Gustavo Pilger, worldwide GEOVIA R&D Strategy & Management Director, answers the most frequently asked questions, principal concerns and upcoming trends in the Mining-tech sector.
What are the key digital technologies that mines are starting to exploit?
Many players in the industry have been adopting, adapting and even developing technology along many years to improve aspects related to safety, productivity and sustainability.  Many examples come to mind, but more frequently than not, these are invariably point solutions that aim at improving specific parts of processes in isolation.
Examples of applied technologies include sensors & IoT, robotics & automation, virtual & augmented reality, digital photogrammetry & LiDAR, data analytics & AI amongst others. Efforts to master and improve applications of these technologies across mining processes must not be understated, but there is more to be done in terms of connecting the dots between them. Once these technologies and the data they interact with are connected and “converse” with one another, then significant levers – those that transform businesses – can start to be pulled. The ultimate target, in my opinion, is to make the mine to operate like an automated factory within the framework of a Virtual Twin.
This framework provides a live virtual replication of the real world in which processes (or systems) are interlinked and associated with one another with the underlying data that informs and describes those processes. It is this data associativeness combined with smart methods and algorithms that allows one to constantly chase value while in operation, adjusting to uncertainty and unplanned events, being of technical, mechanic, or of market nature. The Virtual Twin framework allows closing the loop between IT (information technology) and OT (operational technology) while leveraging AI algorithms, cloud and edge computing, within an environment that is cybersecure by design. It allows miners to think out of the box and virtually test ideas before physically implementing them. So, the Virtual Twin aggregates a range of technologies as described above within a dynamic, live, and scientifically-accurate system-of-systems framework.
How do these key solutions assist mines to improve their planning and project implementation?
The technologies I mentioned before are all enablers of the Virtual Twin, which provides miners a safe cyber sandbox to test ideas or hypotheses before committing to physically implement them. These could be for instance, innovative mining methods, energy substitution configurations and even methodologies to track material/mineral within their mining complexes.
For example, within the context of monitoring and predictive maintenance, the Virtual Twin, which is powered by a network of connected devices carrying a range of fit-for-purpose sensors that distill data 24/7 aided by AI algorithms, help to predict equipment failure based on data (past and live) from direct input and from adjacent interacting systems as well as environmental conditions within the spatial context where the equipment is located.
Embedded algorithms supported by AI techniques then suggest decision options based on simulated scenarios that account for not only the primary process but also for the network of processes the equipment interacts with or influences. The idea is to constantly have a “finger on the pulse” of the operation to monitor, assess root causes, simulate possible scenarios, and propose corrective measures to keep the operation performing as best as possible under environmental (e.g., unexpected geology variations) and contextual variables (e.g., energy cost).
What is the importance of data and data management in this space?
Data and their effective management do play a key role in this whole context, as decisions should be data-driven based on verified behavior (e.g., reconciled data), estimated or simulated within the time and spatial context of the mine as a whole, within a system-of-systems framework. This may sound a bit futuristic or aspirational, but this should be the end game we strive to achieve. Once one have identified key bottlenecks that hinder achieving operation targets, then one can frame the problem at hand and develop a plan to address it. This involves determining main processes to focus on, input/output data, types of data to collect and devices or sensors to leverage.
In terms of data, it needs to be federated and consolidated in a single common repository so it is not only safe and secured but it is indexed (for quick retrieval), standardized through semantic dictionaries (so data is understood by human and machines) and contextualized, enabling establishing meaningful links and associativeness between processes and data. Once data is federated, indexed, standardized and contextualized in a safe and secured single repository and systems are connected and input and output are associated through common data models then one can test multiple hypotheses or scenarios in the virtual world (Virtual Twin) to then efficiently apply a given design or plan in the real world – eliminating unnecessary waste, reducing risk, minimizing material re-handling while maximizing productivity!
Following this, how should mines best leverage data analytics and AI to obtain real-time insights, and what impact will this have on productivity?
AI is arguably already embedded in pre-processing algorithms of many OT assets across mine sites – those cyber-physical devices that drill, dig, load, haul, crush, convey, sense and monitor. This is great as those embedded algorithms offer a range of insights at real- or near real-time. However, more often than not, these insights are confined within the boundaries of their immediate space of action. As I mentioned above, more powerful insights could be taken and business transformation decisions made when looking to the data and the insights they offer from a holistic system-of-systems configuration point of view. This way, one makes decisions that not necessarily optimize isolated processes but do optimize a network of process within the context and scope of the target KPIs.
Of course, within the system-of system Virtual Twin framework, AI-based algorithms put forward a range of possible decisions as well as their possible consequences for decision making (decision support). Therefore, the impact on productivity could be significant. Following on the “finger on the pulse” analogy, the Virtual Twin powered by AI algorithms can provide actionable insights to operators, enabling them to make informed decisions and respond quickly to changing conditions, optimizing processes, increasing throughput, and therefore improving overall productivity.  It enables predictive maintenance schedules, reducing unplanned downtime and maximizing equipment availability, also leading to increased productivity.
The Virtual Twin powered by AI can also help capturing knowledge and know-how of a generation of experienced mining professional who are on their way to retirement. Decision, actions, conditions are captured and knowledge and know-how are enhanced over time. It can also help to attract talent to the industry at the same time.
How do digital technologies minimise technical risks and produce optimal pit designs?
Digital technologies aggregated and interconnected through common data models within a Virtual Twin framework have the potential to unlock value across the value chain including the mine planning and design processes. Overall, the key is to have the data federated, indexed, contextualized and standardized in a manner that machines/algorithms can understand them in order to make inferences, predictions and ultimately prescriptions. The latter is the stage in which the machine prescribes or suggests possible decision or actions when faced with unpredicted events in order to adjust on the fly to keep chasing value while in operation. Specifically about mine planning & design, the potential value to unlock revolves around safety, cost, productivity and sustainability.
The ability to evaluate thousands of possible scenarios within a reasonable timeframe opens up unprecedented levels of optionality that enables the planner to ultimately select a robust plan that meets a given set of criteria – i.e., a plan that meets the targets and that is realizable faced a range of uncertainties including possible disruptive unplanned events. Since processes are parametrically defined and digitally interconnected, scenarios are evaluated and optimized in their entirety (within the mine planning and design scope).
This means that hypotheses tested out within the ultimate pit shell optimization and scheduling stage propagate through the chain to the mine design stage ensuring that options for the overall plan meet the criteria from geotechnical constraints, design practicality down to ESG targets, all the while maximizing NPV. Therefore, the capabilities of mine planning optimization and parametric design combined with data associativeness between processes unlock by itself massive productivity gains when compared with the siloed traditional approach.
Technical risks from geotechnical, environmental to technology choices can be minimized by testing them out in the Virtual Twin sandbox. For example, one could safely and efficiently assess the design impact due to the adoption of an autonomous truck fleet or of an in-pit crushing and conveying system.
What are the challenges in implementing such digital technologies, looking at:
1) Technological concerns (connectivity, sustainability issues, determining ROI etc)
First, we need to recognize that many industries parallel to Mining have gone through similar digital transformation journeys. So, I think we need to learn and leverage as much as possible from them. I often hear people saying that mining is different, so technologies and processes successfully adopted across other industries cannot simply be adopted in mining. Sure, the mining industry has its peculiarities and particularities, but other industries have them too. For example, the tyre industry: they need to produce tyres within strictly defined specs sourcing rubber of different qualities from different suppliers across the globe. Agriculture is another industry which deals with raw materials and their inherent uncertainties.
Nevertheless, yes we have a few challenges to overcome in order to turn our vision into reality. Connectivity within and across sites is one that immediately comes to mind. However, in my view it is more like a capital investment problem than it is one of technology. What I mean is that there are technologies capable of connecting sites in many parts of the world that are today lagging behind cloud adoption, for instance. Of course, there are connectivity blindspots around the world, but I think it’s a matter of time and investment to close them.
Beyond connectivity, I think other key challenges to solve include closing the IT-OT loop so ensure that the mining IoT works as intended or designed. Associated to that are challenges related to standards for interoperability, including defining ontologies and semantic dictionaries so that machines, devices, algorithms can “talk” the same language so that they can exchange the right data for informing adjacent processes. Still related to that is the role of AI into the technology mix including aspects about regulation and data ownership to train, build, and use predictive models, for instance.
Another challenge or opportunity is to rationalize, connect, digitalize, and integrate supply chains for further agility and productivity gains.
As mining operations become increasingly digitized and connected, the risk of cyber attacks grows, from hacking to data breaches and operational disruption – all with potential severe consequences. So, this is another ongoing and increasing challenge to overcome. Prevention starts with education of mining personnel to manage associated risks.
2) People concerns (change management, skills & training, job losses due to automation etc)
On the social-economic or people side there are equally challenging issues to address for successfully implementing digital technologies. For example, change management: it’s important to take people on a journey, explain them the rationale (for change) in simple terms, have a common vocabulary to facilitate those conversations and avoid misunderstandings. It’s also important to clearly communicate and (again) explain what changes mean for each team and individuals so they are reassured the change in question is not a threat but represents opportunities (for learning, career development, etc.).
Then there is the issue about skilled labour shortage. In recent times we have seen more Geology and Mining courses being shut down or reformulated with direct consequences on the workforce entering the industry. This is an industry problem that is unfortunately increasing year-over-year as a generation of skilled and experienced professionals retire from the industry. How do we capture knowledge and know-how of a generation of experienced mining professional who are on their way to retirement AND attract talent to the industry at the same time? This is a key question to answer! Also, the increasing adoption of advanced technologies in mining tends to exacerbate the existing shortage of skilled labour force as specialised expertise is required to operate and maintain this new generation of technologies being adopted across the industry.
We as an industry have a challenge to redefine the perception general society have about mining in order to be able to compete with other industries in the current market for talent. In addition, we need to be able to attract talent but with a different set of skills than what we have been traditionally bringing in into the industry. A skilled labour force will be essential to the successful implementation of digital technologies in the industry.  Automation and the adoption of technologies such as AI should be seen as opportunities to attract talent to the industry.
Beyond the above, other broader challenges particularly related to AI and people include: governance frameworks and AI ethics and accountability. On the former, there will be need for robust governance policies establishing oversight, access control, auditability and responsible data stewardship practices. In relation to ethics and accountability, there are growing concerns around AI bias, transparency, and accountability, in particular for high-stakes decision impacting personnel, communities and of course the environment.
How do you anticipate technology changing the way miners operate and manage tasks over the next few years? Will we ever see a fully automated mine?
Indeed, technology development and their adoption play a key role in the mine of future. Navigating through the challenges outlined above will be essential to eventually realize the vision of an automated mine. Will we achieve the vision of a fully automated mine? The vision in which a mine would be a data-driven, fully automated and simulated, electrified system-of-systems operation, functioning continuously and reliably, adjusting on the fly as conditions shift and plans change based on continuous updates and input from intelligent sensors as well as human decision-making in a fully integrated Industrial Internet of Things ecosystem?Â
It’s definitely an ambitious target! And I would rather have an ambitious target to chase than not! More importantly, these and future technologies and the efficiencies promoted by their application could open up new spaces of opportunity. For example, it can help us to conceive new innovative mining methods to improve resource efficiency, minimize waste and hence reduce the environmental footprint of a typical mine.
It can help us to develop and implement innovative advanced mineral processing methods capable of higher recoveries at minimal environmental impacts. So, in summary, I believe in future we will be working in tandem with technology and derivative devices to enhance human potential and foster creativity, and by doing so transforming the way we traditionally mine and run a mining operation.
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