These attempts to develop a validated DEM model structure for compressive breakage in cone crushers have been continued in lab-oratory 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 investi-gated at levels significantly higher than normal for cone crushers。 It was found that the original liner design with a distinct short par-allel 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 machinedand two laboratory experiments have been conducted at eccentric speeds 10 Hz and 20 Hz with a close side setting of2。2 mm。 The eccentric throw of the crusher is fixed at4。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 experi-mental results are shown followed by the DEM simulation results。 Finally the experimental and simulation findings are discussed and conclusions are proposed。

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 eccen tric 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 mod elled 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 crushe controlled by a variable speed drive。 A National Instruments Lab-

VIEW 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 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。