A dynamic flotation model to infer process characteristics from online measurements

Abstract

A dynamic flotation model incorporating fundamental and phenomenological relationships, information from froth images and steady-state models is described. Model outputs correspond with online measurements commonly available on flotation circuits, and the model parameters are estimated from industrial data. Simulation results are presented, highlighting important non-linearities that need to be taken into account for optimal flotation operation. Observability and controllability analyses are performed, proving that key flotation parameters can theoretically be estimated from online process measurements, and that the set of modelled inputs can control all the model outputs. This model can be used in advanced model-based control and optimisation applications. The ability to estimate key flotation parameters opens up opportunities for improved optimisation of operating variables such as aeration rates, froth depth and the reagent recipe.