Optimization of Compressive Crushing
Abstract: With almost all infrastructures being dependent on the supply of crushed rock materials, minerals, and ores, it is fair to say that the foundation of modern society is literally built upon these materials. As society continues to develop and standards of living progressively increase, the subsequent growing demand for crushed rock materials, minerals, and ores will result in a need for improving the performance and efficiency of rock crushing equipment. The main hypothesis of this research is that better crushing machines can be achieved by first optimizing a given crushing process theoretically, and then designing the actual crusher. The conducted research can therefore be divided into five main stages, namely rock material characterization, modeling, optimization, evaluation, and implementation. In this thesis, the complex compressive breakage behaviors of four different rock materials (i.e. gneiss, diabase, marble, and quartzite) and two different iron ores were experimentally studied and mathematically modeled. A genetic algorithm was also applied to theoretically optimize the compressive crushing of these rock materials and ores. The obtained results indicated that optimal compressive crushing differs depending on the application and optimization objective. Different types of crushing applications, such aggregate and mining, should therefore not be operated in the same way. Similarly, crushing applications with different optimization objectives, e.g. the same type of application but different production situations, should not be run identically. Analyses also showed that existing cone crushers and crushing applications are not operating optimally. In fact, defined theoretical performance efficiencies of 30-40 % were calculated for studied aggregate applications. These numbers indicate great improvement potential despite possible mechanical and practical restraints. More specifically, comparison between existing cone crushers and theoretical crushing concepts showed that the implementation of optimization results can be more or less difficult depending on the type of crusher. For aggregate applications, the optimization results particularly suggested that rock materials are currently being over-crushed, and that the size reduction process should be separated from the process of particle shaping. In comparison, the results for mining applications indicated that a larger amount of size reduction should be performed by single particle crushing, if the overall size reduction of the process is to be maximized and the energy consumption is to be kept to a minimum. These optimization results for both aggregate and mining applications were implemented in prototypes, which were then tested in full scale experiments. The subsequent analysis of the results indicated that the performance of cone crushers can be improved in terms of product yield as well as reduction ratio. In conclusion, considering the variety of applications as well as rock materials, minerals and ores, a truly optimal performance of a crushing application must be based on an optimized crusher design as well as a continuously optimized crusher operation.
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