Work

State and parameter estimation of a dynamic froth flotation model using industrial data

Year: 2024

Type: article

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.

Source: Minerals Engineering

DOI: 10.1016/j.mineng.2024.109059

Authors: Jaco-Louis Venter, Johan Derik le Roux, Ian Keith Craig

Institution: University of Pretoria

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,080

Funder: National Research Foundation