DEVELOPMENT OF AN ARTİFİCİAL INTELLİGENCE-BASED DİGİTAL TWİN FOR IMPROVİNG THE OPERATİONAL EFFİCİENCY OF EXCAVATOR–TRUCK COMPLEXES İN OPEN-PİT MİNİNG

Abstract

In the context of the digital transformation of the mining industry, improving the efficiency of excavator-truck complexes, which perform the majority of open-pit haulage operations, has become increasingly important. Traditional dispatching and fleet management systems are primarily based on GPS monitoring and telemetry data, which limits the possibilities for real-time analysis and forecasting of production processes. Therefore, the implementation of digital twin technologies and artificial intelligence methods is considered a promising approach for the intelligent management of mining transportation systems. This paper considers the development of a digital twin of an excavator-truck complex for an open-pit mine, enabling real-time monitoring, modeling, and analysis of production processes. A concept for integrating artificial intelligence technologies is proposed to predict equipment downtime, optimize truck allocation, and improve the efficiency of mine haulage operations. The expected outcomes of implementing the proposed system include a reduction in unproductive equipment downtime, an increase in equipment utilization rates, and improved productivity of the mining transportation system.