An approach to parameters estimation of a chromatography model using a clustering genetic algorithm based inverse model
| Autor: | Leôncio Diógenes Tavares Câmara, Antônio José da Silva Neto, Mirtha Irizar Mesa, Francisco Herrera Fernández, David Curbelo Rodríguez, Orestes Llanes-Santiago |
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| Rok vydání: | 2010 |
| Předmět: |
education.field_of_study
Mathematical optimization Partial differential equation Chromatography Estimation theory Population System identification Function (mathematics) Inverse problem Theoretical Computer Science Genetic algorithm Geometry and Topology education Cluster analysis Software Mathematics |
| Zdroj: | Soft Computing. 15:963-973 |
| ISSN: | 1433-7479 1432-7643 |
| DOI: | 10.1007/s00500-010-0638-3 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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