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Parameter identification of Droop model: an experimental case study
Micaela Benavides1, Anne-Lise Hantson, Jan Van Impe
1BioSys, University of Mons, Boulevard Dolez 31, 7000, Mons, Belgium, Micaela.Benavides@umons.ac.be.
Bioprocess and Biosystems Engineering
|June 3, 2015
Summary
This study identifies a simple Droop model for microalgae growth, crucial for optimizing bioprocess control. The research details a lab-scale photobioreactor and a robust parameter identification method for accurate dynamic modeling.
Area of Science:
- Biotechnology
- Biochemical Engineering
- Mathematical Biology
Background:
- Mathematical modeling is vital for optimizing and controlling bioprocesses.
- Predictive dynamic models are essential for state estimation and process management.
- Microalgae cultivation requires accurate growth models for efficient production.
Purpose of the Study:
- To identify a simple model for microalgae growth under substrate limitation, specifically the Droop model.
- To describe the design and instrumentation of a lab-scale flat-plate photobioreactor for experimental data collection.
- To present a comprehensive parameter identification methodology for the developed model.
Main Methods:
- Design and instrumentation of a lab-scale flat-plate photobioreactor with on-line and off-line monitoring.
- Collection of experimental data on microalgae growth under substrate-limited conditions.
- Parameter identification using an analytical procedure for initial estimates, cost function selection, and validation tests.
Main Results:
- Successful identification of the Droop model parameters for microalgae growth.
- Demonstration of a robust methodology for parameter estimation and model validation.
- Establishment of a validated dynamic model for microalgae cultivation.
Conclusions:
- The identified Droop model provides a simplified yet effective representation of microalgae growth under substrate limitation.
- The developed parameter identification methodology ensures accurate and reliable model parameter estimation.
- The validated model can be applied to optimize and control microalgae bioprocesses for enhanced productivity.
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