Deep Underground Mines Research Articles

A multiphysics simulator for stope-coupled heat exchanger operation in deep underground mines

The ever-increasing mine depth offers unprecedented opportunities to access high-grade geothermal resources. By pre-installing pipeline systems in mined-out cavities before waste tailings placement, the stope-coupled heat exchanger (SCHE) has drawn growing attention for its superior socio-economic advantages. However, despite the extensive focus on energy efficiency, the system stability during heat production has been typically overlooked. This study therefore introduces a novel high-fidelity simulator for replicating practical SCHE operation. By embedding non-isothermal pipe flow into an evolutive thermo-poromechanics framework for cemented tailings, the outlet water temperature and multiphysics backfill response in diverse production settings are meticulously studied. Our calculations reveal for the first time that circulating chill fluid within mine backfill can substantially alleviate pressure development, suggesting enhanced system stability with heat-exchanger implementation. We also demonstrate that albeit targeting higher-grade geothermal resources, prioritizing cold-season operation, and pre-charging heat exchangers with production delays could spur heat productivity, the companion thermal pressure generation might still impose significant overloading risks. Conversely, while fast circulation would diminish the end-utilization temperature, the rapid heat removal could indeed facilitate superior efficiency and stability performance for the geothermal system. We believe these new findings hold critical implications for better positioning SCHE as a safely sustainable pathway towards hybrid utilization of deep resources.

Smart Wearable Device for Enhancing Safety and Efficiency of Coal Miners

The task of ensuring worker safety in underground coal mines has never been simple. It has always been challenging to ensure worker safety in underground coal mines. Coal miners are seriously injured or killed as a result of numerous fatal and non-fatal accidents all over the world. Accidents occur as a result of lack of monitoring of mining areas and failure to implement proper safety measures. In this review, the coal miner's smart wearable safety device is implemented. A smart wearable safety device that will monitor the miner's health and provides precautionary measures for the miner's safety has been developed with the advent of the Industrial Internet of Things (IIoT). Integrating smart wearable safety devices with various health sensors such as a pulse rate sensor, temperature sensor, blood oxygen sensor, gas sensor, and camera, and then connecting it to Node MCU, and the internet enhances the safety of coal miners. Sensors constantly transmit sensor data to the cloud, and if an unusual situation arises, it notifies the responsible person in the control room as well as the miners. Since deep underground mining is especially susceptible to toxic gases, low levels of oxygen, and hazardous gases, the MQ gas sensor family can detect them. The proposed system is primarily used to improve working conditions inside coal mines and to ensure workers' safety.

Factor of safety analysis for mine pillar considering the influence of the intermediate principal stress component

Failure of mine pillars, especially in deep underground mines, significantly threatens the safety of miners and equipment. Previous studies on mine pillar stability design have used classical constitutive models that ignore the intermediate principal stress component when determining the factor of safety. In this study, we develop and implement a three-dimensional modified Hoek–Brown (HB) constitutive model that incorporates the intermediate principal stress component into the numerical simulation tool FLAC3D. Furthermore, we propose and apply a strength-reduction technique to determine a more accurate factor of safety for mine pillars. This novel approach provides a more comprehensive and realistic method for geomechanical analysis and pillar design, enhancing our understanding of pillar stability. Through numerical analysis, we illustrate the impact of the intermediate principal stress component on mine pillar plasticity. The factor of safety is calculated via the strength reduction method, revealing a substantial improvement from 1.7 with the classical HB model to 2.0 with the 3D HB model. Including the intermediate principal stress component reduces the evolution of plasticity in the mine pillar. For instance, the volume of plastic zones diminishes, and the factor of safety increases as the width-to-height ratio increases. Exemplary simulations show that ignoring the effect of the intermediate principal stress component, including underestimating safety levels, designing suboptimal pillar design, and misinterpreting in situ observations and measurements, can lead to severe consequences.

Development of a Deep Neural Network Model for the Relocation of Mining-Induced Seismic Event

The precise relocation of seismic events is critical for many engineering projects. Swarms of minor or micro earthquakes typically reveal stress concentration and spots of greater seismic hazards. Particularly in the context of deep underground mining, advanced techniques that can accurately relocate microseismicities are urgently in demand. Here, we developed a neural network-based modeling training method that can precisely relocate seismicities and invert for velocities at the same time, with preconfigured receiver network locations. Our model can be iteratively improved with field recorded data. We showed that, with roughly eight iterations, we can reasonably resolve for the earthquake locations for both clusters of events, namely spatially distributed with linear pattern or randomly scattered. Our initially trained model, which only focused on events that had a linear distribution pattern, was used as the base for the training of the subsequent models which could better resolve for randomly scattered event locations. Although we stopped at the eighth iteration, the process reported here can be continued, as the model will have a better performance with more iterations.

Evaluation of underground hard rock mine pillar stability using gene expression programming and decision tree‐support vector machine models

AbstractAssessing the stability of pillars in underground mines (especially in deep underground mines) is a critical concern during both the design and the operational phases of a project. This study mainly focuses on developing two practical models to predict pillar stability status. For this purpose, two robust models were developed using a database including 236 case histories from seven underground hard rock mines, based on gene expression programming (GEP) and decision tree‐support vector machine (DT‐SVM) hybrid algorithms. The performance of the developed models was evaluated based on four common statistical criteria (sensitivity, specificity, Matthews correlation coefficient, and accuracy), receiver operating characteristic (ROC) curve, and testing data sets. The results showed that the GEP and DT‐SVM models performed exceptionally well in assessing pillar stability, showing a high level of accuracy. The DT‐SVM model, in particular, outperformed the GEP model (accuracy of 0.914, sensitivity of 0.842, specificity of 0.929, Matthews correlation coefficient of 0.767, and area under the ROC of 0.897 for the test data set). Furthermore, upon comparing the developed models with the previous ones, it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy. This suggests that these models could serve as dependable tools for project managers, aiding in the evaluation of pillar stability during the design and operational phases of mining projects, despite the inherent challenges in this domain.

A Trial Evaluation of Rock Core DCDA Absolute Shear Stress Measurement for Routine Quantitative Mining Hazard Assessment in Deep Underground High Stress Mines

A Trial Evaluation of Rock Core DCDA Absolute Shear Stress Measurement for Routine Quantitative Mining Hazard Assessment in Deep Underground High Stress Mines

Effects of Coal Mining Activities on the Changes in Microbial Community and Geochemical Characteristics in Different Functional Zones of a Deep Underground Coal Mine

Effects of Coal Mining Activities on the Changes in Microbial Community and Geochemical Characteristics in Different Functional Zones of a Deep Underground Coal Mine

Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings

Ground vibrations induced by underground mining blasting has a significant impact on the stability and safety of surface buildings near mines. Due to the thick rock layers overlying underground mines, there is presently limited accuracy in regard to predicting ground vibrations induced by underground mine blasting. Therefore, this study aims to improve the accuracy of predicting ground vibrations induced by underground blasting by comprehensively measuring the peak particle velocity (PPV) in all three directions and independently considering on the impact of vertical distance. Random forest regression (RFR), bagging regression (BR), and gradient boosting regression (GBR) were used to regress the X-axis PPV (X-PPV), Y-axis PPV (Y-PPV), and Z-axis PPV (Z-PPV) based on blasting records measured at an iron mine. In addition, a genetic algorithm, gray wolf optimizer (GWO), and a particle swarm optimization were used to optimize the parameters of the RFR, BR, and GBR. The comparison results show that GWO-GBR is the optimal model for the prediction of the X-PPV (R2 = 0.8072), Y-PPV (R2 = 0.9147), and Z-PPV (R2 = 0.9265), respectively. Thus, the GWO-GBR model proposed in this study is considered a highly reliable model for predicting ground vibrations induced by underground mine blasting to ensure the safety of the mines’ surroundings.

A Review on the 3D Cartographic and Spatiotemporal GIS Models for Safety of Accidents in Deep Underground Coal Mines

The aim of this study was the safety risk assessment of accidents that occur because of production dynamics in deep coal mining, one of the highly dangerous business class, with spatiotemporal GIS. In this study, accidents that occur at certain times during production in underground mining workplaces are discussed based on evaluations of spatial and temporal dimensions. Accordingly, various analyses have been handled using criminology science tactics and strategies such as 80–20, kernel density, space–time cube, and hot spot, which have been adapted to GIS tools. Implementations have been realized on 3D underground cartographic models covering accident data from deep coal mine workplaces in the Zonguldak-Kozlu basin in Turkey between 2019 and 2021, at an average elevation of – 400 m. Space–time cube, kernel density, and time series analyses suggest that the Annual Bonus Incentives, which are repeated every year and given before the new year, increase the psychosocial risks for employees. It has been determined that this situation can lead to problems such as poor concentration among miners and loss of focus in some shifts. Additionally, significant relationships were found between the number of accidents and monthly production progress rates as a temporal parameter. The results demonstrate that applying a reactive approach with spatiotemporal GIS to accidents in deep coal mines is becoming increasingly important.

Failure mechanism features and precursor characteristics of coal roadway groups in fault zone under confined conditions

After the stress wave penetrates the fault, it changes the stress direction and destroys the stress balance of the deep underground mining surface and roadway during mining activities, causing the roadway and underground equipment to collapse. To reveal the failure mechanism, we studied the precursor characteristics of unstable failure of rocks in deep roadway groups. With conducting two PFC numerical models based on common roadway layouts, we studied crack development and failure conditions of surrounding rocks near roadway groups by investigating the force chain fracture, coordination number, energy evolution, and acoustic emission moment tensor in the models. The results show that the overall deformation failure of the roadway group first occurs in the upper roadway, and the weak force chain break in the force chain field of the lower roadway is obvious, especially before and after the peak value, accompanied by the coordination number in the direction of 80° with the joint plane significantly reduced. At the same time, obvious force patterns were observed in the roadway group area: irregular gathering (>90°) and swirl (<90°). These findings clarify the mechanism of fault disturbance in roadways surrounding rocks and help to better understand the deformation failure feature and energy evolution process of coal.

NOx Emission Prediction of Diesel Vehicles in Deep Underground Mines Using Ensemble Methods

The mining industry faces persistent challenges related to hazardous gas emissions. Diesel engine-powered wheeled vehicles are commonly used during work shifts and are a primary source of nitrogen oxides (NOx) in underground mines. Despite diesel engine manufacturers providing gas generation data, mining companies need to predict NOx emissions from numerous load-haul-dumping (LHD) vehicles operating under dynamic conditions and not always equipped with gas sensors. This study focused on two ensemble methods: bootstrap aggregation (bagging) and least-square boosting (boosting) to predict NOx emissions. These approaches combine multiple weaker statistical models to yield a robust result. The innovation of this research is in the statistical analysis and selection of LHD vehicles’ working parameters, which are most suitable for NOx emission prediction; development of the procedure of source data cleaning and processing, model building and analyzing factors, which may influence the accuracy; and the comparison of two ensemble methods and showing their advantages and limitations for this specific engineering application, which was not previously reported in the literature. For datasets obtained from the same LHD vehicle and different operators, the more efficient bagging method gave a coefficient of determination R2 &gt; 0.79 and the RMSE (root mean square error) was under 30 ppm, which is comparable with the measurement accuracy for transient regimes of physical NOx sensors available in the market. The obtained insights can be utilized as input for mine ventilation systems, enhancing mining transport management, reducing workplace air pollution, improving work planning, and enhancing personnel safety.

Characteristics of resistivity variation in deep granite and in-situ detection applications

During the construction of deep vertical shafts, water inrush and flooding accidents are prone to occur, which seriously affect construction safety. Accurately determining the groundwater conditions is a prerequisite for effectively controlling water hazards and conducting risk management. In order to ensure the accuracy of the resistivity method in deep vertical well water exploration construction, a combination of indoor rock physics, mechanical testing, and on-site engineering measurements was used to analyze the influencing factors of granite resistivity. The corresponding relationship between resistivity and formation integrity was revealed, and water exploration experiments were conducted in the working face of deep underground mines. The results show that: (1) Rock resistivity is influenced by metallic minerals, saturation, temperature, ion content of fracture water, and joints. Regarding deep subsurface detection issues, the main factors affecting the detection results are water content and rock integrity. (2) During the loading process, rock resistivity exhibits significant stage response characteristics, which are closely related to rock integrity and damage accumulation. (3) A degradation model for aquifer zoning based on resistivity benchmark line was established. When the formation resistivity is higher than the benchmark line, it indicates a well-integrated formation with low water content. (4) Resistivity cloud maps and zoning degradation models can be used to visually determine and evaluate the occurrence status of formations and the effectiveness of grouting.

Testing the properties of cemented tailings backfill under multiaxial compressive loading

The engineering design of cemented paste backfill (CPB) typically relies on technical data obtained from tests conducted on CPB samples cured under conventional laboratory conditions. However, these methods often fall short in capturing the complex multiaxial stress curing conditions that CPB masses experience in many underground mines, particularly in deep underground mines. In these mining environments, CPB is subjected to multi-axial stress conditions during the curing process that are challenging to replicate in a controlled laboratory setting. No lab-scale apparatus can currently accurately reproduce these actual field multiaxial stress conditions, especially the intricate interplay of horizontal rockwall closure stresses, vertical self-weight stress, and overburden stresses from upper layers. Moreover, the impact of these conditions on the key engineering properties of CPB is unknown. To address this significant challenge and technology gap, this study has developed a new apparatus for curing and monitoring CPB under realistic multiaxial compressive loading conditions, simulating the complex stress environment encountered in underground mines. The developed apparatus was then utilized to assess the impact of multiaxial stress curing conditions, including vertical stress and horizontal stresses induced by rockwall closures, on key engineering properties (strength, deformation behaviour, self-desiccation) of CPB at early ages. The research results clearly indicate that the application of multiaxial stresses leads to a significant enhancement in the mechanical and physical properties of CPB. This enhancement is supported by observable densification of the pore structure in CPB subjected to multiaxial compressive stresses. In addition, a comprehensive analysis of deformation and strain patterns establishes a direct correlation between multiaxial stresses and the progressive increase in stiffness and hardness of CPB over time. Furthermore, the evolution of matric suction and degree of saturation provides compelling evidence of the significant impact of multiaxial stresses on pore water consumption or self-desiccation. The development of this new apparatus fills a substantial void in CPB research capabilities. Furthermore, the results obtained provide a better understanding of the complex behavior of CPB in multi-stress loading environments, paving the way for the design and optimization of backfill solutions that effectively address the unique challenges posed by deep underground mining operations.

Rockburst prevention by microwave destressing: a numerical investigation

Rockbursts are a complex phenomenon characterized by a violent ejection of rock fragments from the free face of deep underground mines. Due to the sudden, disruptive, and complex nature of these events, accurate predictions of rockbursts are difficult. Therefore, geotechnical intervention is imperative to prevent the occurrence of a rockburst. The scenarios of microwave destressing is introduced here as an application method to potentially prevent their occurrence. To understand the influences of microwave destressing, such as microwave exposure duration, spatial layout, and geometric parameters of the microwave preconditioned zone (MPZ), on the occurrence of a rockburst, numerical simulation was conducted based on an impact-induced rockburst model. The feasibility of microwave destressing technology was validated numerically. This paper also provides guidance for the utilization of microwave destressing in practice. Discontinuous MPZ is suitable for a known blasting source with a fixed position, and continuous MPZ can be applied for a working condition with multiple blasting sources with variable positions. A closer distance of MPZ from the free face results in a better microwave destressing effect. However, the closer distance of MPZ from the free face also introduces more negative influences on the static stability of surrounding rock.

Damage evolution and failure characteristics of a cemented gangue backfill considering direct shear and dynamic tests

Backfill mining is an effective technique that can prevent and control the impact of dynamic disasters which can occur in deep underground coal mines. This technique allows to reduce the surface subsidence and achieve a greener mining. The backfill shear mechanical properties under mining disturbances, are of great significance for ensuring the surrounding rock stability in deep mining. Therefore, studying the shear mechanical properties of the cemented gangue backfill body (CGBB) under coupled dynamic and static loads actions in deep coal mining is of great importance. In this paper, shear mechanical tests and the acoustic emission (AE) technology were used to study the shear mechanical response characteristics of CGBB under low-frequency dynamic normal load conditions. The results show that a dynamic normal load with an amplitude of 5 kN improves the shear CGBB strength, while a dynamic load with an amplitude greater than 10 kN will cause a shear strength deterioration, and the degree of deterioration is positively correlated with the load amplitude. Under low-frequency dynamic normal load conditions, the shear CGBB stress-strain curve exhibits classical characteristics. AE indicate that the main CGBB damage changes from a nonlinear deformation to an elastic stage under low-frequency dynamic normal load conditions. During the shear failure process, the cumulative AE counts are positively correlated with the load amplitude. The crack type identification based on RA-AF shows that the low-frequency dynamic normal load affects the CGBB crack type. Under constant normal load (CNL) conditions, tensile or mixed cracks account for 63.95% and are the main fracture mode. Conversely, under low-frequency dynamic normal load conditions, shear cracks account for more than 60% and are the main failure mode. This research provides a reference for the CGBB stability analysis under complex stress conditions for the case of a deep underground coal mining.

Predicting Sandstone Brittleness under Varying Water Conditions Using Infrared Radiation and Computational Techniques

The brittleness index is one of the most integral parameters used in assessing rock bursts and catastrophic rock failures resulting from deep underground mining activities. Accurately predicting this parameter is crucial for effectively monitoring rock bursts, which can cause damage to miners and lead to the catastrophic failure of engineering structures. Therefore, developing a new brittleness index capable of effectively predicting rock bursts is essential for the safe and efficient execution of engineering projects. In this research study, a novel mathematical rock brittleness index is developed, utilizing factors such as crack initiation, crack damage, and peak stress for sandstones with varying water contents. Additionally, the brittleness index is compared with previous important brittleness indices (e.g., B1, B2, B3, and B4) predicted using infrared radiation (IR) characteristics, specifically the variance of infrared radiation temperature (VIRT), along with various artificial intelligent (AI) techniques such as k-nearest neighbor (KNN), extreme gradient boost (XGBoost), and random forest (RF), providing comprehensive insights for predicting rock bursts. The experimental and AI results revealed that: (1) crack initiation, elastic modulus, crack damage, and peak stress decrease with an increase in water content; (2) the brittleness indices such as B1, B3, and B4 show a positive linear exponential correlation, having a coefficient of determination of R2 = 0.88, while B2 shows a negative linear exponential correlation (R2 = 0.82) with water content. Furthermore, the proposed brittleness index shows a good linear correlation with B1, B3, and B4, with an R2 &gt; 0.85, while it shows a poor negative linear correlation with B2, with an R2 = 0.61; (3) the RF model, developed for predicting the brittleness index, demonstrates superior performance when compared to other models, as indicated by the following performance parameters: R2 = 0.999, root mean square error (RMSE) = 0.383, mean square error (MSE) = 0.007, and mean absolute error (MAE) = 0.002. Consequently, RF stands as being recommended for accurate rock brittleness prediction. These research findings offer valuable insights and guidelines for effectively developing a brittleness index to assess the rock burst risks associated with rock engineering projects under water conditions.

Mechanical, energy evolution, damage and microstructural behavior of cemented tailings-rock fill considering rock content and size effects

Cemented tailings-rock fill (CTRF) is crucial for engineering intention and durability of re-filled stopes in deep underground mines. The current work examined the effects of fragment contents and sizes on the mechanical, energy evolution and microstructure of CTRF with 70 wt% solid. All CTRFs were subjected to uniaxial compression, energy dissipation and scanning electron microscopy experiments. Strength outcomes exhibit that adding rock fragments to CTRF had a clear mitigating influence on the backfill strength. Total/elastic-strain energy at peak point decreased with growing rock content in backfill. SEM-EDS studies demonstrate that backfill defects were chiefly micro-porosity and fractures. The findings are of great significance to guarantee the success and security of the CTRF fulfilled in deep underground hard rock and metalliferous mines.

Hybridized intelligent multi-class classifiers for rockburst risk assessment in deep underground mines

Hybridized intelligent multi-class classifiers for rockburst risk assessment in deep underground mines

Study on the energy and damage characteristics of gas-containing coal under confining pressure unloading process

Research on energy and damage evolution patterns of gas-containing coal under constricting pressure unloading conditions is urgently needed in the process of deep underground mining and is crucial for understanding the mechanisms underlying coal and gas composite rockburst occurrences. Prior reaching peak stress, cyclic loading and unloading experiments were carried out on gas-containing coal specimens under varied confining pressures and unloading circumstances. According to the experimental findings, gas pressure significantly degrades the mechanical characteristics of coal specimens, with a higher gas pressure causing worsening of the mechanical properties. The degree of mechanical property deterioration in coal specimens caused by gas pressure steadily reduces as confining pressure rises, indicating that confining pressure has an inhibitory influence on the deterioration of mechanical characteristics brought on by gas. Additionally, the degradation of coal mechanical characteristics is a result of the unloading of confining pressures. The peak stress and elastic modulus of coal decrease as confining pressure unloading increases in magnitude. Prior to the peak stress, the capacity of coal to convert external work into elastic energy is mostly indicated by the compression storage energy coefficient, whereas the peak elastic energy predominantly denotes the upper bound of compressed storage elastic energy for coal. Based on the Weibull statistical damage theory and employing elastic energy as the distribution variable, a statistical damage model is proposed. Comparisons of coal damage evolution curves indicate that both the confining pressures unloading process and gas pressure exert a promoting effect on the damage evolution of coal.

An improved Burgers creep model of coal based on fractional-order

The instability and destruction of various types of coal pillars left in deep underground mining are frequently attributed to cumulative creep deformation exceeding the permissible limit, with high in situ stress playing a critical role in this phenomenon. To understand the complicated creep behavior of coal, triaxial compression creep tests were conducted. It has been proven that the coal specimens subjected to high confining pressure exhibit typical three-stage creep characteristics. Furthermore, with the increase in confining pressure, the creep threshold value increases while the creep threshold coefficient decreases. By fitting the creep strain-time results, an improved Burgers creep model based on fractional-order was proposed. This model can describe the primary, secondary, and tertiary stages and reflect the nonlinear behavior, contributing to the understanding of the long-term stability evaluation of deep coal pillars.