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A Novel Bioreactor for High Density Cultivation of Diverse Microbial Communities

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Toward an operational dynamic model for tertiary nitrification by submerged biofiltration.

E Vigne¹, J M Choubert, J P Canler

¹Civil Engineering Department, Faculty of Sciences and Engineering, Pavilion Adrien-Pouliot, Laval University, Quebec (Quebec) G1K 7P4, Canada. emmanuelle.vigne.1@ulaval.ca

Water Science and Technology : a Journal of the International Association on Water Pollution Research

|June 6, 2007

Summary

This study refines a biofiltration model (BAF) for tertiary nitrification, improving predictions of ammonia and nitrate levels in wastewater treatment. Key parameters influencing effluent quality were identified, enabling a more accurate calibration process.

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Area of Science:

Environmental Engineering
Water Treatment Technologies
Wastewater Management

Background:

Tertiary nitrification is crucial for effective wastewater treatment.
Biofiltration models (BAF) are used to simulate nitrification processes.
Accurate parameterization of BAF models is essential for reliable predictions.

Purpose of the Study:

To develop and validate a methodology for fitting and validating parameters of a biofiltration model (BAF).
To assess the model's performance under dynamic loading conditions in tertiary nitrification.
To identify key BAF parameters influencing effluent concentrations and head loss.

Main Methods:

Utilized a semi-industrial pilot to apply different time-loading rates during filtration-backwash runs.
Compared predicted values from the BAF model with observed data for NH4-N, NO3-N, TSS, and head loss (deltaP).
Performed a sensitivity analysis to rank BAF parameters based on their influence on effluent quality and head loss.

Main Results:

BAF model predictions, using default parameters, generally overestimated measured values but reproduced trends accurately.
Sensitivity analysis identified critical parameters: filtration module, biofilm density (for TSS and head loss), specific autotrophic growth rate, maximum biofilm thickness, and diffusivity reduction (for NH4-N and NO3-N).
A hierarchy of BAF parameters was established, categorizing them by their influence (strong vs. low).

Conclusions:

The established parameter hierarchy facilitates a more targeted calibration procedure for the BAF model.
Direct measurement of identified key BAF parameters can significantly enhance model accuracy.
The validated methodology improves the reliability of biofiltration models in dynamic tertiary nitrification treatment.