Dynamically predicting nitrous oxide emissions in a full-scale industrial activated sludge reactor under multiple...

Area of Science:

• Environmental Engineering
• Wastewater Treatment Technologies
• Greenhouse Gas Emission Monitoring

Background:

• Digital tools are crucial for quantifying greenhouse gas (GHG) emissions in wastewater treatment plants (WWTPs) to meet net-zero targets.
• Validation studies of model predictions in industrial settings, particularly for effluent quality, economics, and emission factors, are scarce.
• Industrial wastewater presents unique challenges for accurate emission prediction and mitigation.

Purpose of the Study:

• To develop a decision support tool (DST) for dynamic prediction of nitrous oxide (N2O) emissions in industrial activated sludge reactors (ASRs).
• To incorporate biological and physico-chemical processes, including specialized gas-liquid mass transfer routines for covered reactors.
• To validate the DST using full-scale industrial data and test its efficacy across various operational strategies.

Main Methods:

• Development of a DST integrating biological and physico-chemical processes with unique gas-liquid mass transfer routines.
• Validation using high-frequency (minute-level) and daily data from Northern Europe's largest industrial WWTP.
• Testing the DST across different aeration patterns, influent COD/N ratios, and operational strategies.

Main Results:

• The DST accurately reproduced daily COD/nitrogen removal, sulfur transformations, and phosphorus precipitation (8.6% deviation over six weeks).
• High-frequency dynamics of nitrogen species and dissolved oxygen were captured with NRMSEs between 0.11 and 0.16.
• Emission factors (EFs) correlated strongly (R² up to 0.9) with influent COD/N ratios, influenced by oxygen supply and aeration duration (EFs 0.2%–1.4%).
• Denitrification (DEN) was identified as the primary N2O production pathway, with nitrifier-denitrification (ND) contributing less.
• Optimized strategies derived from the DST achieved up to 71% reduction in N2O emissions (1.4% to 0.4%), potentially mitigating over 15,000 tons CO2-e annually.

Conclusions:

• The developed DST effectively predicts N2O emissions and key performance indicators in industrial WWTPs.
• The DST provides valuable insights into operational parameters influencing N2O production and allows for targeted mitigation strategies.
• The tool demonstrates significant potential for optimizing WWTP operations to achieve substantial GHG emission reductions and environmental benefits.