Modeling microbial diversity in anaerobic digestion.

Area of Science:

• Environmental Microbiology
• Biochemical Engineering
• Process Modeling

Background:

• Anaerobic digestion processes are sensitive to microbial community shifts.
• Existing models like the IWA Anaerobic Digestion Model No1 (ADM1) may not fully capture microbial diversity dynamics.
• Process imbalances can arise from changing influent conditions or toxicant presence.

Purpose of the Study:

• To develop a robust modeling approach for anaerobic digesters that explicitly accounts for microbial diversity.
• To enhance the capability of existing models to handle both normal operational fluctuations and abnormal situations, such as toxicant inhibition.
• To provide a framework for improved monitoring and control strategies in anaerobic digestion.

Main Methods:

• Augmenting a well-established model (IWA Anaerobic Digestion Model No1) with a stochastic term to represent microbial diversity.
• Utilizing experimental data from a pilot-scale anaerobic digester (1 m3) treating wine distillery wastewater.
• Validating the proposed modeling approach against real-world operational data.

Main Results:

• The developed stochastic modeling approach successfully handles microbial diversity under varying conditions, including the presence of toxicants.
• The model demonstrates applicability in predicting and managing process behavior in anaerobic digesters.
• Experimental validation confirms the effectiveness of the approach in a pilot-scale setting.

Conclusions:

• The integration of stochastic terms offers a promising way to incorporate microbial diversity into anaerobic digestion models.
• This approach can improve the understanding and control of digester performance during both normal and abnormal operational states.
• Future monitoring strategies for anaerobic digesters should consider explicit inclusion of microbial diversity for enhanced control objectives.