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The water quality of mine water is obviously improved after being stored in underground reservoir, but the process of water–rock interaction and the purification mechanism of mine water quality are not clear. In this study, the water samples and rock samples collected in the underground reservoir of Daliuta coal mine were taken as the research object. Based on the analysis of the hydrochemical characteristics of the reservoir water samples and the characterization of the rock samples, combined with PHREEQC analysis, the mechanism of water quality purification of mine water was discussed. The results showed that the rocks in the underground reservoir had layered silicate structure and flaky kaolinite structure, with some irregular edges and microcracks, and higher specific surface area and total pore volume. These characteristics made the rocks have a certain adsorption and removal capacity for heavy metal ions and other pollutants in the mine water. The water–rock interaction, such as the dissolution of albite and halite, the precipitation of gypsum and kaolinite, and the cation exchange, resulted in the increase of the concentration of Na + and the decrease of the concentration of Ca 2+ , Mg 2+ , and TDS in the outlet water, and the hydrochemical type changed from SO 4 2− -Cl − /Ca 2+ type to SO 4 2− -Cl − /Na + type. Moreover, this study shows that PHREEQC analysis can be used to analyze the water–rock interaction of coal mine underground reservoir and can obtain more detailed information; therefore, it may have the potential ability to help assess the migration and transformation of pollutants during the storage process of mine water in underground reservoirs.

Fault rock is a typical hazardous material of water–silt inrush during the excavation in underground mines. To investigate hydraulic characteristics of fault rock during the water–silt inrush, a one-dimensional radial three-phase flow model of water–rock–silt was established in this study. In the proposed model, the mass conservation and continuity equations of the three-phase flow were obtained; the rock particle migration and the momentum conservation of the three-phase fluid migration were described by erosion constitutive equations and non-Darcy flow equations, respectively. The laboratory tests of porosity and the evolution of volume discharge rate were compared, and the accuracy of the proposed three-phase model was verified by the comparison results. From the test and numerical results, a high standard deviation of repeated results is observed in the case with high silt concentrations, and the erosion effect is inhibited by the silt flow. Last but not least, the temporal–spatial distribution of hydraulic properties is obtained by the numerical simulation: With the progress of the three-phase flow, rock particles near the fluid outlet are first fluidized and constantly migrate outward, resulting in an increase of the porosity and permeability in fault rock. Subsequently, water-conducting pathways are gradually formed inside the fault rock, and then more fluidized rock particles flow out. Finally, the fluidized rock particles have completely migrated, and the porosity and permeability tend to be stable with the more significant non-uniform spatial distribution.HighlightsA one-dimensional radial three-phase flow model of water-rock-silt is established.Mass conservation, continuity, erosion constitutive and non-Darcy flow relations are considered.Laboratory tests are designed to verify the proposed model from porosity and volume discharge rate.Temporal-spatial distribution of hydraulic properties during water-silt inrush is obtained.

Underground mines embroil several occupational hazards, including airborne dust generation from various mining operations. Line-of-sight remote Load Haul Dumper (LHD) mucking is adopted to draw the blasted muck from unsupported open stopes in underground metalliferous mines. Assessment of particulate matter (PM) concentrations and remote LHD operator’s exposure is crucial for devising appropriate dust control measures. In this study, PM generated due to mucking in longhole open stope by line-of-sight remote LHD during downcast airflow was measured using real-time aerosol spectrometers. The particulate concentrations at upstream and downstream of dust source were analysed for various particle sizes as well as occupational dust types, such as alveolic and thoracic. The airborne dust concentration of ≤ 10 μm (PM 10 ), ≤ 5 μm, and ≤ 1 μm (PM 1 ) size at operator’s location in downstream was measured 71.3%, 28.5%, and 3.0%, respectively. The alveolic and thoracic dust types, respectively, were determined 25.1% and 74.2% in downstream and 48.9% and 84.6% in upstream total airborne dust concentration (311 ± 246 μg/m 3 ). Dilution of airborne dust generated due to muck sliding inside the stope was analysed with time. Moreover, dust concentrations under typical airflow scenarios encountered in open stope were simulated using Ventsim software to identify the potential dust exposure hazard for remote LHD operator. The simulation revealed that downcast airflow causes maximum exposure of harmful airborne dust for remote LHD operator. This study enhanced the understanding of exposure potential of airborne dust during remote LHD mucking. Moreover, it emphasised adoption of tele-remote-operated LHD and automated mucking operation in open stopes.

Working against nature and an uncertain environment makes underground mining a hazardous profession. Every year hundreds of miners lose their valuable lives due to mine hazards. Increasing demand for coal necessitates the extraction of coal at a higher rate. As a result, easily minable shallow coal deposits are depleting speedily, and in near future, deep-seated deposits will be left for mining by underground methods. With rising depth and deployment of high-capacity machines, increasing heat stress becomes a major hazard in the underground mine environment posing threat to the miners’ health, productivity and safety. Ignoring the effect of heat stress may lead to dangerous circumstances, even result in death. To avoid such unwanted event, it has become imperative to predict the heat stress to reduce its adverse impact in underground coal mines. Therefore, in this study a detailed field survey is conducted to collect the environmental data of three underground coal mines. Genetic programming (GP) is done to develop a relation between the environmental parameters and heat stress, by taking the mine survey data as input. A good correlation coefficient (R = 0.9816) is obtained between the GP predicted heat stress and actually measured heat stress, which indicates that GP can be effectively used to predict the heat stress in underground mines. A sensitivity analysis (SA) is done to determine the effect of input parameters on heat stress. The SA results revealed that all six input parameters have a considerable effect on the heat stress; however, the dry-bulb temperature has the highest effect (0.98) on heat stress.

This article presents the results of precise measurements of the volume flow rate of an air-methane mixture flowing out of a mine shaft and compares them with measurements at the air intakes to the shaft. Continuous measurement of the volume flux of the air-methane mixture in underground mine workings is a complex issue, and given the need to determine methane emissions by mining companies based on quantitative methane measurements in air discharged through ventilation shafts, it represents a current and important problem for both ecological and economic reasons. The development of measurement methods based on a purpose-built instrument was complemented by the creation of a multipoint system for measuring methane velocity and concentration using SOM 2303-type methane anemometers. With the cooperation of the mine, simultaneous measurements were taken in the collection duct of Shaft VI and at all four air intakes to the shaft. The results were presented as determined air and methane fluxes, followed by a comparative analysis of the volume flux measurements taken in the workings at the inlet to Shaft VI.

Organic and nitrogen pollutants in mine water could be removed effectively during the storage and transport of water in a coal mine underground reservoir packed with coal gangue through various water-rock interactions. However, little is known about the effect of the released dissolved organic matter (DOM) derived from the packed matrix on their removal. Column experiments were performed at a Darcy flux of 1.56 cm·h −1 at 25 °C to investigate the characteristics of DOM derived from Jurassic and Permian coal gangue individually packed in underground reservoirs of Bulianta (BL1) and Baode (BD2) coal mines. Chemical characteristics of the DOM were analyzed by using the ultraviolet and visible (UV-Vis) absorbance and fluorescence spectroscopy techniques. Results showed that the values of dissolved organic carbon (DOC) and electricity (EC) in the outlet of column packed with BL1 were obviously higher than those from BD2 due to the higher permeability of BL1 with more complex mineralogical and chemical compositions. The parallel factor analysis (PARAFAC) indicated that the fluorescence components in the DOM derived from BL1 and BD2 were individually dominated by the humic-like and tryptophan-like substances. Thus, the higher aromaticity, hydrophobicity, and humification indicated by the specific ultraviolet absorbance at 254 nm (SUVA 254 ) and 260 nm (SUVA 260 ) and humification index (HIX) were observed in the DOM from the younger Jurassic BL1, implying that the DOM may contain more plant-derived precursors. Meanwhile, the higher values of fluorescence index (FI) and biological/autochthonous index (BIX) confirmed the stronger autochthonous characterization of DOM originated from the earlier Permian BD2. The observed characterization of DOM will further extend the understanding of purification mechanism of mine water during its storage and transport in coal mine underground reservoirs packed with coal gangue of different geologic ages.

A procedure is presented for finding the shortest network connecting three given undirected points, subject to a curvature constraint on both the path joining two of the points and the path that connects to the third point. The problem is a generalisation of the Fermat–Torricelli problem and is related to a shortest curvature-constrained path problem that was solved by Dubins. The procedure has the potential to be applied to the optimal design of decline networks in underground mines.

Object detection in low-light scenarios has been widely acknowledged as a significant research area in the field of computer vision, presenting a challenging task. Aiming at the low detection accuracy of mainstream single-stage object detection models in low-light scenarios, this paper proposes a detection model called DK_YOLOv5 based on YOLOv5, specifically designed for such scenarios. First, a low-light image enhancement algorithm with better results is selected to generate enhanced images that achieve relatively better visual effects and amplify target features. Second, the SPPF layer is improved to an R-SPPF module with faster inference speed and stronger feature expression ability. Next, we replace the C3 module with the C2f module and incorporate an attention mechanism to develop the C2f_SKA module, enabling richer gradient information flow and reducing the impact of noise features. Finally, the model detection head is replaced with a decoupled head suitable for the object detection task in this scenario to improve model performance. Additionally, we expand the Exdark dataset to include low-light data of underground mine scenario targets, named Mine_Exdark. Experimental results demonstrate that the proposed DK_YOLOv5 model achieves higher detection accuracy than other models in low-light scenarios, with an mAP0.5 of 71.9% on the Mine_Exdark dataset, which is 4.4% higher than that of YOLOv5.

The mining industry relies on extracting valuable minerals through underground mining. Many industries have implemented automation to enhance workplace safety, optimize operations, improve responses to events, and achieve cost-effectiveness. A real-time communication and monitoring system is indispensable in underground mines to prevent significant hazards and improve safety in underground mines. However, the environmental conditions of underground mines are affected by toxic, flammable, combustible gases and dust. The harmful gases are a significant concern as they can cause gas explosions. Internet of Things (IoT) enabled real-time communication system with Long Range (LoRa) transceiver module is designed and developed to measure the underground mine environmental parameters, temperature, and humidity. The LoRa-based proof of concept (POC) system is tested and evaluated at the surface level and in two underground mines. The LoRa module radio waves range test is carried out to measure the received signal strength indicator (RSSI) value at the surface level. In addition, the developed system is tested and evaluated at different positions of underground mines to measure environmental parameters in straight and curved tunnels. The experimental results represent successful IoT with LoRa-based wireless communication between underground mine tunnels to the surface, wireless transmission of parameters at the straight tunnels, and curved tunnels of underground mines.

Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines.