Enhanced productivity through data mining

We are producing data like never before and machinery is now equipped with sensors that collect data about performance and environment. Efficiently utilising data for predictive maintenance is an example of how digitalization optimises performance and cost effectiveness. 


“I have absolutely no doubt that the growing application of digital solutions is the right answer to many of the challenges facing the mining industry. Advanced digital technologies can potentially disrupt conventional mining operations in a positive manner. It can, for instance, help avoid unscheduled downtime and help achieve greater throughput or better and faster recovery, or both. This is the productivity enhancement the miners are waiting for,” says Manfred Schaffer, Group Executive Vice President at FLSmidth.

Digital data, however, is of little help to plant management if the organisation does not have the capability to analyse and act upon it.

“In the last decade, data analytics has become increasingly important in order to optimise processes. Advances in connectivity, software usability and capacity to store large amounts of data have created a range of potential applications for digitalization, all driving productivity,” says Skage Hem, Vice President of R&D Mining in FLSmidth.

Hem stresses predictive maintenance as an example. When implemented, it enables miners to predict and manage operational risk in a cost-effective way. This requires a digital setup with the capability to collect and analyse data. The difficulty is then to interpret these findings into an actionable form – enabling the organisation to create and prioritise maintenance tasks in order to minimise risk and increase uptime.

“An example of one of these productivity potentials lies in the interplay between the quality variation of the ore and the wear state of the equipment. By understanding how these parameters tie in to process performance, energy consumption and wear rates, it is possible to optimise all or some of these variables. Once data is available, it opens up for different types of maintenance schemes and operational strategies. Combining these with selective mining, stockpile management and sorting of ore we will realize significant increases in productivity," says Hem.

Digitalization improves automation Sensors in equipment detect rates of wear and tear, and data from these sensors translate into information that enables predictive maintenance or repairs. By allowing operators to schedule maintenance, modifications are undertaken while production is low rather than having to shut down unexpectedly in peak periods – helping miners save time and uphold effective management resources and money.

Automation has already made the process more efficient. By utilising the options within digitalization, it is now possible to enhance productivity and take automation to the next level: “Automation itself has improved many work processes in the mining industry. By collecting and analysing data, we can optimise the automation processes. However, it is when we combine data with a spark of human experience and creativity, that automation can realise its fullest potential. That is the big picture we are working towards,” says Hem.