REAL TIME OPTIMIZATION OF ROCK CRUSHERS
Licentiatavhandling, 2008
Cone crushers are used in the mineral, mining, and aggregate industry for fragmentation of rock materials. Cone crusher control systems are widely used for wear compensation and machine protection. These systems ordinarily focus on the crusher and not the crushed products.
The eccentric speed in a cone crusher determines the number of times a material is compressed and thus the particle size distribution of the product. The speed of the crusher is usually fixed since speed changes are only possible by shifting pulleys, a process which is quite time-consuming. By applying a frequency converter to the crusher motor power supply, it is possible to continuously adjust the eccentric speed. The cost for frequency converters has decreased significantly over the last decade.
By applying mass-flow sensors to the process, the crusher can be run optimally to yield the most sellable products in a given moment. The manufactured product type varies with season, e.g., materials for road maintenance in the winter or asphalt during the summer. Existing systems normally only protect the machine. The sensors can be mass-flow meters, e.g., conveyor-belt scales. To analyze data from the process and calculate the appropriate value for the Closed Side Setting (CSS) and eccentric speed, algorithms have been developed. The algorithms are loaded into a computer that can communicate with sensors and crushers.
The developed algorithms are tested and evolved at real aggregate crushing plants. Crushing stage performance increased 3.5% compared to a fixed CSS when the algorithm was implemented on top of the existing control system. The algorithm automatically compensates for changes in the feed material and also decreases the need for calibration of the CSS. The crushing stage where the speed algorithm was tested increased its performance (yield of required products) by 4%. As a bonus, the lifetime of the mantles increased 27% on the evaluated crusher.
crushing plant
cone crusher
real time optimization
rock crusher