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Research Article

Study on coal and gangue recognition method based on the combination of X-ray transmission and diffraction principle

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Pages 9716-9728 | Received 02 May 2022, Accepted 08 Oct 2022, Published online: 20 Oct 2022

References

  • Chang, C., and C.-J. Lin. 2011. LIBSVM : A library for support vector machines [J]. ACM Transactions on Intelligent Systems and Technology 2(27):1–27. doi:10.1145/1961189.1961199.
  • Chen, Y., X. Wang, Q. Song, J. Xu, and B. Mu. 2018.Development of a high-energy-resolution EDXRD system with a CdTe detector for security inspection [J]. AIP Advances 8:105113. doi:10.1063/1.5052027
  • Cook, E., R. Fong, J. Horrocks, D. Wilkinson, and R. Speller. 2007.Energy dispersive X-ray diffraction as a means to identify illicit materials: A preliminary optimisation study [J]. Applied Radiation and Isotopes 65:959–67. doi:10.1016/j.apradiso.2007.02.010
  • Dou, D., W. Wu, J. Yang, and Y. Zhang. 2019.Classification of coal and gangue under multiple surface conditions via machine vision and relief-SVM [J]. Powder Technology 356:1024–28. doi:10.1016/j.powtec.2019.09.007
  • Guo, Y., L. He, P. Liu, and X. Wang. 2021. Multi-dimensional analysis and recognition method of coal and gangue dual-energy X-ray images [J]. Journal of China Coal Society 46(1):300–09. in chinese.
  • He, L., S. Wang, Y. Guo, G. Cheng, K. Hu, Y. Zhao, and X. Wang. 2022. Multi-scale coal and gangue dual-energy X-ray iamge concave point detection and segmentation algorithm [J]. Measurement 196:111041. doi:10.1016/j.measurement.2022.111041
  • Hu, F., M. Zhou, P. Yan, K. Bian, and R. Dai. 2019. Multispectral imaging: A new solution for identification of coal and gangue [J]. IEEE Access 7:169697–704. doi:10.1109/ACCESS.2019.2955725
  • Ketelhodt, L. V., and C. Bergmann. 2010. Dual energy X-ray transmission sorting of coal [J]. Journal of the Southern African Institute of Mining and Metallurgy 110(7):371–78.
  • Kuerten, A. 2017. Preconcentration of mineral carvao of the moatize mine with sensor-based sorting (SBS). In Rio de Janeiro technology, Brazil: Federal University of Rio Grande Sul. pp. 48–63.
  • Lin, M., N. Nguyen, T. Nguyen, and A. Nguyen. 2019. Characterization of breakage and washability of ROM coal using X-ray computed tomography [J]. International Journal of Coal Preparation and Utilization 39(3):145–58. doi:10.1080/19392699.2017.1305364.
  • Luo, Z., Y. Zhao, X. Yu, C. Duan, S. Song, and X. Yang. 2017. Effects of characteristics of clapboard unit on separation of <6 mm fine coal in a compound dry separator [J]. Powder Technology 321:232–41. doi:10.1016/j.powtec.2017.08.028
  • Peterzol, A., P. Duvauchelle, V. Kaftandjian, and P. Ponard. 2011. Modeling-based optimization study for an EDXRD system in a portable configuration [J]. Nuclear Instruments and Methods in Physics Research A 645(1):450–63. doi:10.1016/j.nima.2011.06.043.
  • Robben, C., P. Condori, A. Pinto, R. Machaca, and A. Takala. 2020.X-ray-transmission based ore sorting at the San Rafael tin mine [J]. Minerals Engineering 145:105870. doi:10.1016/j.mineng.2019.105870
  • Sampaio, C.H., W. Aliaga, E.T. Pacheco, E. Petter, and H. Wotruba. 2008. Coal beneficition of Candiota mine by dry jigging [J]. Fuel Processing Technology 89(2):198–202. doi:10.1016/j.fuproc.2007.09.004.
  • Shrivastava, N. A., A. Khosravi, and B. K. Panigrahi. 2015. Prediction interval estimation of electricity prices using PSO-tuned support vetor machines [J]. IEEE Transactions on Industrial Informatics 11(2):322–31. doi:10.1109/TII.2015.2389625.
  • Sun, X., Z. Cao, Y. Yue, Y. Kuang, and C. Zhou. 2018. Online prediction of dense medium suspension density based on phase space reconstruction [J]. Particulate Science and Technology 36(8):989–98. doi:10.1080/02726351.2017.1333180.
  • Sun, A., W. Jia, D. Hei, Y. Yang, C. Cheng, J. Li, Z. Wang, and Y. Tang. 2021. Application of concave point matching algorithm in segmenting overlapping coal particles in X-ray images [J]. Minerals Engineering 171:107096. doi:10.1016/j.mineng.2021.107096
  • Sun, Z., W. Lu, P. Xuan, H. Li, S. Zhang, S. Niu, and R. Jia. 2019. Separation of gangue from coal based on supplementary texture by morphology [J]. International Journal of Coal Preparation and Utilization 42(3):1–17. doi:10.1080/19392699.2019.1590346.
  • Sutherland, J. L., J. E. Dickinson, and K. P. Galvin. 2020. Flotation of coarse coal particles in the Reflux™ Flotation Cell [J]. Minerals Engineering 149:106224. doi:10.1016/j.mineng.2020.106224
  • Tripathy, A., A. K. S. Lopamudra Panda, R. K. Biswal, S. K. Dwari, A. K. Sahu, and A. K. Sahu. 2016. Statistical optimization study of jigging process on beneficiation of fine size high ash Indian non-coking coal [J]. Advanced Powder Technology 27(4):1219–24. doi:10.1016/j.apt.2016.04.006.
  • Wang, W., and C. Zhang. 2017.Separating coal and gangue using three-dimensional laser scanning [J]. International Journal of Mineral Processing 169:79–84. doi:10.1016/j.minpro.2017.10.010
  • Wo, X., G. Li, Y. Sun, J. Li, S. Yang, and H. Hao. 2022.The changing tendency and association analysis of intelligent coal mines in China: A policy text mining study [J]. Sustainability 14:11650. doi:10.3390/su141811650
  • Yang, X., Z. Fu, J. Zhao, E. Zhou, and Y. Zhao. 2015.Process analysis of fine coal preparation using a vibrated gas-fluidized bed [J]. Powder Technology 279:18–23. doi:10.1016/j.powtec.2015.03.047
  • Yang, Y., and Q. Zeng. 2021.Multipoint acceleration information acquisition of the impact experiments between coal gangue and the metal plate and coal gangue recognition based on SVM and serial splicing data [J]. Arabian Journal for Science and Engineering 46:2749–68. doi:10.1007/s13369-020-05227-6
  • Zhou, E., Y. Zhang, Y. Zhao, Z. Luo, J. He, and C. Duan. 2018. Characteristic gas velocity and fluidization quality evaluation of vibrated dense medium fluidized bed for fine coal separation [J]. Advanced Powder Technology 29(4):985–95. doi:10.1016/j.apt.2018.01.017.
  • Zou, H. 2019. Ruiqing Jia.Visual positioning and recognition of gangues based on scratch feature detection [J]. Traitement du Signal 36(2):147–53. doi:10.18280/ts.360204.

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