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When you think of mining, does your mind go to pickaxes and dangerous underground environments? Think again. Autonomous vehicles, artificial intelligence, and machine learning are being more and more integrated into today’s mining landscape to address safety, sustainability, and efficiency issues. Applications of mined metal and mineral products also look different these days: they are used to create some essential components of modern life – from smartphones to electric cars – and important pieces of infrastructure like electrical grids.

In a world increasingly dependent on these technologies, it is crucial for countries to secure the domestic supply of elements that allow them to produce and commercialize these items. Copper, lithium, cobalt, nickel, rare earth elements, and graphite are examples of the critical minerals that governments, including Canada, are paying special attention to.

The Gazette spoke to (Mining Engineering), head of the , to learn more about how Queen’s is at the forefront of research and capacity building when it comes to mining and critical minerals.

What is the definition of critical minerals?

When today’s governments talk about “critical minerals” they are referring to metals for which we have identified some sort of limited supply and/or supply risk, and we have no viable substitute. For example, it could be because a particular element’s supply is isolated to a single geographic region, meaning that, if there was any sort of natural disaster or change in geopolitical conditions in that region, the supply of that material would no longer be available.

How does technology support the development of new mining processes?

Many technological advancements that we see today in mainstream society have roots in the mining industry. A good example is autonomous vehicles, which are incredibly useful in mining. One the one hand, autonomous vehicles can work non-stop, allowing mines to be running and operating 24-hours a day. On the other hand, there is an important safety component: we have vehicles running underground without subjecting people to this environment. Automation is a huge topic in the field and there are a few mines around the world that are now operating with autonomous haul truck fleets.

AI and machine learning are also finding their way into the mining industry, as they are into all parts of our day-to-day lives. One important example is mineral processing. Mining industries have been collecting process data online, in real time, for decades. There is this huge wealth of data associated with processing minerals and rocks that is now being leveraged through machine learning models to improve process efficiency. These models take information about the rocks that are mined and help us dial in process conditions to increase the recovery of valuable minerals from the rock and reduce waste.

What challenges is your research currently trying to solve?

My research focuses on the processing stage. After the actual mining part, when the rocks come to surface and have been drilled and blasted and dug up, we need to concentrate the minerals that contain valuable metals from the waste minerals. Hundreds of years ago, this process was mainly done by labour intensive methods like hand sorting and gold panning, but today our techniques are far more advanced and productive. One of the ways we now separate minerals is through a process called flotation.

In this process, we make the minerals that we want to recover repel water and be attracted to air, so that, when we mix the rock slurry with air, the valuable minerals stick to the surface of air bubbles and float to the top of the tank.

My team mainly investigates flotation and we are currently focusing on lithium mineral flotation. As you may know, lithium is in high demand for energy transition as it is an important component in electric vehicle batteries. We have a lot of lithium deposits in Canada, but we only have one that is currently producing lithium, and one of the main reasons for this are the challenges we have in the flotation process.

What are some of the other projects that the Critical Minerals Processing Lab is working on?

We have been working with industry partners to use AI in copper-nickel flotation: we place several different sensors in flotation tanks and use the data to predict process performance and behavior. The lab is also working to develop a process to separate graphite from waste minerals using no water, through air separation.

What does the future of critical minerals mining in Canada look like?

There is a growing interest in critical minerals, and we expect thousands of jobs in mining and processing will be created in the coming decade – capacity building is imperative for the Canadian mining industry to stay globally competitive.

Queen’s is particularly well positioned to train this new generation of mining engineers, as we have one of the strongest programs in Canada, including unique facilities like an explosives test site. Our curriculum also has a strong focus on the societal and environmental impact side of the mining industry and we built a virtual reality mineral processing plant that has been integrated into several of our undergraduate courses. The use of virtual reality will also improve our capacity for training outside the university, and we are already partnering with industry to provide staff training.

How do you see industry and academia collaborating to advance mining in Canada?

In recent years, and in light of all of the excitement around critical minerals, we saw a huge increase in engagement and support for mining research, which is a big change from a decade or two ago.
Canada doesn’t have a long operating history in exploring some of these critical minerals, like lithium, so we still have a lot of unanswered questions that mining companies just aren't set up to address, and that’s where research comes in. Collaborations between industry and academia can lead to more productivity, more sustainable practices, and to raise the next generation of talent that will work in this field.