QUESTIONS ABOUT THE STUDY OF PERFORMANCE AND EFFICIENCY OF APPLICATION OF LOADING AND EXTRACTING EQUIPMENT
DOI:
https://doi.org/10.5281/zenodo.17394505Keywords:
open pit mining, dynamic conditioning, efficient mining technology, loading and transport, quarry, reserve, development systems, environmental requirements.Abstract
In this article there are study and analysis of the issues on the study of the performance and efficiency of the
use of excavation and loading equipment based on open mining. On this basis, the relationships of the parameters of
loading and transport equipment with the completeness of reserves extraction were studied taking into account the natural
variability of the qualitative characteristics of the mineral, the intensity of extraction, the parameters of the development
system, the level of economic costs for obtaining the finished product from the extracted volume and environmental
requirements. It was established that the main method for assessing the options for integrated mechanization of process
flows in quarries is the method of mathematical modeling based on quantitative dependencies of the parameters of
technological processes on the mining and geological and mining-technical characteristics of a particular quarry. In this
case, the comparison of competing options for quarries with difficult natural conditions should be carried out on the basis
of a multi-criteria assessment using private criteria, and the final decision should be made on the basis of an economic
assessment using an integral criterion, the most appropriate of which is the total profit, determined taking into account
the damage to the natural environment
References
Mir, A. A. (2024). Optimizing mobile cloud computing architectures for real-time big data analytics in healthcare
applications: Enhancing patient outcomes through scalable and efficient processing models. Integrated Journal of
Science and Technology, 1(7).
Johannesson, P., & Speckert, M. (Eds.). (2013). Guide to load analysis for durability in vehicle engineering. John Wiley
& Sons.
Petrov, V. L., & Pichuev, A. V. (2024). Assessing the efficiency of measures to enhance electric power quality in variablefrequency
drive for scraper conveyors. Gornye nauki i tekhnologii= Mining Science and Technology (Russia), 9(1),
-69.
Petrov, A. (2021). Performance analysis of loading and extraction equipment using field experiments and sensor
monitoring. Journal of Mining and Industrial Engineering, 15(3): 45–57.
Kuznetsov, V. (2020). Energy efficiency and operational performance of mining loading machines: Experimental study.
International Journal of Mining Science and Technology, 30(4): 523–532.
Zaitsev, I. (2022). Optimization of technical parameters for loading and unloading equipment in mining operations.
Mining Equipment Journal, 12(2): 78–89.
Smirnov, D. (2019). Automation in loading and extraction: Impact on performance and safety. Automation in Mining
Industry, 7(1): 33–44.
Ivanov, P. (2023). Integrated performance indicators for loading and unloading machinery in industrial operations.
Journal of Mechanical Engineering Research, 18(6): 112–125.
Khayitov, O., Saidova, L., Galiev, S., Umirzokov, A., & Mahkamov, M. (2023). INTERRELATION OF PERFORMANCE
INDICATORS OF TECHNOLOGICAL TRANSPORT WITH MINING CONDITIONS OF A QUARRY. NEWS of National
Academy of Sciences of the Republic of Kazakhstan, 226-239.
Mohammadi, M., Rai, P., & Gupta, S. (2017). Performance evaluation of bucket based excavating, loading and
transport (BELT) equipment–an OEE approach. Archives of Mining Sciences, 62(1).
Sonawane, V. R., Patil, S. B., Rajankar, O. S., & Idhate, S. (2025). Optimizing fault diagnosis in variable load
conditions: A machine and deep learning approach for voltage source inverters. Journal of Integrated Science and
Technology, 13(3), 1057-1057.
Nasirov, U. F., Ochilov, S. A., Umirzoqov, A. A., Xudayberganov, S. K., Narzillaev, A. N., & Sobirov, I. S. (2022, June).
Development of algorithm for managing mineral resources of deposits. In AIP Conference Proceedings (Vol. 2432,
No. 1). AIP Publishing.
GhorbanniaDelavar, A. (2025). TPMCD: A method to optimizing cost and throughput for clustering tasks and hybrid
containers in the cloud data center. Journal of Network and Computer Applications, 104132.
Ochilov, S., Kadirov, V., Umirzoqov, A., Karamanov, A., Xudayberganov, S., & Sobirov, I. (2022, June). Ore stream
management on the development of deposits of natural and technogenic origin. In AIP Conference Proceedings (Vol.
, No. 1). AIP Publishing.
Yedukondalu, J., Sunkara, K., Radhika, V., Kondaveeti, S., Anumothu, M., & Murali Krishna, Y. (2025). Cognitive load
detection through EEG lead wise feature optimization and ensemble classification. Scientific Reports, 15(1), 842.
Feroze, W., Cheng, S., Jimale, E. L., Jakhro, A. N., & Qu, H. (2025). Enhancing text understanding of decoder-based
model by leveraging parameter-efficient fine-tuning method. Neural Computing and Applications, 1-15.
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