Stably controlling concentration and fineness of first and second overflow in their quality index range are
the control objectives of grinding and classification process. Grinding and classification process is a fat system, and the
controller still has free degree after its dynamic optimization objectives realized, so local steady-state economic optimization
is considered. For this objective, a multiple model predictive control considering local steady-state economic objectives is
proposed. Firstly, based on the field database, transfer function matrix models of ball-mill and classifications are built up.
By considering local economic performance, steady-state economic objectives are embedded into dynamic objectives as a
penalty function. To eliminate the effect of model mismatch, based on the law of ball changing, a multiple model switching
strategy is built. The simulation result shows the effectiveness of the proposed control method.