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.

Keywords: HNA and LNA cells; anaerobic membrane bioreactor; flow cytometry; microbial diversity; predictive model.

Figure 1

Pilot AnMBR and its performance considering the variation in the HRT: (A) different operational phases conducted and sample points for this study, (B) COD removal efficiency, and (C) methane production.

Figure 2

Gating strategy to quantify the HNA and LNA populations in sludge samples and the monitoring of their actual abundance: (A) removal of events on the edges of SSC/FSC, (B) selection of the cell population for HNA/LNA analysis with FITC/SSC, (C) establishing gates between HNA and LNA subpopulations, and (D) number of HNA and LNA cells and the ratio of HNA/LNA abundance at different HRTs.

Figure 3

Microbial diversity of the anaerobic microbial consortium derived from 16S rRNA gene-based amplicon sequencing: (A) microbial richness represented on the basis of the Chao 1 index at different HRTs and (B) associated standard deviation in Chao 1 index values of each examined HRT.

Figure 4

Spearman correlation matrix of the relationships among microbial diversity, FCM-derived fingerprint data, and reactor performance. The colors and sizes of the dot indicate the correlation coefficient. The darkest blue color indicates a perfect positive correlation (r = 1); the darkest red indicates a perfect negative correlation (r = −1); and colors of different gradients indicate a gradual loss in correlation from both spectrum ends.

Figure 5

Comparison of the predicted and measured (A) COD removal efficiency and (B) methane production based on the corresponding proposed linear regression models with two separated stages.