To accommodate surrounding rock structure stability control problem in underground mining, we study the coupling effect principle between hydraulic support and surrounding rock, and develop a series of longwall mining technology and equipment, which solves four common technical problems that significantly undermine coal mining safety, efficiency, and high recovery and extraction rates. Based on the coupling characteristic between mining-induced stress field and supporting stress field of hydraulic support, we identify six controllable factors in the application of hydraulic support to surrounding rock, and further reveal the relationship between hydraulic support and surrounding rock in terms of the strength, the stiffness, and the stability coupling. Our findings provide a plausible solution to the longwall mining technical problem with 6-8 m mining height. By analyzing the dynamic disequilibrium characteristics between hydraulic support and surrounding rock, we propose the intelligent top coal caving control method and the high-coal-recovery-rate technology for fully mechanized caving faces. With the invention of this technology, China is likely to lead the world in terms of the fully mechanized top coal caving mining technology. We are also the first to employ the intelligent coupling technology between hydraulic support and surrounding rock, and automated mining mode, and supporting system cooperative control with automatic organization. We develop the comprehensive multi-index intelligence adjusting height decision-making mechanism and three-dimensional navigation automatic adjusting straightness technology based on shearer cutting height memory association, cutting power parameters, vibration, and video information, leading to the first set of intelligent longwall mining technology and equipment for thin seam. Our innovation makes a solid contribution to the revolution of intelligence mining technology. With the innovative use of three-dimensional coupling control principle for surrounding rock, we successfully resolve the technological difficulties of longwall mining equipment and surrounding rock control for steep dipping seam, making a breakthrough of longwall mining technology with steep dipping seam.
In order to study the mechanism of confined water inrush from coal seam floor, the main influences on permeability in the process of triaxial seepage experiments were analyzed with methods such as laboratory experiments, theoretical analysis and mechanical model calculation. The crack extension rule and the ultimate destruction form of the rock specimens were obtained. The mechanism of water inrush was explained reasonably from mechanical point of view. The practical criterion of water inrush was put forward. The results show that the rock permeability ''mutation'' phenomenon reflects the differences of stress state and cracks extension rate when the rock internal crack begins to extend in large-scale. The rock ultimate destruction form is related to the rock lithology and the angle between crack and principal stress. The necessary condition of floor water inrush is that the mining pressure leads to the extension and transfixion of the crack. The sufficient condition of floor water inrush is that the confined water's expansionary stress in normal direction and shear stress in tangential direction must be larger than the internal stress in the crack. With the two conditions satisfied at the same time, the floor water inrush accident will occur.Keywords Triaxial permeability experiment Á Floor water inrush model Á Floor water inrush mechanism Á Necessary and sufficient conditions of water inrush 1 Introduction
Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.
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