ArcelorMittal Mines Canada GP (AMEM) is an integrated pit-to-port business that mines, processes, and delivers iron ore products to the international steel market.
With activities in both the mining and primary processing sectors, the company has impressive facilities in Québec, on the north shore of the Gulf of St Lawrence.
It’s been a process information (PI) system user for over 20 years, and in those years the company has gone from working hard to working smart in a big way.
Better data, better efficiency
In 2010, with over ten years of experience with the PI system under its belt, ArcelorMittal invested in an expansion project at the mines with the goal of increasing annual production from 16 to 24 million tonnes.
“We had to push more ore through with no additional capital investment,” AMEM director of innovation and technology Michel Plourde recalled.
“All we could do was apply a bit of smarts to what we had and see what we could do.”
Centralised data = profit
The answer was creating an integrated remote operations centre (IROC) and bringing the full power of the PI system to bear.
By bringing all their data into one system, rolling out OSIsoft’s asset framework (AF), and using PI Vision to make easy-to-understand data accessible to everyone, a new collaborative mindset emerged, where employees of all levels were excited and engaged with the data.
“We met our targets in 2015, translating to about $120 million of additional revenue,” Plourde said.
Since then, AMEM has had many more improvements in the works.
You can only improve what you can measure
Once the team solved the bottleneck at the port, it was ready to chase bottlenecks all along the value chain.
What it needed, according to Plourde, was “to bring in the tools to provide us real time analytics.”
Using PI Vision, the company created visualisation portals for management, analysts, foremen, and truck operators to offer users “a single source of truth” for real-time data.
Foremen began using data to determine whether they had the correct number of mining trucks in relation to the shovels in operation.
Viewing real-time operational data in PI Vision, the company could confidently deploy more trucks when necessary, or park trucks when they weren’t needed to save on maintenance and fuel costs.
Mining truck data was fed into a system that analysed “hot spots” in the roads, so road crews could be deployed where they were needed most on any given day, depending on where mining activity was focused.
Image-analysis programs began to catch irregularities in individual truckloads, allowing those loads to be intercepted before large rocks could jam the machinery.
Data-driven strategy creates serious results
All this work paid off, with a significant increase in daily hauled tonnage due to increased truck productivity, resulting in increased profits; a 300 per cent reduction in concentrator slowdown due to low feed at the crushers; and a 5 per cent increase in conformity to the mine plan, saving time, money, and resources across the board.
With much more predictable results, ArcelorMittal Mines Canada was able to identify and solve bottlenecks in real-time, making its data-driven business plan the key to a successful future.