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Abstract It is of great significance for coal mining and utilization to study the adsorption process of mixed gas in coal. In this paper, the Monte Carlo method (MC) is employed to study the competitive saturation adsorption of oxygen and water vapor inside coal particles, and then the convection, diffusion and adsorption inside and between particles are studied by lattice Boltzmann method (LBM). In addition, this study examines the impacts of porosity, average particle size, and gas concentration on the process of adsorption in coal porous media. The research results show that oxygen and water vapor present in the mixed gas experience increased permeability, diffusion rate, and saturated adsorption capacity as the porosity and average particle size of the coal porous medium increase. However, the time required to achieve saturated adsorption decreases. Under the condition of maintaining the proportion of gas components and altering the initial gas concentrations from 4.087 to 53.131 mol/m3, saturated adsorption capacity of both gases remains nearly unchanged. Yet, the effective diffusivity of gases declines with increasing initial concentration. Additionally, it is also found that water vapor diffuses more quickly than oxygen in the mixed gas and achieves adsorption saturation faster.

As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

Weak inclusions play a significant role in regulating the deformation and fracturing behavior of rock masses. In this study, sandstone specimens filled with circular inclusions were subjected to diametrical compression until failure. The local strain concentration intensity and cracking process at the rock-inclusion interface were specifically investigated using the integrated strain gauge (SG) and digital image correlation (DIC) techniques. The experimental results reveal that the presence of inclusions introduces more complex deformation and fracturing behaviors in the infilled sandstone specimens. The stiffness of the infilled sandstones, with either low-strength or high-strength inclusions, initially increases and then decreases as the inclusion diameter increases. The effective load-carrying capacity of the infilled sandstone is influenced by the competing effects of inclusion enhancement and hole weakening. Moreover, a more pronounced strain concentration occurs at the inclusion boundary when the inclusion diameter is larger and the inclusion strength is lower. The crack mode in the infilled red sandstone is independent of the strength of the inclusion. For larger hole diameters, fracture planes form along the compression line, whereas interfacial debonding occurs in red sandstone with smaller hole diameters. Finally, the stress distribution characteristics along the loaded diameter and transverse diameter were analyzed using elastic finite element analyses, which can well explain the cracking mechanism of the infilled disc specimens.

To better grasp the status quo of urban rail transit (URT) safety research and explore the knowledge base and hotspot trends, 1249 URT safety research papers from 1983-2018 were collected from the SCIE and SSCI databases of the WOS search platform as data samples. Co-occurrence analysis, burst analysis of keywords and co-citation analysis were adopted to analyze the current research situation of URT safety in the world by means of information visualization. The study found that risk analysis in the construction/operational phase, methods for predicting ground movements, and cause analyses of tunnel deformation and settlement are the knowledge bases in the field of URT safety research. Tunneling and Underground Space Technology, Safety Science, Atmospheric Environment, Lancet and Transportation Research Record are the core journals in the field. At present, the basic theory and research system of URT safety have been basically completed, but the research directions are too concentrated, and the frontier branches are too few. The research themes of URT safety were roughly grouped into three core paths: the “health risk” path, the “risk analysis - safety management” path and the “tunnel displacement/deformation” path. In the process of research and evolution, the research paths are refined step-by-step, and the research directions are transformed from macro to micro. The emergency evacuation of personnel in subway stations, the effects of particulate matter in subway air on human health and the use of computer technology for engineering optimization are the current frontiers of URT safety. URT safety research also tends toward risk analysis in the operational phase.

The presence of water is one of the most important factors in coal mining, and it has a dual influence on the mechanical behavior of rock. To study the influence of water content on the mechanical properties of coal under complicated stress conditions, dry coal specimens and wet coal specimens with water contents of 1.8% and 3.6% were conducted by uniaxial and conventional triaxial compression tests. The relations between the uniaxial compressive strength, deformation, and water content were observed. The reductions in the strength and elastic modulus under different confining pressures were obtained. The mechanical properties of coal specimens with different water contents under triaxial compression were studied. The influences of water content on the microstructure, clay minerals, internal friction angle, and cohesive force of coal were discussed. The results show that the strengths and elastic moduli of wet specimens are clearly lower than those of dry specimens under different confining pressures. The water content has a significant influence on the postfailure mechanical behavior of coal. The loss rates of strength and elastic modulus decrease with increasing confining pressure. The water content has almost no effect on the internal friction angle, while the cohesive force of the saturated specimens is 36.5% lower than that of the dry specimens. The results can provide a reference for inhibiting the occurrence of disasters during coal mining and exploiting coal efficiently.

The issue of coal spontaneous combustion greatly threatens the production safety of coal mining, storage, and transportation. It is significant to study oxygen adsorption understand the mechanism of coal spontaneous combustion. In this paper, based on the fractal dimension of coal and the self-similar fractal geometry, the internal pore structure of coal is modeled. Then, the lattice Boltzmann method is employed to conduct the numerical simulation of oxygen adsorption in coal. Compared with the existing experimental data and numerical simulation, the lattice Boltzmann method is verified to be correct. The numerical results indicate that in the process of oxygen adsorption in coal, the preferential flow occurs when the large pores connect to the channel. In the meantime, the large diffusion coefficient leads to an early time for adsorption equilibrium. The oxygen adsorption increases with an increased adsorption rate constant. Pore structure plays a significant role in the adsorption behavior of oxygen in coal. The results can provide theoretical support for reducing coal spontaneous combustion and ensuring coal mine safety in production. [ABSTRACT FROM AUTHOR]