advanced identification techniques. By diving deep into theoretical, model-based, and technological facets, the main goal is to underline the primary challenges and suggest potential domains for future innovation. • Comprehensive review of diverse risk identification methods across scales • Thorough analysis of the historical evolution of outburst indicators • Intelligent algorithms excel in uncertain static prediction and dynamic gas emission • Discussion on pros and cons of dynamic identification technologies • Proposal of future core intelligent identification directions from system, method, and technology perspectives [ABSTRACT FROM AUTHOR]

Coal and gas outburst disaster of the No. B-1 Coalbed, Henan, China, have lasted for nearly 60 years, and the threat will become more and more serious as mining depths continuously increase. However, coal and gas outburst characteristics and factors of the coalbed have not been studied in detail. To effectively prevent and control coal and gas outburst, we analyzed the type, intensity, location and precursors of coal and gas outburst occurring in the No. B-1 Coalbed. Moreover, the effects of geological conditions (burial depth, faults, folds, coalbed thickness and dip) and mining disturbances on coal and gas outburst were studied. The results showed that these outburst accidents were mostly typical, small-sized and medium-sized outburst, which mainly occurred at the coal roadway working faces. There were many precursors such as blasting sound, changes in coal structure, and abnormal gas emission prior to the accidents. Within a burial depth of 500 m, the average outburst intensity had a stronger correlation with the burial depth, which was more obvious at a burial depth of 301∼400 m, and less obvious at a burial depth of 401~500 m. However, the distribution of these outbursts barely changed within the burial depth. Up to 99.15% of coal and gas outburst occurred at faults, folds, and areas with changes in coalbed thickness and dip. Up to 68.25% were induced by blasting and 18.04% occurred during shelving, coal-shoveling and no operation. The No. B-1 Coalbed was characterized by delayed outbursts. Moreover, specific suggestions were recommended for the prevention and control of coal and gas outburst. The study is useful to government regulators and staffs engaged in the prevention and control of coal and gas outburst. [ABSTRACT FROM AUTHOR]

Many gas pre-drainage boreholes are generally constructed in coal seams to ensure safe underground operation. True triaxial experiments were conducted to study the mechanical response and gas flow characteristics of pre-drilled coal to reveal the in-situ behavior at pre-drilled regions in coal seams. The mechanical parameters, failure modes, and flow rate evolutions of pre-drilled coal samples were collected. The gas flow rate of pre-drilled coal samples showed an S-shaped trend with the increase of strain, and no obvious descending progress was found in the flow rate-strain curves. The typical failure mode for pre-drilled coal samples was shear-type during the true triaxial stress compression. V-shaped macro-fractures around the boreholes were observed when boreholes were perpendicular to bedding planes and parallel to face cleats. In contrast, single shear macro-fractures around the boreholes were observed when boreholes were parallel to butt cleats. The pre-drilled coal samples exhibited lower peak strength values when boreholes were parallel to butt cleats, induced by the relatively low cohesion parameter. Pre-drilled coal's initial flow rates perpendicular to bedding planes and parallel to butt cleats directions were only 33.33% and 41.67% of values measured along face cleats. More complex macro-fractures were formed, and up to a 39.30% decrease in strength was observed in pre-drilled coal samples under true triaxial unloading stress paths. The variations in volumetric strain level and the flow rate evolution were further discussed with an anisotropic conceptual failure model. These mechanical responses and flow characteristics of pre-drilled coal would guide the design of borehole layout and prevention of coal dynamic disasters. • Mechanical parameters and flow rate evolutions of pre-drilled coal were measured. • Failure mechanisms of pre-drilled coal were revealed under true triaxial stress. • The effects of anisotropy on the mechanical and flow characteristics were studied. [ABSTRACT FROM AUTHOR]

• A new gas pressure inversion model is established. • The application result of the new model proves the uneven distribution of gas pressure. • The gas pressure value shows an approximate normal distribution law in statistics. • The evolution law of outburst elimination in the coal seam presents the characteristics of non-uniformity. China is one of the countries most seriously affected by coal and gas outburst disasters. Gas pressure is an important parameter to evaluate the risk of coal and gas outburst. In order to solve the problem existing in the traditional gas pressure measurement method, a dynamic inversion model of gas pressure while drilling is established based on the gas emission rule of coal particle and the gas radial migration equation of borehole wall in this paper. Combined with the directional drilling system and the special gas emission measuring device, the dynamic inversion model is applied to measure the gas pressure in the coal seam. The results show that: (1) The theoretical value of gas pressure obtained from the new model has a small error range by comparing with the actual value of gas pressure, which indicates that the new model has good reliability and applicability; (2) The distribution range of gas pressure value is wide, which shows the non-uniformity of gas occurrence in coal seam. The heterogeneity of gas pressure value shows an approximate normal distribution law in statistics; (3) Influenced by the occurrence of original gas in coal seam, the evolution law of outburst elimination in each area of coal seam also presents the characteristics of non-uniformity. The research results have important practical significance to assist the early warning of coal and gas outburst and to achieve coal and gas co-mining with safety mining. [ABSTRACT FROM AUTHOR]

• Coal and gas outburst experiments with different conditions are conducted. • A fragmentation index reflecting coal and gas outburst intensity is developed. • The relationship between fragmentation index and hardiness coefficient is analyzed. • A new method for coal and gas outburst prediction and prevention is proposed. Coal and gas outbursts are among the most serious disasters affecting the safety of coal mines, with 39 deaths reported during 2019 in China. The cause of outbursts is fairly complicated and involves many influencing factors. Thus, methods of accurate prediction and prevention are quite immature. For this study, two groups of coal and gas experiments under different conditions are carried out to explore more effective prediction and prevention measures. The results show that a two-phase flow of coal and gas is ejected from an outburst mouth at high speed, crushing large particles into smaller ones. The crushing effect increases with a higher stress concentration factor and gas pressure, as does the relative intensity of outburst (RIO) nonlinearly. By analyzing the ejected coal, an outburst fragmentation index is developed based on a new surface theory, which can be linearly fitted with the RIO. The fitting parameters reflect the outburst risk from two dimensions. Next, the η prediction method is proposed, offering many advantages compared with current prediction methods. Furthermore, its relationship with the f prediction method is analyzed. Five grades of outburst risk (i.e., negligible-risk zone, low-risk zone, medium-risk zone, high-risk zone, and very-high-risk zone) are classified according to the ranges of fitting parameters. Finally, based on the η prediction method, a new method for coal and gas outburst prevention is specified, and its applications and prospects are discussed. The results have guiding significance for better preventing outbursts and ensuring safe coal-mine operations. [ABSTRACT FROM AUTHOR]

The determination of gas pressure is a prerequisite for preventing coal and gas outburst. The traditional measurement method has some shortcomings, such as few test points, long test time and complicated process. In this paper, after analyzing the gas emission law of coal particle and the radial gas flow rule of borehole wall, a real-time and fast method for calculating gas pressure with drilling is proposed to establish gas pressure dynamic inversion model based on gas emission quantity of borehole, which is applied to inversion calculation of gas pressure in the directional drilling process. The results show that: (1) Inversion of gas pressure shows that the physical properties of coal seam and the characteristics of gas adsorption affect the amount of gas emission, resulting in different calculation results of gas pressure, which proves the uneven distribution of gas pressure the in coal seam; (2) According to the comparison results, the error range between the theoretical value and the measured value of gas pressure is from 3.6% to 18.1%, which indicates the correctness and reliability of the new model. The error is mainly influenced by the gas emission quantity of coal particle and the permeability coefficient of coal seam after gas desorption; (3) Field application results show that there are many abnormal regions of gas pressure in the coal seam because of geological structure existence. The research results have important practical significance for the analysis of distribution law of coal seam gas and the identification of gas outburst risk. [ABSTRACT FROM AUTHOR]

Coal and gas outbursts are among the most severe disasters threatening the safety of coal mines around the world. They are dynamic phenomena characterized by large quantities of coal and gas ejected from working faces within a short time. Numerous researchers have conducted studies on outburst prediction, and a variety of indices have been developed to this end. However, these indices are usually empirical or based on local experience, and the accurate prediction of outbursts is not feasible due to the complicated mechanisms of outbursts. This study conducts outburst experiments using large-scale multifunctional equipment developed in the laboratory to develop a more robust outburst prediction method. In this study, the coal temperature during the outburst process was monitored using temperature sensors. The results show that the coal temperature decreased rapidly as the outburst progressed. Meanwhile, the coal temperature in locations far from the outburst mouth increased. The coal broken in the stress concentration state is the main factor causing the abnormal temperature rise. The discovery of these phenomena lays a theoretical foundation and provides an experimental basis for an effective outburst prediction method. An outburst prediction method based on monitoring temperature was proposed, and has a simpler and faster operation process and is not easily disturbed by coal mining activities. What is more, the critical values of coal temperature rises or temperature gradients can be flexibly adjusted according to the actual situations of different coal mines to predict outbursts more effectively and accurately. [ABSTRACT FROM AUTHOR]

• A coal and gas outburst visual simulation system was developed and outburst experiments were conducted. • The propagation and characteristics of the outburst two-phase flow in the roadway were studied. • The evolution of shock wave overpressure was analyzed and the velocity of wave front was calculated. Coal and gas outbursts are serious disasters that occur during coal mine production. In these instances, the outburst two-phase flow is the main cause of casualties. To investigate the propagation and mechanism responsible for the disaster of the outburst two-phase flow, a visual test system for coal and gas outburst simulation was independently developed. The outburst simulation experiments under different gas pressures were carried out and the propagation process of outburst two-phase flow in a visual roadway was monitored in real time to analyze the propagation mechanism and outburst intensity. The experimental results showed that the first shock wave overpressure reached its peak value immediately after the outburst and the characteristics of turbulent pulse appeared at the front of the outburst cavern, which was followed by one to two successive waves with lower peak values in the negative-pressure region. The flow pattern of pulverized coal observed in the visual roadway included suspended, stratified, dune, and plug flows. The velocity of pulverized coal flow was calculated based on the image method, which can be divided into the acceleration phase, and the acceleration and deceleration cycling phases. When the gas pressure increased from 0.35 MPa to 2.0 MPa, the velocity of pulverized coal flow increased from 34.19 m/s to 71.20 m/s, while they had a nonlinear relationship. The shock wave propagated in the roadway ahead of the pulverized coal flow at supersonic speed, and the velocity of shock wave front was as high as 344.75–370.02 m/s, which was much higher than that of pulverized coal flow. The higher the gas pressure was, the more pulverized coal ejected, and the relative outburst strengths increased from 36.1% to 63.7% with the increase of gas pressure from 0.35 MPa to 2.0 MPa. [ABSTRACT FROM AUTHOR]