Optimized Compressive Crushing with respect to Energy Consumption, Pressure and Multiple Product Yield
Paper i proceeding, 2009
The mining and aggregates industries are essential for the development and continuous prosperity of today’s societies. However, in spite of the maturities of these industries, real-time optimized control of crusher settings is still rarely applied, and crusher design is still solely based on long term experience and simplistic design criteria. In an age of growing eco-efficiency, focus and effort must be directed towards improving the performance and efficiency of existing crushers.
Finding the best way in going about solving a complex problem is far from always being simple and straight-forward. In situations when the solutions become of such high complexity that simple mind-work cannot handle the solution, computer-assisted evaluation, i.e. optimization, is needed.
In the present study, theoretical energy optimizations, as well as pressure minimizations, and yield optimizations with multiple target products are performed. Obtained results in terms of crushing sequences, consisting of different number of compressions and compression ratios, indicate how different rock materials should be crushed to minimize the energy consumption, while maximizing the product yield, or to decrease the wear in crushers through pressure minimization.
The presented optimizations are performed using genetic evolutionary algorithms (GEA or GA). Breakage models, calibrated by laboratory scale breakage characterization tests, are relied upon for the predictions of particle size distributions, which in turns enable the computerized optimization to find the theoretically optimal crushing sequence under the given circumstances. Studies have shown that optimization results are directly dependent on the formulation of the fitness functions used in the GA evaluation process (Lee and Evertsson, 2008 and Hulthén et al., 2006). The aim of the present study is therefore to further the knowledge of optimal fragmentation through different formulations of GA fitness functions.