Real-time algorithm for cone crusher control with two variables

Cone crushers are used in the mineral, mining, and aggregate industry for fragmentation of rock materials, minerals and ores. Systems used for controlling the Closed Side Setting (CSS) on cone crushers, and thereby the size reduction, 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. Especially the number of compressions the material is exposed to is affected and also the local compression of the rock material is affected, thus the particle-size distribution of the product. Eccentric speed also affects crusher capacity. 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 including a two variable online algorithm for the selection of the setpoint for eccentric speed with respect to the current CSS. The different product yields from the crushing plant were monitored by mass-flow meters and continuously evaluated by a fitness function. A model for the outcome of the crushing stage, with the two parameters eccentric speed and CSS, was fitted mathematically to the measurement data. However, 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 400,000 tonnes aggregates per year. The algorithm was implemented in a computer that communicated with the frequency converter and retrieved data from ten mass-flow meters in the process. The operator was able to interact and supervise the system through a Human Machine Interface (HMI). 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%. (C) 2011 Elsevier Ltd. All rights reserved.