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

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.