Abstract 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.
Sarsenova et al. (Thu,) studied this question.