Mine Safety Research Articles

Mining Injuries 2012-2019: Using Workers' Compensation Claims Data From 35 States to Identify Rates and Costs Associated by Nature of Injury, Event/Exposure, and Body Part Affected.

This study summarized the frequency and cost of mining-related injuries. Mining-related workers' compensation (WC) claims data from 35 states were summarized to report counts, claim rates, and costs for 2012-2019. These data were compared with Mine Safety and Health Administration injury and employment data for the same period. Despite system differences, both WC and Mine Safety and Health Administration counts and rates declined over time and injury patterns were similar. Total WC costs were approximately $2.325B. Medical-only claims represented 59.4% of the claims by count, but only 3.3% of costs. Lost-time nonfatal claims represented 40.2% of the claims by count, but 90.2% of costs. Claims frequency and costs varied greatly by injury event/exposure, part of body, and nature. Injury frequency has declined but costs remain high. The most costly and disabling cases were identified.

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information

Understanding the precursors leading to rock fracture is crucial for ensuring safety in mining and geotechnical engineering projects. To effectively discern these precursors, a collaborative monitoring approach that integrates multiple sources of information is imperative. This paper considered a rock multi-parameter monitoring loading system, incorporating infrared radiation and acoustic emission monitoring technologies to simultaneously track the rock fracture process. The study delves into the spatiotemporal evolution patterns of infrared radiation and acoustic emission in rock under loading. Utilizing stress, cumulative acoustic emission count, and average infrared radiation temperature (AIRT), the paper establishes a comprehensive evaluation model termed “acoustic-thermal-stress” fusion information, employing principal component analysis (PCA). The research reveals that the sensitivity to rock sample damage response follows the sequence of cumulative acoustic emission count, AIRT, and stress. Furthermore, a novel method for identifying rock fracture precursors is proposed, based on the first derivative of the comprehensive evaluation model. This method addresses the limitations of single physical field information, enhancing the robustness of monitoring data. It determines the average stress level of fracture precursors to be 0.77σmax. Subsequently, the study defines the probability function of rock damage during loading and fracture, enabling the realization of probability-based warnings for rock fracture. This approach introduces a new perspective on rock fracture prediction, significantly contributing to safety monitoring and warning systems in mine safety and geotechnical engineering. The findings of this research hold paramount engineering significance, offering valuable insights for enhancing safety measures in such projects.

An experimental investigation on the foam proportion for coal dust suppression in underground coal mines

Coal dust presents a grave peril to the well-being of miners and mine safety. In the absence of preventive measures, the concentration of dust in the working area can exceed one thousand milligrams per cubic meter. Foam-based dust removal, owing to its noteworthy merits such as minimal water consumption and high efficiency in removing dust, has emerged as one of the prevailing technologies for dust elimination. To mitigate dust generation during excavation, extensive investigations have been undertaken to explore the impact of foam reagent solution ratios on the efficacy of foam dust removal. By meticulously selecting surfactants, the consequences of varying concentrations and combinations of foam reagents, wetting reagents and foam stabilizers on foaming performance are examined. Valuable insights into the functional relationship between initial foam volume, volume decay rate, and foam reagent concentration are obtained. Furthermore, an in-depth analysis is conducted to probe the influence of wetting reagent addition on coal dust wetting, foam formation, and stability, thereby elucidating the functional connection between wetting time and the concentration of the wetting reagent. Additionally, the ramifications of stabilizer addition on foam stability and other relevant foam properties undergo comprehensive exploration, ultimately leading to the discovery of the functional relationship between foam performance and stabilizer concentration. Notably, experimental findings demonstrate that the optimum ratio for the foam reagent solution is AOS: SDS: STPP: CDEA: water=5:1:4:1:9989. This specific composition exhibits superlative foam foaming performance, stability, and wetting characteristics. Following a meticulous evaluation of gas supply volume, the optimal foam proportion is determined to be a foam reagent solution of 8 m3/h accompanied by a gas supply rate of 100 m3/h. The dust suppression efficiency of the foam produced by the optimal foam proportion reaches an about 97%, substantiating its effectiveness in dust removal.

Numerical Study on Failure Mechanisms of Deep Roadway Sidewalls with Different Height-Width Ratios and Lateral Pressures

Numerical Study on Failure Mechanisms of Deep Roadway Sidewalls with Different Height-Width Ratios and Lateral Pressures

Controlled reconstruction and application research of lateral overlying strata structure in high-gas mines with thick-hard roofs for a fully-mechanized top-caving face

Due to the large mining area, the fully-mechanized top-caving mining with thick-hard roof is easy to form cantilever structure on the lateral roof of the working face, which on the one hand causes high stress level of adjacent roadway and serious deformation of roadway, on the other hand causes gas accumulation in corners, which brings severe challenges to safe and efficient mining of the mine. In this study, a mine facing such problems in Jincheng, China was taken as the research object. Based on the mechanical characteristics of coal and rock, the characteristics of overlying strata activity in the mining process of working face are mastered, and the dual effects of controlled transformation of lateral overlying strata structure on stress field and gas field were revealed. On this basis, roadway reinforcement and gas drainage schemes were put forward and applied. The results showed that the strength of the hard rock stratum was high in the triaxial stress environment, and it was not easily destroyed. However, once the strata exceed their strength threshold, they break down. In addition, the strength of coal is relatively low, and it is continuously deformed when the force exceeds its strength. The overlying strata structure after thick-hard roof fully-mechanized top-caving mining evolves in the following manner: “long cantilever length formed by the main roof being broken in the initial stage, voussoir beam formed by the upper hard roof being broken in the middle stage, and double cantilever beam formed by overlying strata compaction.” The stress carried by upper hard rock stratum is transferred to coal pillars, which is the main reason for the high stress environment of multi-purpose roadway with large coal pillars. The controlled transformation of lateral overlying strata structure by pre-splitting and roof cutting can realize the "transfer-unloading" of coal pillar stress and the "plugging and driving" of corner gas. Based on the hydraulic fracturing reconstruction of lateral overburden structure, the grouting reinforcement scheme of roadway and dynamic gas drainage scheme were put forward. The results demonstrated that after roof cutting, the maximum deformation of the surrounding rock in the multi-purpose roadway was reduced by approximately 90 %, and the maximum concentration of corner gas was decreased by 15.28 %. This approach successfully achieved a collaborative control effect on roadway surrounding rock stability and gas emission well within the safety limits.

Management of methane emission in coal mines using artificial neural networks: A systematic review

Underground mines are responsible for a large number of the released methane worldwide. Our study paper can contribute to mitigating methane emissions, hence reducing the concentration about greenhouse emissions within the environment and mitigating the associated hazardous risks. In this study, more than 45 recent journals on Methane emission in Coal Mines were gathered from Web of Science, IEEE Xplore, ScienceDirect, and ResearchGate. A systematic review is accomplished of the past four years of various parts of Methane gas emission such as anthropogenic emission sources, gas emissions detection, prediction of methane using different technology and the Artificial intelligence projection model for methane emission. The outcomes reveal that since the methane emission management has obtained increasing attention over the past four years. This study also shows that big countries are using technology to control and utilize the methane emission to reduce the energy crisis. To decrease the coal mine injuries, academic understanding of underground methane management has increased, different technologies are integrated and support from various IT departments has amplified for the forecasting. In the future, the most critical task for coal mines risk assessment is to restore the worker's trust in mine safety, and the primary solution is to give more awareness to the underground management and workers through utilizing Artificial Intelligence (AI) mainly Artificial Neural Networks (ANN).

Experimental Study on the Distribution Law of Wind Speed in Rectangular Section Tunnels of Coal Mines

Ventilation is the cornerstone of coal mine safety production, and accurate monitoring of ventilation parameters is the fundamental guarantee for ventilation technology decision-making. A calculation model for the average wind speed line position in rectangular section tunnels of coal mines was established based on the Boussinesk theory and Prandtl turbulence theory to address the issue of difficulty in meeting decision-making needs in wind speed monitoring accuracy. The airflow field of rectangular section tunnels under different wind speed conditions was measured at the coal mine site, and the average position of the measured average wind speed line was obtained. The comparison and verification with the theoretical calculation results were carried out, with an absolute error of less than 5%.

A two-dimensional model test system for floor failure during automatic roadway formation mining without pillars above confined water

The coal mining in North China is seriously threatened by floor water inrush from the Ordovician limestone, so it is particularly important to explore failure characteristics. Automatic roadway formation mining without pillars (ARFMP) provides an effective method for reducing floor failure. To explore failure characteristics above confined water, a two-dimensional model test system is built up. Such a system can be applied to compare the difference of floor failure between ARFMP above confined water and traditional retained coal pillar mining, and to intuitively analyze the lifting law of confined water under dynamic disturbance. The core features of this system include the customized spring sets and water bag adopted as materials to simulate the floor aquifer, the guide lift hose and branch conduit used as the lifting channel of confined water, and the flow rate and cumulative volume monitored in real time. The customized “cutting-support-blocking” integrated device is the key equipment to simulate ARFMP. Then, the model test is conducted on a typical engineering case. By comparing stress, displacement, and water inrush characteristics of floor in different areas, failure characteristics of the roof-cutting side, the middle of working face, the collapse column area, and the coal pillar side were obtained, and it was clarified that ARFMP can effectively prevent floor water inrush. According to the results of model test, a comprehensive treatment scheme combining ARFMP with the floor regional control is proposed to ensure the safe mining of working face.

Research on chlorine salt antifreeze road dust suppressants for open-pit coal mines

In addressing the significant challenge of trucking road dust emanating from open-cast coal mines, the prevalent method of conventional water sprinkling for dust removal on transport roads has proven suboptimal. This inadequacy is attributed to the pronounced mobility of truck transport roads, substantial dust loads, and extensive open dust sources. Particularly in cold mining regions, the use of water in winter exacerbates the issue by causing icy road surfaces, thereby increasing the risk of truck skidding and overturning, posing a severe hazard to both mine safety and the well-being of truck drivers. A pragmatic imperative exists to devise a solution that effectively addresses both anti-freezing and dust suppression in open-pit coal mine roads, while minimizing corrosive impact. Against the backdrop of Hebei Open-pit Coal Mine in Huolin, Inner Mongolia, this study focuses on the development of a chloride salt antifreeze-type road dust inhibitor tailored for open-pit transportation roads. The proprietary anti-freezing dust suppressant achieves a freezing point as low as −36.4 °C, exhibiting corrosion efficiency merely at 56.5% of that of water. Notably, the effective dust suppression duration surpasses that of water by a factor of 150, resulting in a 53% reduction in dust suppression costs. This innovation is specifically applicable to high-cold and arid surface coal mines in northern regions and similar mining contexts. It offers technical support for the establishment of environmentally sustainable practices in surface coal mines, representing a pivotal step toward the realization of green mining initiatives.

Determination of Critical Damage Size of Inclined Waterproof Coal Pillar under Asymmetric Load

Quantitative determination of the critical size of an inclined coal pillar in an old goaf water-affected area is of great significance for water damage prevention and safe mining. The critical size of the inclined waterproof coal pillar is derived by using mechanical analyses, numerical calculations, and field engineering practices to determine the stability of the waterproof coal pillar in the old goaf water-affected area of the 1303 working face of Dananhu No. 1 Mine in the Xinjiang region. Firstly, a force model of the inclined waterproof coal pillar was established to reveal the law that the critical size of the coal pillar increases with the increase in coal seam inclination under the action of asymmetric load. Then, numerical simulation was applied to reveal the dynamic evolution processes of plastic deformation–destabilization of the coal pillar under the influence of mining and single-side water pressure, and the critical size of the coal pillar in the study area was determined to be 19.09 m. Finally, measures such as pumping pressure relief and slurry reinforcement were adopted to reduce the deformation rate of the roadway on the side of the coal pillar, which ensured the stability of the waterproof coal pillar and the safe mining of the working face.

IDENTIFIKASI POTENSI BAHAYA K3 DAN PENGENDALIAN RISIKO PADA KEGIATAN PEMBORAN DI BALAI DIKLAT TAMBANG BAWAH TANAH SAWAHLUNTO IDENTIFICATION OF POTENTIAL OHS HAZARDS AND RISK CONTROL IN DRILLING ACTIVITES IN THE SAWALUNTO UNDERGROUND MINING AND TRAINING CE

OHS in mining activities is generally known as the Mining Occupational Health and Safety Management System (MOHS) which will regulate how and what forms of prevention and control both potential hazards or risks occur during mining activities. Drilling is one of the stages carried out before blasting activities. This is to obtain drilling fast, with the right position. The drilling, which was carried out at the Underground Mining Training Center, used a Jumbo drill and Jackleg machine. Risk identification is the process of identifying hazards, assessing risks and developing management strategies. The application of risk management has benefits including being able to guarantee business continuity by reducing the risk of every activity that contains a hazard, while the purpose of this study is to identify potential hazards and control the risk of work accidents for both workers and trainees at the Sawalunto Underground Mining Training Center.

Chemical reaction process and dynamic characteristics of urea pyrolysis products in inhibiting gas explosion

During the coal mining process, gas explosions pose significant hazards, causing casualties and property losses. In order to develop new types of inhibitors for gas explosions, this paper explores the reaction mechanisms of urea pyrolysis products NH3 and HNCO inhibiting gas explosions based on density functional theory (DFT), transition state theory, and grand canonical ensemble Monte Carlo method (GCMC). It analyzes the reaction processes and kinetic characteristics of urea pyrolysis products with key radicals and gas molecules involved in explosions from a microscopic dynamic perspective. The results show that urea pyrolysis products exhibit good inhibition effects on active radicals involved in explosion reactions·NH3 and HNCO show stronger van der Waals forces in electrostatic attraction towards O2 and *H, respectively, and faster rate advantages in reaction rate constants. The lower Gibbs free energy barrier indicates higher reaction activity of pyrolysis products, thereby diluting the concentration of key radicals involved in explosion reactions and effectively inhibiting explosion key elementary reactions (R32, R38, R53, R57, R156, and R170). This study provides new insights into the microscopic inhibition mechanism of urea pyrolysis products on gas explosions, offering a new approach for designing more targeted modified inhibitors, which helps reduce the hazards of gas explosions during coal mining and provides scientific support and technical guidance for safe coal mining production.

Analysis of coal mining accident risk factors based on text mining

Frequently occurring coal mine safety accidents have caused great casualties and economic losses. Coal mine intelligence is the core technical support for the high-quality development of the coal industry. The deep integration of coal mining safety production management and Artificial Intelligence (AI) technology is practically significant to achieve accident prevention. To efficiently identify mining accident risk factors and explore mechanism of coupling between risk factors, this study mined 400 reported cases of mining accidents in Shanxi Province and identified 64 accident risk factors through custom, stopword, synonym dictionary construction, keyword extraction and keyword correlation analysis. Then, this study constructed association rules and a Bayesian causal network. The major risk factors are identified using a comprehensive high-frequency, sensitivity, strength and key path analysis of the Bayesian causal network. The following seven risk factors are found to play a major role in the occurrence of mine accidents: inadequate safety supervision, disordered safety management, illegal organization of production, inadequate staff safety education and training, operation against rules, command against rules and weak safety consciousness among the staff. Finally, a case study is conducted to validate the reliability of the results. This study solves the problem of incomplete extraction of key feature information in coal mine reports and the lack of analyses of coupling mechanisms between coal mine risk factors in traditional accident analysis methods, providing the methodological support for the effective use of unstructured coal mine safety production data for risk analysis.

Investigation and mapping of toxic fumes produced by detonation of ANFO explosives in underground space

AbstractPost‐blast fumes are hazardous and known to cause severe health related issues of workers. Further, these harmful gases have a significant impact on the surrounding environment. Thus, it is imperative to have an in‐depth understanding of the real time detonation fume generation in underground space to avoid hazardous health risk of the worker. In this context, the mapping of toxic fume concentrations generated by the detonation of ANFO explosives in the actual field is a fascinating area of research that has a great environmental impact. This article examined the real‐time analysis of toxic fumes generated by ammonium nitrate fuel oil (ANFO) explosives at various locations of a metalliferous underground mine. Furthermore, detonation parameters of various ANFO explosive compositions were also studied at the mining site. On‐site blasting studies were performed with ANFO explosives, and post‐detonation fume measurements enabled us to map the CO and NOx concentrations in underground spaces. Toxic fumes like CO and NOx were analyzed before and after each blasting operation at different intervals, and found within the allowed limit as per the Directorate General of Mines Safety guidelines. Additionally, an empirical correlation has been established to evaluate the maximum detonation velocity based on the alteration of ammonium nitrate and fuel oil composition.

Study on time–frequency features of induced charge signals during the damage and failure process of coal medium

Monitoring and preventing coal-rock dynamic disasters are vital for safe mining. To investigate the time–frequency features of induced charge signals with coal damage and failure of roadways, the generation mechanism of free charge in loaded coal is analyzed and the induced charge monitoring test is conducted. According to the stress-induced charge-time curves, the time-domain features of charge signals at each loading stage are obtained. The wavelet threshold denoising approach and generalized Morse wavelet transform method are applied to denoise the raw signals and study the frequency-domain features. Further, the quantitative relationship between the de-noised induced charge signals and the degree of coal damage is established. The results show that the event number, amplitude and fluctuation degree of available induced charge signals are all at a low level in the compaction and elastic stages of the coal, which are mainly generated by the piezoelectric effect and predominantly represent discreteness. When entering the plastic and failure stages, the available signals are primarily produced by the crack propagation and triboelectric effects, with a significant increase in the event number, amplitude, and fluctuation degree. Then the induced charge signals gradually transit from discrete to continuous. Generally, the dominant frequency of the available induced charge signals during the coal damage process is concentrated at 0 ~ 11 Hz. The available induced charge is positively correlated with the degree of coal damage, which can perform the damage degree of coal mass, providing a new approach to evaluate the stability of roadway surrounding rocks.

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s.

Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model's detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model's generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.

Research on Pressure Relief and Anti-impact Technology for Segmental Hydraulic Fracturing in Directional Long Borehole in Roof

After large-scale underground mining operations in coal mines, the stress of the overlying strata above the goaf is transferred to the surrounding support areas, creating support pressures. These pressures can easily trigger dynamic disasters such as gas outbursts and roof collapses, seriously threatening mine safety and production. To ensure the safety of underground mining operations, directional long borehole segment hydraulic fracturing technology was used in the roof strata of the 2305 comprehensive caving working face of Cuijiagou Coal Mine in Shaanxi Province to conduct experimental research on the technology of directional long borehole segment fracturing for pressure relief and anti-impact. The hydraulic fracturing of the roof’s directional long borehole reduced the stress peak coefficient of the coal body in front of the working face (K≈2.9), blocked the propagation of mining stress and high stress in the goaf to the mining roadway, reduced the deformation of the mining roadway, and reduced the average working resistance of the working face support by 22% compared to the working face without any measures. The effective reduction of the length of the hanging roof, the improvement of the support resistance, and timely roof collapse during the working face mining period helped ensure the initial pressure did not form a strong impact. The initial pressure interval of the thick hard roof calculated theoretically was shortened by 53%, and the periodic pressure interval during normal mining was reduced by 27% compared with the 2303 working face under the same geological conditions. The experimental results show that the coverage of the directional long hole on the roof is extensive, with the utilization rate of segmented hydraulic fracturing wells reaching up to 80%. This technology effectively diminishes the strength of the roof above the coal seam on a large scale, thereby reducing the periodic pressure interval and intensity. As a result, it prevents the impact caused by the hanging roof during the mining period of the working face, ensuring a safe working environment for underground workers and facilitating the safe and efficient progress of mining operations.

Decision Intelligence-Based Predictive Modelling of Hard Rock Pillar Stability Using K-Nearest Neighbour Coupled with Grey Wolf Optimization Algorithm

Pillar stability is of paramount importance in ensuring the safety of underground rock engineering structures. The stability of pillars directly influences the structural integrity of the mine and mitigates the risk of collapses or accidents. Therefore, assessing pillar stability is crucial for safe, productive, reliable, and profitable underground mining engineering processes. This study developed the application of decision intelligence-based predictive modelling of hard rock pillar stability in underground engineering structures using K-Nearest Neighbour coupled with the grey wolf optimization algorithm (KNN-GWO). Initially, a substantial dataset consisting of 236 different pillar cases was collected from seven underground hard rock mining engineering projects. This dataset was gathered by considering five significant input variables, namely pillar width, pillar height, pillar width/height ratio, uniaxial compressive strength, and average pillar stress. Secondly, the original hard rock pillar stability level has been classified into three types: failed, unstable, and stable, based on the pillar’s instability mechanism and failure process. Thirdly, several visual relationships were established in order to ascertain the correlation between input variables and the corresponding pillar stability level. Fourthly, the entire pillar database was randomly divided into a training dataset and testing dataset with a 70:30 sampling method. Moreover, the (KNN-GWO) model was developed to predict the stability of pillars in hard rock mining. Lastly, the performance of the suggested predictive model was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The findings of the proposed model offer a superior benchmark for accurately predicting the stability of hard rock pillars. Therefore, it is recommended to employ decision intelligence models in mining engineering in order to effectively prioritise safety measures and improve the efficiency of operational processes, risk management, and decision-making related to underground engineering structures.

Predicting sandstone water abundance using seismic dispersion attribute inversion: A case study of Yuwang coal mine, China

AbstractPredicting the water abundance of coal‐bearing strata is crucial for ensuring mining safety. However, owing to the dispersion and attenuation characteristics caused by pore fluid flow, it is difficult to estimate the water abundance of coal seam roof aquifers using seismic data. To overcome this challenge, we provide the relationship between the frequency‐dependent seismic wave velocity and water saturation based on the Chapman fracture model and the mixing fluid model. We propose a seismic dispersion attribute technique that can use dispersion information as an indicator of water abundance. Numerical experiment results show that the water saturation of the sandstone aquifer is positively correlated with the dispersion attribute. The results of low‐frequency rock physical experiments are roughly consistent with those predicted by the model for the given parameters. Using seismic dispersion attribute inversion and the frequency slice wavelet transform method, we predicted the water abundance of sandstone in the coal seam roof of the Yuwang coal mine in Yunnan Province, China. The predicted sandstone water abundance was compatible with the actual water‐rich scenario observed in well logs and downhole drilling in the study area. Therefore, the method proposed herein has the potential to quantitatively determine the water abundances of sandstone aquifers in coal seam roofs.

Experimental investigation on the mixture optimization and failure mechanism of cemented backfill with coal gangue and fly ash

Using coal gangue and fly ash for mining backfill materials can create a win-win situation for safe mining and waste disposal, with the critical challenge being the optimization of coal gangue grading and fly ash proportions. Uniaxial compression tests and scanning electron microscopy (SEM) observation were conducted on backfill specimens with different coal gangue grading and fly ash proportions, and in-situ monitoring of acoustic emission (AE) parameters and infrared radiation temperatures (IRT) was carried out. The elastic modulus, uniaxial compressive strength (UCS), failure pattern, infrared temperature, AE ringing count and energy evolution of the backfill were discussed. Further, the b-value and entropy-value of AE during the loading were analyzed. The results show that the UCS and elastic modulus decrease with the increase in the proportions of fly ash and large-size gangue. Tensile cracks generally dominate the failure of specimen under unaxial compressive load but shear cracks are more prone to occur for specimens with high proportions of fly ash and large-size gangue. The inner defects and weak interfaces between large-size gangue and cement pastes significantly affect the mechanical performance for specimens with high proportions of fly ash and large-size gangue. A sudden drop of the b-value occurs before the peak stress while the AE entropy-value reaches maximum at the peak stress. The methods and findings can guide the mixture optimization and mechanical failure analysis for backfill mining.