An approach to parameters estimation of a chromatography model using a clustering genetic algorithm based inverse model

Genetic algorithms are tools for searching in complex spaces and they have been used successfully in the system identification solution that is an inverse problem. Chromatography models are represented by systems of partial differential equations with non-linear parameters which are, in general, dif...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2011-05, Vol.15 (5), p.963-973
Main Authors: Irizar Mesa, Mirtha, Llanes-Santiago, Orestes, Herrera Fernández, Francisco, Curbelo Rodríguez, David, Da Silva Neto, Antônio José, Câmara, Leôncio Diógenes T.
Format: Article
Language:English
Subjects:
Adsorption
Artificial Intelligence
Chromatography
Clustering
Computational Intelligence
Computing time
Control
Engineering
Fermentation
Genetic algorithms
Genetic engineering
Inverse problems
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Methods
Neural networks
Optimization
Original Paper
Parameter estimation
Partial differential equations
Protein adsorption
Proteins
Robotics
System identification
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Summary:Genetic algorithms are tools for searching in complex spaces and they have been used successfully in the system identification solution that is an inverse problem. Chromatography models are represented by systems of partial differential equations with non-linear parameters which are, in general, difficult to estimate many times. In this work a genetic algorithm is used to solve the inverse problem of parameters estimation in a model of protein adsorption by batch chromatography process. Each population individual represents a supposed condition to the direct solution of the partial differential equation system, so the computation of the fitness can be time consuming if the population is large. To avoid this difficulty, the implemented genetic algorithm divides the population into clusters, whose representatives are evaluated, while the fitness of the remaining individuals is calculated in function of their distances from the representatives. Simulation and practical studies illustrate the computational time saving of the proposed genetic algorithm and show that it is an effective solution method for this type of application.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-010-0638-3