4.8 Article

High efficiency in-situ biogas upgrading in a bioelectrochemical system with low energy input

Journal

WATER RESEARCH
Volume 197, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.watres.2021.117055

Keywords

Biogas upgrading; Direct electron transfer; Methanothrix; CO2 reduction; Artificial neural network

Funding

  1. Fundamental Research Funds for the Central Universities [2019ZY19]
  2. National Natural Science Foundation of China [51708031]

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This study successfully upgraded biogas using a bioelectrochemical system with low energy input, achieving higher methane content and lower CO2 concentration. The high efficiency of this approach may be attributed to the enrichment of Methanothrix and Actinobacillus species, as well as the use of artificial neural network models.
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 x10(-3) and R-2 of 0.99. (C) 2021 Elsevier Ltd. All rights reserved.

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