Mine Disaster Research Articles

Quantitative characterization of constitutive model of mudstone under uniaxial loading after immersion in acid solution by infrared radiation

The fracturing of coal and rock is a fundamental cause of mine disasters, such as coal pillar instability,water inrush and rock failure. To prevent the occurrences of these failures, it is important to accurately monitor and detect stress fractures in coal and rock. Furthermore, it is also essential to develop stress–strain constitutive models for an accurate and effective monitoring, early warning of coal and rock stress fracture process. The development of stress–strain constitutive model is the important basis for effectively realizing water conservation mining and rock strata control in mines. In this research, mudstone samples were collected from coal mining sites and mining faces. The mudstone samples were subjected to chemical corrosion using both strong acid and neutral solutions, followed by conducting infrared radiation observation experiments during uniaxial loading. The research findings revealed that 1) strong acid can promote the infrared radiation of the mudstone surface. The Average Infrared Radiation Temperature (AIRT) value of the mudstone corroded by the solution with pH 1.3 during uniaxial loading fracture is changed and noted 6.71 times to the solution with pH 7.3. 2) strong acid has an significant impact on the failure of mudstone samples. The mudstone corroded by the solution with pH 1.3 is mainly damaged by tension and shear composite cracks, while the mudstone corroded by the solution with pH 7.3 is primarily damaged by tension cracks. 3) A nearly linear relationship between the strain of mudstone during uniaxial loading and AIRT was found. This relationship defines a new index of deformation infrared radiation coefficient, allowing for the quantitative characterization between strain and infrared radiation. 4) A one-dimensional statistical damage constitutive model based on AIRT is established using Weibull distribution, while considering the stress–strain compaction stage. This model effectively predicts the stress of mudstone during uniaxial loading. The research results lay a theoretical and practical foundation for the monitoring and evaluation of rock stability in underground engineering such as coal mines, subways and tunnels.

Monitoring and evaluation of disaster risk caused by linkage failure and instability of residual coal pillar and rock strata in multi-coal seam mining

Comprehensive research methods such as literature research, theoretical analysis, numerical simulations and field monitoring have been used to analyze the disasters and characteristics caused by the linkage failure and instability of the residual coal pillars-rock strata in multi-seam mining. The effective monitoring area and monitoring design method of linkage instability of residual coal pillar-rock strata in multi-seam mining have been identified. The evaluation index and the risk assessment method of disaster risk have been established and the project cases have been applied and validated. The results show that: ①The coal pillar will not only cause disaster in single-seam mining, but also more easily cause disaster in multi-seam mining. The instability of coal pillars can cause not only dynamical disasters such as rock falls and mine earthquakes, but also cause surface subsidence and other disasters. ②When monitoring the linkage instability of residual coal pillar-rock strata, it is not only necessary to consider the monitoring of the apply load body (key block), the transition body (residual coal pillar) and the carrier body (interlayer rock and working face), but also to strengthen the monitoring of the fracture development height (linkage body). ③According to the principles of objectivity, easy access and quantification, combined with investigation, analysis, and production and geological characteristics of this mining area, the main evaluation indexes of the degree of disaster caused by linkage instability of residual coal pillar-rock strata are determined as: microseismic energy, residual coal pillar damage degree, fracture development height. And the evaluation index classification table was also given. ④According to the measured value of the evaluation index, the fuzzy comprehensive evaluation method was used to calculate the disaster risk degree in the studied mine belongs to class III, that is, medium risk level. The corresponding pressure relief technology was adopted on site, which achieved a good control effect, and also verified the accuracy and effectiveness of the risk evaluation results.

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis.

In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems of disaster-causing factors, disaster-prone environments, and vulnerable bodies, establishing an early warning index system for gas explosion disasters. Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. Subsequently, a training model based on the random forest classification algorithm is constructed, and the model is optimized using two parameters, Mtry and Ntree. Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. Specific coal mine case studies are conducted to verify the applicability of the optimized random forest algorithm. The experimental results demonstrate that: The optimized random forest model has achieved 100% accuracy in predicting gas explosion disaster of coal mines, while the accuracy of SVM model is only 75%. The optimized model also shows lower model error and relative error, which proves its high performance in early warning of coal mine gas explosion. This study innovatively combines intelligent mining system with multidimensional data analysis, which provides a new method for coal mine safety management.

Numerical and theoretical study of load transfer behavior during cascading pillar failure

To further study the load transfer mechanism of roof–multi-pillar–floor system during cascading pillar failure (CPF), numerical simulation and theoretical analysis were carried out to study the three CPF modes according to the previous experimental study on treble-pillar specimens, e.g. successive failure mode (SFM), domino failure mode (DFM) and compound failure mode (CFM). Based on the finite element code rock failure process analysis (RFPA2D), numerical models of treble-pillar specimen with different mechanical properties were established to reproduce and verify the experimental results of the three CPF modes. Numerical results show that the elastic rebound of roof–floor system induced by pillar instability causes dynamic disturbance to adjacent pillars, resulting in sudden load increases and sudden jump displacement of adjacent pillars. The phenomena of load transfer in the roof–multi-pillar–floor system, as well as the induced accelerated damage behavior in adjacent pillars, were discovered and studied. In addition, based on the catastrophe theory and the proposed mechanical model of treble-pillar specimen–disc spring group system, a potential function that characterizes the evolution characteristics of roof–multi-pillar–floor system was established. The analytical expressions of sudden jump and energy release of treble-pillar specimen–disc spring group system of the three CPF modes were derived according to the potential function. The numerical and theoretical results show good agreement with the experimental results. This study further reveals the physical essence of load transfer during CPF of roof–multi-pillar–floor system, which provides references for mine design, construction and disaster prevention.

Stoneflies left over from a mining disaster: new species and records of Perlidae (Plecoptera) from the Doce River basin, southeastern Brazil.

Seven years after a mudflow reached an important area of the Doce River basin, southeastern Brazil, and with the objective of reporting the current Plecoptera fauna, specimens of Anacroneuria Klaplek, 1909 (Plecoptera: Perlidae) were studied. Three new records from this basin are included: A. mineira Novaes & Bispo, 2014; A. atrifrons Klaplek, 1922; and Anacroneuria itatiaiensis Baldin, Bispo & Novaes, 2013. A new species is described: A. piranga sp. nov. Castillo-Velsquez, Gonalves & Salles. The new species is morphologically similar to A. annulicauda Stark & Kondratieff, 2004 and A. atrifrons, but can be distinguished by the keel of the penial armature, which is flat and pointed at the apex. In addition to its description, photographs of all species are included, as well as a detailed description of the habitat of the new species.

A Method for Dividing Rockburst Risk Zones—A Case Study of the Hegang Mining Area in China

Rockbursts are serious mine disasters. Through the division of rockburst risk zones, risks can be predicted in advance and measures can be implemented to prevent disasters. In this paper, taking the Hegang mining area as an example, we propose a method for dividing rockburst risk zones based on in-situ stress measurements. First, 24 survey points were established in the Hegang mining area to measure the in-situ stress. Second, based on the in-situ stress measurement data, eight representative prospecting lines were selected to establish a numerical model, and the distribution characteristics of the in-situ stress field at mining elevations of −330 m and −450 m in the Hegang mining area were obtained via the linear differential method. Afterward, division criteria for rockburst risk zones were proposed in accordance with the energy criterion and the minimum energy principle. Finally, the Hegang mining area was divided into rockburst risk zones in accordance with the in-situ microseismic monitoring data and simulation results for the in-situ stress field. Coal seam #3 was chosen as an example to illustrate rockburst risk-zone division in the Hegang mining area considering the division criterion proposed herein, and a rockburst risk zoning map of coal seam #3 in the Hegang mining area was finally obtained. The locations of the five rockbursts that have occurred in coal seam #3 of the Hegang mining area were marked on the risk zoning map and were found to have occurred in the threatened zone. Thus, it was shown that the proposed rockburst risk-zone division method is reasonable. Therefore, the results of this study could serve as a reference for the division of rockburst risk zones.

Research on Automatic Classification of Coal Mine Microseismic Events Based on Data Enhancement and FCN-LSTM Network

Efficient and accurate classification of the microseismic data obtained in coal mine production is of great significance for the guidance of coal mine production safety, disaster prevention and early warning. In the early stage, the classification of microseismic events relies on human experiences, which is not only inefficient but also often causes some misclassifications. In recent years, the neural network-based classification method has become more favored by people because of its advantages in modeling procedures. A microseismic signal is a kind of time-series signal and the application of the classification method is widely optimistic. The number and the balance of the training data samples have an important impact on the accuracy of the classification result. However, the quality of the training data set obtained from the production cannot be guaranteed. A long short-term memory (LSTM) network can analyze the time-series input data, where the image classification at the pixel level can be achieved by the fully convolutional network (FCN). The two structures in the network can not only use the advantages of the FCN for extracting signal details but also use the characteristics of LSTM for conveying and expressing the long time-series information effectively. In this paper, a time-series data enhancement combination process is proposed for the actual poor microseismic data. A hybrid FCN-LSTM network structure was built, the optimal network parameters were obtained by experiments, and finally a reasonable microseismic data classifier was obtained.

Optimization and Investigation of Rapid Sealing Technology Based on Mine Disaster Period

Due to the particularity of mine spaces and the limitations of underground ventilation, the gas in disaster areas changes greatly after a fire occurs. Rapid sealing technology is beneficial for preventing the development of fires and gas explosions by controlling oxygen. Using the Fuzzy Analytic Hierarchy Process (FAHP), in this research, we analyzed the three most effective rapid sealing processes, conducted experimental research on the three sealing processes, and developed a further optimized design. At the same time, according to different stages of blasting damage, the change characteristics and migration laws of explosive hazardous gases in a disaster area were analyzed using fluent numerical simulation. Additionally, the ability of the three optimal processes to create an airtight area was measured in this research. The applicable scenarios and scope of the three technologies were found, which provides a wider range of application scenarios and more diverse options for rapid airtightness during catastrophic periods, mine fire prevention, and explosion protection.

Repair and the 2014 Mount Polley Mine disaster: Antirelationality, constraint, and legacies of socio-ecological disruption in settler colonial British Columbia

In this article I situate the 2014 Mount Polley Mine disaster within centuries-long relations of colonial-modernity in the region currently known as British Columbia, Canada. Guided by the work of Gilmore and Moten, I argue that repairing colonial systems of mass disruption and death requires attending to the logics that enable and normalize these systems of violence. To support this argument, I turn to British Columbia’s early settler colonial history—a violent and destructive history forged through mining—and outline large-scale socio-ecological violence that occurred throughout this period. By turning to this history, I show how these disruptions are connected to the Mount Polley Mine disaster through ongoing and pervasive logics that enable, and often even celebrate, these processes of violence and disruption.

Microseismic event waveform classification using CNN-based transfer learning models

The efficient processing of large amounts of data collected by the microseismic monitoring system (MMS), especially the rapid identification of microseismic events in explosions and noise, is essential for mine disaster prevention. Currently, this work is primarily performed by skilled technicians, which results in severe workloads and inefficiency. In this paper, CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multi-channel microseismic signal waveforms. First, data collected by MMS was generated into 6-channel original waveforms based on events. After that, sample data sets of microseismic events, blasts, drillings, and noises were established through manual identification. These datasets were split into training sets and test sets according to a certain proportion, and transfer learning was performed on AlexNet, GoogLeNet, and ResNet50 pre-training network models, respectively. After training and tuning, optimal models were retained and compared with support vector machine classification. Results show that transfer learning models perform well on different test sets. Overall, GoogLeNet performed best, with a recognition accuracy of 99.8%. Finally, the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.

Experimental and numerical research on the evolution law of precursor information of rock apparent resistivity during the process of water inrush in metal mines

Mine water inrush is one of the most threatening disasters in the production process of metal mines. Therefore, the analysis and research of precursor information in the process of water inrush disasters in metal mines is a key issue in solving and further preventing water inrush disasters. To study the evolution law of precursor information of rock apparent resistivity during the process of metal mine water inrush disaster, this paper adopts a self-designed metal mine water inrush experimental monitoring platform, combined with numerical simulation using ANSYS finite element software, to provide rock mass monitoring information to study the changes in apparent resistivity before rock mass fracture and instability under water inrush conditions. By analyzing the data from physical models and numerical simulations, it was found that the variation patterns of physical model experiments and numerical simulation calculations were basically consistent, providing a new approach for the joint revelation of the mechanism of mine water inrush disasters. The research results indicate that with the evolution of water inrush disasters in metal mines, the physical information of the apparent resistivity of rock masses will change accordingly; Long before the formation of a water-bearing zone due to rock mass fracture and instability, the apparent resistivity value of the rock mass will exhibit obvious precursor characteristics, that is, the apparent resistivity of the rock mass at that location will appear as a “bump”. Therefore, early warning of water inrush accidents can be achieved through key points of precursor information of rock mass apparent resistivity.

On strata damage and stress disturbance induced by coal mining based on physical similarity simulation experiments

Extensive studies have been conducted on the movement of overlying strata when a single coal seam is mined. However, structural characteristics and associated stress field variation of the overlying strata over multiple coal seam mining remain unclear. Here we performed physical modelling experiments analogous to No. 42108 working face of Buertai coal mine, Shendong coalfield, where No. 22 coal seam (2.9 m thickness) was mined first, preceding No. 42 upper coal seam (6.1 m thickness) with an inter-coal-seam distance of 72.8 m. We employed DIC (digital image correlation) measurement and systematically-laid pressure cells to visualize the overlying strata movement and monitor stress field variations over multiple coal seam mining. We found that the stress of the inter-coal-seam strata increased significantly in the late mining stage of No. 22 coal seam due to the strata collapse, and culminated after compaction of the caved blocks. The inter-coal-seam strata stress gradually decreased over mining of No. 42 upper coal seam and arrived at zero after the inter-coal-seam strata collapsed. The mining of No. 42 upper coal seam aggravated the roof settlement of No. 22 coal seam; and the floor stress was noticeably lower than that of No. 22 coal seam due to the pressure-relief caused by the former mining activity. Our physical modelling findings advanced our understanding on structural characteristics and stress evolutions of overlying strata over multiple coal seam mining and offered guidance for prediction and mitigation of strata movement associated disasters in underground coal mining with geomechanical and mining conditions similar to those of Buertai coal mine.

Prediction of gas drainage changes from nitrogen replacement: A study of a TCN deep learning model with integrated attention mechanism

Accurate prediction of pure gas volume changes in nitrogen injection replacement coal seams is of great significance to increase coalbed methane (CBM) production and prevent underground gas disasters in coal mines. However, the mechanism of coal seam gas flow during nitrogen injection replacement is complex, and it is extremely difficult to achieve quantitative prediction of the pure volume of gas in nitrogen injection replacement coal seams. In this study, we propose a new model for predicting the change of pure amount of gas in coal seam of nitrogen injection replacement in coal mines (Attention-TCN). Meanwhile, we believe that a stable and reliable predictive model should have the ability to capture the underlying patterns and relationships of regular and noisy datasets during the learning process, rather than memorizing training data. Therefore, in this paper, the Attention-TCN model is trained and tested using the results of nitrogen injection and replacement methane double diffusion test (regularity dataset) and the results of nitrogen injection and replacement experiment in coal mine underground engineering (noisy dataset), respectively. The difference between the Attention-TCN model and the baseline models of TCN, gated recurrent neural network (GRU) and long and short-term memory neural network (LSTM) was also compared. The validity of the model was demonstrated using ten-fold cross-validation results. The results show that the proposed Attention-TCN prediction model achieves good prediction performance on both regular and noisy datasets, with prediction R2 greater than 0.99, and is highly flexible and robust. The model is better able to extract the basic features and structures in different types of datasets, enabling it to perform well on unseen data. The ten-fold cross-validation results clearly show that the Attention-TCN model is more accurate and all outperforms the TCN, GRU and LSTM prediction models. In addition, the prediction results of all models for different mine underground nitrogen injection replacement coal seam pure gas volume datasets further validate that the Attention-TCN model has better ability to generalize to new datasets and is more effective and practical for predicting the variation of pure gas volume in underground nitrogen injection replacement coal seams in coal mines.

Coal and Gas Outburst Prediction Model Based on Miceforest Filling and PHHO–KELM

Coal and gas outbursts are some of the most serious coal mine disasters, and effective prediction of coal and gas outbursts can reduce the likelihood of accidents and fatalities. Previously conducted studies have established that machine learning has achieved results in the prediction of coal and gas outbursts, but there is a problem that the available accident data of coal and gas outbursts are diminished or missing. This paper proposes a prediction model based on multiple filling of chained equations for random forests (miceforest) and the Harris Hawk optimization algorithm with Piecewise chaos mapping (PHHO) to optimize the kernel extreme learning machine (KELM) to solve the problem of missing data in coal and gas outburst prediction and to improve prediction accuracy in the case of missing data. Firstly, the miceforest algorithm was adopted to fill missing values in the salient samples, and then the PHHO algorithm was used to optimize the parameters of KELM. Finally, the datasets before and after filling were input into the PHHO–KELM model for experimentation and comparison with other models. The results show that miceforest filling is effective in improving the salient sample accuracy and overall accuracy of predictions, but the improvement is not significant for non-salient samples. The use of the PHHO–KELM model can effectively avoid falling into a local optimum and further improve the prediction accuracy of the KELM algorithm. The salient sample accuracy and overall accuracy of the miceforest–PHHO–KELM model prediction are 96.77% and 98.50%. And an effective coal and gas outburst model has been proposed, which is the miceforest–PHHO–KELM model.

Failure analysis of overlying strata in fault fracture zone during coal mining

Abstract Faults encountered during coal mining can compromise the continuity and integrity of the overburden, resulting in considerable differences in the stress, displacement, and failure fields of the rocks surrounding the fault zone. When a working face is located adjacent to a fault, the fault-disturbed overburden becomes activated and unstable along the fault plane, which could lead to mining disasters. The fault-adjacent overburden morphology during mining was analyzed using a physical model. A mechanical model of the stability of the fault-disturbed overburden was constructed. The criteria for determining the sliding failure of the overburden during mining were defined, from which the critical coal pillar width required to maintain the overburden stability was determined. The results indicate that an inverted trapezoidal block forms in the overburden due to the combined effects of mining and faulting. The morphology of this block is influenced by the coal pillar width, the height of the fractured zone, and the dip angles of fault and coal seam. The block is prone to sliding or rotational failure along the fault plane during mining. As the coal seam and fault dip angles increase, the critical coal pillar width for maintaining overburden stability decreases. Conversely, increasing coal seam thickness increases the critical coal pillar width. The critical width of coal pillar was determined to be 176 m, which was verified through field observations performed in the #3307 working face.

Risk Analysis of Transport Requalification Projects in the Urban Mobility Problem Caused by a Mining Disaster

Background: This paper proposes a risk analysis of transport requalification projects in the urban mobility problem caused by a mining disaster related to irregular rock salt extraction in the city of Maceió, Brazil. Methods: The model is composed of three main steps: problem definition, risk management, and decision analysis. For this purpose, we used the Picture Fuzzy-Delphi method for data collection and experts’ judgment elicitation and the Delphi method was used to assess the problem without interference from others. In addition, we used Picture Fuzzy Sets (PFSs) to incorporate uncertain information in the decision-making process. Results: The results of the proposed model demonstrated consistency and relevance to the discussion. The application of methods shows the risks of the project based on a general perspective. It evaluates the sustainability tripod: economic, environmental, and social points of view, assessing the occurrence risk and intensity of the risk. Conclusions: The main objective of the work was achieved; however, some limitations of this study are related to the methods used to assess risks and the options of projects of requalification available at the moment of data analysis. This paper contributes because it systematizes the risk management of projects related to requalification in urban mobility.

The role of waves in the resuspension and transport of fine sediment and mine tailings from the Fundão Dam failure, Doce River, Brazil

In one of the world's worst mining disasters, after flowing 670 km down the Doce River, about 20 million m3 of tailings reached the coast of Brazil in November 2015. The occurrence of persistent northerly winds favored the detachment of the river plume from the coastline and its displacement southward. However, observations of the sediment plume along the coast during the year following the breach, using aerial photographs, indicated that the plume is more extensive and persistent in a northerly direction. Previous studies have shown that although the most persistent winds are from the northern sector (70%), there is a greater incidence of waves from the southern sector (62%), especially with greater heights and periods. In this research, the effect of gravity waves on the transport of sediments is investigated, and, therefore, tailings from the Doce River and deposited on the adjacent inner continental shelf, mixed with the fluvial mud deposit also are investigated. Two mechanisms were investigated: 1) the effect of waves on sediment resuspension and their combination with tidal and wind forcings, using a two-dimensions in the horizontal directions (2DH) numerical model within the DELFT3D software, and 2) the capture of the fluvial plume by littoral drift, calculated by means of analytical models using data from wave propagation modeling. Both mechanisms were studied for 2019, also considering two major meteorological events in the region. Both effects were relevant for the northward dispersion of fine sediment, highlighting the effect of waves on the potential for northward transport of tailings towards the Abrolhos Bank.

Notifications of work-related injuries and diseases: an observational study on a mining disaster

IntroductionAccidents at work are events that endanger the health and life of workers. They are considered a public health problem, being the object of studies and actions in the Brazilian health system. The collapsed of the ore tailings dam in the municipality of Brumadinho, Brazil, in January 2019, shocked the world due to the proportion of environmental and human damage caused. In this context, concern for the health of workers gained prominence. This paper evaluated the impact of the collapse of the mining tailings dam in Brumadinho, on notifications of occupational health problems before and after the disaster.MethodsAn observational, longitudinal and retrospective study, of the time series type with a statistical approach was carried out on notifications of work-related injuries and diseases, available in the local database of the Notifiable Diseases Information System between January 2017 and December 2021.ResultsDuring the study period, 520 notifications of work-related injuries and conditions were registered. Of this total, 67.3% were serious work accidents, 26.0% mental disorders, 12.3% accidents with biological material and 2.9% repetitive strain injuries/musculoskeletal disorders, occupational dermatoses and induced hearing loss by noise. Occurrences were more frequent in 2019, the year in which the mining disaster occurred, recording 65.2% of total notifications. Regarding the volume of notifications after the disaster, there was a statistically significant reduction (p < 0.001) for accidents with biological material; significant increase for severe work accident and mental disorder. The average time between the occurrence of the injury and the notification showed a statistically significant reduction for accidents involving biological material (p = 0.001) and a significant increase for serious accidents at work (p = 0.016).ConclusionsIt was demonstrated that there were changes in the number of notifications when comparing the period before and after the mining disaster, with a consequent impact on the health of workers, which may persist over the years.

Evaluating Fractal Damage and Acoustic Emissions of Soft Rock–Coal Combinations in a Deep Mining Area

Weakly cemented soft rock mines in the Ordos Basin are susceptible to mining disasters, including roof collapse and substantial deformation of surrounding rocks, during coal mining operations. Researching the damage characteristics of structures composed of low-strength “soft rock–coal” combinations is crucial for effectively preventing and controlling disasters in deep soft rock mining. To investigate the fractal damage characteristics of soft rock–coal combinations with different height ratios, uniaxial compression tests were conducted on specimens containing soft rock percentages of 20%, 40%, 50%, 60%, and 80%. The results show that the uniaxial compressive strength and modulus of elasticity of the soft rock–coal combinations increased with increasing proportions of soft rock. The soft rock–coal combination was clearly segmented, and the 40%, 50%, and 60% soft rock–coal combinations had good self-similarity. The fractal dimensions were 2.374, 2.508 and 2.586, which are all within the interval [2, 3]. When the percentage of soft rock was 20%, the specimen damage yielded flaky coal bodies with smaller grain size, whereas the coal–rock interface was spalled by small conical rock bodies. As the soft rock proportion increased, the percentage mass of fragments with particle size greater than 20 mm increased from 83.34% to 94.15%. The failure mode in soft rock–coal combinations is primarily attributed to the partial tensile splitting of the coal body. As the proportion of soft rock increased, there was a gradual reduction in the extent of coal body damage. Moreover, the acoustic emission absolute energies and counts decreased as the proportion of soft rock increased. The acoustic emission energy was reduced from 2.46 × 109 attoJ to 3.41 × 108 attoJ, and the acoustic emission counts were reduced from 18,276 to 7852.

Analysis of the Current Status and Hot Technologies of Coal Spontaneous Combustion Warning

Coal spontaneous combustion disasters are one of the most serious types of mine disasters in China at present, posing a huge threat to underground personal safety and coal production operations. In order to prevent and control coal spontaneous combustion hazards and construct an efficient early warning system, this paper presents a review of coal spontaneous combustion early warning based on the Web of Science database search of 583 papers related to coal spontaneous combustion early warning collected from 2002 to 2021, using VOSviewer visualization software. The number of publications and partnerships at the author, institution and country levels are obtained, and the research hotspots in the field of coal spontaneous combustion warning are obtained based on keyword co-occurrence and clustering. The results show that the research results of scholars with a high publication volume have significant influence in the field of coal spontaneous combustion warning and prevention and control, and a more mature camp has been formed among the research authors; a more stable core group of institutions has been formed in the field of coal spontaneous combustion warning; most of the national publications are concentrated in mineral resource-mining countries; the analysis of hot keywords shows that “sign gas warning” and “warning models and technologies” are the key contents of this field. The analysis of hot keywords shows that “sign gas early warning” and “early warning model and technology” are the key contents of this field. The research content of this paper is helpful for researchers to find the latest information on the current research and trends in the field of spontaneous combustion prevention and coal seam monitoring.