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Effective control of high-purity distillation columns is one
of most challenging topics in the field of process control over
the years. The unit is commonly employed to separate the
final products and would normally consume vast amount of
energy. Maintaining satisfactory separation of high-purity
distillation columns is one of the most important concerns in
in the chemical industry. Due to nonlinearity and loop inter-
ation characteristics of high-purity distillation columns, it
is difficult to describe dynamic behavior of such columns
using simple linear mathematical models. A realistic dynamic
simulation of a dual composition and temperature column is
used in this study and the process is identified by using
artificial neural network(ANN). Because ANN is capable of
learning essential process nonlinearity from plant data , this
ANN model can provide another means to describe the dynamic
behavior of high-purity distillation column. Also in this
work, different manipulated input excitation methods will be
used to investigate the best input/output data as training
set. The nonlinear model obtaioned via ANN will be compared to
another nonlinear model using nonlinear ARX model.
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