Integrated comminution and flotation neurocontrol using evolutionary reinforcement learning

Comminution and flotation circuits are frequently perturbed by disturbances, such as feed particle size variations, ore hardness and varying mineral liberation, which can have a significant impact on the recoveries and grades. Although advanced control strategies can neutralize many of these disturbances, few such systems have been implemented at present. One reason is that it is only recently that the requisite sensor systems have been developed and used to control froth flotation systems. Another is owed to the complexity of these non-linear interactive systems. In this paper, a plant-wide control strategy is considered, wherein both unit operations have a single overall control objective. The automated design of the controller is based on an advanced evolutionary algorithm viz., Symbiotic, Memetic Neuro-Evolution (SMNE), for developing neural network controllers in a reinforcement learning framework. A rigorous phenomenological comminution and flotation model was used to develop suitable neurocontrollers, which used all possible sensor inputs and available manipulated variables. The neurocontroller managed to identify optimal or near-optimal operating conditions consistently and showed robust performance in the presence of significant disturbances. The results suggest that evolutionary reinforcement learning offers a very competitive approach towards the development of integrated non-linear controllers for mineral processing plants. Keywords: neural networks, control, flotation, comminution, evolutionary reinforcement learning