On the application of genetic programming to chemical process systems

Author

McKay, Ben ; Willis, Mark J. ; Barton, Geoffrey W.

Author_Institution

Dept. of Chem. Eng., Sydney Univ., NSW, Australia

Volume

2

fYear

1995

fDate

29 Nov-1 Dec 1995

Firstpage

701

Abstract

A genetic programming approach is utilised to develop mathematical models of chemical process systems. Having discussed genetic programming in general, two examples are used to reveal the utility of the technique. It is shown how the method can discriminate between relevant and irrelevant process inputs, evolving to yield parsimonious model structures that accurately represent process characteristics. This removes the need for restrictive assumptions about the form of the data and the structure of the required model. In addition, as the technique determines complex nonlinear relationships in the data, non-intuitive process features are revealed with comparative ease

Keywords

chemical engineering computing; chemical industry; genetic algorithms; chemical process systems; complex nonlinear relationships; genetic programming; irrelevant process inputs; mathematical models; nonintuitive process features; parsimonious model structures; relevant process inputs; Artificial neural networks; Australia; Chemical processes; Costs; Data engineering; Genetic programming; Information retrieval; Mathematical model; Memory; Performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1995., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2759-4

Type

conf

DOI

10.1109/ICEC.1995.487470

Filename

487470