Morphological characterization of crushed coal gangue by micro X-ray computed tomography scanning

Zheng, Kaiyu; Gao, Kuidong; Yang, Zhenjun; Wang, Z. Y.

doi:10.1080/19392699.2020.1732946

Cited by 9 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the above analysis, it can be determined that the research on the intelligent sorting of coal gangue focuses on finding the image features of coal gangue under visible light, which is mainly reflected in the optimization of the training model and the diversification of image features. − There is less research on this fundamental difference between coal and gangue mineral components. To further develop the photoelectric separation technology of coal and gangue, the impact of thickness and density of single and mixed minerals on the gray value was studied via the regression equation of the gray value. − …”

Section: Introductionmentioning

confidence: 99%

Research on the Identification Mechanism of Coal Gangue Based on the Differences of Mineral Components

Yang

Yin

et al. 2022

ACS Omega

View full text Add to dashboard Cite

For coal and gangue, intelligent sorting processes for separation, the use of coal and gangue mineral components with different fundamental differences, and the study of different properties of minerals and coal with different scales and density regarding the gray value change law are presented. The results show that the gray value of single minerals and mixed minerals gradually decreases with the increase of their thickness and density. The greater the density of minerals, the smaller the gray value at the same thickness, and the same rule applies to different coal ranks. Via regression analysis methods, the values of the regression equation parameter a of pure minerals for graphite, quartz, kaolinite, and montmorillonite are 59. 25, 65.69, 61.61, and 58.02 in the high-energy region, respectively. In the low-energy region, they are 174. 95, 177.31, 186.95, and 161.81. For the regression equation parameter of mixed minerals in the form of two mixed minerals (graphite and quartz, kaolinite, or montmorillonite) and three kinds of mineral mixing (graphite−kaolinite and quartz; graphite−montmorillonite and quartz; graphite−kaolinite and montmorillonite),

show abstract