Search Results (230)

Underground coal gasification (UCG) is an emerging energy technology that involves the in situ conversion of coal into syngas through controlled combustion within a subsurface excavation. The geomechanical processes associated with UCG can lead to significant overburden caving and surface subsidence, posing risks to surface infrastructure and groundwater systems. To accurately predict the size of overburden caving zones and associated surface subsidence, a prediction model was developed based on simulation results using discrete element method (DEM) numerical models. The main purpose of developing such a model is to establish a systematic and computationally efficient method for the rapid prediction of the height of overburden caving and its associated surface subsidence induced by underground excavation. The model is broadly applicable to different types of underground excavations, and UCG is used in this study as a representative application scenario to demonstrate the relevance and performance of the model. Sensitivity analysis indicates that excavation span, tensile strength, and burial depth are the primary controls on the height of the caving zone within the ranges of parameters investigated. Rock density is retained as a secondary background parameter to represent gravitational loading and its contribution to the in situ stress level. The derived model was validated using published numerical, experimental, and field measurement data, showing good agreement within practical ranges. To further demonstrate the application of the model developed, the predicted caving geometries were incorporated into finite element method (FEM) models to simulate surface subsidence under different geological conditions. The results highlight that the arch structure formed by overburden caving can help redistribute stresses and thereby reduce surface deformation. The proposed model provides a practical, parameter-driven tool to assist in underground excavation design, environmental risk evaluation, and ground stability management. Full article

Existing coal spontaneous combustion liability assessments suffer from incomplete temperature range coverage, poor cross-rank comparability, and weak correlations between microscopic essence and macroscopic criteria—issues that undermine reliability and risk coal mine safety. This study aims to establish a structure-driven intrinsic identification system to address these gaps. Using 10 cross-rank coal samples (lignite, bituminous coal, and anthracite), we conducted systematic research via experiments, model building, and theoretical verification. We integrated three stage-specific parameters (each matching a combustion phase): saturated oxygen uptake (VO₂, 30 °C chromatographic adsorption), average heating rate R70 (40–70 °C adiabatic oxidation), and Fuel Combustion Characteristic index (FCC, 110–230 °C crossing point method). With Information Entropy weighting (VO₂: 0.296; R70: 0.292; and FCC: 0.412), we constructed the Multi-Factor Comprehensive Spontaneous Combustion Index (MF-CSCI). We also screened functional groups via FTIR, built a microstructure-driven model (MD-CSEI, linking groups to MF-CSCI), and verified mechanisms via DFT. Results show MF-CSCI covers the full “adsorption-heat accumulation-self-heating” process: HG lignite (MF-CSCI = 1.0) had high liability and YCW anthracite (MF-CSCI = 7.98) had low liability, solving cross-rank issues. Pearson analysis found –OH positively correlated with MF-CSCI (r ≈ −0.997), C=C negatively (r ≈ −0.951); MD-CSEI achieved R² = 0.863 (p = 0.042). This study improves cross-rank assessment accuracy, enables rapid micro-to-macro risk prediction, and provides a theoretical basis for on-site coal safety management. Full article

Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate, and reliable pre-diction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. Four main geological indicators were identified by examining the attributes of these factors and their association to outburst intensity. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K Nearest Neighbors (KNN), Back Propagation (BP), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision support tool for mine executives to prevent and control outburst incidents. Full article

To address the problem of wellbore instability in the development of deep coalbed methane reservoirs in Daniudi gas field, this study takes the coal seam cores from Member 1 of the Taiyuan Formation at a depth of approximately 2880 m as the research object. Through CT scanning, scanning electron microscopy (SEM), mineralogical analysis, laboratory mechanical tests, and drilling fluid interaction experiments, the study investigated the coal seam fabric characteristics, mechanical response, anisotropy, and the interaction between drilling fluids and the formation. Based on the double-weak-plane criterion, a wellbore collapse prediction model was established, and instability risk assessment under multi-factor coupling conditions was carried out. Experimental and computational results indicate that the deep coal seam exhibits significant heterogeneity in fabric structure, the clay minerals show low swelling potential, and the bright coal and semi-bright coal are prone to instability due to their dual pore structures. The average uniaxial compressive strength (UCS) of the coal cores is 16.3 MPa, which is weaker than that of the roof, floor, and dirt band. The coal also exhibits anisotropy, with the lowest strength occurring when the loading direction forms an angle of 30–60° with the weak planes, corresponding to 67.5% of the intrinsic compressive strength. Immersion in drilling fluid causes the coal rock strength to decay in a pattern of “rapid decline in the initial stage—gradual decrease in the middle stage—stabilization in the later stage.” After 24 h, the strength is only 55–65% of that in the dry state. Due to its excellent plugging and inhibition performance, HX-Coalmud drilling fluid delays strength loss more effectively than the strongly inhibitive composite salt drilling fluid. The wellbore instability risk assessment indicates that as the drilling time is extended, the collapse pressure rises significantly. After 7 and 20 days of contact between the wellbore and drilling fluid, the equivalent collapse pressure density increases by 0.08–0.15 g/cm³ and 0.13–0.20 g/cm³, respectively. Therefore, homogeneous isotropic models tend to underestimate the risk of wellbore collapse. The findings can provide theoretical and technical support for the safe drilling of deep coalbed methane in Daniudi gas field. Full article

Improving the energy efficiency of advanced ultra-supercritical (USC) power plants by increasing steam operating temperature up to 700 °C can be achieved, at reduced cost, by using novel engineering design concepts, such as coated steam pipe systems manufactured from high temperature materials commonly used in current operational power plants. The durability of thermal barrier coatings (TBC) in advanced USC coal power systems is critically influenced by thermally grown oxide (TGO) evolution and interfacial stress under thermo-chemo-mechanical coupling. This study investigates a novel dual-pipe coating system comprising an inner P91 steel pipe with dual coatings and external cooling, designed to mitigate thermal mismatch stresses while operating at 700 °C. A finite element framework integrating thermo-chemo-mechanical coupling theory is developed to analyze TGO growth kinetics, oxygen diffusion, and interfacial stress evolution. Results reveal significant thermal gradients across the coating, reducing the inner pipe surface temperature to 560 °C under steady-state conditions. Oxygen diffusion and interfacial curvature drive non-uniform TGO thickening, with peak regions exhibiting 23% greater thickness than troughs after 500 h of oxidation. Stress analysis identifies axial stress dominance at top coat/TGO and TGO/bond coat interfaces, increasing from 570 MPa to 850 MPa due to constrained volumetric changes and incompatible growth strains. The parabolic TGO growth kinetics and stress redistribution mechanisms underscore the critical role of thermo-chemo-mechanical interactions in interfacial degradation. These research findings will facilitate the optimization of coating architectures and the enhancement of structural integrity in high-temperature energy systems. Meanwhile, clarifying the stress evolution within the coating can improve the ability to predict failures in USC coal power technology. Full article

Sustainable materials engineering necessitates the valorization of industrial by-products, such as coal fly ash, into functional, high-performance materials. This research addresses a core challenge in materials synthesis: establishing a deterministic technology for controlled porous structure formation to optimize the thermophysical properties of lightweight thermal insulation composites. The primary objective was to investigate the structural evolution kinetics during the high-intensity thermal processing of fly ash-based precursors to facilitate precise property regulation. We developed a novel, integrated process, underpinned by mathematical modeling of simultaneous bloating and non-equilibrium heat transfer, to evaluate key operational parameters within a vortex-layer reactor (VLR). This framework enables the a priori prediction of structural outcomes. The synthesized composite granules were subjected to comprehensive characterization, quantifying apparent density, total porosity, static compressive strength, and effective thermal conductivity. The developed models and VLR technology successfully identified critical thermal exposure windows and heat flux intensities of the heating medium required for the reproducible regulation of the composite’s porous architecture. This precise structure process control yielded materials exhibiting an optimal balance between low density (<400 kg/m³) and adequate mechanical integrity (>1.0 MPa). This work validates a scalable, energy-efficient production technology for fly ash-derived porous media. The established capability for predictive control over microstructural development provides a robust engineering solution for producing porous materials, significantly contributing to waste reduction and sustainable building practices. Full article

Based on comprehensive experimental datasets—proximate/ultimate analyses, XPS, solid-state ¹³C NMR, and Raman spectroscopy—we constructed and optimized a compositionally faithful macromolecular model of SG coking coal. Using density-functional theory (DFT) calculations, we simulated electrostatic-potential (ESP) fields and frontier molecular orbitals (FMO) to probe elementary oxidation steps relevant to combustion, and focused on how heteroatom speciation and carbon ordering govern site-selective reactivity. Employing multi-peak deconvolution and parameter synthesis, we obtained an aromatic fraction f_a = 76.56%, a bridgehead-to-periphery ratio X_BP = 0.215, and Raman indices I_D1/I_G ≈ 1.45 (area) with FWHM(G) ≈ 86.7 cm⁻¹; the model composition C₁₉₀H₁₄₄N₂O₂₁S and its predicted ¹³C NMR envelope validated the structural assignment against experiment. ESP–FMO synergy revealed electron-rich hotspots at phenolic/ether/carboxyl and thiophenic domains and electron-poor belts at H-terminated edges/aliphatic bridges, rationalizing carbon-end oxidation of CO, weak electrostatic steering by O₂/CO₂, and a benzylic H-abstraction → edge addition → O-insertion/charge-transfer sequence toward CO₂/H₂O, with thiophenic sulfur comparatively robust. We quantified surface functionalities (C–O 65.46%, O–C=O 24.51%, C=O 10.03%; pyrrolic/pyridinic N dominant; thiophenic-S with minor oxidized S) and determined a naphthalene-dominant, stacked-polyaromatic architecture with sparse alkyl side chains after Materials Studio optimization. The findings are significant for mechanistic understanding and control of coking-coal oxidation, providing actionable hotspots and a reproducible workflow (multi-probe constraints → model building/optimization → DFT reactivity mapping → spectral back-validation) for blend design and targeted oxidation-inhibition strategies. Full article

To improve the accuracy and reliability of coal quality assessment via near-infrared spectroscopy, this study proposes a multi-method analysis framework for robust spectral feature selection. A core challenge is reconciling the trade-offs between different analytical approaches: statistical methods often yield smooth but diffuse results, while machine learning models can identify sharp, localized features that may lack stability. Our framework addresses this by integrating diverse analytical perspectives, including statistical correlations, SHAP-interpreted machine learning models, and latent-variable regression. We then introduce a novel fusion strategy that synthesizes the importance profiles from these methods based on inter-method consistency, curve smoothness, and local concentration. Experimental results demonstrate this fusion yields more interpretable and physicochemically coherent wavelength importance profiles for both Moisture ($M_{ad}$) and Volatile Matter ($V_{ad}$). The selected features consistently achieve superior prediction performance across various regression models, showing particular robustness with limited training data. This work offers a structured methodology for identifying compact and informative spectral features, facilitating the development of efficient models for online monitoring and contributing to improved process control. Full article

Pressure-preserved coring enables in situ encapsulation of deep coal samples at the borehole bottom. By effectively reducing gas desorption, it supports reliable reserve assessment. Achieving reliable pressure-preserved sealing within the confined space of drilling tools remains a critical technical challenge in the field. Focusing on the pressure controller, this study investigates three key aspects: configuration design, sealing interface behavior, and structural performance. The investigation employs both theoretical modeling and laboratory experiments. First, a geometric configuration design methodology was proposed for the pressure controller using intersecting contact and tapered sealing principles. This was followed by the creation of a spatial motion interference prediction model for the assemblies. Secondly, the contact behavior of intersecting sealing interfaces was studied; analysis of the failure mechanisms showed that the design achieves a pressure-preserved capacity of about 24 MPa. Finally, laboratory tests validated the sealing performance of the pressure controller. The tests confirmed that seal ring failure is characterized by high-pressure extrusion, which is caused by an increased sealing clearance. The research findings elucidate the sealing formation mechanism of the pressure controller, establishing a theoretical foundation for advancing pressure-preserved coring technologies in coalbed methane and gas hydrate exploration and development. Full article

The calorific value of coal is a key parameter for pricing, trade, and combustion management. Conventional bomb calorimetry provides accurate results but is time-consuming, labor-intensive, and destructive. Near-infrared (NIR) spectroscopy offers a rapid and non-destructive alternative, yet its application is limited by strong band correlations, nonlinear spectral responses, and the lack of interpretability in many predictive models. In this study, the Kolmogorov–Arnold Network (KAN) is applied to the prediction of coal calorific value, demonstrating its capability to describe nonlinear spectral relationships within an interpretable mathematical structure. Based on this framework, a Radial Basis Function KAN (RBF-KAN) is further developed by replacing the B-spline bases in the KAN with radial basis functions, allowing improved representation of localized and irregular spectral variations while maintaining model transparency. Using 671 coal-powder samples measured by a portable MicroNIR spectrometer, the RBF-KAN achieved an RMSE of 1.35 MJ/kg and an MAE of 0.92 MJ/kg under five-fold cross-validation, outperforming conventional regression models, deep neural networks, and other KAN variants. Analysis of RBF activations and spectral attribution maps indicates that the model consistently responds to characteristic O-H and C-H overtone regions, which correspond to known absorption features in coal. These results suggest that the RBF-KAN provides a practical and interpretable framework for on-site estimation of coal calorific value, complementing traditional calorimetric analysis. Full article

Permeability evolution is one of the key parameters influencing the efficient exploitation of deep unconventional energy resources, as it reflects the dynamic development of pore-fracture structures under complex engineering effects. Using fractal geometry to describe the pore-fracture system, rock permeability enhancement can be quantitatively evaluated. In this study, fractured coal specimens were analyzed under simulated mining-induced stress relief and CH₄ release conditions based on fractal geometry theory. The permeability-enhancement rate was derived and verified through CT (Computed Tomography) characterization of the pore-fracture network. The fractal dimension of the fracture aperture distribution and the tortuosity of fracture paths were determined to establish a fractal permeability-enhancement model, and its sensitivity was analyzed. The results indicate that permeability evolution undergoes four distinct stages: a stable stage, a slow-growth stage, a rapid-growth stage, and a stable or declining stage. The mining-induced stress relief and gas desorption effects significantly accelerate permeability enhancement, providing new insights into the mechanisms governing gas flow and pressure relief in deep coal seams. The proposed model, highly sensitive to the fracture aperture ratio (λ_min/λ_max), reveals that a smaller aperture span leads to greater permeability enhancement during the damage and fracture stage. These findings offer practical guidance for predicting permeability evolution, optimizing gas drainage design, and enhancing the safety and efficiency of coal mining and methane extraction operations. Full article

Karst collapse columns (KCCs) represent key concealed hazard-inducing factors that threaten the safety of coal mines in North China. To clarify their primary controlling geological factors and evolutionary processes, this study focuses on KCCs in the Hancheng Mining Area, situated on the southeastern margin of the Ordos Basin, China. A comprehensive methodological approach—integrating field surveys, petrographic and mineralogical identification, geochemical analysis, and structural interpretation—was employed to investigate the dominant factors controlling KCC development and their evolutionary mechanisms. The results indicate the following: (1) Thick-bedded dolomites of the 5th Member of the Majiagou Formation (Middle Ordovician Series) serve as the material foundation for karstification. These dolomites were deposited in an oxidized shallow-water tidal flat setting, which endowed them with favorable lithological properties for subsequent dissolution. (2) NE-SW trending erosional grooves within the paleogeomorphology of the Ordovician top surface functioned as preferential flow paths for karst water, channeling fluid movement and intensifying localized dissolution. (3) Multi-phase tectonic activities, particularly extensional deformation during the Himalayan orogeny, created the necessary stress conditions to trigger cave collapse. (4) KCCs undergo a multi-stage formation and evolution process: Starting with the Majiagou Formation’s 5th Member dolomites as the primary lithology, initial modification occurred via Caledonian weathering–crust karstification. Subsequently, compressional tectonism during the Yanshanian orogeny generated void spaces that facilitated deep-seated dissolution. Rapid uplift in the Paleogene exacerbated vertical dissolution, leading to extensive cavity development, which ultimately collapsed under the extensional tectonic regime of the Neogene. This study provides theoretical support for predicting and mitigating sudden water inrushes caused by KCCs in the Hancheng Mining Area. Furthermore, it offers novel insights and a scientific basis for advancing understanding of the developmental mechanisms of North China-type KCCs. Full article

The energy sector, as a major source of carbon emissions, has a significant impact on the operation of the carbon market and the management of carbon emissions. With the introduction of the “dual carbon” goals, the Chinese government has actively implemented measures to reduce carbon emissions, making the carbon market an important tool for emission reduction. Therefore, characterizing the inter-market relationships helps enhance decision-making for market participants and promotes sustainable economic development. This study selects the price of the Chinese carbon emission trading market, which began trading on 16 July 2021, as a representative of the carbon market price. In terms of energy market selection, the prices of electricity, new energy, and coal are chosen as representatives of the energy market. From the perspective of the nonlinear dependency structure between market prices, a “carbon ↔ electricity ↔ new energy ↔ coal market” multi-to-multi interaction model is constructed, and the MSVAR model is employed to study the nonlinear dependency characteristics between market prices under interactive influences. The results show that there is a significant nonlinear dependency structure between the four market prices, especially between the carbon market and the new energy market. These market prices exhibit different behavioral characteristics under different states, with non-stationary states being the most common. There is a strong positive correlation between the electricity market and new energy market prices, while the relationship between the carbon market and other market prices is relatively weaker. The relevant conclusions provide valuable insights for policymakers and investors, helping them better understand and predict future market dynamics. Full article

This study investigates the mechanisms of hazardous gas outbursts in geologically complex non-coal tunnels. This is a critical safety concern during excavation, particularly at specific locations and during time-sensitive periods. To address this, a gas–solid coupled numerical model is established to simulate gas seepage processes under such conditions. The simulations systematically reveal the spatiotemporal evolutionary patterns of the velocity and direction of the gas seepage and elucidate the migration mechanism driven by excavation-induced pressure gradients. The model specifically analyzes how geological structures, such as rock joints and fractures, control the seepage pathways. The model also demonstrates the dynamic variations in and enrichment behavior of the gas escape velocities near these discontinuities. Field measurements obtained from the Hongdoushan Tunnel validated the simulated emission patterns along jointed fissures. The findings clarify the intrinsic relationships between the outburst dynamics and key factors that include pressure differentials, geological structures, and temporal effects. This work provides a crucial theoretical foundation and practical strategy for the prediction and prevention of hazardous gas disasters in analogous tunnel engineering projects, thereby enhancing overall construction safety. Full article

With the progressive depletion of coal resources, the recovery of shaft pillars has become an important means of improving resource utilization and reducing waste. Taking the main shaft pillar recovery of the Longxiang Coal Mine at the stage of mine closure as the engineering background, this study systematically investigates ground subsidence prediction and shaft stability control under strip mining with symmetrical extraction. An improved subsidence prediction model was established by integrating the probability integral method with superposition theory, and its validity was verified through numerical simulations and field monitoring data. The results demonstrate that the proposed method can accurately capture the subsidence behavior under complex geological conditions, with prediction errors ranging from 6.4 mm to 399.1 mm. In fully subsided zones, the percentage error was as low as 1.1–3.5%, while larger deviations were observed in areas where subsidence was incomplete, confirming both the reliability and the practical limitations of the method under different conditions. Furthermore, the deformation mechanisms of the shaft during pillar recovery were analyzed. Monitoring results indicated that the maximum subsidence at the east and west sides of the shaft reached 7620.6 mm, accompanied by local cracks exceeding 1500 mm, which caused significant damage to surface structures. To address these risks, a safety control scheme based on an integrated “prediction–monitoring–control” framework is proposed, including shaft wall reinforcement, optimization of mining parameters, and continuous ground subsidence monitoring. By combining real-time monitoring with the superposition of small working face predictions, the scheme enables maximum recovery of shaft pillar coal while ensuring operational safety. This study provides a scientific basis and technical support for shaft pillar recovery in Longxiang Coal Mine and offers valuable theoretical guidance for similar mine closure projects, with significant implications for engineering practice. Full article