An Online Flexible Sorting Model for Coal and Gangue Based on Multi-Information Fusion

Wang, Zixiang; Xie, Shuxin; Chen, Guodong; Chi, Wenzheng; Ding, Zegang; Wang, Peng

doi:10.1109/access.2021.3090780

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…Based on the aforementioned intelligent recognition methods, the primary sorting methods include pneumatic nozzles and multi-axis robotic arms. Yuan Huaxin 17 , Dou Dongyang 18 , and Zixiang Wang et al 19 proposed a jet-type coal and gangue sorting device that utilizes a camera to obtain the postural information of coal and gangue and employs an array of pneumatic nozzles for sorting. Chenguang Yang 20 and Viljoen Jacob 21 used X-rays to identify coal and gangue, achieving separation with high-pressure nozzles.…”

Section: Introductionmentioning

confidence: 99%

Design and application of coal gangue sorting system based on deep learning

Zhang,

Wang,

Zhang

et al. 2024

Sci Rep

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With the advancement of science and technology, coal-washing plants are transitioning to intelligent, information-based, and professional sorting systems. This shift accelerates the construction a modern economic system characterized by green and low-carbon development, thereby promoting the high-quality advancement of the coal industry. Traditional manual gangue picking and multi-axis robotic arm gangue selection currently suffer from low recognition accuracy, slow sorting efficiency, and high worker labor intensity. This paper proposes a deep learning-based, non-contact gangue recognition and pneumatic intelligent sorting system. The system constructs a dynamic database containing key feature information such as the target gangue's contour, quality, and center of mass. The system elucidates the relationships between ejection speed, mass, volume, angle of incidence, and the impact energy matching mechanism. Demonstration experiments using the system prototype for coal gangue sorting reveal that, compared to existing robotic arm sorting methods in coal washing plants, this system achieves a gangue identification accuracy exceeding 97%, a sorting rate above 91%, and a separation time of less than 3 s from identification to separation, thereby effectively enhancing raw coal purity.

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Section: Introductionmentioning

confidence: 99%

Design and application of coal gangue sorting system based on deep learning

Zhang,

Wang,

Zhang

et al. 2024

Sci Rep

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“…Manual gangue sorting is gradually being eliminated due to high labor intensity, poor working environment, and low efficiency [3]. To change the above gangue sorting method and enhance the level of coal mine intelligence, many scholars have conducted in-depth research on an automatic gangue sorting system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Gangue Sorting System Based on Dual-Energy X-ray and Improved YOLOv5 Algorithm

Qin,

Kou,

Han

et al. 2023

Applied Sciences

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Intelligent gangue sorting with high precision is of vital importance for improving coal quality. To tackle the challenges associated with coal gangue target detection, including algorithm performance imbalance and hardware deployment difficulties, in this paper, an intelligent gangue separation system that adopts the elevated YOLO-v5 algorithm and dual-energy X-rays is proposed. Firstly, images of dual-energy X-ray transmission coal gangue mixture under the actual operation of a coal mine were collected, and datasets for training and validation were self-constructed. Then, in the YOLOv5 backbone network, the EfficientNetv2 was used to replace the original cross stage partial darknet (CSPDarknet) to achieve the lightweight of the backbone network; in the neck, a light path aggregation network (LPAN) was designed based on PAN, and a convolutional block attention module (CBAM) was integrated into the BottleneckCSP of the feature fusion block to raise the feature acquisition capability of the network and maximize the learning effect. Subsequently, to accelerate the rate of convergence, an efficient intersection over union (EIOU) was used instead of the complete intersection over union (CIOU) loss function. Finally, to address the problem of low resolution of small targets leading to missed detection, an L2 detection head was introduced to the head section to improve the multi-scale target detection performance of the algorithm. The experimental results indicate that in comparison with YOLOv5-S, the same version of the algorithm proposed in this paper increases by 19.2% and 32.4% on mAP @.5 and mAP @.5:.95, respectively. The number of parameters decline by 51.5%, and the calculation complexity declines by 14.7%. The algorithm suggested in this article offers new ideas for the design of identification algorithms for coal gangue sorting systems, which is expected to save energy and reduce consumption, reduce labor, improve efficiency, and be more friendly to the embedded platform.

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“…Visual recognition combined with machine learning has been successfully used to classify coal and miscellaneous materials, with effective outcomes reported in previous studies. [10][11][12][13] However, this method requires the introduction of various devices, which can adversely affect the convenience and efficiency of LIBS for coal analysis in power plants.…”

Section: Introductionmentioning

confidence: 99%

A Step-By-Step Classification Method Of Coal And Miscellaneous Materials By Laser-Induced Breakdown Spectroscopy

Yao¹

2023

At.Spectrosc.

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The miscellaneous materials, including gangue, plastic, and wood commonly present in coal. These miscellaneous materials affect the reliability of coal analysis using laser-induced breakdown spectroscopy in power plants, but have significantly distinct spectral characteristics from coal. Hence, this paper proposes a step-by-step classification method to screening the false spectra of miscellaneous materials. The first step aims to identify the plastic and wood spectra by determining the existence of specific characteristic spectral lines using the standard deviation (SD) values. The spectral lines Si Ι 288.16 nm with the SD value of more than 850 counts and Li Ι 670.78nm with SD value of more than 1750 counts were used as the distinguishing markers. The classification accuracy of first step was 100%. Due to the high similarity between gangue and coal, the second step utilized the random forest (RF) classification model to identify the gangue spectra. The number of trees and random variables in the RF model was optimized. The accuracy of classification model without and with the proposed step-by-step method was 98.30 and 99.96%, respectively. To assess the necessity of spectra classification, a set of calorific value analysis was performed by adding false spectra of different proportions, which were compared with analysis after removing the false spectra. The root mean square error of prediction (𝑅𝑀𝑆𝐸𝑃) was 0.42 MJ kg -1 (after removing), compared with 0.50 MJ kg -1 (mixing with 10% gangue spectra), 0.56 MJ kg -1 (mixing with 20% gangue spectra) and 0.57 MJ kg -1 (mixing with 30% gangue spectra). The results demonstrated that the proposed stepby-step classification method could effectively identify the spectra of coal and miscellaneous materials and improve the accuracy of coal analysis.

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An Online Flexible Sorting Model for Coal and Gangue Based on Multi-Information Fusion

Cited by 11 publications

References 22 publications

Design and application of coal gangue sorting system based on deep learning

Design and application of coal gangue sorting system based on deep learning

Intelligent Gangue Sorting System Based on Dual-Energy X-ray and Improved YOLOv5 Algorithm

A Step-By-Step Classification Method Of Coal And Miscellaneous Materials By Laser-Induced Breakdown Spectroscopy

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