Abstract:
This paper investigates an observable dynamic model of froth flotation circuits aimed at online state and
parameter estimation and model-based control. The aim is to estimate the model states and parameters online
from industrial data. However, in light of limitations in the plant data, additional model analysis is conducted.
It is shown that without online compositional measurements, only the states and parameters of a reduced
model can be estimated online. The reduced model lumps all recovery mechanisms into a single empirical
equation. The reduced model is used to develop a moving horizon estimator (MHE) which is implemented
on the industrial data. The state and parameter estimates from the MHE are used to evaluate the model
prediction accuracy over a receding control horizon as would be done in model predictive control (MPC).
Given the uncertainty of the available data, unmeasured disturbances and missing online measurements, the
estimation and prediction results are reasonably accurate, at least in a qualitative sense. If accurate and reliable
online measurements are available for estimation, the reduced model shows potential to be used for long-term
model-based supervisory control of a flotation circuit.
