TOMRA Mining has launched CONTAIN™, a deep learning solution purpose-built to classify complex inclusion-type ores that are difficult to detect using traditional sorting methods.
Engineered for seamless integration with TOMRA Mining’s ecosystem, CONTAIN represents the latest evolution in the company’s AI-driven sorting platform. Developed entirely in-house by TOMRA’s software engineers and mining experts, the technology uses convolutional neural networks to perform real-time analysis of X-ray imagery, visually classifying rocks based on the probability of subsurface ore mineral inclusions. These include complex mineralisations such as in tungsten, nickel, and tin ores – materials that traditionally result in high misclassification or excessive product loss.
Stefan Jürgensen, Software Team Lead at TOMRA Mining, comments:
“Our system was trained on tens of thousands of ore samples and designed from the ground up for sorting inclusion-type ores. With CONTAIN, operators can dynamically adjust the grade-recovery threshold via a touchscreen interface, enabling precise control over yield and product specifications.
“Existing technologies can be configured to detect low-grade material in such ores, but this results in a high quantity of waste rocks being sorted into the product stream, diluting the product beyond economic viability. CONTAIN is exceptionally accurate in evaluating the value of a rock, making sorting thresholds for such relatively low-grade ores economically viable.”
Proven success at Wolfram Bergbau
Field trials at Wolfram Bergbau in Mittersill, Austria, confirmed the transformative potential of CONTAIN. Integrated alongside TOMRA’s latest COM XRT and OBTAIN™ technologies, the system delivered immediate performance gains. The operation rapidly increased total plant throughput by 8%, achieved a 33% reduction in ore mineral losses, and recorded its lowest-ever tails grade. The visual impact of the improvements was so striking within the first minutes of operation that the team immediately requested a second installation.
What truly set CONTAIN apart was its ability to identify tungsten-bearing inclusions that would otherwise go undetected – particularly those embedded deep within host rock. Traditional sorting systems often fail to distinguish such subtle mineralisation, resulting in either excessive gangue or compromised concentrate quality. With CONTAIN, operators were able to fine-tune the balance between grade and recovery in real time, producing consistent, high-spec output with a higher tails rejection volume and reduced ore mineral losses. The downstream effect was a more stable, efficient operation and a notable drop in overall production costs.
David Comtesse, Production Manager, Wolfram Bergbau- und Hütten AG, said:
“We were absolutely overwhelmed by what CONTAIN could do. It picked up mineral inclusions we didn’t think were detectable, and did it with incredible precision even at larger grain sizes up to 65 mm. It immediately changed the way we think about sorting and processing. This isn’t just an upgrade – it’s a completely new level of performance.”