Improved calibration of a solid substrate fermentation model
Background: Calibration of dynamic models in biotechnology is challenging. Kinetic models are usually complex and differential equations are highly coupled involving a large number of parameters. In addition, available measurements are scarce and infrequent, and some key variables are often non-meas...
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| Autores principales: | , , , , , |
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| Lenguaje: | English |
| Publicado: |
Pontificia Universidad Católica de Valparaíso
2011
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582011000500007 |
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| Sumario: | Background: Calibration of dynamic models in biotechnology is challenging. Kinetic models are usually complex and differential equations are highly coupled involving a large number of parameters. In addition, available measurements are scarce and infrequent, and some key variables are often non-measurable. Therefore, effective optimization and statistical analysis methods are crucial to achieve meaningful results. In this research, we apply a metaheuristic scatter search algorithm to calibrate a solid substrate cultivation model. Results: Even though scatter search has shown to be effective for calibrating difficult nonlinear models, we show here that a posteriori analysis can significantly improve the accuracy and reliability of the estimation. Conclusions: Sensibility and correlation analysis helped us detect reliability problems and provided suggestions to improve the design of future experiments. |
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