Acetoclastic and Hydrogenotropic pathway shift in anaerobic digestion

Fig. 1. Conversion rates and efficiencies across key anaerobic digestion phases: The relationship between reactor configurations (R1: 100% food waste (FW), R2: 75% FW : 25% sewage sludge (SS), R3: 50% FW : 50% SS, R4: 25% FW : 75% SS, and R5: 100% SS) and their corresponding performance metrics. Calculations were based on influent and effluent concentrations, converted to gram COD (Chemical Oxygen Demand), using specific conversion factors for each parameter across reactors.Fig. 2. Temporal dynamics of methanogenic pathway shifts in HOLAnD® reactors: The relationship between pH–pKa differentials and digestion time was assessed using measured pH values and pKa calculated from volatile fatty acid (VFA) concentrations. These trends were analyzed alongside biogas, methane, and carbon dioxide yields from the reactors. Hydrogen concentrations were estimated stoichiometrically based on propionic and butyric acid levels.Fig. 3. Profiles of dissociated and undissociated volatile fatty acids: The speciation of VFAs into dissociated and undissociated forms was computed using daily pKa values and corresponding pH measurements from the reactors. Additionally, the visualization were performed using Python, with custom code applied to the associated datasetFig. 4. Multivariate correlation analysis of physicochemical variables and performance indicators in the HOLAnD® system: A composite matrix illustrating the interrelationships among 16 key process parameters, integrating Pearson correlation coefficients (heatmap) and Mantel test results (network overlay). Pearson correlations were computed using reactor-derived data, while the Mantel test was conducted using the scikit-bio package in Python. Visualization was performed using OriginPro for the Pearson heatmap and Gephi (software) for the Mantel network overlay.Fig. 5. Taxonomic and functional organization of microbial communities: A chord diagram illustrating species-level compositional shifts in the microbial species based on the 16S rRNA metagenomic abundance data. Connecting lines indicate variations in relative abundance, with each microbial taxon distinguished by a unique color. Functional annotation was derived through KEGG-based analysis, while the visualization was generated using Tableau for the chord diagram and Gephi (version 0.10.1) for functional network representation.<br>