Experimental Investigation on the Influence of Water Content on the Mechanical Properties of Coal under Conventional Triaxial Compression

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

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM

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