Improve Mine Safety Research Articles

Optimizing Methane Control in Composite Goaf Areas: Insights from Multisource Emission Analysis and Numerical Modeling in the Tongxin Coal Mine.

Coal mine methane poses a threat to both coal production and the well-being of workers. This study investigates the complex challenge of managing methane emissions from multiple sources in coal mines prone to gas. Coal mining creates multiple air leakage channels in the mine goaf, increasing the risk of gas explosions. Tracer SF6 measurements were employed to identify multiple air leakage channels. A comprehensive numerical model, incorporating multiple air leakage channels, was developed to simulate methane distribution in the goaf and upper corner. By adjusting the combination of gas drainage rate (U) and working face ventilation flux (Q), the concentration of methane at the upper corner is expected to be decrease, ensuring safe production at the Tongxin mine. A total of 48 simulation cases were conducted, determining that U = 1080 m3/min and Q = 2250 m3/min are recommended for the 8201 working face in the Tongxin coal mine. This combination provides not only an effective solution but also a cost-efficient one for methane control. The findings offer a new insight into effective methane control providing a foundation for improving mine safety.

Research on the thermo-hydro-mechanical coupling simulation and deformation spatiotemporal evolution for the entire process of oil shale in-situ mining

The ground surface deformation (GSD) caused by oil shale in-situ mining poses a threat to land resources and human's lives and property. This study, for the first time, conducts a full stratum simulation of the Fuyu oil shale in-situ pyrolysis pilot base, analyzing the evolution characteristics of the temperature field, stress field, and deformation field of the entire stratum profile during the heating and cooling processes of convective heating mining. Considering the changes in the pore structure, thermophysical, and mechanical properties of the stratum, the environmental geological effects of rock deformation during in-situ mining were identified. Simulation results show that after heating, the temperature within 80 cm of the heating well reaches above the initial pyrolysis temperature of 350 °C for oil shale organic matter, and there is a significant stress concentration near the heat source. In the simulation, ground displacement rises in a wave-like manner during heating, quickly subsides after cooling, and finally stabilizes. Eventually, the entire stratum exhibited subsidence, with a subsidence amount of 0.59 cm. The spatiotemporal deformation trend obtained from SBAS-InSAR real-time monitoring results is similar to the simulation results. By comparing the monitoring results with the simulation results, the synergistic deformation mechanism of underground and ground surface co-deformation during in-situ mining of geochemical reactions in the study area was analyzed. The deformation rate is determined by the thermal hysteresis phenomenon and the temperature difference between the heated fluid and the rock layer. This provides scientific support for the geological effect evaluation of oil shale in-situ mining, which helps to improve mining safety and efficiency.

Analisis Resiko Pekerjaan Dengan Metode Hazard Identification Risk Assesment Di Perusahaan Tambang Batu Bara

Data from the Social Security Administering Agency (BPJS) for Employment in 2021 - 2022 recorded 499,604 cases of work accidents, then the Circular Letter of the Chief Mining Inspector number: B- 6234/MB.07/DBT.KP/2023 dated 9 October 2023 regarding Increasing Vigilance in Efforts Preventing Mining Accidents and Improving Mining Safety Performance, throughout 2023, 46.9% of accidents will result in deaths due to interaction activities on mine roads. Based on preliminary studies at PT. The true Saptaindra Site SERA in 2023 found that the number of property damage accidents (equipment damage) was 11 cases and near misses were 4 cases. The aim of this research is to identify risks that can occur both in terms of humans, the environment and operational processes, then be able to analyze the level of risk and provide recommendations for controlling dangerous risks to minimize the potential for work accidents at the PT Saptaindra Sejati Site SERA. This research uses HIRA (Hazzard Identification Risk Assessment) method with the main focus being able to identify potential hazards through calculating the level of opportunity (Likelihood) and level of severity (Saverity) in order to obtain the level of risk using a risk matrix. And then control is carried out according to the work safety hierarchy, namely elimination, substitution, engineering, administration and PPE (personal protective equipment). The results of this research show that after controlling jobs with a medium risk level, there are 3 jobs including Topsoil removal, Over burden removal and Coal getting and less than 9 according to table 2.3 Risk Matrix and jobs with a low risk level. risk), namely 3 jobs including Land clearing, Drill and blast and Mine water pumping work in the sump.

Seismic fault identification in coal mines based on the self-organizing map-gray wolf optimizer-support vector machine algorithm

Accurate fault identification in coal mines is important to improve mine safety and economic benefits. We compare various intelligent algorithms for data preprocessing and optimization and analyze the construction methods of seismic attribute data sets and the performance of intelligent optimization algorithms using fault identification accuracy as the discrimination index to find a better combined model for seismic fault identification. First, the training data set is constructed by mining the fault and nonfault information revealed by the roadway. The distribution characteristics of the seismic attribute data indicate similarities among them, and they are nonlinearly separable. Directly using the attributes to construct the data set, the accuracy of fault identification using the support vector machine (SVM) model is 78.41%. Principal component analysis (PCA) and self-organizing mapping (SOM) neural networks are used to extract effective information and then combined with the SVM classification model, and the accuracy of fault identification is 83.82% and 87.47%, respectively. Compared with the original data and PCA dimensionality reduction data, the accuracy of fault detection is improved by 9.06% and 3.66%, respectively, indicating that SOM can effectively improve the accuracy of fault detection by eliminating similar attributes and reducing the weight of redundant information. Then, through a fixed attribute data set, genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimizer (GWO) intelligent optimization algorithms are used to find the optimal kernel function parameter and penalty parameter of the SVM classifier. The accuracy rate of the SOM-GWO-SVM model reaches 91.12%, compared with the SOM-PSO-SVM and SOM-GA-SVM, and the model accuracy is increased by 5.2% and 5.61%, respectively. Compared with PSO and GA, the GWO algorithm has a better global search ability. The identification result of the SOM-GWO-SVM model is closest to the actual fault exposure, especially for the identification of “short” faults and associated faults, which has obvious advantages over the traditional manual interpretation in terms of efficiency and accuracy.

Analysis and Research on Mine Safety and Management Methods in the Context of Big Data

Abstract The optimization of mine safety and management methods is critical in the context of big data. This study will explore improving mine safety and management efficiency by utilizing CPS. A mine data interaction mechanism is constructed using CPS technology to realize efficient data collection and processing. The study profoundly explores the data interaction of end devices and the value model of computing resources by analyzing the mine CPS perceptual execution layer. The results show that CPS technology can significantly improve the efficiency of mine safety management. For example, the introduced CRV incentive mechanism significantly improves the utilization efficiency of computing resources. In addition, this study also explores the scheduling strategy of computing resources under multi-objective constraints and the implementation of mine scheduling based on the MOCA-PSO algorithm, which effectively optimizes resource allocation and utilization. The conclusion shows that applying big data technology and CPS can effectively improve mine safety and management, which is of great significance in guiding the future development of the mining industry.

Evaluation of roof cutting by directionally single cracking technique in automatic roadway formation for thick coal seam mining

Automatic roadway formation by roof cutting is a sustainable nonpillar mining method that has the potential to increase coal recovery, reduce roadway excavation and improve mining safety. In this method, roof cutting is the key process for stress relief, which significantly affects the stability of the formed roadway. This paper presents a directionally single cracking (DSC) technique for roof cutting with considerations of rock properties. The mechanism of the DSC technique was investigated by explicit finite element analyses. The DSC technique and roof cutting parameters were evaluated by discrete element simulation and field experiment. On this basis, the optimized DSC technique was tested in the field. The results indicate that the DSC technique could effectively control the blast-induced stress distribution and crack propagation in the roof rock, thus, achieve directionally single cracking on the roadway roof. The DSC technique for roof cutting with optimized parameters could effectively reduce the deformation and improve the stability of the formed roadway. Field engineering application verified the feasibility and effectiveness of the evaluated DSC technique for roof cutting.

Increasing Safety in an Underground Coal Mine through Degasification by Vertical Wells—Influence of the Relationship between the Permeability of Carbon and the Filter Cake of the Bentonite Suspension

The Hullera Vasco Leonesa (HVL) underground coal mine in northern Spain is subject to violent methane (CH4) outbursts. Vertical wells are used to extract CH4 from coal layers to improve mine safety. Bentonite suspensions are used as drilling fluids in this degasification system. The relationship between the soil and filter cake permeabilities, the filter cake thickness, and the filtrate loss significantly affects the fluid’s rheological properties. Fann mud balance, marsh funnel viscometer, and Fann 300 press filter tests have been carried out to determine the rheological properties of the bentonite suspension. A drilling fluid study was carried out for three drilling zones (across which the rheological properties of the drilling fluid vary for the reasons mentioned above): Zone 1, the lower zone, wherein drilling cuts through the coalbed; Zone 2, the upper zone, wherein drilling cuts through layers of sand; and Zone 3, an intermediate zone consisting mainly of rock. When drilling cuts through the coalbed, the release of methane, which improves the safety of underground operations, depends on the relationship between the permeability of the coal and the permeability of the filter cake of the drilling fluid. The effect of sand contamination increases the filtrate loss, and therefore also increases the permeability of the filter cake. The filtrate reducer decreases filtrate loss by recovering the permeability.

Insight into proactive inertisation strategies for spontaneous combustion management during longwall mining of coal seams with various orientations

ABSTRACT Spontaneous combustion of coal occurs in the longwall (LW) goaf during mining cycles due to coal oxidation at low temperatures and air ingress. Coal seam orientations, dictated by the elevations of maingate (MG) and tailgate (TG) and the height of working face and starting-up line, significantly impact the gas distribution in the goaf. Despite this, there has been limited study on the effects of coal seam orientations on spontaneous heating management. To fill this knowledge gap, extensive computational fluid dynamics (CFD) modeling was conducted based on the actual conditions of an Australian underground coal mine and verified with onsite gas monitoring data, after which extensive parametric studies of how coal seam orientations influenced gas distribution were conducted. Simulation results indicate that coal seam orientations significantly impact spatial gas distribution in the LW goaf and the development of proactive goaf inertisation strategies. Regardless of coal seam orientations, nitrogen performs better than carbon dioxide in reducing the oxidation zone area. In addition, at least 1.5 m3/s of nitrogen is required to effectively prevent spontaneous heating, and the area ratio of oxidation zone to active goaf is approximately 10%, reducing by about 15% compared to scenarios without inertisation. The modeling results shed insight into the goaf gas distribution characteristics under various coal seam orientations and provide guidance on developing corresponding proactive inertisation strategies for managing spontaneous heating in the goaf, thus improving mining safety.

Study on mechanical properties and energy change of rock materials in whole splitting process based on peridynamics

Aiming to bypass the inability to directly observe the evolution process of rock internal deformation and fracture, this paper proposes that rock samples with different inclination angles can be analyzed from the standpoint of energy, using the bond-base peridynamic theory and the PMB model of brittle materials, combined with laboratory experiments. The whole process of shearing is analyzed, and the LAMMPS software is used to simulate the internal energy change of rock-like materials under shear conditions, while the damage evolution law of samples with different dip angles is studied from macro and micro perspectives. The result shows that prefabricated cracks and the inclination of cracks are important factors for specimen damage, a finding that has important theoretical value for rock mechanics research. The research results can reduce the occurrence of rock burst accidents, the difficulty of mine support, and the cost of mining engineering, as well as improve mine safety levels.

Potential of CBM as an Energy Vector in Active Mines and Abandoned Mines in Russia and Europe

The energy sector is in the spotlight today for its contribution to global warming and its dependence on global geopolitics. Even though many countries have reduced their use of coal, the COVID-19 crisis, the drop in temperatures in Central Asia, and the war between Russia and Ukraine have shown that coal continues to play an important role in this sector today. As long as we continue to depend energetically on coal, it is necessary to create the basis for the successful extraction and industrial use of coal mine methane (CMM), for example, as an unconventional energy resource. Early degassing technology is a technique that allows for the extraction of the methane contained within the coal seams. The application of this technology would reduce emissions, improve mine safety, and even increase their profitability. However, this technology has been understudied and is still not implemented on a large scale today. Moreover, mines with this technology generally burn the extracted methane in flares, losing a potential unconventional fuel. This study, therefore, presents different scenarios of the use of coalbed methane (CBM), with the aim of generating an impact on pollutant emissions from coal mines. To this end, a model has been designed to evaluate the economic efficiency of degasification. In addition, an emissions analysis was carried out. The results showed that the use of this technology has a negative impact on the economy of mines, which can be completely reversed with the use of CBM as fuel. Furthermore, it is observed that degasification, in addition to reducing the number of accidents in coal mining, reduces emissions by 30–40%.

Analysis of near-miss incidents (NMI) reporting in mining operations

Purpose. In safety management, near-miss incident (NMI) reporting has been recognised as an essential practice in the prevention of mining accidents. This research aims to investigate near-miss incidents in Kosovo mining operations based on previous researches and in-situ surveys. Methods. For this purpose, both qualitative and quantitative methods were used as an observation tool, with 115 questionnaires randomly assigned among the employees at mining companies. To analyse collected data, the Excel Spread-Sheet and SPSS software were used. The survey was conducted to get basic information on the number of near-miss incidents, how they occurred and were brought to the light, as well as to reveal their similarity with mining accidents that happened during mining operations in Kosovo. Findings. The research brings to the light that mining activities related to production are the most predominant sources of near-miss incidents. From the qualitative observation approach in different mining companies, it has been found that the majo-rity of mines do not report even in an informal way about near-miss incidents that the employees experience. Originality. The legislative framework of Kosovo does not require a formal reporting on near-miss incidents. Therefore, the present research aims not only to develop proper legislation, but also to comprehensively study the main factors causing near-miss incidents in order to improve mining safety. Increasing the transparency of data on near-miss incidents and their publication should facilitate research work related to improving the safety of mining operations, in other words, the prevention of mining accidents. Practical implications. Apart from the literature review and questionnaire survey conducted in two underground hard rock mines and one surface coal mine, owned and operated by Kosovo authorities, this research attempts to examine sources, influencing factors associated with near-miss incidents, as well as to analyse the best practices of NMI reporting in order to enhance mining safety management in mining sector.

Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis

Methane is one of the most dangerous gases encountered in the mining industry. During mining operations, methane can be broadly classified into three states: mining excavation, stoppage safety check, and abnormal methane concentration, which is usually a precursor to a gas accident, such as a coal and gas outburst. Consequently, it is vital to accurately predict methane concentrations. Herein, we apply three deep learning methods—a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU)—to the problem of methane concentration prediction and evaluate their efficacy. In addition, we propose a novel prediction method that combines classical time series analysis with these deep learning models. The results revealed that GRU has the least root mean square error (RMSE) loss of the three models. The RMSE loss can be further reduced by approximately 35% by using the proposed combined approach, and the models are also less likely to result in overfitting. Therefore, combining deep learning methods with classical time series analysis can provide accurate methane concentration prediction and improve mining safety.

Early Warning Method for Coal and Gas Outburst Prediction Based on Indexes of Deep Learning Model and Statistical Model

The early warning models for coal and gas outburst have become increasingly more important and have gained more attention in the mining industry in an effort to further improve mine safety. In the warning process, however, the theoretical models do not always work in a timely manner largely due to the delayed capture of the real time parameters. Based on the evolving mechanism of gas outburst, the gas emission is considered a dominant factor in this work because its data is attainable in real time and clearly characterizes the entire outburst process. In order to characterize and distinguish the variation of the gas emission during an outburst and normal mining activity, a total of four statistical methods were employed to quantify the variation of gas emission: the moving average, the deviation ratio, the dispersion ratio, and the fluctuation ratio. Also, the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are also included to demonstrate the accuracy of the deep learning model for predicting the variation of gas emission. Developed from these six indicators, the multi-factor fuzzy comprehensive evaluation model forms the outburst early warning system by calculating the combined index of the difference among the indicators. The accuracy of the early warning system is examined in the case study of the “3.25” gas outburst hazard in Shigang Coal Mine. The results show advantages of the comprehensive evaluation model established from the six characteristic indicators when predicting an outburst.

Methane emissions and dynamics from adjacent coal seams in a high permeability multi-seam mining environment

Reliable prediction of methane emission sources and dynamics from adjacent strata of longwall coal mining is critical for developing efficient methane capture designs to maximise emission reduction and improve mining safety. This paper presents an integrated field monitoring study that has resulted in many new insights into mine methane emissions and dynamics in a relatively high permeability, multi-seam mining environment. The field study comprised of measuring gas content pre- and post-mining at three different locations across the longwall panel, and continuous monitoring of seam pore pressure using fibre optic piezometers.The gas content profile pre- and post-mining enabled delineation of the gas emission zone. The lateral extent of the gas emission zone was found to be far beyond the goaf vicinity, which contradicts the baseline assumption of the conventional Flugge model used at the mine. Significant discrepancies were also found in the degree of gas emission between the measurement and the predictive modelling. For instance, the coal seam 62 m below the mining seam had no discernible gas released, whereas the Flugge model predicts a 24% gas emission. The causes underlying these discrepancies were analysed, and the methods to improve gas emission prediction were discussed. The monitored pore pressure change in different seams validates the gas content measurement results, and is also invaluable for characterising gas emission dynamics from seams in the roof and floor. Monitoring seam pore pressure change can therefore be an effective means to guide methane capture planning and design.

Development of integrated roof monitoring system and continuous miner working index for improving mine safety

Development of integrated roof monitoring system and continuous miner working index for improving mine safety

FRACTAL TREELIKE FRACTURE NETWORK MODEL FOR HYDRAULICALLY AND MECHANICALLY INDUCED DYNAMIC CHANGES IN THE NON-DARCY COEFFICIENT DURING THE PROCESS OF MINE WATER INRUSH FROM COLLAPSED COLUMNS

An accurate and reliable mathematical model for water inrush can help ensure operational safety in coal mines. In this study, a numerical model for water inrush caused by collapsed columns that couples mechanical rock deformation with flow in porous media based on a fractal treelike fracture network was developed. In particular, the proposed model considers collapsed columns and mining damage zones as fractal dual-porosity media. The model incorporates Darcy’s law, the Brinkman equation, and the Navier–Stokes equation to govern the groundwater flow. The Brinkman equation was adopted to simulate the groundwater flow in collapsed columns, for which the non-Darcy coefficient can be derived based on damage criteria for the fracture zone. To simulate the dynamics of water inrush in collapsed columns, the finite element method was used to solve the governing equations for the groundwater flow, solid mechanics, and rock damage. The proposed model was validated against two sets of field data, and the results showed that dynamic changes in the non-Darcy coefficient could be used to identify the occurrence of water inrush, which can be employed to improve mining safety.

Investigation and analysis of fatal accidents reporting practices in the Punjab province of Pakistan and remedial measures

Mining is one of the most hazardous industries as mineworkers are exposed to unsafe working conditions. One of the key steps in improving mine safety is through study and analysis of mine accident data. In recent years researchers have rigorously worked towards the analysis of mine accidents in Pakistan, but very little work if none is conducted to investigate and analyze the mine accidents reporting system in Pakistan. This research focuses towards analyzing the occurrence of surface mining accidents in Punjab from year 2004–2018 during which 377 fatalities occurred. Intensive literature review is done to understand the current body of knowledge in mine accidents in Pakistan. This work identifies that mining fatalities are increasing with each year unlike global trends with a pattern of consistent occurrence due to primitive mining practices, negligence of workers, lack of resources, slackness on part of regulatory bodies and mine management and worse accident reporting mechanism. The work finds that only fatal accidents or extremely serious accidents are reported to regulatory body. The work identifies the limitations of accident reporting tool (Form IX) as being outdated and generic that abstains from binding cause of accident to any of the stakeholder to be held accountable along with no centralized system to investigate indigenous solutions for improving safety. This work presents a new accident reporting tool that will help improving the overall safety of mining activity by binding occurrence of accidents with responsible stake holders. The approach to make stake holders accountable will result in overall ownership of mine safety in the Pakistan. The future work can investigate the proposed work and quantify the outcomes resulting from the opting the proposed system.

An Experimental Study on Preparation of Reconstituted Tectonic Coal Samples: Optimization of Preparation Conditions

The uniquely soft and fragile nature of tectonic coal makes it difficult to obtain core samples suitable for laboratory experimentation. Preparation of reconstituted tectonic coal (RTC) samples generally adopts the secondary forming method. Reliable coal samples are needed to obtain credible permeability and mechanical parameters that can guide Coalbed Methane (CBM) extraction and improve mining safety. In this study, the compaction mechanism of coal particles is analyzed based on the Kawakita model, and optimal sample preparation conditions are systemically investigated, particularly particle size and particle size distribution, forming pressure, and moisture content. The density and P-wave velocity of coal samples were used to test whether the RTC samples were realistic. Finally, the mechanical properties and deformation characteristics of the RTC samples were determined. The results indicate that RTC samples prepared for laboratory testing of mechanical properties require (1) the original particle size of the tectonic coal to be retained as much as possible; (2) a forming pressure that compacts the sample similar to the original tectonic coal; and (3) an optimum moisture content.

Study on Gas Emission Characteristics in Lower Stress-relief Coal Seam for Improving Mining Safety of Extremely-close Upper Seam: Implementation of Coupled Permeability Model

In two extremely close coal seams, when the upper coal seam being first mined, large amount of gas normally emits into the mining area from lower stress-relief seam. This is a significant safety issue which may lead to gas-related incidents. In this paper, a coupled permeability model was developed. Then COMSOL Multiphysics numerical simulation was conducted to study multi-factor coupling gas emission flow characteristics. Furthermore, effects of coal stress, gas pressure and initial coal permeability on gas pressure change in lower stress-relief coal seam was analysed. The numerical model established is based on a real case of Ji15 coal seam and Ji16-17 coal seam in Pingdingshan No. 8 coalmine, China. Results show that after upper seam (Ji15 coal seam) being mined, the closer to bottom of working face, the lower the gas pressure because of increasing gas emission performance. As the distance from floor of working face increases, gas pressure becomes closer to its original value. Meanwhile, due to gas emission, gas pressure drop in lower stress-relief coal seam (Ji16-17 coal seam) is symmetrical from the middle of mining face to two ends. Along the parallel direction of mining face, gas pressure curve is approximately in “W”-shape, with gas pressure at both ends tends to be original. From two working face ends to the middle, gas pressure first decreases before increasing, with a maximum decrease of 9.36%. Furthermore, coal stress, gas pressure and initial coal permeability are all positively correlated with gas pressure decrease. Significance of initial coal permeability’s effect is higher than the other two factors. Above results could provide references on drainage borehole design for related scholars and on-site engineers, which is of significance for improving mining safety.

Analysis of Mine Safety Performance Evaluation Law Based on Matter-Element Analysis and Rough Set of Concept Lattice Reduction

The mine safety performance evaluation and its law were taken as research objects; the human factor, mechanical equipment, management system, environment and technology were taken as mine safety state indexes; accident loss and disabling incident frequency rate as mine safety performance evaluation indexes. The classification standard of safety performance was determined according to the range of influence index and evaluation index, and then the influence index value of safety performance was discretized. According to the classification standard, the classical domain and node domain of safety performance evaluation index classification were determined. Combined with the weight of each safety performance evaluation index, the correlation degree of each mine safety performance evaluation was calculated, and then the mine safety performance was evaluated, and the mine safety performance evaluation decision system was constructed. Based on the dominance-based rough set, the evaluation law of mine safety performance evaluation decision system was analyzed. To generate more reliable laws, an efficient concept lattice reduction method was introduced to reduce the attributes of decision system. The example shows that when the concept lattice is used for reduction, there are 84 concepts in the Hasse diagram, and 24 of them can be identified. The decision system has 8 conditional attributes. And 255 power sets can be obtained under 8 conditional attributes. After eliminating the defect attribute, there are 148 reducible sets, among which there are 4 optimal reducible sets with two conditional attributes. The results show that the proposed method can be used to analyze the law of mine safety performance, so as to improve mine safety work.