Coal Mining Operations Research Articles

Hazard and pollution characteristics of rock-containing dust in coal mine tunneling faces based on experimental and numerical study

The rock-containing dust poses a serious threat to the safe and efficient operation of coal mine tunneling faces (CMTFs). This study investigates the hazard and pollution characteristics of rock-containing dust in CMTFs by testing wettability, lithology surface functional groups, and particle size distribution of dusts with varying rock content. Furthermore, the spatial and temporal evolution of rock-containing dusts is explored through CFD simulation and the effects of rock-containing dust on the dedusting characteristics of water spray are analyzed. The results show that the crystallinity exhibits an exponential growth trend as the mudstone content increases, and the respirable dusts < 2.5μm and < 7μm increase by 15.04% and 24.28%, respectively. The rock-containing dust reaches the end of the simulated tunnel in just 50seconds. On both the driver’s and pedestrian’s sides, the dust concentration shows an approximate S-shaped increase over time. Under the condition that only traditional water spraying measures are implemented, the concentration of respirable rock-containing dust will be approximately 1.2 to 1.5 times higher than the concentration of respirable coal dust. The results have provided valuable insights into the hazard and pollution characteristics of rock-containing dust in CMTFs, which are crucial for guiding dust disaster management in coal mines.

Numerical analysis of stress distribution due to upper protective layer mining and its impact on gas extraction

Pressure-relief gas extraction through the floor directional long boreholes is an advanced and effective gas control technology for the upper protective layer of contiguous coal seams. This study systematically analyzed the effects of mining activities on stress distribution in the underlying strata and developed an analytical equation to calculate the permeability distribution of the protected layer under mining-induced conditions. The results indicate that the effective extraction radius of directional long boreholes increased by 186% as the mining distance of the upper protective layer extended from 50 to 150 m. Furthermore, increasing the borehole diameter from 89 to 153 mm led to a 14.8% improvement in gas extraction efficiency, while raising the negative pressure from 15 to 35 kPa resulted in a 19.6% increase in the effective extraction radius. These findings provide valuable guidance for optimizing borehole design parameters and gas extraction efficiency, ensuring safer and more effective gas control in coal mining operations.

Study on the Identification Method of Planar Geological Structures in Coal Mines Using Ground-Penetrating Radar

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations.

Study on the evolution rules of coal deformation and failure characteristics caused by multiple mining disturbances

During coal mining operations, the coal will be deformed and damaged due to multiple mining disturbances (MMD), often resulting in disasters, like rock burst. To understand the evolution rules of coal deformation under MMD and its final fracture characteristics after impact dynamic load loading, reduce the adverse effects of mining disturbances, and improve disaster prevention and control capabilities, quasi-static uniaxial cyclic loading-unloading (L-U) and dynamic axial compression tests were conducted on large-sized coal-like samples. During the tests, three-dimensional (3D) laser scanning and acoustic emission (AE) monitoring technology were utilized to accurately capture the full-field deformation and AE response data, facilitating a systematic analysis of deformation and fracture characteristics. The results show that: (1) Under the cyclic L-U effect induced by MMD, each loading cycle causes compression deformation with partial recovery during unloading, presenting an overall “wavy” variation trend. (2) The maximum load is the most critical factor affecting the damaged coal deformation, with smaller load resulting in less overall sample deformation. (3) After the impact dynamic loading, the damaged samples suffered large-scale impact splitting failure, with the compressive-shear layer failure mainly occurred inside the holes. (4) Lower loading during cyclic L-U process correlate with reduced damage degree, and smaller debris particles with a higher fractal dimension when impact failure occurs, indicating a more severe impact failure. (5) With multiple cycles of L-U, the cracks inside the sample gradually extend and expand from around the hole to the outside. The greater the load and the number of cycles, the more serious the crack damage will be. (6) In the practical mining process, it is crucial to reinforce roadway interiors while minimizing low-loading cyclic disturbances induced by MMD. The study has obtained the deformation evolution rules and failure characteristics of coal under MMD, providing a theoretical basis for the prevention and control of corresponding engineering disasters.

Applying similar modeling to study vehicle exhaust transport in a confined mining space

To scientifically and effectively manage exhaust pollution in underground confined spaces and reduce hazards from personnel exhaust exposure, a systematic study of the distribution characteristics of diesel locomotive exhaust emissions in mines is needed. Aiming at large underground coal mining operations, an experimental platform for the physical simulation of vehicle exhaust transport in restricted spaces is designed and created. The Hongliulin Coal Mine in Shaanxi Province, China, is used as a prototype, and the platform consists of a coal mining face system, ventilation system, exhaust emission system, monitoring and surveillance system, and control platform. The experimental platform with carbon dioxide simulation of diesel exhaust pollution, in the role of different wind speed, the concentration of carbon dioxide is a downward trend, the wind speed is in the low wind speed on the 0.2 m of the concentration difference of 25.75%; the experimental platform exhaust transport process presents a clear “three-zone” change rule; experimental platform carbon dioxide concentration and the field nitrogen dioxide concentration change trend is consistent. The platform functions and features can provide guidance for mine exhaust management and exhaust exposure hazard prevention and control.

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.

Signal recognition and prediction system based on random forest model

To accurately predict the occurrence of rock bursts during deep coal mining and ensure the safe and efficient operation of coal mines, a signal recognition and prediction system based on the Random Forest model is proposed. This system utilizes electromagnetic radiation (EMR) and acoustic emission (AE) signal data, employing feature extraction and point biserial correlation analysis to screen out the most relevant feature parameters. A Random Forest binary classification model is constructed to identify interference signals. Subsequently, by introducing new features such as moving average slope and exponentially weighted moving average (EWMA), a time series analysis of precursor feature signals is conducted. A real-time warning model based on the Random Forest algorithm is developed, dynamically calculating the probability of precursor feature signal occurrence by integrating historical data and real-time data changes. This approach improves the accuracy and recall rate of signal recognition and prediction, providing reliable data support for mine safety management.

Effect of Variation of Dragline Bench Height and Cut Width on Performance of Tandem Dragline Operations in Large Opencast Coal Mines in India

Effect of Variation of Dragline Bench Height and Cut Width on Performance of Tandem Dragline Operations in Large Opencast Coal Mines in India

A lightweight object detection approach based on edge computing for mining industry

AbstractCoal Mining enterprises deploy numerous monitoring devices to ensure safe and efficient production using target detection technologies. However, deploying deep detection models on edge devices poses challenges due to high computational loads, impacting detection speed and accuracy. A mining target detection dataset has been created to address these issues, featuring key targets in coal mining scenes such as miners, safety helmets, and coal gangue. A model is proposed to improve real‐time performance for edge mining detection tasks. Detection performance is enhanced by incorporating a Pixel‐wise Normalization Spatial Attention Module (PN‐SAM) into the MobileNet‐v3 bneck structure and replacing the h‐swish activation function with Mish, providing more prosperous gradient information transfer. The proposed model, YOLO‐v4‐LSAM, shows a 3.2% mAP improvement on the VOC2012 dataset and a 2.4% improvement on the mining target dataset compared to YOLO‐v4‐Tiny, demonstrating its effectiveness in mining environments. These enhancements enable more accurate and efficient detection in resource‐constrained edge environments, contributing to safer and more reliable monitoring in coal mining operations.

Numerical Investigation of coupling hazard zone of coal spontaneous combustion and gas in gob for high-gas mines

As coal mining operations continue to deepen, the frequency of coal spontaneous combustion (CSC) and gas explosion disasters in high-gas mines is increasing. Understanding the distribution patterns of CSC and gas compound disaster zones in the gob during the extraction process is crucial for reducing the risks of combustion and explosion. This study focuses on the 201 working face of the Shuanglong Coal Mine, where a multi-physical field coupling model of pressure, temperature, gas seepage, and diffusion was established to investigate the evolution characteristics of compound disaster zones and propose corresponding prevention and control measures. The results demonstrate that the area with the most severe composite hazard is mainly located at the return roadway side, extending to the middle of the gob. With increased ventilation volume and advance rate, the area of the CSC and gas coupling hazard zone expands continuously. By implementing measures such as brattice cloth and nitrogen injection, the areas of spontaneous combustion and CH4 explosion zones were reduced by 53.6 % and 49.5 %, respectively, effectively controlling the extent of the compound disaster zone and eliminating the risk of gas explosions.

Разработка трудноизвлекаемых запасов угля мобильными горнодобывающими комплексами без постоянного присутствия людей в зоне ведения горных работ

Today, millions of tons of coil remain in the subsoil and are written off as losses. These include hard-to-recover reserves unfit for mining with traditional high-performance means of integrated mechanization. One of the all-purpose technical solutions to the problem of developing such reserves can be technologies using robotic mobile mining equipment based on a heading-and-winning machine and a self-propelled car. Flowcharts for coal deposit development have been designed for seams unfit for mining with integrated mechanization means (medium thick seams, thick seams, flat and steeply inclined seams). These include open-pit and underground mining of marginal reserves from the side of the pit wall and limited reserves from the daytime surface performed by mobile mechanization means. During the operation of these means, the following process operations are repeated: coal breaking, loading into a self-propelled car, transportation along a preset trajectory, unloading into a reloader or other transport vehicle, or a warehouse on the bench. These operations can be robotized, which will increase both the safety and efficiency of coal mining. In order to perform coal-mining operations without constant attendance of maintenance staff, the existing mechanization means must be retrofitted with machine vision and a remote control system. Moreover, a robotized control system for the set of equipment must be developed to resolve the following problems. For the heading-and-winning machine: transportation (feed) along a straight line; transportation along the preset curve (formation of a diagonal entry); coal breaking at the preset thickness; coal breaking within coal-rock boundaries or along a preset cross-sectional contour; loading of broken coal into a self-propelled car; and control of the car loading level. For a self-propelled car: transportation along a preset trajectory from the loading point to the unloading point; control and management of the completeness of loading (unloading).

Visual analysis of coal mine safety using CiteSpace V

AbstractTo intuitively and systematically grasp the development status and trend of safety evaluation research in China's coal mining operations, we consulted relevant literature in the fields of “coal mine” and “safety evaluation” collected by China Knowledge Network, and deployed CiteSpace V software to summarize and analyze research pertaining to safety evaluation in China's coal mines from three aspects: research institutions, authors, and hot keywords. With respect to research institutions, the results show that although many researchers have conducted in‐depth evaluations of coal mine safety, most of these studies were independent, and two‐way information exchange and cooperation between research institutions remains scarce. With respect to authors, most cooperation between authors has been limited to team members, and relatively stable research teams have been formed. However, most of these research teams are independent. With respect to hot keywords, the trends of coal mine safety evaluation studies exhibit continuous change, with an overall increase in richness. According to the frequency burst times of important keywords, “entropy weight method” and “mining with pressure” are expected to remain important keywords in future evaluations of coal mine safety in China.

Research on Comprehensive Evaluation Indicators and Methods of World-Class Open-Pit Coal Mines

Since the 20th century, with the rapid development of the global economy, human demand for fossil energy such as coal has been increasing. In the face of fierce competition, the state and various ministries and commissions have put forward the goals and requirements of building a world-class enterprise, and how to become a world-class open-pit coal mine and establish the corresponding evaluation standards has become an urgent scientific problem to be solved. According to the characteristics of open-pit coal mine production and operation, from the perspective of benchmarking world class, the key factors affecting the construction of world-class open-pit coal mines are extracted by using the fuzzy DEMATEL method, and the comprehensive evaluation index system, evaluation standards, and methods of open-pit coal mines are established, which provides benchmarks and guidelines for the construction of world-class open-pit coal mines and improves the construction level of world-class open-pit coal mines.

The Construction and Application of a Digital Coal Seam for Shearer Autonomous Navigation Cutting.

Accurately obtaining the geological characteristic digital model of a coal seam and surrounding rock in front of a fully mechanized mining face is one of the key technologies for automatic and continuous coal mining operation to realize an intelligent unmanned working face. The research on how to establish accurate and reliable coal seam digital models is a hot topic and technical bottleneck in the field of intelligent coal mining. This paper puts forward a construction method and dynamic update mechanism for a digital model of coal seam autonomous cutting by a coal mining machine, and verifies its effectiveness in experiments. Based on the interpolation model of drilling data, a fine coal seam digital model was established according to the results of geological statistical inversion, which overcomes the shortcomings of an insufficient lateral resolution of lithology and physical properties in a traditional geological model and can accurately depict the distribution trend of coal seams. By utilizing the numerical derivation of surrounding rock mining and geological SLAM advanced exploration, the coal seam digital model was modified to achieve a dynamic updating and optimization of the model, providing an accurate geological information guarantee for intelligent unmanned coal mining. Based on the model, it is possible to obtain the boundary and inclination information of the coal seam profile, and provide strategies for adjusting the height of the coal mining machine drum at the current position, achieving precise control of the automatic height adjustment of the coal mining machine.

Integrated assessment for groundwater quality and flood vulnerability in coal mining regions.

Coal mining activities greatly damage water resources, explicitly concerning water quality. The adverse effects of coal mining and potential routes for contaminants to migrate, either through surface water or infiltration, into the groundwater table. Dealing with pollution from coal mining operations is a significant surface water contamination concern. Consequently, surface water resources get contaminated, harming nearby agricultural areas, drinking water sources, and aquatic habitats. Moreover, the percolation process connected with coal mining could alter groundwater quality. Subsurface water sources can get contaminated by toxins generated during mining activities that infiltrate the soil and reach the groundwater table. The aims of this study are the creation of models and the provision of proposals for corrective measures. Twenty-five scenarios were simulated using MODFLOW; according to the percolation percentage and contamination, 35% of the study area, i.e., the middle of the research area, was the most affected. About 38.08% of the area around the mining zones surrounding Margherita is prone to floods. Agricultural areas, known for applying chemical fertilizers, are particularly vulnerable, generating a risk of pollution to surrounding water bodies during flooding. The outputs of this research contribute to identifying and assessing flood-vulnerable regions, enabling focused measures for flood risk reduction, and strengthening water resource management.

Batch Electrocoagulation Process for the Removal of High Colloidal Clay from Open-Cast Coal Mine Water Using Al and Fe Electrodes

Open-cast coal mining operations can produce water with high amounts of total suspended solids (TSS). We tested the use of electrocoagulation for the removal of colloidal clay from mine water. Monopolar batch electrocoagulation was performed at the laboratory scale using Al and Fe electrodes and varying the DC current (0.5, 1, and 2 A) and contact time (15, 30, and 45 min). Aluminum electrode electrocoagulation with a current of 2 A and a contact time of 15 min had the greatest TSS removal efficiency (99%), with concentrations decreasing from 5,400 to 23 mg/L. The greatest removal (98 and 99%, respectively) was obtained using an Al electrode with an electric current of 0.5 and 1 A, with 30 min of contact time. With an Fe electrode, the greatest efficiency was achieved with a current of 2 A and a contact time of 30 min. The TSS removal efficiency reached 98% while the concentration dropped to 66 mg/L, followed by 89% and 95% for the 0.5 A-45 min and 1 A-15 min variations, respectively.

Characteristics and environmental implications of coal fines at Hwange Colliery Company, Zimbabwe: A comprehensive analysis

The study investigates the characteristics and environmental implications of coal fines at Hwange Colliery Company in Zimbabwe, aiming to address gaps in understanding and propose sustainable management strategies. The research encompasses a comprehensive analysis, including particle size distribution, proximate properties, elemental composition of coal fines, water and soil quality analysis. Sample collection and analysis were conducted following standardized procedures, revealing significant variations among sites in terms of particle size distribution and proximate properties. The sieve analysis revealed that around 80 % of the coal fines passed through the 2 mm sieve. Sites A and B exhibited lower moisture content and higher carbon content in contrast to site C, possibly due to variances in disposal duration. With an average gross calorific value of 17.43 MJ/kg, the coal fines showcase notable potential for energy extraction. Elemental analysis uncovered traces of heavy metals like Fe, Co, Cr, Ni, and Cu, posing an environmental risk if not managed appropriately. The water quality analysis revealed generally low turbidity levels in groundwater, with readings ranging from 0.34 to 0.55 NTU. However, elevated sulfate concentrations in some samples, averaging 113.67 mg/l, coupled with traces of nickel at levels up to 0.07 mg/l, warrant continuous monitoring to ensure compliance with safety standards. Soil analysis indicated the presence of heavy metals near coal fines dump sites, with lead levels reaching up to 52.32 mg/L, nickel at 36.17 mg/L, and copper at 53.70 mg/L, impacting soil enzyme activity and agricultural productivity. The study emphasizes the importance of improved waste management practices and sustainable utilization strategies to mitigate environmental risks and enhance resource efficiency in coal mining operations.

Experimental and simulation studies on suppressing gas explosions with diverse atomized liquid droplets: Insights for improved safety in mining

Gas explosions represent a prevalent thermodynamic hazard in coal mining, significantly endangering worker safety and the operation's overall safety. Water mist stands out among various explosion suppression techniques due to its high heat absorption capacity and environmental sustainability, offering a promising avenue for application. This study introduces a custom-built pipeline gas explosion testing apparatus to investigate the suppression effects of different atomized liquid droplets on gas explosions. By collecting and analyzing experimental data from pressure sensors under varying conditions within the pipeline, we conduct a thorough comparison of the explosion suppression characteristics attributed to different atomized droplets. Based on this foundation, the Fluent software was used for numerical simulation research to further analyze the explosion suppression effects of different atomized droplets. Additionally, numerical simulations were conducted to optimize the nozzle arrangement. Our findings reveal that an increase in atomization pressure, leading to smaller droplet sizes, significantly mitigates the impulse of the gas explosion shock wave. This indicates a marked inhibitory effect of atomized droplets on gas explosions, with finer droplets showing enhanced suppression capabilities. The simulation results from the optimized nozzle arrangement can provide valuable guidance for on-site deployment. Through a combination of experimental and simulation data, this study conducts a qualitative and quantitative analysis of the suppression mechanisms offered by different atomized droplets, considering parameters such as explosion impulse, blast energy, and explosion indices. The insights gained provide a theoretical foundation for reducing the ring-breaking effect of gas explosions and enhancing explosion suppression strategies, which are crucial for ensuring the safety of coal mining operations.

Isotope hydrology of the intermontane Elk Valley, British Columbia: an assessment of water resources around coal mining operations

ABSTRACT This study aimed to synthesise and interpret stable isotopic data (δ 2H and δ 18O) from various sources to understand the isotope hydrology around coal mine operations in Elk Valley, B.C., Canada. The data, including precipitation, groundwaters, seeps, and mine rock drains, were used to construct a local meteoric water line (LMWL) for the Elk Valley, evaluate the spatiotemporal isotopic composition of its groundwater, and assess mine seepage and mine rock drain discharge. The study revealed a robust LMWL relation (δ 2H = 7.4 ± 0.2 · δ 18O – 4.3 ± 4.1). The groundwater and seep data indicated a winter season bias and a north–south latitudinal gradient, suggesting rapid near-surface groundwater flow without significant post-precipitation evaporation. Porewater isotope samples from unsaturated mine rock piles (MRPs) showed site-specific evaporation patterns, potentially due to convective air flows or exothermic sulphide oxidation. This research revealed the influence of groundwater and meltwater on rock drain discharge. Based on evaporative mass balance calculations, MRPs seasonally contributed ca. 5 %(December base flow) and 22 % (snowmelt) to drain discharge. The findings underscore the value of stable isotope data collections in the Elk Valley to help better define and quantify the hydrology–hydrogeology, including a better understanding of evaporative conditions in MRPs.

Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach

Disasters caused by mine water inflows significantly threaten the safety of coal mining operations. Deep mining complicates the acquisition of hydrogeological parameters, the mechanics of water inrush, and the prediction of sudden changes in mine water inflow. Traditional models and singular machine learning approaches often fail to accurately forecast abrupt shifts in mine water inflows. This study introduces a novel coupled decomposition-optimization-deep learning model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks. We evaluate three types of mine water inflow forecasting methods: a singular time series prediction model, a decomposition-prediction coupled model, and a decomposition-optimization-prediction coupled model, assessing their ability to capture sudden changes in data trends and their prediction accuracy. Results show that the singular prediction model is optimal with a sliding input step of 3 and a maximum of 400 epochs. Compared to the CEEMDAN-LSTM model, the CEEMDAN-NGO-LSTM model demonstrates superior performance in predicting local extreme shifts in mine water inflow volumes. Specifically, the CEEMDAN-NGO-LSTM model achieves scores of 96.578 in MAE, 1.471% in MAPE, 122.143 in RMSE, and 0.958 in NSE, representing average performance improvements of 44.950% and 19.400% over the LSTM model and CEEMDAN-LSTM model, respectively. Additionally, this model provides the most accurate predictions of mine water inflow volumes over the next five days. Therefore, the decomposition-optimization-prediction coupled model presents a novel technical solution for the safety monitoring of smart mines, offering significant theoretical and practical value for ensuring safe mining operations.