Anglo American has highlighted its predictive maintenance efforts on equipment at its Barro Alto nickel mine in Brazil in its recently-published annual report.
The company said it is developing predictive models so it can make better informed operational decisions. These models, built by data scientists and often powered by artificial intelligence and machine learning, contain advanced algorithms that leverage the power of data to generate predictions, according to the company.
“At the operational level, we are using customised learning algorithms across a range of different applications,” Anglo said. “In one such instance, we monitor equipment health at a number of our operating sites, with the aim of improving operational performance through predictive maintenance.”
The company said at Barro Alto, which has two rotary kilns and two electric furnaces that smelt nickel ore, it is focusing its predictive maintenance efforts on key pieces of high-power equipment.
Anglo said: “By building a comprehensive data platform that monitors 38 major elements of the Barro Alto operation, we are increasing our knowledge of the performance of the equipment and we are using data to accurately forecast failures before they happen.”
Soon, the company will be able to “dynamically manage” maintenance intervals – only replacing parts when required – Anglo said. This ensures greater operational uptime and product throughput, according to the company. “The implementation is expected to improve furnace reliability, as well as realise cost savings for the nickel business,” Anglo said.
The learnings from Barro Alto are also being applied to fixed-plant assets in other operations, Anglo said. “This nascent project is on track to deliver considerable value from just one data analytics application.”
On the technology in general, Anglo said: “Data analytics augments the intelligence in our people by helping them make better, confident data-driven decisions. Remote monitoring of assets takes people away from physical equipment and helps avoid high-energy failures, which leads to a safer working environment. Reducing unplanned equipment failures can also bring significant environmental benefits owing to the reduced likelihood of spillages.”
Anglo plans to extend the reach of its data analytics platforms to all aspects of its value chain and extend operational decision support to the mining and processing phases of its assets, it said.
