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Identification in an anaerobic batch system: global sensitivity analysis, multi-start strategy and optimization
Andres Donoso-Bravo1, Johan Mailier, Gonzalo Ruiz-Filippi
1Automatic Control Laboratory, University of Mons, 31 Boulevard Dolez, 7000 Mons, Belgium. adonosobravo@gmail.com
This study introduces a reliable 3-step method for estimating parameters in anaerobic digestion (AD) models, improving accuracy and understanding of the process. Global sensitivity analysis and a multi-start strategy were key to reliable kinetic and stoichiometric parameter estimation.
Area of Science:
- Biochemical Engineering
- Environmental Science
- Mathematical Modeling
Background:
- Anaerobic digestion (AD) systems are crucial for waste management and biogas production.
- Mathematical models are widely used to simulate AD processes, but parameter estimation and validation remain challenging.
- Identifiability issues in AD models are often overlooked, hindering reliable parameter estimation.
Purpose of the Study:
- To present a robust 3-step procedure for reliable estimation of kinetic and stoichiometric parameters in a simplified AD model.
- To address the common neglect of structural and parametric identifiability in AD modeling studies.
- To validate the proposed procedure using experimental data from a lab-scale sequencing batch reactor.
Main Methods:
- A 3-step procedure combining global sensitivity analysis, multi-start strategy, and optimization criteria selection.
- Global sensitivity analysis to assess parameter interactions.
- Application of the classical least-squares cost function for parameter estimation.
Main Results:
- Successfully estimated two kinetic parameters and two stoichiometric coefficients for the simplified AD model.
- Determined the accuracy of the estimated parameters.
- Identified the classical least-squares cost function as the optimal choice for this specific case study.
Conclusions:
- The proposed 3-step procedure offers a reliable approach for parameter estimation in AD models.
- Accurate parameter estimation is crucial for effective model validation and process optimization in anaerobic digestion.
- The classical least-squares method proved effective for parameter estimation in the studied lab-scale reactor.
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