Maximizing performance of microbial electrolysis cell fed with dark fermentation effluent from water hyacinth

Abstract

The performance of microbial electrolysis cell (MEC) fed with dark fermentation effluent (DEF) from water hyacinth (WH) was enhanced in this study. First, the single effects of the auxiliary processes, including centrifugation, dilution, buffering, and external power input, were investigated. Then, the interaction of these processes was further evaluated using response surface methodology (RSM) and a combination of artificial neural network (ANN) and particle swarm optimization (PSO). Statistical analysis results revealed that ANN-PSO outperformed RSM in predictability. Consequently, the ANN-PSO approach determined that a 2.2-fold dilution of centrifuged-DFE (∼1.64 g of soluble metabolite products per L), buffer concentration of 75 mM, and an applied voltage of 0.7 V were the optimal conditions for simultaneously maximizing H2 production yield and energy efficiency of DFE@WH-fed MEC. Under co-optimized conditions, H2 yield (560.8 ± 10.8 mL/g-VS) and electrical energy recovery (162.2 ± 4.7%) significantly improved compared to unoptimized conditions.