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A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

Author

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  • Shuai Luo

    (Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
    These authors contributed equally to this work.)

  • Hongyue Sun

    (Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
    These authors contributed equally to this work.)

  • Qingyun Ping

    (Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA)

  • Ran Jin

    (Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA)

  • Zhen He

    (Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA)

Abstract

Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:2:p:111-:d:64007
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    References listed on IDEAS

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    1. Grendár, M., 2012. "Is the p-value a good measure of evidence? Asymptotic consistency criteria," Statistics & Probability Letters, Elsevier, vol. 82(6), pages 1116-1119.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    4. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    5. Venkata Mohan, S. & Velvizhi, G. & Annie Modestra, J. & Srikanth, S., 2014. "Microbial fuel cell: Critical factors regulating bio-catalyzed electrochemical process and recent advancements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 779-797.
    6. Escapa, A. & Mateos, R. & Martínez, E.J. & Blanes, J., 2016. "Microbial electrolysis cells: An emerging technology for wastewater treatment and energy recovery. From laboratory to pilot plant and beyond," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 942-956.
    7. Ran Jin & Xinwei Deng, 2015. "Ensemble modeling for data fusion in manufacturing process scale-up," IISE Transactions, Taylor & Francis Journals, vol. 47(3), pages 203-214, March.
    8. Chen, Yinguang & Luo, Jingyang & Yan, Yuanyuan & Feng, Leiyu, 2013. "Enhanced production of short-chain fatty acid by co-fermentation of waste activated sludge and kitchen waste under alkaline conditions and its application to microbial fuel cells," Applied Energy, Elsevier, vol. 102(C), pages 1197-1204.
    9. Xiaojin Li & Ibrahim M. Abu-Reesh & Zhen He, 2015. "Development of Bioelectrochemical Systems to Promote Sustainable Agriculture," Agriculture, MDPI, vol. 5(3), pages 1-22, June.
    10. 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.
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    1. Luo, Shuai & Wang, Zhi-Wu & He, Zhen, 2017. "Mathematical modeling of the dynamic behavior of an integrated photo-bioelectrochemical system for simultaneous wastewater treatment and bioenergy recovery," Energy, Elsevier, vol. 124(C), pages 227-237.
    2. Daniele Cecconet & Arianna Callegari & Andrea G. Capodaglio, 2018. "Bioelectrochemical Systems for Removal of Selected Metals and Perchlorate from Groundwater: A Review," Energies, MDPI, vol. 11(10), pages 1-21, October.

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