Climate change adaptation and mitigation in urban water treatment systems - numerical modelling of aeration and greenhouse gas emission

Abstract

Climate change, caused by the release of greenhouse gases (GHG), stands as a critical contemporary global challenge, marked by its impacts such as severe weather events and associated risks. These consequences have the potential to induce hydraulic shocks to urban water systems (UWS), thereby affecting the operational capacity and effluent quality of Water Resource Recovery Facilities (WRRFs). The main focus of this study is adaptation to and mitigation of climate effects in activated sludge WRRFs. Increased frequency of hydraulic shock events – thanks to climate change in Northern Europe – requires effective adaptation means to increase resilience of WRRFs. Additionally, WRRFs themselves are also a source of greenhouse emission in the form of N2O gas – a by-product of biological nitrogen removal that requires mitigation strategy. The main areas of research presented here focus on adaptation to climate change impacts and mitigation of greenhouse gas emissions in WRRFs. A novel mechanistic soft-sensor is developed for online calibration of the most hydraulically sensitive unit operation in WRRF, i.e., secondary settling tank. This can be used in model-predictive control to build climate resilience in WRRFs. Additionally, a new numerical model of the surface aeration, combining oxygen and N2O gas-liquid mass-transfer models, is developed. Furthermore, experimental and modelling work were used to identify sub-models of aeration efficiency and associated N2O gas stripping for surface and fine-bubble aeration systems, thus facilitating the development of effective control strategies to mitigate N2O emission.

In activated sludge processes, climate change impact – through more frequent occurrence of wet weather periods – can increasingly cause the hydraulic overload of systems. This impact can be exacerbated by proliferation of filamentous bacteria by significantly impacting the settleability of activated sludge. But the dynamic prediction of secondary settling velocity parameters is a major knowledge gap. To address this gap, key parameters was involved and focusing on biokinetic modeling of filamentous bacteria growth (Cand. Microthrix) to predict the settleability of activated sludge, under hydraulic shock conditions. The primary factor influencing the growth of Cand. Microthrix and hindered settling velocity (v0/rH) is identified as the lipid fraction in the influent (fLip) during the cold season (T = 10℃), while the maximum Microthrix growth rate (μM) takes precedence during the warm season (T = 20℃).

Besides climate impact adaptation, climate impact mitigation through the reduction of GHG emission – especially N2O in WRRFs – is necessary. Regarding the reduction of N2O emission from WRRFs, while biokinetic modelling of N2O production has been intensively studied in the past fifteen years, the comprehension of the physical chemical hydrodynamics of gas mass transfer in activated sludge reactors, and their impacts on N2O emission are less well-understood. This significant knowledge gap was addressed by developing and using computational fluid dynamic (CFD) simulation models, integrating biokinetic and hydrodynamic processes to predict aeration efficiency and N2O emissions. Due to their wide-used by utilities, full-scale surface aerated oxidation ditch type reactors were investigated in depth. Single- and two-phase model calibration practices will be evaluated. Our results show that, while the calibrated single-phase model effectively predicts liquid sensor measurements (DO – 1.6 mg/L and N2O concentration – 0.25 mg/L), the two-phase model is limited to predict oxygen gas mass transfer and N2O stripping. Using simulation results obtained with the calibrated single-phase CFD model, regression meta-models have been identified to predict oxygen gas mass transfer (KLaO2,clean) and N2O emissions, as a function of design and operating parameters.

The variability of aeration efficiency in wastewater – characterised by the α-factor, which is the ratio of wastewater to clean water mass transfer coefficient – can significantly influence aeration efficiency and N2O emission. There exists a significant knowledge gap regarding the dynamic calibration of the α-factor using sensor data – key to reliable prediction of N2O emission. Besides the key factors influencing the α-factor - mixed liquor solid suspensions (MLSS) concentration, surfactant, COD, etc. – filamentous bacteria were recently found to disrupt oxygen gas mass transfer. The filaments grow from the cell can protrude through the bubble interface and then affect the gas mass transfer. Therefore, a continuous flow lab-scale reactor system was operated to encourage growth of filamentous bacteria and to assess its impact on gas mass-transfer using surface aeration and fine-bubble aeration. Based on these experiment results, a novel approach was introduced using sensor measurements (DO and N2O concentration) to dynamically predict the α-factor as a function of MLSS concentration and sludge settleability for both fine-bubble and surface aeration. Plant-wide WRRF simulations indicate the superiority of fine-bubble aerated systems over surface aerated systems in treatment efficiency (28% higher), energy consumption (80% higher), and N2O emission (20% higher).

Additionally, an exciting outcome from the lab-scale measurements was the finding of another effective filamentous microbial community predictor of settling velocity, i.e., Thiothrix sp. – besides Cand. Microthrix. an extension to the biokinetic model was developed to predict the growth of Cand. Microthrix and Thiothrix sp. in the same culture. A regression meta-model for predicting v0/rH was additionally identified that accounts for the relative abundances of both Cand. Microthrix and Thiothrix sp.

Overall, this thesis contributes to advancing knowledge for providing new extension to WRRF process models to evaluate effective solutions for control strategies as well as reactor design and operation to combat climate change. The integrated models and control strategies developed for practitioner to predict and enhance aeration efficiency in full-scale WRRFs, thus reducing their environmental impact through GHG emission. Future research directions include a deeper exploration of the two-phase model and further investigation into Thiothrix sp., with a focus on N2O production biokinetics, to continue advancing understanding in this critical field.

Date of Award	2 Oct 2024
Original language	English
Awarding Institution	University of Bath
Supervisor	Benedek Plosz (Supervisor) & John Chew (Supervisor)