TOMRA Mining has unveiled CONTAIN, a proprietary deep-learning solution designed to classify complex inclusion-type ores with unprecedented accuracy. The software seamlessly integrates with the manufacturer’s XRT sorters, representing the latest evolution in its AI-driven sorting platform.
Unlike traditional optical sorting systems, CONTAIN uses advanced deep-learning algorithms to perform real-time analysis of X-ray imagery, visually classifying rocks based on the probability of subsurface ore mineral inclusions. These include complex mineralization such as seen in tungsten, nickel and tin ores. The OEM said this capability allows mining operations to adapt their strategies in real time – whether the goal is to maximize concentrate grade, minimize valuable material loss, or align with processing cost constraints.
“Our system was trained on tens of thousands of ore samples and designed from the ground up for sorting inclusion-type ores,” said Stefan Jürgensen, Software team lead at TOMRA Mining. “With CONTAIN, operators can dynamically adjust the grade-recovery threshold via a touchscreen interface, enabling precise control over yield and product specifications.”
TOMRA said it is actively testing the CONTAIN software on gold and chromite, and is exploring expanded applications in iron and copper. While still in the early stages, the OEM noted that initial results indicate promising possibilities for expanding applications across a broader range of ore types.