The number of size classes in a cumulative rates model of a grinding mill circuit is reduced to determine the minimum
number required to provide a reasonably accurate model of the circuit for process control. Each reduced size class set is
used to create a non-linear cumulative rates model which is linearised to design a linear model predictive controller. The
accuracy of a model is determined by the ability of the corresponding model predictive controller to control important
process variables in the grinding mill circuit as represented by the full non-linear cumulative rates model.
Results show that a model with 25 size classes that provides valuable information for plant design and scale-up, can be
reduced to a model containing only a small number of size class sets and still be suitable for process control. Although
as few as 3 size classes can be used to obtain a fairly accurate model for process control, the distribution of these 3 size
classes influences the accuracy of the model. For a model to be useful for process control, the model should at least
provide the directions in which the process variables change.