Extracting aluminum from FA is an effective way to improve its utilization rate. Since aluminum oxide is found in high polymerization degree, inert substances such as mullite and sodium aluminosilicate make the reaction process difficult because of their stable chemical properties; within these highly polymerized matrices, the chemical stability of alumina typically persists across a broad temperature range from 1000 °C to over 1600 °C. To address the issue of stable mullite structure that hinders aluminum extraction, a combined acid-base method using sodium carbonate as an activating agent and hydrochloric acid at a temperature of 100 °C as a leaching agent is employed. XRD and SEM were used to analyze the phase characterization and microstructure of fly ash before and after activation and acid leaching, examining the effects of activation parameters and acid leaching parameters on the activation of FA and the aluminum extraction rate. The research results indicate that after calcination activation, mullite is transformed into zeolite, which is easily soluble in acid, and the aluminum within the activated molten material is transferred to the filtrate through acid leaching, achieving the goal of extracting aluminum. Under the activating conditions with sodium carbonate flux, the Al-O bonds in mullite are broken, the crystal structure is transformed, and the aluminum compounds obtained from hydrochloric acid leaching have a stable form of existence, which has a low impact on the error of the experimental results. When the material ratio of fly ash to sodium carbonate is 1:0.7, after reacting at a calcination temperature of 880 °C for 1.5 h, and leaching in 6 mol/L hydrochloric acid at 100 °C with a solid-liquid ratio of 1:6 for 2 h, the extraction rate of aluminum in fly ash is the highest, reaching 97%.

To investigate the impact of bedding on the multifractal characteristics of acoustic emission (AE) signals during the tensile failure process of coal rock, Brazilian tests were conducted on coal samples with varying bedding angles. AE systems were employed for real-time monitoring, and analyses were performed on the mechanical properties, failure patterns, and ringing counts. The multifractal spectrum of the coal samples was estimated using the MF-DFA method. The time-varying response characteristics of the spectrum width Δα and the frequency spectrum measure subset Δf(α) were examined, which facilitated the identification of precursors to tensile failure in coal rock through multifractal analysis.The research findings indicate that as the bedding angle increases from 0°to 90°and subsequently to C0°, the tensile strength of coal samples initially decreases before increasing again. The failure mode transitions from bedding tensile failure to bedding shear sliding failure, and eventually to matrix tensile failure or a composite failure characterized by bedding shear sliding. Throughout the loading process, the coal samples display a fluctuating decrease followed by an increase in Δ α, while Δf(α) exhibits an overall fluctuating decrease. The fractal aggregation activity of cracks is enhanced, and when significant primary fractures develop, both Δ α and Δf(α) reach their extreme values, maximizing the non-uniform distribution characteristics of the signals and the proportion of large-scale fractures.The propagation mode of cracks is significantly influenced by bedding, with the bedding dip controlling the direction of crack propagation. This indicates that as the bedding dip increases, cracks transition from bedding-parallel to bedding-perpendicular propagation, resulting in a greater variety of macroscopic cracks. The evolution of crack patterns in coal samples shifts from a “unidirectional gradual” mode to a “multidirectional multifocal” mode. Consequently, the parameter Δ α initially decreases and then increases, while the variation of Δf(α) is inconsistent. Overall, the more singular the crack propagation mechanism in coal samples, the smaller the Δf(α). The time-varying response characteristics of the multifractal parameters Δ α and Δf(α) in coal samples are closely linked to their crack evolution, with a sudden increase in Δ α and a sudden decrease in Δf(α) serving as precursors to tensile failure in coal rock.

Given the severe water hazard in the coal seam roof of the Binchang mining area, existing research methods still primarily rely on traditional approaches such as empirical formula and numerical simulation—resulting in insufficient accuracy and convenience in predicting the height of the water-conducting fracture zone (WCFZ). By comprehensively considering three influencing factors—mining thickness, mining depth, and working face length—a data-driven approach was employed to construct a multiple nonlinear regression prediction model and a Convolutional Neural Network (CNN) prediction model based on 27 sets of measured data. Both models were subsequently applied to the ZF1403 and ZF1405 working faces in the Yadian coal mine. The results indicate that when considering only single factor of mining thickness, the coefficient of determination (R²) value of the multiple nonlinear regression model was 0.64. When considering all influencing factors, R² improved to 0.84. The mean absolute percentage error (MAPE) of multiple nonlinear regression model was 7.52%. The established CNN model achieved a R² of 0.97, a root mean square error (RMSE) of 9.78, and a MAPE of 4.67%. Compared to the Back Propagation Neural Network model, the prediction accuracy of the CNN model was significantly improved. The relative prediction errors of the developed height of WCFZ in the ZF1403 and ZF1405 working faces at Yadian mine were 6.30% and 2.54% for the multiple nonlinear regression model, respectively, and 0.97% and 3.15% for the CNN model, respectively. Both models met practical engineering requirements. This paper can provide reliable technical support for the prediction of water-conducting fracture zone height under mining conditions similar to the Binchang mining area.