Data Analytics is being deployed to predict the dissolved nitrous oxide (N2O) concentration in a full-scale sidestream sequence batch reactor (SBR) treating the anaerobic supernatant. On average, the N2O emissions are equal to 7.6% of the NH4eN load and can contribute up to 97% to the operational carbon footprint of the studied nitritation-denitritation and via-nitrite enhanced biological phosphorus removal
process (SCENA). The analysis showed that average aerobic dissolved N2O concentration could significantly vary under similar influent loads, dissolved oxygen (DO), pH and removal efficiencies. A combination of density-based clustering, support vector machine (SVM), and support vector regression (SVR) models were deployed to estimate the dissolved N2O concentration and behaviour in the different phases
of the SBR system.