Abstract
In this contribution, a Genetic Programming (GP) algorithm is used to develop empirical models of chemical process systems. GP performs symbolic regression, determining both the structure and the complexity of a model. Initially, steady-state model development using a GP algorithm is considered, next the methodology is extended to the development of dynamic input-output models. The usefulness of the technique is demonstrated by the development of inferential estimation models for two typical processes: a vacuum distillation column and a twin screw cooking extruder.
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