SYSTEMS MODELING USING GENETIC PROGRAMMING

In this contribution, a Genetic Programming (GP) algorithm is used to develop empirical models of chemical process systems. GP performs symb olic regression, determining both the structure and the complexity of a model. Initially, steady-state model development using a GP algorith m is considered, next the methodology is extended to the development o f dynamic input-output models. The usefulness of the technique is demo nstrated by the development of inferential estimation models for two t ypical processes: a vacuum distillation column and a twin screw cookin g extruder.