Enhanced production of short-chain fatty acid by co-fermentation of waste activated sludge and kitchen waste under alkaline conditions and its application to microbial fuel cells

Chen, Yinguang; Luo, Jingyang; Yan, Yuanyuan; Feng, Leiyu

doi:10.1016/j.apenergy.2012.06.056

Cited by 213 publications

(78 citation statements)

References 29 publications

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“…Note that OFAT can only study the effect of a certain parameter on the MFC performance once at a time, resulting in interpretation problems of interactions or joint effects of multiple factors on the MFC performance. Misleading results can be yielded in such circumstances [125,126,133,152]. For instance, it was found that the interaction effect of pH and buffer concentration was very significant, while a false conclusion could be made if the interaction effect was ignored [125].…”

Section: Data Generationmentioning

confidence: 99%

“…Misleading results can be yielded in such circumstances [125,126,133,152]. For instance, it was found that the interaction effect of pH and buffer concentration was very significant, while a false conclusion could be made if the interaction effect was ignored [125].…”

Section: Data Generationmentioning

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%

“…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%

“…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%

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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 Generationmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Data Generationmentioning

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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Mesophilic and Thermophilic Anaerobic Digestion of Model Kitchen Waste with Variation of Fat Content

Sobon‐Mühlenbrock

Schlienz

Greger

2020

Chemie Ingenieur Technik

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Anaerobic digestion of model kitchen waste (MKW) produced on basis of real kitchen waste (KW) is studied in batch tests. The impact of fat content of three MKWs (14.7 %, 23 %, and 27.2 % rapeseed oil content) on the biogas production at mesophilic and thermophilic conditions and two loadings is examined. The loading of 5 g VS L-1 leads to fast degradation under both temperature modes and the volume of biogas produced is only weakly correlated to the oil content. For the loading of 25 g VS L-1 a strong retardation of biogas production, high concentration of volatile fatty acids and no correlation of oil content is observed.

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Enhanced production of short-chain fatty acid by co-fermentation of waste activated sludge and kitchen waste under alkaline conditions and its application to microbial fuel cells

Cited by 213 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

Mesophilic and Thermophilic Anaerobic Digestion of Model Kitchen Waste with Variation of Fat Content

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