Risk Assessment
AI can transform mining operations, but as Dr Penny Stewart warns, its real value will only be unlocked if the technology is transparent, tested and trusted.
New rules, stricter enforcement and a state-wide crackdown are forcing South Australian mines and quarries to radically rethink how they manage crystalline silica exposure - or risk being shut down.
For an industry under mounting scrutiny and regulatory oversight, there is perhaps no role more critical - or misunderstood - than that of the Engineer of Record (EoR).
In the wake of evolving regulatory expectations and maturing risk management frameworks, mining companies are being urged to reassess how they apply critical controls to tailings storage facilities (TSFs).
When it comes to tailings management, the mining industry is no stranger to technical standards, risk registers, or operational frameworks.
The Unites States' Mine Safety and Health Administration (MSHA) has released its final report into a fatal blasting accident at Calhoun Quarry #1 in Jersey County, Illinois, citing multiple safety violations and enforcement actions.
When BHP needed a new Engineer of Record for one of Australia’s most complex tailings sites, the Olympic Dam handover became a masterclass in how to get it right.
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.
A state-wide mine safety blitz has revealed widespread compliance issues in New South Wales operations, with mechanical engineering control plans emerging as the most significant area of concern.
In an era where emerging technologies promise to transform mining operations, from AI-driven optimisation to fully automated systems, the real challenge is not finding the next breakthrough.
