| dc.contributor |
Civil and Environmental Engineering |
|
| dc.contributor |
Industrial and Systems Engineering |
|
| dc.creator |
Luo, Shuai |
|
| dc.creator |
Sun, Hongyue |
|
| dc.creator |
Ping, Qingyun |
|
| dc.creator |
Jin, Ran |
|
| dc.creator |
He, Zhen |
|
| dc.date |
2017-09-20T18:24:53Z |
|
| dc.date |
2017-09-20T18:24:53Z |
|
| dc.date |
2016-02-18 |
|
| dc.date |
2017-09-20T18:24:53Z |
|
| dc.date.accessioned |
2023-03-01T18:51:25Z |
|
| dc.date.available |
2023-03-01T18:51:25Z |
|
| dc.identifier |
Luo, S.; Sun, H.; Ping, Q.; Jin, R.; He, Z. A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects. Energies 2016, 9, 111. |
|
| dc.identifier |
http://hdl.handle.net/10919/79268 |
|
| dc.identifier |
https://doi.org/10.3390/en9020111 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/281508 |
|
| dc.description |
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. |
|
| dc.description |
Published version |
|
| dc.format |
application/pdf |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
MDPI |
|
| dc.rights |
Creative Commons Attribution 4.0 International |
|
| dc.rights |
http://creativecommons.org/licenses/by/4.0/ |
|
| dc.subject |
bioelectrochemical systems |
|
| dc.subject |
data mining |
|
| dc.subject |
differential equations |
|
| dc.subject |
engineering models |
|
| dc.subject |
regression |
|
| dc.subject |
statistical models |
|
| dc.title |
A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects |
|
| dc.title |
Energies |
|
| dc.type |
Article - Refereed |
|
| dc.type |
Text |
|