Underground Mining Environment Research Articles

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers.

The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.

Genetic programming for prediction of heat stress hazard in underground coal mine environment

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.

Improving Workplace Environment of a Deep Underground Coal Mine with Multiseam Workings: An Alternative Approach

Improving Workplace Environment of a Deep Underground Coal Mine with Multiseam Workings: An Alternative Approach

Framework for Implementing Load Balancing Within Cluster in Underground Mining Environments

Abstract: Underground mines are more hazardous in nature and always contain unsafe for their workers. Underground mining causes huge losses due to accidents and disaster. There is the need to develop an effective, secure, reliable and low-cost surveillance and safety system in underground mine environments. Wireless Sensor Networks (WSNs) are increasingly being used for such application. Since wireless sensor nodes are highly energy constrained devices and they have limited battery life due to various constraints of sensor nodes. Therefore, optimal usage of node energy is the major challenge in wireless sensor networks. Clustering of sensor nodes is an effective method to use the node energy optimally and prolong lifetime of such energy constrained wireless sensor networks. Here we propose a framework for load balancing within clusters in the underground mine environment. The proposed clustering algorithm ensures balanced size cluster formation and it has been proposed to achieve balanced energy consumption among nodes. This project aims to give a secure and efficient communication for the underground are using these respective sensors and here a concept of cluster and their rotation is implemented so that the loss of signals can be restricted and helps in a secure communication

Evaluation of Work-related Musculoskeletal Injuries Among Coal Miner

Mining work is quite tedious, and it takes the whole body into account. Underground mine environment has sensory issues, and working conditions are not ideal. It results in maximum chances for the occurrence of work-related musculoskeletal injuries (WRMSIs). WRMSIs are the conditions which affect muscles, bones, joints, nerves, vessels, and supporting system. These are the significant cost occurring disorders in the workplace. This research aims to assess the WRMSIs in Lakhra coal mines to identify critical issues. Fifty operational workers completed the self- administrated questionnaire survey. Data related to personal and work information, physical activities age, nature of job, and WRMSIs was collected. A physical wellbeing checklist and Body Part Diagram were used to know the response of the worker. The research findings show that shoulder and knee disorders occur more frequently among workers. Almost 54% of cases were reported knee pain, whereas 44% reported toe/foot disorders, 38% for a wrist disorder, 36% stuck with shoulder pain, 16% had neck pain, 10% were with elbow pain, and only 2% have the hip disorder. The study is a positive addition in the research area to carry out better health and safety measures for the miners.

The LOS/NLOS Classification Method Based on Deep Learning for the UWB Localization System in Coal Mines

A localization system is one of the basic requirements for coal mines. Ultra-wideband (UWB), as a technology with broad application prospects, is considered to have great potential in the absence of satellite signals, especially in the underground mine environment, as it can provide rescue assistance. However, state-of-the-art UWB position systems in coal mines cannot efficiently differ the line-of-sight from all communication links, which results in deterioration of the localization accuracy. In this paper, we propose a LOS/NLOS classification method based on a deep learning algorithm. Specifically, we utilize the Generative Adversarial Networks (GAN) to generate diagnostic data for frame transmission under non-line-of-sight (NLOS) condition. Then, a Convolutional Neural Network (CNN) is adopted to identify the NLOS communication. Finally, the trilateral centroid positioning algorithm (TCPA) based on ranging data is used to verify the effect of our method for a localization system in coal mines. Field experiments show that our method can accurately differ the LOS/NLOS with the accuracy of 91.19%. The TCPA algorithm with our method can obtain 3.11% improvement compared with the scenario without using our method.

A Numerical Modelling Approach to Find the Stability of RIB and Snook in Mechanised Depillaring Panel — A Case Study of Kurja Mine

Depillaring by conventional techniques created many irregular shaped pillars, mainly located at shallow depths in different coalfields of India. Continuous Miner Technology (CMT) has been introduced to extract these coal pillars scientifically with safety and productivity with greater depth. Leaving a proper sized rib or snook is a legal requirement as it decides the efficiency and safety of the pillar. However, CMT of square/rectangular shaped pillars created irregular shaped rib and snook. With the conventional empirical formula it is difficult to estimate the strength of such ribs or snooks. These ribs or snooks should be of sufficient size to protect the adjacent slicing operation and junction as well as they should fail in a controlled manner when machines shift inside the extraction. Field studies of different mines found several factors affecting the design of rib or snook. They are various types of induced stress, geological disturbances, manner of extraction etc. Proper assessment of the performance of different sizes and shapes of ribs and snooks in the field is complex due to the problematic underground mining environment for depillaring. For successful operation of the CMT required to study every depending parameter scientifically. Therefore, a numerical model is conducted to estimate the factor of safety of rib and snook to calculate the rib and snook stability. Results of field and simulation studies are presented and discussed in this paper to determine the rib’s stability in the continuous miner panel to create a safe environment for the men and machines.

3D Global Localization in the Underground Mine Environment Using Mobile LiDAR Mapping and Point Cloud Registration.

This study proposes a 3D global localization method that implements mobile LiDAR mapping and point cloud registration to recognize the locations of objects in an underground mine. An initial global point cloud map was built for an entire underground mine area using mobile LiDAR; a local LiDAR scan (local point cloud) was generated at the point where underground positioning was required. We calculated fast point feature histogram (FPFH) descriptors for the global and local point clouds to extract point features. The match areas between the global and the local point clouds were searched and aligned using random sample consensus (RANSAC) and iterative closest point (ICP) registration. The object’s location on the global coordinate system was measured using the LiDAR sensor trajectory. Field experiments were performed at the Gwan-in underground mine using three mobile LiDAR systems. The local point cloud dataset formed for the six areas of the underground mine precisely matched the global point cloud, with a low average error of approximately 0.13 m, regardless of the type of mobile LiDAR system used. In addition, the LiDAR senor trajectory was aligned on the global coordinate system to confirm the change in the dynamic object’s position over time.

Concurrent Solar-Illumination and Power Line Voice Communication for Indian Underground Coal Mines – An Experimental Study

The illumination systems in underground coal mines in India, generally operating in unfavourable, hazardous, and restricted environments, are badly in need of updating and revamping as they play a vital role in mine safety and productivity. Moreover, person-to-person communication in underground coal mines is very inadequate. In view of limited resources, the provision of illumination and communication using the same infrastructure forms an attractive proposition. As wireless communication is not a viable alternative because of the high attenuation profile there, wired personal communication employing the proposed DC distribution system along with LED illumination proves to be a very efficient and economical approach. The proposed technique establishes the design and implementation of the very low voltage DC (LVDC) power distribution system especially for mine illumination in consort with power line voice communication for underground coal mines. The environment of an Indian underground coal mine is simulated inside a sub-way tunnel and a suitable model designed. An experimental study is made to validate the technical aspects. The major contribution of the present work is a novel solution comprising of a low-cost mine-safe illumination system along with essential voice communication by the same underground infrastructure.

Lanthanide-Dependent Methanol Metabolism of a Proteobacteria-Dominated Community in a Light Lanthanide-Rich Deep Environment.

This study investigated the occurrence and diversity of proteobacterial XoxF-type methanol dehydrogenases (MDHs) in the microbial community that inhabits a fossil organic matter- and sedimentary lanthanide (Ln3+)-rich underground mine environment using a metagenomic and metaproteomic approach. A total of 8 XoxF-encoding genes (XoxF-EGs) and 14 protein sequences matching XoxF were identified. XoxF-type MDHs were produced by Alpha-, Beta-, and Gammaproteobacteria represented by the four orders Methylococcales, Nitrosomonadales, Rhizobiales, and Xanthomonadales. The highest number of XoxF-EG- and XoxF-matching protein sequences were affiliated with Nitrosomonadales and Rhizobiales, respectively. Among the identified XoxF-EGs, two belonged to the XoxF1 clade, five to the XoxF4 clade, and one to the XoxF5 clade, while seven of the identified XoxF proteins belonged to the XoxF1 clade, four to the XoxF4 clade, and three to the XoxF5 clade. Moreover, the accumulation of light lanthanides and the presence of methanol in the microbial mat were confirmed. This study is the first to show the occurrence of XoxF in the metagenome and metaproteome of a deep microbial community colonizing a fossil organic matter- and light lanthanide-rich sedimentary environment. The presented results broaden our knowledge of the ecology of XoxF-producing bacteria as well as of the distribution and diversity of these enzymes in the natural environment.

An Enhanced VLC Channel Model for Underground Mining Environments Considering a 3D Dust Particle Distribution Model.

Underground Mining (UM) is a hostile industry that generally requires a wireless communication system as a cross-cutting axis for its optimal operation. Therefore, in the last five years, it has been shown that, in addition to radio-frequency-based communication links, wireless optical communications, such as Visible Light Communication (VLC), can be applied to UM environments. The application of VLC systems in underground mines, known as UM-VLC, must take into account the unique physical features of underground mines. Among the physical phenomena found in underground mines, the most important ones are the positioning of optical transmitters and receivers, irregular walls, shadowing, and a typical phenomenon found in tunnels known as scattering, which is caused by the atmosphere and dust particles. Consequently, it is necessary to use proper dust particle distribution models consistent with these scenarios to describe the scattering phenomenon in a coherent way in order to design realistic UM-VLC systems with better performance. Therefore, in this article, we present an in-depth study of the interaction of optical links with dust particles suspended in the UM environment and the atmosphere. In addition, we analytically derived a hemispherical 3D dust particle distribution model, along with its main statistical parameters. This analysis allows to develop a more realistic scattering channel component and presents an enhanced UM-VLC channel model. The performance of the proposed UM-VLC system is evaluated using computational numerical simulations following the IEEE 802.1.5.7 standard in terms of Channel Impulse Response (CIR), received power, Signal-to-Noise-Ratio (SNR), Root Mean Square (RMS) delay spread, and Bit Error Rate (BER). The results demonstrate that the hemispherical dust particle distribution model is more accurate and realistic in terms of the metrics evaluated compared to other models found in the literature. Furthermore, the performance of the UM-VLC system is negatively affected when the number of dust particles suspended in the environment increases.

Accurate Real-Time Localization Estimation in Underground Mine Environments Based on a Distance-Weight Map (DWM).

The precise localization of an underground mine environment is key to achieving unmanned and intelligent underground mining. However, in an underground environment, GPS is unavailable, there are variable and often poor lighting conditions, there is visual aliasing in long tunnels, and the occurrence of airborne dust and water, presenting great difficulty for localization. We demonstrate a high-precision, real-time, without-infrastructure underground localization method based on 3D LIDAR. The underground mine environment map was constructed based on GICP-SLAM, and inverse distance weighting (IDW) was first proposed to implement error correction based on point cloud mapping called a distance-weight map (DWM). The map was used for the localization of the underground mine environment for the first time. The approach combines point cloud frames matching and DWM matching in an unscented Kalman filter fusion process. Finally, the localization method was tested in four underground scenes, where a spatial localization error of 4 cm and 60 ms processing time per frame were obtained. We also analyze the impact of the initial pose and point cloud segmentation with respect to localization accuracy. The results showed that this new algorithm can realize low-drift, real-time localization in an underground mine environment.

The development of specialised modular protective structure on continuous miners against coal burst hazards

As mining progresses into deep ore deposits in Australia, geo-hazards such as coal burst and outbursts are becoming a major concern for mine workers. The occurrence of geo-hazards involved the ejection of coal lumps and sometimes large volumes of hazardous gases such as methane and carbon dioxide. Whilst it is extremely important to de-stress and de-gas the seam and adjacent strata before roadway development and install competent support systems such as steel mesh and bolt, the last line of protection will be the installation of a protective canopy on the Continuous Miner (CM), which is typically used for roadway developments, to shield mine workers from these deadly dynamic impacts of coal and rock resulting from a burst or outburst. This paper aims to introduce the design, manufacture and testing of an innovative modular protective structure on the CM in underground coal mines. The developed protective system can be easily assembled in the underground mining environment and provide a high level of protection against flying debris hazards in the event of a coal burst. The extensive experimental program and numerical simulations have confirmed the high performance of the protective system against high-speed impact loading by single and multiple coal rocks and projectiles.

Preparation of Transparent Sandwich-like Superhydrophobic Coating on Glass with High Stability and Self-Cleaning Properties

High stability and transparent superhydrophobic coating on a glass substrate that can effectively repel the wetting dust as a self-cleaning property are beneficial traits for solving the decrease in optical lens clarity in an unmanned underground mining environment. However, the transparent superhydrophobic coating has still not been applied due to the contradiction between visibility, hydrophobicity and durability. Herein, a sandwich-like superhydrophobic coating was designed and prepared on borosilicate glass, which consisted of a micro/nanostructure body of neutral silicone sealant (primer) and hydrophobic silica nanoparticles (interlayer), as well as a protective layer of ultraviolet (UV) gel. The coated glass exhibited excellent superhydrophobicity towards many aqueous solutions, and had highly visible light transparency of 80% at 4 wt.% primer mass content. Furthermore, significant tests including the droplet impact, hot water boiling, stirring in acetic acid aqueous solution and sandpaper abrasion were performed on our superhydrophobic coating, which indicated that the obtained transparent coating had good stability and excellent mechanical durability. The coated glass also showed a more wonderful self-cleaning property compared with that of the original glass. This superhydrophobic coating on glass substrate, fabricated by a facile and cost-effective layer-by-layer construction approach, has great potential for general and practical application in the unmanned mining environment under multiple dust and atomized water conditions.

Performance analysis of IEEE 802.15.7-based visible light communication systems in underground mine environments

Performance analysis of IEEE 802.15.7-based visible light communication systems in underground mine environments

Research on Intellectualized Location of Coal Gangue Logistics Nodes Based on Particle Swarm Optimization and Quasi-Newton Algorithm

The optimization of an integrated coal gangue system of mining, dressing, and backfilling in deep underground mining is a multi-objective and complex decision-making process, and the factors such as spatial layout, node location, and transportation equipment need to be considered comprehensively. In order to realize the intellectualized location of the nodes for the logistics and transportation system of underground mining and dressing coal and gangue, this paper establishes the model of the logistics and transportation system of underground mining and dressing coal gangue, and analyzes the key factors of the intellectualized location for the logistics and transportation system of coal and gangue, and the objective function of the node transportation model is deduced. The PSO–QNMs algorithm is proposed for the solution of the objective function, which improves the accuracy and stability of the location selection and effectively avoids the shortcomings of the PSO algorithm with its poor local detailed search ability and the quasi-Newton algorithm with its sensitivity to the initial value. Comparison of the particle swarm and PSO–QNMs algorithm outputs for the specific conditions of the New Julong coal mine, as an example, shows that the PSO–QNMs algorithm reduces the complexity of the calculation, increases the calculation efficiency by eight times, saves 42.8% of the cost value, and improves the efficiency of the node selection of mining–dressing–backfilling systems in a complex underground mining environment. The results confirm that the method has high convergence speed and solution accuracy, and provides a fundamental basis for optimizing the underground coal mine logistics system. Based on the research results, a node siting system for an integrated underground mining, dressing, and backfilling system in coal mines (referred to as MSBPS) was developed.

Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine

In this paper, an efficient model of the channel matrix is developed for 2 &#x00D7; 2 Wireless Body Area Network Multiple Input Output (WBAN-MIMO) system, based on deep learning algorithms. The model is composed of three deep learning algorithms. Moreover, the model predicts simultaneously the channel matrix <i>H</i> in underground mine and identifies the position of the collected data in both Line of Sight (LoS) and Non-Line of the Sight (NLoS) scenarios. The model is trained and evaluated using the magnitude and phase of the collected data in an underground mine environment within the frequency range of 2.3 GHz &#x2013; 2.5 GHz. These measurements, conducted with different antenna configurations in LoS and NLoS scenarios, constitute an input to the model. The latest predicts the channel matrix <i>H</i> with the position and identifies whether the channel is LoS or NLoS. Finally, the path loss and the channel impulse response models are compared with the measurements-based ones. The modeled channel prediction exhibited lower Root Mean Square Error (RMSE) for channel prediction and high classification accuracy for LoS-NLoS and position identification, respectively. The numerical results reveal that the deep learning MIMO WBAN modeling offers a powerful solution for future wireless systems in underground mine environments.

Achieving Reliable Underground Positioning With Visible Light

Visible light technology shows great potential in the underground mine positioning today. Most visible light-based underground positioning technology deploy the receiver, such as photodiodes (PDs), on the mine tunnel ground and consider the miner’s lamp on the ceiling as the transmitter. This incurs the unreliable reception of visible light signals and the difficult implementation of VLP systems due to the complex underground mine environment, such as the receiver being obscured or trampled by objects (e.g., Minecarts and Ore). We present a reliable underground positioning algorithm with visible light considering the actual mine environment. The key innovation is that we design a novel reverse mechanism, i.e., utilizing the existing miner’s headlamp as the transmitter for broadcasting the specific light beacon representing the mine’s identity information, and the PDs deployed on the ceiling of the mine tunnel as the receiver for optical signal reception. We can infer the current locations of all miners using a series of base stations constructed by PDs. If some special mine environment limits the large-scale deployment of base stations and causes the positioning blind spots, the inertial navigation is combined to achieve all miners’ real-time positioning. We overcome some technical challenges for this new mechanism and the underground mine environment, such as optimal frequency selection, robust frequency identification, and accurate base station positioning. We conduct extensive experiments on our algorithm in a simulated mine tunnel, and the experimental results verify that our algorithm exhibits good performance.

Development of empirical model for strength estimation of irregular-shaped-heightened-rib/snook for mechanised depillaring

Issues with conventional depillaring techniques resulted in unscientific development of a number of coal seams into rectangular/square shaped pillars, mainly, located at shallow depths in different coalfields of India. Continuous miner based mechanised depillaring (MD) has been introduced to extract these coal pillars which is the main stage of production. The practise of leaving a proper sized rib/snook is a legal requirement as it decides the efficiency and safety of the MD operation. However, MD of square/rectangular shaped pillars created irregular-shaped-heightened-rib/snook. Conventional empirical formula is not capable to estimate strength of such ribs/snooks. These ribs/snooks should be of sufficient size to protect the adjacent slicing operation and junction as well as it should fail in a controlled manner when shifted inside the goaf. Field studies conducted at 39 panels of 7 different mines found a number of factors affecting the design of rib/snook. Proper assessment of performance of different sizes and shapes of ribs/snooks in field is difficult due to the complex and challenging underground mining environment of depillaring. These challenges prohibit a detailed study to be carried out in field. Therefore, parametric study on calibrated numerical model is conducted to estimate the strength of rib/snook with variation in sizes and heights of rib/snook and depth of cover. Further, an empirical relationship is developed to estimate the strength of rib/snook based on multivariate regression analysis . The values of strength estimated by the developed relationship is found to be least when compared with the popular pillar strength formulations due to effect of pillar/rib/snook spalling observed during field investigation with increase in depth of working. Results of field and simulation studies are presented and discussed in this paper for the design of irregular-shaped-heightened-rib/snook.

Dispatch Optimization Model for Haulage Equipment between Stopes Based on Mine Short-Term Resource Planning

The working environment of underground mines is complicated, making it difficult to construct an underground mine production plan. In response to the requirements for the preparation of a short-term production plan for underground mines, an optimization model for short-term resource planning was constructed, with the goal of maximizing the total revenue during the planning period. The artificial bee colony optimization algorithm is used to solve the model using MATLAB. According to the basic requirements of underground mine ore haulage and ore hoisting, a haulage equipment inter-stopes dispatch plan model was constructed, with the primary goal of minimizing the haulage equipment wait time. A non-dominated sorting genetic algorithm is used to solve the optimization model. An underground mine is examined using the two models, and the optimization results are compared and verified with the scheme obtained by using traditional optimization algorithms. Results show that based on the improved optimization algorithm, the use of short-term production planning schemes to guide mine production operations can increase the haulage equipment utilization rate, thereby increasing mine production revenue.