What Digital Twins Bring to Modern Mining Operations
Digital twins in mining are detailed, real-time digital models of equipment, processes or entire plants that mirror physical operations, allowing operators to simulate scenarios, test interventions and optimise production before applying changes on site. These real-time digital models connect live sensor data, process history and predictive analytics to support production optimization and faster, better decisions. As ore grades fall and deposits become harder to reach, digital twins give mining companies a way to squeeze more value from existing assets while limiting downtime and safety risks. They also help align mining operations technology with energy transition goals by improving efficiency in power-hungry processes such as grinding, leaching and refining. The approach is now moving from isolated pilots to coordinated platforms that can host dozens or even hundreds of digital twins across refineries and complex production chains.
BHP, Microsoft and Copper Extraction Innovation
Copper extraction innovation is becoming critical as demand surges for electric vehicles, digital infrastructure and renewable energy systems. BHP, described as the world’s largest mining company, is working with Microsoft Discovery to speed up research into more efficient copper leaching reagents, especially for lower-grade, harder-to-reach deposits tightly bonded to surrounding rock. Microsoft Discovery runs on Azure AI, using teams of AI agents to scan scientific literature, propose new molecules and run quantum chemistry simulations at scale. According to BHP Vice President Innovation Jessica Farrell, “This project is about giving our scientists excellent tools to focus on the most promising copper leaching solutions, sooner.” By screening more than half a million candidate molecules digitally before laboratory testing, BHP is effectively building a form of scientific digital twin around the leaching process, reducing trial-and-error cycles and aiming for higher recovery rates from challenging ore bodies.
RUSAL Scales Real-Time Digital Models Across Alumina Refineries
RUSAL’s alumina business shows how digital twins mining strategies are moving into large-scale deployment. The company is developing a corporate platform to manage the full life cycle of digital twins across alumina refineries, expanding on existing real-time optimization projects at UAZ, BAZ, AGK, Friguia and Ewarton. Between 2026 and 2028, the platform is expected to support more than 100 digital twins, forming the backbone of RUSAL’s Model Predictive Control framework. These twins continuously analyse process data, evaluate operating conditions and calculate optimal parameters, feeding recommendation systems that guide operators through a dedicated web interface. The initiative aims to improve process stability, equipment reliability and production safety while raising overall production optimization performance. By standardising best practices in one environment, RUSAL can roll out proven digital models faster and unlock more value from analytics and real-time digital models across its alumina operations.
From Pilots to Platforms: Solving Old Problems with New Tools
For decades, mining and refining teams have struggled with the same operational challenges: variable ore quality, complex multi-stage circuits, and difficult trade-offs between recovery, throughput and energy use. Digital twins and related mining operations technology now give engineers tools to examine these trade-offs in real time, test changes safely in a virtual environment and push the best settings back to the plant. BHP’s agentic AI-driven exploration of new copper leaching reagents and RUSAL’s Model Predictive Control platform both show how digital twins mining initiatives connect scientific models, process control and operator workflows. As energy transition metals like copper and aluminium stay in high demand, these systems help miners raise yields from lower-grade resources without proportionally increasing environmental or energy footprints. The result is a gradual shift from reactive troubleshooting toward continuous, data-driven optimisation at scale.
