- Email: [email protected]
Study of waste stabilization pond geometry for the wastewater treatment efficiency
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/ecoleng
Study of waste stabilization pond geometry for the wastewater treatment efficiency Hamdy Abbas a,∗ , Rabeia Nasr a , Hamdy Seif b a b
Irrigation and Hydraulic Engineering Department, Faculty of Engineering, Alexandria University, Egypt Sanitary Engineering Department, Faculty of Engineering, Alexandria University, Egypt
a r t i c l e
i n f o
a b s t r a c t
Article history:
The simulation of hydrodynamics in waste stabilization ponds is a developing tool worth
Received 23 December 2005
studying in order to understand their internal processes and interactions. Pond design
Received in revised form
involves several physical, hydrological, geometrical and dynamic variables to provide high
8 March 2006
hydrodynamic efficiency and maximum substrate utilization rates. Computational fluid
Accepted 11 March 2006
dynamic modelling (CFD) allows the combination of these factors to predict the behavior of ponds by using different configurations. The two-dimensional depth-integrated model SMS was used in this study to simulate hydrodynamics and water quality. A set of 12 configura-
Keywords:
tions including baffling and pond geometry was modeled. The model was run at steady state
Waste stabilization ponds
with raw wastewater to study the effect of the assumed rectangular shapes and dimensions
Computational fluid dynamic
with constant area, for various values of water depth, flow rate and hydraulic retention
Water quality
time (HRT) of raw wastewater. The model was also run for different rectangular shapes with
Hydrodynamics
baffles. The area was manipulated by increasing the ratio between rectangular width and
Modelling
length as one, two, three and four times, respectively. Biochemical oxygen demand (BOD),
Wastewater treatment
dissolved oxygen (DO) concentrations and velocities distribution were recorded for different rectangular shapes with different numbers of baffles. Results showed that the rectangular shape ratio (L1 /L2 = 4) with the provision of two and four cross baffles at 1/3L (two baffles) and 1/5L (four baffles), respectively, most efficient to improve overall water quality. © 2006 Elsevier B.V. All rights reserved.
1.
Introduction
There are many methods of wastewater treatment; undoubtedly, the stabilization pond is one of the most common wastewater treatment techniques, especially if low-cost land is available. In modern societies, environmental management programs use models of systems that will examine and evaluate the possible alternatives. The mathematical models are commonly used in the areas of the fluid dynamic, surface water hydrology, the subsurface water hydrology and sedimentation processes and flow velocities in lakes. Numerical models or mathematical simulation models are currently
∗
Corresponding author. Tel.: +20 3 5853533; fax: +20 3 5551242. E-mail address: [email protected] (H. Abbas). 0925-8574/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2006.03.008
widely used in the field of water quality management (to predict the concentrations of biochemical oxygen demand, dissolves, toxic, etc.) and in the field of hydrodynamics. The simulation of hydrodynamic in bioreactors supported by modern computing technology is an important tool to gain an improved understanding of the process functioning and performance. Natural wastewater treatment systems such as waste stabilization ponds (WSP) are particularly subjected to stochastically varying environmental factors of different kinds, for example, temperature, rainfall and evaporation regimes, wind speed and direction and solar energy intensity. WSP designers have some control only upon one sin-
26
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Fig. 1 – Schematic representation of WSP configuration shapes modeled (case of no baffles).
gle process variable—hydraulic retention time (HRT) (Vega et al., 2003). However, the HRT distribution of influent wastewater volumes will be affected by some of the other factors. Despite these difficulties, some researchers have recently carried out work on WSP hydrodynamics by using computational fluid dynamic (CFD) modelling packages (Wood et al., 1995, 1998; Shilton, 2000; Salter et al., 2000; Baleo et al., 2001). CFD modelling offers an alternative way to study and predict the performance of WSP based upon their hydrodynamic features (Wood et al., 1995). It seems to be a powerful tool to help in the design and evaluation of new and existing WSP systems. The present study aims to apply a two-dimensional CFD modelling on (WSP) treating wastewater with various rectangular shape configurations. It was evaluated in order to establish the most likely interventions for future improvements. An additional aim of this paper is to com-
plement and extend the previous valuable work found in the literature.
1.1.
Governing dynamic equations and modelling
The following governing equations are used to solve the depthintegrated equations of fluid mass and momentum conservation in two horizontal directions. The forms of the solved equations are: h
∂u ∂u ∂u h + hu + hv − ∂t ∂ ∂y
+
gun2 (1.486h1/6 )
2
+ (u2 + v2 )
Exx 1/2
∂2 u ∂2 u + Exy 2 2 ∂x ∂y
− Va2 cos
+ gh
∂a ∂x
+
∂h ∂x
− 2hωv sin ˚ = 0,
Fig. 2 – Schematic representation of WSP configuration shapes modeled (case of two baffles).
(1)
Table 1 – Summary of BOD effluent and removal, DO effluent and velocities at different rectangular shape ratios and different cases of baffles L1 /L2 ratio
Case of no baffles Effluent BOD (mg/l)
Percent of removal
Range Mean
L1 /L2 ratio
6.347 7.998 10.393 12.50
Effluent BOD (mg/l)
Percent of removal
233 96.563 43.738 22.008
22.33 67.8 85.42 92.669
Case of no baffles Effluent DO (mg/l)
Range
Mean
Effluent concentrations of DO: (influent DO = 0.00 mg/l) 1 0.507 0.434 0.074 2 0.454 0.372 0.074 3 0.456 0.351 0.098 4 0.263 0.2006 0.060
L1 /L2 ratio
Standard deviation
257.134 160.78 112.516 95.21
19.086 61.98 74.887 88.62
Effluent BOD (mg/l)
Percent of removal
Range
Mean
Standard deviation
54.22 15.972 14.31 12.642
81.92 94.68 95.23 95.786
245.78 284.028 285.68 287.358
127.985 70.702 54.977 51.001
71.533 76.702 65.72 64.437
Standard deviation
Effluent DO (mg/l)
0.507 0.454 0.456 0.263
6.373 8.864 9.676 9.961
Range
Mean
6.373 8.864 9.676 9.961
4.373 6.353 7.43 7.673
Case of four baffles Standard deviation 1.79 2.618 2.764 3.057
Effluent DO (mg/l) 9.579 10.015 10.029 10.032
Case of Two baffles
Mean Minimum Maximum Range velocity (×10−3 ) (×10−3 ) velocity (×10−3 m/s) (×10−3 m/s) 2.69 2.78 3.01 2.8
66.47 203.43 256.26 277.99
Standard deviation
Case of Two baffles
Case of no baffles
Maximum and minimum velocities 1 0.002 2.69 2 0.001 2.78 3 0.005 3.01 4 0.015 2.8
Range Mean
Case of four baffles
0.39 0.52 0.62 0.59
Range
Mean
9.579 10.015 10.029 10.032
7.195 8.397 8.842 8.840
Standard deviation 2.69 2.566 2.195 2.191
Case of four baffles
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Effluent concentrations of BOD: (influent BOD = 300 mg/l) 1 251.7 16.1 48.293 257.658 2 246.61 17.79 53.38 255.308 3 243.41 18.87 56.584 253.60 4 235.19 21.60 64.806 249.68
Case of Two baffles
Maximum Range Mean Standard Minimum Maximum Range Mean Standard Standard Minimum velocity velocity (×10−3 ) (×10−3 ) deviation velocity velocity (×10−3 ) (×10−3 ) deviation deviation (×10−3 ) (×10−3 m/s) (×10−3 m/s) (×10−3 ) (×10−3 m/s) (×10−3 m/s) (×10−3 ) 0.452 0.479 0.525 0.137
0.038 0.062 0.018 0.091
3.19 44.10 79.86 169
3.19 44.10 79.86 169
4.24 1.58 18.9 27.40
3.02 7.5 12.55 21.77
0.047 0.065 0.1 0.5
105 267 317 765
105 267 317 765
0.14 35.1 51.1 85.4
11.2 27.1 35.5 69.7
27
28
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Fig. 3 – Schematic representation of WSP configuration shapes modeled (case of four baffles).
∂v ∂v ∂v h h + hu + hv − ∂t ∂x ∂y +
gvn2 (1.486h1/6 )
2
∂2 v ∂2 v Eyx 2 + Eyy 2 ∂x ∂y
+ (u2 + v2 )
1/2
− Va2 sin
+ gh
∂a ∂y
+
∂h ∂y
+ 2hωv sin ˚ = 0, (2)
∂h +h ∂t
∂u ∂x
+
∂v ∂y
+u
∂h ∂h +v = 0, ∂x ∂y
(3)
where h is the water depth, u and v are the velocities in the Cartesian directions, x, y and t are the Cartesian coordinates and time, the density of fluid, E the eddy viscosity coefficient
Fig. 4 – WSP simulations plots (case of four baffles) (L1 /L2 = 1.0; L2 /L2 = 0.2; a/L1 = 0.9). (a) Finite element mesh; (b) velocity distribution; (c) BOD concentration; (d) DO concentration.
29
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Fig. 5 – WSP simulations plots (case of four baffles) (L1 /L2 = 2.0; L2 /L2 = 0.2; a/L1 = 0.9). (a) Finite element mesh; (b) velocity distribution; (c) BOD concentration; (d) DO concentration.
(for xx is the normal direction on x-axis surface; for yy is the normal direction on y-axis surface; for xy and yx are the shear direction on each surface), g the acceleration due to gravity, a the elevation of bottom, n the Manning’s roughness n-value, 1.486 the conversion from SI (metric) to non-SI units, the empirical wind shear coefficient, Va the wind speed, the wind direction, ω the rate of earth’s angular rotation and ˚ is the local latitude. Eqs. (1)–(3) are solved by the finite element method using the Galerkin method of weighted residuals. The solution is fully implicit and the set of simultaneous equations is solved by Newton–Raphson non-linear iteration. The computer code executes the solution by means of a front-type solver, which assembles a portion of the
matrix and solves it before assembling the next portion of the matrix. For determination of water quality parameters, the following governing equation solves the depth-integrated equations of the transport and mixing process. The form of the depth averaged transport equation is
h
∂c ∂c ∂ ∂ R(c) ∂c ∂c ∂c +u +v − Dx − Dy − + kc + ∂t ∂x ∂y ∂x ∂x ∂y ∂y h
= 0, (4)
where h is the water depth, c the concentration of pollutant for a given constituent, t the time, u and v are the velocity in xdirection and y-direction, Dx and Dy are the turbulent mixing
Fig. 6 – WSP simulations plots (case of four baffles) (L1 /L2 = 3.0; L2 /L2 = 0.2; a/L1 = 0.9). (a) Finite element mesh; (b) velocity distribution; (c) BOD concentration; (d) DO concentration.
30
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Fig. 7 – WSP simulations plots (case of four baffles) (L1 /L2 = 4.0; L2 /L2 = 0.2; a/L1 = 0.9). (a) Finite element mesh; (b) velocity distribution; (c) BOD concentration; (d) DO concentration.
Fig. 8 – BOD concentrations curves (cases of four baffles). Sections (1-1), (2-2) and (3-3) are shown in Fig. 3.
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
(dispersion) coefficient, k is the first order decay of pollutant, the source/sink of constituent and R(c) the rainfall/evaporation rate. The equation is solved by the finite element method using Galerkin weighted residuals. Spatial integration of the equations is performed by Gaussian techniques. The model, which was selected for the present study, is surface-water modelling system (SMS) version 7.0. SMS is a comprehensive environment for two-dimensional hydrodynamic modelling.
31
(31 days), constant depth (1.5 m) and constant water temperature (15 ◦ C). • Optional boundary conditions (isotropic eddy viscosities, diffusion coefficients, Manning’s n-values and dispersion coefficients) were also calculated. • Raw wastewater characteristics were assumed: ◦ influent BOD concentration = 300 mg/l; ◦ influent DO concentration = 0.0 mg/l; ◦ surface organic loading rate of BOD = 150 kg/ha day. All study cases are shown in Figs. 1–3.
2.
Boundary conditions 3.
The model was run at steady-state case with raw wastewater to study the effect of various rectangular shapes with constant area, depth, flow rate and hydraulic retention time on rectangular stabilization ponds performance. The model was run with different rectangular shapes. The area was manipulated by increasing the ratio between the width and the length ratio one, two, three and four times, respectively. The operated boundary conditions for the present study were chosen as follows: • The model was operated hydraulically for each case at constant flow rate of 0.18 m3 /s hydraulic retention time of 744 h
Results and discussion
The model was run at steady state with raw wastewater to study the effect of various rectangular shapes at constant area, depth, flow rate and hydraulic retention time (HRT) on each pond performance. The model was run at different rectangular shapes. The area was manipulated by increasing the length to width ratio as 1, 2, 3 and 4 with different number of baffles 0, 2, and 4, respectively. BOD, DO concentrations and velocities distributions were determined for different rectangular shape cases with different numbers of baffles. The effect of pond shapes with or without baffles on the treatment efficiency is studied for all the cases given.
Fig. 9 – DO concentration curves (cases of four baffles). Sections (1-1), (2-2) and (3-3) are shown in Fig. 3.
32
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
The summary of effluent concentrations and removal efficiency of BOD, DO concentrations and velocities values, for different cases of baffles are given in Table 1. The results for the studied three cases are concluded as follows. • Case 1: without baffles. The effluent BOD concentration was found in the range of 235.19–251.7 mg/l for different rectangular shape ratios (L1 /L2 = 1, 2, 3 and 4). The effluent concentration of DO ranged between 0.263 and 0.507 mg/l and velocity ranged between 0.002 × 10−3 m/s and 0.015 × 10−3 m/s for the minimum and between 2.69 × 10−3 m/s and 3.01 × 10−3 m/s for the maximum for the same conditions. • Case 2: with two baffles. The effluent BOD concentration was found in the range of 22.008–233 mg/l at different rectangular shapes ratios (L1 /L2 = 1, 2, 3 and 4) with two baffles. The effluent concentration of DO ranged between 6.373 and 9.961 mg/l and velocity ranged between 0.018 × 10−3 m/s and 0.091 × 10−3 m/s for the minimum and between 3.19 × 10−3 and 169 × 10−3 m/s for the maximum for the same conditions. • Case 3: with four baffles. The effluent BOD concentration was found in the range of 12.64–54.22 mg/l at different rectangular shapes ratios (L1 /L2 = 1, 2, 3 and 4) with four baffles. The effluent concentration of DO ranged between 9.57 and 10.03 mg/l and velocity ranged between
0.047 × 10−3 m/s and 0.05 × 10−3 m/s for the minimum and between 105 × 10−3 m/s and 765 × 10−3 m/s for the maximum for the same conditions. Case 3 results were given in Table 1 and will be used for illustrating the simulation display, whereas Figs. 4–7 display simulation of some of the waste stabilization pond configurations which were studied. Figs. 8–10 present the results at the entrance, the middle and the outlet sections for the same cases to demonstrate the relationships between the effluent concentrations of BOD, DO and velocities with distances which resulted from different shape ratios of the pond. Figs. 11 and 12 present the relationship between BOD removal and DO effluent concentration and pond shape ratios at different cases of baffling. The study has demonstrated that the BOD removal efficiency increased from 16% at L1 /L2 = 1–22% at L1 /L2 = 4 (case 1), from 22% at L1 /L2 = 1–93% at L1 /L2 = 4 (case 2) and from 82% at L1 /L2 = 1–96% at L1 /L2 = 4 (case 3). The DO effluent concentration decreased from 0.5 mg/l at L1 /L2 = 1–0.26 mg/l at L1 /L2 = 4 (case 1), increased from 6 mg/l at L1 /L2 = 1–9.9 mg/l at L1 /L2 = 4 (case 2) and increased from 9.5 mg/l at L1 /L2 = 1–10.0 mg/l at L1 /L2 = 4 (case 3). The results in Figs. 11 and 12 show that increasing the ratio between length and width causes bulk increase in the removal
Fig. 10 – Velocity values curves (cases of four baffles). Sections (1-1), (2-2), and (3-3) are shown in Fig. 3.
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
33
Fig. 11 – Relationship between percent of BOD removal and pond shape ratios (for constant area and depth).
efficiency of BOD and causes a slight increase in the DO effluent concentration (cases 2 and 3). On the other hand, in case 1, increasing the ratio between length and width causes slight decrease in the DO effluent concentration. This result similar to Vega et al. (2003) who argued that crosswise (diagonally opposite) inlet–outlet layout, a lengthto-breadth ratio of 2:1, plus provision of two cross baffles at 1/3L and 2/3L increases the pond retention factor and BOD removal efficiency. It also agrees with observations by Shilton and Harrison study (2003), which states that a minimum of two baffles in a pond is recommended. A further improvement was achieved using four baffles. Fig. 13 presents the relationship between the minimum and the maximum velocities, related to the pond shape ratios at each case. Fig. 13 shows that the minimum velocity increased from 0.002 × 10−3 m/s at L1 /L2 = 1 to 0.015 × 10−3 m/s at L1 /L2 = 4 (case 1), from 0.038 × 10−3 m/s at L1 /L2 = 1 to 0.09 × 10−3 m/s at L1 /L2 = 4 (case 2) and from 0.047 × 10−3 m/s at L1 /L2 = 1 to 0.5 × 10−3 m/s at L1 /L2 = 4 (case 3). It also shows that the maximum velocity increased from 2.6 × 10−3 m/s at L1 /L2 = 1 to 2.8 × 10−3 m/s at L1 /L2 = 4 (case 1), from 3 × 10−3 m/s at L1 /L2 = 1 to 0.169 m/s at L1 /L2 = 4 (case 2) and from 0.105 m/s at L1 /L2 = 1 to 0.765 m/s at L1 /L2 = 4 (case 3). From the results in Fig. 13 it can be concluded that increasing the ratio between length and width causes slight increase in the flow velocity. This result agrees with Arceivala who
Fig. 13 – Relationship between minimum and maximum velocities and pond shape ratios (for constant area and depth).
found that units possessing baffles which lengthen the flow path invariables gave much higher D (dispersion coefficient) values than those without baffles but having same width. These bends in flow increase dispersion so, inserting a baffle. And also introduced that inserting a baffle in the unit helps to quickly reduce the D/UL value since the baffle reduces width, and increases both the velocity and length of the flow path.
4.
Conclusion
The results obtained through the present study show that the increase in pond length and width ratio causes bulk increase in the removal efficiency of BOD, slight increase in the DO effluent concentration and slight increase in the flow velocity. The results also show that the minimum of two baffles in a pond is recommended. A further improvement was achieved using four baffles. The results show that the length to width ratio of 4:1 with two and four cross baffles lies at 1/3L and 1/5L, respectively, are the most effective measures to improve overall waste stabilization pond hydrodynamics and BOD removal efficiency.
references
Fig. 12 – Relationship between DO effluent concentration and pond shape ratios (for constant area and depth).
Baleo, J.N., Humeau, P., Le Cloirec, P., 2001. Numerical and experimental hydrodynamic studies of a lagoon pilot. Water Res. 35, 2268–2276.
34
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Salter, H.E., Ta, C.T., Ouki, S.K., Williams, S.C., 2000. Three-dimensional computational fluid dynamic modelling of a facultative lagoon. Water Sci. Technol. 42 (10/11), 335–342. Shilton, A., 2000. Potential application of computational fluid dynamics to pond design. Water Sci. Technol. 42 (10/11), 327–334. Shilton, A., Harrison, J., 2003. Guidelines for the Hydraulic Design of the Waste Stabilization Ponds. Institute of Technology and Engineering, Massey University, Palmerston North, New Zealand.
Surface-water Modeling System (SMS) Program Version 7.00. ˜ M.R., Ram´ırez, C., Mara, D.D., 2003. Application of Vega, G.P., Pena, CFD modelling to study the hydrodynamics of various anaerobic pond configurations. Water Sci. Technol. 48 (2), 163–171. Wood, M.G., Greenfield, P.F., Howes, T., Johns, M.R., Keller, J., 1995. Computational fluid dynamic modelling of wastewater ponds to improve design. Water Sci. Technol. 30 (12), 111–118. Wood, M.G., Howes, T., Keller, J., Johns, M.R., 1998. Two-dimensional computational fluid dynamic models for waste stabilisation ponds. Water Res. 32, 958–963.
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/ecoleng
Study of waste stabilization pond geometry for the wastewater treatment efficiency Hamdy Abbas a,∗ , Rabeia Nasr a , Hamdy Seif b a b
Irrigation and Hydraulic Engineering Department, Faculty of Engineering, Alexandria University, Egypt Sanitary Engineering Department, Faculty of Engineering, Alexandria University, Egypt
a r t i c l e
i n f o
a b s t r a c t
Article history:
The simulation of hydrodynamics in waste stabilization ponds is a developing tool worth
Received 23 December 2005
studying in order to understand their internal processes and interactions. Pond design
Received in revised form
involves several physical, hydrological, geometrical and dynamic variables to provide high
8 March 2006
hydrodynamic efficiency and maximum substrate utilization rates. Computational fluid
Accepted 11 March 2006
dynamic modelling (CFD) allows the combination of these factors to predict the behavior of ponds by using different configurations. The two-dimensional depth-integrated model SMS was used in this study to simulate hydrodynamics and water quality. A set of 12 configura-
Keywords:
tions including baffling and pond geometry was modeled. The model was run at steady state
Waste stabilization ponds
with raw wastewater to study the effect of the assumed rectangular shapes and dimensions
Computational fluid dynamic
with constant area, for various values of water depth, flow rate and hydraulic retention
Water quality
time (HRT) of raw wastewater. The model was also run for different rectangular shapes with
Hydrodynamics
baffles. The area was manipulated by increasing the ratio between rectangular width and
Modelling
length as one, two, three and four times, respectively. Biochemical oxygen demand (BOD),
Wastewater treatment
dissolved oxygen (DO) concentrations and velocities distribution were recorded for different rectangular shapes with different numbers of baffles. Results showed that the rectangular shape ratio (L1 /L2 = 4) with the provision of two and four cross baffles at 1/3L (two baffles) and 1/5L (four baffles), respectively, most efficient to improve overall water quality. © 2006 Elsevier B.V. All rights reserved.
1.
Introduction
There are many methods of wastewater treatment; undoubtedly, the stabilization pond is one of the most common wastewater treatment techniques, especially if low-cost land is available. In modern societies, environmental management programs use models of systems that will examine and evaluate the possible alternatives. The mathematical models are commonly used in the areas of the fluid dynamic, surface water hydrology, the subsurface water hydrology and sedimentation processes and flow velocities in lakes. Numerical models or mathematical simulation models are currently
∗
Corresponding author. Tel.: +20 3 5853533; fax: +20 3 5551242. E-mail address: [email protected] (H. Abbas). 0925-8574/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2006.03.008
widely used in the field of water quality management (to predict the concentrations of biochemical oxygen demand, dissolves, toxic, etc.) and in the field of hydrodynamics. The simulation of hydrodynamic in bioreactors supported by modern computing technology is an important tool to gain an improved understanding of the process functioning and performance. Natural wastewater treatment systems such as waste stabilization ponds (WSP) are particularly subjected to stochastically varying environmental factors of different kinds, for example, temperature, rainfall and evaporation regimes, wind speed and direction and solar energy intensity. WSP designers have some control only upon one sin-
26
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Fig. 1 – Schematic representation of WSP configuration shapes modeled (case of no baffles).
gle process variable—hydraulic retention time (HRT) (Vega et al., 2003). However, the HRT distribution of influent wastewater volumes will be affected by some of the other factors. Despite these difficulties, some researchers have recently carried out work on WSP hydrodynamics by using computational fluid dynamic (CFD) modelling packages (Wood et al., 1995, 1998; Shilton, 2000; Salter et al., 2000; Baleo et al., 2001). CFD modelling offers an alternative way to study and predict the performance of WSP based upon their hydrodynamic features (Wood et al., 1995). It seems to be a powerful tool to help in the design and evaluation of new and existing WSP systems. The present study aims to apply a two-dimensional CFD modelling on (WSP) treating wastewater with various rectangular shape configurations. It was evaluated in order to establish the most likely interventions for future improvements. An additional aim of this paper is to com-
plement and extend the previous valuable work found in the literature.
1.1.
Governing dynamic equations and modelling
The following governing equations are used to solve the depthintegrated equations of fluid mass and momentum conservation in two horizontal directions. The forms of the solved equations are: h
∂u ∂u ∂u h + hu + hv − ∂t ∂ ∂y
+
gun2 (1.486h1/6 )
2
+ (u2 + v2 )
Exx 1/2
∂2 u ∂2 u + Exy 2 2 ∂x ∂y
− Va2 cos
+ gh
∂a ∂x
+
∂h ∂x
− 2hωv sin ˚ = 0,
Fig. 2 – Schematic representation of WSP configuration shapes modeled (case of two baffles).
(1)
Table 1 – Summary of BOD effluent and removal, DO effluent and velocities at different rectangular shape ratios and different cases of baffles L1 /L2 ratio
Case of no baffles Effluent BOD (mg/l)
Percent of removal
Range Mean
L1 /L2 ratio
6.347 7.998 10.393 12.50
Effluent BOD (mg/l)
Percent of removal
233 96.563 43.738 22.008
22.33 67.8 85.42 92.669
Case of no baffles Effluent DO (mg/l)
Range
Mean
Effluent concentrations of DO: (influent DO = 0.00 mg/l) 1 0.507 0.434 0.074 2 0.454 0.372 0.074 3 0.456 0.351 0.098 4 0.263 0.2006 0.060
L1 /L2 ratio
Standard deviation
257.134 160.78 112.516 95.21
19.086 61.98 74.887 88.62
Effluent BOD (mg/l)
Percent of removal
Range
Mean
Standard deviation
54.22 15.972 14.31 12.642
81.92 94.68 95.23 95.786
245.78 284.028 285.68 287.358
127.985 70.702 54.977 51.001
71.533 76.702 65.72 64.437
Standard deviation
Effluent DO (mg/l)
0.507 0.454 0.456 0.263
6.373 8.864 9.676 9.961
Range
Mean
6.373 8.864 9.676 9.961
4.373 6.353 7.43 7.673
Case of four baffles Standard deviation 1.79 2.618 2.764 3.057
Effluent DO (mg/l) 9.579 10.015 10.029 10.032
Case of Two baffles
Mean Minimum Maximum Range velocity (×10−3 ) (×10−3 ) velocity (×10−3 m/s) (×10−3 m/s) 2.69 2.78 3.01 2.8
66.47 203.43 256.26 277.99
Standard deviation
Case of Two baffles
Case of no baffles
Maximum and minimum velocities 1 0.002 2.69 2 0.001 2.78 3 0.005 3.01 4 0.015 2.8
Range Mean
Case of four baffles
0.39 0.52 0.62 0.59
Range
Mean
9.579 10.015 10.029 10.032
7.195 8.397 8.842 8.840
Standard deviation 2.69 2.566 2.195 2.191
Case of four baffles
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Effluent concentrations of BOD: (influent BOD = 300 mg/l) 1 251.7 16.1 48.293 257.658 2 246.61 17.79 53.38 255.308 3 243.41 18.87 56.584 253.60 4 235.19 21.60 64.806 249.68
Case of Two baffles
Maximum Range Mean Standard Minimum Maximum Range Mean Standard Standard Minimum velocity velocity (×10−3 ) (×10−3 ) deviation velocity velocity (×10−3 ) (×10−3 ) deviation deviation (×10−3 ) (×10−3 m/s) (×10−3 m/s) (×10−3 ) (×10−3 m/s) (×10−3 m/s) (×10−3 ) 0.452 0.479 0.525 0.137
0.038 0.062 0.018 0.091
3.19 44.10 79.86 169
3.19 44.10 79.86 169
4.24 1.58 18.9 27.40
3.02 7.5 12.55 21.77
0.047 0.065 0.1 0.5
105 267 317 765
105 267 317 765
0.14 35.1 51.1 85.4
11.2 27.1 35.5 69.7
27
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e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Fig. 3 – Schematic representation of WSP configuration shapes modeled (case of four baffles).
∂v ∂v ∂v h h + hu + hv − ∂t ∂x ∂y +
gvn2 (1.486h1/6 )
2
∂2 v ∂2 v Eyx 2 + Eyy 2 ∂x ∂y
+ (u2 + v2 )
1/2
− Va2 sin
+ gh
∂a ∂y
+
∂h ∂y
+ 2hωv sin ˚ = 0, (2)
∂h +h ∂t
∂u ∂x
+
∂v ∂y
+u
∂h ∂h +v = 0, ∂x ∂y
(3)
where h is the water depth, u and v are the velocities in the Cartesian directions, x, y and t are the Cartesian coordinates and time, the density of fluid, E the eddy viscosity coefficient
Fig. 4 – WSP simulations plots (case of four baffles) (L1 /L2 = 1.0; L2 /L2 = 0.2; a/L1 = 0.9). (a) Finite element mesh; (b) velocity distribution; (c) BOD concentration; (d) DO concentration.
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e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Fig. 5 – WSP simulations plots (case of four baffles) (L1 /L2 = 2.0; L2 /L2 = 0.2; a/L1 = 0.9). (a) Finite element mesh; (b) velocity distribution; (c) BOD concentration; (d) DO concentration.
(for xx is the normal direction on x-axis surface; for yy is the normal direction on y-axis surface; for xy and yx are the shear direction on each surface), g the acceleration due to gravity, a the elevation of bottom, n the Manning’s roughness n-value, 1.486 the conversion from SI (metric) to non-SI units, the empirical wind shear coefficient, Va the wind speed, the wind direction, ω the rate of earth’s angular rotation and ˚ is the local latitude. Eqs. (1)–(3) are solved by the finite element method using the Galerkin method of weighted residuals. The solution is fully implicit and the set of simultaneous equations is solved by Newton–Raphson non-linear iteration. The computer code executes the solution by means of a front-type solver, which assembles a portion of the
matrix and solves it before assembling the next portion of the matrix. For determination of water quality parameters, the following governing equation solves the depth-integrated equations of the transport and mixing process. The form of the depth averaged transport equation is
h
∂c ∂c ∂ ∂ R(c) ∂c ∂c ∂c +u +v − Dx − Dy − + kc + ∂t ∂x ∂y ∂x ∂x ∂y ∂y h
= 0, (4)
where h is the water depth, c the concentration of pollutant for a given constituent, t the time, u and v are the velocity in xdirection and y-direction, Dx and Dy are the turbulent mixing
Fig. 6 – WSP simulations plots (case of four baffles) (L1 /L2 = 3.0; L2 /L2 = 0.2; a/L1 = 0.9). (a) Finite element mesh; (b) velocity distribution; (c) BOD concentration; (d) DO concentration.
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e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
Fig. 7 – WSP simulations plots (case of four baffles) (L1 /L2 = 4.0; L2 /L2 = 0.2; a/L1 = 0.9). (a) Finite element mesh; (b) velocity distribution; (c) BOD concentration; (d) DO concentration.
Fig. 8 – BOD concentrations curves (cases of four baffles). Sections (1-1), (2-2) and (3-3) are shown in Fig. 3.
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
(dispersion) coefficient, k is the first order decay of pollutant, the source/sink of constituent and R(c) the rainfall/evaporation rate. The equation is solved by the finite element method using Galerkin weighted residuals. Spatial integration of the equations is performed by Gaussian techniques. The model, which was selected for the present study, is surface-water modelling system (SMS) version 7.0. SMS is a comprehensive environment for two-dimensional hydrodynamic modelling.
31
(31 days), constant depth (1.5 m) and constant water temperature (15 ◦ C). • Optional boundary conditions (isotropic eddy viscosities, diffusion coefficients, Manning’s n-values and dispersion coefficients) were also calculated. • Raw wastewater characteristics were assumed: ◦ influent BOD concentration = 300 mg/l; ◦ influent DO concentration = 0.0 mg/l; ◦ surface organic loading rate of BOD = 150 kg/ha day. All study cases are shown in Figs. 1–3.
2.
Boundary conditions 3.
The model was run at steady-state case with raw wastewater to study the effect of various rectangular shapes with constant area, depth, flow rate and hydraulic retention time on rectangular stabilization ponds performance. The model was run with different rectangular shapes. The area was manipulated by increasing the ratio between the width and the length ratio one, two, three and four times, respectively. The operated boundary conditions for the present study were chosen as follows: • The model was operated hydraulically for each case at constant flow rate of 0.18 m3 /s hydraulic retention time of 744 h
Results and discussion
The model was run at steady state with raw wastewater to study the effect of various rectangular shapes at constant area, depth, flow rate and hydraulic retention time (HRT) on each pond performance. The model was run at different rectangular shapes. The area was manipulated by increasing the length to width ratio as 1, 2, 3 and 4 with different number of baffles 0, 2, and 4, respectively. BOD, DO concentrations and velocities distributions were determined for different rectangular shape cases with different numbers of baffles. The effect of pond shapes with or without baffles on the treatment efficiency is studied for all the cases given.
Fig. 9 – DO concentration curves (cases of four baffles). Sections (1-1), (2-2) and (3-3) are shown in Fig. 3.
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e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
The summary of effluent concentrations and removal efficiency of BOD, DO concentrations and velocities values, for different cases of baffles are given in Table 1. The results for the studied three cases are concluded as follows. • Case 1: without baffles. The effluent BOD concentration was found in the range of 235.19–251.7 mg/l for different rectangular shape ratios (L1 /L2 = 1, 2, 3 and 4). The effluent concentration of DO ranged between 0.263 and 0.507 mg/l and velocity ranged between 0.002 × 10−3 m/s and 0.015 × 10−3 m/s for the minimum and between 2.69 × 10−3 m/s and 3.01 × 10−3 m/s for the maximum for the same conditions. • Case 2: with two baffles. The effluent BOD concentration was found in the range of 22.008–233 mg/l at different rectangular shapes ratios (L1 /L2 = 1, 2, 3 and 4) with two baffles. The effluent concentration of DO ranged between 6.373 and 9.961 mg/l and velocity ranged between 0.018 × 10−3 m/s and 0.091 × 10−3 m/s for the minimum and between 3.19 × 10−3 and 169 × 10−3 m/s for the maximum for the same conditions. • Case 3: with four baffles. The effluent BOD concentration was found in the range of 12.64–54.22 mg/l at different rectangular shapes ratios (L1 /L2 = 1, 2, 3 and 4) with four baffles. The effluent concentration of DO ranged between 9.57 and 10.03 mg/l and velocity ranged between
0.047 × 10−3 m/s and 0.05 × 10−3 m/s for the minimum and between 105 × 10−3 m/s and 765 × 10−3 m/s for the maximum for the same conditions. Case 3 results were given in Table 1 and will be used for illustrating the simulation display, whereas Figs. 4–7 display simulation of some of the waste stabilization pond configurations which were studied. Figs. 8–10 present the results at the entrance, the middle and the outlet sections for the same cases to demonstrate the relationships between the effluent concentrations of BOD, DO and velocities with distances which resulted from different shape ratios of the pond. Figs. 11 and 12 present the relationship between BOD removal and DO effluent concentration and pond shape ratios at different cases of baffling. The study has demonstrated that the BOD removal efficiency increased from 16% at L1 /L2 = 1–22% at L1 /L2 = 4 (case 1), from 22% at L1 /L2 = 1–93% at L1 /L2 = 4 (case 2) and from 82% at L1 /L2 = 1–96% at L1 /L2 = 4 (case 3). The DO effluent concentration decreased from 0.5 mg/l at L1 /L2 = 1–0.26 mg/l at L1 /L2 = 4 (case 1), increased from 6 mg/l at L1 /L2 = 1–9.9 mg/l at L1 /L2 = 4 (case 2) and increased from 9.5 mg/l at L1 /L2 = 1–10.0 mg/l at L1 /L2 = 4 (case 3). The results in Figs. 11 and 12 show that increasing the ratio between length and width causes bulk increase in the removal
Fig. 10 – Velocity values curves (cases of four baffles). Sections (1-1), (2-2), and (3-3) are shown in Fig. 3.
e c o l o g i c a l e n g i n e e r i n g 2 8 ( 2 0 0 6 ) 25–34
33
Fig. 11 – Relationship between percent of BOD removal and pond shape ratios (for constant area and depth).
efficiency of BOD and causes a slight increase in the DO effluent concentration (cases 2 and 3). On the other hand, in case 1, increasing the ratio between length and width causes slight decrease in the DO effluent concentration. This result similar to Vega et al. (2003) who argued that crosswise (diagonally opposite) inlet–outlet layout, a lengthto-breadth ratio of 2:1, plus provision of two cross baffles at 1/3L and 2/3L increases the pond retention factor and BOD removal efficiency. It also agrees with observations by Shilton and Harrison study (2003), which states that a minimum of two baffles in a pond is recommended. A further improvement was achieved using four baffles. Fig. 13 presents the relationship between the minimum and the maximum velocities, related to the pond shape ratios at each case. Fig. 13 shows that the minimum velocity increased from 0.002 × 10−3 m/s at L1 /L2 = 1 to 0.015 × 10−3 m/s at L1 /L2 = 4 (case 1), from 0.038 × 10−3 m/s at L1 /L2 = 1 to 0.09 × 10−3 m/s at L1 /L2 = 4 (case 2) and from 0.047 × 10−3 m/s at L1 /L2 = 1 to 0.5 × 10−3 m/s at L1 /L2 = 4 (case 3). It also shows that the maximum velocity increased from 2.6 × 10−3 m/s at L1 /L2 = 1 to 2.8 × 10−3 m/s at L1 /L2 = 4 (case 1), from 3 × 10−3 m/s at L1 /L2 = 1 to 0.169 m/s at L1 /L2 = 4 (case 2) and from 0.105 m/s at L1 /L2 = 1 to 0.765 m/s at L1 /L2 = 4 (case 3). From the results in Fig. 13 it can be concluded that increasing the ratio between length and width causes slight increase in the flow velocity. This result agrees with Arceivala who
Fig. 13 – Relationship between minimum and maximum velocities and pond shape ratios (for constant area and depth).
found that units possessing baffles which lengthen the flow path invariables gave much higher D (dispersion coefficient) values than those without baffles but having same width. These bends in flow increase dispersion so, inserting a baffle. And also introduced that inserting a baffle in the unit helps to quickly reduce the D/UL value since the baffle reduces width, and increases both the velocity and length of the flow path.
4.
Conclusion
The results obtained through the present study show that the increase in pond length and width ratio causes bulk increase in the removal efficiency of BOD, slight increase in the DO effluent concentration and slight increase in the flow velocity. The results also show that the minimum of two baffles in a pond is recommended. A further improvement was achieved using four baffles. The results show that the length to width ratio of 4:1 with two and four cross baffles lies at 1/3L and 1/5L, respectively, are the most effective measures to improve overall waste stabilization pond hydrodynamics and BOD removal efficiency.
references
Fig. 12 – Relationship between DO effluent concentration and pond shape ratios (for constant area and depth).
Baleo, J.N., Humeau, P., Le Cloirec, P., 2001. Numerical and experimental hydrodynamic studies of a lagoon pilot. Water Res. 35, 2268–2276.
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Salter, H.E., Ta, C.T., Ouki, S.K., Williams, S.C., 2000. Three-dimensional computational fluid dynamic modelling of a facultative lagoon. Water Sci. Technol. 42 (10/11), 335–342. Shilton, A., 2000. Potential application of computational fluid dynamics to pond design. Water Sci. Technol. 42 (10/11), 327–334. Shilton, A., Harrison, J., 2003. Guidelines for the Hydraulic Design of the Waste Stabilization Ponds. Institute of Technology and Engineering, Massey University, Palmerston North, New Zealand.
Surface-water Modeling System (SMS) Program Version 7.00. ˜ M.R., Ram´ırez, C., Mara, D.D., 2003. Application of Vega, G.P., Pena, CFD modelling to study the hydrodynamics of various anaerobic pond configurations. Water Sci. Technol. 48 (2), 163–171. Wood, M.G., Greenfield, P.F., Howes, T., Johns, M.R., Keller, J., 1995. Computational fluid dynamic modelling of wastewater ponds to improve design. Water Sci. Technol. 30 (12), 111–118. Wood, M.G., Howes, T., Keller, J., Johns, M.R., 1998. Two-dimensional computational fluid dynamic models for waste stabilisation ponds. Water Res. 32, 958–963.