Quantifying, predicting, and mitigating nitrous oxide emissions in a full-scale partial nitritation/anammox reactor treating reject water.

In this paper, a set of mathematical tools are developed and assembled to quantify, predict and virtually assess NO emission mitigation strategies in partial nitritation (PN) / anammox (ANX) granular based reactors. The proposed approach is constructed upon a set of data pre-treatment methods, process simulation models, control tools (and algorithms) and key performance indicators to analyze, reproduce, and forecast the behavior of multiple operational variables within aerobic granular sludge systems. All these elements are tested on two full-scale data sets (#D1, #D2) collected over a period of four months (Sept-Dec 2023). Results show that data pretreatment is essential for noise reduction, filling data gaps, and ensuring smooth process simulations. The model accurately predicts (normalized RMSE< 1) multiple N oxidation states (NH, NO, NO, NO) and dissolved oxygen (DO), demonstrating its capability to describe bacterial behavior within the studied system. Special emphasis is placed on weak acid-base chemistry where pH is reliably reproduced, and it can be used for control purposes. Both biological and physico-chemical aspects are predicted at different time scales (months, days, minutes). While nitritation mainly occurred in the bulk, biofilm distribution showed inactive inner granule parts and increasing biomass (mostly ANX) towards the surface, with distinct organic concentrations. Gradients for multiple soluble compounds could also be reflected. Nitrifier denitrification (ND) is identified as the main NO production pathway. The model revealed that the system was suffering from low ANX activity leading to NO accumulation. This in combination with low DO levels resulted in an unusually high emission factor (EF). The validation data set also yielded satisfactory results (normalized RMSE< 1). The scenario analysis revealed that modification of the operational parameters could improve the ANX activity and lead to NO emission rates that are in line with what is normally expected from similar systems. The study includes a discussion on transitioning from process models to digital shadows/ twins for real-time process monitoring. Additionally, it emphasizes the necessity of evaluating reject water technologies from a plant-wide perspective.