Journal of China University of Mining and Technology最新文献

A fully-mechanized coal mining (FMCM) technology capable of filling up the goaf with wastes (including solid wastes) is described. Industrial tests have proved that by using this technology not only can waste be re-used but also coal resources can be exploited with a higher recovery rate without removing buildings located over the working faces. Two special devices, a hydraulic support and a scraper conveyor, run side-by-side on the same working face to simultaneously realize mining and filling. These are described in detail. The tests allow analysis of rock pressure and ground subsidence when backfilling techniques are employed. These values are compared to those from mining without using backfilling techniques, under the same geological conditions. The concept of equivalent mining height is proposed based on theoretical analysis of rock pressure and ground subsidence. The upper limits of the rock pressure and ground subsidence can be estimated in backfilling mining using this concept along with traditional engineering formulae.

Wireless sensor networks (WSNs) are very important for monitoring underground mine safety. Sensor node deployment affects the performances of WSNs. In our study, a chain-type wireless underground mine sensor network (CWUMSN) is first presented. A CWUMSN can monitor the environment and locate miners in underground mines. The lowest density deployment strategies of cluster head nodes are discussed theoretically. We prove that the lifetime of CWUMSN with a non-uniform deployment strategy is longer than with a uniform deployment strategy. Secondly, we present the algorithm of non-uniform lowest density deployment of cluster head nodes. Next, we propose a dynamic choice algorithm of cluster head nodes for CWUMSN which can improve the adaptability of networks. Our experiments of CWUMSN with both non-uniform lowest density and uniform lowest density deployments are simulated. The results show that the lifetime of CWUMSN with non-uniform lowest density deployment is almost 2.5 times as long as that of the uniform lowest density deployment. This work provides a new deployment strategy for wireless underground mine sensor networks and then effectively promotes the application of wireless sensor networks to underground mines.

A spectrophotometric method for the determination of ruthenium(III) is described, based on its catalytic effect on the oxidation reaction of dimethyl yellow (DMY) with potassium bromate in an acid solution medium and in the presence of an OP emulsifier (p-iso-octyl phenoxy polyethoxy ethanol) at 100 °C. This reaction was followed spectrophotometrically by measuring the decrease in the absorbance at 530 nm of the catalytic reaction of DMY. The calibration curve for the recommended method was linear in the concentration range over 0.0–1.0 μg/Land the detection limit of the method for Ru(III) was 0.01 μg/L. The method is highly sensitive, selective and very stable and has been successfully applied for the determination of trace amounts of ruthenium in some ores and metallurgy products with the relative standard deviations (RSD) over 1.6%–2.8% and a recovery over 98.7%–104.0%.

Accuracy of time-depth conversion in data processing of transient electromagnetic prospecting always affects the accurate positioning of water bodies in coal mines. In order to improve the accuracy of time-depth conversion, we established a mathematical model of time-depth conversion for a transient electromagnetic method based on the theory of “double smoke ring effect” of full space transient electromagnetic field transmission. Using a 3-layer as well as a 4-layer geo-electric model for roadway floors, we performed the time-depth conversion of theoretical curves of apparent resistance varying over time. In these curves, the depth corresponding to extreme value points is nearly the same as the depth of a geo-electric model. The position of water body determined by our time-depth conversion method agrees well with the result of borehole drilling, indicating that the established time-depth conversion model can clearly improve the accuracy of spatial positioning of water bodies in coal mines.

A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Component Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimension feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.

The laws of motion of particle groups in a jigging process are studied. These describe the macroscopic phenomena that occur during jigging. During jigging the heavier and bigger particles concentrate at the bed bottom while lighter and smaller particles move to the upper part of the bed. Particles with equivalent properties tend to concentrate at a certain position centered around the inherent height of their distribution. The particle distribution variance gradually diminishes to some asymptotic value. The state equation group of the jigging bed is deduced and a calculation method, called the λ value judgment method, is proposed. The method is used to calculate the layer number and the inherent height of each particle group. A mathematical expression for the particle distribution variance is also given.

Xin'an coal mine, Henan Province, faces the risk of water inrush because 40% of the area of the coal mine is under the surface water of the Xiaolangdi reservoir. To forecast water disaster, an effective aquifuge and a limit of water infiltration were determined by rock-phase analysis and long term observations of surface water and groundwater. By field monitoring, as well as physical and numerical simulation experiments, we obtained data reflecting different heights of a water flow fractured zone (WFFZ) under different mining conditions, derived a formula to calculate this height and built a forecasting model with the aid of GIS. On the basis of these activities, the coal mine area was classified into three sub-areas with different potential of water inrush. In the end, our research results have been applied in and verified by industrial mining experiments at three working faces and we were able to present a successful example of coal mining under a large reservoir.