While discussions of AI in mining often focus on software, Glencore technology He says the bottleneck is the underlying data.
The company said that traditional flotation circuits are slow, complex, and difficult for algorithms to interpret. With its short residence time and streamlined design, Jameson Cell provides fast feedback and supports direct measurement of grade and recovery – conditions the company says are essential for AI-driven optimization.
“AI has huge potential for metal processing, but software alone will not be able to deliver it,” said Chris Anderson, principal metallurgist at Glencore Technology. “The advantage of the Jameson cell is its simplicity,” he added.
Jameson Seal’s ability to integrate direct measurement tools is key, the company said. With the use of online grading analyzers, flow meters, density meters, particle size sensors, and automated samplers, data from direct readings are more readily available, reducing reliance on inference.
“If the industry wants to realize the full potential of AI, we first need to move beyond traditional float thinking,” Anderson said. “Simplicity is key. Decisions made while designing circuits today will determine how effectively processes can use AI tomorrow.”
The adoption of AI in mineral processing is still in its early stages, with most sites experimenting on the margins rather than fixing core circuits. Glencore asserts that design choices made today will determine how easily factories can deploy automation in the future.
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