Automation of LHDs and their increasing use in mines make it critical to understand their performance in actual mining environments. Cycle times of semi-autonomous and manual LHDs were compared to determine their productivity differences. Manual LHDs had shorter cycle times in 57% of the areas, while the semi-autonomous were faster in 43% of the areas. Cycle time distributions were evaluated, and a log-logistic distribution was proposed to simulate the total cycle time, a lognormal distribution to simulate the loading duration, a logistic distribution to simulate the dumping duration, and a small extreme-value distribution to simulate the speed of semi-autonomous LHDs.

Mining, like other industries, has progressed through a series of industrial revolutions, transitioning from disconnected, manually operated mines to operations dominated by safe, sustainable, semi-automated, and automated operations, driven by technological advancements such as digitalisation and automation. These changes have resulted in enhanced safety measures, cost reductions, and increased efciency, while simultaneously altering the nature of mining work. This paper presents a study to assess the impacts, challenges, and opportunities of automation and digitalisation in the mining industry from an end-user’s perspective. The study is based on the overall combined results from two previous studies, one surveying the opinions of LHD operators and the other the opinions of mining production workers, extracted through two workshops. The fndings indicate digitalisation and automation are predominantly perceived positively, but there are some negative attitudes. End-users have diverse opinions about the impact of digitalisation and automation on their work and the skill sets that will be required in the future, but they agree computer skills and understanding of the mining processes will continue to be crucial competencies in the future. Another common opinion is that machine maintenance is the most challenging aspect of the work to automate. The results highlight an increased need for further education enabling workers to manage new technologies as they are implemented.

Many devices used in mining are not monitored by sensors or do not have their own intelligence. By retrofitting sensors and real-time data processing, machines can be integrated into an existing digital infrastructure. Within the European funded project Mine.io a Sandvik DE110 exploration drill rig is being digitized using various AI algorithms at Research and Education Mine of TU Freiberg. The aim is to increase the productivity of drilling with real-time evaluation of the current rock to be drilled and the current maintenance status of the drill rig. The drill rig is equipped with a speed sensor, pressure sensors in the hydraulics, 3-axis vibration sensor, borehole length measurement and temperature sensor. The sensors are additionally attached to the drilling rig and no structural changes on the drill rig are necessary. All data are processed real-time at a server structure outside of the mine using AI algorithms to analyze which rock (ore or host rock) is being drilled and to perform predictive maintenance. Processed Data are visualized for the operator to optimize the workflow and the productivity.

Oversize rock fragments are highly undesired in a sublevel caving (SLC) operation as they affectthe production cycle, equipment, and infrastructure. In this study, afield test was carried out inMalmberget mine to analyse the impact of oversize fragments on the production cycle and thecosts of different procedures for handling such fragments. The tests involved monitoring ofdumping oversize fragments in two orepasses, one with a grizzly and the other one withouta grizzly, using cameras. The cycle times of load-haul-dump (LHD) machines weredetermined for both orepasses. The results indicate that the grizzly increased the availabilityand productivity of the orepass despite increasing the cycle time of the LHD machines.Moreover, installation of a boulder breaker system along with the grizzly can furtherincrease the productivity and the cost of such a system will be paid offin a shorter time interms of enhanced productivity.

Rock fragmentation is vital in a sublevel caving operation. The oversize fragments are the most undesiredfragmentation category because of their challenges; as such, they require special attention. This study carried outa field test in one of the LKAB’s iron ore mines in northern Sweden to analyse the occurrence of oversizefragments. The analysis involved correlation and regression tests and was performed for different types of rockmasses. The results showed that an increase in the percentage of solid rock mass caused an increase in thepercentage of oversize fragments. The other rock types, including slightly fractured, highly fractured, and rockmass with minor and major cavities, tended to have a reduced percentage of oversize fragments. The resultsindicate that oversize fragments can be predicted using linear regression or partial least square regression modelswith R2 values of 0.78 and 0.73, respectively. 

Mining is a high-risk industry, so efficiency and safety are key priorities. Technological advancements, such as digitisation, digitalisation, and automation have made mines safer. These developments have also highlighted the need for operators with updated skills and improved education programs. This study analysed the training of semi-autonomous and manual Load Haul Dump (LHD) operators’ at LKAB’s Kiirunavaara mine, focusing on operators’ training, perspective and integration of more recent tool such as simulator training. The survey questionnaire was sent to all 120 LHD operators. 86 answers were received, giving response rate of 70%. Results showed that operators generally were satisfied with how the training was structured, organised, and delivered. However, they wanted to add more topics, including practical loading, spending time with departments of other sub-processes, etc. In addition, 36% of the operators, including 20% of those operating semi-autonomous LHDs, and 80% of those operating manual LHDs, found simulator training difficult.

Sublevel caving (SLC) is a mass mining method based upon the utilization of gravity flow of blasted ore and caved waste rock. Drilling and blasting are the initial and the major impact on primary fragmentation and later material flow characteristics. The heterogeneity of rock mass has a great impact on materials’ mechanical properties and plays a significant role in rock fragmentation by blasting. In this work, a numerical model for sublevel caving based on 3D Voronoi tessellations was developed to section rock mass into arbitrarily shaped regions for modelling the heterogeneous rock mass. A coupled finite element and discrete element method was employed to investigate both the fragmentation due to blasting and the gravity flow of ore in a heterogeneous orebody. In the coupled model, the caved waste rock was modelled with loose discrete particles while the orebody to be blasted was simulated with bonded particles. The detonation of explosives was described with a particle blast method and a finite element model was used to model the remaining orebody. The results indicated that the fragmentation at the upper part of the ring is coarse while it is fine at the lower part of the ring. The heterogeneity of rock mass induced some boulders after blasting.

Sublevel caving operation relies on the estimation of ore grade at drawpoints, as the mine management uses grade to decide whether the material at a certain ring should be loaded or abandoned. Grade is estimated in various ways, including visual estimation, density-based calculation, and sampling and assay methods. The grade estimation at the world’s two largest underground iron ore mines owned by LKAB in northern Sweden is based on the density difference between ore and waste. The calculations assume a constant swell factor, a theoretical fill of 100%, and a linear relationship between bucket weight and material grade. This study evaluated these assumptions in detail based on the loading data for 12,237 buckets and concluded that the method has some shortcomings which render the assumptions invalid. Further research is required to deal with these shortcomings to improve estimation of the material grade.

Comminution is a major contributor to the production costs in a mining operation. There-fore, process optimization in comminution can significantly improve cost efficiency. The mine-to-mill concept can be utilized to optimize the comminution chain from blasting to grinding. In order to evaluate the mill performance of the ore from a specific location of the deposit, a direct link needs to be established between the mill performance and the place of origin in the mine. Today, technology enables the accurate positioning of drilling, loading, and dumping points in the mine, making the ore flow between loading and crushing more transparent. However, the material flow from the crusher to the mill is not yet fully understood and monitored. This paper presents the development of an ore transportation model, based on the virtual silo concept, between the crusher and the mill for Boliden’s Aitik mine in northern Sweden. The proposed model helps to establish a link between in situ ore location and mill performance. Two transportation time calculations are used, one based on mass balance, and one based on momentary values. Historical data are used to test the capabilities of the model and the results are compared with the transportation time calculated from the mean capacity values, commonly used in previous studies to connect mill parameters with in situ ore location. The comparison of the results show that the mean parameter-based values can be as much as 50% lower than the transportation times, even in normal operation. In the tested times, the transportation time based on momentary values systematically underestimated the cumulated times. The developed model will also serve as a starting point to analyze the effect of geotechnical parameters, in addition to drill and blast design, on the mill performance of the blasted ore.