The mining industry faces a significant challenge as the demand for metals continues to grow, particularly for the clean energy transition. The World Bank estimates that three billion metric tons of metals are required for this transition. However, the extraction process generates an enormous amount of waste, with over 98% of mined material becoming tailings in some cases, such as copper mining. These tailings are typically stored in massive structures called tailings dams or tailings storage facilities (TSFs). With more than 30,000 TSFs worldwide, including about 25% that are abandoned and unmonitored, the risk of catastrophic failures looms large.
Digital twins technology offers a promising solution to this pressing issue. By creating virtual replicas of physical assets, digital twins can provide real-time monitoring and analysis of tailings dams. These systems integrate data from various sources, including satellite imagery, ground-based sensors, and geotechnical models. The Digital Twins 4 Tailings Dams project, developed by Maral Bayaraa and her team at the University of Oxford, aims to push the boundaries of what is technologically possible in TSF monitoring. Their research combines geotechnical engineering, satellite remote sensing, and machine learning to create a comprehensive early warning system.
One of the key advantages of digital twins for tailings dams is their ability to fuse data from multiple sources. Bayaraa's team has demonstrated the complementarity of ground deformation monitoring using Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar), finite element modeling, and ground-based prism data. This approach provides a more complete picture of a TSF's stability and potential risks. Additionally, digital twins can compare "as-designed" versus "as-implemented" conditions, helping to identify any human errors or deviations from the original plan that could lead to unexpected behaviors.
The real power of digital twins lies in their ability to forecast and simulate alternative future scenarios. This capability allows mining companies and regulators to make data-driven decisions by understanding the potential impacts of various actions on the entire system. For example, operators can test different management strategies and assess their long-term consequences before implementing them in the real world. This proactive approach can significantly enhance the safety and sustainability of tailings storage facilities.
Implementing digital twins for tailings dams presents several challenges. The sheer volume of data involved requires advanced machine learning techniques to process and interpret effectively. There are also issues surrounding data access and sharing between mining companies, regulators, and other stakeholders. While mining companies possess detailed data on their dams, government agencies and communities often lack access to this critical information. Overcoming these barriers will be essential for realizing the full potential of digital twins in tailings management.
Satellite technology plays a crucial role in the development of digital twins for tailings dams. Both optical and synthetic aperture radar satellites provide frequent, wide-area monitoring that complements ground-based instruments. Medium-resolution SAR data, such as that from the Sentinel-1 mission, is available globally and open-source. Higher resolution commercial alternatives also exist, though they are not globally available and can be expensive. The integration of satellite data into digital twin systems offers a unique "macroscope" view of these massive structures, enabling more comprehensive and timely monitoring.
Major mining companies are recognizing the potential of digital twins and similar technologies for improving tailings dam safety. Rio Tinto, for instance, has partnered with technology firms to develop its own tailings monitoring system using satellite imagery and artificial intelligence. BHP has also invested in advanced monitoring technologies, including the use of drones and remote sensing. As these technologies continue to evolve and become more widely adopted, they have the potential to significantly reduce the risks associated with tailings storage facilities and improve the overall sustainability of the mining industry.
