What is the role of measurement in the mining industry?
To work efficiently, processing plants need to constantly track several process variables—flow, temperature, level, pressure, pH, weight and so on. They also need to keep measurements stabilized in the desired ranges, so that processes run correctly.
Having the right tools and capabilities to detect instrument failures can help miners reduce downtime, improve maintenance and speed troubleshooting processes. In turn, these benefits support effective, fact-based decision making.
However, all of this relies on assessing operational efficiencies in innovative ways, such as real-time equipment health monitoring, using effectiveness key performance indicators (KPIs) based on available information and methodology for predictive maintenance. The ability to integrate, measure and analyze the operational data that miners collect is critical in order to improve productivity and drive down cost.
What are the biggest challenges when managing operational data?
In my Accenture project work, I frequently see mining companies hindered by poor quality of data and ineffective analytical tools to evaluate risk.
On the one hand, miners are starting to install sensors and image processing-based measuring devices in order to add new data and calculations to every process. This makes it possible to monitor various indicators: on-line noise, vibration, humidity, chemical elements concentration, ore-size distribution, scanners for 3D mapping and more.
On the other, this new scenario is also a “double-edged sword” because any uncertainty about the data can be a significant issue. Defects or anomalies in instruments, for example, can cause measurements to be unreliable. If the collected data is not accurate, it will introduce errors into downstream calculations and make process control ineffective.
What can mining companies do to face these challenges quickly and effectively?
Inaccurate data leads to impaired decision making and can cause miners to have less confidence in their information systems. The answer lies in using today’s digital technologies, including smart instruments, which provide automatic diagnostics of field devices in order to understand their condition and reduce unplanned maintenance.
Many mining companies are already realizing quick wins using these tools in terms of higher productivity, lower operating costs, better working conditions and effective machine utilization.
What are the main differences between digital instruments and conventional instrumentation, and what is the contribution from Accenture in this field?
In our recent analysis, Data integrity: Enabling effective decisions in mining operations, we describe in detail how smart instrumentation differs from the traditional approach in three aspects. Specifically, smart instruments:
Are not limited to measuring and transmitting the value of only one process variable, such as temperature. Instead, they can diagnose their own internal health, detecting failures in sensors, electronic boards, communications and, in some cases, the process they are measuring.
Can communicate bi-directionally, sending and receiving data over the network.
Can store valuable data such as equipment type, supplier and model; engineering characteristics; date of calibration; and other useful information.
While conventional analog instruments only track the process variable, smart instruments–based on digital technology–also provide complementary digital and diagnostic information.
For digital instruments, the detailed self-diagnoses can be integrated into Accenture’s solution. For conventional instrumentation, we have developed a library of data integrity algorithms for diagnosis. The two instrumentation classes—digital and conventional—are part of our overall data integrity and asset management solution.
In your experience, what are the key success factors to implement a strong data integrity strategy and improve mining operations?
First and foremost, miners need to adapt their processes and operations to take advantage of the new digital technologies that detect and integrate information for improved data accuracy. This goes hand in hand with broader capabilities for transformation: a solid business case, alignment of operational technology and information technology, an affinity for technology change, the ability to swiftly reskill the workforce to effectively derive insights from the digital technologies, and agile deployment of industrial security solutions.