Statistical Optimization of Medium Components to Enhance Bioelectricity Generation in Microbial Fuel Cell

Alshehri, A.N.Z.; Ghanem, Khaled M.; Al-Garni, Saleh M.

doi:10.1007/s13369-012-0397-9

Cited by 7 publications

(9 citation statements)

References 17 publications

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“…In general, the data can be collected from controlled experiments, observational studies, and simulations based on engineering models. Most MFC model works were performed based on controlled experiments with studying the effect of parameters on system performance [125][126][127][128][129][130][131][132][133]. More details on data collection for controlled experiments will be introduced in Section 3.2.…”

Section: Overviewmentioning

confidence: 99%

“…A discussion on selecting the appropriate type of model is provided in Supplementary Materials (Model Selection and Figure S1). Most models used for MFC modeling are based on linear regression (LR), and these models usually have good interpretability and simple model structures [125][126][127][129][130][131][132][133]. In recent years, data mining methods were also introduced for modeling the MFC performance such as artificial neural network (ANN), genetic programming (GP), support vector machine (SVM) and relevance vector machine (RVM) [128,137,138].…”

Section: Overviewmentioning

confidence: 99%

“…MFCs have been studied via controlled experiments intensively [125][126][127][128][129][130][131][132][133]141,145]. The controlled experiments are performed in OFAT or design of experiments (DOE) fashion.…”

Section: Data Generationmentioning

confidence: 99%

“…Moreover, the number of experimental runs required can be significantly reduced in DOE compared with all possible combinations, or random selections of factor levels. The past MFC studies have used factorial design (FD), central composite design (CCD), box-Behnken design (BBD), Placket-Burman design (PBD), and uniform design (UD) [125,127,128,[130][131][132][133]142,153]. More general designs can be found in [152].…”

Section: Data Generationmentioning

confidence: 99%

“…The authors also concluded that two-factor and three-factor interactions were less significant, and might be negligible in the final identified mode. The ANOVA or RSM were used in several other works studying the effects of parameters on MFC performance (as summarized in Table 1) [126,[129][130][131][132][133]153].…”

Section: Linear Regressionmentioning

confidence: 99%

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A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

et al. 2016

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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.

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Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Linear Regressionmentioning

confidence: 99%

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A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

et al. 2016

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Resource recovery from low strength wastewater in a bioelectrochemical desalination process

Stuart-Dahl

Martinez‐Guerra

Kokabian

et al. 2019

Engineering in Life Sciences

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In this research, low strength synthetic wastewaters with chemical oxygen demand less than 300 mg L−1 were treated at different concentrations in a bioelectrochemical desalination process. A process optimization model was utilized to study the performance of the photosynthetic bioelectrochemical desalination process. The variables include substrate (chemical oxygen demand) concentration, total dissolved solids, and microalgae biomass concentration in the cathode chamber. Relationships between the chemical oxygen demand concentration, microalgae, and salt concentrations were evaluated. Power densities and potential energy benefits from microalgal biomass growth were discussed. The results from this study demonstrated the reliability and reproducibility of the photosynthetic microbial desalination process performance followed by a response surface methodology optimization. This study also confirms the suitability of bioelectrochemical desalination process for treating low substrate wastewaters such as agricultural wastewaters, anaerobic digester effluents, and septic tank effluents for net energy production and water desalination.

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Optimization of factors affecting current generation, biofilm formation and rhamnolipid production by electroactive Pseudomonas aeruginosa FA17

Allam

Elnouby

Sabry

et al. 2021

International Journal of Hydrogen Energy

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Statistical Optimization of Medium Components to Enhance Bioelectricity Generation in Microbial Fuel Cell

Cited by 7 publications

References 17 publications

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

Resource recovery from low strength wastewater in a bioelectrochemical desalination process

Optimization of factors affecting current generation, biofilm formation and rhamnolipid production by electroactive Pseudomonas aeruginosa FA17

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