Abstract:
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poorly modeled or non-modeled parts of the process have techniques
applied on them to deduce the structure of the model. With the choice of
these techniques is often tied the choice of the control. This is however not
always the case as other factors such as speed, robustness and accurate
control may come into play thereby reducing the importance attached to
deducing the poorly modeled or non-modeled parts of the process.
In this context it becomes important what the requirements from a certain
process are on an industrial or research level as these are often the
motivating factors behind the study, and indeed, control of natural process.
What this thesis aims is to control a natural process highly employed in
industry with a control scheme that has never been used to control it before.
The process is Fed-Batch Sugar Crystallization and the control scheme is
Sliding Mode Control (SMC). It is a non-linear non-stationary process having
presence of non-modeled system dynamics. However the model utilized is
reliable and the purpose of this thesis is to show that, for a particular
simulator under study, the performance of SMC is more reliable and accurate
as compared to the standard control for this process employed in industry:
PID. The robustness of the two schemes is also compared.
An accurate system simulator is developed in this thesis that may aid future
work in this field as the main papers containing extensive study of this
process contain inaccuracies in system equations. While these have been
corrected newer techniques to carry out the process (in addition to the
control) are also suggested which may boost up the speed of the system
considerably. It is expected that this unique combination of process and
control that has not been tried before will open new doors to further increase
the robustness of the control schemes applied hence completely making it
irrelevant the lack of accurate system dynamics in certain parts of the
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process. This could have major economic advantage for those interested in
carrying this research further and implementing it on an industrial scale.
