Real-time Optimization of Crushing Processes using EVOP

Övrigt konferensbidrag, 2011

Cone crushers are used in the mineral, mining, and aggregate industry. Systems used for controlling the Closed Side Setting (CSS) on cone crushers are widely used to compensate for wear of the manganese crushing liners and to protect the machines from overloads. With a frequency converter also the eccentric speed in a cone crusher can be adjusted in real-time in addition to the CSS. The eccentric speed affects the dynamic interaction between the rock material and the crusher liners. Real-time feedback data on the sellable product streams can be obtained by applying mass-flow sensors to the process. The adjustment of these two online parameters in real time can result in an increased potential for production yield; however, a nontrivial optimization problem with a large solution space also arises. As the feed material also varies, the optimal setting for the parameters varies in time. Herein, we report the development of a monitoring and control system. The different product yields from the crushing plant were monitored by mass-flow meters and continuously evaluated by a fitness function. Since the process varies continuously, due to the wear of crushers and screens and feed-material variations, the performance landscape is also continuously varying. Therefore, an Evolutionary Operation (EVOP) approach was adopted, wherein the variations are instead used to continuously find an operating point closest to the optimal. The developed algorithm was tested and evolved at a crushing plant for aggregates that produces around 400 kton aggregates per year. The result is an algorithm that can determine the position and direction of a dynamic speed control to continuously improve the process-operation point. The magnitude of the improvement potential compared to a fixed-speed operation is from 5 to 20%.