Dynamic modeling and parameter estimation of biomethane production from microalgae co-digestion

Area of Science:

• Biotechnology and Bioengineering
• Environmental Science
• Chemical Engineering

Background:

• Microalgae residual co-digestion offers a sustainable route for biomethane production.
• Accurate dynamic modeling is crucial for optimizing anaerobic digestion processes.
• Understanding acidogenesis and methanogenesis pathways is key to efficient biomethanation.

Purpose of the Study:

• To develop and validate dynamic models for biomethane production from microalgae residual co-digestion.
• To assess the impact of biomass dynamics on model performance.
• To evaluate the potential of these models as digital twins for process monitoring.

Main Methods:

• A two-stage anaerobic digestion model was employed, encompassing acidogenesis and methanogenesis.
• Parametric structural and local identifiability analyses were conducted on candidate models.
• An original parameter estimation procedure was used for model validation and cross-validation.
• Experimental data from the University of Mons Laboratories formed the basis for modeling.

Main Results:

• Several dynamic models were proposed, with and without biomass dynamics.
• Identifiability studies informed model selection and parameter estimation.
• The developed models demonstrated successful direct validation and cross-validation.
• The models proved effective even with incomplete process data.

Conclusions:

• Dynamic models can accurately represent biomethane production from microalgae co-digestion.
• These models can function as advanced digital twins for real-time process monitoring.
• The methodology is robust, handling incomplete datasets effectively.
• This approach enhances the efficiency and control of anaerobic digestion systems.