Search Results (6)

Mine water hazards represent one of the principal threats to safe coal mine operations; therefore, accurately predicting mine water inflow is critical for drainage system design and water hazard mitigation. Because mine water inflow is governed by the combined influence of multiple hydrogeological factors and thus exhibits pronounced non-linear characteristics, conventional approaches are inadequate in terms of forecasting accuracy and medium- to long-term predictive capability. To address this issue, this study proposes a Chaos-Autoformer-based method for predicting mine water inflow. First, the univariate inflow series is mapped into an m-dimensional phase space by means of phase-space reconstruction from chaos theory, thereby fully preserving its non-linear features; the reconstructed vectors are then used to train and forecast inflow with an improved Chaos-Autoformer model. On top of the original Autoformer architecture, the proposed model incorporates a Chaos-Attention mechanism and a Lyap-Dropout scheme, which enhance sensitivity to small perturbations in initial conditions and complex non-linear propagation paths while improving stability in long-horizon forecasting. In addition, the loss function integrates the maximum Lyapunov exponent error and earth mode decomposition (EMD) indices so as to jointly evaluate dynamical consistency and predictive performance. An empirical analysis based on monitoring data from the Liangshuijing Coal Mine for 2022–2025 demonstrates that the trained model delivers high accuracy and stable performance. Ablation experiments further confirm the significant contribution of the chaos-aware components: when these modules are removed, forecasting accuracy declines to only 76.5%. Using the trained model to predict mine water inflow for the period from June 2024 to June 2025 yields a root mean square error (RMSE) of 30.73 m³/h and a coefficient of determination (R²) of 0.895 against observed data, indicating excellent fitting and predictive capability for medium- to long-term tasks. Extending the forecast to July 2025–November 2027 reveals a pronounced annual cyclical pattern in future mine water inflow, with markedly higher inflow in summer than in winter and an overall slowly declining trend. These findings show that the Chaos-Autoformer can achieve high-precision medium- and long-term predictions of mine water inflow, thereby providing technical support for proactive deployment and refined management of mine water hazard prevention. Full article

To address the limitations of traditional hardened concrete road surfaces in coal mine tunnels, which are prone to damage and entail high maintenance costs, this study proposes using modular concrete blocks composed of fly ash and coal gangue as an alternative to conventional materials. These blocks offer advantages including ease of construction and rapid, straightforward maintenance, while also facilitating the reuse of substantial quantities of solid waste, thereby mitigating resource wastage and environmental pollution. Initially, the mineral composition of the raw materials was analyzed, confirming that although the physical and chemical properties of Liangshui Well coal gangue are slightly inferior to those of natural crushed stone, they still meet the criteria for use as concrete aggregate. For concrete blocks incorporating 20% fly ash, the steam curing process was optimized with a recommended static curing period of 16–24 h, a temperature ramp-up rate of 20 °C/h, and a constant temperature of 50 °C maintained for 24 h to ensure optimal performance. Orthogonal experimental analysis revealed that fly ash content exerted the greatest influence on the compressive strength of concrete, followed by the additional water content, whereas the aggregate particle size had a comparatively minor effect. The optimal mix proportion was identified as 20% fly ash content, a maximum aggregate size of 20 mm, and an additional water content of 70%. Performance testing indicated that the fabricated blocks exhibited a compressive strength of 32.1 MPa and a tensile strength of 2.93 MPa, with strong resistance to hydrolysis and sulfate attack, rendering them suitable for deployment in weakly alkaline underground environments. Considering the site-specific conditions of the Liangshuijing coal mine, ANSYS 2020 was employed to simulate and analyze the mechanical behavior of the blocks under varying loads, thicknesses, and dynamic conditions. The findings suggest that hexagonal coal gangue blocks with a side length of 20 cm and a thickness of 16 cm meet the structural requirements of most underground mine tunnels, offering a reference model for cost-effective paving and efficient roadway maintenance in coal mines. Full article

In view of the current situation where research on the dust diffusion laws of different dust source points is limited and the gap with the actual field situation is too large; this study employs an innovative gas–liquid–solid triphase coupling method to investigate how dust moves and spreads in the fully mechanized excavation face 431305 at the Liangshuijing Mine; focusing on both the dust field and the dust–fog coupled field. The results indicate that using the long-pressure short-suction ventilation method; dust movement in the roadway is primarily influenced by the airflow; which can be classified into vortex; jet; and return flow regions. The analysis reveals that different dust source points affect dust distribution patterns. Dust source 1 generates the highest dust concentration; primarily accumulating on the duct side and return air side of the roadway. By contrast; dust source 2’s dust mainly gathers at the heading and the front of the cutting head. Dust sources 3 and 4 show lower dust concentrations near the top of the roadway. Dust source 5 achieves the most effective dust removal; aided by airflow and a suction fan; showcasing superior dust performance. A comprehensive comparison indicates that dust source 1 has the highest overall dust concentration. Therefore; further simulation of the distribution law of dust generated at dust source 1 under the action of water mist reveals that the dust concentration near the heading face is reduced from 2000 mg/m³ under the action of single air flow to about 1100 mg/m³. At t = 5 s; the spray droplets almost cover the entire tunneling face; leading to a significant decrease in dust concentration within 10–25 m from the tunneling face. Within 40 s; both coal dust and spray droplets are significantly reduced. The field measurement results verify the accuracy of the simulation results and provide certain guidance for promoting the sustainable development of the coal industry. Full article

Most of the existing coal mine water inrush risk assessment methods are global assessment methods, which have the following problems: they ignore the difference in importance of the evaluation indicators at different locations in the study area and assign the same weight value; the area of the danger zone in the evaluation results is thus too large. The evaluation results improve the prediction accuracy by reducing the safe zone area. To address the aforementioned issues, this study employs a local analysis method based on a moving circular window, taking into account the spatial heterogeneity of criterion indicators in the decision-making process. By traversing each position of the raster with a circular moving window, the method performs local standardization and calculates local weights of indicators within the local window range. Based on the obtained maps of locally standardized evaluation criteria and local weights, a local water inrush risk assessment model is established using Geographic Information Systems (ArcGIS), considering the differences in the importance of evaluation indicators within the study area. Taking the Liangshuijing mining area as an example, both global and local evaluation models were employed to assess its water inrush risk. The evaluation results obtained from these two models were compared and validated against geological survey data and historical water inrush points. The comparative analysis between the two methods reveals that the local evaluation model demonstrates higher accuracy. It offers a more precise delineation of the distribution of water inrush risk zones, which better corresponds to the actual conditions within the mine. The localized water inrush risk assessment method proposed in this paper breaks away from the traditional approach of uniformly weighting evaluation indicators across the entire area, offering a novel method for assessing water inrush risk. Full article

This study aims to reveal the impact of immersion duration on the internal structural damage and mechanical property degradation of coal rocks. Coal rocks from the post-mining area of Liangshuijing Coal Mine were selected as the research subject. Physical and mechanical tests were carried out on these with different immersion durations (0 d, 15 d, 30 d, 60 d, 120 d, and 240 d) using scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), acoustic emission (AE), digital image correlation (DIC), and compression testing, further elucidating the damage degradation mechanisms of water-immersed coal rocks. The research demonstrates that changes in the pore structure of coal rocks can be divided into two stages as the soaking time varies: the stage of water swelling (saturation process) and the stage of soaking damage (long-term immersion process). The water swelling stage of coal rock extends from surface drying and contraction to water swelling, and the soaking damage stage of coal rock extends from expansion to soaking damage. During the stage of soaking damage, the water showed dynamic changes from macropores to mesopores to micropores, with a gradual increase in the number of micropores. The AE count and cumulative count of coal rock decrease first and then increase, and the four stages’ acoustic characteristics and macroscopic characterization phenomena appear. The mechanical properties declined. After 240 d of immersion, the uniaxial compressive strength and elastic modulus decreased by 48.93% and 29.53%, respectively, and the plastic characteristics were enhanced. These research results provide a beneficial reference for understanding and predicting the instability and destruction of water-immersed coal rocks. Full article

A combination of geographic information system (GIS) and spatial multicriteria decision making (MCDA) in mine water inrush risk evaluation is widely used, but the randomness in the process of index weight determination and the risk-coping attitude of decision makers are not considered in the decision making process. Therefore, this paper proposes a probability-based roof water inrush risk evaluation method (GIS-MCDA) by combining the Monte Carlo analytic hierarchy process (MAHP) and ordered weighted averaging (OWA) operator. This method uses MAHP to determine the weight of the evaluation indicators, reducing the randomness of the analytic hierarchy process (AHP) to determine the weight of the evaluation indicators using the OWA operator to quantify the five risk-coping attitudes of decision makers and incorporate the risk attitude of decision makers into the evaluation process. Taking the Liangshuijing Coal Mine in northern Shaanxi as an example, the application of the GIS-MCDA method showed that the method makes the risk results of roof water inrush more objective and comprehensive and reduces or avoids the risk of decision making due to human subjective tendency change. Full article