Dataset Information

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells.

ABSTRACT: Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t - 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.

SUBMITTER: Cheng H

PROVIDER: S-EPMC10851436 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells.

Cheng Hong H Medina Julie Sanchez JS Zhou Jianqiang J Pinho Eduardo Machado EM Meng Rui R Wang Liuwei L He Qiang Q Morán Xosé Anxelu G XAG Hong Pei-Ying PY

Environmental science & technology 20240123 5

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets o ...[more]

PMID: 38261758

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Project description:Despite numerous studies undertaken to define the development and significance of the dynamic membrane (DM) formed on some coarse materials, the optimization of reactor configuration and the control of the membrane fouling of anaerobic dynamic membrane bioreactor (AnDMBR) need to be further investigated. The aim of this study was to design a novel anaerobic gravity-driven dynamic membrane bioreactor (AnGDMBR) for the effective and low-cost treatment of municipal wastewater. An 800 mesh nylon net was determined as the optimal support material based on its less irreversible fouling and higher effluent quality by the dead-end filtration experiments. During the continuous operation period of 44 days, the reactor performance, DM filtration behavior and microbial characteristics were studied and compared with the results of recent studies. AnGDMBR had a higher removal rate of chemical oxygen demand (COD) of 85.45 ± 7.06%. Photometric analysis integrating with three-dimensional excitation-emission matrix fluorescence spectra showed that the DM effectively intercepted organics (46.34 ± 16.50%, 75.24 ± 17.35%, and 66.39 ± 17.66% for COD, polysaccharides, and proteins). The addition of suspended carriers effectively removed the DM layer by mechanical scouring, and the growth rate of transmembrane pressure (TMP) and the decreasing rate of flux were reduced from 18.7 to 4.7 Pa/h and 0.07 to 0.01 L/(m2·h2), respectively. However, a dense and thin morphological structure of the DM layer was still observed in the end of reactor operation and plenty of filamentous microorganisms (i.e., SJA-15 and Anaerolineaceae) and the acidogens (i.e., Aeromonadaceae) predominated in the DM layer, which was also embedded in the membrane pore and led to severe irreversible fouling. In summary, the novel AnGDMBR has a superior performance (higher organic removal and lower fouling rates), which provides useful information on the configuration and operation of AnDMBRs for municipal wastewater treatment.

Dataset Information

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells.

Publications

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells.

Similar Datasets

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