IT in Manufacturing


Real-time data increases metal recovery at Peñasquito mine

November 2019 IT in Manufacturing

Newmont Goldcorp, the world’s largest gold producer, has embarked on a digital transformation journey to optimise its portfolio of high-quality mining assets, reinvest in its people and technology, and drive increasing margins and returns on investment. To this end, the company is exploring a wide variety of digital technologies, including autonomous drilling, drones, mixed and augmented reality, machine learning, and data analytics and visualisation.

At a recent OSIsoft User Conference in California, ARC Advisory Group had an opportunity to learn about a related project at Newmont Goldcorp’s flagship Peñasquito gold, silver, zinc, and lead mine in Mexico. According to Derek Shuen, superintendent, electrical, instrumentation, process control & energy management at Newmont Goldcorp, the company had been using the PI System at its flagship Peñasquito mine since 2012 to integrate and historise data sources across the mine site, but was not getting the most value from the data.

In 2017, as part of Newmont Goldcorp’s larger “20/20/20” five-year initiative to improve business performance, the corporate IT group hosted a joint workshop in conjunction with OSIsoft. The workshop participants wanted to focus on an area that would be relatively easy to achieve, did not require a capital investment, and had the potential for good results. While several other options were discussed, the team decided that enhanced metal recovery stood out as the best opportunity for quantifiable improvement that could be achieved in a relatively short time frame.

Feed variations require prompt operator response to maximise metal recovery

The flotation circuits at open pit mining operations such as Peñasquito are highly susceptible to feed variations. To optimise metals recovery, operators have to manually adjust up to eight different reagents. The operator’s ability to react to feed variations will often largely determine recovery performance.

Previously, the mine had seen its metal recoveries dip for no apparent reason. These types of losses can extend for several hours if the operator is not vigilant or does not have the right data.

Prior to this pilot project, to establish baseline performance targets for the operators, Newmont Goldcorp’s Technical Services had used regression analysis on daily, weekly and monthly historical data to correlate and establish baseline targets for economic recovery of the various precious (gold and silver) and base (zinc and lead) from the feed grades. Since Technical Services only updated these equations every two years or so, the targets rarely varied, regardless of the nature of the ore feeds.

For the flotation cell operators, the recovery target was typically pegged at 70 percent and rarely adjusted. Since the established targets were based on past historical data, rather than current operations, they were not really meaningful for the operators who thus tended to operate the cell in a largely ‘open loop’ manner. This resulted in inconsistent operating practices between shifts and individual operators and the unexplainable dips in extraction performance, resulting in recovery losses.

Developing more meaningful recovery targets

To develop more meaningful recovery targets for the flotation cell operators, the team incorporated the equations previously developed by the Technical Services group into PI Performance Calculations, which generate dynamic baseline recovery targets based on real-time data. On-stream analyser measurements taken at the head and tail of the Sulfide Plant flotation circuit are correlated in the PI System to provide operators with real-time performance trend feedback. In effect, this became what Shuen referred to as “a dynamic simulator” for recovery performance. By operating closer to these targets, the operators would be able to enhance recovery performance. Of course, the operators first had to be trained to understand and make best use of these new data to respond to ore-related and other recovery dips.

PI Vision dashboards were placed on the plant operating floor and in the control room, providing field and control room operators alike with the needed access to real-time performance data. Operators now rely on the trends from the dynamic simulator, which, according to Shuen, serve as KPIs, to guide them so that they can make decisions based on where they should be performing.

Improving metal recovery

Along with other factors, being able to visualise real-time recovery rates resulted in tangible improvements in metal recoveries at Peñasquito’s flotation circuit. Now, recoveries meet and exceed the calculated predictions. Once operators were properly trained and understood how to use the data, the company achieved notable performance improvements in economic metals extraction.

Benefits of using the dynamic simulator

According to Shuen, in the first six months of implementation, the mine saw a four percent improvement in zinc recovery alone, not accounting for lead improvements over six months. Over 12 months, the company saw a seven percent overall improvement in zinc recovery. These recovery improvements equated to an additional 4,5 days of production per month. Improved equipment reliability also contributed to improvements, including:

• Improved stability of operator performance.

• Reduction in recovery variations.

• Greater accountability of operators.

• Overall improvement in metal recoveries for lead, zinc, gold, and silver which equates to huge bottom line benefits.

As we’ve seen, by making better use of its data, Newmont Goldcorp achieved tangible operational and business improvements without requiring additional capital investment. According to Shuen, the dynamic simulator’s ability to show operators where they should be at any time, has driven a major cultural change at the mine. “We went from a tool that was largely ignored, to one that operators could not live without,” he concluded.

For more information contact Paul Miller, ARC Advisory Group, +1 781 471 1141, pmiller@arcweb.com, www.arcweb.com




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