Sludge bound-EPS solubilization enhance CH bioconversion and membrane fouling mitigation in electrochemical anaerobic membrane bioreactor: Insights from continuous operation and interpretable machine learning algorithms.

Bound extracellular polymeric substances (EPS) are complex, high-molecular-weight polymer mixtures that play a critical role in pore clogging, foulants adhesion, and fouling layer formation during membrane filtration, owing to their adhesive properties and gelation tendencies. In this study, a novel electrochemical anaerobic membrane bioreactor (EC-AnMBR) was constructed to investigate the effect of sludge bound-EPS solubilization on methane bioconversion and membrane fouling mitigation. During the 150-days' operation, the EC-AnMBR demonstrated remarkable performance, characterized by an exceptionally low fouling rate (transmembrane pressure (TMP) < 4.0 kPa) and high-quality effluent (COD removal > 98.2 %, protein removal > 97.7 %, and polysaccharide removal > 98.5 %). The highest methane productivity was up to 38.0 ± 3.1 mL/L/d at the applied voltage of 0.8 V with bound-EPS solubilization, 107.6 % higher than that of the control stage (18.3 ± 2.4 mL/L/d). Morphological and multiplex fluorescence labeling analyses revealed higher fluorescence intensities of proteins, polysaccharides, total cells and lipids on the surface of the fouling layer. In contrast, the interior exhibited increased compression density and reduced activity, likely attributable to compression effect. Under the synergistic influence of the electric field and bound-EPS solubilization, biomass characteristics exhibited a reduced propensity for membrane fouling. Furthermore, the bio-electrochemical regulation enhanced the electroactivity of microbial aggregates and enriched functional microorganisms, thereby promoting biofilm growth and direct interspecies electron transfer. Additionally, the potential hydrogenotrophic and methylotrophic methanogenesis pathways were enhanced at the cathode and anode surfaces, thereby increasing CH₄ productivity. The random forest-based machine learning model analyzed the nonlinear contributions of EPS characteristics on methane productivity and TMP values, achieving R² values of 0.879 and 0.848, respectively. Shapley additive explanations (SHAP) analysis indicated that S-EPS and S-EPS were the most critical factors affecting CH₄ productivity and membrane fouling, respectively. Partial dependence plot analysis further verified the marginal and interaction effects of different EPS layers on these outcomes. By combining continuous operation with interpretable machine learning algorithms, this study unveils the intricate impacts of EPS characteristics on methane productivity and membrane fouling behaviors, and provides new insights into sludge bound-EPS solubilization in EC-AnMBR.