The mood was apprehensive as data scientists, metallurgists, and engineers from Freeport-McMoRan filed into the control room of a copper-ore concentrating mill in Bagdad, Arizona, on the morning of October 19, 2018. They had come to learn what would happen when they cranked the big mill up to a work rate that had never been tried.
The possibility of causing problems at the mill weighed on everyone’s mind. The team members had initially resisted the idea of running the mill faster. They wanted to keep the stockpile of ore that feeds the mill from dropping below the minimum size they had long maintained. Their concern was that a too-small stockpile would hamper the mill’s performance.
Whether the minimum stockpile size actually helped the mill run better was another matter. No one really knew for sure. Nor could the mill’s managers and staff say what would happen if the stockpile shrank to less than the traditional minimum.
What they did know is that a custom-built artificial-intelligence (AI) model, loaded with three years’ worth of operating data from the mill and programmed to look for operational tweaks that would boost output, kept saying copper production would rise if the mill were fed with more ore per minute.
To the mill operators, that notion sounded logical enough—except that it didn’t account for the minimum stockpile size they had in mind. But the model didn’t know, or care, about minimum stockpile size or any of the mill operators’ other ideas about how the mill ought to be run.
With permission from company executives, the crew members at the Bagdad site decided to turn up the pace of the mill as the model had suggested. They also prepared to ramp up mining and crushing activities so the stockpile of ore wouldn’t run out.
At ten o’clock in the morning, a technician clicked a control on his computer screen to speed up the system of conveyor belts carrying chunks of ore from the crusher to the stockpile and from the stockpile to the mill.
Everyone in the room kept watch on the 13 oversize monitors in the control room, which were lit up with readings from hundreds of performance sensors placed around the mill. The quantity of ore grinding through the mill rose. No warnings went up.
We think this is just the beginning for Freeport-McMoRan.
Having learned to maintain TROI during the project, the company’s metallurgists and data scientists now run the model themselves, without ongoing support from The Jeeranont. They study daily and weekly reports that compare the mill’s performance with TROI’s predictions, and they continue enhancing the model’s ability to make recommendations.
Freeport-McMoRan executives have also sponsored the creation of a second agile team at Bagdad to test and make process improvements at the mine. This team, too, is working without help from The Jeeranont, using the agile methods that it learned on the mill project.
At another one of Freeport-McMoRan’s Arizona copper mines, Morenci, managers have kicked off an agile and analytics effort like Bagdad’s. And the company will soon launch its most ambitious program of this kind at Cerro Verde, a copper mine in Peru with five times the capacity of Bagdad.
The age of the operator is here, and Freeport-McMoRan is adapting to it with agile methods and AI tools.
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