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Author Spotlight: Scaling Microalgal Biotechnology for Enhanced Biomethane Production
Published on: March 22, 2024
Estimation of in-situ biogas upgrading in microbial electrolysis cells via direct electron transfer: Two-stage
Jiewen Xiao1, Chuanqi Liu1, Bangmin Ju1
1Beijing Key Laboratory for Source Control Technology of Water Pollution, Engineering Research Center for Water Pollution Source Control and Eco-remediation, College of Environmental Science and Engineering, Beijing Forestry University, 35 Tsinghua East Road, Beijing 100083, China.
Abstract:
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.
