Prediction of biogas production from food waste in a continuous stirred microbial electrolysis cell (CSMEC) with backpropagation artificial neural network

Abstract

In this study, a three-layer backpropagation neural network (BPNN) model was utilised as an estimation model of biogas production from a continuous stirred microbial electrolysis cell (CSMEC) and a continuous stirred tank reactor (CSTR) treating food waste. The study focuses on the effects of several factors, such as chemical oxygen demand removal, oxidation reduction potential (ORP), volatile fatty acids (VFAs), organic loading rate (OLR), influent pH, effluent pH, and influent ammonium on biogas production. The biogas recovery target for the model was set at 75–85%. Levenberg Marquardt backpropagation algorithm was chosen as the algorithm for the model out of the seven-benchmark comparison. Determination coefficient (R2), index of agreement (IA) and fractional variance (FV) used for the exactitude of optimal BPANN model were 0.8902, 0.925 and 0.0715 in the CSMEC and 0.9414, 0.966 and 0.0484 in the CSTR, respectively. The results of this study showed a higher accuracy and dependability of BPANN in modelling and optimizing the process parameter interactions in relation to biogas production in both the CSMEC and CSTR.