Search Results (76)

Considering the serious hazard of respiratory dust in underground coal mines and the low efficiency of traditional dust-reduction technology, this study optimizes the atomisation parameters of the gas–liquid two-phase flow nozzle through numerical simulation and experimental testing, and designs an on-board dust-reduction system. Based on the Fluent software (version 2023 R2), a flow field model outside the nozzle was established, and the effects of the air supply pressure, gas-phase inlet velocity, and droplet mass flow rate on the atomisation characteristics were analyzed. The results show that increasing the air supply pressure can effectively reduce the droplet particle size and increase the range and atomisation angle, and that the dust-reduction efficiency is significantly improved with the increase in pressure. The dust-reduction efficiency reached 69.3% at 0.6 MPa, which was the economically optimal operating condition. Based on the parameter optimization, this study designed an annular airborne gas–liquid two-phase flow dust-reduction system, and a field test showed that the dust-reduction efficiency of this system could reach up to 86.0%, which is 53.5% higher than that of traditional high-pressure spraying, and that the dust concentration was reduced to less than 6 mg/m³. This study provides an efficient and reliable technical solution for the management of underground coal mine dust and guidance for promoting the development of the coal industry. Full article

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article

Abrasive waterjet technology is a promising sustainable and green technology for cutting underground structures. Abrasive waterjet usage in demolition promotes sustainable and green construction practices by reduction of noise, dust, secondary waste, and disturbances to the surrounding infrastructure. In this study, a numerical framework based on a coupled Smoothed Particle Hydrodynamics (SPH)–Finite Element Method (FEM) algorithm incorporating the Riedel–Hiermaier–Thoma (RHT) constitutive model is proposed to investigate the damage mechanism of concrete subjected to abrasive waterjet. Numerical simulation results show a stratified damage observation in the concrete, consisting of a crushing zone (plastic damage), crack formation zone (plastic and brittle damage), and crack propagation zone (brittle damage). Furthermore, concrete undergoes plastic failure when the shear stress on an element exceeds 5 MPa. Brittle failure due to tensile stress occurs only when both the maximum principal stress (σ₁) and the minimum principal stress (σ₃) are greater than zero at the same time. The damage degree ($\chi$) of the concrete is observed to increase with jet diameter, concentration of abrasive particles, and velocity of jet. A series of orthogonal tests are performed to analyze the influence of velocity of jet, concentration of abrasive particles, and jet diameter on the damage degree and impact depth (h). The parametric numerical studies indicates that jet diameter has the most significant influence on damage degree, followed by abrasive concentration and jet velocity, respectively, whereas the primary determinant of impact depth is the abrasive concentration followed by jet velocity and jet diameter. Based on the parametric analysis, two optimized abrasive waterjet configurations are proposed: one tailored for rock fragmentation in tunnel boring machine (TBM) operations; and another for cutting reinforced concrete piles in shield tunneling applications. These configurations aim to enhance the efficiency and sustainability of excavation and tunneling processes through improved material removal performance and reduced mechanical wear. Full article

Integrating autonomous haulage systems into underground mining has revolutionized safety and operational efficiency. However, deploying 3D detection systems for autonomous truck navigation in such an environment faces persistent challenges due to dust, occlusion, complex terrains, and low visibility. This affects their reliability and real-time processing. While existing reviews have discussed object detection techniques and sensor-based systems, providing valuable insights into their applications, only a few have addressed the unique underground challenges that affect 3D detection models. This review synthesizes the current advancements in 3D object detection models for underground autonomous truck navigation. It assesses deep learning algorithms, fusion techniques, multi-modal sensor suites, and limited datasets in an underground detection system. This study uses systematic database searches with selection criteria for relevance to underground perception. The findings of this work show that the mid-level fusion method for combining different sensor suites enhances robust detection. Though YOLO (You Only Look Once)-based detection models provide superior real-time performance, challenges persist in small object detection, computational trade-offs, and data scarcity. This paper concludes by identifying research gaps and proposing future directions for a more scalable and resilient underground perception system. The main novelty is its review of underground 3D detection systems in autonomous trucks. Full article

This study reports a CFD investigation of spray-based dust suppression strategies in mining tunnels, focusing on the dynamic operation of roadheaders, onboard spraying systems, and water curtains. The simulations assess how these systems affect airflow patterns, velocity distributions, and pressure variations under various operating conditions. The results indicate that cutterhead sprays produce conical dispersion patterns directed toward the rear of the tunnel under forced ventilation, while transfer point sprays establish localized zones of extended residence time, with stable droplet distributions achieved in 3.5 s. Spray activation markedly increases local air velocity, with peak values near the cutterhead rising from 0.88 m/s to 32.29 m/s. Meanwhile, water curtains, modeled as porous media, induce stepwise pressure drops from 186.89 Pa to 91.15 Pa. These findings underscore the distinct effects of spraying and water curtain systems on tunnel ventilation and offer valuable insights for the design and optimization of airflow control and dust suppression in underground mining environments. Full article

Coal dust seriously affects the underground working environment. The current water-spray dust reduction technology uses a large amount of water and has a poor effect on coal dust with poor wettability. This study proposed a clean and sustainable technology using plasma-activated water (PAW) to alter the wettability of coal dust and improve its dust control effect. The PAW was prepared and its physical and mathematical properties were tested by a device designed in-house. The influence of PAW on the wettability of coal dust was determined by the coal dust contact angle experiments. The effect of PAW on the surface morphology of coal dust was analyzed by a scanning electron microscope. The effect of PAW on the pore structure of coal dust was analyzed through the specific surface area and pore size experiments. The results showed that PAW contained a large number of active substances such as H₂O₂, NO₃⁻, and NO₂⁻, showing strong and stable oxidation. PAW could significantly reduce the instantaneous contact angle of coal dust, and the higher the degree of coal dust metamorphism, the more significant the reduction effect. The surface morphology, pore volume, specific surface area, and fractal dimension of the coal dust were significantly changed after PAW treatment. PAW could transform the non-uniform three-dimensional spatial distribution of the coal dust surface into an approximate two-dimensional planar distribution, thus enhancing the wettability of the coal dust. With the increase in PAW ionization intensity, the contact angle of long-flame coal was negatively correlated with the mesoporous pore volume. The contact angle of gas coal was negatively correlated with the micropore volume and micropore specific surface area, and was positively correlated with the mesopore volume. The contact angle of meager lean coal was positively correlated with the macropore specific surface area. The surface morphology, pore volume, specific surface area, and fractal dimension changes in coal dust treated with PAW can reveal the wettability enhancement mechanism to some extent. The results of the study can provide pre-theoretical guidance for the field application of PAW coal mine dust reduction technology. Full article

Mine ramps, serving as a critical transportation hub in underground mining activities, are beset by severe issues of dust pollution and secondary dust generation. While dust suppressants are more efficient than the commonly used sprinkling methods in mines, traditional single-function dust suppressants are inadequate for the complex application environment of mine ramps. Building on the development of conventional single-function dust suppressants, this research optimized the components of bonding, wetting, and moisturizing agents. Through single-factor optimization experiments, a comparison was made of the surface tension water retention property and viscosity of diverse materials, thus enabling the identification of the primary components of the dust suppressant. By means of synergistic antagonism experiments, the optimal combination of the wetting agent and bonding agent with excellent synergy was ascertained. Ultimately, the wind erosion resistance and rolling resistance were measured through three-factor orthogonal experiments, and the optimal ratio of the dust suppressant was established. Specifically, fenugreek gum (FG) was selected as the bonding agent, cane sugar (CS) as the moisturizing agent, and alkyl phenol polyoxyethylene ether (Op-10) as the wetting agent. The research findings demonstrate that the optimal ratio of dust suppressant is 0.3 wt% fenugreek gum (FG) + 0.06 wt% alkyl phenol polyoxyethylene ether (Op-10) + 3 wt% cane sugar (CS). Under these conditions, the dust fixation rate can reach up to 97~98% at a wind speed of 8 m/s. The maximum rolling resistance can reach 65~73% after grinding the samples for 1 min. The surface tension of the solution is 13.74 mN/m, and the wetting performance improved by 81% compared to pure water. This dust suppressant is of great significance for improving the working environment of workers and ensuring the sustainable development of the mining industry. Full article

Throughout China’s 14th five-year plan, the national natural gas pipeline network has been interconnected, and the gas quality became increasingly complex. A certain amount of dust particles widely spread in the natural gas pipeline and lead to abnormal wear of the reciprocating compressor’s compression cylinder within the underground storage cavern. The wear characteristics of the compression cylinder are effectively demonstrated based on the tangential impact energy model, and combined with field measurement and the moving-grid method of computational fluid dynamics. The results reveal that the lubricating oil forms “grinding paste” when mixed with dust particles. With an increase in the dust mass concentration from 0.01% to 3.00%, the viscosity of the “grinding paste” increases from 450,800 mPa·s to 1,274,000 mPa·s, and the density increases from 890 kg/m³ to 980 kg/m³. The abnormal wear frequently occurs at the 12 o’clock and 6 o’clock directions of the compression cylinder. When the piston is in the midpoint of the stroke, the wall shear rate and the wear rate are the highest. When the piston is at both endpoints of the stroke, the wall shear rate and the wear rate are the lowest. For every 1000 h of operation without repairing the cylinder, the dust concentration should be controlled below 0.60%. For every 5000 h and 10,000 h of operation without replacing the cylinder, the dust concentration should be controlled below 1.3% and 0.4%. When the dust mass concentration is 0.01%, the wear rate decreases with decreasing lubricating oil viscosity. When the dust mass concentration is 0.51% and 1.0%, and the lubricating oil viscosities are 259,700 mPa·s, 220,500 mPa·s, and 196,980 mPa·s, the wear rate increases dramatically with decreasing lubricating oil viscosity. Full article

As a critical task in underground coal mining, personnel identification and positioning in fully mechanized mining faces are essential for safety. Yet, complex environmental factors—such as narrow tunnels, heavy dust, and uneven lighting—pose significant challenges to accurate detection. In this paper, we propose a personnel detection network, MSS-YOLO, for fully mechanized mining faces based on YOLOv8. By designing a Multi-Scale Edge Enhancement (MSEE) module and fusing it with the C2f module, the performance of the network for personnel feature extraction under high-dust or long-distance conditions is effectively enhanced. Meanwhile, by designing a Spatial Pyramid Shared Conv (SPSC) module, the redundancy of the model is reduced, which effectively compensates for the problem of the max pooling being prone to losing the characteristics of the personnel at long distances. Finally, the lightweight Shared Convolutional Detection Head (SCDH) ensures real-time detection under limited computational resources. The experimental results show that compared to Faster-RCNN, SSD, YOLOv5s6, YOLOv7-tiny, YOLOv8n, and YOLOv11n, MSS-YOLO achieves AP50 improvements of 4.464%, 10.484%, 3.751%, 4.433%, 3.655%, and 2.188%, respectively, while reducing the inference time by 50.4 ms, 11.9 ms, 3.7 ms, 2.0 ms, 1.2 ms, and 2.3 ms. In addition, MSS-YOLO is combined with the SGBM binocular stereo vision matching algorithm to provide a personnel 3D spatial position solution by using disparity results. The personnel location results show that in the measurement range of 10 m, the position errors in the x-, y-, and z-directions are within 0.170 m, 0.160 m, and 0.200 m, respectively, which proves that MSS-YOLO is able to accurately detect underground personnel in real time and can meet the underground personnel detection and localization requirements. The current limitations lie in the reliance on a calibrated binocular camera and the performance degradation beyond 15 m. Future work will focus on multi-sensor fusion and adaptive distance scaling to enhance practical deployment. Full article

To address the challenges of low accuracy in coal gangue image recognition and poor segmentation performance under the influence of dust in underground coal mines, a scaled simulation platform was constructed to replicate the longwall top coal caving face. This platform utilized real coal gangue particles as the raw material and employed dust simulation to mimic the dust conditions typically found in coal mines. Images of coal gangue without dust and under varying dust concentrations were then collected for analysis. In parallel, an improved DeeplabV3+ coal gangue image segmentation model is proposed, where ResNeSt is employed as the backbone network of DeeplabV3+, thereby enhancing the model’s capability to extract features of both coal and gangue. Furthermore, two channel attention modules (ECAs) are incorporated to augment the model’s ability to recognize edge features in coal gangue images. A class-label smoothing training strategy was adopted for model training. The experimental results indicate that, compared to the original DeepLabV3+ model, the optimized model achieves improvements of 3.14%, 4.70%, and 3.83% in average accuracy, mean intersection over union (mIoU), and mean pixel accuracy, respectively. Furthermore, the number of parameters was reduced from 44.18 M to 43.86 M, the floating-point operations decreased by 8.33%, and the frames per second (FPS) increased by 45.03%. When compared to other models such as UNet, PSANet, and SegFormer, the proposed model demonstrates superior performance in coal gangue segmentation, accuracy, and parameter efficiency. A method combining dark channel prior and Gaussian weighting was employed for defogging coal gangue images under varying dust concentration conditions. The recognition performance of the coal gangue images before and after defogging was assessed across different dust concentrations. The model’s segmentation accuracy and practical applicability were validated through defogging and segmentation of both indoor and underground dust images. The recognition accuracy of coal and gangue, before and after defogging, improved by 6.8–71.8% and 5.8–45.8%, respectively, as the dust concentration increased, thereby demonstrating the model’s effectiveness in coal gangue image defogging segmentation in underground dust environments. Full article

Current regulations mandate the monitoring of respirable coal mine dust (RCMD) mass and crystalline silica in underground coal mines to safeguard miner health. However, other RCMD characteristics, such as particle size and chemical composition, may also influence health outcomes. This study collected RCMD samples from two underground coal mines and performed detailed chemical speciation. Source apportionment was used to estimate RCMD and silica contributions from various sources, including intake air, fire suppression limestone dust, coal dust, diesel engine exhaust, and rock strata. The mine dust mass-based size distributions were comparable to those recorded over a decade ago, with a peak around 10 μm and the majority of the mass in the supermicron size range. The current mine conditions and mining practices do not appear to have significantly increased the generation of smaller particles. Limestone rock dust was prevalent in many locations and, along with coal dust, was the main contributor to RCMD at high-concentration locations. Silica accounted for over 10% of RCMD mass at several active mining locations, primarily from limestone and rock strata dust. Reducing the concentration of limestone dust and its silica content could reduce RCMD and silica levels. Further cleaning of the intake air could also improve the overall mine air quality. Full article

Cement production may involve excessive use of natural resources and have negative environmental impacts, as energy consumption and CO² emissions can cause air pollution and climate change. Cement kiln dust (CKD), a by-product waste material, is also a primary issue associated with cement production. Utilizing CKD in mining applications is a pathway to eco-sustainable solutions. Cemented paste backfill (CPB) made with mine tailings is an efficient method for void backfilling in underground mines. Therefore, this study investigated the eco-sustainable utilization of CKD as a co-binder material that can partially replace cement in CPB prepared with copper tailings. At 7, 14, 28, 56, and 90-day curing times, the experimental campaign measured the physical and mechanical parameters of the cured CPB samples, including density, UCS, and elastic modulus (stiffness). Additionally, the CPB-cured mixes were analyzed using XRF, X-ray XRD, SEM, and EDX techniques to link the mineral phases and microstructure to mechanical performance. Four proportions (5, 10, 15, and 20%) of CKD represented in 75 samples were prepared to replace ordinary Portland cement (OPC) in the CPB mixtures, in addition to the reference mix (control) with 0% CKD. As all combinations exceed the compressive strength of CPB required for achieving stability in underground mines, the results showed that CKD could be utilized advantageously as a partial substitute for OPC with a proportion of up to 20% in the CPB mixture. When tested after 90 days, the combination modified with 5% CKD exhibited comparatively higher compressive strength than the control mixture. Full article

This paper considers extreme dusty conditions at workplaces in underground coal mine. These extreme conditions stem from various physical factors that affect employees’ performance. The extreme effect of the dust can significantly contribute to permanent health damage or even the death of employees. In this study, we present and discuss the results of measurements of airborne dust and respiratory dust taken during wall cutting in a coal mine and propose effective measures to reduce the burden on the life and health of employees and the environment. Full article

Coal dust is one of the environmental factors that seriously affect the health of workers as well as the mining equipment in underground coal mines. At present, coal dust is commonly generated during drilling, blasting, excavation, and transportation processes in mining operations. During mining blasting processes, coal dust is generated with varying particle sizes and high concentration levels. High concentrations of dust will affect mining operations and increase the ventilation time required for mining faces. In addition, coal dust exists in suspended form in the roadway and is harmful to human health, especially fine dust particles that have a negative impact on work efficiency. To improve ventilation efficiency and eliminate coal dust, this article presents a CFD-DPM numerical modeling method that integrates a DEM collision model based on the finite element method to analyze the motion characteristics of airflow and dust particles in the mine tunnel, while considering collisions between particles and between particles and walls. The article analyzes the distribution of wind speed, the dispersion of dust in the space around the roadway, and dust concentrations at distances of 1 m, 3 m, and 6 m from the working personnel and at a position 1.5 m above the roadway floor, corresponding to the breathing zone of the workers, with varying parameters such as velocity and duct position. The results indicate that with a wind velocity of V = 18 m/s and an air duct height h = 3.0 m, the best dust reduction results are achieved, and they provide theoretical guidance for selecting and optimizing ventilation parameters in dust control. Full article

Battery-powered electric machines have been replacing classic combustion vehicles for many years in the automotive and heavy industry. This change has a positive impact on the environment and, in the case of working machines, also on the safety and comfort of operators. In underground mining plants, due to limited working space and difficult environmental conditions, the use of battery-powered electric vehicles (BEVs) in place of combustion machines with diesel engines brings even greater benefits in terms of the operator’s work conditions. This article presents the results of comprehensive tests of two roof bolters in a BEV and a vehicle with a combustion engine. The tests were performed in underground conditions, during normal operation of the machines. They covered many aspects of machines’ operation, such as availability; traction properties; battery use; cooling system; efficiency; costs; safety; and ergonomics in terms of gas emissions, noise, vibrations, and generally understood work comfort. The research results showed a significant advantage of the battery-powered machine over the one with a combustion engine. The tests in question are unique due to their scope and the fact that they were carried out in underground conditions, during normal operation, both for the internal combustion machine and its battery-powered equivalent. Full article