What Digital Twins Mean for Modern Mining
Digital twins in mining are high‑fidelity virtual replicas of physical assets and processes that combine real‑time operational data, engineering models and analytics to monitor, predict and optimise extraction and refining performance at scale. In mining operations technology, this means every key stage—from ore extraction to refining—can be simulated, tested and tuned before changes touch the plant or the pit. For copper extraction optimization, digital twins allow engineers and geochemists to experiment with new reagents, flow sheets and operating conditions in a digital environment, reducing trial‑and‑error in the field. In alumina refinery automation, they support continuous control of complex thermal and chemical processes that would be hard to steer by manual observation alone. The result is a gradual shift from reactive troubleshooting to anticipatory, model‑driven operations across mines and processing plants.
BHP, Microsoft Discovery and the Race for Better Copper
BHP’s work with Microsoft Discovery shows how digital twins and AI‑driven R&D can unlock harder‑to‑reach copper deposits. Geochemists and data scientists screened more than 500,000 chemical reagents using Azure‑based quantum chemistry simulations, focusing on copper leaching from lower‑grade ores tightly bonded to host rock. Microsoft Discovery coordinates teams of specialised AI agents that mirror the scientific method—from literature review to hypothesis generation, simulations and iterative learning—shortening cycles that would normally take years in traditional labs. “This project is about giving our scientists excellent tools to focus on the most promising copper leaching solutions, sooner,” said BHP Vice President Innovation Jessica Farrell. By narrowing candidates to a smaller set of molecules for physical testing, BHP can pursue copper extraction optimization aimed at higher recovery rates while supporting rising demand from electric vehicles, renewable energy and grid infrastructure.
RUSAL’s Digital Twin Platform for Alumina Refineries
RUSAL is building a corporate platform to manage the full life cycle of digital twins across its alumina refineries, expanding on systems already in place at facilities such as UAZ, BAZ, AGK, Friguia and Ewarton. Between 2026 and 2028, the platform is expected to support more than 100 digital twins, forming a unified ecosystem for alumina refinery automation. These models underpin the company’s Model Predictive Control framework, which continuously analyses process data to determine optimal production parameters and provide real‑time recommendations to operators. According to RUSAL, its existing digital technologies generated an economic effect of approximately ₹798 million in 2025. Engineering teams have already built recommendation systems for three complex process facilities at the Urals Aluminium Smelter, showing how plant‑level digital twins can be scaled into a coordinated, enterprise‑wide production optimisation layer.
From Process Monitoring to Predictive, AI-Driven Mines
Digital twins mining initiatives are moving beyond simple dashboards into predictive, AI‑driven control of entire value chains. In copper, discovery platforms built on high‑performance computing can test thousands of leaching pathways in silico, accelerating the search for reagents that improve recovery from low‑grade, deeply buried deposits. In alumina, real‑time twins feed model predictive control systems that stabilise critical variables, reduce off‑spec output and support safer, more reliable operations. As BHP, RUSAL and partners like Microsoft and Prescience Insilico expand these tools, mining operations technology is becoming more integrated: twins connect to lab robotics, plant sensors and advanced analytics to shorten feedback loops between experiment and execution. This convergence addresses decades‑old extraction challenges while supporting the energy transition by improving copper sourcing, aluminium feedstock reliability and the overall efficiency of resource‑intensive processes.
