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The dynamic optimization for chemical processes usually has the following
properties : (1) highly nonlinear (2) containing complex and discontinuous
constraints and (3) delay arguments. The conventional maximum principle
approaches often encounter som problems such as unstable integration and
difficulties in obtaining global optimum. In this study, the use of iterative
dynamic programming(IDP) presents the advantages of obtaining global optimum,
solving problem without transformation, and in itself constituting a simple
concept. In stead of the conventional penalty function method, the modified
Powell's multiplier algorithm is incorporated to handle each constraint.
As far as the optimization of multiobjective dynamic systems,
the conventional
multiobjective optimization usually requires the adjustment of parameters to
generate many Pareto optimum from which are to be chosen by the decision maker.
In a complex optimization problem, this approach takes rather
high computational
effort. In the current stydy, the use of IDP along with fuzzy decision making
procedure can directly approach the desired solution of the decision maker. In
the multiobjective dynamic optimization of the Nylon-6 batch reactor and the
Penicillin G fed-batch fermentation process, the efficiency of the method is
verified.
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