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

ABSTRACT

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.