Skip to main content
SpringerLink
Log in

A Novel Coal Mine Dust Measurement System and Experimental Analysis

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

An Erratum to this article was published on 01 October 2023

This article has been updated

Abstract

To facilitate the prediction and early warning of coal dust explosions in mines, a real-time online detection system that employs photoelectric diodes as light sources and narrow beam transmission technology based on the extinction statistical method is proposed. The study establishes a coal dust test model using the extinction statistical method, derives the extinction statistical test equations, and numerically analyzes the relationship between the average and standard deviation of transmittance using beams of varying thicknesses based on the theory of light transmission. The experimental devices of the test system and signal acquisition circuit are designed, and the interference of the original background light measure and light signal in the test is eliminated by prism splitting. Subsequently, coal dust measurement experiments are conducted using beams of diameters 0.05, 0.24, 0.72, and 1 mm. The experimental results indicate that selecting an appropriate beam thickness can enhance the accuracy of coal dust testing. The proposed system is capable of real-time online detection of coal dust particle size and concentration, demonstrating the practicality and feasibility of the system.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Similar content being viewed by others

Change history

REFERENCES

  1. Wei, M., Tong, M., Hao, J., Cai, L., and Xu, J., Detection of coal dust in a mine using optical tomography, Int. J. Min. Sci. Technol., 2012, vol. 22, no. 4, pp. 523–527. https://doi.org/10.1016/j.ijmst.2012.01.014

    Article  Google Scholar 

  2. Wang, Z., Zheng, X., Pan, H., and Li, D., Information entropy multi-decision attribute reduction fuzzy rough set for dust particulate imagery characteristic extraction, IEEE Access, 2020, vol. 8, no. 8, pp. 77865–77874. https://doi.org/10.1109/access.2020.2989831

    Article  Google Scholar 

  3. Yu, H., Cheng, W., Wang, H., Peng, H., and Xie, Ya., Formation mechanisms of a dust-removal air curtain in a fully-mechanized excavation face and an analysis of its dust-removal performances based on CFD and DEM, Adv. Powder Technol., 2017, vol. 28, no. 11, pp. 2830–2847. https://doi.org/10.1016/j.apt.2017.08.010

    Article  Google Scholar 

  4. Miller, A.L., Weakley, A.T., Griffiths, P.R., Cauda, E.G., and Bayman, S., Direct-on-filter α-quartz estimation in respirable coal mine dust using transmission Fourier transform infrared spectrometry and partial least squares regression, Appl. Spectrosc., 2017, vol. 5, no. 71, pp. 1014–1024.

    Article  Google Scholar 

  5. Zhang, R., Liu, S., and Zheng, S., Characterization of nano-to-micron sized respirable coal dust: Particle surface alteration and the health impact, J. Hazard. Mater., 2021, vol. 413, no. 413, p. 125447. https://doi.org/10.1016/j.jhazmat.2021.125447

    Article  Google Scholar 

  6. Liu, Y. and Heng, M.A., Remote on-line detection system for coal dust particle based on machine vision technology, J. Liaoning Tech. Univ. (Nat. Sci.), 2012, vol. 31, pp. 822–825.

    Google Scholar 

  7. Li-Xiu, M., Bo-Xue, T., Jing, J., and Xue-Ling, L., Pulverized coal fineness detection based on double-D optical sensor, China Coal Soc., 2012, vol. 37, no. 8, pp. 1369–1372.

    Google Scholar 

  8. Yuan, C. and Hui-Min, R., The denoising of acoustic emission signal in coal mine, Int. Symp. on Coal Gas Control Technology, 2008, pp. 319–322.

  9. Wang, H., Jia, J., Yang, Yu., Buschle, B., and Lucquiaud, M., Quantification of gas distribution and void fraction in packed bubble column using electrical resistance tomography, IEEE Sens. J., 2018, vol. 18, no. 21, pp. 8963–8970. https://doi.org/10.1109/jsen.2018.2868463

    Article  Google Scholar 

  10. Qin, Sh. and Cai, X., Indirect measurement of the intensity of incident light by the light transmission fluctuation method, Opt. Lett., 2011, vol. 36, no. 20, p. 4068. https://doi.org/10.1364/ol.36.004068

    Article  Google Scholar 

  11. Gregory, J., Turbidity fluctuations in flowing suspensions, J. Colloid Interface Sci., 1985, vol. 105, no. 2, pp. 357–371. https://doi.org/10.1016/0021-9797(85)90309-1

    Article  Google Scholar 

  12. Singh, P. and Bose, S.S., Ambiguous D-means fusion clustering algorithm based on ambiguous set theory: Special application in clustering of CT scan images of COVID-19, Knowl.-Based Syst., 2021, vol. 231, p. 107432. https://doi.org/10.1016/j.knosys.2021.107432

    Article  Google Scholar 

  13. Singh, P. and Bose, S.S., A quantum-clustering optimization method for COVID-19 CT scan image segmentation, Expert Syst. Appl., 2021, vol. 185, p. 115637. https://doi.org/10.1016/j.eswa.2021.115637

    Article  Google Scholar 

Download references

Funding

This work was supported by the Key Research and Development Plan of Xuzhou (KC22083).

Author information

Authors and Affiliations

  1. Department of Science and Technology, Xuzhou College of Industrial Technology, 221004, Xuzhou, China

    Bencheng Yu

  2. School of Information and Control Engineering, China University of Mining and Technology, 221116, Xuzhou, China

    Bencheng Yu, Ren Zihui & Tang Shoufeng

Authors
  1. Bencheng Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ren Zihui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tang Shoufeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ren Zihui.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

The original online version of this article was revised: Modifications have been made to the Affiliations. Full information regarding the corrections made can be found in the erratum/correction for this article.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bencheng Yu, Zihui, R. & Shoufeng, T. A Novel Coal Mine Dust Measurement System and Experimental Analysis. Aut. Control Comp. Sci. 57, 348–354 (2023). https://doi.org/10.3103/S0146411623040107

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411623040107

Keywords:

Search

Navigation