Assessment of energy efficiency improvement strategies for ventilation and hoisting systems during the reconstruction of the Molibden mine

Cover Page

Cite item

Full Text

Abstract

The economic efficiency of high-performance mining enterprises largely depends on the parameters and operating modes of energy-intensive equipment. Ventilation fans and hoisting machines are traditionally considered among the most energy-intensive equipment. This study focuses on analyzing the operation of the main ventilation fans and hoisting equipment at the Molibden mine and on developing measures to ensure optimal operating conditions aimed at improving energy efficiency and reducing operating costs. The paper presents methods for evaluating the efficiency of mine ventilation systems, including analytical approaches applied in system design and performance assessment. The study draws on operational data from the Molibden mine. The analysis revealed that the ventilation fans were operating inefficiently, with excessive specific energy consumption. Consequently, the replacement of electric motors is proposed to reduce energy use and operational expenditures. Calculations indicate that the expected economic benefit from replacing the ventilation fan motors at the Molibden mine amounts to 4.9 million rubles per year. Based on an analysis of the hoisting equipment characteristics, a required motor power assessment was performed. The study demonstrates that the use of modern multi-rope hoisting systems with balanced designs is essential for improving operational efficiency. Measures to optimize equipment utilization are proposed, which would reduce the specific energy consumption associated with ore extraction. An analysis of eight years of data revealed an inverse correlation between ore output and specific energy use: a 10–15% increase in productivity results in a 2–5% reduction in specific energy consumption. Avoiding periods of low equipment utilization and implementing automated control systems can significantly enhance overall system efficiency. The findings of this study may be applicable to other mining enterprises operating under similar conditions, particularly those engaged in deep-level mining.

About the authors

R. V. Klyuev

Moscow Polytechnic University

Email: kluev-roman@rambler.ru
ORCID iD: 0000-0003-3777-7203

References

  1. Баловцев С. В. Аэрологические риски высших рангов в угольных шахтах. Горные науки и технологии. 2022;7(4):310–319. https://doi.org/10.17073/2500-0632-2022-08-18
  2. Пелипенко М. В., Баловцев С. В., Айнбиндер И. И. К вопросу комплексной оценки рисков аварий на рудниках. Горный информационно-аналитический бюллетень. 2019;(11):180–192. https://doi.org/25018/0236-1493-2019-11-0-180-192
  3. Liu J., Ma Q., Wang W. et al. Risk level assessment and co prediction of underground mines for poisoning and asphyxiation accidents. Sustainability (Switzerland). 2022;14(24):16640. https://doi.org/10.3390/su142416640
  4. Brigida V. S., Zinchenko N. N. Methane release in drainage holes ahead of coal face. Journal of Mining Science. 2014;50:60–64. https://doi.org/10.1134/S1062739114010098
  5. Dzhioeva A. K., Brigida V. S. Spatial non-linearity of methane release dynamics in underground boreholes for sustainable mining. Journal of Mining Institute. 2020;245:522–530. https://doi.org/10.31897/PMI.2020.5.3
  6. Semin M., Kormshchikov D. Application of artificial intelligence in mine ventilation: a brief review. Frontiers in Artificial Intelligence. 2024;7:1402555. https://doi.org/10.3389/frai.2024.1402555
  7. Du D., Lei W., Li X., Li Z. Research on simulation and optimization of complex ventilation system in multiple level of Shaxi copper mine. Journal of Applied Science and Engineering. 2024;27(10):3283–3293. https://doi.org/10.6180/jase.202410_27(10).0002
  8. Wang J., Xiao J., Xue Y. et al. Optimization of airflow distribution in mine ventilation networks using the modified sooty tern optimization algorithm. Mining, Metallurgy and Exploration. 2024;41(1):239–257. https://doi.org/10.1007/s42461-023-00895-y
  9. Семин М. А., Попов М. Д. Теоретический анализ влияния распределенных тепловых источников на устойчивость течения воздуха в наклонных горных выработках. Устойчивое развитие горных территорий. 2024;16;3(61):1374–1383. https://doi.org/10.21177/1998-4502-2024-16-3-1374-1383
  10. Босиков И. И., Клюев Р. В., Силаев И. В., Стась Г. В. Комплексная оценка трудноформализуемых вентиляционно-технологических процессов на угольных шахтах. Устойчивое развитие горных территорий. 2023;15(3):516–527. https://doi.org/10.21177/1998-4502-2023-15-3-516-527
  11. Li S., Huang Y., Qiu G. et al. Research and application of dust removal performance optimization of exhaust ventilation system in fully-mechanized excavation rock tunnel. Tunnelling and Underground Space Technology. 2025;155:106160. https://doi.org/10.1016/j.tust.2024.106160
  12. Chen J., Zhi Y. Experimental study on the dust control performance of rotating fog curtain under the perturbation of long-pressure and short-pumping ventilation. Scientific Reports. 2024;14(1):29844. https://doi.org/10.1038/s41598-024-81560-2
  13. Босиков И. И., Клюев Р. В., Майер А. В., Стась Г. В. Разработка метода анализа и оценки оптимального состояния аэрогазодинамических процессов на угольных шахтах. Устойчивое развитие горных территорий. 2022;14(1):97–106. https://doi.org/10.21177/1998-4502-2022-14-1-97-106
  14. Валиев Н. Г., Голик В. И., Габараев О. З., Лебзин М. С. Алгоритм определения эффективности комбинирования технологий добычи металлов. Горный информационно-аналитический бюллетень. 2022;(11–2):52–62. https://doi.org/10.25018/0 236_1493_2022_112_0_52
  15. Nevskaya M. A., Raikhlin S. M., Chanysheva A. F. Assessment of energy efficiency projects at russian mining enterprises within the framework of sustainable development. Sustainability (Switzerland). 2024;16(17):7478. https://doi.org/10.3390/su16177478
  16. Петров В. Л., Кузнецов Н. М., Морозов И. Н. Управление спросом на электроэнергию в горнопромышленном секторе на основе интеллектуальных электроэнергетических систем. Горный информационно-аналитический бюллетень. 2022;(2):169–180. https://doi.org/10.25018/0236_1493_2022_2_0_169
  17. Giraud L., Galy B. Fault tree analysis and risk mitigation strategies for mine hoists. Safety Science. 2018;110(Part A):222-234. https://doi.org/10.1016/j.ssci.2018.08.010
  18. Klyuev R., Bosikov I., Gavrina O. et al. Improving the energy efficiency of technological equipment at mining enterprises. Advances in Intelligent Systems and Computing. 2021;1258:262-271. https://doi.org/10.1007/978-3-030-57450-5_24
  19. Shchemeleva Y. B., Sokolov A. A., Labazanova S. H. Development of hardware and a system for analyzing energy parameters based on simulation in SimInTech. Journal of Physics: Conference Series. 2022;012082. https://doi.org/10.1088/1742-6596/2176/1/012082
  20. Pervuhin D. A., Trushnikov V. E., Abramkin S. E. et al. Development of methods to improve stability of underground structures operation. International Journal of Engineering, Transactions B: Applications. 2025;38(2):472–487. https://doi.org/10.5829/ije.2025.38.02b.20

Supplementary files

Supplementary Files
Action
1. JATS XML


Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).