Kanish Jindal
ABE 527: Class Project
March 21, 2004
Modeling of a West Lafayette chlorine disinfection chamber:
Progress report:
Objective:
The objective of this project is computational fluid dynamics(CFD)
analysis of existing chlorine contact chamber in West Lafayette waste
water treatment plant using Fluent, compare it with the actual results and
suggest improvements if any.
Introduction
Chlorination is the most widely practiced disinfection process for water
and waste water treatment. This is primarily done in disinfection
chambers where sufficient contact time is provided between treated waste
water and chlorine. The US EPA determines the effectiveness of these
contactors for disinfection by the CT method. where C is the
concentration of the disinfectant at the outlet of tank and T is the T10
value (The time required for 10% of the liquid to leave the tank, or the
time at which 90% of the liquid is retained in the tank and subject to at
least disinfection level of C).
The contactor hydraulic efficiency can be measured by the ratio of T10 and
theoretical hydraulic residence time. The configuration of baffles, inlet
and outlet conditions, length and width of compartments influence this
hydraulic efficiency. Computational fluid dynamics analysis provides the
better understanding fluid flow phenomena that affect mixing, short
circuiting, particle dispersion and other critical issues. It helps to eliminate
different configuration of the contactor which are inefficient and evaluate
designs in terms of mixing characteristics. It can help to reduce the cost
of chlorine by increasing efficiency of its disinfection reservoirs.
In this study I am evaluation the efficiency of disinfection chamber of West
Lafayette waste water treatment plant. One of the greatest challenges in
designing water treatment equipment is that its large size makes it very
expensive and time consuming to perform physical experiments.
Tracer test is conducted in full scale plants to calculate this residence
time distribution. The entire RTD can be used to predict the microbial
inactivation in the disinfection chamber. Another alternative of this full
scale tracer test is to simulate them using CFD models which will thus
same time and money. In this research I have modeled chlorine contact
chamber of West Lafayette waste treatment plant and predicted its
residence time distribution.
Experimental Tracer Test:
I did the tracer test on actual chlorine disinfection chamber at West
Lafayette treatment plant. During the experimental period the flow rate
ranged from 7.46-14.63 MGD, and the average flow rate during the
experiment was 9.91 MGD (26.06 m3/min). Figure 1 shows that the tracer
concentration at time 56min is abnormally high, and it is related as an
outlier. The concentration at this point is assumed to be the average
concentration of its two neighboring points. The mean residence time can
be simplified for a discrete data set as follows:
t

alli
(
t i 1  t i
2
)  ( Fi (t )  Fi 1 (t ))
Method for tracer test:
In order to determine the Residence time distribution (RTD), fluorescent
dye Rhoda mine-wt was added as a pulse(mass=10g) at the influent end of
the chlorine contact chamber at WLWWTP. The fluorescent dye provided
for the flow visualization or the characteristics of the mixing behavior
within the system until the dispersed to the point where it was no longer
available.
The calculation results are shown in Table 2, and the mean residence
time of the chamber is 62.60 minutes.
Tracer concentration (ug/l)
40
Data used in
calculation
original data
35
30
25
20
15
10
5
0
0
10
20
30
40
50
60
70
80
90
100 110
Time (min)
Figure 1: Tracer concentration profile of chlorine contact chamber
Table 2: Calculation of mean hydraulic residence time at the WWTP
Time(min)
50
51
52
53
54
Concentration
(μg/l)
0
0.377
0.372
1.3335
7.078
E(t)
0.000
0.001
0.001
0.003
0.018
Corrected
E(t)
0.000
0.001
0.001
0.003
0.018
F(t)
taveΔF(t)
0.000
0.001
0.002
0.005
0.024
0.000
0.049
0.049
0.180
0.972
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
15.184
19.6553
24.1265
25.7345
29.059
29.611
28.014
26.0755
24.527
23.5835
22.6725
21.0095
17.921
11.3445
9.285
7.4365
7.05
7.156
5.7065
4.33
2.975
2.981
2.47
2.079
1.776
1.577
1.2055
1.1615
0.779
0.678
0.512
0.3885
0.2905
0.364
0.2375
0.173
0.165
0.142
0.1475
0.095
0.040
0.051
0.063
0.067
0.076
0.077
0.073
0.068
0.064
0.061
0.059
0.055
0.047
0.030
0.024
0.019
0.018
0.019
0.015
0.011
0.008
0.008
0.006
0.005
0.005
0.004
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.039
0.050
0.062
0.066
0.075
0.076
0.072
0.067
0.063
0.061
0.058
0.054
0.046
0.029
0.024
0.019
0.018
0.018
0.015
0.011
0.008
0.008
0.006
0.005
0.005
0.004
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.000
0.000
0.000
0.063
0.113
0.175
0.241
0.316
0.392
0.464
0.530
0.593
0.654
0.712
0.766
0.812
0.841
0.865
0.884
0.902
0.921
0.935
0.946
0.954
0.962
0.968
0.973
0.978
0.982
0.985
0.988
0.990
0.992
0.993
0.994
0.995
0.996
0.996
0.997
0.997
0.998
0.998
0.998
2.125
2.801
3.500
3.799
4.365
4.524
4.351
4.117
3.936
3.845
3.755
3.533
3.060
1.966
1.633
1.327
1.276
1.314
1.062
0.817
0.569
0.578
0.485
0.414
0.358
0.322
0.249
0.243
0.165
0.145
0.111
0.085
0.065
0.082
0.054
0.040
0.038
0.033
0.035
0.023
95
96
97
98
99
100
101
102
103
Sum
0.0915
0.101
0.0595
0.1045
0.072
0.051
0.0655
0.028
0.0265
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
1.015
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.999
0.999
0.999
0.999
0.999
1.000
1.000
1.000
1.000
0.022
0.025
0.015
0.026
0.018
0.013
0.017
0.007
0.007
62.60
E(t) (min-1)
0.08
0.06
0.04
0.02
0.00
0
10 20 30 40 50 60 70 80 90 10 11
0 0
Time (min)
Figure 2: Residence Time Distribution Function
E (t ) 
26.06m3 / min
g
1g
103 l
 c (t )


6
10g
l
10 g m 3
Corrected E(t)= E(t)/sum(E(t))
F(t) = Cumulative sum of E(t)
Sample calculation:
At t=52min
E (t ) 
26.06m3 / min
g
1g
103 l 26.06
g
1g
103 l
 c (t )



 0.372


 0.001(min1)
6
3
6
10g
l 10 g m
10
l
10 g m3
Corrected E(t=52) = 0.001/1.05 = 0.001 min-1
F(t=52) = F(t=51)+ E(t=52) = 0.01 + 0.01 =0.02
t  t52
t
t
51  52
taveF (t  52)  i 1 i  ( F (t i )  F (t i 1))  51
 ( F (t52 )  F (t51)) 
 (0.00192 0.00097)  0.049min
2
2
2
Numerical Tracer test:
Fluent follows the following steps for numerical solution:



Division of the domain into discrete control volumes using a
computational grid.
Integration of the governing equations on the individual control
volumes to construct algebraic equations for the discrete
dependent variables (``unknowns'') such as velocities, pressure,
temperature, and conserved scalars.
Linearization of the discretized equations and solution of the
resultant linear equation system to yield updated values of the
dependent variables.
Turbulent flow produced in the initial part of the tank is being modeled
using the RNG k- Model. The RNG-based k- turbulence model is
derived from the instantaneous Navier-Stokes equations, using a
mathematical technique called ``renormalization group'' (RNG) methods.
The analytical derivation results in a model with constants different from
those in the standard k- model, and additional terms and functions in the
transport equations for k and . Here k is the turbulent kinetic energy and
represents the turbulent energy.
Fluent allows you to choose either of two numerical methods: The two
numerical methods employ a similar discretization process (finite-volume),
but the approach used to linearize and solve the discretized equations is
different.
Segregated Solution Method: Using this approach, the governing equations
are solved sequentially (i.e., segregated from one another).
Coupled Solution Method: The coupled solver solves the governing
equations of continuity, momentum, and (where appropriate) energy and
species transport simultaneously (i.e., coupled together). Governing
equations for additional scalars will be solved sequentially (i.e.,
segregated from one another and from the coupled set.
For this project I am using segregated solution method for solving the
discretized equations. Although the flow rate varied during the actual
tracer test performed at the plant, for the simplicity I am assuming the
steady state flow conditions existed in the chamber. Rhoda mine (Inert
organic dye) used in the actual experiment has been replaced by liquid
water therefore there is no chemical kinetics between the two phases.
Steps followed in numerical similitude of disinfection tank:
I collected the actual plots or design drawings of the chlorine disinfection
tank from treatment plant. I used gambit being a pre-processor for
modeling the tank. Tank being rectangular in shape, I used Cartesian coordinate system to define various points on the tank. The modeled was
developed from top to bottom i.e. volumes were generated first and nodes,
edges and surface
The following diagram shows the geometry of the disinfection chamber.
The chamber is 41.41 m long in length.
The first chamber in the following plot shows the mixing zone. Chlorine is
injected at a distance from 14mts from the initial boundary. A hydraulic
jump is provided for uniform mixing just below the injection.
For the same of simplicity I am assuming there’s turbulent zone for the
first few 14 meters in this tank. As a result of this there is uniform mixing
of chlorine inside the tank. Plug flow regime occurs beyond this hydraulic
jump.
Meshing of the fluid volume
The following plot depicts the mesh structure of the disinfection tank.
Cooper meshing was employed for meshing the whole tank.
Inlet and outlet faces were meshed separately to ensure a good mesh
density on the boundaries.
The following plots show’s an alternate mesh of the structure.
Boundary conditions: Appropriate boundary conditions were applied to the
tank. The inlet boundary had uniform flux across the whole surface. The
inlet velocity as calculated from the flow coming in was used in the
negative-x direction.
The meshed file is exported as neutral file for the analysis.
Analysis of Tank: Processing
Fluent is very powerful in modeling fluid flow in disinfection tanks. The
solution adaptive grid capability is particularly useful for accurately
predicting flow fields in regions with large gradients.
The neutral mesh file generated using gambit was imported in fluent.
Grid check is applied to see if there’s any error in geometry. The
minimum volume obtained came out to be positive. Segregated flow model
was used for the analysis under steady state conditions. Water was used
as a fluid material with a density of 998.2 kg/m3 and viscosity of 0.001003
kg/(m.sec). For tracking the residence time again water was assumed as
an injector. Initially 1000 iterations were carried out. Convergence was
achieved after 107 iterations.
The following plot shows that convergence was achieved after 107
iterations.
The following plot shows the velocity vector of water particles in the
chlorine disinfection chamber. It can be seen from the plot that the
velocity of particles reaches a very small value at the edges of the reactor
resulting in dead zones. The flow regime tends to deviate from plug flow
conditions.
The following plot is again a contour plot of velocity of particles inside the
disinfection chamber. The velocity tends to be more at the center and
decreases with distance from center. Similarly the velocity tends to reach
very low values on the corners.
More analysis is required to depict the actual flow conditions in the tank.
There are some issues in terms of flow regimes, boundary conditions and
phase interactions which still need to be resolved.