Engineering, Faculty of Engineering, University of Porto, Porto, Portugal

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1 CFD Approach on the Energy Analysis of an Oxidation Ditch Aerated with Hydrojets Anna M. Karpinska, 1 Madalena M. Dias, 1 Rui R. Boaventura, 1 José Carlos B. Lopes 1 and Ricardo J. Santos 1 Anna M. Karpinska 1 1 Laboratory of Separation and Reaction Engineering (LSRE), Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal Abstract This work focused on Computational Fluid Dynamics (CFD) simulations of the hydrodynamics and macromixing (RTD) in an oxidation ditch using several turbulence models: Reynolds Averaged Navier-Stokes Simulations (RANS) with the standard k ε model; unsteady RANS (URANS); and Large Eddy Simulation (LES). The specific performance of the hydrojets, based on reinjection of the fluid already saturated with oxygen and its impact on the mixing phenomena within the ditch is evaluated. The effect of the turbulence models on the macromixing is also studied. Finally, the overall energy expenditure of the proposed hydraulic scheme of the oxidation ditch system, considering power demand for aeration, mixing and supporting equipment operation, is determined. Keywords: CFD, energy, hydrojets, mixing, oxidation ditch Introduction An oxidation ditch is an Activated Sludge (AS) process, generally operated in extended aeration mode and with long solids retention times (SRTs) (EPA, 2000). This technology was developed in 1950s in the Netherlands (Pasveer, 1962) and it is still commonly used in small to medium-sized municipalities due to its robustness and efficiency in terms of Biological Oxygen Demand (BOD) and nutrients removal (75-95%) (Benefield and Randall, 1980) and requirement of relatively simple operational skills. The oxidation ditch can be operated as a continuous or intermittent process, due to its buffer capacity against variable flows, loadings and also unfavorable environmental temperatures. The efficiency of biological wastewater treatment in the oxidation ditches depends on the efficient homogenisation of the tank volume (activated sludge + dissolved oxygen + nutrients), hence it is linked to the overall mixing phenomena. Mixing efficiency, and thus oxidation ditch volume usage depends on the hydraulic features of the tank and on the operating parameters of aeration and mixing devices. Originally these systems were equipped only with horizontal surface brush-type aerators, but nowadays separation of aeration and mixing is more common, and fixed diffused air units, such as fine-bubble diffusers, are used in combination with a mechanical agitation unit, such as axial slow speed mixers. Aeration is energy-intensive and accounts for % (Rosso et al., 2008) of the total energy usage of the Wastewater Treatment Plant (WWTP). In order to reduce energy usage there is an urgent task in wastewater sector to apply advanced design and modelling tools for development, diagnostics and elaboration of smart aeration technologies, preserving the objective of water and energy nexus. Page 1 of 8

2 Nowadays, due to increasing availability of computational resources, dynamic modelling of the oxidation ditches involves two different approaches, both providing relevant and mutually complementary information. The systemic approach is based on well established ASM models and focus mainly on the reactions of biochemical conversion within ideal reactors: a single or a cascade of Continuous Stirred Tank Reactors (CSTRs); or a plug flow reactor (PFR). The results enable the prediction of quantitative oxygen consumption and pollutants removal and qualitative biomass growth and decay. However these models neglect the influence of the temperature, reactor hydraulics, oxygen transfer and consumption dynamics on the treatment efficiency. Therefore, the kinetics modelling needs to be coupled with the reactor hydrodynamics. Computational Fluid Dynamics (CFD) modelling and simulation enables the prediction of the influence of the operating parameters and of local scale phenomena, such as flow field coupled with mass and heat transfer and chemical reactions, giving detailed insight into mixing efficiency and the distribution of the system components. However, complete CFD simulation of the oxidation ditch is difficult to handle, due to the complex and coupled local hydrodynamics, mutual interactions between the phases (AS focks, mixed liquor and air bubbles) and requires a parallel processing with a large number of CPUs and/or long computational times. A possible strategy is to simulate separately with CFD codes individual parts of the ditch system and afterwards couple the results. Hence some works focus mainly on the study and optimization of the aerator performance (Do-Quand et al., 1999; Cockx et al., 2001; Dahikar et al., 2007), as other works simulate flow field and Residence Time Distribution (RTD) of the AS tank (Le Moullec et al., 2008, 2010a, 2010b). This work focuses on the hydrodynamics and macromixing (RTD) simulation, involving main features of the aeration performance in the boundary conditions of the CFD models. The specific application of the hydrojets, based on reinjection of the mixed liquor saturated with oxygen and its impact on the ditch hydrodynamics is assessed. The effect of the turbulence models on the macromixing will also be studied. Finally, overall energy expenditure of the oxidation ditch system, considering power demand for aeration, mixing and supporting equipment operation, is determined. Methods Simulated ditch system consists of a closedloop open channel filled with fluid, having two slot jet aerators, see Fig. 1 (Karpinska et al., 2008). Here, the aeration is based on the reinjection of fluid saturated with oxygen in an external aeration unit: the Pressurized Aeration Chamber (PAC) (Karpinska et al., 2009). The hydraulic design of the system consists of: an oxidation ditch with two hydrojet modules; a PAC; a recirculation pump; and a pipeline connecting all equipments. The 3D ditch geometry used in the CFD simulations was designed for pilot scale applications (scale 1:10) and has the following dimensions: 12.8 m long, 3.0 m wide and 0.75 m deep. The surfaces of the inlet and outlet are located on the top of the ditch. The pair of opposite positioned slot hydrojet has 0.1 m height, and extended across the ditch width. Inlet, outlet and hydrojets are distanced from each other for half of the ditch length. In this work, three different geometry scenarios were taken into consideration where the hydrojets were: fixed above the bottom, at 1/3 depth; in the middle of the ditch; and below the fluid surface, at 2/3 depth. Page 2 of 8

3 Fig. 1. Oxidation ditch aerated with hydrojet scheme Water is set as a working fluid and a mean hydraulic retention time of the fluid of 4 h is assumed. Hydrojets are set as momentum sources, guarantying acceleration of the fluid flow to 0.30 m s -1. Non-slip condition is set on the surface of the ditch, modelling a free surface flow. The hydrodynamics of the oxidation ditch is obtained from the single-phase flow simulations with the ANSYS Fluent commercial code, using: Reynolds Averaged Navier-Stokes Simulations (RANS); unsteady RANS (URANS); and Large Eddy Simulation (LES). In RANS and URANS, flow is governed by continuity and momentum- Navier-Stokes equations ρυi = 0 x i ( υυ ) 2 i j 1 p υ ( υυ i j) uur = + µ ρ + ρ g i x j ρ x 2 i x j x j i (1) (2) where p is the averaged pressure field, ρ and µ are the fluid density and viscosity, respectively, t is the time, x i are the spatial coordinates, g i is the gravitational acceleration and υ i, j are the velocity components. In RANS and URANS, Reynolds decomposition is enabled to separate velocity components into a time average term, υ i, and a fluctuating term, υ ' i, so that υ = υ + υ ' i i i (4) Flow structures smaller than the numerical grid discretization, represented by the Reynolds stresses υ i υ j are unclosed, and thus must be modelled. In this study, the turbulence model used for υυ i j closure is the standard k ε model, where k denotes turbulence kinetic energy of velocity fluctuation and ε represents its dissipation rate. LES was used as an alternative approach in hydrodynamics modelling, in which the large scale motions, large eddies, are directly and explicitly solved in timedependent simulation, using filtered Navier- Stokes equations. The filtering operation results in introduction of a subgrid scale stress tensor term, τ i, j = υυ i j υυ i j (5) The term τ i, j cannot be directly solved and is modeled using the Smagorinsky s subgrid scale model. Macromixing is simulated using particle tracking in Discrete Phase Model (DPM) through determination of the RTDs in the oxidation ditch for the different turbulence models. The RTD is computed from the simulation of a pulse injection of particles introduced into the ditch system through the inlet in a very short period of 10-3 s. In this approach, the continuous (3) phase of working fluid is simulated by solving time-averaged Navier-Stokes equations in an Eulerian reference frame. The particle tracking is simulated using random-walk Lagrangian trajectory calculations for the dispersed phase through the flow field of the continuous phase. The particles trajectories are predicted by integrating the force balance on the single particle and recording the Page 3 of 8

4 particle position. The flux of the particles exiting the oxidation ditch through the outlet is recorded. The physical characteristics of the particles were chosen in order to give them the features of a perfect fluid flow tracer, thus their density is identical to the density of the working fluid, and they are spherical with a diameter of 10-6 m. The power expenditure for the aeration was determined from the data obtained from the CFD simulation of PAC and validated in pilot studies. Power consumption is propotional to the pressure drop on the chamber, p, and the water flowrate, Q. Depending on the use of air or pure oxygen feed system, power demand for air blowers or oxygen separation in PSA (Pressure Swing Adsorption) process needs to be taken into account. For the blowers it was assumed that (Metcaf & Eddie, 2003) (6) where P w is the power requirement for each blower in kw, w is the air mass flow rate in kg s -1, R is the universal gas constant, T is the inlet temperature in K, P 1 and P 2 are the inlet and outlet pressures in atm, and η is equipment efficiency, usual in the range of For oxygen separation in PSA process, power demand accounts for around 0.35 kwh per cubic meter of oxygen. The energy expenditure for mixing is determined directly from the RANS, URANS and LES simulations of the oxidation ditch and depends on the hydrojets performance. Power required for recirculation and reinjecting mixed liquor saturated with air/oxygen depends on the friction and local head loss in the pipeline. The simplified head loss computation during sludge pumping is based on multiplying the head loss obtained for water by a constant and is a Page 4 of 8 function of the suspended sludge concentration in the mixed liquor. Besides the viscosity of the mixed liquor, the friction head loss also depends on: the internal hydraulic diameter and the roughness of the internal surface of the pipes; changes in elevations within the system; and the length of the straight conduits. Following the design guidelines for the wastewater tubing, pipes are assumed to be made of galvanized iron with a roughness factor of 0.15 mm and with an internal diameter of 250 mm. Due to the risk of sludge precipitation in the conduits, and since turbulent conditions of the flow have to be ensured, the assumed velocity of the flow in the pipeline is 1.5 m s -1. From the ditch hydraulic layout (Fig. 1), the pipeline system of the oxidation ditch consists on sections of straight conduits of the following lengths: 20 m (ditch bottom elbow distance), 1 m (elbow PAC and PAC - pump), 1 m (PAC pump), 23 m (elbow ditch) and 7.0 m (connection with hydrojets). For each length, the sum of the friction head losses are determined using the Darcy-Weisbach formula which is valid for any liquid or gas, h f 2 Lυ =λ 2gD (7) where h f is the friction headloss in m, λ is the friction coefficient, L is the length of the pipe, υ is the average velocity in the pipeline and D is the inner diameter of the pipe. The ditch system layout also includes the following devices and fittings: mixed liquor recirculation pump; two 90 elbows; two inlets and outlets, to and from the oxidation ditch; and the PAC. Local losses are calculated from the Darcy s formula h = k l l 2 υ 2g (8) where h l is a local loss, and k l is a local loss coefficient. The sum of frictional and local

5 head losses determines the head of the pump, using Bernoulli s equation 2 2 p1 υ p0 υ 1 f, l 0 z + ρ h = z + ρ h g g g g where p1 and 0 p (9) p are the static pressures at levels z 1 and z 0, i.e., the level of the oxidation ditch ( z 1 = 20 m) and in the piping system at the level of the recirculation pump, ( z 0 = 0 m); and h p is the energy supplied by the pump. The pressure in the PAC is 2 bars at z 0. The overall energy expenditure in the piping system is given by P ρ gh Q (10) = p Results Fig. 2 shows the vector flow field in the vertical planes obtained from each of the simulations - RANS, URANS and LES - for the geometry with hydrojets placed in the middle depth. For RANS and URANS, the same overall distribution of the flow patterns within the oxidation ditch was obtained. It is clear, that the hydrojet plume creates an average flow with great stratification inside the volume of the ditch characterized by poor vertical transport. The only difference between both results is the smooth waving of the injected stream in URANS while in RANS the jets are steady. In LES, it is observed the evolution of eddies from the hydrojet plume creating a dynamic sinuous path throughout the ditch, which enables convective transport between the fluid in different layers (Fig. 2). Visible difference between the flow fields obtained from the simulations is even clearer when comparing the corresponding maps of the surface velocity (Fig. 3). Distribution of the superficial velocities for all models simulated was validated from the hydraulic profiles of the open channel flow through the 180 bend. Presence of the secondary flow interacting dynamically with the main current and promoting horizontal transport is emphasized. The unique difference between velocity maps obtained for RANS and URANS is a slight acceleration of the segregated flow currents for URANS, to the value of 0.16 m s -1. Dynamic flow field map was obtained for LES, with the iso-velocity regions reaching values of 0.18 m s -1. Considering hydrojet configurations within the ditch, the maps of surface velocity are identical in terms of iso-currents distribution, hence the maximal superficial velocity values are 0.10 m s -1 for the near-bottom, 0.11 m s -1 the mid-depth and 0.13 m s -1 for the near-surface configurations. Considering the influence of the hydrojets position on the vertical velocity profile in the straight section of the ditch (Fig. 4), only near-bottom configuration yields lower surface velocity magnitude. Horizontal profiles of the velocity are independent from the hydrojets position and represent a typical hydraulic profile for the open channel flow. The computation of macromixing, and thus RTD of the fluid particles is strongly dependent on the turbulence model involved in the flow simulation, as can be seen in Figure 5. Even though the mean hydraulic residence time of the fluid in the ditch is 4 h, RTD plots refer to the first 1.6 h, due to the long computational times involved. The mean travel time of the flow across the entire ditch is approximately 200 s. For RANS and URANS the peaks of the RTD at the early time instants are associated with a great amount of particles exiting in the first two turns. These results suggest that the hydrojets caused an intense vertical segregation of the flow, with strong channelling from inlet to outlet. The particles are not being transported to lower regions of the ditch, which means that only a small percentage of the ditch volume is used. The particles that are transported to the Page 5 of 8

6 lower part of the ditch will be trapped there for long time, yielding a long tail in the RTD. The RTD plots obtained from LES do not show the peaks related with significant amounts of particles exiting in the first turns. Here, the convective flow patterns promote transport to the lower regions of the ditch, and thus a greater fraction of the actual volume is used. The percentage of total injected particles exiting during the first 800s is: 30% for RANS, 20% for URANS and 5 % for LES. As seen from these figures, the turbulence model applied in the CFD simulations has a strong impact on the advection patterns taken by the particles in the oxidation ditch. Fig. 3. Surface velocity distribution obtained from the CFD simulations of the oxidation ditch Fig. 2. Velocity vector maps in the oxidation ditch using RANS, URANS and LES Fig. 4. Vertical (a) and horizontal (b) velocity profiles from RANS simulation for near-bottom, middle-depth, and near-surface hydrojets positions Page 6 of 8

7 demand for atmospheric air compression is 0.9 W while for oxygen separation in PSA reduces to the value of 0.2 W. Depending on the turbulence model applied, energy expenditure for mixing is in the range of W. Power consumption of the whole piping and recylculation system depends only on pump elevation, and thus 1.82 m of the head accounts for 0.9 W. Fig. 5. Partial plots of the RTD for RANS, URANS and LES ditch. Energy analysis for the simulated system is obtained with a pre-defined momentum source of the hydrojets of 450 N m³, assumed identical for all turbulence models used in the oxidation ditch simulations. The difference in the energy expenditure between the turbulence models, RANS, URANS and LES is less than 5%, and thus the model that requires less computational resources, RANS, was applied to the analysis of the effects of hydrojets position on the power demand. In this case, the difference in power expenditure between all geometry configurations of the hydrojets placement is less than 4.5 %. Table 1 shows the data set obtained for each of the turbulence models applied in the simulations: average flowrate, superficial velocity and power. Table 1. Flow parameter and power demand obtained with different turbulence models. Simulation Hydrojet υ Q Power position (m s -1 ) (m s -3 ) (W) bottom RANS surface middle URANS middle LES middle Energy demand for aeration in the PAC obtained from CFD simulations and validated in pilot study accounts for 0.1 W. Depending on the oxygen source, power Discussion and Conclusions The results of the simulations have shown that the cases where the average flow field is directly solved, RANS and URANS, demand around 5% less energy, than LES simulation. However RANS and URANS do not give clear information on the actual flow dynamics. LES, where the larger vorticity scales of turbulence are solved, gives better approximation of the actual flow conditions within the ditch, but the simulation has to be run for a sufficiently long flow-time to obtain stable statistics of the flow being modeled. As a result, the computational cost involved with LES is much higher than that for RANS calculations in terms of RAM and CPU usage. Macromixing computation was shown to be a useful tool to describe the actual reactor performance. The RTD simulations based on the average flow field, RANS and URANS, may lead to errors; however this work proves that both these methods are suitable for design studies regarding power consumption. The flow dynamics underlies mixing at all its scales, both macro- and micro-, and thus it must always be accounted for. Furthermore, since the AS reactions orders are greater than zero, the treatment efficiency depends on the oxidation ditch hydrodynamics. The RTDs obtained from CFD simulations of the oxidation ditch can be used to generate a reactor model, where the ASM can be implemented, hence the usefulness of the CFD data must account with the dynamics Page 7 of 8

8 components of the flow and LES simulations, which demand for more computational resources have to be used. References Benefield L.D. and Randall C.W. (1980). Biological Process Design for Wastewater Treatment. Activated Sludge and its Process Modification. Prentice Hall Inc, Englewood Cliffs, NJ, USA Cockx A., Do-Quang Z., Audic J.M., Liné A., Roustan M. (2001). Global and local mass transfer coefficients in waste water treatment process by computational fluid dynamics. Chem. Eng. Process 40(2), Dahikar S.K., Gulawani S.S., Joshi J.B., Shah M.S., Rama Prasad C.S., Shukla D.S. (2007). Effect of nozzle diameter and its orientation on the flow pattern and plume dimensions in gas-liquid jet reactors. Chem. Eng. Sci. 62(24), Do-Quang Z., Cockx A., Liné A., Roustan M. (1999). Computational fluid dynamics applied to water and wastewater treatment facility modeling. Environ. Eng. Pol. 1(3), EPA (2000). Wastewater Technology Fact Sheet. Oxidation Ditches. United States Environmental Protection Agency, EPA 832-F , Washington DC, USA. Karpinska A.M., Pereira J.P., Dias M.M. and Santos R.J. (2008). CFD simulation of an oxidation ditch. In Proceedings of ChemPor- 10th International Chemical and Biological Engineering Conference, Braga, Portugal, 4-6 Sept Karpinska A.M., Pereira J.P., Lopes J.C.B., Dias M.M., Santos R.J. (2009). Aeration of activated sludge processes with hydrojets: energy efficiency analysis. In Proceedings of IWA Water & Energy, Copenhagen, Denmark, Oct Le Moullec Y., Gentric C., Potier O., and Leclerc J.P. (2008). Flow field and residence time distribution simulation of a cross-flow gas-liquid wastewater treatment reactor using CFD. Chem. Eng. Sci., 63(9), Le Moullec Y., Gentric C., Potier O., and Leclerc J.P. (2010a). Comparison of systemic, compartmental and CFD modelling approaches: Application to the simulation of a biological reactor of wastewater treatment. Chem. Eng. Sci., 65(1), Le Moullec Y., Gentric C., Potier O., and Leclerc J.P. (2010b). CFD simulation of the hydrodynamics and reactions in an activated sludge channel reactor of wastewater treatment. Chem. Eng. Sci., 65(1), Metcalf & Eddie Inc., Tchobanoglous G., Burton, F.L., Stensel, H.D. (2003). Wastewater Engineering, Treatment and Reuse, 4th Edn, McGraw Hill, New York. Pasveer A. (1962). Verfahren und Anlage zum Reinigen von Abwasser (Process and Device for the Purification of Sewage). CH360350; Switzerland, Passavant Werke. Rosso D., Stenstrom M.K., and Larson L.E. (2008). Aeration of large-scale municipal wastewater treatment plants: state of the art. Wat. Sci. Tech., 57(7), Acknowledgments Financial support for this work was in part provided by national research grant PTDC/EBB-EBI/103761/2008 project PEst- C/EQB/LA0020/2011, financed by FEDER through COMPETE - Programa Operacional Factores de Competitividade and by FCT - Fundação para a Ciência e a Tecnologia. Anna Karpinska Portela acknowledges her Ph.D. scholarship by FCT (SFRH/BD/47673/2008). Disclosures The authors have nothing to disclose Page 8 of 8