Baolin Li

This Reprint addresses China’s energy landscape as defined by rich coal, scarce oil and gas, ensuring coal will remain the country’s core energy pillar through 2050 under the dual carbon goals and energy security demands. With shallow coal reserves depleted, deep mining has become essential; yet, deep seams feature high geostress, strong gas adsorption and low permeability. Mining disturbances exacerbate intertwined coal-gas outburst and rock burst disasters, creating complex prevention challenges. Focusing on deep mining coal-rock dynamic disasters, this book clarifies disaster control difficulties and delivers innovative mitigation solutions for mining and civil engineering to advance global sustainability. It collects original studies and reviews covering outburst and rock burst prevention, regional risk evaluation, hazard early warning, mine ventilation, disaster management models and gas extraction technologies and materials.

Underground mine ventilation is a safety-critical engineering system and one of the largest electricity consumers in mining, typically accounting for 40–60% of a mine’s electrical demand. Allocating airflow economically without violating safety limits is therefore central to energy-efficient ventilation regulation. This study proposes an optimization framework that couples loop-space embedding with a sine–cosine and Cauchy mutation-enhanced sparrow search algorithm (SCSSA). Loop-space embedding reparameterizes the branch airflow vector on the null space of the network incidence matrix, so that nodal mass conservation holds identically rather than approximately, and the decision dimension falls from the number of branches m to the number of independent loops L = m − n + 1. The remaining airflow, fan-operation and safety constraints are treated by a hierarchical exterior penalty function. Within SCSSA, refraction-based opposition learning improves the initial population, a sine–cosine strategy with adaptive inertia replaces the rigid producer step, and Cauchy mutation supplies the heavy-tailed perturbations needed to leave local optima. On ten classical benchmark functions, together with ablation and parameter-sensitivity studies, SCSSA attains better convergence accuracy and lower run-to-run variability than SSA, GWO and PSO. For a synthetic but engineering-scale 15-node, 21-branch ventilation network, the framework lowers fan shaft power from 32.604 kW to 14.784 kW relative to a 1.3-times over-ventilation design baseline, a scenario-based saving of 54.65% that at 8000 operating hours corresponds to about 1.43 × 10⁵ kWh of electricity, about 8.55 × 10⁴ CNY and roughly 87 t of CO₂ per year, with both working-face airflow requirements satisfied. The framework thus offers a practical route to energy-efficient mine ventilation regulation in which mass conservation is structurally exact.

Fractures are widely developed in deep coal-mine surrounding rocks. They weaken the load-bearing capacity and energy-storage capacity of rock specimens, which may induce surrounding-rock deformation, roof collapse, and other hazards. Current studies on fractured rock masses mainly focus on a single parameter, such as fracture number or fracture dip angle. However, their coupled effects remain unclear. Integrated analyses of mechanical behavior, crack propagation, and energy evolution are also limited. In this study, uniaxial compression simulations of intact sandstone, single-fracture sandstone, and double-fracture sandstone were conducted using PFC^2D. The effects of fracture number and fracture dip angle on mechanical properties and failure characteristics were investigated. The results show that fractures reduced the peak stress and modulus of elasticity. A stronger weakening effect was observed with increasing fracture number. With increasing fracture dip angle, both peak stress and modulus of elasticity showed a V-shaped trend. The minimum peak stress occurred at 15°, while the minimum modulus of elasticity occurred at 45°. Sandstone failure was mainly dominated by tensile cracks. At 15°, the total crack number was the lowest, with 932 and 818 cracks for single-fracture and double-fracture specimens, respectively. Energy analysis showed that increasing fracture number reduced elastic strain energy and promoted dissipated energy. The weakest energy-storage capacity was observed at 30°. Overall, fracture number and fracture dip angle jointly controlled strength degradation, crack propagation, and energy evolution. This study provides a reference for fracture–damage assessment and disaster prevention in deep coal-bearing sandstone.

Microseismic monitoring systems enable real-time acquisition of vibration signals generated by coal and rock fracturing, deriving key parameters (e.g., event time, spatial location, and energy release) via inversion algorithms for rock burst risk prediction in coal mining operations. However, the underground coal mining environment is highly complex, and signals acquired by sensors are often contaminated with substantial blasting-induced interference, thereby significantly degrading the accuracy of monitoring and early warning. While existing research predominantly utilizes time-frequency transform and fractal analysis to characterize mining microseismic and blasting signals, systematic investigations into data-driven automatic identification of these two signal types remain conspicuously limited. To address this gap, this study investigated the characteristic differences between mining microseismic signals and blasting signals across three analytical dimensions: time-domain, frequency-domain, and fractal dimension. Using the Relief characteristic selection algorithm, discriminative characteristics for distinguishing the two signal types were selected. Moreover, discrimination models for mining microseismic and blasting signals were developed using three machine learning algorithms: Fisher's linear discriminant analysis, support vector machine (SVM), and adaptive boosting (AdaBoost). The results indicated that the discriminative characteristics for distinguishing mining microseismic signals from blasting signals include duration, rise time, post-peak attenuation coefficient, high-frequency/low-frequency ratio, and multifractal spectrum width (Δα). In terms of model performance, the classification model based on AdaBoost exhibited good stability, whereas the SVM model achieved the highest identification performance with an overall recognition accuracy of 91.2%. These models can effectively satisfy the real-time monitoring requirements of engineering sites.

Electromagnetic radiation (EMR) can reflect coal rock stress, rupture and gas seepage outflow, and is an important means of monitoring coal and rock dynamic disasters such as rock burst, coal and gas outburst. However, there are also problems such as electromagnetic radiation is susceptible to interference and high signal noise, which affect the accuracy of early warning indicators and risk early warning. At present, there is still a lack of effective EMR de-noising means, so it is necessary to find the optimal de-noising method of EMR signal. In view of the above problems, this paper carried out the coal and rock uniaxial compression EMR monitoring experiment, collected the EMR signals of different noise levels in the whole process of coal and rock loading. time-varying filtering empirical mode decomposition (TVF-EMD), variational mode decomposition (VMD) and successive variational mode decomposition (SVMD) algorithms optimized by gray wolf optimization (GWO) algorithm are applied to the de-noising process of EMR signal of coal rock fracture. Compared with the conventional empirical mode decomposition (EMD) and wavelet packet transform (WPT) algorithms, the signal to noise ratio (SNR) after denoising is analyzed. The experimental results show that GWO-TVF-EMD algorithm can adapt to the de-noising of EMR signals with different noise levels, and the average increase in SNR after de-noising is 19.74 compared with the original signal, which is significantly better than other algorithms. The research results are of great significance for the improvement of EMR monitoring and early warning technology.

Electromagnetic radiation, as a non-contact and real-time monitoring technology, has been widely used in coal rock fracture and rock burst disaster monitoring during coal mining. Electromagnetic radiation signals can reflect the loading state and fracture degree of coal rocks. However, when electromagnetic radiation is used to predict rock fracture, the current study mainly focuses on the trend change of signal strength, counts and other statistical indicators. There is a lack of research on rock fracture prediction based on the electromagnetic radiation signal itself. Therefore, experiments on monitoring electromagnetic radiation in uniaxial compression of rocks were carried out. Differences in the features of ordinary signals (corresponding to the 0–80% σ stage) and precursory signals (corresponding to the 80% σ—σ stage) of electromagnetic radiation during the loading process of rocks were analyzed. The results showed that different signal features distinguished the two types of electromagnetic radiation signals to different degrees. Automatic ranking of feature importance can be achieved by the random forest method. Adaptive boosting method was used to establish intelligent recognition models for two types of electromagnetic radiation signals. The model recognition accuracy was also analyzed when the feature sets were different. It was finally determined that the highest recognition accuracy (92.25%) of the intelligent recognition model for the two types of signals was achieved when the combination of four features was used as the feature set. The research results provide new ideas and methods for the rock fracture prediction using electromagnetic radiation.

Generally, there are two important types of microseismic (MS) signals caused by mining and blasting activities at coal mines. The waveform characteristics of MS signals using FFT, STA/LTA method, and envelope analysis were studied to distinguish these two types of MS signals. The main results are as follows: the dominant frequency and duration of two types of signals are significantly different. The following peak envelope curves of two types of MS signals fit a power function. The power exponent was obtained to describe the attenuated speed of the MS signals. The attenuation of the coal mining MS signals is slower and more fluctuant than that of the blasting signal. Waveform characteristics consisting of the dominant frequency, duration, and attenuation coefficient were extracted as the discriminating parameters. The discriminating performance of these parameters was compared and discussed. Based on the waveform characteristics, a discriminant model for coal mining MS and blasting signals was established by using Fisher linear discriminant method and its performance was checked. The results show that the accuracy of the discriminant model is more than 85%, which can meet the requirements of MS monitoring at coal mines.