Improving coal/gangue recognition efficiency based on liquid intervention with infrared imager at low emissivity

Zhang, Jinwang; Han, Xing Jiang; Cheng, Dongliang

doi:10.1016/j.measurement.2021.110445

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…Images [2,3] The actual production environment of the mine is dark and dusty, which is far from the experimental conditions, resulting in great challenges in the practical application of coal gangue image recognition. [13][14][15][16][17][18] Acoustic signals [4,5] The composition of acoustic signals is complex, and it is difficult to perform multiple blind source separation of noise.…”

Section: References Limitationsmentioning

confidence: 99%

“…The recognition accuracy of cutting coal rock specimens with different wear degrees was studied, and the fusion recognition accuracy of coal rock interface was improved. Zhang et al [13][14][15][16] studied coal gangue recognition by using the difference of infrared thermal images of coal and gangue with liquid intervention, effectively overcoming the problem of low accuracy of traditional coal gangue recognition using infrared images. Wang et al 17 studied the accurate recognition of gangue that may occur in the process of coal caving, and formed a "trinity" intelligent recognition technology of gangue.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved deep learning based multi‐index comprehensive evaluation system for the research on accuracy and real‐time performance of coal gangue particles recognition

Zhang,

Yang,

Zeng

2023

Energy Science & Engineering

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The problems of insufficient recognition accuracy, poor real‐time performance and lack of consideration of actual working conditions in the process of intelligent construction of coal mines make this technology still in the research stage and not applied in practical engineering. The purpose of this paper is to establish an accurate and real‐time recognition model, which can quickly distinguish the vibration acceleration signals of coal and gangue under the influence of external factors such as impact position, velocity, and direction by using the different physical properties of coal and gangue particles. Therefore, the accuracy and real‐time of coal gangue recognition model established by different convolutional neural networks (CNN) structures and different position signal input are studied. First, to meet the real‐time requirements, an original CNN recognition model composed of single convolution layer and single pooling layer is established, and the data collected by seven sensors are input in the form of two‐dimensional matrix. However, the stability of the training and test results is insufficient. To solve this problem, once improved CNN (OI‐CNN) recognition model with multiconvolution layers and multipooling layers is built by deepening the network. The experimental results show that the stability and accuracy are improved, but the real‐time performance is poor. Furthermore, through parameter adjustment, the OI‐CNN is changed to the twice improved CNN (TI‐CNN), and the sensor data at different positions are input in the form of one‐dimensional vectors. The results show that the accuracy and real‐time performance of the TI‐CNN coal gangue recognition model are further improved. Finally, according to the research purpose of this paper, the weights of CNN indexes are given, and a multi‐index comprehensive evaluation system (MICES) is established. With the original CNN recognition model as the control, the OI‐CNN recognition model and the TI‐CNN recognition model at different positions are quantitatively compared to obtain the comprehensive evaluation scores of each model. The results show that the MICES of the coal gangue recognition model established based on the TI‐CNN structure and the data input of a single position sensor is the highest, while the sensor position has little effect on the recognition results.

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Section: References Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved deep learning based multi‐index comprehensive evaluation system for the research on accuracy and real‐time performance of coal gangue particles recognition

Zhang,

Yang,

Zeng

2023

Energy Science & Engineering

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“…Many studies have been carried out based on image, − vibration, − sound, − γ rays, , Lidar, , and other media. Recently, Wang et al proposed a semantic segmentation network for coal gangue image recognition.…”

Section: Introductionmentioning

confidence: 99%

Research on Coal Gangue Recognition Based on Multi-source Time–Frequency Domain Feature Fusion

Zhang,

Yang,

Zeng

2023

ACS Omega

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The over-exploitation of resources caused by the increasing coal demand has resulted in a sharp increase in solid waste emissions mainly gangue, which has made the burden on the environment, economy, resources, and society of our country heavier. In order to achieve a balance between energy consumption and solid waste emission in the process of top coal caving, this study carried out coal gangue recognition research based on multi-source time–frequency domain feature fusion (MS-TFDF-F). First, the process of coal gangue symbiosis and the harm of gangue in top coal caving are analyzed, and the fundamental method of comprehensive treatment of gangue is put forward, which is the accurate recognition of the coal gangue interface. Second, by building a top coal caving simulation test bed, the MS signals generated in the caving process of the coal gangue mixture with a gangue content of 0–100% are collected and the TFDFs are extracted. Third, the MS-TFDF-F-based coal gangue recognition model is established. Then, the recognition effect of the two TFDF-F sample sets was compared, and the results show that the time–frequency domain feature selection fusion method (TFDFS-FM) has higher accuracy. On this basis, this paper studies the variation law of the number of sensors on the coal gangue recognition accuracy of MS information fusion. Finally, the economic, social, environmental, and resource benefits of the model are qualitatively described. The final results show that the MS-TFDF-F-based coal gangue recognition model has the strongest recognition ability when fusing six sensor signals, and the recognition accuracy reaches 99% under the AdaBoost algorithm. The establishment of this model brings huge benefits to China’s environment, economy, resources, and society, and it is helpful to realize the balance between loss reduction mining and solid waste emission reduction in the process of top coal caving.

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“…Alfarzaeai et al [ 37 ] studied coal gangue identification by convolutional neural networks and thermal images. Zhang et al [ 38 ] used infrared imager with low emissivity to improve the coal gangue recognition accuracy based on liquid intervention. Yang et al [ 39 ] used vibration, sound, pressure and other signals to classify and identify the mixing ratio of coal and gangue mixture.…”

Section: Introductionmentioning

confidence: 99%

Simulation Research on Impact Contact Behavior between Coal Gangue Particle and the Hydraulic Support: Contact Response Differences Induced by the Difference in Impacted Location and Impact Material

et al. 2022

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In the process of top coal caving, coal gangue particles may impact on various parts of the hydraulic support. However, at present, the contact mechanism between coal gangue and hydraulic support is not entirely clear. Therefore, this paper first constructed the accurate mathematical model of the hydraulic cylinder equivalent spring stiffness forming by the equivalent series of different parts of emulsion and hydraulic cylinder, and then built the mesh model of the coal gangue particles and the support’s force transmission components; on this basis, the rigid–flexible coupling impact contact dynamic model between coal gangue and hydraulic support was established. After deducing contact parameters and setting impact mode, contact simulations were carried out for coal particles impacting at the different parts of the support and coal/gangue particles impacting at the same component of the support, and the contact response difference in the support induced by the difference in impacted component and coal/gangue properties was compared and studied. The results show that the number of collisions, contact force, velocity and acceleration of impacted part are different when the same single coal particle impact different parts of the support. Various contact responses during gangue impact are more than 40% larger than that of coal, and the difference ratio can even reach 190%.

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Improving coal/gangue recognition efficiency based on liquid intervention with infrared imager at low emissivity

Cited by 20 publications

References 17 publications

Improved deep learning based multi‐index comprehensive evaluation system for the research on accuracy and real‐time performance of coal gangue particles recognition

Improved deep learning based multi‐index comprehensive evaluation system for the research on accuracy and real‐time performance of coal gangue particles recognition

Research on Coal Gangue Recognition Based on Multi-source Time–Frequency Domain Feature Fusion

Simulation Research on Impact Contact Behavior between Coal Gangue Particle and the Hydraulic Support: Contact Response Differences Induced by the Difference in Impacted Location and Impact Material

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