Next Article in Journal
Synthesis and Characterization of Sulfonated Poly(Phenylene) Containing a Non-Planar Structure and Dibenzoyl Groups
Previous Article in Journal
Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study
Open AccessFeature PaperEditor’s ChoiceReview

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

by 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
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Thomas E. Amidon
Energies 2016, 9(2), 111; https://doi.org/10.3390/en9020111
Received: 14 December 2015 / Revised: 22 January 2016 / Accepted: 3 February 2016 / Published: 18 February 2016
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop