The future of mining lies in digitalization
Everyone involved with mining knows that the industry has had tough time in the past years. Still the number one priority remains the same as always – safety. And it’s becoming more important all the time because society in general is not accepting safety or health risks at work, no matter what the work is. Current technology can make work as safe as we choose to make it.
Experience shows that safety is also good business. Employee safety and process efficiency have an undeniable relationship. Even taking time to prevent a safety issue improves productivity, so safe processes are also efficient processes.
So, how can we improve safety? One of the things that modern operations are focusing on is removing people from hazardous situations. And one way of doing that is through the increased mechanization of mining processes. That takes different forms but one is to move people away from machines. People may still be in proximity to the machine but they are not in a hazardous area.
But there is no such thing as a free lunch. As you become more automated and people are no longer near the machines, you lose that sensory perception that people have when they can feel the vibrations and hear the tone of the engine, so you must have measured data to replace what they would have been sensing themselves had they been there.
This is where big data comes into play. But big data is more than just a tool to handle huge quantities of data. When all machines are continuously collecting data, a very large amount of processing capacity is required to process all that data into usable from.
To make the data valuable we need the industry and process expertise to interpret the collected data so that we can make the right conclusions and decide on the right actions. This expertise needs to be programmed into the data system. When large amount of data is processed, analyzed and presented in easily readable form automatically, you have big data.
How to get your payback from the big data
In order to have big data it needs to be collected and for this you need connectivity. Using industry standards or other common protocols the platform provides mechanisms to facilitate collection of data from multiple devices.
Connectivity can be a simple connection to a device, connection to multiple devices, or to systems such as programmable logic controllers, distributed control systems or production management. As you most likely have systems form a multitude of suppliers, you need to make sure that your chosen communication protocol can support all devices you have.
To benefit from the massive amount of data collected from machines and processes, the data needs to be managed, analyzed, optimized and visualized. Just looking at vast data charts doesn’t do the trick.
Data management covers the storage and management of the information received from the connectivity components. There can be multiple underlying data storage systems, all of which are managed by a single platform and transparent to the user of the platform. One solution for managing data is ABB Ability™.
The analytics and optimization includes the ability to perform simple calculations, to determine KPI’s, and very complex calculations that can be used to compare machine performance against specifications. Examples of the usage would include:
- Key Performance Indicator (KPI) Calculation
- Trending and comparison of collected values
- Equipment performance analysis
Optimization includes a complete Advanced Process Control (APC) engine that can perform complex predictive calculations, including the capability to perform soft sensor calculations as well as linear and non-linear mathematical optimizations. It also includes the capability to generate empirical models from collected data sets.
The visualization platform should provide a tool to facilitate building common, re-usable dashboards that can be used to view consolidated information, or drill down to visualize information at the device level. The visualization tool supports building dashboards for:
- Real-time monitoring of data and events
- Display of aggregated or filtered data
- Comparison of data from different devices or sites
Additionally, it must be possible to export the data to a third-party system using public interfaces.
Are you already utilizing big data in your daily operations?…
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