Multi-objective optimization for microbial electrolysis cell-assisted anaerobic digestion of swine manure

Abstract

As a new technology, microbial electrolysis cell-assisted anaerobic digestion (MEC-AD) has been applied to the utilization of swine manure. Methane production and total energy efficiency are both important indicators in MEC-AD; thus, it is necessary to simultaneously optimize methane production and total energy efficiency. In this study, the Box–Behnken design (BBD) was used as the experimental design, the response surface methodology (RSM) and back propagation neural network (BP-NN) methods were used to construct multi-objective models, and the non-dominated sorting genetic algorithm II (NSGA II) was used for multi-objective optimization. The BP-NN model was superior to the RSM model in terms of goodness of fit. Additionally, the relative error between the predicted and experimental values of the Pareto front was determined to be <3%. The maximum methane production and total energy efficiency were 333.97 mL/g total solid and 61.38%, respectively. Therefore, multi-objective optimization of MEC-AD systems may be accomplished using BBD-(BP-NN)-NSGA II.