SELF-OPTIMIZATION OF AN AUTOGENOUS GRINDING CIRCUIT

This paper deals with the optimization of an autogenous grinding circu it using a random search technique. This technique is based on a hiera rchical structure of learning automata operating in a random environme nt constituted by the autogenous circuit to be optimized. The ore feed rare to the mill is considered as the control variable while the mass flow rate of the concentrate of the subsequent separation process con stitutes the controlled variable. The variation domain of the manipula ted variables is discretized into a set of regions which are associate d to the actions of the automata of the last level of the hierarchical learning system. A probability is associated to each action (region). The learning system selects one of the available actions and, based o n the response of the environment, modifies the strategy (the probabil ities associated to the set of actions) using an adaptation procedure called reinforcement scheme.