Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning...

Area of Science:

• Environmental Engineering
• Data Science
• Wastewater Treatment Technology

Background:

• Nitrous oxide (N2O) emissions from wastewater treatment plants (WWTPs) show significant seasonal variations.
• Conventional biokinetic models struggle to predict N2O emissions due to complex biochemical processes.

Purpose of the Study:

• To develop data-driven models for forecasting N2O emissions from WWTPs.
• To explore the integration of these models into a predictive control framework for emission reduction.

Main Methods:

• Utilized long short-term memory (LSTM) based encoder-decoder models.
• Trained and tested models on 15 months of data from a full-scale WWTP, including seasonal emission peaks.
• Evaluated model performance across various prediction horizons (0.5 to 6.0 hours).

Main Results:

• The best-performing LSTM model (256-256 architecture) achieved high accuracy (R² up to 0.98).
• Key input features included past N2O emissions, influent flowrate, NH4+, NOx, and DO.
• Model performance decreased with longer prediction horizons, emphasizing the importance of process variables.

Conclusions:

• LSTM models show strong potential for accurate short-term N2O emission forecasting in WWTPs.
• These models can support operational control for reducing N2O emissions.
• Hybrid approaches combining deep learning with mechanistic insights may improve long-term forecasting.