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Optimizing multi-variables of microbial fuel cell for electricity generation with an integrated modeling and experimental approach

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  • Fang, Fang
  • Zang, Guo-Long
  • Sun, Min
  • Yu, Han-Qing

Abstract

Microbial fuel cell (MFC) is a device that transforms chemical energy in wastewater into electricity, and its performance is influenced by multi-variables. Mathematic modeling approach could be a useful alternative to design and optimize such a complex system for power generation and wastewater treatment. Here we develop a novel integrated modeling approach with uniform design (UD), a machine learning approach of relevance vector machine (RVM) and a global searching algorithm of accelerating genetic algorithm (AGA) to optimize the operation of multi-variable MFCs after they are constructed. With the integrated UD–RVM–AGA approach, a maximum Coulombic efficiency of 73.0% and power density of 1097mW/m3 of MFC are estimated under the optimal conditions of ionic concentration of 102mM, initial pH of 7.75, medium nitrogen concentration of 48.4mg/L, and temperature of 30.6°C. The Coulombic efficiency and power density in the verification experiments, 70.9% and 1156mW/m3, are close to those calculated by the modeling approach. The results demonstrate that the integrated UD–RVM–AGA approach is effective and reliable to optimize the complex MFC and improve its performance.

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  • Fang, Fang & Zang, Guo-Long & Sun, Min & Yu, Han-Qing, 2013. "Optimizing multi-variables of microbial fuel cell for electricity generation with an integrated modeling and experimental approach," Applied Energy, Elsevier, vol. 110(C), pages 98-103.
    • Handle: RePEc:eee:appene:v:110:y:2013:i:c:p:98-103
    DOI: 10.1016/j.apenergy.2013.04.017
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    References listed on IDEAS

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    1. Gonzalez del Campo, A. & Lobato, J. & Cañizares, P. & Rodrigo, M.A. & Fernandez Morales, F.J., 2013. "Short-term effects of temperature and COD in a microbial fuel cell," Applied Energy, Elsevier, vol. 101(C), pages 213-217.
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    Cited by:

    1. Shuai Luo & Hongyue Sun & Qingyun Ping & Ran Jin & Zhen He, 2016. "A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects," Energies, MDPI, vol. 9(2), pages 1-27, February.
    2. Shi, Xian-Yang & Li, Wen-Wei & Yu, Han-Qing, 2014. "Key parameters governing biological hydrogen production from benzoate by Rhodopseudomonas capsulata," Applied Energy, Elsevier, vol. 133(C), pages 121-126.
    3. Almatouq, A. & Babatunde, A.O., 2018. "Identifying optimized conditions for concurrent electricity production and phosphorus recovery in a mediator-less dual chamber microbial fuel cell," Applied Energy, Elsevier, vol. 230(C), pages 122-134.
    4. Pasternak, Grzegorz & Greenman, John & Ieropoulos, Ioannis, 2016. "Regeneration of the power performance of cathodes affected by biofouling," Applied Energy, Elsevier, vol. 173(C), pages 431-437.

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