Mining has been a mainstay of the Australian economy since the gold rushes of the mid-nineteenth century. The continent is home to some of the world’s richest mineral resources and is a global leader in the production and export of iron ore, lead, gold, and other metals. Perhaps unsurprisingly, Australia is also home to some of the world’s most innovative and forward-thinking mining companies.
Newcrest Mining Limited is a front runner among them, pioneering smart mining technologies to automate many of its operations, increasing worker safety and boosting productivity while controlling costs. Such innovation is a necessity in the highly competitive mining industry. Because large-scale mining operations produce a commodity whose market is driven by complex geopolitical forces, those companies best able to maximise operational efficiencies and create a lean production environment stand to increase their margins and thereby gain a significant advantage over their competitors.
Newcrest turned to Microsoft partner Insight, a Fortune 500 digital transformation consultancy, to develop an intelligent IoT solution in which AI workloads are pushed to the edge. Their solution integrates data science to enable predictive maintenance of mining equipment and optimise its performance throughout its operational lifecycle, reducing unplanned downtime and setting a new performance standard for the mining industry.
Deep within the earth, sensor failures disrupt operations
In the Cadia Valley mines, Newcrest relies on a large-scale mining method called block caving that supports rapid production and requires minimal drilling and blasting, reducing costs and surface land impacts. It’s an efficient and cost-effective process, but to maintain production, ore crushers and a series of other machines must be kept running continuously.
One component that’s critical to keeping Newcrest’s mining operations running smoothly is the crushed ore bin. Located downstream from the ore crusher and within a group of machines functioning together to extract ore, process it, and then transport it to the surface, the crushed ore bin is equipped with a hard sensor that constantly measures the level of ore in the bin.
These sensors provide essential information to mine operators, allowing them to maintain peak production rates and avoid downtime. If the ore levels fall too low, too little material will be fed onto the conveyor, and production slows. If they’re too high, production must be halted and the bin emptied manually, a process that can be dangerous and time-consuming.
But, subject to harsh conditions underground, the sensors in Newcrest’s mines experienced an unacceptably high failure rate. “The hard sensors in our crushed ore bins were failing at a rate of two to three times every month per crusher, and it often took hours to get the process up and running again,” says Gary Slater, Digital and Data Science Architect at Newcrest. “Over a period of six months, just one of our Cadia Valley mines experienced 4,780 minutes of downtime due to sensor failure in the crushed ore bins, which equates to a significant dollar value in lost opportunity.”
Partnering to boost productivity, reduce costs, and improve worker safety
Together with Microsoft and Insight, Newcrest created an IoT and intelligent edge solution to optimise the operations of the underground crushing circuit. The machine learning-based model gathers data from additional sensors upstream from the crushed ore bin, incorporating data on tons of ore tipped, apron feeder speeds, weightometers, and other sources. “The model then analyses the data to predict the level of crushed ore in the bin, and uses that information to control the flow of ore to the crusher, keeping the ore moving at an optimal level of productivity and preventing the bins from overfilling,” explains Richard Lee, Data & AI Principal Consultant at Insight.
By leveraging data science to predict the level of ore in the bin, the solution is mitigating the effects of sensor failure, and thus reducing downtime. “By implementing a soft sensor that uses a machine learning model, we can constantly predict the level of crushed ore in the bin with about 85 percent accuracy,” says Slater. “That allows us to maintain full production for at least four hours if a hard sensor fails. That’s a tremendous improvement,” he adds.
The new solution has streamlined operations and lowered costs. All in all, Newcrest expects a full return on their technology investment within three months of deployment. In addition, the new predictive maintenance process is keeping production on schedule and poses fewer safety risks to workers.
Because the new solution combines the power and performance of Microsoft Azure with a microservices architecture and the flexibility and responsiveness of the Intelligent Edge, Newcrest has been able to greatly reduce the amount of time it takes to implement new solutions underground in the harsh conditions of the mine. “Azure IoT Edge has enabled Newcrest to build and deploy new machine learning models to the edge in a matter of days instead of months,” says Lee.
The intelligent edge will power future innovations
Newcrest plans to expand its use of the new solution beyond the Cadia Valley mines where it was initially developed, and it will eventually be implemented in all mining operations within the company that rely on that same crushing process.
Newcrest has also developed an open data science platform that leverages Azure Industrial IoT and Big Data Platform. Ingesting data feeds from approximately 100,000 sensors monitoring machinery across Newcrest’s network of international mines, the new platform is enabling the company to pursue a variety of new data science initiatives, both internally and with partners.
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