Coal Mine Safety Research Articles

Double verification and quantitative traceability: A solution for mixed mine water sources

Accurate identification of mine water sources is one of the keys to safe coal mining. However, conventional water source identification methods rely on groundwater-level responses and hydrochemical characteristics. The identification results often have multiple interpretations, lack verification of the actual situation, and cannot trace source quantitatively for mixed water sources. In this paper, we propose a new double verification and quantitative traceability (DV-QT) method. The double verification consists of verification and the consistency of water source identification results based on conventional methods and verification of the rationality of both the water source identification results and the inrushing water channel results. When the verification fails, the groundwater-level or hydrochemical data shall be analyzed in depth, or the hydraulic connection between groundwater in the channel and water source needs to be determined. The quantitative traceability includes the multivariate mixing and mass balance calculations (M3) that calculate the proportion of water sources. Through an example application, the proposed method identified the causes of the identification error of the traditional hydrochemical characteristic analysis method, that is, the hydrochemical evolution process during water inrush cannot be ignored, and allowed for accurate water source identification and quantitative mine water traceability. The new approach is especially useful for mixed water sources and in mines with complicated hydrogeological conditions.

An analytical methodology of rock burst with fully mechanized top-coal caving mining in steeply inclined thick coal seam

Rock burst disaster is still one of the most serious dynamic disasters in coal mining, seriously restricting the safety of coal mining. The b value is the main parameter for monitoring rock burst, and by analyzing its changing characteristics, it can effectively predict the dangerous period of rock burst. This article proposes a method based on deep learning that can predict rock burst using data generated from microseismic monitoring in underground mining. The method first calculates the b value from microseismic monitoring data and constructs a time series dataset, and uses the dynamic time warping algorithm (DTW) to reconstruct the established b value time series. A bidirectional short-term and short-term memory network (BiLSTM) loaded with differential evolution algorithm and attention mechanism was used for training, and a prediction model for the dangerous period of rock burst based on differential algorithm optimization was constructed. The study used microseismic monitoring data from the B1+2 fully mechanized mining face and B3+6 working face in the southern mining area of Wudong Coal Mine for engineering case analysis. The commonly used residual sum of squares, mean square error, root mean square error, and correlation coefficient R2 for time series prediction were introduced, which have significant advantages compared to basic LSTM algorithms. This verifies that the prediction method proposed in this article has good prediction results and certain feasibility, and can provide technical support for the prediction and prevention of rock burst in steeply inclined thick coal seams in strong earthquake areas.

Research on the Application of Intelligent Diagnosis of Major Equipment Failures in Coal Mines

Abstract Currently, the implementation of a coal mine production safety monitoring system, capable of real-time monitoring of the safety condition of large coal mine equipment and timely dispatch management, is crucial for the efficient and safe operation of coal mine enterprises. This paper designs the coal mine major equipment failure process based on the rolling bearing’s vibration mechanism, analyzes the major equipment’s failure signal using both time domain and frequency domain analysis methods, and employs the intelligent fuzzy control method to identify the major equipment failure. We have designed and practically applied a real-time, multi-threaded, and comprehensive fault diagnosis and prediction system for coal mine major equipment, combining information from coal mine safety, production, and operation. The results show that the normal state’s time-domain amplitude approximates (-0.2, 0.2), and the time-domain waveforms all display periodic shock signals when the bearing sustains damage. The faulty vibration amplitude of the inner ring is within 0.08 ms 2 and the faulty vibration amplitude of the outer ring is within 0.44 ms 2, and the diagnostic accuracy using the method of this paper reaches 97.2%. This study demonstrates the effectiveness of remote monitoring and fault analysis of major equipment in coal mines, which effectively improves the equipment start-up rate and ensures coal mine production safety.

Design and optimization of intelligent ventilation system in coal mine

With the continuous development of the coal industry, coal mine safety and environmental protection have gradually become the focus of important attention. The design and optimization of mine ventilation systems is a key factor in ensuring air quality and safety in the working area. In order to improve the application level of automation and intelligent equipment of mine ventilation system, under the existing conditions of mine, the design significance and goal of mine intelligent ventilation system are taken as the starting point, and the design and application effects are analyzed. By optimizing the layout of ventilation routes, increasing the number of sensors, building an intelligent ventilation system, optimizing the ventilation scheme in the main production scenarios such as coal face, driving face and opening up main roadway in the daily production process of the mine, realizing rapid wind regulation and wind counteraction when disasters occur, and using air flow short circuit to form protective measures, effectively avoiding the expansion of disasters and accidents. At the same time, remote automatic control of ventilation facilities and equipment is carried out to effectively improve the optimal utilization of air supply, avoid the phenomenon of insufficient air supply and waste of air volume, and improve the intelligent level of mine ventilation system.

Safety Stressors and Coal Miners' Safety Performance: The Mediating Role of Resilience and Coping Styles.

This study aimed to investigate the effects of three different safety stressors (safety role ambiguity, safety role conflicts, and safety interpersonal conflicts) on safety performance of coal miners under the mediating role of resilience and coping styles. The study is cross-sectional. To collect data to analyze the hypothesized relationships in the present study, a total of 450 questionnaires were distributed to coal miners in Shannxi Province of China. Regression analysis was employed as the main statistical technique in analyzing the data using SPSS 22.0 and Process 4.1. The results of regression analysis indicate that the three kind of safety stressors have a negative predictive effect on coal miners' safety performance. Resilience and coping styles both were the mediating variables between the safety stressors (safety role ambiguity, safety role conflicts, and safety interpersonal conflicts) and coal miners' safety performance, and resilience and coping styles play a chain mediating role between the safety stressors (safety role ambiguity, safety role conflicts, and safety interpersonal conflicts) and safety performance of coal miners. This study further explores the mechanism between safety stressors and safety performance, providing theoretical guidance for improving the safety performance of coal mines. It emphasizes the importance of coal miner's resilience intervention, positive coping styles promotion, and negative coping styles prevention in coal mine safety production.

Human-caused accident evaluation of coal mine safety relying on ant colony clustering algorithm

Human-caused accident evaluation of coal mine safety relying on ant colony clustering algorithm

Human-caused accident evaluation of coal mine safety relying on ant colony clustering algorithm

Human-caused accident evaluation of coal mine safety relying on ant colony clustering algorithm

Dynamic Control System of Coal Mine Safety Risk Map Based on Cloud Model

Dynamic Control System of Coal Mine Safety Risk Map Based on Cloud Model

Formation Mechanism and Prevention Technology of Mine Water Disaster under Complex Conditions

With the continuous westward shift of China’s coal production center, the western mining areas will play an increasingly important role in ensuring national energy security. It is urgent to extract coal economically and efficiently and realize scientific mining under the premise of ensuring the safe and environmental capacity. This paper systematically summarizes the research progress of coal seam separation water disaster at home and abroad, and elaborates the problem of coal seam separation water disaster from four aspects: formation mechanism of coal seam separation water, disaster-causing mechanism, prediction and early warning of water disaster and key prevention and control technologies. This paper summarizes the research on mining overlying strata separation and the formation mechanism of overlying strata separation water, and summarizes the formation of coal seam overlying strata separation water into three basic conditions: water separation, the existence of recharge water around the separation bed, and the separation space lasting long enough. However, the prevention and control of coal mine water disaster under complex geological conditions is an important problem restricting coal mine safety at present. The water damage prediction of overlying strata separation is summarized from three aspects: prediction of the location of available water separation, prediction of the periodicity of water inrush and prediction of the amount of water inrush, according to the development characteristics of bed separation. In this paper, the water damage prediction of overlying strata separation is summarized from three aspects: prediction of the location of available water separation, prediction of the periodicity of water inrush and prediction of the amount of water inrush. According to the development characteristics of bed-separation and the location of bed-separation water, the main prevention and control methods of bed-separation water damage, such as diversion hole, advance drain hole and ground direct pump hole, are proposed.

Experimental Study on Wind Speed Distribution in Semi-circular Arch Tunnels Based on Mirror Stepped Grid Method

Ventilation is the cornerstone of coal mine safety production. Accurate monitoring of ventilation parameters is the basic guarantee for ventilation technology decision. For the problem that the accuracy of wind speed monitoring in coal mine tunnels is difficult to meet decision-making needs, a combination of theoretical calculation and experimental research was used to study the relationship between the average wind speed line and the position of the tunnel wall. The theoretical calculation obtained the expression of the relationship between the average wind speed line of the semi-circular arch tunnel section and the position of the tunnel wall. The proposed multi section mirror stepped grid format was used to investigate the airflow field in a semi-circular arch roadway under different conditions in the laboratory. The contour map of wind speed distribution was drawn, and the position of the measured average wind speed line was obtained. The absolute error between the measured average wind speed line and the theoretical model calculation results was within 5%, which verified the reliability of the theoretical calculation results.

Analysis on accident types of coal mine in global major coal producing countries

Coal plays an extremely important role in the global primary energy consumption structure; It is of great significance to analyze the types of coal mine accidents in major coal producing countries. This paper makes a comparative analysis of the types of coal mine accidents in the USA, Australia, Russia, India, and China, which has a significance for further improving coal mine safety in various countries.

Geochemical and mineralogical responses to thermal alteration by an igneous intrusion in the Dashucun Coal Mine of the Fengfeng coalfield, Hebei, North China

Igneous intrusions into coal affect the safety, productivity, and economic viability of many coal mines. Also, the alterations in coal caused by igneous intrusions encompass a deterioration in coal quality, a decrease in permeability, an enhancement of coal rank, and geochemical and mineralogical compositions. Although there have been some studies related to the effects of igneous intrusions on the inorganic constituents in the coal, the mechanism of newly formed chemical and mineralogical associations caused by igneous intrusions has not been fully understood.This paper investigates the mineralogical and geochemical responses of coal to thermal alteration by igneous intrusion into an Early Permian coal seam in Hebei Province, North China. The Fengfeng coalfield is structurally complex, experiencing variable rates of burial and uplift, with abundant intrusions during the upper Jurassic and maximum burial in the Cretaceous resulting in an increase in rank to the north. In the search for critical minerals and elements, intrusions and hydrothermal alteration are often cited as the source for elevated concentrations.Towards the intrusion, the coal rank indicators increase from semi-anthracite to anthracite over a 500-cm distance, as does the mineral matter. The clay minerals present in coals include NH4-illite, kaolinite, paragonite, and chlorite (chamosite and cookeite), which were all resulted from igneous intrusions. The epigenetic minerals mainly include quartz, ankerite, calcite, pyrite, and apatite, which were derived from fluids permeating into the coal seam and chemically interacting with the original minerals or precipitating directly to form new minerals.Geochemical indicators in this coal indicate hydrothermal solutions derived from igneous intrusion. The enrichment of Li and Hg is linked to the igneous activity, potentially influencing the utilization of the coal adjacent to the intrusions. However, the concentrations of the remaining elements in the coal closest to the intrusion show no significant changes, thereby supporting the notion of a “non-universal effect” of igneous intrusions on the elemental composition and concentrations.

Effect of confining pressure strength on the characteristics of energy evolution and fatigue fracture of the coal‐rock structural body

AbstractThe stability of coal seam‐overlying strata is significant to the safety of coal mines. The coal‐rock height ratio and confining pressure strength affect the stability of coal seam‐overlying strata. The research purpose of this paper is to obtain the effects of confining pressure and coal‐rock height ratio on the mechanical properties, energy evolution characteristics, and damage variables of structural body through the uniaxial loading, uniaxial cyclic loading, and triaxial cyclic loading experiment. Based on the evolution characteristics of the energy, the concept of energy dissipation ratio is put forward, and the effect of height ratio and confining strength on the energy dissipation ratio are compared and analyzed. The research results of this paper can provide theoretical reference for exploring the stress and energy evolution of coal seam‐overlying strata during chamber excavation and working face mining.

Fault complexity degree in a coal mine and the implications for risk assessment of floor water inrush

Faults significantly affect the stability of surrounding rock and stress distribution, which increases the possibility of water inrush disasters in a coal mine. Also, the precisely advanced prevention and control of floor limestone water become the foundation of ensuring mine safety and efficient production in coal mines. Fault development evaluation is one of the elements that control further transparent and fine studies. In this paper, the fault control index (FCI) is built based on the fractal dimension (Ds) and fault influence factor (E), which is determined according to the measured water inflow of water inrush points. The box meshing level is analyzed to calculate and compare the FCI. The results show that the box-meshing level affects the value of Ds and FCI and a meshing level of 6 seems to be the minimum standard of Ds for high accuracy. To verify the results, a comprehensive risk evaluation model of floor water inrush is established to illustrate the implications of fault complexity degree on safety mining. A stricter meshing grade when using the box-counting method in fault complexity degree is necessary for further studies in the precise prevention and control of mine hazards.

Knowledge graph construction and visualization analysis of coal mine accident safety management research based on CiteSpace and VOSviewer

Although the coal mining industry has implemented mechanization and automation, frequent coal mine accidents still occur, making coal mine safety a primary concern. To gain a comprehensive understanding of global progress in preventing coal mine accidents and managing safety, we utilized the Web of Science database (WOS) as our sample source. With the support of CiteSpace and VOSviewer software, we constructed a knowledge graph depicting publication numbers, research institutions, and keyword clustering to visually analyze coal mine accident prevention and safety management. Our research reveals that this field has undergone three stages of development: initial growth, steady progress, and rapid advancement. Particularly noteworthy is the current golden age of rapid development from 2018 to 2023 which has garnered significant attention from researchers. In terms of author collaboration networks, notable contributors with high publication rates and citation counts include Wu Qiang, Liu Quanlong, et al. Regarding institutional cooperation networks, major research institutions such as China University of Mining & Technology, Shandong University of Science & Technology, and Anhui University are actively engaged in important scientific research related to coal mining. Through keyword clustering analysis based on relevant literature review, it is evident that research on coal mine safety management primarily focuses on high-frequency keywords such as "coal and gas outburst","risk assessment" and "coal miners" By extracting the top nine emergent keywords with high-intensity levels and arranging them chronologically according to their emergence year, the evolution pattern over time was obtained for emergent words. Furthermore, it was observed that emerging keywords like "China", "subsidence" and "workers" have also gained increasing attention during this period.

Spontaneous combustion properties and quantitative characterization of catastrophic temperature for pre-oxidized broken coal under stress

Coal Spontaneous Combustion (CSC) is one of the most important natural disasters in coal mining safety production. To deeply restore the real state of CSC left in goaf during the face mining of coal mines, the experiments are carried out to simulate the oxidation characteristics of coal left in goaf under the coupling of the stresses (0 MPa, 4 MPa, 8 MPa) and the pre-oxidation temperatures (25 °C, 70 °C, 120 °C, 180 °C) employing the load-pressure-based device equipped with temperature-programmed. The oxidation characteristic parameter of CO gas generation, the O2 consumption rate, the apparent activation energy (Ea), and the critical temperature(T1) are calculated. It is found that both the pre-oxidation temperature and the stress have a promotion range for coal spontaneous combustion, and although they inhibited each other beyond the promotion range, they still show a promotion result. The pre-oxidation temperature and stress mainly affect the content of oxygen-containing functional groups and the permeability, thus influencing the process of coal spontaneous combustion. Based on the engineering application, a cusp catastrophe model is established to predict the catastrophic temperature of coal spontaneous combustion, and it is verified that the catastrophic temperature could correctly characterize the transition of coal spontaneous combustion, and it is found that the catastrophic temperature lagged behind the critical temperature, which provides sufficient time and space for the prevention and control of coal spontaneous combustion.

Nondimensional analysis and application of gas desorption and diffusion driven by density gradient in coal particles.

An in-depth understanding of gas diffusion characteristics in coal is of great value to coalbed methane (CBM) production planning and coal mine safety management. However, the mechanism and model of gas diffusion is still unclear, and some methods for determining diffusion coefficients are not accurate enough. Accordingly, a free gas density gradient (FGDG)-driven coal particle gas desorption and diffusion model was established in this work, and numerical solutions were performed via finite difference method (FDM) and dimensionless method. The variation rules of dimensionless gas pressure, gas content, desorption capacity, and desorption rate were obtained. Finally, the application of the dimensionless method in diffusion modeling and diffusion coefficient inversion was discussed. The results show that the dimensionless method can simplify mathematical equation processing and analyze the common phenomena of desorption and diffusion under different parameters. The gas desorption diffusion in coal particles is from the surface to the inside, and there is obvious desorption hysteresis effect. The larger the dimensionless radius or dimensionless time, the smaller the dimensionless gas pressure, gas content, and dimensionless desorption rate. The dimensionless cumulative gas desorption amount increased rapidly first and then tended to flat with dimensionless time. The simulated curve can be easily converted into the variation curves of several different measured parameters, and the diffusion coefficient can be calculated accurately. The prediction curve of the FGDG diffusion model is in good agreement with the experimentally measured data, which verifies its reasonableness. The research content aims to provide some ideas for modeling gas desorption and diffusion behavior.

Prediction of Coal Mine Pressure Hazard Based on Logistic Regression and Adagrad Algorithm—A Case Study of C Coal Mine

Effectively avoiding coal mine safety accidents has always been an important issue in the process of coal mining. In order to predict mine pressure hazard and reduce the occurrence of mine safety accidents, this paper innovatively combines logistic regression and mine pressure hazard prediction to establish a mine pressure hazard prediction model. By standardizing the data, the model improves the reliability of the mine pressure data and reduces the interference of the prediction effect of random errors. Based on the batch gradient descent algorithm and the Adagrad optimization algorithm, the prediction model is solved innovatively, which greatly improves the calculation speed and prediction accuracy of the model. Accuracy rate, precision rate, recall rate, and F1-score were selected as the evaluation indices to evaluate the prediction effect of the Adagrad optimization algorithm to solve the logistic regression model for mine pressure hazard. Compared with the existing classification algorithms, such as SVM and decision tree, the Adagrad optimization algorithm has the highest four indices when solving the logistic regression prediction model, and it takes the least time to predict. The results show that the model can efficiently predict mine pressure hazard. Finally, C Coal Mine was selected as the example for analysis. The prediction function was added to the mine pressure monitoring interface design. The practical application effect is similar to the theoretical verification. The establishment of this model provides a reliable guarantee for the secure and efficient production of coal mines and provides helpful research for the prediction of mine pressure.

Multi-Indicator Early-Warning Model for Mine Water Inrush at the Yushen Mining Area, Shaanxi Province, China

Previously conducted studies have established that the early warning of water inrush is crucial for the prevention and control of mining water catastrophes in the panel. In order to ensure the safety of coal mining, in this paper, monitoring indicators were determined using the sensitivity analysis method, and then a multi-indicator early-warning model for water inrush was established mainly based on the geological data of the Yanghuopan coal mine. The monitoring stage of the early-warning model should be determined according to the distance between the monitoring borehole and the mining position. Then, the development of a water-conducting fracture zone and the fluctuation and stochastic oscillator of monitoring indicators are comprehensively analyzed to determine the early-warning level. A multi-indicator early-warning model was applied to panel 30302 of the Yanghuopan coal mine and panel 1304 of the Zhaoxian coal mine in the Yushen mining areas. The abnormal warning of the water disaster in panel 1304 was sent 3 days before the disaster, which shows the effectiveness of the model. It can provide a reference for the development of an early-warning model for mine water inrush in Yushen mining areas.

An efficient detection of non-standard miner behavior using improved YOLOv8

To prevent coal mine safety accidents caused by non-standard mining operations, higher requirements have been proposed for identifying miners' behavior in surveillance videos. In this study, a single-stage detection model, YOLOv8, is employed, which effectively predicts the center of objects and is more suitable for recognizing miner behavior in complex environments. First, the video is segmented sampled to capture long-term information. Afterwards, deformable convolutions are integrated, applying offset learning to enhance fine-grained feature extraction and simultaneously, the introduction of the parameter-free SimAM improves the saliency mapping of miner behavior. Furthermore, an improved cooperative loss function is used for high-quality prior box regression. To validate the performance of non-standard miner behavior recognition, the proposed approach is conducted on a self-built dataset and achieves a mAP of 95.7 %, accuracy of 95.3 %, and recall of 95.1 % compared to the state-of-the-arts. It maintains an effective balance between computational cost and recognition accuracy.