This paper deals with the application of neural networks and evolutionary techniques to the area of process identification and control. A distillation process is simulated with the dynamic flow-sheet simulator DIVA which employs a first-principle-based model. Several neural paradigms were implemented to adaptively model the concentration dynamics. A combined PI-Neural Net controller for concentration control is presented. Using genetic algorithms it was possible to optimize the network structure and reduce the size of the training set. Finally, some parallelization methods for neural and evolutionary algorithms, implemented on Connection Machine architectures, are briefly explained.

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