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Издательство:
Дата издания
25.02.2026На каком языке издана
ISSN
23915439Область исследования
Тип исследования
Аннотация (краткое описание)
In open-pit mining, bulk material transportation largely determines productivity and energy costs. Air-cushion belt conveyors reduce friction and wear compared to traditional systems, yet their comprehensive evaluation under real mining and geometric conditions remains limited. Intelligent modeling and automated monitoring enable real-time assessment of key parameters and justify modernization aimed at improving energy efficiency and reliability of logistics systems. The objective of this study is to develop and validate a methodology for assessing the production and economic performance of air-cushion belt conveyors in open-pit mining operations using intelligent modeling and an automated monitoring system. The proposed approach takes into account complex mining and geometric conditions and is aimed at improving the energy efficiency, reliability, and sustainability of transport systems. The methodology combines real-time data collection, digital modeling using artificial intelligence algorithms, and industrial testing at an operating mining facility. Key performance parameters, such as energy consumption, stability, fault tolerance, and automation, are assessed. A comparative analysis of technical and economic indicators before and after the implementation of intelligent technologies is conducted. The results confirm the feasibility and benefits of upgrading air-cushion conveyor systems in combination with artificial intelligence-based tools. This methodology can be applied to optimize bulk material transportation in quarries and improve the overall efficiency of production logistics in the mining and construction industries.DOI
10.1515/eng-2025-0146Тип публикаций
Практическое значение
The practical significance of this research lies in substantiating the effectiveness of artificial intelligence technologies for improving the operation of modern manufacturing and industrial transport systems. The proposed approaches to the integration of AI into intelligent conveyor systems enable optimization of production processes, implementation of predictive diagnostics, automated control, and real-time monitoring of equipment operation. The obtained results demonstrate that the application of AI technologies in air-cushion conveyor systems contributes to increased productivity, reduced equipment downtime, minimization of operational losses, and lower energy and labor costs. The implementation of intelligent quality control and predictive maintenance systems improves the reliability, safety, and economic efficiency of industrial enterprises. The research findings can be applied in mining, logistics, manufacturing, and transport industries for the development of scalable intelligent production systems based on digital technologies. In addition, the study provides a scientific and practical foundation for the further modernization of industrial infrastructure and supports the transition toward sustainable and highly automated Industry 4.0 production models.Квартиль
Источник квартиля
SCImago (Scopus)Impact Factor
3.3Данный документ располагается в коллекциях
Международные публикации ученых SU
Scopus
Файлы документа
2026.pdf