Mine Safety Research Articles

IOT Based Mining, Tracking and Worker Safety Helmet

Abstract: This project addresses a cost-effective, flexible solution of underground mine workers safety. A module of MEMS and temperature-based sensors are used for underground environment monitoring and automating progression of measurement data through IOT communication technique is proposed with high accuracy, smooth control and reliability. Arduino microcontroller is used for collecting data and making decision, based on which the mine worker is informed through server. Sensor data transforms into digital signal and effectively communicate wirelessly with the ground control centre computer.

The Re-Crushing Spatiotemporal Evolution Law of Broken Coal in the Goaf for Sustainable Utilization of Abandoned Mines

The broken coal samples’ (BCS) re-crushing characteristics in the goaf during roof compaction directly affect the mechanics and seepage characteristics of the caving zone. This will further affect the safety of coal mining and the sustainable utilization of abandoned mines. Thus, the experiment of BCS compaction is carried out with the help of an acoustic emission (AE) monitoring system. The Hurst exponent changes of the AE counts at different stages were obtained using the R/S analysis method. The results indicate that the compaction and re-crushing of the BCS at the laboratory scale have long-term memory. When providing sufficient stress, the AE activity of BCS will continue to develop according to the current trend. Based on the AE breakage location technology, the spatial distribution re-crushing characteristics of the BCS are obtained. Re-crushing of the BCS demonstrates uniform breakage in the horizontal direction and layered breakage in the vertical direction. In the horizontal direction, the boundary area first began to break, and the damage gradually spread evenly to the central area. In the vertical direction, the upper layer was the first to be broken, and then the damage began to shift to the middle and lower layers.

Coal mining activities driving the changes in bacterial community

The mechanism of the difference in bacterial community composition caused by environmental factors in the underground coal mine is unclear. In order to reveal the influence of coal mining activities on the characteristics of bacterial community structure in coal seam, 16S rRNA gene amplicon sequencing technology was used to determine the species abundance, biodiversity, and gene abundance of bacterial community in a coal mine in Shanxi Province, and the environmental factors such as metal elements, non-metal elements, pH value, and gas concentration of coal samples were determined. The results showed that environmental factors and bacterial communities had obvious regional characteristics. Mining activities greatly affected the α diversity of bacterial communities, mining working face > main airway > roadway roof > unexposed coal seam > tunneling roadway. The bacterial community composition of each sample point is also very different. The main airway, roadway roof, and unexposed coal seam are dominated by Actinobacteria while the mining working face and tunneling roadway are dominated by Proteobacteria. Among the gene abundances of metabolic pathways in each site, Citrate cycle had the greatest difference, followed by glycine, serine and threonine metabolism, and oxidative phosphorylation and methane metabolism had little difference. RDA analysis showed that the environmental factors affecting the bacterial community were mainly cadmium, oxygen, hydrogen, and gas content. CCA analysis divided the bacterial community into three categories. Degradation functional bacteria are located in mining working face, bacteria that tolerate poor environments are located in main airway and tunneling roadway, and human pathogens are mostly located in roadway roof and unexposed coal seam. The research results would provide support for realizing green and safe mining in coal mines.

Advanced modeling of seepage dynamics and control strategies in thick coal seams under high-confined aquifer conditions: A case study

The hydraulic behavior of the connection between the floor failure area and the aquifer water-conductive zone is considered to be the root cause of mine water inrush disasters. Therefore, unraveling the floor failure mechanism is particularly important for safe coal mining above the high-confined aquifer. This paper estimates the depth of the baseplate failure to be 18.4–27.3 m by combining network parallel electrical methods with drilling visualization technology. The FLAC3D-based numerical model considering the strain hardening of caved rock was established with rigorous calibration and verification. The results showed that the depth of damage to the floor is 23.1 m, and the dominating floor failure mechanism is shear failure caused by the vertical stress exceeding the rock bearing capacity. Moreover, the stress recovery process of the baseplate does not alter the failure morphology of the baseplate. Based on the above research findings, the in-situ floor control technique of the working face No. 4305 is proposed and practiced in the field. Field measurements show that floor control performance is satisfactory with water inflow in the goaf being roughly stable at 50 m3/h. Our results can provide useful reference for safe mining above confined aquifer and prevention and mitigation of water-related hazards.

Influence of polypropylene fiber length and geometric shape on the compressive strength of cemented lepidolite tailings backfill

Lepidolite ore contains abundant lithium resources; however, the extraction process generates a large number of tailings, which are environmentally hazardous solid waste. Currently, cemented fiber reinforcement and tailings filling technologies are commonly used methods for tailings treatment. The fiber length and geometric shape significantly affect the performance of fiber-reinforced cemented lepidolite tailing backfill (CLTB). However, there is limited research on the impact of these two factors on the performance of CLTB. Consequently, Polypropylene fiber-reinforced CLTB of four sizes and four fiber lengths were prepared and used for uniaxial compressive strength (UCS) tests. The max UCS of fiber-reinforced CLTB was 2.84 MPa, and the maximum increase percent was 83.7% compared with the non-fiber-reinforced CLTB. The experimental results show that when the fiber length was 12 mm the CLTB had the maximum UCS, longer fibers did not necessarily result in a higher UCS. The end effect was significant when the difference in cross-sectional area was small. The UCS of the L-40 sample was higher than that of the Y-50 sample under the same fiber length. The differences in the size effect and geometric shape were the main factors influencing their mechanical performance. When the fiber length was from 0 mm to 6 mm, the size effect was obvious, the UCS values gradually decreased with an increase in the volume ratio and cross-sectional area. However, the fiber length was the primary factor influencing the fitting curve of the UCS when the fiber length was from 12 mm to 19 mm. Additionally, the addition of fibers enhanced the integrity of CLTB. In other words, fiber-reinforced CLTB exhibited improving structural integrity. This study can provide theoretical references for the research and practical applications of fiber-reinforced fillers and size effects, as well as the treatment of lepidolite tailings, while also reflecting the CLTB performance under the action of different sizes and different fiber lengths, improving the filling efficiency, mining, and backfill safety.

Research on the surface subsidence characteristics and prediction models caused by coal mining under the reverse fault

Predicting and understanding the phenomenon of surface subsidence caused by coal mining in working faces with faults are important issues for safe coal mining and efficient production. In numerical simulation experiments, it was found that the phenomenon of surface subsidence manifests when faults exist, and the degree of influence of faults with different dip angles on surface subsidence varies. This phenomenon is attributed to fault activation. According to the experimental results, the impact of faults with different dip angles on surface subsidence falls into three levels: level I for 35° faults, level II for 45° and 55° faults, and level III for 65° and 75° faults. Similarly, the relationship between the difficulty of fault activation and the dip angle of faults can be categorized as 35° faults prone to activation, 45° and 55° faults difficult to activate, and 65° and 75° faults not prone to activation. The probability integral correction model for fault mining, which integrates the surface subsidence values caused by fault-induced attenuation and the subsidence arising from separation spaces, was introduced, thereby constructing a surface subsidence prediction model. This proposed prediction model can accurately predict surface subsidence, with a root mean square error of 10.74 mm between the predicted and measured values, as validated using DInSAR results from the III 6301 working face in the Jincheng mining area.

Review of Major Influencing Factors Contributing to Persisting Safety Problems in Coal Mines: Addressing Systemic Challenges

The coal mining industry in Pakistan faces recurring fatal accidents due to data scarcity, lack of research, and technology adoption. This paper reviews the current status of coal mining, the ongoing energy crisis, the utilization of indigenous coal resources, and the prevailing safety challenges. By comparing coal mining safety standards in Pakistan with global benchmarks, this study proposes advanced mining technologies to improve productivity and safety. This study emphasizes the importance of investigating the underlying factors causing mining accidents in order to devise effective strategies to mitigate them. The lack of relevant data and the reluctance to adopt technology in the industry are identified as major obstacles to improving safety conditions. The proposed strategies for overcoming safety issues include improving data collection and analysis, increasing research efforts, and promoting the adoption of advanced technology. Thus, this paper highlights the urgent need to address safety concerns in Pakistan’s coal mining industry to avoid further loss of lives and resources.

Research on the Evolutionary Law of Fracture Formation in Loose Seams Under High-Intensity Mining with Shallow Depth

The western mining regions of China, known for shallow-buried and high-intensity mining activities, face significant ecological threats due to damage to loose strata and the surface. The evolution of fissures within the loose layer is a critical issue for surface ecological environment protection in coal mining areas. The study employed field measurements, mechanical experiments, numerical simulations, and theoretical analysis, using the ‘triaxial consolidation without drainage’ experiment to assess the physical and mechanical properties of various strata in the loose layer. Additionally, the PFC2D numerical simulation software was employed to construct a numerical model that elucidates the damage mechanisms and reveals the evolution of loose layer fissures and the development of ground cracks. The research findings indicate that during shallow-buried high-intensity mining loose layer fissures undergo a dynamic evolution process characterized by “vertical extension-continuous penetration-lateral expansion”. As the working face advances, these fissures eventually propagate to the surface, forming ground cracks. The strong force chains within the overlying rock (or soil) layers develop in the form of an “inverted catenary arch”. As the arch foot and the middle of the arch overlap, fissures propagate along these strong force chains to the surface, resulting in ground cracks. The study elucidates the surface damage patterns in shallow-buried, high-intensity mining, offering theoretical insights for harmonizing coal mining safety with ecological conservation in fragile regions.

Shear creep deformation of rock fracture distrubed by dynamic loading

The long-term stability of jointed rock masses is usually dominated by fault activation, which may be triggered by the dynamic disturbance generated by blasting during mining activities, leading to the occurrence of disasters such as landslides in open-pit and rockbursts in deep mining. The initial stress and dynamic disturbance are key factors that strongly affect the shear creep behavior of rock fractures. In this work, the shear failure instability of rock fractures of sandstone under creep-impact loading was experimentally investigated by using a creep-impact test machine, which allows for applying creep loading and an additional dynamic disturbance on rock fractures. Three stages of shear creep deformation, creep strain rate, and time-to-failure are examined under different creep stress levels and impact energies. Experimental results show that the tangential and normal creep rates increase with the increase of creep stress and impact energy, but the increment of tangential creep rate is higher than that of the normal creep rate. The time-to-failure of the creeping specimen is shortened under high creep stress and large impact energy, while the time-to-failure after the last dynamic disturbance of the specimen is determined by the total impact energy and creep stress level. By using high-speed photography, it is found that the failure types of rock depend on the magnitude of impact energy and creep stress level; that is, rock mainly slides with low stress levels and shears off with high stress levels. In addition, under different impact energy and creep stress levels, the variation of height is between 0.38 and 0.52, while the defined fracture factor, which describes the degree of failure of serrations, is between 0.30 and 0.54. The findings can provide deep insight into the fault sliding mechanism caused by mining activities, which provides theoretical support for the safe mining of ore in fault fracture zones.

Unsupervised anomaly detection in shearers via autoencoder networks and multi-scale correlation matrix reconstruction

As the main equipment of coal mining production, the anomaly detection of shearer is important to ensure production efficiency and coal mine safety. One key challenge lies in the limited or even absence of labeled monitoring data for the equipment, coupled with the high costs associated with manual annotation. Another challenge stems from the complex structure of the mining machines, making it difficult to reflect the overall operational state through local anomaly detection. Consequently, the application of decoupled local anomaly detection for mining machines in practical production remains challenging. This paper presents an unsupervised learning-based method for detecting anomalies in shearer. The method includes a module for constructing a Multi-scale Correlation Matrix (MSCM) of mining machine operating conditions, as well as the CNN-ConvLSTM Autoencoder (C-CLA) network. The module for constructing an MSCM enhances the representation of interrelationships between various features of the equipment from different perspectives using multiple correlation analysis methods. The C-CLA network integrates convolutional and convolutional recurrent neural networks, with the convolutional structure extracting local spatial features and the ConvLSTM structure further capturing information from different time scales and feature scales, thereby enhancing the model’s perceptual capabilities towards changes in equipment status. Finally, shearer anomaly detection is achieved through the analysis of reconstructed residual matrices. The rationality and practicality of the proposed method have been validated on our dataset, and the model’s generalization capability has been verified through repeated experiments in similar scenarios. However, due to variations in the working environment of different mining faces and differences in equipment models, implementing detection on other mining faces often requires retraining the model with new data. Furthermore, we compared our method with other anomaly detection techniques, and our detection efficiency was superior by approximately 3%. This method effectively detects anomalies in the shearer.

Investigation of Load Characteristics and Stress-Energy Evolution Laws of Gob-Side Roadways Under Thick and Hard Roofs

The stress environments of gob-side roadways (GSRs) are becoming increasingly complex during deep coal mining under thick and hard roofs. This leads to strong strata behaviors, including roadway floor heave, roof subsidence, and even coal bursts. Among them, coal bursts pose the greatest threat to production safety in coal mines. Coal bursts in a GSR strongly correlate with the load characteristics and stress-energy evolution laws of the roadway. This study analyzes the roof structures of double working faces (DWFs) during the initial weighting stage (IWS) and full mining stage (FMS) of gob-side working faces (GSWFs). This study also explores how varying roof structures affect the stability of GSRs. Three-dimensional roof structure models of DWFs and mechanical models of dynamic and static loads superposition on a GSR throughout the IWS and FMS of a GSWF were developed. An analysis identified the primary stress sources affecting the GSR throughout various mining stages of the GSWF. Subsequently, the principle of “three-load” superposition was developed. A novel method was proposed to quantify the stress state in the GSR surrounding rock across different mining stages of the GSWF. The method quantitatively characterizes the load of the GSR surrounding rock. Based on this, the criterion for judging the burst failure of the roadway was established. Numerical simulations are used to analyze the stress-energy evolution laws of the working face, coal pillar, and GSR surrounding rock during the mining process of the GSWF. These findings offer valuable references for studying and preventing coal bursts in GSRs under equivalent geological situations.

Theoretical and numerical investigations of the floor failure characteristics affected by coal mining near complex fault structures

Water inrush from underlying aquifers under the synergistic action of complex faults and mining activities seriously threatens the mining of coal seams in North China. The failure characteristics of the coal seam floor under complex fault conditions are the keys to understanding the water inrush mechanism during mining. To investigate floor failure characteristics and water inrush mechanisms influenced by complex fault structures, theoretical analysis and numerical simulation were carried out. An improved theoretical model is first proposed, including complex fault structures such as horst, graben, and step fault structures. The calculation formula for waterproof coal pillars under complex fault conditions is established based on the improved theoretical model. The failure and displacement characteristics of the coal floor under complex faults are analyzed via numerical simulation. Compared with those under normal mining conditions, the theoretical and numerical simulation results reveal the following: (1) Under the horst structure, the floor failure characteristics show an increase in the compression failure area on both sides, and shear failure occurs on the large-dip fault side. The simulation results reveal that the failure width on the large-dip fault side increases by 25%, indicating a failure trend toward the large-dip fault side. The width of waterproof coal pillars needs to be increased by 20% to ensure mining safety. (2) Under the graben structure, the floor failure characteristics show an increase in the compression failure area on both sides, and shear failure occurs on the small-dip fault side. The simulation results reveal that the failure width on the small-dip fault side increases by 25%, indicating a failure trend toward the small-dip fault side. The width of waterproof coal pillars needs to be increased by 40% to ensure mining safety. (3) Under the step fault structure, the floor failure characteristics show compression failure on both sides, increasing the floor compression failure area on the large-dip fault side. The simulation results show that the failure width on the small-dip fault side increases by 15%, the large-dip fault side increases by 50%, and the floor failure depth increases by 20%. The width of waterproof coal pillars needs to be increased by 50% to ensure mining safety.

Contract labour in mining and occupational health and safety: A critical review

Abstract In recent decades, there has been a global growth of the use of contract labour in the mining industry, primarily driven by cost/flexibility considerations. At the same time, contracting has been associated with poorer occupational health and safety (OHS) outcomes across a range of industries. Drawing on published research, theses, and government reports, this paper critically reviews the available evidence on the OHS effects of contract labour in mining and the likely implications of further growth in this trend. This evidence confirms that the use of contract labour is associated with worse OHS outcomes, and that the Ten Pathways and Pressure, Disorganisation, and Regulatory failure (PDR) models are both valuable in explaining this. The latter point is confirmed by a more detailed examination of four serious mine incidents in NSW and Queensland. The paper identifies some gaps and areas for further research as well as the actions that mining companies, regulators, and unions could take to improve contractor safety. Notwithstanding the latter, the paper argues that the most effective way of improving contractor safety in mines is reducing the use of contractors overall and concentrating their activities in areas such as major shutdowns/repairs, where contractors have specialised expertise to undertake non-routine tasks. Despite oft-repeated phrases such as zero-harm and management systems, the corporate shift to using contractors is primarily driven by cost-cutting and highlights how OHS is compromised by such priorities.

Influence of roof confined water drainage on stress distribution in coal seam

Coal and gas outbursts, along with rock bursts, are common dynamic disasters in coal mining, threatening the safety and green production of coal mines. The distribution of coal seam stress is an external factor contributing to coal mine power disasters. The drainage of confined water in coal seam roof is the primary factor of coal seam stress changes. Using theoretical analysis and numerical simulations, the influence of roof confined water on coal seam stress by looking at the law of stress and stress conservation transfer in the drainage layer is investigated. Our results show that: (1) after drainage, there is an increase in the local stress within the coal seam, with a stress concentration factor of 1.35. The influence range of theoretically calculated stress is consistent with the measured results. (2) the maximum value of drainage boundary stress correlated positively with the angle of the water-rich abnormal area, with a linear fitting of R2 = 0.98. These findings hold significant theoretical and practical implications for preventing and controlling dynamic disasters during the mining of water-rich coal seams.

Signal recognition and prediction system based on random forest model

To accurately predict the occurrence of rock bursts during deep coal mining and ensure the safe and efficient operation of coal mines, a signal recognition and prediction system based on the Random Forest model is proposed. This system utilizes electromagnetic radiation (EMR) and acoustic emission (AE) signal data, employing feature extraction and point biserial correlation analysis to screen out the most relevant feature parameters. A Random Forest binary classification model is constructed to identify interference signals. Subsequently, by introducing new features such as moving average slope and exponentially weighted moving average (EWMA), a time series analysis of precursor feature signals is conducted. A real-time warning model based on the Random Forest algorithm is developed, dynamically calculating the probability of precursor feature signal occurrence by integrating historical data and real-time data changes. This approach improves the accuracy and recall rate of signal recognition and prediction, providing reliable data support for mine safety management.

Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks.

Wind speed effect on infrared-image-based coal and gangue recognition with liquid intervention in LTCC

The commitment to achieving carbon neutrality by 2060 makes the clean transition of fossil energy production inevitable in China. As the primary fossil energy source, China produced approximately 4.66 billion tons of raw coal in 2023. Among them, in the fields of mining engineering and environmental science, the accurate recognition of gangue (one of the environmental pollutants produced by coal combustion is black-gray solid waste) from coal is crucial because it is a key step in reducing resource waste and environmental pollution. Hence, coal and gangue automatic recognition with high accuracy is very important for intelligent longwall top coal caving (LTCC) mining. A novel approach of “liquid intervention + infrared detection” is introduced to enhance the recognition accuracy when the exterior color of coal and gangue is similar. This study focuses on the infrared image recognition of coal and gangue after liquid intervention under varying wind speed conditions, and optimizes the image processing path. The effect of varying wind speed conditions on the recognition accuracy of coal and gangue after liquid intervention was analyzed, and the optimal wind speed range was determined. The morphological evolution characteristics of droplets under varying wind field conditions were studied by COMSOL two-phase flow interface. The results show that with the increase of wind speed, the surface contour of coal is gradually clear, and the overall recognition accuracy is also improved. When the high wind speed is 1.55 m/s, the recognition accuracy reaches 99.89%, and the recognition accuracy of other wind speed regions is also above 90% (within 7s). In addition, the numerical simulation can maintain the volume fraction of the droplet in a short time under the constant flow wind flow field, thus inhibiting the phenomenon that the recognition accuracy decreases with time. The simulation results are in good agreement with the physical experimental phenomena, which proves the reliability of the numerical simulation. This study is of great significance to the recognition of coal gangue types with low identification characteristics in LTCC, so as to ensure the primary economic benefits, ecological environment safety and sustainable development of coal mines.

Research on the Characteristics of Seepage Failure in the Surrounding Rock (Coal) of the Goafs

During mining, the brittle fracture structure of coal makes it highly susceptible to disturbance, leading to changes in the permeability of the coal seam from non-conductive to water-conductive, which poses a significant threat to the stability and safety of coal pillars in goafs. Therefore, understanding the damage mechanisms of coal during water–rock interactions is crucial for ensuring mine safety. In this paper, based on laboratory seepage tests, the impact of hydrodynamic forces on the microstructure of fissured coal and its subsequent effect on permeability is examined. The study found that increasing confining pressure causes the “closure” of coal fissures, leading to a reduction in permeability. Additionally, during the initial stage of seepage, fine particles within the coal samples are mobilized due to seepage damage, leading to channel blockages and further reductions in permeability. However, as seepage continues, the hydraulic channels eventually open fully, resulting in a sharp increase in permeability. Furthermore, using a two-dimensional fracture seepage model, the study investigated how the scale of fractures in the water-conducting channels influences seepage behavior. A critical fracture width method was proposed to predict permeability surges, offering a new approach for analyzing the stability of coal pillars in mining areas.

Bayesian optimized deep Q-network for diagnosing mine ventilation systems windage alteration fault targeting imbalanced data

Fault diagnosis of mine ventilation system is of great significance for mine safety production. Traditional machine learning algorithms have been widely applied in the field of mine ventilation systems windage alteration faults (WAFs) diagnosis, but these algorithms have poor intelligence and weak generalization ability. Therefore, this research constructs a WAFs diagnosis model (WAFs-BODQN) based on Bayesian optimized (BO) deep Q-network (DQN). Meanwhile, in order to solve the problem of poor performance of intelligent models when the dataset is imbalanced, a reinforcement learning reward function (KMeans-SDF) was designed, which combines the KMeans++ algorithm and spatial distance function (SDF). The WAFs-BODQN model with KMeans-SDF reward function has fault location diagnosis accuracies of 98.75 %, 97.50 %, and 95.00 % for mild, moderate, and extreme imbalanced datasets UB1, UB2, and UB3, respectively. This effectively avoids the "eccentricity" phenomenon of the intelligent model on majority class and achieves accurate prediction of fault locations. The accuracy of fault location diagnosis using the WAFs-BODQN model in simulation experiments and on-site empirical applications is higher than 95 %, demonstrating the intelligence, generalization, and feasibility of WAFs-BODQN model in the field of WAFs diagnosis in mine ventilation systems. By comparing the results with traditional machine learning and deep learning fault diagnosis models, it has been proven that the WAFs-BODQN model has advantages in intelligence and generalization ability. It is expected to establish a universal architecture for diagnosing WAFs, providing theoretical guidance and technical support for achieving intelligent mine ventilation.

Research on Enhancing Gas Extraction Efficiency through CO2 Gas Fracturing in Outburst-Prone Coal Seams.

Given the background of significant Coal Mine gas disasters worldwide, the research and development of efficient gas control technologies have become critical to ensuring mining safety. CO2 gas fracturing (CO2-Frac), an innovative technology for Coal Mine gas control, has demonstrated efficiency in various mining areas across China. However, its application in outburst-prone coal seams is not yet fully understood. This study presents a field-scale CO2-Frac project conducted in an outburst Coal Mine in China, specifically the Pingshu Coal Mine, where the previously employed dense-borehole gas extraction technology failed to efficiently achieve gas extraction and outburst prevention. The CO2-Frac plan was evaluated through fracture propagation experiments utilizing both single-hole and dual-hole configurations, highlighting the advantages of the dual-hole CO2-Frac method. Subsequently, on-site gas extraction tests were conducted to further assess the efficacy of the CO2-Frac plan. The results indicate that (1) In the dual-hole CO2-Frac scheme, the fractured and pressure relief area expanded to approximately 26.82 m2, which is 220% larger than that of the single-hole scheme. (2) The dual-hole CO2-Frac significantly enhanced gas extraction effectiveness, increasing the flow rate from 0.026 to 0.216 m3/min in a 100m borehole, a 7.3-fold improvement. Additionally, the gas extraction period to reach the standard was reduced from 20 to 6 days. These findings conclusively demonstrate that the dual-hole CO2-Frac technique is an effective method for safe excavation in outburst-prone coal seams, providing both theoretical and practical validation for its application in similar geological settings.