Skip to main content
Log in

A review of coal gangue identification research—application to China’s top coal release process

  • Review Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Top coal caving is a process for the rational extraction of large amounts of coal resources. However, this process readily causes release of excessive amounts of gangue during the coal release process. The conventional technique, which involves visual inspection, is not only labor-intensive but also can introduce inaccuracies. Coal and gangue identification methods used during the top coal caving process are reviewed herein. The methods developed and studied over the last 20 years, such as gamma identification, acoustic identification, radar detection, vibration technology, image identification, and infrared positioning, are described separately, and their principles for use are explained. The advantages and disadvantages of these methods are analyzed, and an evaluation index for coal gangue identification methods is established by using hierarchical analysis (AHP). The results indicate that the γ-ray method is optimal to use under the current system. A future gangue identification method is conceived and proposed, which has reference value for the research and applications of gangue identification technology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

References

  • Bai FY, Fan MQ et al (2021) Rapid ash content determination method for coal particles through images captured under multiple ring light sources with various incident angles. Fuel 296:120528

  • Bi DZ (2014) Design on portable terminal of coal rejects identification based on ZICM2410 communication module. Coal Sci Technol 42(08):83–85+94

    CAS  Google Scholar 

  • Chen B, Yang Y, Xin ZY (2019) Explicit dynamic analysis of coal gangue particles impacting the metal plate. Int Conf Oil Gas Eng Geol Sci 2019:012166

  • Chen YG, Silvestri L et al (2022) Optically powered gas monitoring system using single-mode fibre for underground coal mines. Int J Coal Sci Technol 9(1):26

    Article  Google Scholar 

  • Chen GJ, Zhao WY, Zhu X (2004) On-line identification device for coal and gangue based on dual-energy γ-ray transmission and microcontroller. Nuclear Electronics and Detection Technology 2004(02):140–142.

  • Dou XJ, Wang SB, Xie Y et al (2020) Coal and gangue identification based on IMF energy moment and SVM. Journal of Vibration and Shock 39(24):39–45

    Google Scholar 

  • Fan L, Liu SM (2021) Respirable nano-particulate generations and their pathogenesis in mining workplaces: a review. Int J Coal Sci Technol 8(2):179–198

    Article  CAS  Google Scholar 

  • Guo YC, He L, Liu PZ et al (2021a) Multi-dimensional analysis and recognition method of coal and gangue dual-energy X-ray images. J China Coal Soc 46(01):300–309

    Google Scholar 

  • Guo YC, Wang XQ, Wang S (2021b) Identification method of coal and coal gangue based on dielectric characteristics. IEEE Access 9:9845–9854

  • He AX, Zhang MK, Wang CC (2013) Coal gangue interface recognition based on MFCC research. Appl Mech Mater 2700:1058–1061

    Google Scholar 

  • Li M, Duan Y, He XL et al (2020) Image positioning and identification method and system for coal and gangue sorting robot. Int J Coal Prep Util 42(6):1759–1777

  • Hou W (2019) Identification of coal and gangue by feed-forward neural network based on data analysis. Int J Coal Prep Util 39(1):33–43

    Article  CAS  Google Scholar 

  • Hu F, Zhou MR, Yan PC et al (2021) Multispectral imaging: a new solution for identification of coal and gangue. IEEE ACCESS 7:169697–169704

    Article  Google Scholar 

  • Huo P, Zeng HL, Huo KY (2015) Research on density identification system of coal and refuse based on image processing technology. Coal Prep Technol 2015(02):69–73

  • Jangara H, Ozturk AC (2021) Longwall top coal caving design for thick coal seam in very poor strength surrounding strata. Int J Coal Sci Technol 8(4):641–658

    Article  Google Scholar 

  • Lai WH, Zhou MR, Hu F et al (2020) A study of multispectral technology and two-dimension autoencoder for coal and gangue recognition. IEEE ACCESS 8:61834–61843

    Article  Google Scholar 

  • Li M, He XL, Duan Y et al (2021) Experimental study on the influence of external factors on image features of coal and gangue. Coal Sci Technol 6(27):1–8

    Google Scholar 

  • Li X, Gu T (2011) Study on wavelet singularity-Fisher discriminant of vibration signals of coal and rock during caving. Comput Eng Des 32(05):1800–1803

    Google Scholar 

  • Liu FQ, Qian JS, Wang XH et al (2000) Automatic separation of waste rock in coal mine based on image procession and recognition. J China Coal Soc 2020(05):534–537

  • Liu W (2011) Study on theory and method of coal-gangue interface recognition on fully mechanized caving face [D].China University of Mining and Technology-Beijing,2011

  • Liu W, Hua Z, Wang RL (2011) Vibrational feature analysis for coal gangue caving based on information entropy of Hilbert spectrum. China Saf Sci J 21(04):32–37

    Google Scholar 

  • Liu W, Hua Z, Zhang SX (2011) Coal and rock recognition based on wavelet denosing and independent component analysis. Control Eng China 18(02):279–282+ 289

    Google Scholar 

  • Lv HY, Cheng ZB, Liu F (2021) Study on the mechanism of a new fully mechanical mining method for extremely thick coal seam. Int J Rock Mech Min Sci 142:104788

  • Ma R, Wang ZC, Wang BP (2010) Coal-rock Interface recognition based on wavelet packet transform of acoustic signal. Coal Mine Machinery 31(05):44–46

    CAS  Google Scholar 

  • Ma XM (2009) Coal gangue image identification and classification with wavelet transform. International Conference on Intelligent Computing Technology and Automation 1:562–565

  • Ma XM, Jiang Y (2004) Digital image processing method of coal gangues. Colliery Mechanical & Electrical Technology 05:9–11

    Google Scholar 

  • Ministry of Natural Resources (2022) China mineral resources. Geological Publishing House, Beijing (in Chinese)

    Google Scholar 

  • Pan WD, Li XY, Yuan MT et al (2020) Technology principle and field application of automatic coal drawing based on the top coal tracker. J China Coal Soc 45:23–30

    Google Scholar 

  • Qin JQ, Zheng JR, Zhu X et al (1996) Theoretical modeling and experimental verification of natural T-ray coal rock interface recognition sensor. J China Coal Soc 05:67–70

    Google Scholar 

  • Ren F (2003) Study on the theory and method of coal-rock interface recognition based on multi-sensor data fusion technique [D].Taiyuan University of Technology

  • Ren F, Xiong XY, Yang ZJ (2006) Key state parameters for coal-rock interface recognition. Colliery Mechanical & Electrical Technology 2006(05):37–39

  • Song XM, Zhu DF, Wang ZL et al (2021) Advances on longwall fully-mechanized top-coal caving mining technology in China during past 40 years: theory, equipment and approach. Coal Sci Technol 49(03):1–29

    CAS  Google Scholar 

  • Sun ZY, Huang LL, Jia RQ (2021) Coal and gangue separating robot system based on computer vision. Sensors (Basel, Switzerland) 21(4):1349

  • Wan LR, Chen B, Yang Y et al (2019) Dynamic response of single coal-rock impacting tail beam of top coal caving hydraulic support. J China Coal Soc 44(09):2905–2913

    Google Scholar 

  • Wang JC (2018) Engineering practice and theoretical progress of top-coal caving mining technology in China. J China Coal Soc 43(01):43–51

    Google Scholar 

  • Wang JC, Li Y (2017) Thick seam coal mining and its ground control. Woodhead Publishing Series in Energy 2017:379–407

  • Wang JC, Lv HY, Wang ZH et al (2019) Technical principle and experimental study on fully mechanized top-coal caving mining after extracting the middle slice in extremely thick coal seam. J China Coal Soc 44(03):906–914

    Google Scholar 

  • Wang JC, Yang SL, Huang GJ et al (2013) Research on top coal tracker of fully mechanized top coal caving mining and measurement of top coal recovery rate. Coal Sci Technol 41(01):36–39

    Google Scholar 

  • Wang SY, Wang SB, Ge SR et al (2020a) Near-infrared spectrum characteristics and mechanism of coal and rock in mechanized caving face. J China Coal Soc 45(8):3024–3032

    Google Scholar 

  • Wang YY, Liu Y, Wang C et al (2020b) Optimization and application research of soft rock roadway support scheme based on AHP. Journal of Mining and Strata Control Engineering 2(2):023037

    Google Scholar 

  • Wang WD et al (2017) Weidong Wang and Chen Zhang. Separating coal and gangue using three-dimensional laser scanning[J]. Int J Miner Process 169:79–84

    Article  CAS  Google Scholar 

  • Wang X (2017) Study of coal-rock identification method based on electromagnetic wave technology [D]. China University of Mining and Technology

  • Wang X, Ding EJ, Hu KX (2016) Effects of coal-rock scattering characteristics on the GPR detection of coal-rock interface. J China Univ Min Technol 45(01):34–41

    Google Scholar 

  • Wang YF, Xia YT, Wang XC (2011) Application on overcomplete ICA with noise in coal and rock identification of fully mechanized mining. J China Coal Soc 36(S1):203–206

    Google Scholar 

  • Wang ZC (2002) Study on distributing coal and rock in the process of fully-mechanized coal winning sublevel caving coal technology. Coal Mine Machinery 2002 (08):13–14

  • Wang ZC, Fu Q (2006) Attenuation of natural γ ray passing throughcoal seam and hydrulic support. J Liaoning Tech Univ, Nat Sci Ed 06:804–807

    Google Scholar 

  • Wang ZC, Meng HR (2002) Influence of canopy of hydraulic support upon measurting thickness of coal seam by natural gamma ray. J China Univ Min Technol 03:110–113

    Google Scholar 

  • Wang ZC, Zhang XJ, Zhang HX et al (2003) The research on detection of rock content in coal rock mixture in top coal caving by natural gamma ray. J Transduction Technol 04:442–446

    Google Scholar 

  • Wu Q, Tu K, Zeng YF et al (2019) Discussion on the main problems and countermeasures for building an upgrade version of main energy(coal) industry in China. J China Coal Soc 44(06):1625–1636

    Google Scholar 

  • Xiang Z et al (2021) Cooperative control mechanism of long flexible bolts and blasting pressure relief in hard roof roadways of extra-thick coal seams: a case study. Appl Sci 11(9):4125–4125

    Article  CAS  Google Scholar 

  • Yang E, Ge SR, Wang SB (2018) Characterization and identification of coal and carbonaceous shale using visible and near-infrared reflectance spectroscopy. J Spectrosc 2018:2754908

    Article  Google Scholar 

  • Yang Y, Zeng QL (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. Arab J Sci Eng 46(3):2749–2768

  • Yang Y, Zeng QL, Yin GJ (2019) Vibration test of single coal gangue particle directly impacting the metal plate and the study of coal gangue recognition based on vibration signal and stacking integration. IEEE ACCESS 106783–106804

  • Yi D, Shao PH (2013) Research on coal and gangue identification method based on infrared thermal wave detection. Appl Mech Mater 2370:1285–1287

    Google Scholar 

  • Yu B, Xia HC, Meng XB (2016) Top coal arching mechanism and arch removal strategies in fully mechanized top coal caving mining of ultra-thick coal seam. J China Coal Soc 41(07):1617–1623

    Google Scholar 

  • Yu B, Xu G, Huang ZZ et al (2019) Theory and its key technology framework of intelligentized fully-mechanized caving mining in extremely thick coal seam. J China Coal Soc 44(01):42–53

    Google Scholar 

  • Yu L, Yan SH (2020) The basic principle of roof strata control in fully mechanized caving mining of extra thick coal seam. J China Coal Soc 45(S1):31–37

    Google Scholar 

  • Yuan L (2018) Strategies of high efficiency recovery and energy saving for coal resources in China. Journal of China University of Mining & Technology 20(01):3–12

    Google Scholar 

  • Yuan L, Zhang PS (2020) Framework and thinking of transparent geological conditions for precise mining of coal. J China Coal Soc 45(07):2346–2356

    Google Scholar 

  • Zhang JW, Cheng DL, Yang YY et al (2022) Numerical and theoretical investigations of the effect of the gangue-coal density ratio on the drawing mechanism in longwall top-coal caving. Int J Coal Sci Technol 9(1):31

    Article  CAS  Google Scholar 

  • Zhang C, Wang FT, Bai QS (2021) Underground space utilization of coalmines in China: a review of underground water reservoir construction. Tunn Undergr Space Technol 107:103657-

    Article  Google Scholar 

  • Zhang GJ, Zhang Y (2018) Immediate roof first fracture characteristics of suberect and extermely thick coal. J China Coal Soc 43(05):1220–1229

    Google Scholar 

  • Zhang L, Niu JF, Dai G et al (2014) Design of automatic identification system of coal and gangue for fully-mechanized coal caving working face and its application. Journal of Mine Automation 40(09):121–124

    CAS  Google Scholar 

  • Zhang NB (2015) Detection and radiation law of natural gamma ray from coal and roof-rock in the fully mechanized top coal caving mining [D].China University of Mining and Technology

  • Zhang NB, Liu CY, Chen XH et al (2015) Measurement analysis on the fluctuation characteristics of low level natural radiation from gangue. J China Coal Soc 40(05):988–993

    Google Scholar 

  • Zhang Q, Gu JY, Liu JM (2021b) Research on coal and rock type recognition based on mechanical vision . SHOCK AND VIBRATION 661–7717

  • Zhang SX, Zhang YL, Wang YQ et al (2007) Caved coal and rock spectrum on longwall face. J China Coal Soc 09:971–974

    Google Scholar 

  • Zhang Y, Zhao NZ, Zhang SX (2010) Design on embedded identification system of coal and rejects based on DSP. Coal Sci Technol 38(02):81–83+ 103

    Google Scholar 

  • Zhang YL, Zhang SX (2010) Analysis of coal and gangue acoustic signals based on Hilbert-Huang transformation. J China Coal Soc 35(01):165–168

    Google Scholar 

  • Zhao SF, Guo W (2009) Coal-rock interface recognition based on multiwavelet packet energy. Intelligent Systems and Applications, Wuhan, 2009:1–4

  • Zhao SF (2009) Coal-rock interface recognition based on multiwavelet packet energy. J Xi’an Univ Sci Technol 29(05):584–588

    Google Scholar 

  • Zou L, Yu XH, Li M et al (2020) Nondestructive identification of coal and gangue via near-infrared spectroscopy based on improved broad learning. IEEE Trans Instrum Meas 69(10):8043–8052

    CAS  Google Scholar 

Download references

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (2021YJSNY28).

Author information

Authors and Affiliations

Authors

Contributions

Data analysis and collection were done by B. Xue and J. Li. Methodology was done by Y.Y. Wang. Writing—original draft preparation was done by B. Xue. Writing—review and editing was done by B. Xue. Supervision was done by Y. Zhang. Project administration was done by Y.Zhang and Y.Y. Wang. Funding acquisition was done by Y. Zhang.

Corresponding author

Correspondence to Bo Xue.

Ethics declarations

Ethics approval and consent to participate

No approval of research ethics committees was required to accomplish the goals of this study because experimental work was conducted with an unregulated species.

Consent for publication

This is not applicable for this section.

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Responsible Editor: Shimin Liu

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, B., Zhang, Y., Li, J. et al. A review of coal gangue identification research—application to China’s top coal release process. Environ Sci Pollut Res 30, 14091–14103 (2023). https://doi.org/10.1007/s11356-022-24866-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-24866-w

Keywords

Navigation