Cone crusher performance evaluation using DEM simulations and laboratory experiments for model validation

Minerals Engineering xxx (2016) xxx–xxx

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Minerals Engineering journal homepage: www.elsevier.com/locate/mineng

Cone crusher performance evaluation using DEM simulations and laboratory experiments for model validation Marcus Johansson ⇑, Johannes Quist, Magnus Evertsson, Erik Hulthén Chalmers Rock Processing Systems, Department of Product and Production Development, Chalmers University of Technology, SE-41296 Göteborg, Sweden

a r t i c l e

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Article history: Received 31 May 2016 Revised 22 September 2016 Accepted 23 September 2016 Available online xxxx Keywords: Cone crusher DEM Validation Experiment Simulation Modelling

a b s t r a c t Cone crushers are commonly used for secondary and tertiary crushing stages in the aggregate and mining industry. It has previously been demonstrated that the discrete element method (DEM) can be used to simulate rock breakage in crushers using a variety of modelling techniques. In order to provide confidence in the simulation results the DEM models need to be validated against experimental data. Such validation efforts are scarcely reported in the existing literature and there are no standardized procedures defined. In this paper a laboratory cone crusher is simulated using DEM and the results are compared with laboratory experiments. The rock material is modelled using the Bonded Particle Model approach calibrated against single particle breakage experiments. Two case simulations have been performed investigating the influence of eccentric speed. The laboratory crusher is a Morgårdshammar B90 cone crusher that has been equipped with custom machined liners, variable speed drive and a National Instruments data acquisition system. The results provide novel insight regarding the stochastic flow behaviour of particles when exited by the mantle at high frequency. The estimated product size distribution matches the experimental results relatively well when evaluating the corresponding coarse region that is feasible to calculate from the DEM product discharge data. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction The cone crusher is the most common machine type for secondary and tertiary crushing of hard rock materials in the minerals processing industry. During recent years, minerals processing experts and engineers have shown an increased interest in the operation of primary and secondary crushing and potential efforts to optimize the performance and operation have followed. This interest directs focus on modelling and simulation capabilities in order to provide accurate and robust predictions. Models commonly range from relatively simple empirical analytical models to mechanistic analytical models and numerical models which for instance, utilize the discrete element method (DEM). The required quality and applicability of the different modelling approaches depends on why it is applied. If the model is used in a fast and simple steady state simulation; a fitted empirical size reduction model may be enough, at least if the prediction capability limitations are well understood and considered. In cases where, for instance, the influence on circuit performance due to a crusher liner design change is evaluated, a more advanced mechanistic

⇑ Corresponding author. E-mail address: [email protected] (M. Johansson).

model is needed. Such mechanistic models have been developed and successfully implemented by e.g. Eloranta (1995) and Evertsson (2000). The Evertsson Cone Crusher model was later adopted and implemented in a dynamic simulation platform based on Simulink, developed by Asbjörnsson, Hulthén and Evertsson (Asbjörnsson, 2015). Even though the advanced mechanistic cone crusher models are derived based on first principle equations there are some assumptions included in the modelling framework. These assumptions are, for instance, related to how particles flow and where in the crusher they are subjected to a particular type of breakage mode. Further analysis of these assumptions is one of the drivers for developing simulation models that are capable of delivering predictions where the actual machine geometry, dynamics and rock material properties are considered. Other drivers for detailed modelling of comminution machines are related to, for example, development of new machines, machine design optimization or problem solving. The discrete element method, proposed by Cundall and Strack (1979), has proven to be the most suitable modelling methodology for these purposes. Several authors have contributed to the research field of modelling compressive crushers in DEM. It should be noted that breakage is normally not considered in DEM as the most common simulation applications only involve the flow behaviour of the granular media. Hence, when modelling a compressive

http://dx.doi.org/10.1016/j.mineng.2016.09.015 0892-6875/Ó 2016 Elsevier Ltd. All rights reserved.

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crusher some kind of methodology needs to be applied in order to facilitate a useful description of the actual breakage events and how rock particles break apart. The three most common approaches used for modelling breakage in DEM are listed below:  Bonded Particle Model (BPM) - Spheres are arranged in a cluster and bonded together in each contact point using bonding beams (Potyondy and Cundall, 2004).  Particle Replacement Model (PRM) - Particles are replaced by a set of progeny fragments at the breakage event (Cleary, 2001).  Tetrahedral Element Model (TEM) - Particles are modelled using a tessellated mesh structure using voronoi grains, polyhedrons or trigons (Cundall, 1988; Potapov and Campbell, 1996). All of these three approaches have been used for modelling compressive breakage in cone crushers. Herbst and Potapov (2004) used a version of the TEM method but only displayed results from a 2D simulation of a crusher. The TEM approach was later applied in 3D by the same group for modelling a Morgårdshammar B90 laboratory crusher (Lichter et al., 2009). The PRM method has successfully been implemented for cone crusher simulations by Cleary, Sinnott and Delaney (Cleary and Sinnott, 2015; Delaney et al., 2015). The BPM method has previously been applied for modelling of breakage in cone crushers by the authors in a series of publications (Johansson et al., 2015; Quist and Evertsson, 2016, 2010; Quist et al., 2011). The BPM model is implemented on clusters of sub-particles assembled with the shape from 3D scanned rock particles. The micro properties of the BPM model were calibrated against single particle breakage experiments and the results have been compared to industrial scale experiments. The experiments were conducted on a Svedala H6000 cone crusher and the power draw and pressure signals were measured using a custom data acquisition system with a sampling rate of 500 Hz. These attempts to develop a validated DEM model structure for compressive breakage in cone crushers have been continued in laboratory scale experiments using a B90 Morgårdshammar cone crusher by Johansson and Quist (Johansson et al., 2015). In the mentioned work the eccentric speed of the mantle was investigated at levels significantly higher than normal for cone crushers. It was found that the original liner design with a distinct short parallel zone in the CSS region was possibly suitable for laboratory sample size reduction purposes however less suitable for the investigation of high speed crushing. A new liner design CAD model was developed with continuous liner surfaces and this design was evaluated using DEM. In this work new liner components have been machined and two laboratory experiments have been conducted at eccentric speeds 10 Hz and 20 Hz with a close side setting of 2.2 mm. The eccentric throw of the crusher is fixed at 4.3 mm. The corresponding case has been modelled in DEM and the main scope of this paper is to compare the laboratory result with the DEM simulation results. This comparison is both of scientific value in terms of DEM validation but also in terms of understanding the mechanics and phenomena of high speed cone crushing. The motivation for exploring cone crushing at higher speeds is to investigate if it is possible to capitalize on the increased number of single particle breakage compression events. It has previously been shown that single particle breakage is superior in terms of energy utilization when compared to, for instance, single particle impact breakage and interparticle bed breakage (Schönert, 1972). The layout and configuration of this paper follows the IMRDC structure where the methodology of the experiments and DEM

modelling is first presented. In the subsequent section the experimental results are shown followed by the DEM simulation results. Finally the experimental and simulation findings are discussed and conclusions are proposed. 2. Method In this section the methodology for both DEM simulations and laboratory experiments is presented. In order to be able to compare particle size distribution results between the simulation and experimental domains a previously developed post processing script has been refined. This methodology is presented in the end of the section. 2.1. Laboratory experiments The laboratory experiments were carried out in the Chalmers rock processing laboratory in Göteborg, Sweden. Tests were conducted using a Morgårdshammar B90 laboratory cone crusher equipped with a variable speed drive to allow control of the eccentric speed of the main shaft. The performance of the new liner design has previously been evaluated using DEM simulations (Johansson et al., 2015). The virtually tested liner design was then machined using the material Uddeholm Nimax. The liner design can be seen in Fig. 1 and the experimental setup and feeding arrangement in Fig. 2. A novel feed entrance geometry was modelled and 3D printed in order to allow for unrestricted flow into the chamber. The feed rock material was a 5.6–8 mm granite material from Kållered, Sweden. The feed material was presented to the crusher using a vibrating feeder placed above the crusher. The input feed was controlled by a potentiometer and the speed of the crusher controlled by a variable speed drive. A National Instruments LabVIEW graphical interface was used to control and sample power draw and discharge mass flow signals. The discharge mass flow was measured using a custom developed load cell balance placed under the crusher. Before each experiment a set of sequential preparation steps are performed. Firstly the crusher is started and allowed to run for 5 min crushing at 600 rpm. This is done in order for the bearing

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x [m] Fig. 1. Liner geometry design with inner mantle at nominal position for 2 mm CSS.

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Fig. 2. (Left) Image of the crusher set up and feed arrangement, (right) the crusher inlet when the top is removed.

arrangement to reach operating temperature. The crusher is then stopped for a change of feed material. The feeder is emptied and the test material is placed into the vibrating feeder. Once again the crusher is started and the feed carefully adjusted while the speed is ramped up to the maximum speed of the test series. To utilize the crusher capacity the feed mass flow is adjusted until the power consumption settles, for the tests presented in this paper the power level was aimed to be 3 kW at eccentric speed 20 Hz. When the power level has settled to steady state operation the first product material sampling is performed. The sample container is placed onto a mass flow balance, tracking the accumulated mass. From the accumulated discharge mass data the mass flow rate can be calculated by linear regression. The linearity of the mass flow gives an indication of whether or not the sampling was performed at steady state conditions. After 40 s the sample container is removed and stored for later analysis. The speed is then lowered to the new set point and the power draw is allowed to stabilize once again. At this point a new sampling procedure may be initiated and the process is repeated until all eccentric speed set points have been completed. The size distribution of the product samples has been analysed using sieve analysis following the EN933-1 standard (Standardization, 1998).

small compared with the particle fraction size. Bonds of finite stiffness are created at contact points between two particles and these bonds carry load and break when the calculated stress on the bond exceeds a strength criteria. The bonds fail under tensile or shear loads but not due to compression. The micro-properties of the meta-particles are presented in Table 1. When fraction particles are liberated and are no longer part of a cluster, the interaction with surrounding particles and geometrical elements is controlled by the Hertz-Mindlin no slip contact model. The main limitation and challenge of performing DEM simulations of comminution equipment is to balance; the number of meta-particles included in the simulation, the size of the machine section modelled, and operational time needed to be able to draw any useful conclusions. In this work a 40 degree section of the crusher is modelled in order to increase the amount of material in one feeding location with the intention of achieving a choke feeding condition. The section limitation is realised by the boundary wall where the friction parameters are set to low values. The nutational motion of the mantle is created by two sinusoidal rotations defined from a pivot point where one of the

2.2. DEM model configuration

Table 1 Micro-parameters of the BPM model.

The DEM model has been developed and simulated in EDEM 2.7 provided by DEM Solution Ltd. The rock material is based on two different 3D scanned rock shapes in the size range 6–8 mm. The scanned particles shapes are used as moulds for a packed cluster of fraction particles that are bonded with beams according to the BPM modelling approach (Potyondy and Cundall, 2004; Potyondy et al., 1996). The full procedure and methodology of the specific approach of using meta-particles is described by Quist (Quist and Evertsson, 2016). According to the framework by Potyondy and Cundall, particles are allowed to overlap given that the overlap is

Parameters

Value

Unit

Normal stiffness Shear stiffness Normal strength Shear strength Bonded disc radius Fraction nominal radius Fraction contact radius Shear modules Number of fraction particles Number of meta-particles

1670 670 36.0 24.0 1.0 1.0 1.15 557.1 25,914 64

[GN/m3] [GN/m3] [MPa] [MPa] [mm] [mm] [mm] [MPa] – –

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motions is phase shifted p/2 rad. In addition, a third counter rotational motion around the vertical axis is added in order to simulate the mantle’s rolling motion on the concave. In the real crusher the mantle is allowed to freely rotate around the main shaft axis and the interaction between the mantle and concave can be compared with a planetary gear where the rocks act as gear teeth. If this counter rotation is not included the simulated particles will experience a false horizontal force causing particles to report to the boundary wall. During the initial iterations of these simulations it was found that the number of meta-particles was not enough to achieve a satisfactory choke fed condition. One reason is due to the significantly higher eccentric speed of the mantle that excites the feed particles with a force upwards causing an unstable bed. In order to mitigate this issue a new strategy was tested where a set of large spherical particles were created on top of the meta-particles to apply a choke feeding pressure. The success of this strategy needs to be evaluated further as the interaction between the additional choke feed particles and the meta-particles creates a bias in the simulation results. 2.3. DEM post processing The particle size distribution of the surviving clusters of metaparticles has been estimated using the methodology described in Quist and Evertsson (2016). Images of the surviving bonded clusters are recorded, see Fig. 3, and the MATLAB image analysis toolbox is utilized to first apply a Gaussian filter and secondly calculate the major and minor axis lengths of all identified clusters. The particles on image boundaries are removed. From the two dimensional size of each cluster an ellipsoid volume is calculated in order to calculate an estimate of the particle mass of each cluster particle. The particle size distribution is further on calculated by sorting each cluster based on the minor length size and the estimated mass to each corresponding size class. It should be noted that the projection of the bonded clusters is smaller than if the clusters are represented by the fraction particles. This means that the size of each cluster should be compensated for by a size class specific compensation factor corresponding to approximately one radii on each side of the cluster. This size class compensation factor has not been applied so far in this work. Hence it should be noted that the estimated size distributions are slightly finer than the actual clusters surviving after crushing. Since the product size distribution is estimated from the surviving clusters all liberated fraction particles are disregarded. This means that the size of the small fraction particles limits the capability of predicting the fine region of the product size distribution. The fraction particle size distribution in itself could be added in order to calculate the total discharge distribution. However, this distribution is fixed and is not influenced by the interaction with the crusher as fraction particles are non-breakable. As a consequence of this size limitation only the corresponding coarse region

of the experimental size distribution is compared with the DEM results. The power draw is calculated from the total torque on the mantle from all particle interactions multiplied by the angular velocity. This calculation is performed as a default output in EDEM and additional validation of the accuracy should be performed in future work. 3. Experiments The particle size distributions for the two tested eccentric speed levels are presented in Fig. 4. As expected the high speed level of 20 Hz results in a finer size distribution than for 10 Hz. In cone crushing operations it is normally seen that the P80 corresponds roughly to the close side setting. In this case this rule of thumb corresponds well to the 10 Hz case where the P80 was calculated to 2.09 mm which is close to the CSS of 2.2 mm, especially considering the variance from calibrating and measuring the CSS. In the 20 Hz case a considerable amount of fine material is generated and relatively few particles larger than the CSS survive the crushing chamber. Those particles that were retained on larger sieve decks were normally relatively flaky. The measured gross power draw for the two speed levels is presented in Fig. 5. The power draw level was relatively stable during the tests with a slightly higher variability in the 20 Hz case. The feeding arrangement to the crusher is based on a vibrating feeder with a manual potentiometer for controlling the rate. This means that variance in the incoming feed mass flow rate may cause some of the variance in the power draw data. If the crusher would be fully choke fed this variance would possibly be dampened. However, during the tests the crusher was operated at near choke feeding conditions. When attempting full choke feeding the motor and frequency drive tended to overload. This issue needs to be solved for future experiments. Attempts have been made to estimate the idling power of the crusher and it is believed that it ranges from around 0.5–2 kW depending on the speed. It has proven to be practically very difficult to carefully measure the idling power due to the issue of mantle head spin. When operating the crusher with no feed material

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Size [mm] Fig. 3. Example image of bonded cluster product discharge used for the size distribution image analysis.

Fig. 4. Particle size distributions for feed and product for laboratory experiments.

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4500 10 Hz CSS 2.2mm

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the mantle begins to rotate with the main shaft. This phenomenon is normally denoted as head-spin. When operating the crusher at unusually high eccentric speeds the head-spin effect becomes an increasingly troublesome issue. Attempts have been made to limit the relative mantle rotation during idling power tests however it is unknown to what degree this limiting action affects the power draw. Due to these issues the net power is not yet calculated or presented here. It is a fair assumption to make that the idling power is relatively significant, especially in the 20 Hz case. The bearing assembly pre-loading and lubrication viscosity is not optimized for these high speeds; hence, this should be looked into for future test campaigns. A summary of the laboratory test results is presented in Table 2. It should be noted that the specific energy is expected to drop significantly if evaluating the net energy consumption. 4. Simulations A series of images from each simulation is presented in Fig. 6. The particles are displayed as the bond beams which are coloured according to the experienced normal force on the beam. In both cases the cluster size distribution becomes successively finer as particles move down the chamber. The choke level can be seen and qualitatively estimated from the image at 0.4 s into the simulation. In the 10 Hz case the fracturing of particles tends to be more efficient as particles have more time to free flow to a deeper position at which the effective compression ratio is high enough to break the particle. In the 20 Hz case particles are excited by the

Table 2 Summary of results from the laboratory experiments. Unit

P80 F80 Reducation ratio F80/P80 Mean gross power draw Massflow Specific energy

[mm] [mm] [–] [W] [kg/s] [J/kg]

Eccentric speed 10 Hz

20 Hz

2.090 7.697 3.683 1791 0.1209 14,816

1.722 7.697 4.470 2995 0.1098 27,274

5

mantle more frequently squeezing particles upwards more than in the 10 Hz case. The resulting breakage illustrated in Fig. 6, may seem finer in the 10 Hz case, however the particles in the 20 Hz case will receive more compressions because of the higher eccentric speed. The number of compressions usually increases the amount of fines in the final product. It can be observed from animations of the simulations that the particles move in a very erratic way due to the high frequency. Since there is no heat loss accounted for in the breakage events all stored elastic energy is released into kinetic energy for the fragments. This is an effect that needs to be investigated further and possibly a heat loss model needs to be incorporated into the bonded particle model. The images in Fig. 6 indicate that there is no distinct steady state condition reached in the simulations. This can be seen in Fig. 7 where the mass flow is plotted over the simulation time. In both cases the batch of feed particles was too small to achieve steady state operation. In the 20 Hz case there is a slight tendency of relatively steady operation between 0.5 and 1.5 s. The same phenomena of non-steady state condition can be seen in the power draw data, see Fig. 8. Each peak in the data corresponds to a compression event where the mantle has rolled over the material bed in the simulated section of the crusher. In the 20 Hz case there is a longer period of equal power draw level than in the 10 Hz case. The predicted power draw levels are lower than anticipated and the reason for this discrepancy with the experimental data is not yet fully understood. To some degree the difference can be explained by the idling power losses of the machine not present in the simulation, however there is still an order of magnitude difference. One explanation of the low power draw estimation may be due to the calculation method in the DEM software. The power is calculated as the product of the torque and angular velocity added to the product of the linear velocity and force. It is currently unclear if this calculation is done on each particle to geometry interaction and then summed together or if the total torque is otherwise calculated on selected geometry. In order to investigate this further, all force vectors for all particle to mantle interactions should be exported to facilitate external calculation of the actual torque on the mantle around the vertical axis. In order to further evaluate to what degree the crusher has been simulated at steady state the net power draw data from Fig. 8 has been recalculated and presented as the cumulative energy in Fig. 9. For each compression event it can be seen that each peak in Fig. 8 corresponds to a step increase in energy. As can be seen the 10 Hz case is never in a condition where each step is similar for several steps in a row. A higher tendency of linearity can be seen for the 20 Hz case. From the simulations it is possible to extract numerical datasets that enable comparison with experimental data. It is also possible to qualitatively evaluate the behaviour of the particle flow and how well it conforms to the assumptions made regarding, for instance, the dynamics of the compression event. In Fig. 10 an attempt is made to show an example of such qualitative insight drawn from simulations. In the images the trajectory stream is presented for a randomly selected cluster of particles. The clusters were selected from the surviving discharge in order to track it backwards to the feed. The colour represents the vertical velocity component and cold colours correspond to the particles falling downwards in the crusher. When the stream colour turns green or red the particle is hence subjected to a compression event interaction. In the 20 Hz case it can be seen that an additional particle was unintentionally selected which did not belong to the intended cluster. As a consequence this additional particle trajec-

Please cite this article in press as: Johansson, M., et al. Cone crusher performance evaluation using DEM simulations and laboratory experiments for model validation. Miner. Eng. (2016), http://dx.doi.org/10.1016/j.mineng.2016.09.015

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10 Hz CSS 2 mm

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Fig. 6. Series of images from DEM-simulations showing the bond cluster representations. The images are captured every 0.2 s for both 10 and 20 Hz at 2 mm CSS.

tory stream can be seen next to the main cluster stream. In analytical cone crusher models the compression event is normally approximated as a number of nominal compression events depending on the chamber design, particle size, eccentric speed,

throw and CSS. The shape is normally drawn as a repetitive zigzag pattern where the particle is squeezed and lifted at each compression after which it slides or free flows to the next compression event. The patterns of the simulated trajectories display a much

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Fig. 7. Simulated mass flow over time.

500

be due to errors in the image analyses. In some cases clusters may overlap causing the image analysis algorithm to count particles that appear wrongfully large. As there is more mass for particles in the coarse region such errors may influence the final distribution to a relatively high degree.

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more stochastic nature, especially when the cluster is still part of the feed and has not yet entered the chamber fully. There is, however, some distinct resemblance of the zigzag pattern in the 20 Hz case in the lower region of the chamber. In Fig. 11 the product size distributions for the 10 and 20 Hz cases are presented. The results are nearly identical in the finer end for the 20 Hz case and the 10 Hz case. However, in the coarse end the 10 Hz case is finer. When examining the data set and discharge images it is seen that few coarse particles survive in either of the two cases. This indicates that there is probably a relatively high variance for the coarse region of the curves. Another possible reason to the unintuitive result may

As a means of validation of the DEM model the product size distributions are compared with the experimental results in Fig. 12 for 10 Hz and in Fig. 13 for 20 Hz. As previously mentioned it is not possible to predict discharge sizes below a certain threshold established by the size of the smallest fraction particles that may form a bonded cluster discharge element. In order to evaluate the correspondence between experimental and simulated distributions only particles larger than 500 lm have hence been considered. The 10 Hz case shows a relatively poor agreement in the coarse region and slightly better in the finer region however the 20 Hz case matches remarkably well overall. As mentioned above there is possibly a relatively high variance attributed to the data points in the coarse region data hence both poor correspondence and good correspondence should be judged with caution. The mass flow for the experimental 10 Hz case was 0.1209 kg/s and for the simulation 0.11925 when accounting for the sectioning and using the maximum as approximation due to the very short simulation time. In the 20 Hz case the mass flow was 0.1098 in the experiment and 0.0702 in the simulation. It should be mentioned that the simulated mass flow is skewed by the fact that the meta-particles have an initial packing density of around 0.8. The discharge is a mix of liberated fraction particles and surviving clusters, hence it is not an easy task to adjust for the packing density error in a correct manner. In this work the poorest correspondence is found in the power draw measurements. There are fundamental differences between the data from simulations and experiments. In the simulation, power is measured as work per unit of time,

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Fig. 10. Randomly selected cluster path trajectory for a cluster in the 10 Hz case to the left and a cluster from the 20 Hz case to the right. The colour of the trajectory stream corresponds to the velocity magnitude in the vertical direction.

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Fig. 12. Comparison of experiment and simulation particle size distribution for the 10 Hz case.

which is calculated as the sum of the torque multiplied with the angular velocity and force times linear velocity. In the experiments the active power is measured. The active power measurement is an electric measure compared to the mechanical power measured in the simulations. For future work a torque sensor and a tachometer should be installed to determine the mechanical work done by the main shaft for the laboratory experiments. Apart from the measurement uncertainties the DEM model does not compensate for energy dissipating as friction or heat. Work done by Djordjevic (2010) shows that there could be a significant amount of the energy lost to friction and heat. The investigation was done in regard to HPGR crushing using thermal imaging to study surface temperature difference

in different loading conditions and feeds. The results of the study show distinct differences in energy consumption in different situations and a similar phenomenon most likely appears as in the cone crusher and should in the future be included in a more advanced DEM-simulator. Furthermore the DEM model does not include losses due to resistance from the bearings nor the belt drive. These losses depend on the load and for the used crusher the size of these losses has not been thoroughly studied, but is the subject of future work. All the above reasons will result in a lower power consumption for the simulation compared to the laboratory experiments, which is in correspondence with the result but the difference between the simulation and laboratory domain needs to be better understood.

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The most important results of this paper lie in the insights drawn from the identified discrepancies. The simulations performed have not reached steady state condition, however, the modelling approach does facilitate a successful breakage process with sequential breakage of each particle with the capability of calculating the product size distribution. In order to resolve mentioned issues new simulations should be prepared with at least 50% more meta-particles in the feed. It should also be evaluated if the fraction size distribution should be altered to reduce the number of fraction particles in each meta-particle cluster.

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Acknowledgement

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This work has been carried out within the Sustainable Production Initiative and the Production Area of Advance at Chalmers. The support is gratefully acknowledged. The authors would also like to acknowledge the support from Ellen, Walter and Lennart Hesselmans foundation for scientific research and MinFo. Discrete Element Method (DEM) simulations were conducted using EDEMÒ 2.7 particle simulation software provided by DEM Solutions. Ltd., Edinburgh, Scotland, UK.

Size [mm] Fig. 13. Comparison of experiment and simulation particle size distribution for the 20 Hz case.

6. Conclusion In this paper a laboratory cone crusher has been evaluated using laboratory experiments and DEM simulations. Two high eccentric speed levels have been investigated and the experimental and simulated data have been presented and compared. In both the DEM and experimental results a higher power draw is measured for higher speed levels. Further work is needed to calculate the net power draw in the crusher and also evaluate the energy utilization or energy efficiency if considering the energy required to produce for instance, a certain amount of 75 lm material. All calculated responses and results from the DEM simulation point towards a common conclusion; more meta-particles are need in the simulation in order to reach a steady state condition. If a steady state condition is not achieved it is not possible to extract statistically sound data. Considering this fact, the results show a relatively good correspondence in terms of product size distribution and mass flow for the 20 Hz case, however a poor correspondence in terms of power draw in both cases. The poor correspondence in power draw will be further investigated, both regarding the calculation method in the simulations and the measurement system used in the laboratory. When examining the simulation it was found that the motion of the particles may be considered as relatively erratic. This observation led to the insight that no energy dissipation in the form of heat loss is included in the breakage model. As bond beams act as loaded springs before they break, any stored energy will be released purely in the form of kinetic energy. Further work should be aimed towards investigating if this has any significant influence on the flow behaviour and if a heat loss model should be added to create a damping effect on the fragment clusters after breakage. The erratic behaviour may also be a consequence of a slightly too high time-step causing unwanted contact overlaps and hence unrestrained reaction forces.

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Please cite this article in press as: Johansson, M., et al. Cone crusher performance evaluation using DEM simulations and laboratory experiments for model validation. Miner. Eng. (2016), http://dx.doi.org/10.1016/j.mineng.2016.09.015