Validation and Simulation of Optimised Compressive Crushing

Validation and Simulation of Optimised Compressive Crushing Ores and aggregates are used in countless applications all around in the society. The frequent and escalating usage of these natural resources requires efforts aimed at not only making do with what is available, but also undertaking actions to improve the production yield and the processes involved. This strive to improve the performance and efficiency of existing crushers and crushing processes has brought about research concerning optimisation of crushing plants as well as theoretical optimisation of crushing. Previous work shows that different applications and different materials require different compressive crushing in order to accomplish a theoretically optimal size distribution. These optimisation results, obtained by genetic evolutionary algorithms and evaluated with so called fitness functions, convey the theoretically optimal number of compressions and their corresponding compression ratios. Presented in this paper are thus some laboratory crushing tests, which are conducted in order to validate a set of previously optimised crushing sequences. More specifically, interparticle crushing sequences are carried out in a Piston-die testing equipment, where samples are sieved between each compression. The subsequent analysis of these results shows good correlation with model predictions, which combined with a comparative study between presented optimisation results and measured data from a real crusher indicate a significant potential for improvement. However, before redesigning crushers according to what is theoretically optimal, the resulting impact as well as the effects on the outcome of a crushing plant must be examined. Crushing plant simulations using the software PlantDesigner« are consequently performed. Models of existing crushers are altered, resulting in changed particle size distributions from some of the crushers in a crushing plant. Thence, it is concluded that significant gains can be made in terms of capacity, process yield and energy consumption.