Predictive Expert Control System of a Hybrid Pilot Rougher Flotation Circuit

Abstract: In the last decades heuristic approaches based on logic rules have been one favored tool to control flotation units and plants on top of distributed control systems. The lack of fundamental knowledge on flotation processes, expressed in reliable models, and programs to assure the quality of the information, contained in available measurements, have limited the developing of robust predictive control strategies. In this paper we explore the idea of using simplified models jointly with measured disturbances to modify the set points of froth depth and air flow rate controllers. Since these target predictions are only approximate, a conventional feedback expert system, based on logic rules, continues the task of calculating new set points of the distributed controllers, in order to decrease the gap between actual and desirable metallurgical targets. This combined actions permitted a fast response of the process to change the operation of the circuit when a measured disturbance change is detected. This control system was implemented and experimentally evaluated in a pilot rougher flotation circuit, with three pneumatic cells, with froth depth and air flow rate controllers in each cell. Since the circuit is operated for the air-water system, a phenomenological model, with parameters estimated from industrial data, is fed on-line with real operating variables and virtual values, characterizing the feed (grades, solids%, particle size distribution), in order to predict the metallurgical targets (recovery and concentrate grade). Several cases are discussed showing the benefits of this control system and the possible improvements to work on.