Integrated Model for Understanding N₂O Emissions from Wastewater Treatment Plants: A Deep Learning Approach

This study aims to demonstrate the application of deep learning quantitatively describe long-term full-scale data observed from wastewater treatment plants (WWTPs) perspectives process modeling, analysis, and forecasting modeling. Approximately, 750,000 measurements including influent flow rate, air temperature, ammonium, nitrate, dissolved oxygen, nitrous oxide (N2O) collected for more than a year Avedøre WWTP located in Denmark are utilized develop neural network (DNN) through supervised optimal DNN (R2 > 0.90) is selected further evaluation. For global sensitivity analysis based on variance decomposition considered identify key parameters contributing high N2O emission characteristics. forecasting, proposed DNN-based model compared with long short-term memory (LSTM), showing that LSTM-based performs significantly better 0.94 root-mean-squared error reduced by 64%). The results account feasibility data-driven methods describing understanding rather complex dynamics WWTPs. Research into hybrid modeling concepts integrating mechanistic models WWTPs (e.g., ASMs) would be suggested as future direction monitoring emissions