Optimizing multi-variables of microbial fuel cell for electricity generation with an integrated modeling and experimental approach

Fang, Fang; Zang, Guo-Long; Sun, Min; Yu, Han‐Qing

doi:10.1016/j.apenergy.2013.04.017

Cited by 35 publications

(33 citation statements)

References 29 publications

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“…The learning in infinite-dimensional space is usually easier than in finite-dimensional input space [136]. RVM has very similar formulation as SVM, except that the RVM solves the problem in Bayesian framework [128]. The RVM can provide probabilistic classification, and typically use fewer kernel functions than SVM.…”

Section: Data Mining Methodsmentioning

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%

“…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]. These data mining methods can yield good prediction performance.…”

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%

“…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%

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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: Data Mining Methodsmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

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A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

et al. 2016

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Statistical Methods for Modelling and Performance Analysis of Bioelectrochemical Systems

Das,

Jadhav,

Jagtap

et al. 2023

Microbial Electrochemical Technologies

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Machine Learning in Bioelectrocatalysis

Huang,

Gao,

Chang

et al. 2023

Advanced Science

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At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high‐value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

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Optimizing multi-variables of microbial fuel cell for electricity generation with an integrated modeling and experimental approach

Cited by 35 publications

References 29 publications

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects

Statistical Methods for Modelling and Performance Analysis of Bioelectrochemical Systems

Machine Learning in Bioelectrocatalysis

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