Impact-Crush Separation Characteristics of Coal and Gangue

Yang, Dong‐Yeol; Li, Jianping; Zheng, Kaiyu; Du, Chenglie; Liu, Songyong

doi:10.1080/19392699.2016.1207634

Cited by 24 publications

(7 citation statements)

References 15 publications

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“…Therefore, the underground coal-waste separation equipment should have the characteristics of small volume, be explosion proof, and have strong practicability, good separation effects and high reliability. At present, the underground coal-waste separation methods include the coal-waste selective crushing methods, dense medium shallow groove methods, vibro-assisted jigging methods, water separation methods and ray identification methods [36,37].…”

Section: Main Underground Coal-waste Separation Methods and Equipmentmentioning

confidence: 99%

Overview of Solid Backfilling Technology Based on Coal-Waste Underground Separation in China

Zhang¹,

Zhang²,

Wu³

et al. 2019

Sustainability

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China is the world’s largest coal producer country. However, large-scale coal mining has led to severe environmental pollution issues such as surface subsidence and gangue piling up. The gangue discharging amount has ranked the first in the world and coal mine enterprises are facing enormous discharging reduction pressure. This paper summarizes the research progress of the solid backfilling mining technology and then illustrates the realistic demands and significance of implementing underground coal-waste separation. It also focuses on the technical principles, systems and key equipment of the common underground coal-waste separation methods, such as the selective crushing method, the dense medium shallow groove method, the vibro-assisted jigging method and full-size water separation method and ray identification method. In addition, the selection steps of underground coal-waste separation method, the design process of large section separation chamber and the design principle of separation and backfilling system are proposed, finally, the mining-separating-backfilling + X for coal mining is put forward. By combining the technology of mining-separating-backfilling with other technologies, such as gob-side entry retaining with non-pillar mining, gas extraction, solid waste treatment, water protection mining, mining under buildings, railways and water bodies, the integrated mining methods, mining-separating-backfilling + setting pillars, gas drainage, treatment, protection and prevention methods are formed. It also introduced the ‘mining-separating-backfilling + gas extraction’ technology’s whole idea, system arrangement, separation equipment and practical engineering application effects based on the specific engineering case of pingmei no. 12 coal mine. The results indicate that the integration of underground coal-waste separation and solid backfilling technology could achieve gangue discharging reduction, underground washing and surface subsidence control. It is effective at realizing green mining.

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Section: Main Underground Coal-waste Separation Methods and Equipmentmentioning

confidence: 99%

Overview of Solid Backfilling Technology Based on Coal-Waste Underground Separation in China

Zhang¹,

Zhang²,

Wu³

et al. 2019

Sustainability

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“…(3) Gangue Content in the Coal. According to Li and Yang, there were great differences between coal and gangue in hardness, crushing rate, and resistance to impact crushing [27,28]. The different gangue content inevitably affected the chute wear.…”

Section: Experimental Designmentioning

confidence: 99%

Screening the Main Factors Affecting the Wear of the Scraper Conveyor Chute Using the Plackett–Burman Method

Xia

Wang

et al. 2019

Mathematical Problems in Engineering

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The wear of scraper conveyor chute causes both significant economic and environmental losses by shortening the service life. The life of the chute under coal abrasive wear situations is primarily decided by operating conditions and the materials properties. The comprehensive analysis of the influence factors had not been studied before. In this paper, the Plackett-Burman design (PBD) method was used to screen the main influence factors and a regression equation was developed to predict the wear loss. The steel was tested on a modified pin-on-disk apparatus in which coal abrasive was filled in the disk. The influence factors included water content, gangue content, coal particle size, Hardgrove Grindability-Index (HGI) of the coal, normal load, and scraper chain speed. The results of the investigation suggested that the significance of water content, normal load, and gangue content on wear loss was relatively higher than the HGI of coal, scraper chain speed, and coal particle size. The wear loss increased with the increase of water content, gangue content, normal load, and coal particle size while it decreased as increase in HGI of the coal and scraper chain speed. Based on the significance of the parameters, the regression equations were derived and verified further with a number of test cases. Optical microscope studies revealed the main wear mechanism of the chute was mainly micro-cutting and corrosive wear and accompanied by fatigue fracture.

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“…Traditional manual identification work is intense, and coal gangue sorting efficiency is low [9]. Mechanical vibration identification is mainly through coal and coal gangue particles and metal plate collision vibration signal recognition, but this method will reduce the quality of coal [10,11]. Xing et al [12] proposed a coal and gangue identification method based on the intensity image of lidar and DenseNet.…”

Section: Introductionmentioning

confidence: 99%

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

et al. 2023

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Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual underground working environment of the mine, a machine vision platform for coal gangue identification was built and the coal gangue image acquisition experiment was carried out. Then, the images of coal and gangue were denoised by median filtering, and the coal and gangue features were extracted by using the HOG combined with LBP feature extraction algorithm, and these features were normalized and principal component analysis (PCA) reduced dimension to remove the correlation and redundancy between the features. Finally, SVM, SVM optimized by genetic algorithm (GA-SVM), SVM optimized by particle swarm optimization (PSO-SVM) algorithm, and SVM optimized by grey wolf optimization (GWO-SVM) algorithm were used as classifiers for identification and classification, respectively. The experimental results show that the GWO-SVM classification model has the highest accuracy, and the average classification accuracies were 96.49% and 94.82% of the training set and test set, respectively, which shows that grey wolf algorithm to optimize support vector machine has a good effect on classification of coal gangue images, which proves the feasibility and accuracy of the proposed method.

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Impact-Crush Separation Characteristics of Coal and Gangue

Cited by 24 publications

References 15 publications

Overview of Solid Backfilling Technology Based on Coal-Waste Underground Separation in China

Overview of Solid Backfilling Technology Based on Coal-Waste Underground Separation in China

Screening the Main Factors Affecting the Wear of the Scraper Conveyor Chute Using the Plackett–Burman Method

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

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