Self-optimization of an autogenous grinding circuit

This paper deals with the optimization of an autogenous grinding circuit using a random search technique. This technique is based on a hierarchical structure of learning automata operating in a random environment constituted by the autogenous circuit to be optimized. The ore feed rate to the mill is considered as the control variable while the mass flow rate of the concentrate of the subsequent separation process constitutes the controlled variable. riation domain of the manipulated variables is discretized into a set of regions which are associated 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 on the response of the environment, modifies the strategy (the probabilities associated to the set of actions) using an adaptation procedure called reinforcement scheme. cal results illustrate the feasibility and the performance of this self-adjusting optimization algorithm.