Abstract
This project focussed on the investigation, development and evaluation of a closed loop
control system on a rougher flotation cell that could improve PGM flotation performance.
A PGM rougher flotation cell equipped with Online Stream Analysis (OSA) and machine
vision system (SmartFroth) was used during the investigation. Online measurements
included bubble velocity, average bubble area, bubble colour; Pt, Ni and Cu concentrate
grade and concentrate flow rate and density. Air addition and pulp level was used as the
manipulated variables.
A system identification exercise was performed to model the dynamic responses of all
the available outputs to step changes in the air addition and pulp level. Brief analysis of
the conSistency of the output gains for different initial conditions was done to determine
whether the process for the measured outputs were linear. None of the outputs that were
selected to be controlled exhibited non-linear behaviour so that linear control theory
could be applied for the development of a closed loop controller.
Linear dynamic models of the machine vision outputs were analysed and it was
determined that the biggest operating region for MI MO control exists when the air
addition is manipulated to control the bubble velocity and the pulp level is manipulated to
control the blue bubble colour. The multivariable control application was relatively simple
as the interaction between the two control loops was intermittent. Different decoupling
structures with PI controllers were tested in a simulation environment and It was found
that the difference between the decoupling structures were small with no single
decoupling structure outperforming any of the other structures. LOG and IMC MIMO
controllers were also designed and compared in the simulation environment. All the
controllers performed similarly with the exception of LOG control, which was worse.
Implementation of the designed controllers on site exhibited mixed results. The blue
colour measurement proved to be unreliable while the bubble velocity showed good
control potential as an industrial measurement by manipulating the air addition.
Research should focus on relating machine vision outputs directly to the metallurgical
performance of flotation so that optimal machine vision setpoints can be selected.
Schalkwyk, T (2021). Multivariable Control of a Rougher Flotation Cell. Afribary. Retrieved from https://tracking.afribary.com/works/multivariable-control-of-a-rougher-flotation-cell
Schalkwyk, Theo "Multivariable Control of a Rougher Flotation Cell" Afribary. Afribary, 15 May. 2021, https://tracking.afribary.com/works/multivariable-control-of-a-rougher-flotation-cell. Accessed 10 Nov. 2024.
Schalkwyk, Theo . "Multivariable Control of a Rougher Flotation Cell". Afribary, Afribary, 15 May. 2021. Web. 10 Nov. 2024. < https://tracking.afribary.com/works/multivariable-control-of-a-rougher-flotation-cell >.
Schalkwyk, Theo . "Multivariable Control of a Rougher Flotation Cell" Afribary (2021). Accessed November 10, 2024. https://tracking.afribary.com/works/multivariable-control-of-a-rougher-flotation-cell