Temporal Dynamics of Microbial Communities in Anaerobic Digestion: Influence of Temperature and Feedstock Composition on Reactor Performance and Stability

Anaerobic digestion (AD) offers a sustainable biotechnology to recover resources from carbon- and nutrient-rich wastewater streams, such as food-processing wastewater. Despite the wide adoption of crude wastewater characterisation, the impact of detailed chemical fingerprinting on AD remains underexplored. This study investigated the influence of fermentation-wastewater composition and operational parameters on AD over time to identify critical parameters influencing microbiome diversity and reactor performance. Eighteen bioreactors were operated under various operational conditions using mycoprotein fermentation wastewater. Detailed chemical analysis fingerprinted the molecules in the fermentation-wastewater throughout the AD process including sugars, sugar alcohols and volatile fatty acids (VFAs). High-throughput sequencing revealed distinct microbiome profiles linked to temperature and reactor configuration, with mesophilic conditions supporting a more diverse and densely connected microbiome. Importantly, significant elevations in Methanomassiliicoccus were correlated to high butyric acid concentrations and decreased biogas production, further elucidating the role of this newly discovered methanogen in AD. Reactors from different experimental runs had distinct VFA profiles, which impacted microbial taxonomy and diversity. Dissimilarity analysis demonstrated the importance of individual VFAs, sugars and sugar alcohols on microbiome diversity, highlighting the need for detailed chemical fingerprinting in AD studies of microbial trends. Furthermore, machine learning models predicting reactor performance achieved high accuracy based on operational parameters and microbial taxonomy. Operational parameters were found to have the most substantial influence on chemical oxygen demand removal, whilst Oscillibacter and two Clostridium species were highlighted as key factors in biogas production. By integrating detailed chemical and biological fingerprinting with explainable machine learning models this research presents a novel approach to advance our understanding of AD microbial ecology, offering insights for industrial applications of sustainable waste-to-energy systems.