CFD as a Tool to Optimise Aeration Tank Design and Operation 1

8 In a novel development on previous computational fluid dynamics (CFD) studies, the work 9 reported here employed an Eulerian two-fluid model with the shear stress transport (SST) 10 k − ω turbulence closure model and bubble interaction models to simulate aeration tank 11 performance at full scale and to identify process performance issues resulting from design 12 parameters and operating conditions. The current operating scenario was found to produce a 13 fully developed spiral flow. Reduction of the air flow rates to the average and minimum 14 design values led to a deterioration of the mixing conditions and formation of extended 15 unaerated fluid regions. The influence of bubble-induced mixing on the reactor performance 16 was further assessed via simulations of the residence time distribution (RTD) of the fluid. 17 Internal flow recirculation ensured long contact times between the phases; however, hindered 18 axial mixing and the presence of dead zones were also identified. Finally, two optimization 19 schemes based on modified design and operating scenarios were evaluated. The adjustment 20 of the air flow distribution between the control zones led to improved mixing and a 20 % 21 improvement to the mass transfer coefficient. Upgrading the diffuser grid was found to be an 22 expensive and ineffective solution, leading to worsening of the mixing conditions and 23 yielding the lowest mass transfer coefficient compared to the other optimization schemes 24 studied. 25 INTRODUCTION 26 The activated sludge (AS) process is a well documented standard for many municipal 27 wastewater treatment plants (WWTPs) worldwide. Regardless of the site-specific process 28 configuration, AS systems rely on a steady energy supply for operation of air compressors 29 and recycling pumps (Karpinska Portela 2013). Aeration is the most energy-intensive unit 30 process at municipal WWTPs and accounts for the largest fraction of the total net electricity 31 expenditure (45-85 %) (Reardon 1995; WEF 2009). The importance of the linkage between 32 water and energy has been recognized globally in the context of water shortages, increasing 33 energy and material costs, climate change and food availability. To address this, the historical 34 high energy use of aeration systems and relatively low standard oxygen transfer efficiencies 35 of aeration devices must now be addressed via implementation of cost-effective energy 36 management measures and engineering practices. 37 However, design and operation of AS systems is still largely based on empiricism, general 38 guidelines and operator experience, often with little regard for the influence of site-specific 39 tank hydraulics, type, number and distribution of aerators and mixers, energy input or the 40 influence of mixing on efficient distribution of the dissolved and suspended components in 41 the AS bioreactor (Karpinska and Bridgeman 2016; Samstag and Wicklein 2012). Numerical 42 modelling of WWTPs has been recognized as a powerful tool, providing detailed knowledge 43 of the unit processes, as well as reactor behaviour in response to varying input conditions. 44 Over the last two decades the fast growth in computational power and commercialization of 45 advanced software suites for the solution and visualization of complex flows has contributed 46 to the successful spread of computational fluid dynamics (CFD) in wastewater engineering 47 (Karpinska and Bridgeman 2016). 48 A key advantage of CFD-aided modelling of aeration tanks over the traditional and relatively 49 well-established Activated Sludge Models (ASM) is its capacity to predict the actual flow 50 field in multiphase non-ideal reactor systems, accounting for local scale phenomena, 51 interfacial mass transfer and chemical reactions. A few workers have attempted to couple 52 hydrodynamics with biokinetics to simulate the combined physical-chemical-biological 53 processes within different AS system configurations, e.g. a full-scale oxidation ditch aerated 54 with diffusers (Glover et al. 2006) and a lab-scale channel reactor aerated with porous tube 55 (Le Moullec et al. 2010). While it was possible to predict simultaneously the hydrodynamics 56 and its impact on the biochemical conversion of organics and nutrients by AS biomass, 57 differences between concentration profiles obtained experimentally and those predicted by 58 the numerical model were identified and reported. These errors arose from excessive model 59 simplifications, use of a coarse grid necessitated by RAM/CPU constraints, and the need for 60 simulation runs in realistic time frames. Consequently, the coupled CFD-ASM simulation of 61 AS tanks remains a challenge, due to the complexity of the models involved and the solution 62 accuracy which demands a high level of mesh refinement resulting in significant 63 computational requirements and long run times (Karpinska and Bridgeman 2016). Therefore, 64 common practice is to simplify the modelling approach in a computationally efficient 65 manner, to simulate individual components of the AS system separately, and to couple the 66 results afterwards (Pereira et al. 2012). The literature offers several examples of the use CFD 67 to study aeration process in different AS systems, e.g. to acquire an insight into the mutual 68 interaction between the phases and the global and local mass transfer coefficients (Cockx et 69 al. 2001; Fayolle et al. 2007), to assess the impact of the diffuser arrangement on mixing 70 patterns and nitrification (Gresch et al. 2011) and to identify operating scenarios that promote 71 the formation of anoxic zones reducing energy expenditure on aeration (Yang et al. 2011). 72 To date, no agreed protocol for robust CFD modelling of aeration tanks has been defined. 73 Nonetheless, the modelling procedure exploiting the most computationally inexpensive 74 modelling scenario, based on the Reynolds-Averaged Navier-Stokes (RANS) equations 75 closed by the standard k − ε model, a neutral density Eulerian model, and constant bubble 76 size assumption has become widely accepted by the wastewater modelling community as a 77 standard approach (Karpinska and Bridgeman 2016; Samstag et al. 2016). However, the 78 pitfalls of that modelling scheme have been reflected in overestimated values of the 79 volumetric mass transfer coefficient kLa (Fayolle et al. 2007; Le Moullec et al. 2010). 80 Nonetheless, the proposed method to rectify this error focused exclusively on determination 81 of the actual bubble sizes either experimentally or through implementation of add-on 82 statistical models. This underestimated the influence of the dispersed model on predicted 83 values of turbulent kinetic energy dissipation rate ε, involved in computation of the mass 84 transfer coefficient, kLa, and hence the simulations of dissolved oxygen (DO) and organics 85 and nutrients transformations in the AS process (Karpinska and Bridgeman 2017). 86 Unlike most previous CFD models of aeration systems, the work reported here used the SST 87 k − ω turbulence model (Menter 1994) to account for the turbulent interactions between the 88 gas and liquid phases to analyse the performance of a full-scale conventional plug flow AS 89 tank aerated and mixed by means of fine pore diffusers. Moreover, the dynamic changes in 90 bubble sizes due breakage and coalescence were simulated using the Hibiki-Ishii model 91 (Hibiki and Ishii 2000) embedded into the transport equation for interfacial area 92 concentration (IAC). The selection of this modelling approach, developed previously for the 93 lab-scale aeration tanks (Karpinska and Bridgeman 2017) was justified by its reliability in 94 prediction of the turbulent interactions between the phases and resulting oxygen transfer, 95 outperforming the standard k − ε model. The impact of the design and operating parameters 96 on the flow field induced by the aeration system and the resulting gas holdup were assessed 97 to detect and quantify the shortcomings of each operating scenario. Simulations of the RTD 98 of the fluid in the AS tank were performed to determine the influence of the current operating 99 conditions on the macromixing and reactor performance and to quantify site-specific process 100 limitations. Finally, two optimization scenarios based on the modification of either operating 101 parameters or diffuser density were evaluated taking into consideration mixing and oxygen 102 mass transfer in the tank. 103