Control del Nivel de Pulpa en un Circuito de Flotación Utilizando una Estrategia de Control Predictivo

This work presents the design and implementation results of a model predictive control strategy used to control the pulp level of a Rougher flotation circuit in a mine located in the third region of Chile, which is composed by five flotation banks. The strategy considers a state space representation to model the pulp level of each bank (with a multiple input - single output model), which is obtained by a system identification procedure and uses a Kalman filter as a state estimator. To solve the optimization problem that calculates the control law, a genetic algorithm based optimization tool is used. Experimental data is used to show the results of the proposed control strategy.