MODELING AND CONTROLLING CONVEYOR SYSTEMS USING DIGITAL TWIN TECHNOLOGY
Keywords:
Digital Twin, conveyor modeling, industrial automation, real-time monitoring, IoT sensors, predictive maintenance, intelligent control, simulation, cyber-physical systems, digital manufacturing, Industry 4.0, system optimization, operational efficiency, virtual prototyping, smart industry.Abstract
This research examines the modeling and control of conveyor systems using Digital Twin (DT) technology as an innovative approach to improving industrial automation, operational efficiency, and predictive maintenance. Conveyor systems are among the most critical components in mining, manufacturing, logistics, and processing industries, where uninterrupted material flow and optimized performance directly affect productivity. The Digital Twin concept allows engineers to create a virtual, real-time synchronized model of a physical conveyor, enabling simulation, monitoring, optimization, and automated decision-making. Through DT-based modeling, it becomes possible to predict failures, evaluate system loads, reduce downtime, and optimize energy consumption. The study analyzes the fundamental principles of digital twin architecture, including data acquisition via IoT sensors, real-time data processing, machine learning–driven prediction, and feedback-based control. Particular attention is given to developing a virtual conveyor model, synchronizing it with actual operational data, and integrating it with intelligent control algorithms. The research also highlights the advantages of implementing DT technology, such as reduced operational costs, improved safety, higher system reliability, and more efficient maintenance strategies. Ultimately, the work demonstrates that Digital Twin technology is a transformative solution for modern industrial conveyor management, offering significant technical and economic benefits.
References
1. Turg’unov, A. (2018). Conveyor systems monitoring in underground mining: Stability and operational continuity. Tashkent: Mining Science Journal.
2. Qodirov, M. (2020). Sensor technologies in underground mining: Measurement accuracy and environmental challenges. Tashkent: Uzbek National University Research Papers.
3. Tolaganov, B., & Kholmatov, Sh. (2021). Predictive maintenance models for conveyor mechanisms based on statistical analysis. Tashkent: Academy of Sciences, Mining Institute Journal.
4. Qosimov, D. (2022). Machine learning approaches for predictive diagnostics of industrial conveyor systems. Tashkent: Mining and Industrial Automation Journal.
5. Rakhmonqulov, A. (2023). Digital Twin technology for predictive maintenance: Global experience and applicability to Uzbek mining industry. Tashkent: International Mining Research Review.
