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Search Results (518)

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Keywords = open-pit mining

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21 pages, 5809 KiB  
Article
Characterization and Concentration Prediction of Dust Pollution in Open-Pit Coal Mines
by Guilin Wang, Wei Zhou, Zhiming Wang, Xiang Lu and Yirong Zhang
Atmosphere 2024, 15(12), 1408; https://doi.org/10.3390/atmos15121408 - 22 Nov 2024
Viewed by 275
Abstract
Dust pollution is a major problem formed caused by opencast coal mining, and its prevention is a key prerequisite for the realization of green and climate-friendly mining in open-pit coal mines. In this paper, we conducted the real-time monitoring of dust concentration and [...] Read more.
Dust pollution is a major problem formed caused by opencast coal mining, and its prevention is a key prerequisite for the realization of green and climate-friendly mining in open-pit coal mines. In this paper, we conducted the real-time monitoring of dust concentration and meteorological parameter data in different areas of a large-scale open-pit coal mine in China and used multivariate statistical analysis to study the characteristics of the variation in dust concentration and its influencing factors in operating and non-operating areas. The results showed that there was a significant correlation between TSP, PM10, and PM2.5 in the same area. There was a significant difference in the percentage of PM2.5/PM10 between the operation area and the non-operation area, with particles in the range of 2.5–10 μm dominating close to the operation area and particles in the range of 0–2.5 μm dominating away from the operation area. The production intensity had a greater effect on dust concentration in the operation area, and there was no significant relationship with dust concentration away from the operation area. Wind speed—wind force—wind direction, temperature, and humidity are all correlated with particulate matter. The LSTM model is more suitable for predicting the dust concentration in open-pit coal mines. The results of this study can provide a reference for dust prevention and control in open-pit coal mines. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Mining Areas)
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<p>Location of Haerwusu open-pit coal mine.</p>
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<p>RF algorithm framework.</p>
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<p>Simplified structure of LSTM.</p>
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<p>Trend of daily average dust concentration month by month.</p>
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<p>Trend of hourly average dust concentration month by month.</p>
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<p>Percentage of particulate matter concentration.</p>
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<p>Scatter linear fitting diagram of daily average TSP, PM10, and PM2.5.</p>
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<p>Scatter linear fitting diagram of daily average TSP, PM10, and PM2.5.</p>
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<p>The trend of wind speed and PM2.5.</p>
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<p>Wind rose diagram for different dust concentration levels.</p>
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<p>The trend of temperature and PM2.5.</p>
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<p>The trend of relative humidity and PM2.5.</p>
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<p>The trend of relative humidity and PM2.5.</p>
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<p>The trend of particulate matter concentration and stripping volume.</p>
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<p>PM2.5 concentration predicted value and measured value of RA.</p>
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<p>Effects of different parameters of RF on MAPE and RMSE.</p>
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<p>PM2.5 concentration predicted value and measured value of RF.</p>
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<p>PM2.5 concentration predicted value and measured value of LSTM.</p>
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11 pages, 2066 KiB  
Article
Application of Blast-Pile Image Analysis in a Mine-to-Crusher Model to Minimize Overall Costs in a Large-Scale Open-Pit Mine in Brazil
by Vidal Félix Navarro Torres, Fabiano Veloso Ferreira, Victor Albuquerque de Carvalho, Eltton Veras and Felipe França Sitônio
Mining 2024, 4(4), 983-993; https://doi.org/10.3390/mining4040055 - 22 Nov 2024
Viewed by 495
Abstract
Amazon rainforests have many hidden treasures; thus, a balance between mine activities and the environment must be maintained. In the northern region of Brazil, there is a large diversity of metal ore deposits, the exploitation of which requires innovative and sustainable mining operations. [...] Read more.
Amazon rainforests have many hidden treasures; thus, a balance between mine activities and the environment must be maintained. In the northern region of Brazil, there is a large diversity of metal ore deposits, the exploitation of which requires innovative and sustainable mining operations. Historically, mining operations have caused various environmental issues, such as landscape deterioration, damage to natural structures due to detonations, and soil and water pollution, and have also contributed to CO2 emissions from diesel trucks. Here, to estimate and minimize the operating expenses of a large-scale open-pit iron mine, a mine-to-crusher model was developed. The calibration of the mine-to-crusher model was based on rock fragmentation from the blasting phase through the primary crushing phase from an analysis of pictures of the fragmented pile. A reduction in cost was determined for an optimum 90% passing size (P90). The calibration was performed with technical and economic parameters from 2 years before. For the studied iron ore mine site, an optimum P90 value between 0.29 and 0.31 m was determined. Full article
(This article belongs to the Special Issue Feature Papers in Sustainable Mining Engineering 2024)
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<p>Mining cost distribution.</p>
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<p>3D blasting fragmentation analysis.</p>
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<p>Correlation between the P90 (cm) value from blasting and the dig/excavation time (s) for the loading process by lithology: (<b>a</b>) Friable lithologies and (<b>b</b>) compact lithologies.</p>
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<p>Unitary operating costs by P90 value for (<b>a</b>) friable lithologies with an average truck capacity of 240 t; (<b>b</b>) compact lithologies with an average truck capacity of 240 t; (<b>c</b>) friable lithologies with an average truck capacity of 350 t; and (<b>d</b>) compact lithologies with an average truck capacity of 350 t.</p>
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22 pages, 12893 KiB  
Article
Research on Visual–Inertial Measurement Unit Fusion Simultaneous Localization and Mapping Algorithm for Complex Terrain in Open-Pit Mines
by Yuanbin Xiao, Wubin Xu, Bing Li, Hanwen Zhang, Bo Xu and Weixin Zhou
Sensors 2024, 24(22), 7360; https://doi.org/10.3390/s24227360 - 18 Nov 2024
Viewed by 524
Abstract
As mining technology advances, intelligent robots in open-pit mining require precise localization and digital maps. Nonetheless, significant pitch variations, uneven highways, and rocky surfaces with minimal texture present substantial challenges to the precision of feature extraction and positioning in traditional visual SLAM systems, [...] Read more.
As mining technology advances, intelligent robots in open-pit mining require precise localization and digital maps. Nonetheless, significant pitch variations, uneven highways, and rocky surfaces with minimal texture present substantial challenges to the precision of feature extraction and positioning in traditional visual SLAM systems, owing to the intricate terrain features of open-pit mines. This study proposes an improved SLAM technique that integrates visual and Inertial Measurement Unit (IMU) data to address these challenges. The method incorporates a point–line feature fusion matching strategy to enhance the quality and stability of line feature extraction. It integrates an enhanced Line Segment Detection (LSD) algorithm with short segment culling and approximate line merging techniques. The combination of IMU pre-integration and visual feature restrictions is executed inside a tightly coupled visual–inertial framework utilizing a sliding window approach for back-end optimization, enhancing system robustness and precision. Experimental results demonstrate that the suggested method improves RMSE accuracy by 36.62% and 26.88% on the MH and VR sequences of the EuRoC dataset, respectively, compared to ORB-SLAM3. The improved SLAM system significantly reduces trajectory drift in the simulated open-pit mining tests, improving localization accuracy by 40.62% and 61.32%. The results indicate that the proposed method demonstrates significance. Full article
(This article belongs to the Section Sensors and Robotics)
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<p>The system framework diagram. This procedure encompasses data input, front-end visual–inertial odometry, closed-loop detection, back-end optimization, and mapping; The red box in the data input section represents the sparse textured slope.</p>
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<p>An example diagram of the line feature extraction optimization method. The efficacy of line segment identification is enhanced by implementing short line elimination and approximate line segment amalgamation procedures.</p>
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<p>(<b>a</b>) Flowchart of improved LSD line feature detection algorithm; (<b>b</b>) schematic diagram of similar line feature merging.</p>
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<p>Visual observation model and IMU schematic diagram. The IMU data must be integrated and calculated in discrete time due to the fact that its data acquisition frequency is significantly higher than that of the camera. Consequently, a unified data format is necessary to ensure close coupling of the data. This diagram uses hollow circles, hollow triangles, green stars, and black squares to represent image frames, keyframes, IMU data, and the pre-integration process.</p>
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<p>Marginalization model. The relationship model between the camera and the landmark locations during the marginalization process.</p>
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<p>Histogram for the performance comparison of the line feature extraction algorithm: (<b>a</b>) the average time required to derive line features; (<b>b</b>) the average number of line feature extractions.</p>
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<p>Line feature extraction algorithm performance comparison: (<b>a</b>) the LSD algorithm’s effect on line feature extraction; (<b>b</b>) the enhanced LSD method. Utilizing short segment elimination and approximate line merging techniques markedly eliminates redundant short line features while preserving the longer line segments essential for localization precision. The red box highlights the comparison section between the two images, with the green dots and lines representing the extracted point and line features from the images, respectively.</p>
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<p>Histogram of absolute trajectory error. The histogram illustrates that, in the MH sequence, the absolute trajectory error of the enhanced algorithm is less than that of other algorithms, whereas, in the VR sequence, the enhanced algorithm performs comparably to or better than the perfect algorithm.</p>
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<p>Analysis results of trajectory error comparison: (<b>a</b>) comparison of the trajectory for Sequence MH_04_difficult; (<b>b</b>) comparison of difficult trajectories in Sequence V2_03. The black boxes and red arrows in the figure are used to enlarge key areas and mark trajectory deviations, highlighting the accuracy differences among different algorithms in these regions.</p>
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<p>Results of absolute pose inaccuracy for each series: (<b>a</b>) Sequence MH_04_difficult; (<b>b</b>) Sequence MH_05_difficult; (<b>c</b>) Sequence V1_02_medium; (<b>d</b>) Sequence V1_03_difficult. The color-coded line represents varying levels of Absolute Pose Error (APE) along the trajectory, with red indicating higher error and blue indicating lower error, highlighting accuracy differences across segments.</p>
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<p>Three-dimensional point cloud maps: (<b>a</b>) Sequence MH_05_difficult; (<b>b</b>) Sequence V1_03_difficult. The figure shows a 3D mapping visualization where the green lines represent the estimated trajectory, red points indicate mapped features, and black points show additional environmental points.</p>
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<p>Experimental mining intelligent robot platform: (<b>a</b>) left view; (<b>b</b>) front view.</p>
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<p>Scene 1: circular open-pit excavation: (<b>a</b>) real-world scene; (<b>b</b>) diagram of movement trajectory. In (<b>a</b>), the red arrows represent the motion trajectory of the mapping robot. In (<b>b</b>), points A, B, and C represent key checkpoints along the closed-loop path.</p>
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<p>Comparison of trajectory errors in Scenario 1.</p>
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<p>Analysis of experimental outcomes in Scenario 1: (<b>a</b>) comparison of 2D plane trajectories; (<b>b</b>) absolute trajectory error of data series.</p>
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<p>Scene 2: Uneven road conditions in an open-pit mine: (<b>a</b>) real-world scene; (<b>b</b>) diagram of movement trajectory. In (<b>a</b>), the red arrows represent the motion trajectory of the mapping robot. In (<b>b</b>), points A, B, and C represent key checkpoints along the path.</p>
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<p>Comparison of trajectory errors in Scenario 2.</p>
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28 pages, 5430 KiB  
Article
Energy Consumption and Fume Analysis: A Comparative Analysis of the Blasting Technique and Mechanical Excavation in a Polish Gypsum Open-Pit Mine
by Andrzej Biessikirski, Przemysław Bodziony and Michał Dworzak
Energies 2024, 17(22), 5662; https://doi.org/10.3390/en17225662 - 13 Nov 2024
Viewed by 355
Abstract
This article presents a comparative assessment of energy consumption and fume emissions such as NOx, CO2, and CO associated with the excavation of a specified gypsum volume using two mining methods (blasting and mechanical extraction). The analysis was carried out based [...] Read more.
This article presents a comparative assessment of energy consumption and fume emissions such as NOx, CO2, and CO associated with the excavation of a specified gypsum volume using two mining methods (blasting and mechanical extraction). The analysis was carried out based on a case study gypsum open-pit mine in Poland where both extraction methods are applied. The findings indicate that, for the same output volume, blasting operations require significantly less energy (ranging from 1298.12 MJ to 1462.22 MJ) compared to mechanical excavation (86,654.15 MJ). Furthermore, a substantial portion of the energy in blasting operations is attributed to explosive loading and drilling (970.95 MJ). Conversely, mechanical mining results in higher fume emissions compared to blasting. However, during mechanical extraction, the fumes are dispersed over a prolonged period of 275 h, whereas blasting achieves the same gypsum volume extraction in approximately 7.5 h. The prediction model suggests that, based on the obtained data, overall gypsum extraction will decline unless new operational levels are developed or the mine is expanded. This reduction in gypsum extraction will be accompanied by a corresponding decrease in energy consumption and emission of fumes. Full article
(This article belongs to the Special Issue Energy Consumption at Production Stages in Mining)
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<p>Open-pit mine’s aerial view.</p>
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<p>An overview of the deposit quality model.</p>
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<p>Blasting works at open-pit mine.</p>
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<p>Mechanical extraction at the open-pit mine.</p>
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<p>Fumes emitted from mechanical equipment during one hour of operation time: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, and (<b>c</b>) NO<sub>x</sub>.</p>
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<p>Total energy consumption of blasting works where the effective energy of detonation is (<b>a</b>) 20% and (<b>b</b>) 30%.</p>
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<p>Total energy consumption of blasting works where the effective energy of detonation is (<b>a</b>) 20% and (<b>b</b>) 30%.</p>
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<p>Fume volume comparison of ANFO and TNT fumes.</p>
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<p>Total fumes emitted from all blasting operations: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) NO<sub>x</sub>, and (<b>d</b>) overall.</p>
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<p>Total fumes emitted from all blasting operations: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) NO<sub>x</sub>, and (<b>d</b>) overall.</p>
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<p>Energy consumption during mechanical extraction.</p>
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<p>Total fumes emitted from all mechanical extraction operations: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) NO<sub>x</sub>, and (<b>d</b>) overall.</p>
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<p>Total fumes emitted from all mechanical extraction operations: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) NO<sub>x</sub>, and (<b>d</b>) overall.</p>
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<p>Relation of energy intensities and effective productivities of the mining process using blasting works and mechanical extracting.</p>
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<p>Prediction model for overall gypsum extraction.</p>
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<p>Prediction model of overall energy consumption.</p>
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<p>Prediction models for the emission of (<b>a</b>) CO<sub>2</sub>, (<b>b</b>) CO, and (<b>c</b>) NO<sub>x</sub>.</p>
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<p>Prediction models for the emission of (<b>a</b>) CO<sub>2</sub>, (<b>b</b>) CO, and (<b>c</b>) NO<sub>x</sub>.</p>
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16 pages, 5480 KiB  
Article
A Correlation Relating the Residual Strength Parameters to the Proportions of Clay Fractions and Plasticity Characteristics of Overburden Sediments from the Open-Pit Mine Drmno
by Stevan Ćorluka, Dragoslav Rakić, Nikola Živanović, Ksenija Djoković and Tina Đurić
Appl. Sci. 2024, 14(22), 10325; https://doi.org/10.3390/app142210325 - 10 Nov 2024
Viewed by 481
Abstract
One of the prerequisites for the safe exploitation of surface mines is the stability of the working and final slopes of the mine. In order to ensure this, it is necessary to carry out detailed field and laboratory geomechanical tests of the soil [...] Read more.
One of the prerequisites for the safe exploitation of surface mines is the stability of the working and final slopes of the mine. In order to ensure this, it is necessary to carry out detailed field and laboratory geomechanical tests of the soil and, based on the obtained results, make calculations related to stability analyses. The results obtained in this way are used for dimensioning the slope of exploitation slopes (excavation). Landslides occur when the ultimate shear strength is reached, and therefore, the adequate definition of shear strength parameters is one of the essential prerequisites for successfully solving the stability problem. Unlike earlier tests in Serbia, when the residual shear strength parameters were determined based on the usual conventional methods (direct shear apparatus, triaxial apparatus), this time, in addition to the direct shear apparatus, a ring shear apparatus was also chosen for testing. The paper shows the method of determining the residual shear strength parameters of high plasticity gray clays and siltstones of roof sediments from open pit mine Drmno, using direct and ring shear apparatus. The results show that the residual angle of internal friction for gray clays obtained with the ring shear apparatus is 9.9–10.8°, and for the siltstone, it is 11.8–12.9°, both of which are lower than the values obtained with the direct shear apparatus. In addition, correlations between the residual parameters of soil shear resistance and some physical indicators (plasticity index, clay content) are provided, showing high correlation coefficients. The proposed correlations should be used only when time and financial constraints prevent the execution of actual tests to determine residual shear strength, as concrete experimental procedures provide a much more reliable assessment of the residual strength properties of the soil. Full article
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<p>(<b>a</b>). Position of Serbia in relation to Europe. (<b>b</b>). Position of the Kostolac area in relation to Serbia. (<b>c</b>). Position of the Drmno deposit in relation to Kostolac.</p>
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<p>Schematic representation of the final western slope of the open pit mine: 1: humus; 2: sand; 3: gravel; 4: siltstone; 5: second coal layer; 6: third coal layer; 7: gray clay.</p>
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<p>(<b>a</b>) Bromhead’s ring shear apparatus. (<b>b</b>) Three-dimensional model of the ring shear apparatus (Source: manufacturer’s manual). Legend: 1, 2—horizontal force measurement cell, 3—frame for transmitting vertical load, 4—touchscreen display, 5—vertical force measurement cell.</p>
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<p>(<b>a</b>) Matest direct shear apparatus. (<b>b</b>) Two-dimensional model of the direct shear apparatus [<a href="#B34-applsci-14-10325" class="html-bibr">34</a>] (Reprinted/adapted with permission from Ref. [Karimpour F. et al., 2015]) Legend: 1—frame of the apparatus, 2—system for transmitting horizontal load, 3—digital control unit, 4—device for measuring horizontal linear displacements, 5—device for measuring horizontal load, 6—device for measuring vertical linear displacements, 7—system for transmitting vertical load, 8—plate for transmitting uniform vertical load, 9, 10—shear box.</p>
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<p>Appearance of the gray clay samples after testing in the ring shear apparatus (<b>left</b>) and direct shear apparatus (<b>right</b>).</p>
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<p>Particle size distribution graph.</p>
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<p>Identification and classification indicators of the tested samples.</p>
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<p>Shear stress versus rotation angle for (<b>a</b>) siltstone (<b>b</b>) gray clay.</p>
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<p>Shear stress versus horizontal displacement for (<b>a</b>) siltstone (<b>b</b>) gray clay.</p>
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<p>Values of the residual shear strength parameters depending on the testing method.</p>
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<p>Correlation values of the residual angle of internal friction obtained from the DS and RS apparatuses.</p>
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<p>Correlation values between the plasticity index (Ip) and the residual angle of internal friction obtained from the DS (<b>right</b>) and RS (<b>left</b>) apparatuses.</p>
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<p>Correlation values between the percentage of fractions less than 0.002 mm and the residual angle of internal friction obtained from the DS (<b>right</b>) and RS (<b>left</b>) apparatuses.</p>
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24 pages, 16222 KiB  
Article
Monitoring and Analysis of Surface Deformation in the Buzhaoba Open-Pit Mine Based on SBAS-InSAR Technology
by Yu Zheng, Zhifang Zhao, Min Zeng, Dingyi Zhou, Xiaotong Su and Dingshuai Liu
Remote Sens. 2024, 16(22), 4177; https://doi.org/10.3390/rs16224177 - 8 Nov 2024
Viewed by 530
Abstract
The Buzhaoba open-pit mine is an important lignite production base in Yunnan Province, China. As mining activities have continued to progress, varying degrees of deformation have occurred in different areas of the Buzhaoba open-pit mine, threatening normal coal production and mine safety. To [...] Read more.
The Buzhaoba open-pit mine is an important lignite production base in Yunnan Province, China. As mining activities have continued to progress, varying degrees of deformation have occurred in different areas of the Buzhaoba open-pit mine, threatening normal coal production and mine safety. To comprehensively investigate the characteristics of surface deformation and its influencing factors at the Buzhaoba open-pit mine, this study employed the following methods: first, the SBAS-InSAR technique was used to process 86 Sentinel-1A ascending and descending orbit remote sensing images from 2020 to 2023, obtaining LOS surface deformation information for the mining area; second, leveling observation data were used to validate the accuracy of the SBAS-InSAR results, and based on the principle of two-dimensional deformation decomposition, the east–west and vertical surface deformation information of the mining area was obtained; finally, the temporal variation characteristics and influencing factors of the Buzhaoba open-pit mine were analyzed. The study results indicate that (1) the maximum LOS surface deformation rates in the ascending and descending orbits of the mining area were −42.1 mm/a and −114.0 mm/a, respectively; (2) the correlation coefficient between the SBAS-InSAR monitoring results and the leveling observation results was 0.938, confirming the reliability of the SBAS-InSAR monitoring results; (3) the maximum east–west and vertical deformation rates obtained from the two-dimensional deformation decomposition were −103.4 mm/a and −189.2 mm/a, respectively, with the surface deformation in the east–west direction being more pronounced; (4) internal factors such as stratigraphic lithology and geological structures, as well as atmospheric rainfall, have a certain degree of influence on the surface deformation of the Buzhaoba open-pit mine. Therefore, the research results of this study can provide important data support and theoretical references for safety management and disaster prevention in the mining area. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar Interferometry Symposium 2024)
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<p>The location of the study area is depicted as follows: (<b>a</b>) shows the location of the study area within Honghe Prefecture, Yunnan Province, China; (<b>b</b>) indicates the position of the study area within Kaiyuan City, Honghe Prefecture; (<b>c</b>) delimits the boundaries of the Buzhaoba open-pit mine; and (<b>d</b>) displays a photograph of the Buzhaoba open-pit mine.</p>
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<p>Geological map of the mining area.</p>
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<p>The Sentinel-1A images of the Buzhaoba open-pit mine used for ascending and descending orbits are marked in black in the figure.</p>
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<p>Technical flowchart.</p>
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<p>Partial interferograms of the study area: (<b>a</b>) represents ascending orbit; (<b>b</b>) represents descending orbit.</p>
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<p>Imaging geometry of ascending and descending SAR tracks: (<b>a</b>) horizontal view; (<b>b</b>) vertical view.</p>
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<p>LOS deformation rate at Buzhaoba open-pit mine: (<b>a</b>) represents the deformation rate for ascending tracks; (<b>b</b>) represents the deformation rate for descending tracks.</p>
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<p>Schematic diagram of leveling observation point locations.</p>
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<p>Cumulative displacement trend of SBAS-InSAR monitoring results compared to leveling observations.</p>
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<p>Deformation rates in the east–west and vertical directions at the Buzhaoba open-pit mine.</p>
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<p>Cumulative displacement trend of SBAS-InSAR monitoring results compared to leveling observations.</p>
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<p>Schematic diagram of the regions.</p>
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<p>Frequency distribution of deformation rates in different areas of the Buzhaoba open-pit mine: (<b>a</b>) represents the northwest wall; (<b>b</b>) represents the southwest wall; (<b>c</b>) represents the bottom of the pit; (<b>d</b>) represents the northeast wall; and (<b>e</b>) represents the southeast wall.</p>
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<p>Frequency distribution of deformation rates in different areas of the Buzhaoba open-pit mine: (<b>a</b>) represents the northwest wall; (<b>b</b>) represents the southwest wall; (<b>c</b>) represents the bottom of the pit; (<b>d</b>) represents the northeast wall; and (<b>e</b>) represents the southeast wall.</p>
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<p>The east–west spatial evolution process of the Buzhaoba open-pit mine.</p>
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<p>The vertical spatial evolution process of the Buzhaoba open-pit mine.</p>
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<p>Surface deformation and fault development in the Buzhaoba open-pit mine.</p>
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<p>(<b>a</b>–<b>e</b>) Photographs of localized deformation and collapse sites.</p>
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<p>Schematic diagram of the monitoring point locations selected for analyzing the impact of atmospheric precipitation.</p>
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<p>Relationship between cumulative deformation and rainfall at points P1, P2, and P3 on the southern slope of the Buzhaoba open-pit mine: (<b>a</b>) represents east–west cumulative deformation; (<b>b</b>) represents vertical cumulative deformation.</p>
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<p>Relationship between cumulative deformation and rainfall at points P4, P5, and P6 on the southeastern slope of the Buzhaoba open-pit mine: (<b>a</b>) represents east–west cumulative deformation; (<b>b</b>) represents vertical cumulative deformation.</p>
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17 pages, 5812 KiB  
Article
Study on the Development Rule of Mudstone Cracks in Open-Pit Mine Dumps Improved with Xanthan Gum
by Xiang Qi, Wei Zhou, Rui Li, Ya Tian and Xiang Lu
Appl. Sci. 2024, 14(22), 10194; https://doi.org/10.3390/app142210194 - 6 Nov 2024
Viewed by 499
Abstract
The stability of open-pit mine slopes is crucial for safety, especially for spoil dump slopes, which are prone to cracks leading to landslides. This study investigates the use of xanthan gum (XG) to enhance the stability of mudstone in spoil dumps. Various concentrations [...] Read more.
The stability of open-pit mine slopes is crucial for safety, especially for spoil dump slopes, which are prone to cracks leading to landslides. This study investigates the use of xanthan gum (XG) to enhance the stability of mudstone in spoil dumps. Various concentrations of xanthan gum were mixed with mudstone and subjected to dry–wet cycle tests to assess the impact on crack development. Pore and crack analysis system (PCAS) was utilized for image recognition and crack analysis, comparing the efficiency of crack rate and length modification. The study found that xanthan gum addition significantly improved mudstone’s resistance to crack development post-drying shrinkage. A 2% xanthan gum content reduced the mudstone crack rate by 45% on average, while 1.5% xanthan gum reduced crack length by 46.2% and crack width by 26.3%. Xanthan gum also influenced the fractal dimension and water retention of mudstone cracks. The optimal xanthan gum content for mudstone modification was identified as between 1.5% and 2%. Scanning electron microscopy imaging and X-ray diffraction tests supported the findings, indicating that xanthan gum modifies mudstone by encapsulation and penetration in wet conditions and matrix concentration and connection in dry conditions. These results are expected to aid in the development of crack prevention methods and engineering applications for open-pit mine spoil dump slopes. Full article
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<p>Schematic diagram of sieve analysis of mudstone raw material particle size and wet–dry cycling process. (<b>a</b>) Sampling area; (<b>b</b>) morphology of xanthan gum and mudstone; (<b>c</b>) particle size distribution curve; (<b>d</b>) morphology of different particle size ranges; (<b>e</b>) schematic diagram of the wet–dry cycling process.</p>
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<p>Image processing procedure.</p>
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<p>Changes in crack ratio and modification efficiency of xanthan gum. (<b>a</b>) 3D Plot of Sample Crack Ratio at Different Cycle Counts; (<b>b</b>) Average Modification Efficiency at Different Dosages.</p>
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<p>Development of crack length and width and modification efficiency. (<b>a</b>) Average Crack Length; (<b>b</b>) Average Crack Width; (<b>c</b>) Modification Efficiency.</p>
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<p>Average fractal dimension and probability entropy of modified specimens.</p>
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<p>Different numbers of wet–dry cycles reduce the moisture content curve of the specimen.</p>
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<p>Comparison of surface hardness after wet–dry cycles.</p>
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<p>X-ray diffraction curve and phase quantitative analysis results.</p>
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<p>Comparison of microscopic images under scanning electron microscope. (<b>a</b>) 500x U1 specimen SEM image; (<b>b</b>) 1.0kx U1 specimen SEM image; (<b>c</b>) 2.0kx U1 specimen SEM image; (<b>d</b>) 5.0kx U1 specimen SEM image; (<b>e</b>) 500x M6 specimen SEM image; (<b>f</b>) 1.0kx M6 specimen SEM image; (<b>g</b>) 2.0kx M6 specimen SEM image; (<b>h</b>) 5.0kx M6 specimen SEM image.</p>
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<p>XG molecular structure and modification mechanism. (<b>a</b>) XG molecular structure; (<b>b</b>) Mechanism of Xanthan Gum Modification.</p>
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18 pages, 3649 KiB  
Article
Truck Transportation Scheduling for a New Transport Mode of Battery-Swapping Trucks in Open-Pit Mines
by Yufeng Xiao, Wei Zhou, Boyu Luan, Keyi Yang and Yuqing Yang
Appl. Sci. 2024, 14(22), 10185; https://doi.org/10.3390/app142210185 - 6 Nov 2024
Viewed by 507
Abstract
To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the [...] Read more.
To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations. Full article
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<p>Schematic diagram of the battery-swapping truck transportation model.</p>
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<p>Experimental results for the five weight distribution schemes.</p>
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<p>Sensitivity analysis of different weight schemes. (<b>a</b>) Progressive increase in total transportation cost, (<b>b</b>) Decline in waiting time from Scheme 1 to Scheme 5, (<b>c</b>) Incremental increase in total distance traveled, indicating higher utilization, (<b>d</b>) Gradual increase in number of battery changes required.</p>
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<p>Truck operation Gantt chart using the basic genetic algorithm.</p>
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<p>Truck operation Gantt chart using the adaptive genetic algorithm.</p>
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<p>Comparison of the basic and adaptive algorithms.</p>
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36 pages, 46209 KiB  
Article
Subsidence and Uplift in Active and Closed Lignite Mines: Impacts of Energy Transition and Climate Change
by Artur Guzy
Energies 2024, 17(22), 5540; https://doi.org/10.3390/en17225540 - 6 Nov 2024
Viewed by 462
Abstract
This study examines the combined effects of decommissioning lignite mining operations and long-term climate trends on groundwater systems and land surface movements in the Konin region of Poland, which is characterised by extensive open-pit lignite extraction. The findings reveal subsidence rates ranging from [...] Read more.
This study examines the combined effects of decommissioning lignite mining operations and long-term climate trends on groundwater systems and land surface movements in the Konin region of Poland, which is characterised by extensive open-pit lignite extraction. The findings reveal subsidence rates ranging from −26 to 14 mm per year within mining zones, while land uplift of a few millimetres per year occurred in closed mining areas between 2015 and 2022. Groundwater levels in shallow Quaternary and deeper Paleogene–Neogene aquifers have declined significantly, with drops of up to 26 m observed near active mining, particularly between 2009 and 2019. A smaller groundwater decline of around a few metres was observed outside areas influenced by mining. Meteorological data show an average annual temperature of 8.9 °C from 1991 to 2023, with a clear warming trend of 0.0050 °C per year since 2009. Although precipitation patterns show a slight increase from 512 mm to 520 mm, a shift towards drier conditions has emerged since 2009, characterised by more frequent dry spells. These climatic trends, combined with mining activities, highlight the need for adaptive groundwater management strategies. Future research should focus on enhanced monitoring of groundwater recovery and sustainable practices in post-mining landscapes. Full article
(This article belongs to the Section B: Energy and Environment)
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<p>AOI overview. (<b>A</b>) Location of the AOI within Poland. (<b>B</b>) Detailed map of the AOI, indicating current mining operations, major lakes and rivers, roads, railways, urban areas, the meteorological station, landscape features, and the locations of boreholes and hydrogeological cross-sections. Data sources: National Geoportal of Poland [<a href="#B49-energies-17-05540" class="html-bibr">49</a>], Polish Geological Institute–National Research Institute [<a href="#B52-energies-17-05540" class="html-bibr">52</a>]. Basemap: OpenStreetMap contributors [<a href="#B50-energies-17-05540" class="html-bibr">50</a>].</p>
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<p>Schematic hydrogeological cross-sections through the AOI. See <a href="#energies-17-05540-f001" class="html-fig">Figure 1</a> for the locations of the cross-sections. Geological formations are interpolated using kriging with semi-variogram analysis of data from 30 boreholes retrieved from the Polish Geological Institute–National Research Institute [<a href="#B52-energies-17-05540" class="html-bibr">52</a>]. The approximate positions of Quaternary, Paleogene–Neogene, and Cretaceous aquifer systems are based on data from the Polish Geological Institute–National Research Institute [<a href="#B52-energies-17-05540" class="html-bibr">52</a>] and from Wilk et al. [<a href="#B54-energies-17-05540" class="html-bibr">54</a>].</p>
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<p>Lignite mining history, current operations, and water recultivation areas in the AOI, including prospective lignite deposits. The extent of current lignite mines is based on data from the Polish Geological Institute–National Research Institute [<a href="#B52-energies-17-05540" class="html-bibr">52</a>]. Although active lignite mining in September 2024 was limited to the “Tomisławice” mine, the delineated areas of the “Jóźwin” and “Drzewce” mines, where operations ceased in 2023 and 2022, respectively, are still designated as mining areas undergoing water recultivation [<a href="#B52-energies-17-05540" class="html-bibr">52</a>]. The approximate extent of depression cones primarily affecting the Quaternary aquifer system is also shown, based on Wilk et al. [<a href="#B54-energies-17-05540" class="html-bibr">54</a>]. Additionally, the map displays piezometers monitoring groundwater levels in the “Tomisławice” mine [<a href="#B59-energies-17-05540" class="html-bibr">59</a>], as well as the national monitoring network of the Polish Geological Institute–National Research Institute [<a href="#B52-energies-17-05540" class="html-bibr">52</a>].</p>
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<p>Land surface movement in the AOI from EGMS data for periods (<b>A</b>) 2015–2021 and (<b>B</b>) 2018–2022. Points indicate measurement locations and annual displacement rate (mm/year), while contour lines represent interpolated total displacement (mm) for the respective periods.</p>
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<p>Groundwater level and land surface movement time series from piezometers at the “Tomisławice” mine. Raw data, LOESS trend lines, and linear regression fits. Coefficients of determination (R<sup>2</sup>) and Pearson correlation coefficients (R) are provided for each linear regression. The joint observation period is highlighted in red.</p>
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<p>Groundwater level and land surface movement time series from piezometers monitoring the unconfined Quaternary aquifer system, located outside the mining influence zone. Raw data, LOESS trend lines, and linear regression fits. Coefficients of determination (R<sup>2</sup>) and Pearson correlation coefficients (R) are provided for each linear regression. The joint observation period is highlighted in red.</p>
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<p>Groundwater level and land surface movement time series from piezometers monitoring the confined aquifer system, located outside, but proximal to, the mining influence zone. Raw data, LOESS trend lines, and linear regression fits. Coefficients of determination (R<sup>2</sup>) and Pearson correlation coefficients (R) are provided for each linear regression. The joint observation period is highlighted in red.</p>
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<p>Groundwater level and land surface movement time series from piezometers monitoring the confined aquifer system, located outside and farther from the mining influence zone. Raw data, LOESS trend lines, and linear regression fits. Coefficients of determination (R<sup>2</sup>) and Pearson correlation coefficients (R) are provided for each linear regression. The joint observation period is highlighted in red.</p>
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<p>Time series of mean monthly temperature and precipitation with linear trends and confidence intervals as well as means recorded at Kołuda Wielka meteorological station over periods (<b>A</b>) 1991–2023 and (<b>B</b>) 2009–2023.</p>
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<p>Cross-correlation between groundwater level and meteorological data: (<b>A</b>) mean monthly temperature and (<b>B</b>) monthly precipitation data for the period 2009–2023, for piezometers of the Polish national hydrogeological network monitoring the unconfined aquifer.</p>
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<p>Cross-correlation between groundwater level and meteorological data: (<b>A</b>) mean monthly temperature and (<b>B</b>) monthly precipitation data for the period 2009–2023, for piezometers of the Polish national hydrogeological network monitoring the confined aquifer system.</p>
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<p>Land surface movement in the vicinity of the “Tomisławice” mine from EGMS data for periods (<b>A</b>) 2015–2021 and (<b>B</b>) 2018–2022. Points indicate measurement locations and annual displacement rate (mm/year), while contour lines represent interpolated total displacement (mm) for the respective periods.</p>
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<p>Land surface movement in the vicinity of “Drzewce” and “Lubstów” mines from EGMS data for periods (<b>A</b>) 2015–2021 and (<b>B</b>) 2018–2022. Points indicate measurement locations and annual displacement rate (mm/year), while contour lines represent interpolated total displacement (mm) for the respective periods.</p>
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<p>Land surface movement in the vicinity of the “Jóźwin” mine from EGMS data for periods (<b>A</b>) 2015–2021 and (<b>B</b>) 2018–2022. Points indicate measurement locations and annual displacement rate (mm/year), while contour lines represent interpolated total displacement (mm) for the respective periods.</p>
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17 pages, 5532 KiB  
Article
Numerical Investigation of the Slope Stability in the Waste Dumps of Romanian Lignite Open-Pit Mines Using the Shear Strength Reduction Method
by Florin Dumitru Popescu, Andrei Andras, Sorin Mihai Radu, Ildiko Brinas and Corina-Maria Iladie
Appl. Sci. 2024, 14(21), 9875; https://doi.org/10.3390/app14219875 - 29 Oct 2024
Viewed by 508
Abstract
Open-pit mining generates significant amounts of waste material, leading to the formation of large waste dumps that pose environmental risks such as land degradation and potential slope failures. The paper presents a stability analysis of waste dump slopes in open-pit mining, focusing on [...] Read more.
Open-pit mining generates significant amounts of waste material, leading to the formation of large waste dumps that pose environmental risks such as land degradation and potential slope failures. The paper presents a stability analysis of waste dump slopes in open-pit mining, focusing on the Motru coalfield in Romania. To assess the stability of these dumps, the study employs the Shear Strength Reduction Method (SSRM) implemented in the COMSOL Multiphysics version 6 software, considering both associative and non-associative plasticity models. (1) Various slope angles were analyzed, and the Factor of Safety (FoS) was calculated, showing that the FoS decreases as the slope angle increases. (2) The study also demonstrates that the use of non-associative plasticity leads to lower FoS values compared to associative plasticity. (3) The results are visualized through 2D and 3D models, highlighting failure surfaces and displacement patterns, which offer insight into the rock mass behavior prior to failure. (4) The research also emphasizes the effectiveness of numerical modeling in geotechnical assessments of stability. (5) The results suggest that a non-associative flow rule should be adopted for slope stability analysis. (7) Quantitative results are obtained, with small variations compared to those obtained by LEM. (6) Dilatation angle, soil moduli, or domain changes cause differences of just a few percent and are not critical for the use of the SSRM in engineering. Full article
(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)
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<p>Basins in the Oltenia coal mining region [Oltenia Energy Company internal documents].</p>
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<p>Cross-Section of the rock mass model.</p>
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<p>General geometry construction and redefinition region of finite elements model.</p>
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<p>Boundaries of the simulation domain: (<b>a</b>) vertical sliding displacement; (<b>b</b>) fixed; (<b>c</b>) free.</p>
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<p>The finite element mesh of the model.</p>
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<p>FoS as a function of the slope angle.</p>
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<p>Equivalent plastic strain as a function of slope angle, Material 1.</p>
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<p>Equivalent plastic strain as a function of slope angle, Material 2.</p>
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<p>Displacement magnitude for a slope angle of 45°, Material 1.</p>
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<p>Displacement magnitude for a slope angle of 45°, Material 2.</p>
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<p>Slope displacements for Material 1.</p>
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<p>Slope displacements for Material 2.</p>
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12 pages, 1440 KiB  
Article
Health Risks Due to Co-Exposure to Noise and Respirable Crystalline Silica Among Workers in the Open-Pit Mining Industry—Results of a Preliminary Study
by Iryna Myshchenko, Małgorzata Pawlaczyk-Luszczynska, Adam Dudarewicz and Alicja Bortkiewicz
Toxics 2024, 12(11), 781; https://doi.org/10.3390/toxics12110781 - 27 Oct 2024
Viewed by 654
Abstract
Occupational exposure to carcinogenic respirable crystalline silica and noise requires a deeper understanding and an assessment of the possible health risks caused by their combined action. Data on individual exposure to respirable crystalline silica (RCS) and occupational noise (ON) was collected among 44 [...] Read more.
Occupational exposure to carcinogenic respirable crystalline silica and noise requires a deeper understanding and an assessment of the possible health risks caused by their combined action. Data on individual exposure to respirable crystalline silica (RCS) and occupational noise (ON) was collected among 44 open-pit miners. The study group was divided into two groups according to the job tasks performed. The individual exposure, exceeding of maximum admissible concentration/intensity, and predicted hearing thresholds (HTs) (according to ISO 1999:2013) were compared between the groups directly participating in the technological process (group 1; N = 23) and performing auxiliary, supervising, or laboratory activities (group 2; N = 21). All the analysed indices were significantly higher for group 1; therefore, the job category may predict ON and RCS exposure among open-pit miners. A statistically significant relationship (rs = 0.66, p < 0.05) was found between the time-weighted average (TWA) 8 h RCS and individual daily noise exposure levels. Exposure to noise in the course of employment causes the risk of hearing impairment (mean HTs for 2, 3, and 4 kHz > 25 dB) up to 74% and 4.4% (in the case of groups 1 and 2, respectively). Further studies are needed before conclusions concerning the effects of co-exposure to ON and RCS on open-pit miners can be made. Full article
(This article belongs to the Topic New Research in Work-Related Diseases, Safety and Health)
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<p>The number and percentage of subjects within group 1 (<b>a</b>) and group 2 (<b>b</b>) at the studied workplaces.</p>
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<p>The concentrations of respirable crystalline silica and the noise exposure levels among the open-pit mining workers (N = 44).</p>
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<p>Results of the risk assessment of hearing impairment expressed as mean hearing threshold level at the frequencies of 1, 2, and 3 kHz &gt; 25 dB (<b>a</b>) and &gt;45 dB (<b>b</b>). The calculations are based on the energy mean and maximum values of daily noise exposure levels (i.e., L<sub>EX,8h,mean</sub> and L<sub>EX,8h,max</sub>, respectively).</p>
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<p>Results of the risk assessment of hearing impairment expressed as mean hearing threshold level at the frequencies of 2, 3, and 4 kHz &gt; 25 dB (<b>a</b>) and &gt; 45 dB (<b>b</b>). The calculations are based on the energy mean and maximum values of daily noise exposure levels (i.e., L<sub>EX,8h,mean</sub> and L<sub>EX,8h,max</sub>, respectively).</p>
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15 pages, 4796 KiB  
Article
Spatial Distribution, Leaching Characteristics, and Ecological and Health Risk Assessment of Potential Toxic Elements in a Typical Open-Pit Iron Mine Along the Yangzi River
by Yifan Zeng, Zuxin Xu and Bin Dong
Water 2024, 16(21), 3017; https://doi.org/10.3390/w16213017 - 22 Oct 2024
Viewed by 549
Abstract
Potential toxic elements (PTEs) pollution in the soil of abandoned open-pit mines can lead to great ecological risk to the areas around the mining districts. This study selected a typical abandoned open-pit iron mine along the Yangzi River in southeast China to investigate [...] Read more.
Potential toxic elements (PTEs) pollution in the soil of abandoned open-pit mines can lead to great ecological risk to the areas around the mining districts. This study selected a typical abandoned open-pit iron mine along the Yangzi River in southeast China to investigate the spatial distribution, leaching characteristics, and ecological and health risk of the soil PTEs (As, Pb, Cd, Ni, Cr, Cu, and Zn). Leaching tests and sequential extraction were applied to study the migration of PTEs under the condition of rainfall. Different risk assessment methods were used to analyze the pollution and ecological risk of PTEs. The contents of As and Cu exceeded the background value of the Chinese soil guideline, with average contents of 50.71 ± 1.59 and 197.47 ± 16.09, respectively. The leaching test and sequential extraction indicated that sites 8 and 9 posed the greatest risk of PTE migration. According to the map of the Nemerow integrated pollution index (NIPI), the pollution level of the middle bare area of the study area was the highest, and Cu possessed the highest pollution index (PI) of 3.92. The average geo-accumulation index (Igeo) of As and Cu was between 1 and 2, reaching the pollution level of moderately contaminated. The average potential ecological risk coefficient (Ei) of As was the highest, and the contributions of As, Cu, and Cd to the potential ecological risk of the whole study area were 46.7%, 29.7%, and 14.3%, respectively. The range of the hazard index (HI) and the range of the As carcinogenic risk (CRAs) of all the sampling sites for children were 1.30–3.94 and 2.19 × 10−4–7.20 × 10−4, and As accounted for more than 85% of the total noncarcinogenic risk, indicating that the comprehensive pollution of PTEs in the study area posed great carcinogenic and noncarcinogenic risks to children. This study can be a proper reference for the subsequent recovery methods and environmental management of the whole mining area. Full article
(This article belongs to the Special Issue Soil and Groundwater Quality and Resources Assessment)
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<p>Location of the study area and sampling locations.</p>
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<p>Schematic illustration of the dynamic column experiment.</p>
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<p>Maps of As (<b>a</b>), Cd (<b>b</b>), Pb (<b>c</b>), Ni (<b>d</b>), Cr (<b>e</b>), Cu (<b>f</b>), and Zn (<b>g</b>) concentrations and pH value (<b>h</b>) in the study area.</p>
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<p>BCR extractable fractionations (%) of PTEs in soil samples of the study area.</p>
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<p><span class="html-italic">PI</span> values (<b>a</b>) and map of <span class="html-italic">NIPI</span> values (<b>b</b>) of PTEs in the study area.</p>
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<p><span class="html-italic">I<sub>geo</sub></span> values of PTEs in the study area.</p>
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<p><span class="html-italic">E<sub>i</sub></span> values (<b>a</b>) and map of <span class="html-italic">PERI</span> values (<b>b</b>) of PTEs in the study area.</p>
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<p>Contributions of PTEs to <span class="html-italic">PERI</span> value of the whole study area.</p>
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<p>Contributions of PTEs to <span class="html-italic">HI<sub>adults</sub></span> (<b>a</b>) and <span class="html-italic">HI<sub>children</sub></span> (<b>b</b>) values of the whole study area.</p>
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26 pages, 4030 KiB  
Review
Below Water Table Mining, Pit Lake Formation, and Management Considerations for the Pilbara Mining Region of Western Australia
by Cherie D. McCullough
Mining 2024, 4(4), 863-888; https://doi.org/10.3390/mining4040048 - 17 Oct 2024
Viewed by 1173
Abstract
Located in northern Western Australia, the Pilbara is the highest productivity region for iron ore and other metal mining in Australia. As elsewhere, mine closure guidelines typically require post-closure landforms to be safe, stable, non-polluting and sustainable here in the long-term. I reviewed [...] Read more.
Located in northern Western Australia, the Pilbara is the highest productivity region for iron ore and other metal mining in Australia. As elsewhere, mine closure guidelines typically require post-closure landforms to be safe, stable, non-polluting and sustainable here in the long-term. I reviewed the primary literature, including international, national and state government guidelines and regional case studies for mine closure and related socio-environmental topics, to understand the key risks and management strategies needed to achieve these broad expectations for below water table (BWT) mining. Many BWT open cut mining projects will result in pit lakes in this region, many of which will be very large and will degrade in water quality with increasing salinisation over time. As an arid region, risks are dominated by alterations to hydrology and hydrogeology of largely unmodified natural waterways and freshwater aquifers. Although remote, social risks may also present, especially in terms of impacts to groundwater values. This remoteness also decreases the potential for realising practicable development of post-mining land uses for pit lakes. Explicitly considered risk-based decisions should determine closure outcomes for BWT voids, and when pit backfill to prevent pit lake formation will be warranted. However, maintaining an open pit lake or backfilling a void should also be considered against the balance of potential risks and opportunities. Full article
(This article belongs to the Special Issue Post-Mining Management)
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<p>Main map: generalised location of the Pilbara mining region in Western Australia. Inset: locations of major Pilbara mines (◾iron ore, ▲ gold, ▼ specialty metal, and ⯁ steel alloy metal). Fortescue River drainage channels.</p>
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<p>Mt Goldsworthy pit lake still filling in 2000 (photo: Hugh Jones).</p>
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<p>Conceptual water balances of Pilbara pit lakes. From top: terminal, throughflow, and flowthrough.</p>
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<p>Solute sources for Pilbara pit lakes.</p>
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<p>Conceptual long-term water quality evolution for terminal water balance in Pilbara pit lakes.</p>
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<p>Backfill above water table influences on down-gradient groundwater quality.</p>
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<p>Conceptual model of long-term density-driven saline seepage potential in Pilbara hypersaline pit lakes.</p>
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<p>Potential PMLU opportunities for saline and freshwater Pilbara pit lakes.</p>
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16 pages, 8302 KiB  
Article
Effects of Soil Nutrient Restoration Aging and Vegetation Recovery in Open Dumps of Cold and Arid Regions in Xinjiang, China
by Zhongming Wu, Weidong Zhu, Haijun Guo, Yong Zhang, Chaoji Shen, Jing Guo, Ming Liu, Tuanwei Zhao, Hu Teng, Wanli Zhu, Yongfu Kang, Gensheng Li and Weiming Guan
Land 2024, 13(10), 1690; https://doi.org/10.3390/land13101690 - 16 Oct 2024
Viewed by 636
Abstract
Open-pit coal mining inevitably damages the soil and vegetation in mining areas. Currently, the restoration of cold and arid open-pit mines in Xinjiang, China, is still in the initial exploratory stage, especially the changes in soil nutrients in spoil dumps over time. Dynamic [...] Read more.
Open-pit coal mining inevitably damages the soil and vegetation in mining areas. Currently, the restoration of cold and arid open-pit mines in Xinjiang, China, is still in the initial exploratory stage, especially the changes in soil nutrients in spoil dumps over time. Dynamic remote sensing monitoring of vegetation in mining areas and their correlation are relatively rare. Using the Heishan Open Pit in Xinjiang, China, as a case, soil samples were collected during different discharge periods to analyze the changes in soil nutrients and uncover the restoration mechanisms. Based on four Landsat images from 2018 to 2023, the remote sensing ecological index (RSEI) and fractional vegetation cover (FVC) were obtained to evaluate the effect of mine restoration. Additionally, the correlation between vegetation changes and soil nutrients was analyzed. The results indicated that (i) the contents of nitrogen (N), phosphorus (P), potassium (K), and organic matter (OM) in the soil increased with the duration of the restoration period. (ii) When the restoration time of the dump exceeds 5 years, N, P, K, and OM content is higher than that of the original surface-covered vegetation area. (iii) Notably, under the same restoration aging, the soil in the artificial mine restoration demonstration base had significantly higher contents of these nutrients compared to the soil naturally restored in the dump. (iv) Over the past five years, the RSEI and FVC in the Heishan Open Pit showed an overall upward trend. The slope remediation and mine restoration project significantly increased the RSEI and FVC values in the mining area. (v) Air humidity and surface temperature were identified as key natural factors affecting the RSEI and FVC in cold and arid open pit. The correlation coefficients between soil nutrient content and vegetation coverage were higher than 0.78, indicating a close and complementary relationship between the two. The above results can clarify the time–effect relationship between natural recovery and artificial restoration of spoil dumps in cold and arid mining areas in Xinjiang, further promoting the research and practice of mine restoration technology in cold and arid open pits. Full article
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<p>(<b>a</b>) The province map of the study area location; (<b>b</b>) the city map of study area location; (<b>c</b>) distribution map of specific terrain and sampling points of the mining area.</p>
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<p>(<b>a</b>) Drone aerial view of mining area; (<b>b</b>) mine dump; (<b>c</b>) dump section view; (<b>d</b>) vegetated area.</p>
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<p>The FVC distribution map of the study area in (<b>a</b>) 2018, (<b>b</b>) 2020, (<b>c</b>) 2022 and (<b>d</b>) 2023.</p>
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<p>FVC percentage of the study area.</p>
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<p>The distribution map of the RSEI in the study area.</p>
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<p>RSEI percentage of the study area.</p>
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<p>The distribution map of LST and WET by the RSEI in the study area.</p>
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<p>Soil nutrient data map at different sampling sites.</p>
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<p>Correlation analysis of soil nutrient content and vegetation coverage by (<b>a</b>) in situ restoration and (<b>b</b>) vegetation mine restoration.</p>
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15 pages, 4536 KiB  
Article
Slope Stability Analysis of Open-Pit Mine Considering Weathering Effects
by Wei Liu, Gang Sheng, Xin Kang, Min Yang, Danqi Li and Saisai Wu
Appl. Sci. 2024, 14(18), 8449; https://doi.org/10.3390/app14188449 - 19 Sep 2024
Viewed by 1361
Abstract
Weathering processes gradually alter the physical and mechanical attributes of slope materials, weakening the structural integrity and stability of slopes. This paper presents an in-depth analysis of slope stability in an open-pit mine, emphasizing the pivotal role of weathering effects in determining slope [...] Read more.
Weathering processes gradually alter the physical and mechanical attributes of slope materials, weakening the structural integrity and stability of slopes. This paper presents an in-depth analysis of slope stability in an open-pit mine, emphasizing the pivotal role of weathering effects in determining slope stability. To accurately capture the impact of weathering on slope stability, a comprehensive analysis model was developed, incorporating field observations, laboratory testing, and numerical simulations. The effects of weathering on the mechanical properties of black shale were studied through extensive laboratory tests. The uniaxial compressive strength, shear strength, and modulus of elasticity significantly decreased with increasing weathering, indicating a heightened vulnerability to slope failure. The correlation function between mechanical parameters and weathering time was obtained, providing the basis for evaluating the stability of mine slopes. It was found that more severe weathering conditions were strongly correlated with elevated risks of slope failure, including landslides and collapses. Based on these findings, practical recommendations are provided for slope reinforcement and management strategies, aimed at mitigating slope failure risks and ensuring the safe and efficient operation of the mine. By incorporating weathering effects into slope stability analysis, mine operators can make informed decisions that account for the dynamic nature of slope materials and their susceptibility to weathering, thereby improving overall mine performance and safety. Full article
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<p>Examples of the specimens: (<b>a</b>) Brazilian splitting test, (<b>b</b>) uniaxial compression test, and (<b>c</b>) direct shear test.</p>
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<p>Testing equipment: (<b>a</b>) uniaxial compressive strength test, (<b>b</b>) direct shear test, and (<b>c</b>) Brazilian splitting test.</p>
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<p>The failed specimens: (<b>a</b>) Brazilian splitting test, (<b>b</b>) uniaxial compression test, and (<b>c</b>) direct shear test.</p>
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<p>The direct shear testing parameters at different weathering times.</p>
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<p>Constitutive model and material division in the mining areas.</p>
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<p>Calculated results for slope height of 50 m at angle of 45°.</p>
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<p>Calculated results for slope height of 50 m at angle of 45°.</p>
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<p>Calculated results for slope height of 50 m at angle of 47°.</p>
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<p>Calculated results for slope height of 50 m at angle of 47°.</p>
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