Modeling organic nitrogen conversions in activated sludge bioreactors

Area of Science:

• Environmental Engineering
• Water Quality Management
• Biogeochemical Cycles

Background:

• Biological nutrient removal (BNR) systems often struggle with significant organic nitrogen (ON) effluent concentrations (25-50% of total nitrogen).
• Current models for ON conversions lack accuracy, necessitating improved approaches for effective nitrogen removal.
• Understanding ON fractions (dissolved, colloidal, particulate) is crucial for optimizing wastewater treatment processes.

Purpose of the Study:

• To compare existing models of organic nitrogen conversions with a newly developed conceptual model.
• To integrate the new ON conversion model with the Activated Sludge Model No. 2d (ASM2d).
• To enhance the simulation of ammonification, biomass decay, and hydrolysis of particulate and colloidal ON.

Main Methods:

• Developed a new conceptual model for organic nitrogen (ON) conversions, incorporating dissolved (DON), colloidal (CON), and particulate (PON) fractions.
• Combined the new ON model with the Activated Sludge Model No. 2d (ASM2d).
• Utilized field and laboratory data from the "Wschod" Wastewater Treatment Plant (WWTP) in Gdansk, Poland, for model calibration and validation.

Main Results:

• The enhanced ASM2d model accurately predicted dissolved and colloidal ON profiles by adjusting ammonification and hydrolysis rates under various electron acceptor conditions.
• The model successfully simulated the behavior of both inorganic nitrogen (NH4-N, NOX-N) and organic nitrogen (DON, CON) forms in batch experiments.
• Accurate simulation of effluent ON levels remains challenging due to limitations in direct ON measurement analytical methods and insufficient databases for parameterization.

Conclusions:

• The developed conceptual model offers improved simulation capabilities for organic nitrogen dynamics in BNR systems.
• Accurate prediction of effluent ON requires advancements in analytical techniques and the creation of comprehensive, dynamic ON concentration databases.
• Further research is needed to refine models and overcome analytical challenges for precise ON management in wastewater treatment.