posted on 2024-01-23, 20:18authored byHong Cheng, Julie Sanchez Medina, Jianqiang Zhou, Eduardo Machado Pinho, Rui Meng, Liuwei Wang, Qiang He, Xosé Anxelu
G. Morán, Pei-Ying Hong
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.