Mineral Processing
A cloud-hosted machine learning model, linked securely to a plant control system, has helped eliminate costly surging in dense medium cyclones - and in one case, safeguarded millions in weekly coal revenue.
From the outside, the conversation around digital mining often gets framed in broad terms - automation, Information of Things (IoT), Artificial Intelligence (AI) but for those working underground or in control rooms, the real question is more practical: how do these tools actually solve the daily challenges?
For Stewart Johnston, Account Manager - Mine Electrification and Automation at ABB Australia, the key lies in making information usable, timely, and connected across the mining value chain.
Dr Sandra Occhipinti, research director in minerals at Australia’s national science agency, CSIRO, is leading a team of more than 100 scientists focused on one of the most complex challenges in modern exploration: how to accelerate mineral discovery in covered terrains while simultaneously improving geometallurgical insight across the mining value chain.
At the sharp end of metallurgical decision-making, where feasibility meets financial risk, one recurring theme echoes loudest: if you don’t know your orebody, you don’t know your project.
In a mining landscape increasingly defined by lower ore grades, ESG scrutiny, and complex feedstocks, recovery performance has never been more critical.
As mineral exploration enters an era defined by data complexity and digital transformation, one of the biggest hurdles geoscientists face is not a lack of information, but too much of it.
As the mining industry edges closer to a tipping point on tailings management, a panel of global experts at the 2025 Life of Mine | Mine Waste and Tailings Conference in Brisbane issued a clear message: discipline in operations, humility in design, and a more adaptive mindset will be critical to preventing the next tailings disaster.
If there was one thing the panel on safe mine closure made clear at this year’s Life of Mine - Mine Waste and Tailings Conference in Brisbane, it’s this: closure is no longer just about sealing off the last truckload and planting grass.
In the drive to improve energy efficiency, recovery, and metallurgical precision, a global engineering company has released a quiet disruptor: a machine-learning-enabled sensor that’s helping mining operations monitor and optimise grind size with new levels of accuracy.
As ore grades decline and sustainability pressures rise, mining operations are being forced to find new ways to optimise resource extraction.
