Underground Mining Environment Research Articles

Research and Simulation on the Development of a Hydraulic Prop Support System of Powered Roof Support to Increase Work Safety.

The underground mining environment is currently based on technology that uses mainly analogue sensors in machine and equipment control systems. The primary machine performing the most important functions in a mining system is the powered roof support. In order for it to work properly, it is important that it achieves the required power. To ensure this, it is necessary to continuously and precisely monitor the pressure in the under-piston space of the prop. Due to the extreme environmental conditions, pressure sensors should have high sensitivity, large transmission capacity, small size and light weight. To achieve these requirements, the authors of the article propose to implement a monitoring system based on photonics technology. To achieve this goal, several studies were carried out. The range of these studies included simulations, bench tests and tests under real conditions. The obtained test results showed the possibility of developing the control system for the powered roof support, the additional function to supercharge power. Based on the analysis of the obtained test results, assumptions were developed for the development of a power charging system with monitoring sensors. Based on the guidelines obtained from the research results, thedevelopment of the above prototype based on photonics technology is proposed.

UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement

AbstractIn recent years, low‐light image enhancement has become increasingly active. However, in underground mine environments, acquiring high‐quality images is still challenging due to low light, low contrast, and occlusion. To address this problem, this study proposes a low‐light image enhancement method for underground mine based on generative adversarial networks (UM‐GAN), which aims to take full advantage of the ability of GAN to achieve the restoration of details, the reduction of noise, and the improvement of overall image quality. The model proposed in this paper is divided into three main parts. Initially, a generator network adopting an encoder–decoder structure is developed. Subsequently, a novel strategy is introduced to merge information by utilizing inverted greyscale images and low‐light images as inputs. Further image quality enhancement is achieved by incorporating a noise reduction module employing the diffusion model. To ascertain the efficacy of the UM‐GAN method, evaluations are conducted on diverse real‐world and synthetic datasets, juxtaposing the approach against superior methods. Through qualitative and quantitative comparative experiments, the method showcases noteworthy advancements through qualitative and quantitative comparative experiments, substantiating its effectiveness. This research provides new ideas and methods for overcoming image quality problems in underground mine environments and contributes to the development of underground mine image processing.

Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry

Although multiview platforms have enhanced work efficiency in mining teleoperation systems, they also induce “cognitive tunneling” and depth-detection issues for operators. These issues inadvertently focus their attention on a restricted central view. Fully immersive virtual reality (VR) has recently attracted the attention of specialists in the mining industry to address these issues. Nevertheless, developing VR teleoperation systems remains a formidable challenge, particularly in achieving a realistic 3D model of the environment. This study investigates the existing gap in fully immersive teleoperation systems within the mining industry, aiming to identify the most optimal methods for their development and ensure operator’s safety. To achieve this purpose, a literature search is employed to identify and extract information from the most relevant sources. The most advanced teleoperation systems are examined by focusing on their visualization types. Then, various 3D reconstruction techniques applicable to mining VR teleoperation are investigated, and their data acquisition methods, sensor technologies, and algorithms are analyzed. Ultimately, the study discusses challenges associated with 3D reconstruction techniques for mining teleoperation. The findings demonstrated that the real-time 3D reconstruction of underground mining environments primarily involves depth-based techniques. In contrast, point cloud generation techniques can mostly be employed for 3D reconstruction in open-pit mining operations.

Comprehensive heat extraction performance and fractured reservoir cooling effect analysis of a novel mine enhanced geothermal system

Mining deep metal mines poses significant challenges due to the high-temperature conditions in such environments. However, the vast untapped geothermal energy within these mines holds great prospects for development and application. Herein, we proposed a novel technology for the deep mine cooling and enhanced geothermal system (DMC-EGS), which transforms the relationship between deep mining and geothermal resources from a source of heat harm to heat benefit, while also providing conditions for underground mining environment cooling. Understanding the fluid flow and heat transfer processes in the deep mine thermal reservoir is crucial for developing DMC-EGS. Based on the local thermal equilibrium theory, a three-dimensional thermal–hydraulic coupled model of the thermal reservoir in the Sanshan Island Mine containing multiple fractures was established. The impact of natural fractures and fracture number on the thermal recovery performance and cooling effect of the system was considered, and the optimization scheme of heat recovery wellbore was proposed for the deep thermal reservoir of Sanshan Island Mine. It can be found that fractures play a crucial role in fluid-rock heat exchange. Compared with scenarios without fractures, hydraulic fractures could significantly improve the heat transfer capacity and rock cooling effect, resulting in a 45.7% increase in the amplitude of rock matrix temperature decrease and more than a 1.76-fold increase in overall heat recovery efficiency. In addition, the number of fractures significantly impacted the temperature distribution within the rock formation, and an increased number of fractures was associated with more efficient heat recovery performance. When the number of fractures was 7, the cumulative thermal production could reach 6.767 × 109 J. Among the three thermal exploitation schemes compared, the DMC-EGS under the triple-well mode yielded the best heat recovery performance during the 40-year extraction period, superior to the conventional dual-well mode, while the one injection and two production mode demonstrated better heat recovery capacity and cooling effect.

Bioinspired Whisker Sensor for 3D Mapping of Underground Mining Environments.

Traversing through challenging, unstructured environments, particularly in mining scenarios characterized by dust concentration, darkness, and lack of communication presents formidable obstacles for traditional sensing technologies. Drawing inspiration from naked mole rats, characterized as being skilled subterranean navigators that depend heavily on touch to navigate their environment, this study introduces a new whisker-sensing disk designed for 3D mapping in unstructured environments. The disk comprises a circular array of 32 whisker sensors, each featuring a slender flexible plastic rod attached to a compliant base housing a 3D Hall-effect sensor. The whisker sensor is modeled using both analytical and data-driven approaches to predict rotation angles based on magnetic field measurements. The validation and comparison of both models are performed by evaluating data from other whisker sensors. Additionally, a series of experiments demonstrates the whisker disk's capability in performing 3D-mapping tasks, along with successful implementation on diverse robotic platforms, highlighting its future potential for effective 3D mapping in complex and unstructured subterranean environments.

Ventilation characteristics of double-forced air pipe ventilation in comprehensive mechanized excavation face and determination of its optimal parameters

ABSTRACT This study proposes a ventilation and dust control approach using double-forced air pipes at the tunnel excavation face, aiming to optimize the dust control and reduction measures for underground coal mining environment. We designed a physical model in proportion to the 11,139 excavation face in the Zhangji Coal Mine of China Huaihe Energy Holding Group Coal Company. To begin with, CFD numerical simulation was employed to unveil the basic laws of the double-forced air pipe ventilation approach, including airflow characteristics and dust concentrations. The orthogonal experimental method was then adopted to analyze the influence mechanism of installation location and airflow ratio of the double pipes on the dust density around the driver’s operating space. Results suggest that the double-forced air pipe ventilation can efficiently improve the airflow velocity at the excavation face, reaching 5.54 m/s. With the minimum dust density around the driver’s operating space as the optimal parameter standard and combined with the R value, the three factors in the orthogonal experiment can be sorted as: the distance between the forced ventilation pipe at the driver’s side and the excavation face (R = 206.91) > the distance between the forced ventilation pipe at the return air side and the excavation face (R = 171.72) > the airflow ratio of the two pipes (R = 77.52). The optimal combination is that the distance between the ventilation pipe at the driver’s side and the excavation face is 5 m, the distance between the ventilation pipe at the return air side and the excavation face is 5 m, and the airflow ratio of the two pipes is 6:4. In such a case, the dust density around the driver’s operating space is 48.21 mg/m3, achieving an ideal dust control effect.

Research on coal volume detection and energy-saving optimization intelligent control method of belt conveyor based on laser and binocular visual fusion

Real-time coal flow monitoring is crucial for efficient coal mine transportation. Traditional vision acquisition devices generate two-dimensional images that are not accurately identified in the complex underground coal mine environment, such as dust, water mist, and low light. In this paper, we propose a conveyor belt coal flow detection method that integrates laser scanning and binocular vision to address this problem. Our proposed method has several advantages over traditional approaches. Firstly, we calibrate the binocular camera using Zhang’s calibration method to enhance the accuracy of the system. Secondly, we extract the centerline of the laser stripe using the grayscale center of gravity method, which improves the system’s performance in complex environments. Thirdly, we calculate the cross-sectional area of the material accurately using the trapezoidal area accumulation method, and visualize it in two dimensions based on a single frame, while the point cloud data from multiple consecutive frames are visualized in a 3D model at a realistic scale. Finally, we use the continuous multi-frame cross-sectional area to calculate the current conveyor flow, and apply the BP neural network to establish an energy-saving optimization model for the belt conveyor. We also design a PLC fuzzy controller based on fuzzy control algorithms to adjust the belt’s operating speed intelligently according to the coal flow size, achieving energy-saving operation and intelligent control of the belt conveyor. Our experiments show that our method can accurately obtain coal volume and control the belt speed of the conveyor in real-time, making it an innovative and practical solution for coal flow detection and energy-saving operation in underground coal mines.

Highly efficient adiabatic and flame-retardant SiO2 aerogel and its fibrous composite for high-temperature applications within underground chambers

In the present scenario, the underground chambers of deep metal mines are plagued by excessively high temperatures, and the existing adiabatic materials have proven to be inadequate in addressing this challenge. Considering these issues, this study introduces an innovative adiabatic and flame-retardant SiO2 aerogel-based fibrous composite designed specifically for underground applications. The optimal preparation method for SiO2 aerogel preparation was identified through monomer and orthogonal experiments, ultimately achieving a low thermal conductivity coefficient of 0.028 W/(m·K). Subsequently, through integral moulding, a composite material was developed, incorporating the optimal SiO2 aerogel as a precursor along with ceramic fibres. The resulting ceramic fibre/SiO2 aerogel exhibited an increased thermal conductivity coefficient of 0.061 W/(m·K). When subjected to a high-temperature combustion source of approximately 700 °C, the temperature of the upper surface of the sample was maintained at 131.9 °C and its mass loss rate was a mere 1.2%, exhibiting strong adiabatic and flame-retardant capabilities of the composite material. Moreover, the composite material exhibited good hydrophobic, adsorbent, and mechanical properties, making it well-suited for underground chamber applications. The research conducted on SiO2 aerogel and its adiabatic ceramic fibre composite not only holds theoretical significance by guiding the development of improved adiabatic materials but also provides practical engineering solutions for mitigating heat transfer through raw rock and reducing chamber temperatures in underground mining environments.

A Multimodal Robust Simultaneous Localization and Mapping Approach Driven by Geodesic Coordinates for Coal Mine Mobile Robots

Mobile robots in complex underground coal mine environments are still unable to achieve accurate pose estimation and the real-time reconstruction of scenes with absolute geographic information. Integrated terrestrial-underground localization and mapping technologies have still not been effectively developed. This paper proposes a multimodal robust SLAM method based on wireless beacon-assisted geographic information transmission and lidar-IMU-UWB elastic fusion mechanism (LIU-SLAM). In order to obtain the pose estimation and scene models consistent with the geographic information, the construction of two kinds of absolute geographic information constraints based on UWB beacons is proposed. An elastic multimodal fusion state estimation mechanism is designed based on incremental factor graph optimization. A tightly coupled lidar-inertial odometry is firstly designed to construct the lidar-inertial local transformation constraints, which are further integrated with the absolute geographic constraints by UWB anchors through a loosely coupled approach. Extensive field tests based on coal mine robots have been conducted in scenarios such as underground garages and underground coal mine laneways. The results show that the proposed geodesic-coordinate driven multimode robust SLAM method can obtain absolute localization accuracy within 25 cm with practical robustness and real-time performance in different underground application scenarios. The wireless beacon-assisted geodesic-coordinate transmission strategy can provide a plug-and-play customized solution for precise positioning and scene modeling in complex scenarios of various coal mine robot application.

Mining security assessment in an underground environment using a novel face recognition method with improved multiscale neural network

Overstaffing production in underground coal mining is not convenient for daily management, and incomplete information of coal miners hinders the rescue process of firefighters during mine accidents. To address this safety sustainability issue, a novel face recognition method based on an improved multiscale neural network is proposed in this paper. A new depthwise separable (DS)-inception block is designed and a joint supervised loss function based on center loss theory is developed to construct a new multiscale model. The miniers can be recognized in the harsh underground environment during the life rescue. Experimental results show that the accuracy, recall and F1-score indexes of the proposed method for the miner face recognition in the underground mining environment are 97.26%, 94.17% and 95.42%, respectively. Transfer model with joint supervised loss can effectively improve the recognition accuracy by about 0.5–1.5%. In addition, the average recognition accuracy of the proposed face recognition method achieves to 91.34% and the miss detection rate is less than 5% in the dugout tunnel of coal mine.

Study on PM diffusion and distribution of trackless rubber-tyred vehicle under different driving conditions in underground coal mining environment.

Particulate matter (PM) is one of the most harmful exhaust pollutants to human health. In this study, the PM diffusion and distribution emitted by trackless rubber-tyred vehicle under different driving conditions in coal mine were analyzed with numerical simulations and field measurements. The results show that when the vehicle velocity was constant, the PM concentration of the trackless rubber-tyred vehicle decreased with increasing distance from the exhaust pipe orifice. In addition, the proportion of PM with a concentration below 10mg/m3 was the highest owing to the influences of diffusion and airflow dilution. However, when the diffusion distance is less than 3m, the PM concentration far exceeds the occupational exposure limit (10mg/m3). In this case, underground personnel should stay away from the area near and along the exhaust pipe as far as possible. With increasing vehicle velocity, the PM concentration gradient at a diffusion distance of 0-6m showed the most significant slope. Besides, the concentration fluctuation of PM was the largest and relatively high when the diffusion distance was 5-15m. Therefore, the area 15m from the exhaust gas pipe opening of the trackless rubber-tyred vehicle should be controlled. In addition, the relative errors between the measured and numerical simulation results were mostly less than 10%, which proved that the numerical simulation results were reliable.

Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks.

The Mine Internet of Things (MIoT), as a key technology for reconstructing post-disaster communication networks, enables a user to monitor and control the safety of an affected roadway. However, due to the challenging underground mine environment, the MIoT suffers from severe signal attenuation, vulnerable nodes, and limited energy, which result in a low level of network reliability for the post-disaster MIoT. To improve transmission reliability and reduce energy consumption, a directional-area-forwarding-based energy-efficient opportunistic routing (DEOR) approach for the post-disaster MIoT is proposed. DEOR defines a forwarding zone (FZ) for each node to route packets toward the sink. The candidate forwarding set (CFS) is constructed by the nodes within the FZ that satisfy the energy constraint and the neighboring node degree constraint. The nodes in the CFS are prioritized based on a routing quality evaluation, which takes the local attributes of the nodes, such as the directional angle, transmission distance, and residual energy, into consideration. DEOR adopts a recovery mechanism to address the issue of void nodes. The simulation results verify that the proposed DEOR approach outperforms the ORR, OBRN and ECSOR methods in terms of energy consumption, average hop count, packet delivery rate, and network lifetime.

Numerical investigation of seismic wavefield characteristics induced by a tensile source in a heterogeneous mining environment

Mining-induced tremors serve as the main dynamic stress source of coal bursts which represent one type of the most critical mining disasters in underground coal mines. To evaluate coal burst risks and comprehend the response of the coal-rock mass to seismic waves, microseismic monitoring stands as the principal method. By inferring the zones with elevated coal burst risks based on the seismic event clusters, valuable insights can be gained. However, because of limited seismic monitoring results generated from an individual seismic event, the recognition of the corresponding dynamic disturbance process remains ambiguous. The dynamic disturbance process presents immense complexity due to the focal mechanism and heterogeneous medium, particularly under the effect of structural heterogeneity induced by large-scale mining operations. In this study, forward numerical modelling was employed to gain the comprehension of seismic wave propagation affected by extraction zones. The findings revealed heightened intricacy of the wavefields and increased displacement concentrations around the working face. The characteristics of peak particle velocity (PPV) and displacement spectra were investigated in the near- and far-field zones, respectively. Owing to the existence of the extraction zone, both a significant amplification effect and the redistribution of PPV were observed around the near-field zone. The intricate wavefield caused by the focal mechanism and extraction zones resulted in more scattered distributions of the low-frequency spectral level, which can potentially cause an overestimation of source intensity. However, the impact of the complex wavefield on the corner frequency was limited. Through the analysis of the PPV attenuation law, as well as the distribution of both seismic moment and source radius, this study offered valuable insights for seismic risk assessment. The forward modelling approach facilitated the identification of high-risk potential areas resulting from seismic events, which deepened our comprehension of the dynamic disturbance process in the complex underground mine environment.

Development and Evaluation of a UWB-Based Indoor Positioning System for Underground Mine Environments

Accurate positioning information is a central step in the process of increasing automation and digitalization in the mining industry. Underground mine environments are prime examples for GNSS-denied environments that pose several additional challenges for the employed indoor positioning system (IPS). This work presents an ultra-wideband (UWB)–based system specifically tailored for underground mine environments. Initially, the UWB technology is reviewed and basic concepts of IPS are explained. Both software and hardware are described in detail, including positioning algorithms, electrical circuitry, and the mining-compatible enclosure. Finally, the results from field tests in an underground mine are presented and positioning performance and the influence of dilution of precision (DOP) on positioning results are empirically evaluated. The results show that the localization errors are mostly below 1 m. Individual zones of high localization errors are suspected to be caused by noise interference. Significant DOP influence due to the mining drift geometry can be confirmed. The system shows promising localization results for different mining applications, while influences negatively impacting the accuracy are presented as items for future research.

The spread of microbiota in the air of an underground hard coal mine – A case study

The environment of underground hard coal mines, due to high humidity and temperature, combined with the presence of organic substances and very high dust content, provides optimal conditions for the growth and development of microorganisms, including pathogens. The presented research was aimed at determining the quantitative and qualitative diversity of air in underground mining excavations. Microbiological tests of air samples were carried out according to standard microbiological techniques. The study of microbial communities made it possible to determine the unit concentrations of microorganisms, identify potential pathogens, and analyze the manner of their spread in the air of an underground mine. A large number and variety of microorganisms were observed in the bioaerosol, including pathogenic ones (e.g., Staphylococcus aureus, Aspergillus flavus) that may pose a potential threat to humans by causing lung and skin diseases. Due to the insufficient state of global knowledge in this area of research the obtained results are helpful in the modernization of existing and designing new mining excavation ventilation systems, as well as in refreshing particularly hazardous places.

Deployment of XRF Sensors Underground: An Opportunity for Grade Monitoring or Bulk Ore Sorting in Cave Mines

Ore grades are monitored regularly in cave mines through drawpoint sampling. Automating grade monitoring through deploying X-ray fluorescence (XRF) sensors on the buckets of production loaders has been proposed as an alternative approach to address the issues around the traditional practice of drawpoint sampling. Bucket-mounted sensors can also be employed for bulk ore sorting underground. This study is aimed at evaluating the deployment of XRF sensors on production loaders as an opportunity for grade monitoring or bulk ore sorting in caving operations. The mill feed grade prediction performances of the drawpoint sampling program and mine planning software were assessed for the Cadia East panel cave mine. The results showed that the drawpoint samples underestimated the mill feed quality during a 10-month investigation period. The cave portions with bulk ore sorting potential were linked to the extraction level layout to estimate the number of drawpoints where sensors could be situated for diverting ore and waste. Samples obtained from the mine were tested to evaluate the ability of a lab-scale proxy of a bucket-mounted XRF sensor system to measure copper and gold grades. R-squared values of 0.84 and 0.68 were achieved between the predicted and measured copper and gold grades of the samples, respectively. Sensor test results are promising in revealing the potential to utilize XRF sensors underground. Future test work is encouraged to further validate the applicability of XRF sensors in an underground mining environment.

Characterisation of stress corrosion durability and time-dependent performance of cable bolts in underground mine environments

Premature failures of cable bolt caused by stress corrosion cracking (SCC) has been more and more often observed with the increasing depth of underground mines. Investigating and predicting this failure are critical in maintaining and accessing the integrity of the reinforcement system. In this study, with application of the designed SCC-AE testing system, the SCC failure processes of cold-drawn cable bolts used in underground reinforcement system was investigated using the acoustic emission (AE) techniques in simulated underground conditions. Damage evolution of the specimens was monitored with AE in real-time. The scanning electron microscopy was adopted to examine the fractographic features of the failed specimen. Through the integrated analysis of specimens’ elongation, the evolution of acoustic activities and fractographic features, three different stages during SCC failure processes were determined including cracks initiation due to the local corrosion, cracks propagation characterised as tear ridges appearance and fast fracture failure evidenced by the shear lips. The parameters of elongations, AE signals in each failure stage were investigated and the SCC propagation rates were determined. As a result, this study confirms the AE technique in combination with the designed testing system can give important insights into the SCC failure as well as the possibilities of real-time SCC monitoring in underground reinforcement systems.

Methane migration and explosive fringe localisation in retreating longwall panel under varied ventilation scenarios: a numerical simulation approach

Methane-based inflammable underground coal mine environment has led to catastrophic losses in the past. Migration of methane from the working seam and desorption region above and below the seam causes explosion hazard. In this study, the computational fluid dynamics (CFD)-based simulations of a longwall panel in a methane-rich inclined coal seam of the Moonidih mine in India established that the ventilation parameters greatlyinfluence themethane flow in the longwall tailgate and porous medium of the goaf. The field survey and CFD analysis revealed that methane accumulation on the "rise side" wall of the tailgate is attributable to the geo-mining parameters. Further, the turbulent energy cascade was observed to impact the distinct dispersion pattern along the tailgate. The numerical code was used to investigate the changes in ventilation parameters made to dilute the methane concentration in the longwall tailgate. Methane concentration in the tailgate outlet decreased from 2.4 to 1.5% as the inlet air velocity increased from 2 to 4m/s. The oxygen ingress into the goaf increased from 0.5 to 4.5 lps as thevelocity was increased, causing the explosive zone in the goaf to expand from 5 to 100m. Amongst all velocity variations, the lowest level of gas hazard was observed at an inlet air velocity of 2.5m/s. This study, thus, demonstrated the ventilation-based numerical method to assess the coexistence of gas hazard in the goaf and longwall workings. Moreover, it provided impetus to the necessity of novel strategies to monitor and mitigate the methane hazard in U-type longwall mine ventilation.

Survey on 2D Lidar Feature Extraction for Underground Mine Usage

Robust and highly accurate position estimation in underground mines is investigated by considering a vehicle equipped with 2D laser scanners. A survey of available methods to process data from such sensors is performed with focus on feature extraction methods. Pros and cons of the usage of different methods for the positioning application with 2D laser data are highlighted, and suitable methods are identified. Three state-of-the-art feature extraction methods are adapted to the scenario of positioning in a predefined map and the methods are evaluated through experiments conducted in a simulated underground mine environment. Results indicate that feature extraction methods perform in parity with the method of matching each ray individually to the map, and better than the point cloud scan matching method of a pure ICP, assuming a highly accurate map is available. Furthermore, experiments show that feature extraction methods more robustly handle imperfections or regions of errors in the map by automatically disregarding these regions.

Dynamic Response Analysis of a Reinforced Concrete Structure in Underground Coal Mine Environment with Various Strata Conditions

Dynamic Response Analysis of a Reinforced Concrete Structure in Underground Coal Mine Environment with Various Strata Conditions