Mine Safety Research Articles

Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning

Monitoring and predicting ground vibration levels during blasting operations is essential to safeguard mining sites and surrounding communities. This study introduces an IoT-based ground vibration monitoring device specifically designed for limestone mining operations, combined with machine learning algorithms to predict ground vibration intensity. The primary aim is to provide an efficient predictive tool for anticipating hazardous vibration levels, enabling proactive safety measures. A comparative analysis with the industry-standard Minimate Blaster indicates high accuracy of the IoT device, with percentage errors as low as 0.803% across multiple blasts. The study also employed Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Random Forest (RF) algorithms to predict Peak Particle Velocity (PPV) values. Among these, the Random Forest model outperformed the others, achieving an R2 score of 0.92, Mean Absolute Error (MAE) of 0.21, and Root Mean Squared Error (RMSE) of 0.31. These findings underscore the reliability and predictive accuracy of the IoT-integrated Random Forest model, suggesting that it can significantly contribute to enhancing safety and operational efficiency in mining. The research highlights the potential of IoT and machine learning technologies to transform ground vibration monitoring, promoting safer and more sustainable mining practices.

Study on the mechanism and control of strong ground pressure in the mining of shallow buried close-distance coal seam passing through the loess hilly region

Shallow buried close-distance coal seam (SBCCS) is widely found in northern Shaanxi, China. In the process of mining under the loess hilly area (LHA) in SBCCS, many accidents of strong ground pressure have occurred. Taking the in the Zhangjiamao Coal Mine as the background, this study revealed the mechanism of high ground pressure when the working face of lower coal seam passes through the surface loess hilly region. On-site measurements, physical similarity simulation, numerical calculation, and theoretical analysis were combined to study the mining process of SBCCS. The movement characteristics of the activated structure of the interval strata of the lower coal seam were analyzed. The dynamic load and the change pattern of the front abutment pressure (FAP) of the support during the loading stage of entering and exiting the loess hilly were determined. A coupled structural mechanics model of the overlying activated voussoir beam and the step voussoir beam rock beam of the interval strata was established, revealing the dynamic loading mechanism of strong ground pressure during passing through the LHA. The results showed that the dynamic load of the working face was the highest in areas affected by the load of the LHA, followed by the load while entering the LHA, the peak value of the FAP in the load-influenced LHA was high, which was approximately 1.12 times that after leaving the load-influenced LHA and 1.61 times that before entering this area. By establishing a mechanical model of the roof coupling structure when entering and exiting the load-influenced LHA, it was revealed that the dynamic load of the support while mining under the goaf in the LHA is mainly due to the synchronous movement of the activated structure of the collapsed roof of the upper coal seam and the interval rock structure. The load in the LHA was transmitted to the interval rock structure through the activated structure, resulting in a high dynamic load on the support. The study concluded that the determination of the support resistance of the working face should be based on the high-period compressive load of the synchronous movement of the roof structure in the LHA. Through the engineering practice of hydraulic fracturing, the roof structure of the interval strata is changed, which can effectively reduce the dynamic pressure disaster of the working face. The research provides a scientific basis for the safe and efficient mining of shallow coalfields, and it provides reference for similar mining.

Improvement of geodynamic safety of mining at the North Urals bauxite deposits at great depths

Improvement of geodynamic safety of mining at the North Urals bauxite deposits at great depths

Infra-red remote sensing inversion technology for hidden fire sources in mine fire areas – joint GSO and PSO algorithms

Mine fires have always been a serious challenge to mining safety, and the existence of hidden fire sources in mine fire areas is one of the key factors causing coal seam spontaneous combustion. Hidden fire source refers to the potential fire source that cannot be easily detected by traditional detection means in the mine. They gradually accumulate heat in the complex environment of the mine. If not detected and dealt with in time, it can lead to a serious mine fire. However, traditional fire detection methods are difficult to detect hidden fire sources in the early stages, and in severe cases, can lead to catastrophic mine fires. Therefore, this study proposes a combined firefly optimisation algorithm and particle swarm optimisation algorithm for infra-red remote sensing inversion of hidden fire sources in mine fire areas, and verifies it. The results indicated that the inversion model could accurately obtain the location and intensity information of hidden fire sources. The joint algorithm exhibited higher accuracy and faster convergence speed during the training process, with an accuracy of 97.64% and an average temperature error of only 0.73°C. The average accuracy of the inversion model optimised by the joint algorithm improved to 99.48%, the average calculation time reduced to 7.25 seconds, and the efficiency has been improved by 51.92%. This study innovatively combines infra-red remote sensing inversion technology with intelligent algorithms to improve the efficiency and accuracy of detecting hidden fire sources in mine fire areas, which helps to reduce safety risks and economic losses caused by mine fires.

Intelligent rockburst level prediction model based on swarm intelligence optimization and multi-strategy learner soft voting hybrid ensemble

Rockbursts are highly destructive geological events that pose serious risks to the safety of underground engineering projects, including tunnels, mines, and other subterranean structures. Accurate prediction of rockburst occurrence and intensity is crucial for preventing and mitigating their potentially catastrophic impacts. Such predictions are vital not only for ensuring the safety of workers and infrastructure but also for optimizing construction and operational strategies in underground environments. This research is developing a smart model to predict rockburst levels by combining advanced swarm intelligence with a hybrid ensemble method that uses multiple strategies and classifiers for soft voting. By collecting and analyzing 287 rockburst cases from diverse geological settings around the world, a comprehensive and representative database was constructed. This database was subsequently subjected to in-depth statistical and correlation analyses to identify key patterns and relationships. The data preprocessing method proposed in this study, based on an improved version of the Student t-SNE algorithm, effectively reduced the negative impact of data noise on model performance, enhancing the reliability of predictions. Furthermore, the prediction model developed in this study not only demonstrated exceptional prediction accuracy on the test set but also provided valuable insights into key geological parameters influencing rockbursts through rigorous sensitivity analysis. The proposed hybrid ensemble model greatly improves generalization and interpretability over traditional single models. It offers an efficient, data-driven approach to rockburst prediction, providing a strong scientific foundation for safety management and risk mitigation in underground engineering worldwide.

Deformation mechanism and damage energy evolution of coal body under different gas pressures based on the energy principle

With increasing mining depth, the coal pillars of a coal mine will be in a stressful environment characterized by high gas pressures and unidirectional loading. To investigate the damage evolution characteristics and energy evolution mechanism of coal pillars loaded in a gas pressure environment, a uniaxial compression test was performed on a coal body under different gas pressures using a load testing apparatus for gas-containing coal rocks. The obtained results showed that the mechanical properties of the coal body varied with the gas pressure. Specifically, the peak strain, compressive strength, and elastic modulus decreased with increasing gas pressure; the higher the gas pressure, the lower the conversion rate of the elastic strain energy in the elastic deformation stage of the coal body and the lower its total input energy. With increasing gas pressure, the damage threshold of the coal body decreased, whereas the damage variable corresponding to the peak value, as well as the damage threshold value, increased. According to the theory of continuous damage mechanics, an ontological damage model of the coal body under different gas pressures was established based on the principle of minimum energy dissipation, and the rationality of the model was verified through a comparison between the theoretical and experimental data. Our findings can be useful in ensuring the safety of coal mining in terms of preventing gas disasters.

Study on the safety distance determination model for natural gas wells in Ordos Basin subsidence areas of coal mining

To optimize the safe distance between coal mining faces and natural gas wells, with the Ordos Basin as the engineering background for the intersectional development of natural gas and coal resources, a Gaussian function equation for subsidence curves under complete mining was proposed based on the probability integral method, and a model was constructed to determine the coordinated mining safety distance between coal mining faces and natural gas wells. Based on the distributed optical fiber monitoring principle of the BOTDR technology, on-site monitoring of the simulated monitoring well GX1 within the safety distance was carried out. The results indicated that the maximum strain change values of the outer casing and inner production casing of the simulated monitoring well GX1 were 836 με and 766 με, respectively, far less than the maximum ultimate tensile strain value of the overlying rock at the same depth position. Furthermore, the casing structure possessed ample resistance capability, ensuring no noticeable deformation occurred. Through comparison, it was found that the discriminant model reduced the reserved safety distance between the coal mining working face and the natural gas well by 58% compared with the currently used avoidance method of the surface subsidence boundary, liberated coal resources, and achieved a "win–win" situation of the coordinated mining of natural gas resources and coal resources. It can reasonably adjust the coordinated mining problem without a natural gas-coal co-mining mechanism, provide a new method for the layout of natural gas wells in gas-coal overlapping areas, and provide new ideas for the coordinated mining of coal-related associated resources.

Evaluation of Water Inrush Risk in the Fault Zone of the Coal Seam Floor in Madaotou Coal Mine, Shanxi Province, China

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong Coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy C-means clustering method (FCCM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines.

An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models.

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

Assessment of Peak Particle Velocity of Blast Vibration using Hybrid Soft Computing Approaches

Abstract Blasting vibration is a major adverse effect in rock blasting excavation, and accurately predicting its peak particle velocity (PPV) is vital for ensuring engineering safety and risk management. This study proposes an innovative IHO-VMD-CatBoost model that integrates variational mode decomposition (VMD) and the CatBoost algorithm, with hyperparameters globally optimized using the improved hippocampus optimization algorithm (IHO). Compared to existing models, the proposed method improves feature extraction from vibration signals and significantly enhances prediction accuracy, especially in complex geological conditions. Using measured data from open-pit mine blasting, the model extracts key features such as maximum section charge, total charge, and horizontal distance, achieving superior performance compared to 13 traditional models. It reports a root mean square error of 0.28 cm/s, a mean absolute error of 0.17 cm/s, an index of agreement of 0.993, and a variance accounted for value of 97.28%, demonstrating superior prediction accuracy, a high degree of fit with observed data, and overall robustness in PPV prediction. Additionally, analyses based on the SHapley Additive Explanations framework provide insights into the complex nonlinear relationships between factors like horizontal distance and maximum section charge, improving the model's interpretability. The model demonstrates robustness, stability, and applicability in various tests, confirming its reliability in complex engineering scenarios, and offering a valuable solution for safe mining and optimized blasting design.

Collaborative control technology and application of support and modification for soft-rock roadway in a kilometer-deep coal mine

The soft-rock roadways in kilometer-deep coal mines are often damaged by large deformation and have to be periodically expanded and repaired, which seriously restricts the safe and efficient production of coal mines. A typical soft-rock roadway in a kilometer-deep coal mine is selected as the engineering, and the main reasons for roadway deformation are analyzed, and the ground stress and mechanical characteristics are obtained. The Flac3D numerical model, which can accurately reflect the deformation characteristics of surrounding rock in kilometer-deep soft-rock roadway, has been constructed, and the evolution laws of stress field and its damage mechanism have been analyzed with the vertical stress, vertical displacement and plastic zone. The damage range of roadway surrounding rock is elucidated by endoscopic observation, and the synergistic control technology of support and modification is proposed. The on-site engineering application is carried out in Qianyingzi Coal Mine, and the results show that the collaborative control technology of support and modification ensures the safety and stability of deep soft-rock roadway. The results provide important and preliminary technical references for realizing safe and intelligent mining in coal mines.

Study on deformation failure and permeability resistance of large faults under mining

The deformation and failure of large faults caused by coal seam mining are easy to form channels connected with aquifers, which is an important threat to the safe mining of coal seams. This study takes Beigongcun No.1 fault in No.7 mining area of Dongtan Coal Mine as a case, aiming to explore the deformation and permeability resistance of large faults. Through the establishment of engineering geological model, the FLAC 3D software is used to simulate the fluid-solid coupling of the mining process. The results show that as the working face advances, the compressive stress within 40 m from the fault decreases significantly, and it is most affected by mining at 20 m below the coal seam after mining to the fault. The calculated fault permeability strength is lower than the Ordovician limestone water pressure. Further simulation of fault mining with different dip angles shows that the failure modes are basically the same, but the failure depth is different. In the dip angle of 50° to 80°, the fault failure depth of 65° is the smallest, and the fault resistance strength of 60° is the largest. This shows that the depth of fault failure is the largest under a special dip angle.

A Method and Engineering Practice for a Fully Mechanized Caving Coalface to Rapidly Pass Through a Large Fault

One of the technical problems that must be solved in coal mine production is when the coalface rapidly crosses the fault. Based on the occurrence characteristics of the F6 fault (maximum throw: 13.5 m) in the #3up1101 fully mechanized caving coalface at Gaozhuang Coal Mine, two different solutions allowing the coalface to pass through this fault were proposed, and the solution of pre-driven roadways with rock pillars was optimally determined. The main implementation steps of the method include designing the layout parameters of the pre-driven roadways, determining the width for rock pillars between the adjacent pre-driven roadways, construction of pre-driven roadways by smooth wall blasting, and controlling the surrounding rock deformation of the pre-driven roadways. The results of engineering practice show that it took only 23 days for this coalface to pass through fault F6, about one month shorter than the time required by traditional methods (e.g., proactively taking a detour). Moreover, this method helped achieve stable coal production (an 8.5 × 104 t increase), prevented much gangue from mixing with coal, reduced wear and tear on the mining equipment, and enhanced safety. The economic benefits delivered totaled about CNY 71.1 million. Therefore, this method can ensure continuous, safe, and efficient mining at the coalface, alleviating the tight situation of mine production succession. The results of this study can provide a good reference to help coalfaces rapidly move across faults under similar geological conditions in other mines.

A Prediction Model for Methane Concentration in the Buertai Coal Mine Based on Improved Black Kite Algorithm–Informer–Bidirectional Long Short-Term Memory

Accurate prediction of methane concentration in mine roadways is crucial for ensuring miner safety and enhancing the economic benefits of mining enterprises in the field of coal mine safety. Taking the Buertai Coal Mine as an example, this study employs laser methane concentration monitoring sensors to conduct precise real-time measurements of methane concentration in coal mine roadways. A prediction model for methane concentration in coal mine roadways, based on an Improved Black Kite Algorithm (IBKA) coupled with Informer-BiLSTM, is proposed. Initially, the traditional Black Kite Algorithm (BKA) is enhanced by introducing Tent chaotic mapping, integrating dynamic convex lens imaging, and adopting a Fraunhofer diffraction search strategy. Experimental results demonstrate that the proposed improvements effectively enhance the algorithm’s performance, resulting in the IBKA exhibiting higher search accuracy, faster convergence speed, and robust practicality. Subsequently, seven hyperparameters in the Informer-BiLSTM prediction model are optimized to further refine the model’s predictive accuracy. Finally, the prediction results of the IBKA-Informer-BiLSTM model are compared with those of six reference models. The research findings indicate that the coupled model achieves Mean Absolute Errors (MAE) of 0.00067624 and 0.0005971 for the training and test sets, respectively, Root Mean Square Errors (RMSE) of 0.00088187 and 0.0008005, and Coefficient of Determination (R2) values of 0.9769 and 0.9589. These results are significantly superior to those of the other compared models. Furthermore, when applied to additional methane concentration datasets from the Buertai Coal Mine roadways, the model demonstrates R2 values exceeding 0.95 for both the training and test sets, validating its excellent generalization ability, predictive performance, and potential for practical applications.

Risk assessment of water inrush from coal floor based on enhanced samples with class distribution

In the risk assessment of water inrush from coal floors, the amount of measured data obtained through on-site testing is small and random, which limits the prediction accuracy and generalizability of a model based on measured data. Using the distribution characteristics of the measured data and mega-trend diffusion theory, we propose a virtual sample enhancement method based on class distribution mega-trend diffusion technology (CDMTD) and introduce constraints on the class distribution of influencing factors. This method was used to generate virtual samples and enhance the measured database. A prediction model of the water inrush risk for the coal seam floor was established using a coupled algorithm of extreme learning machines, self-adaptive differential evolution, and CDMTD (PCA-CDMTD-SaDE-ELM) and was used to evaluate the water inrush risk in the 19,105 working face of the Yunjialing Mine. The CDMTD method could effectively solve the problem of virtual sample distribution variation in the overall trend diffusion theory and enhance the measured database, reducing the impact of small sample sizes. Compared to other optimization models, our model showed the best prediction performance, with an error reduction of 42.95–51.27% and results biased towards safety. Our results support safe and efficient coal mining above Ordovician limestone-confined water.

Optimizing design and stability of open pit slopes in Tolay coal mine, Ethiopia

Coal is a critical energy resource for global industries, and its extraction from open-pit mines requires effective slope stability management to ensure safe and efficient operations. This study evaluates the slope stability of the Tolay open-pit coal mine in Ethiopia, located in the Jimma zone, where geological conditions, including basalt, mudstone, and weathered soil layers, influence slope behaviour. The primary objective was to assess slope stability and recommend optimization strategies for safer mining. Geological mapping, discontinuity analysis, Schmidt hammer tests for uniaxial compressive strength (UCS), and laboratory testing of soil samples were performed to assess material properties. Stability was evaluated using Limit Equilibrium Methods (LEM) with Slide software and Finite Element Methods (FEM) using Phase2. Geological surveys revealed mudstone, siltstone, and claystone layers up to 39 m deep, with a 10 m coal seam. The results showed instability in most slope benches, with Factor of Safety (FOS) values between 0.220 and 0.430, indicating the need for reinforcement. Bench 4 showed relative stability, with FOS values of 1.228 to 1.487. Reducing the slope angle from 70° to 26° increased the FOS from 0.322 to 1.373, significantly improving stability. This study emphasizes the importance of optimizing slope geometry, particularly slope angle and bench height, to enhance mine safety and operational efficiency.

Research on the optimization of drum structure based on virtual prototyping technology

Drums are the core working mechanism of the coal mining machine for coal mining. The structural design level of the drum is crucial for mining efficiency and safety production. Traditional design methods not only have long design cycles and high costs, but also limited design capabilities. This study used computer numerical simulation methods to establish coupled models for drum cutting complex coal seams, which was used to obtain the working performance of drums with different structures. By fitting the comprehensive performance evaluation function of the drum, the optimal structural parameters are obtained through the improved Non-Dominated Sorting Genetic Algorithms (NSGA-II). Taking into account both technical and economic factors, the optimal helix angle is ultimately determined to be 18°, the optimal installation angle is 45°, and the optimal cutting distance is 71 mm. Through experiments, it has been proven that the performance of the optimized drum has significantly improved. Specifically, the average cutting resistance has been reduced by 12.72%, the load fluctuation coefficient has been reduced by 9.81%, the cutting specific energy consumption has been reduced by 2.85%, and the coal loading rate has been increased by 9.59%, providing a reference for the optimization design of the shearer drum structure.

IoT-Enabled Methane Monitoring and LSTM-Based Forecasting System for Enhanced Safety in Underground Coal Mining

Ensuring safety in the mining industry is a critical concern for a nation's industrial advancement. Industry 4.0, characterized by the integration of advanced technologies, is at the forefront of efforts to enhance mining practices. Coal seams contain a range of hydrocarbon gases, predominantly methane, which is released in significant quantities during mining operations. Effectively mitigating methane emissions is imperative. The inclusion of methane forecasting allows for the early identification of potential methane emissions, hence resulting in significance enhancement in mine safety. The research work is focused on real-time remote monitoring and cloud-based forecasting of methane levels in underground coal mines. An Industrial Internet of Things (IIoT) device is developed for data acquisition in underground coal mines, capturing essential parameters such as methane concentration, temperature, and humidity. The collected data are utilized to train a long short-term memory based multivariate forecasting model. The trained model is subsequently deployed in the cloud. The experiment is performed in a mine of Eastern Coalfields Limited, India. After the deployment of the proposed model, the developed IIoT device transmits real-time data, obtained from the mine, to the cloud. Based on the real-time data, our model conducts methane forecasting and communicates results back to the IIoT device. The device issues immediate alerts when methane levels surpass predefined thresholds. This ensures enhanced safety in mining operations by providing warnings for both current and forecasted methane concentrations. The forecasted methane concentrations, along with real-time data, are accessible through mobile applications and a web-based dashboard. The accuracy of the proposed model is measured by mean absolute error, mean absolute percentage error, and root mean square error, which demonstrate values of 156.95 ppm, 4.23%, and 191.53 ppm, respectively. A comparative study is performed where our model is evaluated against the multivariate multilayer perceptron, vector autoregression, and auto-regressive integrated moving average models. The comparative study demonstrates that our developed model outperforms the others, showing superior results.

Real-Time AI-Driven Hazard Detection: Integrating Computer Vision and Sensor Networks for Enhanced Mining Safety

Real-Time AI-Driven Hazard Detection: Integrating Computer Vision and Sensor Networks for Enhanced Mining Safety

Using Radiogenic Noble Gas Nuclides to Identify and Characterize Rock Fracturing

AbstractFracture‐released radiogenic noble gas nuclides are used to identify locations and constrain the volume of new fracture creation during subsurface detonations. Real‐time, in situ noble gases and reactive gases were monitored using a field‐deployed mass spectrometer and automated sampling system in a multilevel borehole array. Released gases were measured after two different detonations having distinct energy, pressure, and gas volume characteristics. Explosive‐derived gases (N2O, CO2) and excess radiogenic 4He and 40Ar above atmospheric background are used to identify locations of gas transport and new fracture creation after each detonation. Fracture‐released radiogenic 4He is used to constrain the volume of newly created fractures with a model of helium release from fracturing. Explosive by‐product gas was observed in multiple locations both near and distal to the shot locations for both detonations. Radiogenic 4He and 40Ar release from rock damage was observed in locations near the detonation after the second, more powerful detonation. Observed 4He response is consistent with a model of diffusive release from newly created fractures. Volume of new fractures estimated from the 4He release ranges from 1 to 5 m2 with apertures ranging from 0.1 to 1 m. Our results provide evidence that radiogenic noble gases released during fracture creation can be identified at the field scale in real time and used to identify timing and location of fracture creation during deformation events. This technique could be useful in subsurface science and engineering problems where the location and amount of newly created rock fracturing is of interest including fault rupture, mine safety, subsurface detonation monitoring and reservoir stimulation.