Por: Felipe Carrillo, Anglo American Quellaveco y Juan Mansilla, Astay Systems.

Trabajo ganador de Minería 4.0 en el Foro TIS de PERUMIN 37.

Abstract

This paper presents the implementation and results of a digital twin applied to the autonomous mining operation of Quellaveco, using the DataTwin® platform. The main objective was to develop a selective mining strategy assisted by this technology to anticipate and manage the quality of the ore fed to the plant, thereby optimizing metallurgical recovery and productivity.

The methodology focused on integrating data from multiple systems (FMS, MinePlan, PI System), creating a virtual model of the operational environment through “mining blocks” (realistic simulation units), and using a simulation engine to predict mine advancement on an hourly basis.

The case study focused on controlling RSOL (soluble copper), a critical variable that negatively affects recovery. Two scenarios were compared:

Traditional scenario: mining without explicit control of RSOL, which resulted in high variability of this parameter and a drop in recovery. Optimized scenario: a mining sequence recommended by the digital twin to control ore blending and maintain a stable RSOL.

The results showed that, although both scenarios processed the same tonnage, the digital twin-optimized approach drastically reduced recovery variability (standard deviation of 1.14 vs. 3.3 in the traditional case) and has the potential to keep the plant process stable and predictable, leading to higher actual recovery and lower costs.

In conclusion, the project demonstrates the tangible value of the digital twin as a strategic tool that enables a shift from reactive operation to predictive and proactive management, improving decision-making within the Integrated Operations Center (IOC) and setting a new benchmark for Mining 4.0.