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The theme of this year’s MINExpo, held between Sept 24th and 26th, was unmistakable: automation is reshaping the industry. From Caterpillar’s fully autonomous loaders to robotic companies addressing on-site safety concerns, the scale of these intelligent solutions signals a transformation toward intelligence-based automation.
For decades, automation in mining focused on engineering — conveyor belts, robotic arms, and self-operating machinery — that boosted productivity by reducing manual labour and human error. These mechanical systems enhanced efficiency, constrained by upstream operational limits.. Data management automation has redefined how companies handle vast troves of information. Cloud solutions offer scalability and flexibility, enabling real-time operational oversight. Yet, in the chaotic and heterogeneous realm of ore deposits exploration, where data volumes have surged exponentially, geologists still bear the burden of interpretation due to the subsurface’s inherent complexity.
Intelligence-based automation represents a leap beyond mere mechanization or data processing. This form of automation doesn’t just execute commands; it adapts, learns, and anticipates to better constrain geological variables and support operational decision-making. Insights into ore zone variability on a daily basis provide critical information to optimize ore extraction, grade control, and real-time adjustments. In an industry where incremental improvements can translate into millions saved, such intelligence offers a significant return. Whether optimizing blasting grid placement or predicting supply chain disruptions, intelligence-based automation unleashes downstream mechanical and data automation.
A close look at the key stages of the mining process helps better grasp the scale of this disruption. The mining process is comprised of drilling and blasting; loading and hauling; crushing and grinding; concentration; and smelting or refining. Efficiency gains early in this chain create a snowball effect, amplifying benefits downstream. Poor separation of ore from waste at the mine face marginalizes overall performance. Suboptimal positioning of blast hole grids can ripple through operations — more accurate separation means loaders handle less waste, boosting their efficiency. Consequently, the milling process benefits from higher ore grades, improving throughput and reducing energy consumption.
Real-time refinement of block model debottleneck downstream production efficiency, delivering substantial cost savings across the operation. Relying on coarse models intended for resource estimation during production can undermine engineering investments downstream, where where every minute and second counts in calculating gained efficiency. This is a practice that should have been deemed unacceptable long ago.
However, hurdles remain, such as inertia in mining to adapt new technologies, and the absence of one-size-fits-all solutions impedes widespread adoption of intelligent systems. The behavioural cost of adapting to granular-scale intelligence is significant. A data-driven culture can provide actionable insights into daily operations—a shift that requires embracing agile methodologies and continuous improvement, areas where the industry has traditionally lagged.
Implementing intelligent blasthole positioning and advanced ore-waste separation technologies also demands real-time, purpose-fit intelligence before the shovel hits the ground. In the high-stakes environment of mineral extraction, delays or data inaccuracies can cascade into costly inefficiencies. Solutions must process geological data instantaneously and adapt to each site’s unique conditions—a challenge that off-the-shelf solutions rarely meet. This also calls for engineers who think like geologists and vice versa!
Scient Analytics, a Halifax, Nova Scotia-based start-up, is stepping into this gap, providing hardware and software infrastructure for real-time ore body knowledge at mine sites. The Canadian company has deployed its technology on diamond drill core samples at the tale of exploration, at the face of open pit mines, and on chips coming out of blast holes before engaging shovel. This analysis at micro and macro scale offers unparalleled control over mining and excavation process—something the status quo still inherently lacks. Its technology refines geological models through identifying alteration patterns and minerals invisible to the geologist’s eye. Scient technology application in the production stage—where the environment is better constrained—enhances the understanding of mineralization for precision mining.
The winners of automation challenge are those who prioritize automation of ore body intelligence to avoid marginalizing mechanical and cloud-based automation. In mining, where stakes are high and margins thin, such integration promises is crucial.
*Masoud Aali is the founder and CEO of Nova Scotia-based Scient Analytics Inc.
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