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

• Published in: Water research (Oxford) 2020-07, Vol.178, p.115799-115799, Article 115799
• Main Authors: Vasilaki, V., Conca, V., Frison, N., Eusebi, A.L., Fatone, F., Katsou, E.
• Format: Article
• Language: English
• Subjects: Long-term dissolved N2O and Energy consumption monitoring, knowledge discovery and data mining; Short-cut enhanced nutrients abatement - SCENA
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2020.115799

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 (