Analysis of geotechnical factors influencing power consumption of excavators

Relevance. The technical justification of power consumption standards and the feasibility of linking them with factors that determine the degree of influence of the most significant production indicators on changes in electricity consumption is relevant. For the correct and most complete characteristics of power consumption, it is necessary to establish a quantitative assessment of the degree of influence of mining and technological factors and operating modes of mechanisms to identify the most significant factors and establish patterns of power consumption.Purpose and methods. The aim of the work is a comprehensive study of power consumption for individual technological operations of the mining quarry and forecasting consumption rates based on the application of probabilistic calculation methods. Results. To find the patterns of power consumption, probabilistic-statistical methods of calculation and mathematical modeling apparatus are used. Empirical and theoretical dependences of the electric power consumption of the excavator on the scooping parameters are given. The results of calculating the correlation parameters and equations of the dependences of the energy consumption for excavation on selected factors are presented. Conclusions. The nature of the power consumption of excavators is investigated, mining and technological factors are identified that have the greatest impact on their energy performance. The energy characteristics of excavators are constructed in the form of dependences of the total energy consumption as a function of productivity P, mining and geological properties of soil K and the angle of rotation for unloading . It was found that the correlation coefficient, which allows us to assess the degree of influence of the studied factors on power consumption, reaches a value of 0.774, which indicates a sufficient degree of influence of the selected parameters on the amount of power consumption.

Klyuev R.V., Gavrina O.A., Khetagurov V.N., Fomenko O.A. Analysis of geotechnical factors influencing power consumption of excavators. MIAB. Mining Inf. Anal. Bull. 2020;(11-1):146157. [In Russ]. DOI: 10.25018/0236-1493-2020-111-0-146-157.

Klyuev R.V.^1,3, Dr. Sci. (Eng.), Assistant Professor, Head of Chief, e-mail: kluev-roman@ rambler.ru;
Gavrina O.A.¹, Cand. Sci. (Eng.), Assistant Professor;
Khetagurov V.N.¹, Dr. Sci. (Eng.), Professor;
Fomenko O.A.², Ph.D., director of the branch of the Southern Federal University;
¹ North Caucasian Institute of Mining and Metallurgy (State Technological University), 362021, Vladikavkaz, Russia;
² Southern Federal University, Rostov-on-Don, 344006, Russia;
³ Moscow Polytechnic University, 107023, Moscow, Russia.

R.V. Klyuev, e-mail: kluev-roman@rambler.ru.

1. Popova O.V. Elektropotreblenie pri dobyche uglya otkrytym sposobom: Na primere OAO HK “Kuzbassrazrezugol” [Energy consumption in the extraction of coal by opencast methods using the example of OJSC HK “Kuzbassrazrezugol”], Candidate dissertation, Kemerovo, Kuzbass state technical University, 2003, 226 p. [In Russ].

2. Kaboli S., Selvaraj J., Rahim N. Long-term electric energy consumption forecasting via artificial cooperative search algorithm. Energy, 2016, vol. 115, pp. 857—871.

3. Meira de Oliveira E., Oliveira F., Cyrino F. Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 2018, vol. 1441, pp. 776—788.

4. Biel K., Glock C. Systematic literature review of decision support models for energy-efficient production planning. Computers & Industrial Engineering, 2016, vol. 101, pp. 243—259.

5. Bornschlegl M., Bregulla M., Franke J. Methods-Energy Measurement — An approach for sustainable energy planning of manufacturing technologies. Journal of Cleaner Production, 2016, vol. 1351, pp. 644—656.

6. Wei N., Li Ch., Peng X., Zeng F., Lu X. Conventional models and artificial intelligencebased models for energy consumption forecasting: A review. Journal of Petroleum Science and Engineering, 2019, vol. 181, № 106187.

7. He Y., Zheng Y., Xu Q. Forecasting energy consumption in Anhui province of China through two Box-Cox transformation quantile regression probability density methods. Measurement, 2019, vol. 136, pp. 579—593.

8. Klyuev R.V., Bosikov I.I., Mayer A.V., Gavrina O.A. Comprehensive analysis of the effective technologies application to increase sustainable development of the naturaltechnical system. Sustainable Development of Mountain Territories. 2020, №2, pp. 283—290. [In Russ].

9. Klyuev R.V., Bosikov I.I., Egorova E.V., Gavrina O.A. Assessment of mininggeological and mining technical conditions of the Severny pit with the use of mathematical models. Sustainable Development of Mountain Territories. 2020, №3, pp. 418–427. [In Russ]

10. Klyuev R., Bosikov I., Gavrina O., Madaeva M., Sokolov A. Improving the energy efficiency of technological equipment at mining enterprises. Advances in Intelligent Systems and Computing, 2021, Vol. 1258. pp. 262—271.

11. Golik V.I., Razorenov Yu. I., Efremenkov A.B. Recycling of metal ore mill tailings. Applied Mechanics and Materials, 2014, vol. 682, pp. 363—368.

12. Golik V., Komashchenko V., Morkun V., Burdzieva O. Metal deposits combined development experience. Metallurgical and Mining Industry, 2015, vol. 7, no. 6, pp. 591—594.

13. Gabaraev O.Z., Dmitrak Yu. V., Drebenshtedt K., Savelkov V.I. Regularities of interaction of destroyed geo-materials and ore-bearing massif in the processing of processed deposited ore. Sustainable Development of Mountain Territories. 2017, no. 4, pp. 406—413 [In Russ].

14. Golik V.I., Razorenov Yu. I., Karginov K.G. The basis of sustainable development of North Ossetia-Alania is the mining industry. Sustainable Development of Mountain Territories. 2017, no. 2 (32), pр.163—172. [In Russ].

15. Stefanov V.K., Antonenkov D.V., Igumnova S.A. Specific power consumption of quarry excavators operating in the far North. MIAB. Mining Inf. Anal. Bull. 2019, no (S4), pp. 32—38. [In Russ].

16. Lyakhomsky A.V., Petuhov S.V. Energy efficiency of excavating operations of coal mining enterprises. Promyshlennaya energetika, 2019, no 1, pp. 38—41. [In Russ].

17. Spiliotis E., Petropoulos F., Kourentzes N., Assimakopoulos V. Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption. Applied Energy, 2020, vol. 2611, № 114339.

18. Xiao J., Li Y., Xie L., Liu D., Huang J.A hybrid model based on selective ensemble for energy consumption forecasting in China. Energy, 2018, vol. 15915, pp. 534—546.

19. Carvallo J., Larsen P., Sanstad A., Goldman Ch. Long term load forecasting accuracy in electric utility integrated resource planning. Energy Policy, 2018, vol. 119, pp. 410—422.