Survey & investigation |
Digital Twins 4 Tailings Dams
The often-forgotten consequence of our metal hungry future is the enormous amount of resulting mine waste that is stored in tailings facilities, says Maral Bayaraa from the University of Oxford who recently won an award at COP28 for her work on tailings safety. Digital Twins 4 Tailings Dams is a satellite-based early warning system to monitor the stability of tailings storage facilities, preventing potential catastrophic failures
Above: Maral Bayaraa says the objective of this research into tailings storage facilities is to protect human life, safeguard the environment, preserve infrastructure, ensure economic stability, build community resilience, and foster continuous monitoring and risk reduction
Below: Size of metals compared to the amount of waste that needs to be taken out of the ground. The metal blob is to scale for Palabora mine in South Africa
THE WORLD BANK ESTIMATES three billion tons of metals are required for the energy transition. This is a huge challenge set to the mining industry, especially as metals make up a tiny portion of the material that is dug out of the ground, with over 98% being waste, such as in copper mining. The mine waste, also known as tailings, is often stored behind huge dams called tailings dams or tailings storage facilities (TSFs). Because mining is one of the oldest industries, there are more than 30,000 TSFs in the world – a quarter of which are abandoned with nobody monitoring them. If these dams collapse, the results can be catastrophic. Therefore, the often-forgotten consequence of our metal hungry future is the handling of the enormous amount of resulting mine waste. But what if any dangerous signals or changes to the health of tailings storage facilities can be diagnosed straight away and any human error discovered immediately? What if decisions can finally be made in a way that consider long term consequences as well as alternative scenarios that help prepare for extreme events? This is the promise of Digital Twins: virtual replicas of the physical asset developed and updated using Machine Learning (ML) and sensor technologies both on the ground and in space. Our research ambitions are on developing a
satellite based remote early warning system for these structures and which brings together three separate fields – geotechnical engineering, satellite remote sensing and machine learning within a digital twin system – and aims to push the limits of what is technologically possible. The research themes intend to address many of the fundamental questions that still need answering before a true digital twin system can be achieved for TSFs. The latest satellite technologies offer
32 | June 2024 |
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many advantages to TSF monitoring and are complementary to pre-existing ground-based instrumentation and geotechnical modelling. Therefore, the initial function of a digital twin system lies in data fusion – combining many sources of data. In Bayaraa et al. [2022], we have created the first systematic demonstration of the complementarity of ground deformation monitoring for a failed TSF case study using Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar), finite element modelling and ground-based prism data. Although digital models such as building information models (BIMs) are common in mining, the key difference between a true digital twin and any other digital model is in its ability to update itself continuously to the conditions of the physical twin as the physical twin changes and vice versa. This puts an emphasis on the steps that do the checking to ensure that various stages of the planned construction and consolidation cycles have been completed (2]. Both optical and SAR satellite data offer a vantage view of these ’check phases’ with a rating on the degree to which the implementation is following designed intent. This ability to compare as-designed vs. as-implemented on the ground is crucial for identifying any human error that may occur and result in unexpected TSF behaviours. The real value of digital twins is in their ability to
forecast and simulate alternative future scenarios, so that the best actions are recommended for decision making. The promise of truly data-driven decision making is the ability to understand the potential impacts of a decision in one part of the system within the context of the system as a whole. This is especially crucial for ensuring the safety of TSFs and their surrounding environment. For this to happen, the various physical conditions
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