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Energies 2016, 9(2), 111; doi:10.3390/en9020111

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

1,†
,
2,†
,
1
,
2,* and 1,*
1
Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
2
Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Thomas E. Amidon
Received: 14 December 2015 / Revised: 22 January 2016 / Accepted: 3 February 2016 / Published: 18 February 2016
View Full-Text   |   Download PDF [3191 KB, uploaded 18 February 2016]   |  

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. View Full-Text
Keywords: bioelectrochemical systems; data mining; differential equations; engineering models; regression; statistical models bioelectrochemical systems; data mining; differential equations; engineering models; regression; statistical models
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Luo, S.; Sun, H.; Ping, Q.; Jin, R.; He, Z. A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects. Energies 2016, 9, 111.

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