Other Initiatives to Extract Value from Data
In 2016, the company also decided to improve their gold recovery operations by instituting new methods of identifying and reducing operational hidden losses. To achieve this, they built software templates that were used to capture and segment operational events, to identify when they were running within target ranges versus when they had downtime, maintenance, or production difficulties of some type.
By segregating and classifying these events, they were able to further drill down and assess their operating and energy costs during both normal production periods and during downtime/trouble modes. As a result, they were able to identify significant hidden losses, which were not easily identifiable without consolidated time-series data and the online analytical tools that enabled these valuable discoveries.
This new standardization led to other unexpected benefits, such as reducing the burden on the GoldMineCo IT staff. Because of effective system training, employees were able to analyze and visualize the data themselves.
GoldMineCo’s Use of the Digital Plant Template for Metallurgy Analysis
GoldMineCo's innovative use of the digital plant template assisted them greatly with improving operational efficiencies, that is, improved equipment availability and utilization, increased tonnages, and reduced dilution. As described in Chapter 4, the digital plant template helps reduce the production variance between scheduled production targets and actual production levels, while reducing operating costs. Consumables are aggregated based on critical operational modes (running OK, trouble, idle, down, and maintenance). Real-time insights are obtained from both EIDI system analytics, cloud-based analytics, such as Microsoft's Power BI, or both. Knowledge workers can access this information anywhere in the corporation.
Using the PI AF capability, GoldMineCo utilized reusable process unit templates that were configured with a common set of attributes, for example, process feed rate, water consumption, electricity consumption, and so forth, using standard nomenclature that is quickly and efficiently aggregated and visualized.
GoldMineCo used the digital plant template to help transform plant data into actionable insights for metallurgy analytics. They configured the template with standard analytics and calculations to assess and analyze metrics of common interest, for example, downtime.
A top-down approach was followed whereby the stockpile and crusher, semi-autogenous grinding and ball mill, grinding thickener, carbon-in- leach, carbon-in-columns, and tailings sections were modeled as individual units, with each section modeled using the standard process unit template. The total feed, water, electricity, and reagent consumables were aggregated for each section, and main quality variables and KPI parameters were calculated and monitored. Section operating modes were designated as running OK, trouble, idle, maintenance, or down, based on the section's feed rate and electricity consumption values.
Once GoldMineCo identified impediments to efficient production, they worked on continuously improving their processes and workflows. In 2016, they developed three new capabilities that they are currently deploying:
- 1. GoldMineCo developed a integrated gold recovery software model, which identifies behavior of the ore type, crushing, grinding, cya- nidation gold recovery, and tailing management. This metallurgy analytics initiative is underway using the digital plant template.
- 2. They optimized their processes through forensic analysis, utilizing predictive analytics such as machine learning.
- 3. They completed their transition to a proactive CBM program, with real-time notifications generated when equipment performance degrades or exceeds recommended usage. A predictive maintenance program has also been started using IIoT vibration sensors.
The company has plans to further leverage their PI System data to supply real-time data to several of their technology providers in order to diagnose and solve technology and process issues. GoldMineCo also has discussed collecting data from its fleet of drones.
So, Was It Worth It?
As one GoldMineCo stated, "GoldMineCo's digitization initiative completely depends on the PI System to work. We need the data. The data is in PI." He added, "It was money well spent. We have no regrets."