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

Area of Science:

• Environmental microbiology
• Biotechnology
• Wastewater treatment

Background:

• Anaerobic membrane bioreactors (AnMBRs) require efficient monitoring for optimal performance.
• Predicting reactor outcomes like chemical oxygen demand (COD) removal and methane production is crucial for operational control.

Purpose of the Study:

• To develop a predictive model for AnMBR performance using microbial cell abundance data.
• To establish a rapid monitoring tool for facilitating AnMBR operation.

Main Methods:

• Development of a linear regression model incorporating a time-lagging mode.
• Utilizing low nucleic acid (LNA) cell numbers and the high nucleic acid (HNA) to LNA cell ratio as input data.
• Training and validation using pilot-scale AnMBR data and flow cytometry (FCM).

Main Results:

• The model accurately predicted COD removal efficiency and methane production 3.5 days in advance.
• Validation on an independent reactor confirmed the model's predictive accuracy.
• Correlation analysis identified specific HNA and LNA genera contributing to prediction accuracy.

Conclusions:

• Routine enumeration of HNA and LNA cells can be used with the developed model for rapid AnMBR performance prediction.
• This approach offers a fast and effective method for monitoring and managing AnMBR systems.
• The findings support the integration of microbial monitoring into wastewater treatment operational strategies.