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Abstract

The rapid and accurate detection and identification of coal gangue is one of the premises and key technologies of the intelligent separation of coal gangue, which is of considerable importance for the separation of coal gangue. Focusing on the problems in the current deep learning algorithms for the detection and recognition of coal gangue, such as large model memory and slow detection speed, a rapid detection method for lightweight coal gangue is proposed. YOLOv3 is taken as the basic structure and improved. The MobileNetv2 lightweight feature extraction network is selected to replace Darknet53 as the main network of the detection algorithm to improve the detection speed. Spatial pyramid pooling (SPP) is added after the backbone network to convert different feature maps into fixed feature maps in order to improve the positioning accuracy and detection capability of the algorithm, thereby obtaining the lightweight network MS-YOLOV3. The experimental equipment was set up and multi-condition coal and gangue datasets were constructed. The model was trained and the identification and positioning results of the model were tested under different sizes, illumination intensities and various working conditions, and compared with other algorithms. Experimental results show that the proposed algorithm can detect the coal gangue quickly and accurately, with an mAP of 99.08%, a speed of 139 fps and a memory occupation of only 9.2 M. In addition, the algorithm can effectively detect mutually stacking coal and gangue of different quantities and sizes under different lights with high confidence and with a certain degree of environmental robustness and practicability. Compared with the YOLOv3, the performance of the proposed algorithm is significantly improved. Under the premise that the accuracy is unchanged, the FPS increases by 127.9% and the memory decreases by 96.2%. Therefore, the MS-YOLOv3 algorithm has the advantages of small memory, high accuracy and fast speed, which can provide online technical support for the detection and identification of coal and gangue.
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Authors and Affiliations

Deyong Li
1
Guofa Wang
2
ORCID: ORCID
Shuang Wang
3
Wenshan Wang
3
Ming Du
3

  1. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China
  2. Collaborative Innovation Center for Mine Intelligent Technology and Equipment, Anhui University of Science and Technology, Huainan 232001, China
  3. China Coal Technology Engineering Group Coal Mining Research Institute, Beijing 100013, China
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Abstract

The aim of the study is to determine the mercury content in hard coal, randomly taken from the USCB and in by-products of hard coal mining (fresh mining waste), i.e. aggregates (gangue) and hard coal sludge and mining waste from the Siersza dump (weathered waste). The 34 samples were intended for analysis. The total mercury content and the amount of mercury leaching from solid samples was determined. The percentage of the leaching form in the total element content, i.e. the level of mercury release from the material (leaching level), was also calculated. The amount of mercury leaching was determined by a static method using a batch test 1:10. The highest possibility of leaching mercury is characterized by weathered waste from the Siersza dump and slightly lower analyzed hard coal from the U pper Silesian Coal Basin (USCB). For hard coal samples, the total mercury content is between 0.0275–0.1236 mg/kg. However, the amount of mercury leaching from coal samples is 0.0008–0.0077 mg/kg. The aggregate is characterized by a higher total mercury content in the finest fraction 0–6 mm, within 0.1377–0.6107 mg/kg and much lower in the 80-120 mm fraction, within 0.0508–0.1274 mg/kg. The amount of elution is comparable in both fractions and amounts to 0.0008–0.0057 mg/kg. Coal sludge has a total mercury content of 0.0937–0.2047 mg/kg. L ow leaching values of 0.0014–0.0074 mg/ kg are also observed. Weathered mining waste has a total mercury content of 0.0622–0.2987 mg/kg. However, leaching values from weathered waste are much higher than from fresh mining waste. This value is 0.0058–0.0165 mg/kg. In the hard coal extracted from U SCB, the leaching level is 4.7% on average. Mining waste is characterized by a large variation in the proportion of mercury leaching form and the differences result from the seasoning time of the samples. Waste or by-products of hard coal production, such as aggregates and coal sludge, show a mercury washout form at an average level of 1.7%. The proportion of leachable form in weathered waste increased strongly to 7.3%. Elution characteristics vary for different groups of materials tested. Factors such as the type and origin of samples, their granulometric composition and the seasoning time of the material are of fundamental importance and demonstrated in the work.

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Authors and Affiliations

Beata Klojzy-Karczmarczyk
Janusz Mazurek
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Abstract

In the process of extraction and enrichment of coal waste, considerable quantities of waste material are produced, mainly the gangue and coal sludge, considered as waste or raw material. The main directions of the management development of the waste rock are the production of aggregates, the production of energy products and the liquidation works in hard coal mines and the filling of excavations. The paper proposes the extension of these activities to the use of waste material. The possibility of using aggregates or extractive waste to fill open-pit excavations has been proposed, also in areas within the reach of groundwater and the possibility of building insulation layers of waste material and the production of mixtures of hard coal sludge and sewage sludge to produce material with good energy properties. The analysis was based on the author’s own research and literature data related to selected parameters of waste material. This paper presents our own preliminary studies on the amount of combustion heat and the calorific value of coal sludge combined with other wastes such as sewage sludge. The proposed methods and actions are part of the current directions of development, but they allow the extension of the scope of use of both extractive waste and products produced on the basis of gangue or coal sludge. Due to the frequent lack of the stable composition of these materials, their current properties should be assessed each time before attempting to use them. The fact that it is important to continue research to promote existing economic use and to seek new activities or methods has been concluded.

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Authors and Affiliations

Beata Klojzy-Karczmarczyk
Janusz Mazurek
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Abstract

Strip backfilling mining technology is of great significance for eliminating coal gangue, improving coal recovery rate, harmonizing the development between resources and environment in diggings. This paper firstly analyzed the roof control mechanism, the deformation and failure mechanism and characteristics of the filling body through theoretical analysis. Then, through numerical simulation combined with the geological conditions on site, a gangue strip filling scheme was designed for the 61303 working face of the 13th layer of the rear group coal of the Wennan Coal Mine in Shandong Province, and the filling scheme of filling 50 m and leaving 25 m was determined. Finally, an on-site engineering test was carried out on the 61303 working face. Through the analysis of the measured data of “three quantities” after the filling test, it can be seen that the test has achieved a good engineering application effect and verified the rationality of the filling scheme design. It solves the coal gangue problem, improves the resource recovery rate, and provides a reference for other similar mines.
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Bibliography

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Authors and Affiliations

Wenbin Xing
1
ORCID: ORCID
Wanpeng Huang
1
ORCID: ORCID
Fan Feng
1
ORCID: ORCID

  1. Shandong University of Science and Technology, China
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Abstract

Stability control of the roof is the key to safe and efficient mining of the longwall working face for a steeply dipping coal seam. In this study, a comprehensive analysis was performed on the roof destruction, migration, and filling characteristics of a steeply dipping longwall working face in an actual coalmine. Elastic foundation theory was used to construct a roof mechanics model; the effect of the coal seam inclination angle on the asymmetric deformation and failure of the roof under the constraint of an unbalanced gangue filling was considered. According to the model, increasing the coal seam angle, thickness of the immediate roof, and length of the working face as well as decreasing the thickness of the coal seam can increase the length of the contact area formed by the caving gangue in the lower area of the slope. Changes to the length of the contact area affect the forces and boundary conditions of the main roof. Increasing the coal seam angle reduces the deformation of the main roof, and the position of peak deflection migrates from the middle of the working face to the upper middle. Meanwhile, the position of the peak rotation angle migrates from the lower area of the working face to the upper area. The peak bending moment decreases continuously, and its position migrates from the headgate T-junction to the tailgate T-junction and then the middle of the working face. Field test results verified the rationality of the mechanics model. These findings reveal the effect of the inclination coal seam angle on roof deformation and failure and provide theoretical guidance for engineering practice.
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Bibliography

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Authors and Affiliations

Shenghu Luo
1
ORCID: ORCID
Tong Wang
2
ORCID: ORCID
Yongping Wu
2
ORCID: ORCID
Jingyu Huangfu
2
ORCID: ORCID
Huatao Zhao
3
ORCID: ORCID

  1. Xi’an University of Science and Technology, Department of Mechanics, China
  2. Xi’an University of Science and Technology, School of Energy Engineering, China
  3. Shandong Mining Machinery Group Co., Ltd. China
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Abstract

In order to improve the utilization rate of coal resources, it is necessary to classify coal and gangue, but the classification of coal is particularly important. Nevertheless, the current coal and gangue sorting technology mainly focus on the identification of coal and gangue, and no in-depth research has been carried out on the identification of coal species. Accordingly, in order to preliminary screen coal types, this paper proposed a method to predict the coal metamorphic degree while identifying coal and gangue based on Energy Dispersive X-Ray Diffraction (EDXRD) principle with 1/3 coking coal, gas coal, and gangue from Huainan mine, China as the research object. Differences in the phase composition of 1/3 coking coal, gas coal, and gangue were analyzed by combining the EDXRD patterns with the Angle Dispersive X-Ray Diffraction (ADXRD) patterns. The calculation method for characterizing the metamorphism degree of coal by EDXRD patterns was investigated, and then a PSO-SVM model for the classification of coal and gangue and the prediction of coal metamorphism degree was developed. Based on the results, it is shown that by embedding the calculation method of coal metamorphism degree into the coal and gangue identification model, the PSO-SVM model can identify coal and gangue and also output the metamorphism degree of coal, which in turn achieves the purpose of preliminary screening of coal types. As such, the method provides a new way of thinking and theoretical reference for coal and gangue identification.
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Authors and Affiliations

Yanqiu Zhao
1
ORCID: ORCID
Shuang Wang
1
Yongcun Guo
1
Gang Cheng
1
Lei He
1
Wenshan Wang
1

  1. School of Mechanical Engineering, Anhui University of Science and Technology, China
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Abstract

In order to explore the impact of coal and gangue particle size changes on recognition accuracy and to improve the single particle size of coal and gangue identification accuracy of sorting equipment, this study established a database of different particle sizes of coal and gangue through image gray and texture feature extraction, using a relief feature selection algorithm to compare different particle size of coal and gangue optimal features of the combination, and to identify the points and particle size of coal and gangue. The results show that the optimal features and number of coal and gangue are different with different particle sizes. Based on visible-light coal and gangue separation technology, the change of coal and gangue particle size cause fluctuations in the recognition accuracy, and the fluctuation of recognition accuracy will gradually decrease with increases in the number of features. In the process of particle size classification, if the training model has a single particle size range, the recognition accuracy of each particle size range is low, with the highest recognition accuracy being 98% and the average recognition rate being only 97.2%. The method proposed in this paper can effectively improve the recognition accuracy of each particle size range. The maximum recognition accuracy is 100%, the maximum increase is 4%, and the average recognition accuracy is 99.2%. Therefore, this method has a high practical application value for the separation of coal and gangue with single particle size.
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Authors and Affiliations

Xin Li
1 2
ORCID: ORCID
Shuang Wang
1 2
Lei He
1 2
Qisheng Luo
1 2

  1. School of Mechanical Engineering, Anhui University of Science and Technology, Huainan, China
  2. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, China
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Abstract

Sorting coal and gangue is important in raw coal production; accurately identifying coal and gangue is a prerequisite for effectively separating coal and gangue. The method of extracting coal and gangue using image grayscale information can effectively identify coal and gangue, but the recognition rate of the sorting process based on image grayscale information needs to substantially higher than that which is needed to meet production requirements. A sorting method of coal and gangue using object surface grayscale-gloss characteristics is proposed to improve the recognition rate of coal and gangue. Using different comparative experiments, bituminous coal from the Huainan area was used as the experimental object. It was found that the number of pixel points corresponding to the highest level grey value of the grayscale moment and illumination component of the coal and gangue images were combined into a total discriminant value and used as input for the best classification of coal and gangue using the GWO-SVM classification model. The recognition rate could reach up to 98.14%. This method sorts coal and gangue by combining surface greyness and glossiness features, optimizes the traditional greyness-based recognition method, improves the recognition rate, makes the model generalizable, enriches the research on coal and gangue recognition, and has theoretical and practical significance in enterprise production operations.
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Authors and Affiliations

Gang Cheng
1
Yifan Wei
1 2
ORCID: ORCID
Jie Chen
1
Zeye Pan
1

  1. School of Mechanical Engineering, Anhui University of Science and Technology, Huainan, China
  2. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines,Anhui University of Science and Technology, Huainan, China

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