Coal-rock Interface Recognition Research Articles

Research on the Application of THz-TDS in Coal–Rock Interface Recognition

The recognition of coal–rock interface is very crucial for research in the intelligent production of coal mines. To this end, the study investigated the application of terahertz time-domain spectroscopy in the recognition of coal–rock interface, including the identification of coal–rock and coal–rock mixtures, as well as the accurate characterization of coal seam thickness. Terahertz detection was used to obtain the optical parameter information of pressed pellets prepared by mixing two different kinds of coal and two kinds of rock. Based on the experiment’s results, a database was established for the identification of coal–rock interfaces for coal mining machines. The terahertz detection was performed on 10 different kinds of sheet anthracite with different thicknesses, and the terahertz spectra of coal seams with different thicknesses were simulated and calculated using simulation software. By comparing the two effective mining thicknesses, parameters can be provided for coal seam mining. The experiment and simulation show that the terahertz time-domain spectroscopy technology has a promising application prospect in the identification of coal–rock interface.

Study on coal-rock interface characteristics change law and recognition based on active thermal excitation

ABSTRACT For recognizing the coal-rock interface efficiently, the infrared thermal images of coal-rock interface excited by active thermal excitation are tested and extracted which results in rise of coal and rock with different spatiotemporal characteristics and the infrared temperature attenuation law of coal and rock surface are analysed. The coal-rock infrared image is segmented and denoised to recognize the coal-rock interface with different ratio. The experimental results show that: (1) Under the active thermal excitation, the infrared temperatures of coal, rock and transition layer increase at different rates with a faster temperature growth of coal and the growth of rock temperature is relatively gentle. With the increase of excitation distance, the temperature decreases gradually and fluctuation of surface temperature occurs due to closer distance;(2) No matter the coal seam or the rock stratum, the temperature decreases linearly during the cooling process; (3) The results of image recognition technology based on local optimization and adaptive radius constrained principal curve algorithm are consistent with the actual test samples of coal rock ratio and the maximum error is 0.56%. The analysis results in coal-rock interface recognition for improving the cutting efficiency of shearer and realizing intelligent drum height adjustment control.

Performance Analysis of Natural γ-Ray Coal Seam Thickness Sensor and Its Application in Automatic Adjustment of Shearer’s Arms

The technology of coal-rock interface recognition is the core of realizing the automatic heightening technology of shearer’s rocker. Only by accurately and quickly identifying the interface of coal and rock can we realize the fully automatic control of shearer. As the only one used in the actual detection of coal mining machine drum cutting coal seam after the thickness of the remaining coal seam detection method, natural γ-ray has a very practical advantage. Based on the relationship between the attenuation of the natural γ-ray passing through the coal seam and the thickness of the coal seam, the mathematical model of the attenuation of the natural γ-ray penetrating coal seam is established. By comparing the attenuation intensity of γ-ray with or without brackets, it is verified that the hydraulic girders will absorb some natural γ-rays. Finally, this paper uses the ground simulation experiment and the field experiment to verify the correctness of the mathematical model and finally develop the natural γ-ray seam thickness sensor. The sensor has the function of indicating the thickness of the coal seam, measuring the natural γ-ray intensity, and storing and processing the data.

Free Radical Characteristics and Classification of Coals and Rocks Using Electron Spin Resonance Spectroscopy

Coal–rock interface recognition is one of the key unaddressed problems in unmanned mining, so a novel method for it is proposed. Firstly, electron spin resonance (ESR) is used to directly measure 10 kinds of coals/rocks common in China. Secondly, the free radical characteristics of different particle coals/rocks such as the Lande factor g, line width ΔH, and the concentration of the free radical Ng in the X-band ESR are studied. Lastly, the statistical classifier method of support-vector machine is employed to build a classification model with the input of the parameters of the ESR absorption spectra. Based on the ESR-SVM model, the recognition rate of coals/rocks reaches 100%, the recognition rate of different coals reaches 100%, and the recognition rate of different bituminous coals reaches 88.3%. The experimental results demonstrate that the proposed method is fast, stable, and accurate for the detection of the coal–rock interface and can be a promising tool for the classification of different coals.

Coal–Rock Interface Recognition Based on Permutation Entropy of LMD and Supervised Kohonen Neural Network

Coal–Rock Interface Recognition Based on Permutation Entropy of LMD and Supervised Kohonen Neural Network

Ground Penetrating Radar Based Experimental Simulation and Signal Interpretation on Coal-Rock Interface Detection

Coal-rock interface recognition is one of the key unaddressed problems in unmanned mining condition. Therefore, it is important that efforts should be made to detect coal-rock interface. In the study, Ground Penetrating Radar (GPR) has been used as a non-contacting means for coal-rock detection. We first establish three layered model of coal-rock and give the mathematical analysis of this model. Then numerical simulation and field experiment are carried out. Finally, in order to interpret radar image, an improved image segmentation algorithm is proposed. The experimental results show that it is feasible to detect coal-rock interface by GPR and the improved algorithm is well to interpret radar image of coal-rock interface.

Characterization and Classification of Coals and Rocks Using Terahertz Time-Domain Spectroscopy

Being the key unaddressed problem in unmanned mining condition, a new method for the coal-rock interface recognition was proposed in the study. Firstly, terahertz time-domain spectroscopy (THz-TDS) was employed to measure 10 kinds of coals/rocks which were common in China. Secondly, the physical properties of coals/rocks such as absorption coefficient spectra, refractive index, and dielectric properties in THz band were studied. The different responses in THz range caused by diverse components in coals/rocks were discussed, and the dielectric property of coals/rocks in THz band was well fitted by the Lorentz model. Finally, by the means of principal component analysis (PCA), support vector machine (SVM), and THz spectral data, the recognition rate of coals/rocks reaches to 100 % and the recognition rate of different bituminous coals reaches to 97.5 %. The experimental results show that the proposed method is fast, stable, and accurate for the detection of coal-rock interface and could be a promising tool for the classification of different bituminous coals.

Application of Improved Canny Edge Detection Algorithm in Coal-Rock Interface Recognition

The coal-rock interface recognition is one of the critical automated technologies in the fully mechanized mining face. The poor working conditions underground result in the seriously polluted edge information of the coal-rock interface, which affects the positioning precision of the shearer drum. The Gaussian filter parameters and the high-low thresholds are difficult to select in the traditional Canny algorithm, which causes the information loss of gradual edge and the phenomenon of false edge. Consequently, this paper presents an improved Canny edge detection algorithm, which adopts the adaptive median filtering algorithm to calculate the thresholds of Canny algorithm according to the grayscale mean and variance mean. This algorithm can protect the image edge details better and can restrain the blurred image edge. Experimental results show that this algorithm has improved the edge extraction effect under the case of noise interference and improved the detection precision and accuracy of the coal-rock image effectively.

Application of Hilbert Marginal Spectrum to Coal-Rock Interface Recognition

In this paper, a new method based on Hilbert marginal spectrum is presented to solve the problem of coal-rock interface recognition in the top caving process. Coal or waste rock impinges against the tail beam of the hydraulic support. The impact causes the vibration of the tail beam. Because of different intrinsic characteristics, dissimilarities will exist between the two cases. The coal-rock interface is recognized by comparing the Hilbert marginal spectrum. The results show that the proposed method is a promising approach to identifying the coal-rock interface.

Weighted Algorithm of Multi-Sensor Data Conflict in Coal-Rock Interface Recognition

In order to settle such a problem that the multi-sensors data fusion results are not good due to data confliction in the coal-rock interface recognition, the paper first carries out the fusion with D-S evidence theory. The fusion results are not correct when there are high-conflicting in the evidence, so a distance function is introduced and weight fusion correction algorithm is put forward. Through test simulation, fusion results respectively with D-S evidence theory, weight correction algorithm and fuzzy neural network are analyzed. The results show: the good results are achieved in the multi-sensor data conflict of coal-rock recognition through weight fusion correction algorithm, and influence of signal conflict is avoided effectively.

Application of Wavelet Packet Energy Spectrum in Coal-Rock Interface Recognition

The recognition of coal-rock interface in the top caving was investigated via the vibration signals of the tail beam of the hydraulic support. Wavelet packet transform was used to process the vibration signals. A newly feature based on wavelet packet energy spectrum was proposed to identify the coal-rock interface in top coal caving. The interface was determined by comparing the features in the two cases of coal dropping and rock dropping. The experimental results show that the energy proportion of vibration signal in high frequency bands when rock dropping is greater than that when coal dropping.

Fuzzy Neural Network Based Coal-Rock Interface Recognition

. In the coal-rock interface recognition (CIR) technology, signal process and recognition are the key parts. A method for CIR based on BP neural networks and fuzzy technique was proposed in this paper. By using the trail-and-error, the hidden layer dimension of the network was decided. Also the network training and weight modification were studied. In order to get a higher identification ratio, fuzzy neural networks (FNN) based data fusion was studied. For CIR, the structure and algorithm of FNN were determined. The results indicated that the test data can be used to train and simulate with the neural network and FNN. And the proposed method can be used in CIR with a higher recognition ratio.

Study on the coal-rock interface recognition method based on multi-sensor data fusion technique

Study on the coal-rock interface recognition method based on multi-sensor data fusion technique