The process of grinding is complex with many factors affecting the result. As the composition of the ore fed to the concentrator varies, implying changes in grindability, the optimal operation conditions for a pebble mill will also vary. In an attempt to increase the understanding of charge dynamics, a series of statistically planned experiments were done in a pilot-scale pebble mill with differing charge types. This pebble mill is equipped with an in-mill sensor, which measures the deflection of a single lifter as it passes through the mill charge. The experimental setup was a factorial design with two factors; two levels of magnetite pebbles content and three different size distributions. The experiments show that there is an advantage to keep the magnetite pebbles proportion as high as possible. This will increase the power consumption and maximum deflection of the lifters, but at the same time increase the production of <45 μm material, the grindability and the pebbles consumption. A pebble size fraction 10–35 mm improves the grindability the most and the amount of <45 μm material. It is strongly suggested that the 10–35 mm and 100% magnetite pebbles fraction should be tested in a larger scale pebble mill to confirm these findings.

Grinding is a process of reducing the particle size distribution of an extracted ore commonly performed in tumbling mills. The process is complex with many factors affecting the result, predominately the ores physical and chemical properties. The ore feed to a concentrator varies and optimisation is important, since grinding has high energy consumption and therefore is an expensive process. In an attempt to increase the knowledge of pebble mill grinding, experiments were performed with a pilot-scale mill at the LKAB R&D facilities at Malmberget. The purposes of the experiments were to investigate how the mill reacts to changes in the system and to find out how the grinding ability is affected by the changes. The first set of experiments concentrated on different operational settings, varying the filling degree, the volume-% solids and the percentage of critical speed of the mill. In the second set of experiments, different pebbles mixtures with varying magnetite content and different size fractions were tested. An interesting response variable (result) is the product size for the different operational conditions, since higher amount of fine material < 45 μm can be seen as a probable increase of production rate. The environment inside a mill is too harsh for direct measurements and there is a lack of knowledge of the events occurring inside the mill. Information on the events in the charge can be achieved by the use of different sensors. In the experiments, a Continuous Charge Measurement (CCM) system by Metso Minerals has been used to learn more about the charge dynamics. This system consists of a strain gauge detector embedded in one rubber lifter and measures the deflection as the lifter passes through the charge in the mill. The information received from the deflection curve is used in the evaluation of the experiments. The data from the experiments have been analysed with the aid of a statistical program. The analyses show that there will be an increased production of fines at low critical speed especially when the mill has high filling degree. This setting will also increase the power consumption but it improves the grindability of the ore even more. A higher degree of filling also give a smaller toe angle and a higher shoulder angle as expected. In addition, there is an advantage to keep the magnetite pebbles fraction as high as possible. This will increase the power consumption and maximum deflection of lifters, but at the same time increase the amount < 45 μm, the grindability and the pebbles consumption. A pebble size fraction of 10-35 mm will improve the grindability and amount < 45 μm. To further increase the understanding of charge dynamics, simulations are used to possibly illustrate the events inside the mill. However, for simulations to be reliable it demands that they are verified against process data. Previously, a series of experiments with a steel media charge were performed with the CCM system installed and this provides an opportunity to validate simulation results. The measured lifter deflection signal is used to compare with signals from two- and three-dimensional DEM simulations of the pilot-scale mill. The resulting deflection signals from simulation show that the three-dimensional case displays a better profile and the difference of toe and shoulder angles are less than in the twodimensional case. This means that the simulations are more reliable when they are run in three dimensions and they may be used to increase the understanding of the mill and its charge.

Autogenous grinding is a process of reducing the particle size distribution of an extracted ore by using the ore itself as the grinding media. It is a process that is difficult to control and there is a lack of knowledge of the events occurring inside the mill. To find out more about how the mill behaves under different processing conditions, a full factorial test was performed with iron ore in a pilot-scale pebble mill at the LKAB R&D facility in Malmberget. To complement this work, a strain gauge detector was embedded in one of the mill’s rubber lifters, the Metso Minerals continuous charge measurement (CCM) system, and was used to get more information about the charge dynamics. The data from the experiments has been analyzed. For production purposes, an increase in the number of particles smaller than 45 μm can be regarded as a probable increase in the production rate. The analysis shows that there will be an increase in fines at 65% of critical speed, especially when the mill is 45% full. This setting will also increase the power consumption, but improves the grindability of the ore even more. The deflection of the lifters is smaller for lower critical speeds. A higher degree of filling also gives a smaller toe angle and a higher shoulder angle as expected.

Grinding is the process of reducing a particle size distribution of an extracted ore and is commonly performed in a tumbling mill. It is a complex procedure and there is a lack of knowledge of what really happens inside the mill. A number of pilot-scale experiments were done at LKAB's pilot plant at Malmberget, Sweden [1]. In this particular pilot mill, a continuous charge measurement system is installed in one of the lifters and it gives a deflection signal produced by the mill charge. From this signal it is possible to detect features correlated to the settings of the mill. Large, real experiments are very difficult to control and are of course, very costly and time consuming. A 10 cm slice of the mill was simulated with discrete element method (DEM) for different mill operating conditions. From the simulations a deflection signal was extracted and validated against real data. There is a difference in the signal, mainly due to the lack of slurry in the simulations, but the behaviour when the mills operating conditions changes seems to be the same in both the simulated and the measured signals. To analyse the data from the simulation a statistical analysis on a full factorial design was done. Two levels of degree of filling of the mill, two different rotational speeds, two levels of friction and different types of particles were selected as factors. The response data are two angles: toe and shoulder angle. The toe angle is when the lifter hits the charge and the shoulder angle is when the lifter leaves the charge. The analysis show that the toe angle increases when the degree of filling is low and the rotational speed is high. It is also clear that the particle shape influences the charge behaviour. The simulated changes correspond to changes detected in pilot mill runs. This is important since it validates the DEM model. In essence, mill simulations are easily done and the changes of factor levels cause the simulated mill to react in similar manner as in real cases. One advantage is that in simulations one factor can be isolated and changed while the others are kept at constant values, which in turn creates the possibility to investigate one factor at a time. In real experiments, the factors are more dependent on each other and there is a very high disturbance from noise.