Risk In Coal Mines Research Articles

Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks.

Эколого-правовые риски при добыче полезных ископаемых (на примере добычи угля)

The article discusses the problems of legal regulation of environmental risks in coal mining. Various groups of environmental risks in this area, including licensing risks and environmental risks, are analyzed. The conclusion is drawn: any environmental and legal risks, including the risk during mining, should be considered as a certain objective-subjective category. In this case, the basis for the occurrence of negative consequences for the environment can be both action and inaction, upon the occurrence of which this risk will be realized.

Optimization of a coal mine roof characterization model using machine learning

Predicting areas of increased propensity for roof deformation is crucial for the proactive management of geotechnical risk in underground coal mines. Current practices rely largely on assessing rock mass strength or characterization indices in isolation. Validation of applied ground support systems typically, and in part, comprises a review of observed deformation and analysis of extensometer data from adjacent previously mined areas. This information is seldom routinely assessed relative to the rock mass strength or characterization data. Typically, roof deformation is a response to several combining factors such as mining-induced stress, roof lithology, rock mass strength, and roadway geometry. Therefore, it is optimal to develop an approach to integrate these factors to quantify their relative significance to causing roof deformation, with the goal of establishing a reliable quantitative model for predicting roof deformation.This paper provides a case study in machine learning using Artificial Neural Network (ANN) and geophysical log data information from an underground coal mine located in the Bowen Basin (Australia), whereby a suite of parameters is considered pertinent to excavation stability. This machine learning predictive model analyses key geotechnical parameters including field and mining-induced stress, rock mass strength, clay/shale content, and strata monitoring data to identify strata hazards. Finally, the classification model can indicate the risk of roof displacement and observed roof deformation as communicated through a contoured ‘geotechnical hazard map’. This hazard map can assist site engineers understand roof conditions in underground coal mining and thus put in place operational mitigation processes to reduce geotechnical risk on mining. This could potentially improve the identification of high-risk areas and inform future bolting requirements to maintain a safe working environment and minimise production interruptions.

Assessing Drought Risk in Forest Zones Near Coal Mines with TVDI

Assessing Drought Risk in Forest Zones Near Coal Mines with TVDI

Evolutionary Tripartite Game Model in the Coal Mine Risk-Hazard Double Pre-Control and Management in China

The classification, investigation, and management of the safety risk (namely, risk-hazard double pre-control management mechanism) in coal mines were introduced into a behavioral evolution game to assess the decision-making processes of coal mine workers, regulators, and supervisors. A tripartite game tree and a revenue perception matrix were constructed, and the evolutionary stability strategy (ESS) achieved by the evolutionary system under different conditions was obtained according to the replication dynamic equation (RD). The numerical simulation of the evolution game was conducted in MATLAB, which verified the influence of various parameters on the behaviors adopted by all parties. The results show: (1) When the safety, regulatory, and supervisory costs are high, the average expected value of mine workers, regulators, and supervisors to choose safety behaviors is low, so they adopt an unsafe behavior. By contrast, reducing costs can encourage the adoption of safety behaviors. (2) When the punishment for accident losses caused by unsafe behavior and the lack of regulations and supervision is higher than 50% of the initial state, the actors in the tripartite game are more inclined to avoid risks when facing cost losses in the short term, which can lead them to choose a safety behavior. (3) When the actors in the tripartite game face different accident probabilities and accident loss, with the severity of the accident increasing to 80% of the initial state, mine workers and regulators gradually adopt safety and regulation behaviors, and supervisors quickly adopt supervision methods.

Analysis of coal mining accident risk factors based on text mining

Frequently occurring coal mine safety accidents have caused great casualties and economic losses. Coal mine intelligence is the core technical support for the high-quality development of the coal industry. The deep integration of coal mining safety production management and Artificial Intelligence (AI) technology is practically significant to achieve accident prevention. To efficiently identify mining accident risk factors and explore mechanism of coupling between risk factors, this study mined 400 reported cases of mining accidents in Shanxi Province and identified 64 accident risk factors through custom, stopword, synonym dictionary construction, keyword extraction and keyword correlation analysis. Then, this study constructed association rules and a Bayesian causal network. The major risk factors are identified using a comprehensive high-frequency, sensitivity, strength and key path analysis of the Bayesian causal network. The following seven risk factors are found to play a major role in the occurrence of mine accidents: inadequate safety supervision, disordered safety management, illegal organization of production, inadequate staff safety education and training, operation against rules, command against rules and weak safety consciousness among the staff. Finally, a case study is conducted to validate the reliability of the results. This study solves the problem of incomplete extraction of key feature information in coal mine reports and the lack of analyses of coupling mechanisms between coal mine risk factors in traditional accident analysis methods, providing the methodological support for the effective use of unstructured coal mine safety production data for risk analysis.

Mechanism study on the sandstone roof caving characteristics for improving the preconditioning measures to mitigate the windblast risk in coal mines

Mechanism study on the sandstone roof caving characteristics for improving the preconditioning measures to mitigate the windblast risk in coal mines

Mechanism study on the sandstone roof caving characteristics for improving the preconditioning measures to mitigate the windblast risk in coal mines

Mechanism study on the sandstone roof caving characteristics for improving the preconditioning measures to mitigate the windblast risk in coal mines

Mitigating Risks in Coal Mining: Simulation-Based Strategy for Oxidation Zone Control Using Inorganic Paste Backfill at the Working Face Corners

Insufficient stability of the top plate at the corner of an easily combustible coal seam comprehensive mining face may lead to a natural fire within the goaf. While corner sealing is crucial for minimizing air leakage, current sealing methods struggle to effectively prevent such leakage. Additionally, the distribution characteristics of the oxidation zone in the goaf after sealing are unclear, making it difficult to control the extent of the oxidation zone. To address these issues, a new type of inorganic paste filling material was developed, taking into account the conditions of the Cuncaota II Mine. Various corner-filling schemes were developed, and numerical simulations were used to study the effects of different corner-filling strategies and varying filling interval distances on the width of the oxidation zone in the goaf. Based on these findings, a working face corner-filling technology was proposed and applied to the 22,122 working face. The research results indicate that the mountain sand-based paste filling material, using mountain sand as the filling aggregate and cement and fly ash as the binding materials, not only meets the pumping requirements but also exhibits excellent self-supporting characteristics, thereby addressing the corner filling needs of the working face. The width variation in the oxidation zone in the goaf is influenced by the position and interval distance of the corner filling, showing a pattern of initially decreasing and then increasing with the rise in the filling interval distance, reaching a minimum at a filling interval of 50 m. Field observation data demonstrate that, following the application of the aforementioned filling technology, the width of the oxidation zone in the goaf of the 22,122 working face is reduced by 37.5%, and air leakage decreases by 66.7% compared to the unfilled condition. This technology effectively narrows the range of the oxidation zone in the goaf, ensuring the safety of working face production.

Analysis of accidents risk in coal mines taking into account human factor

Analysis of accidents risk in coal mines taking into account human factor

AdaBoost-driven multi-parameter real-time warning of rock burst risk in coal mines

The stope dynamic disaster, which occurs around the mining space and is represented by stress-type rock burst and fracture-type rock burst, seriously affects the safety production of coal mine. How to effectively forewarn rock burst risk to reduce the disaster caused by rock burst is an urgent problem to be solved in stope. In this paper, the spatiotemporal parameters that affect the occurrence of stope dynamic disaster are collected, and the big data of stope state is established. Due to the complex temporal and spatial parameters of rock bursts with different intensities occurrence and their varying degrees, it is difficult for some machine learning methods to excavate their internal relationships and make accurate warning. In this paper, (1) a big data platform is used to record and integrate the multiple parameters of stope dynamic disaster, and data preprocessing technology is used to de-noise and standardize them. (2) Based on the fused historical data, a strong classifier is iteratively found by the classification algorithm AdaBoost to identify the existence of the rock burst risk, so as to achieve the purpose of accurately and timely warning of rock burst risk. The research of this paper uses big data mining technology and machine learning method to carry out intelligent perception warning of the rock burst risk in real time. The experimental results show that the proposed method has a good effect and is of great significance to the prevention and control of rock burst disaster in stope.

Advanced Algorithms for Low Dimensional Metal Oxides-Based Electronic Nose Application: A Review

Low-dimensional metal oxides-based electronic noses have been applied in various fields, such as food quality, environmental assessment, coal mine risk prediction, and disease diagnosis. However, the applications of these electronic noses are limited for conditions such as precise safety monitoring because electronic nose systems have problems such as poor recognition ability of mixed gas signals and sensor drift caused by environmental factors. Advanced algorithms, including classical gas recognition algorithms and neural network-based algorithms, can be good solutions for the key problems. Classical gas recognition methods, such as support vector machines, have been widely applied in electronic nose systems in the past. These methods can provide satisfactory results if the features are selected properly and the types of mixed gas are under five. In many situations, this can be challenging due to the drift of sensor signals. In recent years, neural networks have undergone revolutionary changes in the field of electronic noses, especially convolutional neural networks and recurrent neural networks. This paper reviews the principles and performances of typical gas recognition methods of the electronic nose up to now and compares and analyzes the classical gas recognition methods and the neural network-based gas recognition methods. This work can provide guidance for research in related fields.

Evaluation Cloud Model of Spontaneous Combustion Fire Risk in Coal Mines by Fusing Interval Gray Number and DEMATEL

Coal still occupies a key position in China’s energy consumption structure, and ensuring safe production in coal mines is a key focus for ensuring energy security. Spontaneous combustion fires in coal mines are a serious threat to the sustainability of safe production in coal mines. In order to prevent coal mine fire risk scientifically and effectively and to assess the level of disaster risk effectively and rationally, a study was conducted on the risk of spontaneous combustion fires in underground coal mines. An evaluation cloud model of spontaneous combustion fire risk in coal mines integrating the interval gray number with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) was established. Seventeen representative risk evaluation indicators were selected, and a coal mine spontaneous combustion fire risk evaluation index system was constructed based on four aspects: personnel, machinery, environment, and management. The interval gray number theory was introduced to improve the classical DEMATEL analysis method, which fully expresses the expert empirical knowledge and solves the problem of ambiguity and randomness in the semantic expression of expert evaluation. The relative importance of each indicator was determined by analyzing the influence relationships between risk evaluation indicators through the improved DEMATEL. A cloud model capable of transforming quantitative descriptions and qualitative concepts was used for comprehensive evaluation of risk, and based on the results of DEMATEL analysis, a comprehensive evaluation cloud model of coal mine spontaneous combustion fire risk was formed. Finally, the validity and practicality of the model were verified by using a mine in Shenmu City, Shaanxi Province, China as an example. This study provides a powerful tool to prevent spontaneous combustion fires in coal mines and makes a positive contribution to the sustainable development of coal mine safety management.

Research on Multifactor Analysis and Quantitative Evaluation Method of Rockburst Risk in Coal Mines

Abstract The prevention of rockbursts is significant to ensure mining safety in deep coal mines. The multifactor analysis and a new quantitative evaluation method for rockbursts in coal mines are proposed in this study. In the aspect of rockburst analysis, a multifactor system of rockburst risk based on the material, stress, and large-scale geological structure is proposed. The factors influencing rockbursts in coal mines are analyzed by numerical simulations. Based on a standard mining model, three comparative models considering the rockburst tendency, high stress, and geological structure are established. The distribution of maximum principal stress and plastic zone during the mining process is compared. The reasons why these three types of factors are liable to trigger rockbursts lie in generating high-stress zones in surrounding rock masses. In the aspect of quantitative evaluation, the monitored microseismic signal is selected as the key indicator, and the daily frequency of microseisms is analyzed. A normal distribution function based on the daily frequency of microseisms is established. The interval of daily frequency of microseisms is set to judge whether the microseismic frequency is abnormal and then determine the rockburst risk of coal mines. Considering the results of multifactor analysis, it is proposed that the monitoring system combining microseisms with stress is the direction to accurately and quantitatively evaluate the rockburst risk in the future. This study makes specific explorations in the quantitative evaluation of rockburst risk in coal mines.

Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management

The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve this challenge, this paper studies the mathematical model of coal mine risk early warning in China based on data mining. Firstly, the coal mine risk data was comprehensively analyzed to provide basic data for the risk prediction model of data mining. Then, the adaptive neuro-fuzzy inference system (ANFIS) was optimized twice to build the coal mine risk prediction model. By optimizing the calculation method of the control chart, the coal mine risk early warning system was proposed. Finally, based on the coal mine risk early warning model, the software platform was developed and applied to coal mines in China to control the risks at all levels. The results show that the error of the optimized ANFIS was reduced by 66%, and the early warning error was reduced by 57%. This study aimed to provide implementation methods and tools for coal mine risk management and control, and data collected has reference significance for other enterprises.

Laboratorial Investigation of Coal Fire Extinguishing and Re-burning Risk in Underground Coal Mines.

The extinguishing and re-burning of the closed fire area in an underground coal mine were investigated by laboratory-scale physical simulation. Temperatures in the center of the fire source were recorded, and the typical cooling process was observed to include the rapid cooling stage (900–400 °C) and dilatory cooling stage (400–100 °C). With the increase of coal mass from 20 to 80 kg, the rate of cooling decreases and the time required for fire extinguishing increases by 69.5%–193.2%. At temperatures ranging between 500 and 100 °C, yields of CO and H2 show strong correlation with the attenuation of the coal fire, and the trend in the yield of H2 might be used as the optimal indicator considering the different amounts of coal. A significant difference appears in the concentration of H2 released by samples of different dosages of coal in the early stage of cooling, especially when the temperature exceeds 200 °C. During the extinguishing process, micropores in coal fused into mesopores and macropores, while the content of O-containing groups fluctuated significantly. Variations of elemental C and O also indirectly reflect the combustion state in the fire cooling. Taking the experimental reactor as a physical model, the time required for the fire area from closure to safe re-opening is deduced, that is, t = Cm ln (T1 – T∞)/(T2 – T∞ ). The calculated results were compared with the changes in measured temperatures, providing a theoretical foundation for the re-opening prediction of mine fire areas.

Study on Multi-Indicator Quantitative Risk Evaluation Methods for Different Periods of Coal Spontaneous Combustion in Coal Mines

ABSTRACT For the current coal spontaneous combustion fire risk evaluation method has the problems of single evaluation dimension, incomplete evaluation index and unreliable evaluation results. However, the coal spontaneous combustion fire risk evaluation can effectively guide coal mine fire prevention and control. Therefore, a multi-indicator quantitative risk evaluation method combining analytic hierarchy process (AHP) and linear interpolation for different periods of coal spontaneous combustion is proposed as a way to improve the rationality of coal spontaneous combustion fire risk evaluation. The continuous segmental evaluation model was established based on the factors that influence the three periods of coal spontaneous combustion (Latent Period, Self-heating Period and Combustion Period). The latent period evaluation model was established based on the fire-prone nature of coal, coal seam occurrence, mining technology, and fire prevention measures. The self-heating period evaluation model was established based on the degree of self-heating of coal, absolute CO generation, and Graham index. The combustion period, there was already a spontaneous combustion fire, directly set the fire risk score to 0. The score could fully reflect the level of coal spontaneous combustion fire risk. The higher the score, the relatively smaller the risk of coal spontaneous combustion, and vice versa, the relatively larger. According to this risk evaluation model, the risk of coal spontaneous combustion in the 2−2 coal seam of a coal mine in Shaanxi Province was evaluated. The result shows that the level of coal spontaneous combustion risk of 2−2 coal seam in this coal mine is in a safer state, and this result is consistent with the actual situation. The multi-indicator quantitative evaluation of different periods of coal spontaneous combustion effectively improves the accuracy of fire risk evaluation. Therefore, the evaluation model has certain theoretical guidance for the control of coal spontaneous combustion risk in coal mines.

A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network

The research was aimed at predicting floor water-inrush risk in coal mines and forewarn of such accidents to guide safe production of coal mines in practice. To this end, a prediction method for floor water inrush combining the chaotic fruit fly optimization algorithm (CFOA) and the generalized regression neural network (GRNN) is proposed. Floor water inrush is predicted by virtue of the robust nonlinear mapping capability of the GRNN. However, because the prediction effect of the GRNN is influenced by the smoothing factor, the CFOA is adopted to optimize this factor. In this way, influences of human factors during parameter determination of the GRNN prediction model are decreased, and the prediction accuracy and applicability of the model are improved. Results show that the CFOA–GRNN prediction model has an accuracy of 93.2% for whether floor water inrush will occur or not. Compared with the BPNN, RNN, and GRU network prediction model, the CFOA–GRNN model is superior in the prediction accuracy and generalization, and it can more accurately predict floor water inrush.

TIME-VARYING MULTIFRACTAL ANALYSIS OF CRACK PROPAGATION AND INTERNAL FRACTURE PROCESS OF COAL UNDER DYNAMIC LOADING

Coal failure behavior under dynamic loading is significant for dealing with the failure issues encountered in underground coal mines. In this study, the crack propagation and internal fracture process of coal under dynamic loading were investigated by a split Hopkinson pressure bar (SHPB) experimental system and numerical approach, respectively. The experimental and numerical results show that the coal crack propagation and internal fracture surface manifest significant multifractal features. Further, multifractal analysis suggests that the multifractal feature becomes more and more significant during the dynamic loading. The formation mechanism of the multifractal features was further discussed, and the result demonstrates that the crack propagation path within coal is essentially a multifractal structure, thus causing the total fracture behavior of coal to show multifractal features under dynamic loading. Moreover, the multifractal spectrum parameter [Formula: see text] was proved to be closely related to the brittle fracture property of coal, which transpires that the multifractal feature is feasible to evaluate the coal brittleness under dynamic loading and also applicable to predict the coal failure risk in underground coal mines.

Coal Mine Gas Explosion Vulnerability Assessment Based on the “Glass Heart” Model

Gas explosion, which is the main type of accidents reported in coal mines, comes with serious economic and safety consequences. The present work adopts the “glass heart” model for assessing the vulnerability of coal mines to gas explosions based on a constructed gas explosion vulnerability assessment index system. The proposed index system is applied to a coal mine in Henan Province, China, as an example. The original disturbance index is augmented by including the energy release index factor. The Delphi method is employed by six experts to score the index factors according to a scoring standard. Data analysis and processing yield a gas explosion vulnerability score of 0.431 for the example mine. Accordingly, the mine is classified as grade II, indicating that the mine is likely to result in casualties and/or property loss. A comparison of the assessment results with those obtained via an analysis of records pertaining to previous gas explosion accidents indicates that the proposed assessment of gas explosion vulnerability by means of the “glass heart” assessment model is realistic. The “glass heart” vulnerability assessment method offers a new perspective and methodology for assessing the vulnerability of coal mines to gas explosion and conducting coal mine risk classification.