Optimization of Compressive Crushing
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.
Virtual Development Laboratory, Hörsalsvägen 7A, Göteborg
Opponent: Professor Hakaan Benzer, Hacettepe Üniversitesi, Ankara, Turkiet