High efficiency in-situ biogas upgrading in a bioelectrochemical system with low energy input. (1st June 2021) - Liu, Chuanqi; Xiao, Jiewen; Li, Haoyong; Chen, Qian; Sun, Dezhi; Cheng, Xiang; Li, Pengsong; Dang, Yan; Smith, Jessica A.; Holmes, Dawn E.
Highlights: High quality biogas (97% CH4 ) produced by bioelectrochemical upgrading system. Methanothrix species significantly enriched (92.7%) on the biocathode. Both acetoclastic methanogenesis and electromethanogenesis occurred on biocathode. New ANN model developed to simulate and predict DET biogas upgrading process. Abstract: Biogas produced from anaerobic digestion usually contains 30%-50% CO2, much of which must be removed, before utilization. Bioelectrochemical biogas upgrading approaches show promise, however, they have not yet been optimized for practical applications. In this study, a bioelectrochemical system with low energy input (applied cathode potential of -0.5 V vs. standard hydrogen electrode, SHE) was used for in-situ biogas upgrading. High efficiency CO2 conversion (318.5 mol/d/m 2 ) was achieved when the system was operated with an organic load of 1.7 kgCOD/(m 3 d). Methane content in the upgraded biogas was 97.0% and CO2 concentrations stayed below 3%, which is comparable to biogas upgraded with more expensive and less sustainable physiochemical approaches. The high efficiency of this approach could likely be attributed to a significant enrichment of Methanothrix (92.7%) species on the cathode surface that were expressing genes involved in both acetogenic methanogenesis and direct electron transfer (DET). Electromethanogenesis by these organisms also increased proton consumption and created a higher pH that increased the solubility of CO2 in the bioreactor. In addition, CO2 removal from the biogas was likely further enhanced by an enrichment of Actinobacillus species known to be capable of CO2 fixation. Artificial neural network (ANN) models were also used to estimate CH4 production under different loading conditions. The ANN architecture with 10 neurons at hidden layers fit best with a mean square error of 6.06 × 10 −3 and R 2 of 0.99. Graphical abstract: Image, graphical abstract