With the tragedies that have plagued the mining industry in the last few years, managing tailings dams has gone from an operational issue to a top priority on the agenda of most CEOs. It is such a high risk that it challenges the industry's licence to operate. If another accident happens, it is well understood that communities, shareholders, authorities and all stakeholders will demand action from all players.
As a result, the topic has been at the centre of attention at all major mining conferences and earnings calls. Large mining companies have established task forces to search for solutions, while numerous vendors are simultaneously trying to push their solutions. Still, progress has been slow and underwhelming.
However, three key pitfalls are creating challenges to moving ahead: the complex vendor landscape, organisational fragmentation and narrow scope initiatives.
The first problem mining task forces find is that there are way too many solutions. Better sensing devices, innovative imaging techniques, data management IT solutions, visualisation tools, academic research projects and cool start-ups all claim to have a magic pill to solve the problem.
In addition, for every solution there will be large global players, as well as Canadian, American, European and Australian regional vendors. Most offerings solve specific problems and cannot be considered an end-to-end solution.
Another typical problem is more internal to mining companies. Organisational structures are complex, with different teams covering regions, sites, functions and commodity types. Each of these internal stakeholders have their own set of priorities, distinct operating environments and experience with various technologies. This complexity makes it difficult for teams to narrow down to initiatives that can be properly resourced and executed.
As a result of both external and internal complexity, the projects that do go ahead tend to focus on immediate solutions, leveraging off-the-shelf products. Complex solutions that require deeper collaboration and further technological development may be de-prioritised versus simpler solutions that navigate more easily through internal approval processes.
Finally, mining companies search for solutions almost entirely within traditional sources - consultants, vendors, industry associations and research centres that are well known and respected and have deep roots in the industry. This approach works well for a steady state technology development, but in a moment of much needed disruption, it leaves behind all other solutions developed for similar problems in other industries.
In order to accelerate improvement in tailings monitoring, mining companies need to start from a comprehensive technology scan, develop a technology adoption roadmap and eventually bring R&D efforts together into a cohesive innovation strategy.
Stratalis Consulting has developed an approach that can help clients accelerate tailings monitoring solutions by bringing order to the chaos. A systematic categorisation of the solutions according to a value chain enables clients to better understand the problem, and to compare offerings apples to apples. The result is a ‘tech scan' that maps the ecosystem and uncovers blind spots, enabling internal teams to have a common basis for discussion.
There can be great disparity in adoption even among sites belonging to the same company. While pilot projects involving satellite interferometric synthetic-aperture radar (InSAR), drone imaging and ground-based radar are becoming more common, experimentation with other technologies such as 3-D seismic, electrical resistivity tomography (ERT), muon tomography and fibre optic are still few and incipient.
While a comprehensive view of how different products and solutions can be used and combined is by itself game-changing, it doesn't address the time component of the problem. A number of new technologies are not market ready yet, and this is a very fast-moving space. Adopt the best solution available today, and it might become obsolete quickly.
Stratalis advises clients to consider a technology roadmap, with technologies at different technology readiness levels. By doing so, companies can anticipate new technologies by following their evolution, or even selectively investing or driving their development.
A technology roadmap implies multi-year projects that require proper governance, funding and cross-functional collaboration. All these execution enablers need to be driven by a cohesive innovation strategy under appropriate leadership.
A study conducted by Stratalis Consulting analysed thousands of technology solutions in the mining industry, as well as in related industries such as defence, oil & gas, civil construction, food processing and weather forecasting, with similar problem statements. The study showed significant potential for integrated computer model systems to complement expert judgement and be a valuable tool in risk mitigation.
In fact, there are several industry level initiatives already moving in this direction. Key examples of work groups developing integrated solutions include DAMSAT, STINGS, Programa Tranque and a project jointly conducted by AMIRA International and the University of Western Canada.
The Stratalis study concluded that the future of monitoring is connected, intelligent, and predictive (see Figure 1). Getting there will require companies to go through four stages of evolution, with increasingly powerful sensing devices, connectivity and computer modelling capabilities:
- Stage 1 represents the present state for many sites and consists of semi-automated solutions, in a traditional set-up;
- Stage 2 utilises connected monitoring, leveraging full connectivity, advanced sensors, 2-D/3-D visualisation tools and centralised 24/7 monitoring centres;
- Stage 3 integrates digital twinning, advanced modelling techniques enriched with advanced imaging and detailed characterisation data; and
- Stage 4 includes advancements such as artificial intelligence (AI), data fusion and advanced predictive models to help geotechnical experts interpret data better. This will result in a constantly updating data loop, real-time risk trees, more reliable anomaly detection, internal and external automated alerts, automated compliance and reporting, among other things.
At its evolved stage (see Figure 2), models will seamlessly integrate multiple data inputs, combining detailed structure characterisation, weather application programming interfaces (APIs), connected sensors and access to external databases. These systems will feed all data into both physical and statistical models, leveraging AI for advanced simulation and predictive features, and providing real time alerts to centralised monitoring centres that are staffed 24/7.
As a result, these models will be able to update risk trees in real time, reliably detect anomalies, trigger internal and external alerts, automate compliance and reporting, trigger emergency preparedness plans, as well as constantly update themselves through a continuous data assimilation feedback loop.
Improving tailings dam risk profile requires a change in mindset and immediate action. Updated risk assessments must be followed by a technology roadmap to continuously deliver state-of-the-art monitoring capabilities. In the short term, these roadmaps should focus on quick wins such as InSAR imaging and upgrades in data logging and connectivity, as well as enablers such as data integration technologies.
For the mid- to long-term, focus should shift to transformational changes that may require deeper coordination and longer lead times, such as pilots with new characterisation technologies, implementation of integrated systems and modelling projects.
In parallel to internal projects with immediate impact on existing assets, companies should also plan to continuously interact with academic research projects, develop industry-level partnerships and make selective investments in the ecosystem.