Automation is changing more than just mining machines

The mining industry is investing heavily in digitalisation and automation to improve safety, productivity and sustainability. However, the future of mining is about much more than autonomous machines. Research at Luleå University of Technology shows that the interaction between technology, operational data and human competence is key to making underground mining safer, smarter and more efficient.

“Automation changes not only how work is performed, but also how decisions are made and which skills are required,” says Muhammad Tariq, PhD candidate in Mining and Rock Engineering at Luleå University of Technology.

Different machines, different results

In his thesis Digitalisation and automation perspective of LHD operation, Muhammad Tariq analysed around 1.4 million loading cycles from LKAB's Kiruna mine to compare manual and semi-autonomous load-haul-dump (LHD) machines.

The results show that neither machine type consistently outperformed the other. Manual LHDs carried slightly larger payloads on average, while semi-autonomous machines achieved shorter cycle times in some parts of the mine. Performance varied considerably between mining areas.

“We found that neither machine type consistently outperformed the other. This highlights the importance of understanding the operational conditions that influence performance,” says Muhammad Tariq.

The findings suggest that productivity cannot be explained by the level of automation alone. Geological conditions, ore characteristics and operational practices all play an important role in determining performance.

Better data leads to better decisions

The research also focuses on how operational data can improve iron ore grade estimation. In many underground mines, the bucket weight recorded by LHD machines is used to estimate the iron (Fe) grade of the extracted ore.

The thesis shows that bucket weight has the greatest influence on these calculations, but that the results are also affected by factors such as ore fragmentation, bucket fill and machine type.

“Small variations in loading conditions can have a greater impact on ore grade estimation than many people realise,” says Muhammad Tariq.

As part of the research, he has proposed a model that incorporates these operational variables into grade estimation. The improved method provides more accurate estimates of ore quality, helping mining companies make better decisions about which mining rings to extract. This can improve resource recovery, increase profitability and reduce unnecessary work, contributing to both safer operations and a lower carbon footprint.

New skills for the future workforce

Another part of the research examines how digitalisation is changing competence requirements in the mining industry. Through surveys and studies involving operators and production personnel, the research explored how automation is perceived and which skills will become increasingly important.

The results show that digital and technical competence will play a growing role as mines become more automated. At the same time, a solid understanding of the mining process itself remains essential.

“Computer skills and technical competence are becoming increasingly important, but knowledge of the mining process will continue to be crucial,” says Muhammad Tariq.

Towards smarter and more sustainable mining

The thesis demonstrates that digitalisation and automation involve much more than introducing new technology. Improving underground mining requires a combination of better operational data, more accurate decision-making and a workforce equipped with the skills needed to operate in increasingly digital environments.

“Automation is not only about introducing new technology. It is about how people, processes and machines work together,” says Muhammad Tariq.

The research provides practical solutions that can improve productivity, enhance safety and support the transition towards smarter and more sustainable underground mining.