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MODERN INVERTER CONTROL SYSTEMS FOR REDUCING ENERGY CONSUMPTION IN MINING MACHINERY

Authors

  • Azizov Ozodbek Farxod o'g'li

    Nukus State Technical University Student of Mining Electrical Engineering
    Author

Keywords:

Inverter control systems, variable frequency drives (vfds), energy efficiency in mining, mining machinery optimization, smart motor control, industrial automation, load-adaptive control, power consumption reduction, iot-based monitoring systems, sustainable mining technologies

Abstract

Energy efficiency has become one of the most critical priorities in the mining industry due to the growing cost of electricity, increasing depth of underground operations, and the global trend toward sustainable resource extraction. Mining machinery—such as conveyors, crushers, excavators, drilling rigs, pumps, and ventilation systems—constitutes the largest share of total energy consumption in mining enterprises. Therefore, the introduction of modern inverter control systems (variable frequency drives, VFDs) plays a crucial role in optimizing power usage, stabilizing load distribution, and improving overall operational performance. This study examines the technological, operational, and economic aspects of using inverter control systems to reduce energy consumption in mining equipment. Modern VFDs allow precise control of motor speed and torque according to real-time load conditions, helping to eliminate unnecessary energy losses caused by constant-speed motors, mechanical throttling, or manual control methods traditionally used in mining. The research highlights that the application of inverter-based control enables 25–60% reduction in electricity usage depending on machine type, operational environment, and duty cycle characteristics.

References

1. Turgunov, A. (2018). Monitoring Conveyor Systems and Increasing Reliability in Mining Equipment. Tashkent: Uzbekistan Mining Institute.

2. Qodirov, M. (2020). Sensor Technologies and Real-Time Data Analysis in Underground Mining. Tashkent: National University of Uzbekistan Publishing.

3. c

4. Qosimov, D. (2022). Predictive Maintenance Using Artificial Intelligence and Machine Learning Algorithms. Tashkent: Mining and Metallurgy Journal.

5. Rakhmonqulov, A. (2023). AI-Based Predictive Maintenance of Conveyor Systems in International Mining Practice. Tashkent: Industry and Technology Journal.

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Published

2025-11-18