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Search Results (3)

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Keywords = bio-electrochemical anaerobic digestion (BEAD)

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14 pages, 2694 KiB  
Article
Influence of Organic Loading Rate on Methane Production from Brewery Wastewater in Bioelectrochemical Anaerobic Digestion
by Hongda Pan, Qing Feng, Yong Zhao, Xiaoxiang Li and Hao Zi
Fermentation 2023, 9(11), 932; https://doi.org/10.3390/fermentation9110932 - 26 Oct 2023
Cited by 3 | Viewed by 2334
Abstract
The effect of bioelectrochemical anaerobic digestion (BEAD) on the methanogenic performance of brewery wastewater at different organic loading rates (OLRs) was investigated and compared to conventional anaerobic digestion. A continuous BEAD reactor was used to treat brewery wastewater at different OLRs of 2, [...] Read more.
The effect of bioelectrochemical anaerobic digestion (BEAD) on the methanogenic performance of brewery wastewater at different organic loading rates (OLRs) was investigated and compared to conventional anaerobic digestion. A continuous BEAD reactor was used to treat brewery wastewater at different OLRs of 2, 4, 8, 16, and 20 g COD/L.d. The experimental results showed that the methane production was gradually increased from 0.48 L/L.d at an OLR of 2 g COD/L.d to 5.64 L/L.d at an OLR of 20 g COD/L.d. The methane production of the BEAD system was significantly higher than that of the conventional anaerobic reactor, indicating that BEAD has a better treatment effect for brewery wastewater. The performance of the conventional anaerobic reactor was significantly reduced especially at an OLR of 16 g COD/L.d, while the BEAD system could withstand a higher OLR. Bioelectrochemical systems provide a completely new platform for the anaerobic treatment of brewery wastewater and greatly improve the operation of anaerobic processes. Full article
(This article belongs to the Special Issue Advance in Microbial Electrochemical Technologies)
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14 pages, 2789 KiB  
Article
Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion
by Ain Cheon, Jwakyung Sung, Hangbae Jun, Heewon Jang, Minji Kim and Jungyu Park
Processes 2022, 10(1), 158; https://doi.org/10.3390/pr10010158 - 14 Jan 2022
Cited by 26 | Viewed by 3568
Abstract
The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, [...] Read more.
The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models. Full article
(This article belongs to the Special Issue Bioelectrochemical System for Wastewater Treatment and Energy Recovery)
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11 pages, 1141 KiB  
Article
Changes of Bacterial Communities in an Anaerobic Digestion and a Bio-Electrochemical Anaerobic Digestion Reactors According to Organic Load
by Jun-Gyu Park, Won-Beom Shin, Wei-Qi Shi and Hang-Bae Jun
Energies 2019, 12(15), 2958; https://doi.org/10.3390/en12152958 - 1 Aug 2019
Cited by 20 | Viewed by 3447
Abstract
Bacterial communities change in bulk solution of anaerobic digestion (AD) and bio-electrochemical anaerobic digestion reactors (BEAD) were monitored at each organic loading rate (OLR) to investigate the effect of voltage supply on bacterial species change in bulk solution. Chemical oxygen demand (COD) degradation [...] Read more.
Bacterial communities change in bulk solution of anaerobic digestion (AD) and bio-electrochemical anaerobic digestion reactors (BEAD) were monitored at each organic loading rate (OLR) to investigate the effect of voltage supply on bacterial species change in bulk solution. Chemical oxygen demand (COD) degradation and methane production from AD and BEAD reactors were also analyzed by gradually increasing food waste OLR. The BEAD reactor maintained stable COD removal and methane production at 6.0 kg/m3·d. The maximum OLR of AD reactor for optimal operation was 4.0 kg/m3·d. pH and alkalinity decline and volatile fatty acid (VFA) accumulation, which are the problem in high load anaerobic digestion of readily decomposable food wastes, were again the major factors destroying the optimal operation condition of the AD reactor at 6.0 kg/m3·d. Contrarily, the electrochemically activated dense communities of exoelectrogenic bacteria and VFA-oxidizing bacteria prevented VFAs from accumulating inside the BEAD reactor. This maintained stable pH and alkalinity conditions, ultimately contributing to stable methane production. Full article
(This article belongs to the Special Issue Biomass Pretreatment and Biomass Conversion to Biofuels)
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