A knowledge discovery framework to predict the N₂O emissions in the wastewater sector

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 NH₄-N 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. The results of the study reveal that the aerobic dissolved N₂O concentration is correlated with the drop of average aerobic conductivity rate (spearman correlation coefficient equal to 0.7), the DO (spearman correlation coefficient equal to -0.7) and the changes of conductivity between sequential cycles. Additionally, operational conditions resulting in low aerobic N₂O accumulation (<0.6 mg/L) were identified; step-feeding, control of initial NH₄⁺ concentrations and aeration duration can mitigate the N₂O peaks observed in the system. The N₂O emissions during aeration shows correlation with the stripping of accumulated N₂O from the previous anoxic cycle. The analysis shows that N₂O is always consumed after the depletion of NO₂^- during denitritation (after the "nitrite knee"). Based on these findings SVM classifiers were constructed to predict whether dissolved N₂O will be consumed during the anoxic and anaerobic phases and SVR models were trained to predict the N₂O concentration at the end of the anaerobic phase and the average dissolved N₂O concentration during aeration. The proposed approach accurately predicts the N₂O emissions as a latent parameter from other low-cost sensors that are traditionally deployed in biological batch processes.