At a recent business lunch, Dr Florian Beil of international technology-based solutions provider Siemens stated that an enormous amount of data is now constantly being generated in the planning, production, operation and monitoring of a variety of industrial devices.
“A decade ago, it would have taken 1000 years to produce five billion gigabytes of data. Today, the same volume is generated in less than 10 minutes. This phenomenon we call big data,” Beil said.
The German-based physicist and computer scientist posited that through powerful mathematics and clever programming, this big data could be turned into meaningful information in the form of “smart data tools” that could help industry overcome a range of challenges.
What is smart data?
According to Beil, Australia is “on the cusp of an explosion in this area”. Quarry machinery is already starting to incorporate technology that provides detailed data to operators to help them increase productivity and efficiency; an example of this is Komatsu’s KOMTRAX system and its accompanying smartphone app, which allows customers to monitor and manage their fleets remotely. However, while Beil acknowledged that telematics and remote control are on the rise in the mining and quarrying sectors, he emphasised that there was potential for much more.
“Although measuring data remotely and displaying it might be considered an initial phase of data applications, smart data is more,” Beil explained to Quarry. “In a smart data application you would apply intelligent algorithms to extract information from the data that would ultimately be used to improve productivity and/or resource efficiency.
“For example, you could measure data from a critical asset (eg vibration spectrums of a crusher mill) and apply algorithms to this data in order to predict when the asset would fall into a critical condition. This would allow the operator to proactively bring the asset back into normal operation instead of letting it fail, which would have caused downtime and associated costs.
“Smart data is about the combination of data collection and intelligent algorithms for the benefit of improved operations performance.”
Applications in the minerals and extractive industries
Beil said the minerals and extractive industries could benefit from a number of smart data applications that could increase asset availability and processing quality.
He provided one example as being the monitoring of the mining process and application of algorithms to optimise this. “This would involve identifying online if the material coming from a crusher fulfils the requirements for the next process steps, and adapting the speed of a crusher mill if, say, the quality of crushed material is degrading,” he explained.
In addition to the predictive maintenance algorithms and remote monitoring of critical assets explained in the previous example, Beil stated that other typical smart data applications for the minerals and extractive industries included intelligent fleet management and remote access information about machine status and proposed optimisation actions.
Importance in the operational phase
Beil added that smart data is particularly relevant now as several major Australian projects begin to shift from the build and construct phase to the operational phase.
“Smart data is relevant to both the build and operational phases of projects but it is especially relevant to operational phases because this is where the bulk of the costs come in and it’s also where we generate large amounts of data,” he explained.
“Some of our major projects – such as mine sites, power stations or desalination plants – operate for between 30 to even 100 years. Reducing downtime, improving energy and taking other resource efficiency measures during the operational phase can ultimately amount to hundreds of millions of dollars’ worth of cost savings, so it’s important to use whatever tools you have at your disposal to operate as efficiently as possible.
“The amount of data now available during this phase presents a tremendous opportunity to do something with that data to improve productivity. That’s where turning big data into smart data really makes a difference.”
How to get started
Beil recommended that businesses looking to adopt smart data tools should first identify their critical production processes and assets before seeking advice from companies such as Siemens on how they could improve these.
“As potential applications of data analytics and smart data are so numerous, it is hard to give companies ‘one size fits all’ advice on what a concrete initial application should be,” he explained. “Instead I would propose to identify the most important improvement areas and then together with partners like Siemens, design and deploy an application targeted to this initial area of interest. Once customers become familiar with applying smart data, they should extend the application space and successively add additional use cases.
“We stand on the precipice of a brave new world, with smart data being an immensely powerful tool, set to improve the way society – in all its forms – operates,” Beil concluded.
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