A recent article in Mining Journal titled “Going lower” looks at how mining companies are addressing falling grades at low grade mines. While there are parts of the world where high grade deposits still exist, they are not in the most politically stable parts of the world, and are usually lacking high cost infrastructure such as ports. This has many in the mining industry looking at how they can get more out of their existing properties by becoming more efficient at extracting and processing lower grades.
Larger equipment has been tried and has brought down unit costs, but there is a limit to how much bigger it can go given that there is only so much throughput that an existing plant can be expected to be able to accept. High grading has been another approach that some have taken, but this only leads faster to lower grades and then more challenges in mining them. In the Mining Journal article, the author states that “high grading has created a ticking time bomb.”
In Chile, some of the largest copper mines have begun to look at big data and machine learning to help them become better at mining lower grades. With this, several have achieve 1 to 2% increases in recovery. Real-time analytics, machine learning, and integrated platforms enabled them to extract more ore with little dilution at the rock face.
More reductions may be achieved through the use of automation, with labor costs equaling roughly 40% of the costs to gold miners. Artificial Intelligence will also help, but the amount of data that has been digitized in mining businesses is still limited.
Technologies that integrate the planning process and drive the digitization of mining are best enabled through platforms that allow data to be used in new ways to model and simulate operations in the virtual world to find ways of making them better.
The short video “Integrated Mine Planning”, after the link, looks at such a solution.
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On the web: 3DS.com/natural-resources/