Estimation of in-situ biogas upgrading in microbial electrolysis cells via direct electron transfer: Two-stage machine learning modeling based on a NARX-BP hybrid neural network

With the increasing of data in wastewater treatment, data-driven machine learning models are useful for modeling biological processes and complex reactions. However, few data-driven models have been developed for simulating the microbial electrolysis cells (MECs) and traditional models are too ambiguous to comprehend the mechanisms. In this study, a new general data-driven two-stage model was firstly developed to predict CH4 production from in-situ biogas upgrading in the biocathode MECs via direct electron transfer (DET), named NARX-BP hybrid neural networks. Compared with traditional one-stage model, the model could well predict methane production via DET with excellent performance (all R2 and MES of 0.918 and 6.52 ? 10-2, respectively) and reveal the mechanisms of biogas upgrading, for the new systematical modeling approach could improve the versatility and applicability by inputting significant intermediate variables. In addition, the model is generally available to support long-term prediction and optimal operation for anaerobic digestion or complex MEC systems.

Automated summary

A new general data-driven two-stage model, NARX-BP hybrid neural networks, was developed in this study to predict CH4 production from MECs. The model, with the input of significant intermediate variables, shows excellent performance and reveals the mechanisms of biogas upgrading.