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Kinetic modeling and parameter estimation in a tower bioreactor for bioethanol production
Elmer Ccopa Rivera1, Aline Carvalho da Costa, Betânia Hoss Lunelli
1Laboratory of Optimization, Design and Advanced Control, School of Chemical Engineering, State University of Campinas, P.O. Box 6066, 13081-970, Campinas, SP, Brazil.
Applied Biochemistry and Biotechnology
|April 18, 2008
Summary
A new RGA-PB-QN method improves bioprocess model building for bioethanol production. This systematic approach enhances parameter estimation accuracy in Saccharomyces cerevisiae bioreactors.
Area of Science:
- Biochemical Engineering
- Bioprocess Modeling
- Metabolic Engineering
Background:
- Accurate bioprocess models are crucial for optimizing industrial fermentation.
- Parameter estimation is a key challenge in developing these models.
- Saccharomyces cerevisiae is widely used in bioethanol production.
Purpose of the Study:
- To present a systematic method for bioprocess model building using optimization techniques.
- To improve parameter estimation for a tower bioreactor used in bioethanol production.
- To compare the proposed method with existing techniques.
Main Methods:
- A step-by-step parameter estimation procedure was developed.
- Real-coded genetic algorithm (RGA) was used for global parameter estimation.
- Plackett-Burman (PB) design identified significant parameters.
- Quasi-Newton (QN) algorithm optimized key parameters.
Main Results:
- The RGA-PB-QN method demonstrated improved performance in parameter estimation.
- The proposed method enhanced the accuracy of a deterministic detailed model.
- Model performance was superior compared to using RGA alone.
Conclusions:
- The RGA-PB-QN approach offers a robust and efficient strategy for bioprocess model development.
- This method significantly improves the description of experimental data in bioethanol production.
- The systematic optimization enhances the reliability of bioprocess simulations.
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