The mining industries today experience a range of unique challenges, as well as some they share with other industries but encounter in different ways. In a recent presentation from Charlie Emerson, he says that like so many industries, mining needs to do more with less while assuring minimal environmental impact and maximum worker safety. At the same time, mining operations struggle with declining ore quality, rising energy costs and water scarcity. The need for maximization of capital equipment usage and the staggering cost of downtime place extra demand on production efficiency, while the environmental risk is particularly high. And of course, the difficult environments at mining sites complicate the top priority – worker safety.
A major challenge in the industry is the skills shortage. As older generation mining professionals leave the industries, new workers are not filling in the gaps, but instead are choosing to work in other industries. The primary way that mining industry companies can offset this lack of available personnel is with automation and technology. There, however, other challenges arise.
Like all industries, mining is faced with the combination of opportunity and threat in Industry 4.0, IIoT, and big data. Mining operations perform inadequate processing of big data, and this is particularly problematic because mining collects so much data. The fact that mining sites may be widely separated in remote areas where connectivity is both difficult and very expensive. As a result, connecting and monitoring assets digitally is more challenging for miners.
Some industries, in order to attack the IIoT and data analysis problems, approach big data with cloud solutions. The fact is, however, a cloud networking solution may be impractical and far too costly in mining operations. That’s why a little data strategy is ready-made for mining.
A key enabling technology for creating value from little data is edge computing, whereby data produced by sensors is analyzed in the field to generate insights. This information can be automated to advise the control network to take action or can allow personnel close to the source to quickly assess issues and take appropriate action.
In the past, this type of edge processing would have required the addition of a separate industrial computing device, or an edge gateway connected to a server, to process the data. In either case, the solution would have required integration with the existing controller and manufacturing network. This would have been a cumbersome solution due to the complexity of setting up and programming in two different environments, as well as synchronization requirements, lag/latency issues, cybersecurity concerns, and other factors.
But today, advances in processor technology enable edge controllers to perform two functions within one low energy use and compact form factor. The first function is real-time deterministic control using IEC 61131-3 languages, exactly like a traditional programmable automation controller (PAC). The second function is edge processing in a Linux environment using advanced programming and scripting techniques. Integration effort, cost, and complexity are reduced because both functions are performed in one device.
Each part of the controller is connected virtually via OPC UA, allowing the two functions to operate seamlessly yet also separately such that real-time control tasks and performance are not affected by the edge applications.
PAC-based control is familiar to anyone working in the industrial automation arena, but edge processing contained in the same device is a new concept for many. In traditional implementations, a PAC would simply collect data and then forward it to a host system for processing. This host system would typically be PC-based, and it would often be located some distance from the edge, perhaps even in a data center or the cloud, raising a number of potential issues.
Processing data at the edge, instead of remotely, addresses these issues by providing local:
Edge controllers can be used to provide these capabilities immediately at the edge in all mining commodities.
Have you implemented a little data strategy in your application?
Learn more about the use of edge control in mining applications in this blog post, how edge computing can provide new value in mining & metals applications in this white paper, and on the industrial edge computing and control solutions section on the Emerson web site.