Preview

Mining Science and Technology (Russia)

Advanced search

Measurement of feeder performance during coal discharge from an underroof seam using machine vision

https://doi.org/10.17073/2500-0632-2022-09-22

Abstract

The technology for extracting and discharging coal from an underroof seam uses the so-called gravitational extraction method in which coal is extracted and discharged from under the roof by gravity. Here, coal can be discharged onto the main conveyor (face conveyor, located in the supported area), central conveyor (rear conveyor in Western literature), and tail conveyor (discharge conveyor, located in the unsupported area). The most common facilities used currently are longwall sets of equipment providing discharge onto tail conveyors. The purpose of this study is to measure the performance of a motorised plate feeder supplying coal from the outlet port of a roof support to a conveyor during the extraction of thick seams with discharge onto the face conveyor. To achieve the goal, it is proposed to measure the coal volume using machine vision. Methods for calculating a unit volume in a measuring section using a three-dimensional model were investigated. Laboratory studies were carried out to estimate the relative errors of the methods. The research allowed properly defining: a method for collecting data to calculate the unit volume of coal; a method for calculating the unit volume in the measuring section; a method for calculating the feeder performance using machine vision, and approaches for physically simplifying the video scene examined by machine vision. A relative error of less than 10 % with the existing measurement accuracy for constructing a coal layer surface height map indicates the sufficiency of the proposed calculation method for engineering use. The developed mathematical apparatus for calculating the unit volume of coal at the measuring section and measuring the feeder performance allows creating algorithmic software using the elementary mathematical functions of addition, subtraction, multiplication, and division. This aspect is important because it lower sights for the software development environment, and therefore expands the range of hardware suitable for calculating the feeder performance.

About the Authors

M. S. Nikitenko
Federal Research Centre of Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences (FRC CCC SB RAS)
Russian Federation

Mikhail S. Nikitenko – Cand. Sci. (Eng.), Head of Laboratory

Scopus ID 55748886500

ResearcherID E-3893-2014

Kemerovo



S. A. Kizilov
Federal Research Centre of Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences (FRC CCC SB RAS)
Russian Federation

Sergey A. Kizilov – Researcher

Scopus ID 57203142801

Kemerovo



Yu. N. Zakharov
Kemerovo State University (KemSU)
Russian Federation

Yuri N. Zakharov – Dr. Sci. (Phys. and Math.), Professor

Scopus ID 56825350700

Kemerovo



D. Yu. Khudonogov
Federal Research Centre of Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences (FRC CCC SB RAS)
Russian Federation

Danila Yu. Khudonogov – Researcher

Kemerovo



A. Yu. Ignatova
Kuzbass State Technical University named after T.F. Gorbachev
Russian Federation

Alla Yu. Ignatova – Cand. Sci. (Biol.), Associate Professor of the Department of Chemical Technology of Solid Fuels and Ecology

Scopus ID 6701747560

Kemerovo



References

1. Klishin V. I., Afyorov B. A., Kuznetsova L. V. Areas of improving development of thick seams with drawing of the coal from under the roof strata. In: Innovations in Fuel and Energy Complex and Mechanical Engineering (FEC-2017). Proceedings of the International Scientific and Practical Conference. Kemerovo: T. F. Gorbachev State Technical University; 2017. Pp. 57–63. (In Russ.)

2. Klishin V. I., Klishin S. V. Current state and direction of development of thick coal seams exavation technology by powered roof supports with controlled coal discharge. Izvestija Tulskogo Gosudarstvennogo Universiteta. Nauki o Zemle. 2019;(1):162–174. (In Russ.)

3. Peng S. S. Longwall mining. 3rd edition. Leiden: CRC Press/Balkema; 2020. 562 p.

4. Le T. D., Mitra R., Oh J., Hebblewhite B. A review of cavability evaluation in longwall top coal caving. International Journal of Mining Science and Technology. 2017;27(6):907–915. https://doi.org/10.1016/j.ijmst.2017.06.021

5. Mundry S., Sandgathe C. Automated Cat longwall top coal caving. In: Efficient Mining of High Seams with Automated LTCC Operations. Caterpillar Inc.; 2018. Pp. 12-14. URL: http://s7d2.scene7.com/is/content/Caterpillar/CM20180716-40601-27335 (Accessed: 01.08.2022).

6. Medhurst T., Rankine R., Kelly M. Development of a method for a longwall top coal caveability assessment. In: Coal operators’ conference. 12–14 February 2014. Wollongong: University of Wollongong; 2014. Pp. 42–50. URL: https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2159&context=coal

7. Le T. D. Longwall Top Coal Caving mechanism and cavability assessment. [PhD thesis in Mining Engineering]. Sydney; 2018. https://doi.org/10.26190/unsworks/20236

8. Klishin V. I., Shundulidi I. A., Ermakov A. Yu., Soloviev A. S. Technique for development of reserves of thick shallow seams with coal discharge. Novosibirsk: Nauka Publ.; 2013. 248 p. (In Russ.)

9. Klishin V. I., Opruk G. Yu., Varfolomeev E. L., Borisov I. L. Interaction of mechanized support with interlayer thickness in systems with subvel caving. Mining Informational and Analytical Bulletin. 2018;(S48):87–94. (In Russ.) https://doi.org/10.25018/0236-1493-2018-11-48-87-94

10. Kizilov S. A., Nikitenko M. S., Neodzhy B. et al. Automation of process control in thick seam mining with top coal caving. Russian Mining Industry. 2017;(6):96–99. (In Russ.) URL: https://mining-media.ru/en/articles/articleen/13196-automation-of-process-control-in-thick-seam-mining-with-top-coal-caving

11. Nikitenko M. S., Kizilov S. A., Nikolaev P. I., Kuznetsov I. S. Technical devices of powered roof support for the top coal caving as automation objects. In: IOP Conference Series: Materials Science and Engineering. XI All-Russian Scientific and Practical Conference (with international participation) “Automation systems in education, science and production, 2017”. 14–16 December 2017, Novokuznetsk, Russian Federation. 2017;354:012014. https://doi.org/10.1088/1757-899X/354/1/012014

12. Nikitenko M. S., Kizilov S. A. Technical and technological platforms for creating robotized complexes for the development of thick seam deposits. In: IOP Conference Series: Earth and Environmental Science, Volume 377, International Scientific and Research Conference on Knowledge-based technologies in development and utilization of mineral resources. 4–7 June 2019, SibSIU, Novokuznetsk, Russia. 2019;377:012033. https:// doi.org/10.1088/1755-1315/377/1/012033

13. Starodubov A. N., Sinoviev V. V., Klishin V. I., Kramarenko V. A. Application of simulating modeling for research of subvel caving modes. In: 9th All-Russian Scientific and Practical Conference on Simulation Modeling and its Application in Science and Industry. Yekaterinburg; 2019. Pp. 540–547. (In Russ.) URL: http://simulation.su/uploads/files/default/2019-immod-540-547.pdf

14. Starodubov A., Sinoviev V., Totskiy A., Klishin V. Review of mining equipment with controlled robotized subvel caving with specialized software. In: E3S Web of Conferences. Vth International Innovative Mining Symposium. 2020;174:03012. https://doi.org/10.1051/e3sconf/202017403012

15. Starodubov A. N., Sinoviev V. V., Klishin V. I. The development of simulating system of robotized technologies for thick and acute coal seams. Journal of Physics: Conference Series. 2021;1749(1):012040. https://doi.org/10.1088/1742-6596/1749/1/012040

16. Heyduk A. Bulk density estimation using a 3-dimensional image acquisition and analysis system. In: E3S Web of Conferences. Mineral Engineering Conference MEC2016. 2016;8:01060. https://doi.org/10.1051/e3sconf/20160801060

17. Heyduk A. Laser triangulation in 3-dimensional granulometric analysis. Archives of Mining Sciences. 2016;61(1):15–27. https://doi.org/10.1515/amsc-2016-0002

18. Min F., Lou A., Wei Q. Design and experiment of dynamic measurement method for bulk material of large volume belt conveyor based on laser triangulation method. In: IOP Conference Series Materials Science and Engineering. 7th Annual International Conference on Material Science and Environmental Engineering. 15–16 November 2019. Wuhan, Hubei, China. 2020;735(1):012029. https://doi.org/10.1088/1757-899X/735/1/012029

19. Fojtík D. Measurement of the volume of material on the Conveyor Belt measuring of the volume of wood chips during transport on the Conveyor Belt using a laser scanning. In: Proceedings of the 2014 15th International Carpathian Control Conference (ICCC). 28–30 May 2014. Velke Karlovice, Czech Republic. Pp. 121–124. https://doi.org/10.1109/CarpathianCC.2014.6843581

20. Amorim L. L., Mutz F., De Souza A. F. et al. Simple and effective load volume estimation in moving trucks using lidars. In: 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). 28–30 October 2019. Rio de Janeiro, Brazil. Pp. 210–217. https://doi.org/10.1109/SIBGRAPI.2019.00036

21. Zeng F., Wu Q., Chu X., Yue Z. Measurement of bulk material flow based on laser scanning technology for the energy efficiency improvement of belt conveyors. Measurement. 2015;75:230–243. https://doi.org/10.1016/j.measurement.2015.05.041

22. Dunn M., Reid P., Malos J. Development of a protective enclosure for remote sensing applications—case study: laser scanning in underground coal mines. Resources. 2020;9(5):56. https://doi.org/10.3390/resources9050056

23. Macpherson T., Churchland A., Sejnowski T. et al. Natural and artificial intelligence: a brief introduction to the interplay between AI and neuroscience research. Neural Networks. 2021;144:603–613. https://doi.org/10.1016/j.neunet.2021.09.018

24. Akulov M. S., Gladkikh S. A., Lankina M. Yu., Baklanov A. N. Processing photos and videos by using neural networks in the LABVIEW program. Sovremennyye Naukoyemkiye Tekhnologii. 2019;(3–1):12–17. (In Russ.) URL: https://top-technologies.ru/ru/article/view?id=37434


Review

For citations:


Nikitenko M.S., Kizilov S.A., Zakharov Yu.N., Khudonogov D.Yu., Ignatova A.Yu. Measurement of feeder performance during coal discharge from an underroof seam using machine vision. Mining Science and Technology (Russia). 2022;7(4):264–273. https://doi.org/10.17073/2500-0632-2022-09-22

Views: 2780


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2500-0632 (Online)