Real-Time Optimization of Cone Crushers
Doctoral thesis, 2010
Cone crushers are used in the mineral, mining, and aggregate industry for fragmentation and production of rock materials. Cone crusher control systems are widely used for machine protection, wear compensation and, to some extent, increasing production. These systems ordinarily focus on the crusher and not the yield of production process.
In this thesis real-time optimization is explored to the control of eccentric speed and on-line CSS adjustment based on information from the process. The objective is to develop theories, models, software and hardware that enable real-time optimization of a single crushing and screening stage. The main hypothesis is that fixed parameters can never be optimal over time because many things in the process vary continuously.
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 change by changing pulleys is a labor intensive activity. 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, e.g. conveyor-belt scales, the crusher result can be monitored and the result can be fed back to an operator or a computer. To analyze data from the process and automatically calculate the appropriate value for the Closed Side Setting (CSS) and eccentric speed, algorithms have been developed. The goal for the algorithms is to maximize the product yield in a given moment. The algorithms are loaded into computer systems that can communicate with sensors and crushers.
The developed algorithms are tested and evolved at full-scale aggregate crushing plants. Crushing stage performance increased 3.5% in terms of production yield compared to a fixed CSS when the algorithm was implemented in addition to 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 stages where the speed algorithm were tested increased their performance by between 4.2% and 6.9% compared to a good fixed speed. In real life however, the performance was increased by almost 20% since an inappropriate speed was selected during installation. As a bonus, on one of the test plants for the dynamic speed, the lifetime of the manganese wear parts increased 27% on the evaluated crusher, as a consequence of changed crusher dynamics.
In conclusion, real-time optimization has been demonstrated to be feasible and increases the production yield with significantly numbers and should thus be of commercial interest to the industry.