Underground Coal Mines Research Articles

A study on chronic obstructive pulmonary disease and its determinants among underground coal mine workers in Bankola area, Durgapur, West Bengal

Background: Chronic obstructive pulmonary disease is a leading cause of morbidity and mortality worldwide that effectuate an economic and social burden that is both considerable and progressive. Mortality from respiratory disease remains an important occupational hazard among coal miners. Inhalation of coal mine dust is known to cause several respiratory diseases including COPD. The current study aimed to estimate the prevalence of COPD among coal miners and to find out the predictors of the same in Bankola Area, Durgapur, West Bengal. Methods: A cross-sectional study was conducted among 315 coal miners selected by simple random sampling from the list of mines in Bankola Area, Durgapur, West Bengal. The required data was collected by face-to-face interview with the help of a predesigned pretested questionnaire and subsequently clinical examination along with spirometry in an eligible subset of population. Data thus collected had been analysed by SPSS 16.0 version. Results: Prevalence of COPD among mines was found to be 11.7%. Most of the miners showing obstructive pattern, had Grade 2 severity of airflow limitation (43.2%) whereas 8.2% had Grade 4 severity. Multivariate analyses revealed that miners who were smoking for more than 20 years, working in highly dusty areas and those who were working at coal face had higher odds of developing COPD. Conclusions: Addressing substance use as well as improvement in working environment including use of personal protective equipment can holistically lead to better respiratory health of the miners.

Characterization and differences of acoustic signals response of semi-circular red sandstone under combined monotonous and cyclic loadings

After underground coal mining, rocks are often subjected to tensile damage by the interaction of dynamic and static loadings. The process of rock fracture development under dynamic and static loadings will be released in the form of acoustic energy to form an acoustic signal. In addition, the acoustic signals in dynamic loading differ from that in static loading. Therefore, this study conducted three-point bending experiments with continuous dynamic loading and dynamic–static coupling loading on semi-circular red sandstone specimens. The acoustic signals during red sandstone specimens’ tensile damage were monitored in real-time. The results show that red sandstone’s tensile strength and deformation are enhanced under dynamic–static coupling loading. The red sandstone has a more effective acoustic emission hit rate, energy rate, and r during tensile damage under continuous dynamic loading. In dynamic loading, macroscopic fractures are developed in red sandstone, which has few acoustic emission events but releases strong acoustic signals. In static loading, the pores inside the red sandstone are compacted, the rock particles are rearranged, and the tiny fractures are closed, and its acoustic emission events are many but low in energy. In addition, the rock particles in the front area of the static loading fracture are tightly cemented, which increases the difficulty of separating the rock particles in the front area of the fracture under dynamic loading. Then weakening the red sandstone fracture development process and suppressing its acoustic signals. The research results provide more insight into the differences in tensile damage processes in red sandstone under the interaction of dynamic and static loadings.

Evaluation of mining landscape changes with development landscape metrics in the Ostrava-Karviná Mining District (Czech Republic).

The study presents an analysis of changes in the landscape of the Ostrava-Karviná Mining District (in the Czech Republic) covering the period of more than 170years. In the area of interest affected by underground coal mining, both areas affected by changes and land cover preserving areas were identified in the study. A detailed assessment of the landscape changes was enabled by using landscape metrics and indices, namely the development index and total landscape change index. The underlying data were obtained from maps of stable cadastre (from the year 1836) and aerial images of the years 1947, 1971, and 2009. Visual photointerpretation of aerial images and interpretation of the maps of stable cadastre made it possible to create land cover maps according to CORINE Land Cover categories. Obtained information on the representation of individual land cover categories were used to identify and to analyze changes in the landscape affected by hard coal mining.

Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine

Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine

Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces

Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.

A visible light positioning system for coal mine personnel based on convolutional recurrent neural network

Visible Light Positioning (VLP) technology exhibits promising potential for underground coal mine positioning. While numerous studies have explored VLP in indoor environments, attention to VLP systems for underground mines still needs to be improved. In this paper, based on the traditional indoor visible light communication channel model, the effects of unique features on VLP, such as irregular walls, inclined and rotating receiving ends, and coal dust particles in underground mines, are considered and modeled in detail. In order to improve the accuracy and reliability of personnel VLP in complex mine environments, this paper proposes a deep-learning-based VLP system for underground personnel in coal mines, which consists of two LEDs and four photodetectors (PDs), where the four PDs are mounted on miners' helmets, in conjunction with the actual mine environment. Due to environmental factors and noise, there are apparent spatio-temporal characteristics in the received signal strength of the PDs. Therefore, the system adopts a convolutional recurrent neural network (CRNN) for feature extraction. In the experimental phase, the method's average localization error was 11.24 cm, with 90% of the localization errors within 20.4 cm. The experimental results show that the system has high positioning accuracy and meets the requirements of personnel positioning in underground mines.

Research on positioning control strategy of hydraulic support pushing system based on multistage speed control valve

The precise position control of the hydraulic support pushing system in the coal mining face is a key technical support for intelligent coal mining. At present, the hydraulic support pushing system uses the electro-hydraulic directional valve as the control element. However, the electro-hydraulic directional valve has problems such as discrete input values, low switching frequency, delay, inability to adjust flow, and large flow fluctuations during the switching process, which results in relatively low positioning control accuracy of the hydraulic support pushing system. Therefore, this study introduces a multi-stage speed control valve that is suitable for underground coal mine conditions and can achieve flow regulation. At the same time, a segmented control strategy combining Bang-Bang control and online predictive control is proposed. Bang-bang control is used for fast propulsion with large flow rate, large range, and short time. Online predictive control method is used to achieve precise positioning control with small flow rate and small range, thereby solving the problem of low positioning control accuracy caused by the imperfect characteristics of electro-hydraulic directional valves. Finally, this study verified the effectiveness of the proposed scheme through simulation and experiments. The results showed that compared with existing logic positioning control methods based on electro-hydraulic directional valves, the proposed scheme can improve the accuracy of single cylinder positioning control from 62 mm to within 10 mm.

Development of a Conceptual Model for Environmental Monitoring in Underground Coal Mining

Development of a Conceptual Model for Environmental Monitoring in Underground Coal Mining

Research on risk assessment of coal and gas outburst during continuous excavation cycle of coal mine with dynamic probabilistic inference

Coal and gas outbursts are a major dynamic hazard in underground coal mining, and predicting these events is challenging due to their complex mechanisms. This study introduces an advanced multistate dynamic probabilistic system designed to evaluate the risk of outbursts prior to each excavation cycle. Through the development of a Bayesian network (BN), we establish a dynamic model that captures the complex interactions among essential variables. The novel integration of time slices facilitates the continuous assessment across successive mining cycles. This study tackles the challenge of maintaining network parameter reliability in the static prediction conditions of outburst-prone coal mines. Specifically, in order to overcome the asynchronism of all variables testing process, the expert knowledge integration of expectation-maximization parameter learning and fuzzy set theory (FST) is built inside the system. Finally, we present a case study illustrating the application of this system across fifty-one consecutive cycles, with regular updates to variable states, showcasing the enhanced predictive capability of Dynamic Bayesian Networks (DBN) over traditional BNs for ongoing risk evaluation in mining operations. Additionally, by integrating backward diagnosis with sensitivity analysis, our approach simplifies the identification of key risk factors, offering valuable insights for improving outburst prevention measures and ensuring the safety of coal mining activities.

Vision-Based obstacle detection in dangerous region of coal mine driverless rail electric locomotives

In order to solve the problem of inaccurate obstacle detection as well as frequent start-stops caused by oversensitive obstacle detection in existing driverless rail electric locomotives in underground coal mines, the YOLO-Region model is proposed to realize regional obstacle detection. First, the model backbone uses InceptionNeXt block and the developed New Spatial Pyramid Pooling (NSPP) module; the model neck extends the FPN+PAN architecture; the model head uses improved task-specific context decoupling (Impro-TSCODE) head. In addition, repulsion loss is introduced to improve the detection ability of partially occluded targets. The experimental results show that the YOLO-Region achieves competitive detection performance with mAP of 98.0 % and an average detection speed of 94.5 FPS. Second, a vision-based method for defining dangerous region based on pixel coordinate points is developed and integrated into YOLO-Region, which allows the model to detect obstacles only within a specific region, thereby reducing frequent start-stops of driverless electric locomotives.

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.

Development of productivity model of continuous miner operators working in hazardous underground mine environmental conditions

Engaging in underground coal mining involves inherent risks, as miners confront hazardous conditions and unpredictable surroundings. Despite the increasing prevalence of renewable energy, coal continues to be a significant source of power. Extended work hours and exposure to underground conditions can have detrimental effects on miners’ well-being, safety, and efficiency. The competence of continuous miner (CM) operators is pivotal for the overall effectiveness of mining operations. This study undertakes an examination of how the underground environment influences the health and productivity of CM operators through the measurement of real-time data. Moreover, it investigates the economic implications stemming from environmental and physiological factors. Even slight adjustments in conditions have the potential to bolster annual revenue by up to INR 2.66 crores. Additionally, prolonged work hours and exposure may lead to a notable 5.5% decline in productivity levels.

Consolidation performance and mechanism of composite dust suppressant based on graft modification

AbstractTo solve the severe coal dust pollution and dust hazards in underground coal mines, this study utilized graft copolymerization modification technology and compound technology to develop a mining composite consolidation dust suppressant. On this basis, analysis of its consolidation wettability and dust suppression mechanism was conducted through characterization tests and molecular dynamics simulation methods. The results show that polyacrylamide (PAM) had been successfully grafted onto the soybean protein isolate (SPI) molecule, and form a SPI‐PAM complex. After the dust suppressant acted on the coal dust surface, it utilized its wealthy hydrophilic groups, for the adsorption of water molecules and the positive amide group for binding to produce a large area of agglomeration of coal dust particles at the interface, exhibiting good wetting and consolidation into a shell. At the same time, molecular dynamic simulation verified that the diffusivity of water molecules in the dust suppressant‐coal system was 0.30Å2/ps, decreased by 43.3%, and the interaction energy with coal molecules was −200.27 kcal/mol, absolute value increased by 41.35%, which made the dust suppressant molecules more easily adsorb and agglomerate on the surface, demonstrated an excellent solidification and dust suppression effect.

Framework for ergonomic design of control centres in underground coal mines - A Serbian experience

We present a framework for solving the problem of functional compatibility between process control equipment in control centres at underground mines and operator capabilities. The equipment incorporates displays of current ventilation, gas, and fire hazard data, status of alarm and early warning systems, a voice communication subsystem, and facilities for recording and printing of data and reports. The layout of the existing control panel is compared with a recommended design. The basic principles of ergonomic design and arrangement of the controls and indicators, as well as ease of use, type of operations carried out with these controls, and operators' viewing angles are discussed. The objective is to improve the usability of control panels, display boards, and graphic screens so as to enhance the functioning of control centres and optimize control of technological processes in coal mines.

Study on the water transport characteristics during spontaneous imbibition and its impact on gas desorption

During the exploitation of deep coal resources, the gas hazard becomes increasingly significant, severely threatening the high-quality development of the coal industry and the occupational safety and health of miners. Coal seam water injection is underground coal mines' most commonly used dust control measure. Additionally, it is an effective method for preventing localized coal and gas outburst hazards. After injecting water into the coal seam, spontaneous imbibition causes the retained water in the fractures to migrate under capillary action and widely distribute the water within the coal seam, which enhances the wetting degree of the coal by water and the effectiveness of gas prevention. To reveal the migration characteristics of water in the coal during spontaneous imbibition and its impact on gas control, a self-developed gas-bearing coal spontaneous imbibition system was used to conduct isobaric water injection imbibition and gas desorption experiments on gas-bearing coal samples. The results showed that after the water was injected into the coal sample, it migrated from bottom to top under capillary action, resulting in three stages of abrupt, rapid, and gradual changes in the electrical resistivity of different layers of the coal sample over time. The migration distance of water increased with the imbibition time, but the rate of increase gradually decreased. The compaction load increase reduced the coal sample's average pore size, resulting in faster water migration speed. After imbibition, the water content decreased gradually from bottom to top along the axial direction and showed a non-uniform distribution in the radial direction. During imbibition, the water displaced gas within the coal sample, and the amount of gas displaced increased with the imbibition time. Increasing the amount of water injected and the coal sample compacted load can promote water migration within the coal, expanding the wetted area and displacing more gas. After imbibition, due to the displaced gas being released and the water-lock effect, the atmospheric gas desorption amount of the coal sample decreased with an increase in the amount of water injected, following a modified power function relationship. When the amount of water injected into the coal sample was low, increasing the water-injected amount could significantly reduce the gas desorption expansion energy of the coal sample. However, this trend became less apparent as the amount of water injected continued to increase. To improve the effectiveness of coal seam water injection in preventing gas outburst hazards, it is recommended to carry out water injection operations as early as possible to expand the wetted area of the coal seam and to add gas drainage holes to prevent increased coal body outburst tendencies due to local gas pressure rise in the coal seam.

Trace element profile of TSPM with depth within opencast coal mines for risk assessment and their source apportionment

Trace element profile of TSPM with depth within opencast coal mines for risk assessment and their source apportionment

A Novel Risk Assessment Approach for Open-Cast Coal Mines Using Hybrid MCDM Models with Interval Type-2 Fuzzy Sets: A Case Study in Türkiye

A Novel Risk Assessment Approach for Open-Cast Coal Mines Using Hybrid MCDM Models with Interval Type-2 Fuzzy Sets: A Case Study in Türkiye

Response and prediction of ecosystem service values to land use change resulting from underground coal mining in high groundwater table areas: A case study of Jining City, China

Ecosystem services are crucial for human survival and development. Changes in land use can lead to alterations in ecosystem service functions. However, the mechanism of how land use changes resulting from underground coal mining affect ecosystem service remains unclear, especially in areas with high groundwater levels. To address this issue, we studied the spatiotemporal in land use change in Jining City from 2000 to 2020 and employed Markov-PIM (probability integration method)-PLUS (Patch-generate Land Use Simulation) model to simulate land use change and ecosystem service values (ESV) in Jining City by 2030 under four different scenarios. The results indicate that from 2000 to 2020, the area of ecological space increased by 169.99 km2, and the area of living space expanded by 775.28 km2, while the area of production space decreased by 945.27 km2, conversely. These changes led to a 3 million yuan increase in total ESV. From 2020 to 2030, there was a marked decline in ESV under both natural development (ND) and living space preference (LSP) scenarios, with decreases of 0.59 billion yuan and 1.38 billion yuan, respectively. In contrast, in production space preference (PSP) and ecological space preference (ESP) scenarios, ESV increase by 0.22 billion yuan and 0.34 billion yuan. The ESP scenario is an effective strategy to strengthen environmental protection, significantly improve the ESV level of Jining City and meet the development goals of Jining City. Finally, the study concludes with recommendations like comprehensive management, industrial upgrading, and collaborative development for optimizing Jining City's future land use and enhance ESV for urban development.

Audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional network in strong noise environments

Abstract Fault diagnosis of belt conveyors is crucial for coal mine production, but audio-based fault diagnosis in underground coal mines remains challenging due to the strong noise environment. To address this problem, a method for audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional networks in a strong noise environment is proposed. Firstly, the improved beluga whale optimization is designed by introducing the non-linear balance factor and non-linear probability and combining them with the proposed cyclical shock factor to optimize the variational modal decomposition parameters to achieve noise reduction and signal reconstruction. Secondly, an improved adaptive noise reduction convolutional network is developed using an adaptive threshold activation function and an improved loss function to enhance noise robustness and fault diagnosis accuracy. Finally, the proposed method's effectiveness is evaluated in low and strong noise environments, with experimental results demonstrating superior fault diagnosis performance. In low noise environments, the fault diagnosis accuracy is 98.61%, and in strong noise environments, it is 98.96%, outperforming existing fault diagnosis methods.

Study on the change of miners' psychological state under different illuminance levels

The poor lighting environment in the underground coal mine is easy to cause the miners to have psychological irritability, which leads to the occurrence of accidents. In order to reduce the incidence of accidents and ensure the personal safety of miners. In this paper, different illumination levels are used as control variables. With the help of cognitive ability tests commonly used in psychology, accuracy(AC), reaction time(RT)and performance indicators(PI) are selected to describe how different illumination levels affect the psychological state of miners. The results show that: (1) In the two selected cognitive ability test tasks, when the illumination level is very low, the AC is not high and the RT is longer. As the illumination increases, the AC in the cognitive test task becomes higher and the RT becomes faster. The optimal value is reached at 200lux–300lux.At this time, the illumination level increases again, the AC value decreases, and the RT value increases. When the illuminance is lower than 200Lux, the results of the two tasks are not the same, but when 200Lux∼300Lux, the PI values are larger, and then increase the illuminance, the PI value decreases. (2)The regression analysis of three indicators and illuminance level was carried out, and the binary linear regression equation of different illuminance levels and PI/RT was established to monitor the influence of illuminance on the psychological state of miners. (3) Reasonable control of lighting environment can effectively promote the cognitive ability of miners in the work, and minimize the risk of coal mine accidents.