Substrate Composition Shapes Methanogenesis, Microbial Ecology, and Digestate Dewaterability in Microbial Electrolysis Cell-Assisted Anaerobic Digestion of Food Waste

The compositional heterogeneity of food waste greatly influences its bioconversion in microbial electrolysis cell (MEC)-assisted anaerobic digestion (AD), but the underlying mechanism remains unclear. Therefore, this study assessed two typical food wastes, i.e., starch-rich rice and cellulose-rich vegetables, on methane production, microbial constituents, and digestate dewaterability in single-chamber MECs. The results demonstrated that, while the rice-fed MEC (258.56 mL/g VS) achieved a higher methane yield compared to the vegetable-fed MEC (161.79 mL/g VS), the latter achieved higher methane purity. Temporal profiles of volatile fatty acids (VFAs) revealed rapid acidification and consumption in rice-fed systems, whereas vegetable-fed MEC exhibited delayed degradation. Additionally, the substrate type greatly influenced digestate dewaterability, since digestate from the vegetable-fed MEC exhibited lower specific resistance to filtration (3.25 × 1012 m/kg vs. 12.46 × 1012 m/kg) and capillary suction time (8.16 s·L/g vs. 19.14 s·L/g) compared to that from the rice-fed MEC. This improvement was likely attributed to high polysaccharides in extracellular polymeric substances (EPS) and cellulose’s structural properties, which promoted the formation of a porous, less compressible sludge cake that facilitated sludge dewaterability. Microbial community analysis revealed a substrate-driven specialization, as the rice-fed MECs enriched exoelectrogens (e.g., Geobacter, Trichococcus) and hydrogenotrophic methanogens (i.e., Methanobacterium), while the vegetables enriched Bacteroides and Methanosarcina. Collectively, these results suggest substrate composition profoundly influences methane yield, metabolic pathways, microbial ecology, and digestate properties in MEC-assisted AD. This work provides key insights into the role of feedstock characteristics in shaping MEC-assisted AD systems.