4.4 Article

Membrane-less microbial fuel cell: Monte Carlo simulation and sensitivity analysis for COD removal in dewatered sludge

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AIP ADVANCES
卷 11, 期 6, 页码 -

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AIP Publishing
DOI: 10.1063/5.0039014

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  1. Universiti Sains Malaysia [304/PTEKIND/6315353]
  2. Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme (FRGS) [203/PTEKIND/6711823]

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Dewatered sludge can be used to power membrane-less microbial fuel cells, but the performance may vary depending on the growth of electrogenic bacteria. A stochastic simulation was used to assess the uncertainty of COD removal, showing a range of substrate removal values. Sensitivity analysis revealed that the performance of the ML-MFC is closely related to the growth of electrogenic bacteria.
Dewatered sludge is redundantly found in a municipal wastewater treatment plant, and the amount is increasing every year. However, the dewatered sludge could be used to power the membrane-less microbial fuel cell (ML-MFC), which is operated electrochemically via incorporation of electricity producing micro-organisms. The dewatered sludge normally acts as an electron donating substrate. Results showed that the ML-MFC produced voltage at about 927.7 +/- 11.24 mV whereby 178.7 mg/L of chemical oxygen demand (COD) was removed after 240 h of incubation period. Nonetheless, voltage and COD removal values obtained from the dewatered sludge in the ML-MFC might differ every time the study is repeated because the availability of maximum biomass of electrogenic bacteria (EB) will be different due to the heterogeneous properties and EB performance inside the ML-MFC. The parametric uncertainty analysis of COD removal was then assessed using Monte Carlo simulation (stochastic variable) to determine the distribution probability affected by the fluctuation and variation of kinetic model parameters. From the study of 100000 samples tested (simulation), the results show that the substrate removal (S) value ranged from 172.58 to 185.02 mg/L. The impact of each kinetic parameter on the ML-MFC performance was evaluated via sensitivity analysis. It is found that the ML-MFC performance significantly relied on the growth of EB present. (C) 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

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