A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects
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 f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-02-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/2/111 |
| _version_ | 1811302101469364224 |
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| author | Shuai Luo Hongyue Sun Qingyun Ping Ran Jin Zhen He |
| author_facet | Shuai Luo Hongyue Sun Qingyun Ping Ran Jin Zhen He |
| author_sort | Shuai Luo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T07:21:59Z |
| format | Article |
| id | doaj.art-0147e9becb4443079a17943652d2cc48 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T07:21:59Z |
| publishDate | 2016-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0147e9becb4443079a17943652d2cc482022-12-22T02:56:35ZengMDPI AGEnergies1996-10732016-02-019211110.3390/en9020111en9020111A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical AspectsShuai Luo0Hongyue Sun1Qingyun Ping2Ran Jin3Zhen He4Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USAGrado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USADepartment of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USAGrado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USADepartment of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USABioelectrochemical 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.http://www.mdpi.com/1996-1073/9/2/111bioelectrochemical systemsdata miningdifferential equationsengineering modelsregressionstatistical models |
| spellingShingle | Shuai Luo Hongyue Sun Qingyun Ping Ran Jin Zhen He A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects Energies bioelectrochemical systems data mining differential equations engineering models regression statistical models |
| title | A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects |
| title_full | A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects |
| title_fullStr | A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects |
| title_full_unstemmed | A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects |
| title_short | A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects |
| title_sort | review of modeling bioelectrochemical systems engineering and statistical aspects |
| topic | bioelectrochemical systems data mining differential equations engineering models regression statistical models |
| url | http://www.mdpi.com/1996-1073/9/2/111 |
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