Microbiome-oriented data mining of operational monitoring of anaerobic digestion reactor during steady operation period, failure, and restoration.
Anaerobic digestion (AD) is an essential biotechnology for sustainable waste management and bioenergy production, driven by a complex microbial ecosystem. However, the stability and efficiency of AD reactors are frequently challenged by microbial shifts resulting from operational disturbances. This study employs microbiome-oriented data mining and bioinformatics approaches to analyze microbial community dynamics in an anaerobic digestion reactor under steady-state operation, failure, and restoration phases. Data were extracted from a biogas plant and used to evaluate a system failure, providing crucial insights into microbial shifts associated with operational instability. A custom-built microbiome-oriented database, 2Gas, was developed to integrate sequencing data with physicochemical monitoring results, enabling structured data storage, retrieval, and correlation analysis. This database was instrumental in identifying key microbial taxa responsible for reactor destabilization and in predicting failure events based on microbial and physicochemical trends. Through high-throughput 16S rRNA gene sequencing and advanced bioinformatics analysis, microbial shifts were correlated with key operational parameters such as pH, volatile fatty acids (VFAs), and ammonia concentrations. Results indicate that methanogenic archaea, particularly Methanosarcina, experienced a sharp decline during reactor failure, followed by a gradual recovery, demonstrating their sensitivity to environmental stressors. Additionally, the balance between Firmicutes and Actinobacteria was identified as a crucial determinant of reactor stability, with Firmicutes recovering in tandem with system restoration. Feedstock composition was found to play a significant role in microbial shifts, with seasonal variability influencing community composition and reactor performance. Machine learning models applied to the database suggested the potential for predictive analytics in anticipating system failures. By leveraging microbial abundance patterns, these models were able to identify early warning signs of instability. Restoration efforts, including feedstock adjustments and operational parameter optimization, successfully reinstated core microbial communities essential for biogas production. This study underscores the critical role of microbiome monitoring in anaerobic digestion and highlights the potential of bioinformatics tools for enhancing reactor resilience. Future research should focus on real-time microbiome tracking, machine learning-driven predictive maintenance, and adaptive operational strategies to further improve AD efficiency and biogas yield.