Inverse internal model control of an ethylene polymerisation reactor using artificial neural networks.
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Date
2001
Authors
Dunwoodie, Ryan.
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Abstract
An artificial neural network is a mathematical black-box modelling tool. This tool can
be used to model complex non-linear multivariable processes. In attempting to create
an inverse process model of an industrial linear low density polyethylene reactor,
several interesting results were encountered. Both time-invariant algebraic and time-invariant
dynamic models could adequately represent the process, provided an
identified 50-minute time lag was taken into account.
A novel variation of the traditional IMC controller was implemented which used two
inverse neural network process models. This was named Inverse Internal Model
Control (IIMC). This controller was initially tested on a real multivariable pump-tank
system and showed promising results.
The IIMC controller was adapted to an on-line version for the polymer plant control
system. The controller was run in open loop mode to compare the predictions of the
controller with the actual PID ratio controllers. It was hoped that by incorporating
neural network models into the controller, they would take the non-linearity and
coupling of the variables into account, which the present PID controllers are unable to
do. The existing PID controllers operate on separate loops involving the two main
feeds (co-monomer and hydrogen) to the reactor, which constitute aspects of the
control system in which the scope for advanced control exists. Although the control
loop was not closed, the groundwork has been laid to implement a novel controller that
could the operation of the plant.
Description
Thesis (M.Sc.Eng.)-University of Natal, Durban, 2001.
Keywords
Polyethylene., Polymerization., Neural networks (Computer science), Pid controllers., Process control., Theses--Chemical engineering.