Real-coded genetic algorithms and nonlinear parameter identification

In this study, real-coded genetic algorithms are used in the parameter identification of the macroscopic Chemostat model. The Chemostat model utilized in this work is nonlinear having two distinct operating areas. Thus, the model is identified separately for both operating areas. The process simulat...

Full description

Saved in:
Bibliographic Details
Main Authors: Sorsa, A., Peltokangas, R., Leiviska, K.
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
Biological processes
Biological system modeling
Chemostat model
Continuous-stirred tank reactor
Couplings
Genetic algorithms
Genetic mutations
Intelligent structures
Intelligent systems
Parameter estimation
parameter identification
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, real-coded genetic algorithms are used in the parameter identification of the macroscopic Chemostat model. The Chemostat model utilized in this work is nonlinear having two distinct operating areas. Thus, the model is identified separately for both operating areas. The process simulator is used to generate data for the parameter identification. The optimizations with genetic algorithms are repeated with 200 different initial populations to guarantee the validity of the results. The parameter identification with genetic algorithms performs well giving accurate results.
ISSN:1541-1672
1941-1294
DOI:10.1109/IS.2008.4670495