Dynamic mechanical response and crack evolution law of raw coal loaded by dynamic-static coupling under three-dimensional constraints.

This paper presents the investigation of the dynamic mechanical properties of coal rock under complex stress conditions at depth, based on the improved Separate Hopkinson Pressure Bar Test System. A total of 15 groups of coal samples were used to perform dynamic impact tests under different conditions. The changing rules of dynamic strength, crushing, fractal dimension and damage modes of coal under different stress conditions were analyzed. A total of nine groups of coal samples were selected for numerical simulation using ANSYS/LS-DYNA. The results show that: (1) The stress-strain curves of coal specimens under different strain rates, different confining pressures and axial pressures have basically the same trend and the curves show a certain jump forward. (2) The peak dynamic stress of the coal specimens increased linearly with the increase of strain rate and confining pressure, and the ambient pressure limited the expansion of internal cracks of the coal specimens under impact loading. Based on the experimental and simulated data, the maximum relative errors between the experimental and simulated data were determined to be 2.9578% for Group A, 6.177% for Group B, and 6.382% for Group C, respectively. (3) The damage modes of the coal samples under the three-dimensional dynamic-static combined loading were mainly "X" type and "conical" shear damage. The fractal dimension increases with the increase of strain rate, decreases with the increase of confining pressure, and first decreases and then increases with the increase of axial pressure. This research achievement can provide theoretical support for the prevention of dynamic disasters in deep coal mine engineering.

Research on the Performance of Cement-Based Composite Borehole Sealing Material Based on Orthogonal Test.

In order to achieve better sealing of boreholes, the performance of sealing materials is modified to improve the efficiency of coalbed methane extraction. In this paper, a new type of cement-based hole sealing material was prepared by using silicate cement (PC) and cement sulfoaluminate (SAC) as raw materials, supplemented with various additives, such as fly ash, Na2SO4, Ca(OH)2, and poly(vinyl alcohol) (PVA) fiber. The effects of these additives on the fluidity, setting time, and compressive strength of the PC-SAC compounded cementitious pore sealing material were investigated by orthogonal tests, and the hydration process and hydration products were analyzed by X-ray diffraction (XRD), thermogravimetry-differential thermogravimetry (TG-DTG), and scanning electron microscopy (SEM). The results show that the water-cement ratio has the most significant influence on the various properties of the material; the two additives of Na2SO4 and Ca(OH)2 play a key role in the setting time of the material; the optimal group, i.e., water-cement ratio of 0.5, fly ash of 5%, Na2SO4 of 1%, Ca(OH)2 of 0.75%, and PVA fibers of 0.8%, is obtained by the orthogonal test method, which is the closest to the actual needs of the project. The hydration products of the optimized materials have obvious changes, and the needle-like AFt and C-S-H increase so that the performance of the materials has been significantly improved.

Modeling of gas emission in coal mine excavation workface: a new insight into the prediction model.

Coalbed methane (CBM) is a clean energy source, but its utilization is inefficient due to the complexity and low accuracy of its emission prediction model. In this research, we constructed a mathematical model of gas emission from the excavation workface, and combined the experimental results to propose a new model for accurate and concise prediction. The new model was validated in the field workface and compared with the traditional prediction model. Moreover, the sensitivity of gas emission parameters and the participation ratio of gas emission sources were analyzed. The study results show that the new model has higher calculation accuracy than the old model prediction, with an average error reduction of 4.693%. In the excavation workface, the coal fall gas emission conforms to the negative power function equation, and the coal wall gas emission conforms to the negative exponential function equation. In the early stage of excavation, the proportion of coal fall gas emission is higher than that of coal wall gas emission, and the peak proportion reaches 58.5%. In the later stage, the proportion of coal fall gas emission gradually decreases to below 30%. The order of the sensitivity of gas emission parameters is coal wall gas initial velocity > coal fall gas decay coefficient > coal fall gas initial velocity > coal wall gas decay coefficient. The new model is successfully applied in engineering, which helps to improve the efficiency of coal mine gas disaster control and utilization.

Combination of Manifold Learning and Deep Learning Algorithms for Mid-Term Electrical Load Forecasting.

Mid-term load forecasting (MTLF) is of great significance for power system planning, operation, and power trading. However, the mid-term electrical load is affected by the coupling of multiple factors and demonstrates complex characteristics, which leads to low prediction accuracy in MTLF. Furthermore, MTLF is faced with the "curse of dimensionality" problem due to a large number of variables. This article proposes an MTLF method based on manifold learning, which can extract the underlying factors of load variations to help improve the accuracy of MTLF and significantly reduce the calculation. Unlike linear dimensionality reduction methods, manifold learning has better nonlinear feature extraction ability and is more suitable for load data with nonlinear characteristics. Furthermore, long short-term memory (LSTM) neural networks are used to establish forecasting models in the low-dimensional space obtained by manifold learning. The proposed MTLF method is tested on independent system operator (ISO) New England datasets, and load forecasting in 24, 168, and 720 h ahead is carried out. The numerical results validate that the proposed method has higher prediction accuracy than many mature methods in the mid-term time scale.