Longhe Liu

The work aimed to characterize the composition properties of soft coal seams and the deformation of roadway surrounding rocks in Panzhihua Coal Mine, Guizhou, China. Experimental analysis, numerical simulation, and field tests were conducted to study the composition characteristics of soft coal seams with large inclination angles and large mining heights and their roadway deformation. Both steeply inclined thick soft coal seams and gently inclined thin soft ones share essentially identical primary mineral compositions and coal component types. Besides, pore structures were predominantly characterized by large-pore development and semi-enclosed pores. Compared to gently inclined thin soft coal seams, the steeply inclined thick soft seams exhibited lower porosity, average pore diameter (4 V/A), median pore diameter (A), and total pore volume. In contrast, they had higher bulk density, median pore diameter (V), and total pore area. The apparent density, however, showed a negligible difference between the two seam types. For the number 7 coal seam, increasing the seam inclination angle intensified stress concentration in the roadway surrounding rocks and progressively amplified asymmetric deformation. The increased roadway height-to-width ratio shifted the primary load-bearing zone away from the roadway and increased its thickness, which escalated deformation in surrounding rocks. Significant differences existed in stress distribution and rock movement around roadways intersecting different rock strata, with deformation heavily influenced by cross-stratum tunneling. Borehole observation of number 7 coal seam's 110,706 open-off cut roof determined the loosening zone of 0-8.05 m.

This study proposes a novel deep learning framework for anomaly detection in coal mine hydraulic support pressure data. The framework innovatively combines bidirectional LSTM and CNN architectures, incorporating gated residual connections and self-attention mechanisms to effectively capture both temporal features and local patterns in pressure data. The research utilizes pressure data collected from 10 hydraulic supports on a fully mechanized mining face in western China, spanning from July 1st to July 30th, 2022. Through a systematic data preprocessing pipeline, including temporal resampling, missing value handling, normalization, and other steps, a standardized experimental dataset was constructed. Experimental results demonstrate the model’s excellent performance in anomaly detection tasks. Ablation studies validate the effectiveness of key components, with CNN layers and gated residual mechanisms contributing most significantly to model performance, leading to test loss increases of 472% and 352% respectively when removed. The model exhibits strong capabilities in sample reconstruction and anomaly identification, effectively distinguishing between normal pressure variations and anomalous patterns. However, the method has certain limitations, including dependency on data quality, fixed threshold strategy constraints, and computational complexity issues. These findings provide important references for improving hydraulic support monitoring systems and have practical significance for enhancing coal mine safety production.

The control of hard roof conditions in underground coal mining is critical for ensuring mining safety and efficiency. Hard roof control remains a critical challenge in underground mining, particularly affecting mining safety and efficiency. Traditional support theories often show limitations in addressing the complex interactions between bolt spacing patterns and geological variability. This study focuses on the No. 10904 working face of Jingu Mine, where three distinct roof types are present: Type I (thick limestone roof, TLR, ≥1.2 m), Type II (moderate limestone roof, MLR, 0.5–1.2 m), and Type III (composite mudstone–limestone roof, CLR). Through FLAC3D numerical simulation and field validation, the mechanisms of bolt support under hard roof conditions were systematically investigated, and the optimization of bolt support parameters, including spacing, length, and pre-tension force, was conducted. The results indicate that: (1) when the ratio between lateral and row spacing approaches unity, reducing lateral spacing while increasing row spacing enhances support effectiveness, achieving 2 mm less roof subsidence with the 1.0 m × 1.5 m configuration compared to the 1.4 m × 0.8 m arrangement, despite a 21% reduction in bolt density; (2) an optimal rib bolt length of 1.8 m was determined, with support effectiveness diminishing beyond 2.0 m, and 1.5 m-long bolts reducing rib convergence by 15% compared to unsupported conditions; and (3) when the anchoring length exceeds 60% of the total bolt length, further increases in bolt length have minimal impact on deformation control under TLR, MLR, and CLR conditions. Field implementation of the optimized support scheme confirmed its effectiveness, with borehole television inspection showing no separation or fracturing within the monitored depth of 4 m in the roof strata. These findings provide practical guidelines for support design in similar geological settings, particularly for shallow-buried roadways with hard roof conditions.

The upstream valley-type tailings dam exhibits a longitudinal profile that resembles an inverted triangle or a shape approximating an inverted triangle. This unique dam structure and its mechanical characteristics mean that the influence of longitudinal stress on the stability of the dam cannot be overlooked. However, the two-dimensional Swedish slip circle method calculation model does not take into account longitudinal stress, necessitating the development of a three-dimensional model specifically for the stability analysis of upstream valley-type tailings dams. Utilizing the Beijing Lizheng Geotechnical Calculation Software 5.3 standalone version, a three-dimensional calculation model was constructed based on the Swedish slip circle method. Taking the Cangyuan Jinla Lead-Zinc Mine tailings dam as an engineering example, this model was used to calculate five cross-sections, including 1–1′, of the dam. The results were then compared and analyzed with those obtained from the Swedish slip circle method, the simplified Bishop method, and the Janbu method. Using this three-dimensional model, the safety factors for cross-sections 1–1′, 2–2′, 3–3′, 4–4′, and 5–5′ of the tailings dam were calculated to be 2.798, 1.348, 1.332, 1.466, and 2.134, respectively. Analysis concluded that the most dangerous sliding surface of the tailings dam is located at the 3–3′ cross-section, with an overall dam safety factor of 1.332, indicating that the tailings dam is in a relatively stable condition. The safety factor of the tailings dam calculated using the finite element software MIDAS is 1.3531, verifying the accuracy of this three-dimensional model. For upstream valley-type tailings dams, the results from ordinary two-dimensional models tend to be on the dangerous side and thus should be corrected using this three-dimensional model to obtain more accurate and conforming safety factors.

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.