A biokinetic model based on BioWin's Activated Sludge Digestion Model (ASDM) coupled with a nitrous oxide (N₂O) model was setup and calibrated for a full-scale wastewater treatment plant (WWTP) Amsterdam West, in the Netherlands. The model was calibrated using one year of continuous data to predict the seasonal variations of N₂O emissions in the gaseous phase. This, according to our best knowledge, is the most complete full-scale data set used to date for this purpose. The results obtained suggest that the currently available biokinetic model predicted the winter, summer, and autumn N₂O emissions well but failed to satisfactorily simulate the spring peak. During the calibration process, it was found that the nitrifier denitrification pathway could explain the observed emissions during all seasons while a combination of the nitrifier denitrification and incomplete heterotrophic denitrification pathways seemed to be dominant during the emissions peak observed during the spring season. Specifically, kinetic parameters related to free nitrous acid (FNA) displayed significant sensitivity leading to increased N₂O production. The obtained values of two kinetic parameters, i.e., the FNA half-saturation during ammonia oxidising bacteria (AOB) denitrification and the FNA inhibition concentration related to heterotrophic denitrification, suggested a strong influence of the FNA bulk concentration on the N₂O emissions and the observed seasonal variations. Based on the suboptimal performance and limitations of the biokinetic model, further research is needed to better understand the biochemical processes behind the seasonal peak and the influence of FNA.

Nitrous oxide (N₂O) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal variability, making accurate predictions with conventional biokinetic models difficult due to complex and poorly understood biochemical processes. This study addresses these challenges by exploring data-driven alternatives, using long short-term memory (LSTM) based encoder-decoder models as basis. The models were developed for future integration into a model predictive control framework, aiming to reduce N₂O emissions by forecasting these over varying prediction horizons. The models were trained on 12 months and tested on 3 months of data from a full-scale WWTP in Amsterdam West, the Netherlands. The dataset encompasses seasonal peaks in N₂O emissions typical for winter and spring months. The best performing model, featuring a 256–256 LSTM architecture, achieved the highest accuracy with test R² values up to 0.98 across prediction horizons spanning 0.5 to 6.0 h ahead. Feature importance analysis identified past N₂O emissions, influent flowrate, NH₄⁺, NO_x, and dissolved oxygen (DO) in the aerobic tank as most significant inputs. The observed decreasing influence of historical N₂O emissions over extended prediction horizons highlights the importance and significance of process variables for the model's performance. The model's ability to accurately forecast short-term N₂O emissions and capture immediate trends highlights its potential for operational use in controlling emissions in WWTPs. Further research incorporating diverse datasets and biochemical process inputs related to microbial activities in the N₂O production pathways could improve the model's accuracy for longer forecasting horizons. These findings advocate for hybridising deep learning models with biokinetic and mechanistic insights to enhance prediction accuracy and interpretability.

Current practice of wastewater treatment does not recover the full potential of energy present in wastewater. The potential of using anammox bacteria for autotrophic nitrogen removal combined with a desire for energy optimization brings new attention to the A-stage technology for organic carbon harvesting from municipal wastewater. The goal of this research was to investigate operational conditions of four full-scale A-stage processes and gain insight in the optimal conditions to harvest the maximum amount of organics present in sewage as excess sludge from the A stage. Large differences in removal efficiencies and design aspects were found between the four operational A-stage processes in the Netherlands. Biochemical oxygen demand (BOD) removal efficiencies vary between 40% and 80%, indicating that a good removal efficiency is possible, but that local conditions or design can be very influential. An optimal solid retention time (SRT) for maximal sludge production of 0.3 days was found; a longer SRT resulted in more mineralization of the chemical oxygen demand (COD). SRT control might be an important design aspect for the optimization of A-stage process. A short contact time with a minimum of 15 min and sufficient aeration were found to be optimal for soluble COD removal. Iron addition aided the removal of colloidal/suspended COD by coagulation/flocculation. Sludge flocs formed in the A-stage process are weak and sensitive to anaerobic conditions as well as shear due to, for example, pumping. Besides a good design of the A-stage itself, the further processing of the produced sludge also needs careful attention to optimize the sludge production and energy production.

Sewage fine sieved fraction (FSF) is a heterogeneous substrate consisting of mainly toilet paper fibers sequestered from municipal raw sewage by a fine screen. In earlier studies, a maximum biodegradation of 62% and 57% of the sewage FSF was found under thermophilic (55 °C) and mesophilic (35 °C) conditions, respectively. In order to research this limited biodegradability of sewage FSF, this study investigates the biodegradation of different types of cellulosic fibers-based hygiene papers including virgin fibers based toilet paper (VTP), recycled fiber based toilet paper (RTP), virgin pulp for paper production (VPPP) as a raw material, as well as microcrystalline cellulose (MCC) as a kind of fiberless reference material. The anaerobic biodegradation or digestibility tests were conducted under thermophilic and mesophilic conditions. Results of the experiments showed different biomethane potential (BMP) values for each tested cellulose fiber-based substrate, which might be associated with the physical characteristics of the fibers, type of pulping, presence of lignin encrusted fibers, and/or the presence of additive chemicals and refractory compounds. Higher hydrolysis rates (K_h), higher specific methane production rates (SMPR) and shorter required incubation times to achieve 90% of the BMP (t_90%CH₄), were achieved under thermophilic conditions for all examined substrates compared to the mesophilic ones. Furthermore, the biodegradability of all employed cellulose fiber-based substrates was in the same range, 38-45%, under both conditions and less than the observed FSF biodegradability, i.e. 57-62%. MCC achieved the highest BMP and biodegradability, 86-91%, among all cellulosic substrates.