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A Model for Bioaugmented Anaerobic Granulation.

Anna Doloman¹, Amitesh Mahajan², Yehor Pererva¹

¹Department of Biological Engineering, Utah State University, Logan, UT, United States.

Frontiers in Microbiology

|October 29, 2020

Summary

This summary is machine-generated.

Bioaugmentation successfully integrated lipolytic bacteria into anaerobic granular sludge, speeding up adaptation to new lipid-rich feeds. This computational model optimizes anaerobic digestion efficiency by simulating microbial consortium dynamics.

Keywords:

agent-based model anaerobic granulation bioaugmentation biogas cDynoMiCs

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Area of Science:

Environmental Microbiology
Biotechnology
Biochemical Engineering

Background:

Anaerobic granular sludge harbors complex microbial communities capable of robust anaerobic digestion.
Shifts in substrate composition necessitate long adaptation periods for granular consortia, impacting reactor efficiency.
Bioaugmentation offers a strategy to accelerate adaptation and enhance digestion performance.

Purpose of the Study:

To computationally elucidate the mechanisms of bioaugmenting anaerobic granules.
To model the adaptation of a cellobiose-degrading consortium to a lipid-rich feed using an agent-based approach.
To identify optimal conditions for successful bioaugmentation and efficient methane production.

Main Methods:

Development of an agent-based model within the cDynoMiCs simulation environment.
Simulation of bioaugmentation with lipolytic bacteria into a stable granular consortium.
Analysis of substrate ratios (cellobiose and oleate) to determine optimal augmentation conditions.

Main Results:

Successful in silico incorporation of lipolytic bacteria into granular consortia within 40 days.
Demonstrated that the ratio of cellobiose to oleate is critical for successful augmentation.
Achieved a stable, homogeneous augmented consortium at 0.5 g/L of both cellobiose and oleate, producing 10.9 mg/L methane.

Conclusions:

Computational modeling can effectively predict and guide bioaugmentation strategies for anaerobic digestion.
The ratio of substrates significantly influences the success of microbial consortium adaptation.
The developed model serves as a valuable planning tool for optimizing anaerobic digestion processes during feed transitions.