Aeration of a wastewater lagoon environment to accelerate
BNR (Biological Nutrient Removal) is well known and
acknowledged technology. The delivery mechanism
typically consists of submerged, specialized aerators.
Current technology tends to be ineffective and require
significant investments in equipment over time.
The combined mixing and aerating action of a horizontal
directional aeration system may yield improved oxygen
transfer rates over that of vertical aeration technology. A
horizontal aerator combines conventional aspirator
technology with blower assisted aeration which produces
fine bubbles that significantly improve oxygen transfer. It
also induces horizontal flow that keep solids from settling.
CFD simulation of lagoon configurations with vertical and
horizontal aerators was conducted to provide the qualitative
difference between directional and vertical mixing and
quantitative data for Residence Time Distribution (referring
to a perception that directional aerators/mixers may cause
short circuiting between flow in and out).

A horizontal or directional aerator differs from standard
aeration equipment and is particularly suited for use in
lagoons. In horizontal aerators air is injected in front of an
impeller. The spinning impeller provides energy that is
needed for air dispersion and also provides a far reaching
flow of oxygen saturated fluid. Dispersion relies on
turbulence produced by the impeller. Horizontal aerators
also offer benefits in having a compact size, easy placement
at any location in a lagoon, low pressure drop for oxygen
delivery, efficient gas dispersing head, and produce very
high horizontal flow.
Vertical aspirators or high speed floaters are a common
technology used in lagoon configurations. They use
pumping to aerate and the pumping direction may be up or
down. In down pumping air is entrained from a vortex
created by mixer action and goes through an impeller
where it is dispersed into small bubbles. In an up pumping
a stream of water is thrown into the air where it is saturated
with oxygen then mixes with the body of water. All mass
transfer relies on fine spray that develops a large surface
area.
The challenge in selection of equipment for lagoon aeration
is how to select optimum placement of aerators.
Calculation of oxygen transfer requirements is very straight
forward, however the placement requires a great deal of
experience and the support of CFD. A lagoon that is fitted
with vertical aerators only will experience solids deposit at
the bottom. Solids are lifted only in small areas adjacent to
aerators. Get easy pay through payday advance today.

A new type static mixer composed of σ-shaped element
was developed, in which multilamination of fluid layers
proceeds through systematic splitting and inverse
recombination. The number of elements required for
complete mixing, n, was measured by conducting the
decolourising reaction of iodine with sodium thiosulfate
for various total flow rates of two fluids to be mixed. n
increased with Re when Re is less than 10, but it decreased
with Re at larger Re. When Re exceeds this critical value,
CFD analysis shows that a larger deformation and stretch
of the fluid interface take place due to the bending and
winding channel structure of σ-shaped element as Re
increases. This flow behaviour accelerates mixing rate,
resulting in the considerable decrease in the number of
elements for complete mixing. In addition, an analysis for
Figure
residence time distribution of fluid particles demonstrates
that the flow in the mixer approaches the plug flow with
increasing the Reynolds number and the number of
elements.
NOMENCLATURE
a largest width of channel of mixing element
b depth of channel of mixing element
n number of mixing elements for complete mixing
Re Reynolds number = ρuava/μ
uav cross-sectional average velocity = Q/ab
Q total flow rate of two fluids fed to mixer
MIXER STRUCTURE
Figure 1 shows channel geometry of a unit element of σ-
type plate static mixer (Hirata, 2006), in which the dotted
circles represent the cross-sections of inlet and outlet for
the fluids to be mixed. Channels with rectangular crosssection
were grooved in a plate to conform this geometry
and each grooved element was aligned in a row. A
packing sheet or plate with circular holes is sandwiched
by two plates with a row of unit elements prepared in this
way, to one of which a Y-shaped channel was connected
for introducing two fluids to be mixed as shown in Figure
2. Each hole of the sandwiched sheet or plate serves as a
channel connecting the outlet of a unit element on a plated
to the inlet of the following unit element on the other plate.
In this way channels for mixing fluids can be constructed.
We call this type of mixer σ-type plate static mixer since
the shape of the unit element resembles σ in Greek
character.

this way, static mixing with splitting and inverse
recombination progresses in the mixer.
VISUALIZATION OF MIXING PTROGRESS
Mixing progress was visualized by using the decolourising
reaction of iodine with sodium thiosulphate. An example
of the visualized images is shown in Fig.3, which were
taken at Re=1.2 using the square channel with a=b=3mm.
At low Reynolds numbers, the static mixing progresses
identically by splitting and inverse recombination as
shown in the figure. As the Reynolds number increases,
mixing progress deviates from the ideal static mixing
because of the secondary flows generated in the threedimensionally
bent portions in the mixer. The occurrence
of secondary flows has been confirmed by CFD
calculation.
NUMBER OF ELEMENTS REQUIRED FOR
COMPLETE MIXING
The numbers of elements required for completing the
decolourising reaction, n, which were obtained for a
square channel with a=b=1mm, is plotted against
Reynolds number in Fig. 4. n increases with Reynolds
number at Re < 10. This is due to that the molecular
diffusion is limited to narrow regions adjacent to the
interface of two liquids because the residence time in the
element is decreased with increasing the flow velocity. At
larger Reynolds number, n decreases with Re. The
decrease in n is mainly caused by the secondary flows
generated in the three-dimensionally bent portions in the
mixer. At Reynolds number greater than 102, the number
of elements required for complete mixing is less than 10.
RESIDENCE TIME DISTRIBUTION
F-curves in a unit element are shown for various Reynolds
numbers in Fig. 5. Using the three-dimensional velocity
data obtained by CFD calculation, F-curves were obtained
by tracking 105 fluid particles that were uniformly
distributed on the mid-plane of the interconnecting
circular channel at a time. Although this curve does not
represent the normal F-curve obtained by a step change in
concentration, reactor performance of σ-type mixer may
be discussed based on it. As shown in the figure, F-curve
for fluid particles, which are sharp compared with that in
the laminar pipe flow, approach the distribution of plug
flow as Reynolds number increases. It has also been
confirmed that the flow in the mixer tends to approach the
uniform residence time distribution with increasing the
number of mixing elements. You can even create solid metal hitch covers using this method.

Quantifying the flow and residence time distribution (RTD) in a reactor is critical for predicting reactor performance measures such as yield and selectivity. This work investigated a continuous industrial scale reactor (diameter 11 ft, height 37 ft) employing a 3-stage agitation system with a plunging jet inflow. In operation, the agitation and liquid level are adjusted based on throughput and the specific product being produced. The objective of this work was to quantify RTD under the different operating conditions of liquid level, throughput and agitation. Flow in the reactor was modeled with computational fluid dynamics (CFD). A combination of different modeling approaches was used to better facilitate the CFD simulations. The Volume of Fluid (VOF) method was used to model the plunging jet (gas-liquid) two-phase flow with unsteady-state simulations, while the Multiple Reference Frames (MRF) model was used to model the stirred tank with steady-state simulations. The two modeling approaches were interfaced by using the plunging jet simulation result as the input boundary condition for the reactor flow simulation. The effect of gas entrainment from the plunging jet impingement was accounted for by using the (dampened) velocity profile of the impinging jet. The RTD was obtained from stochastic particle tracking which tracks trajectories and residence times of massless tracers in the reactor. The Random Walk Model was used for dispersion of tracers due to turbulent eddies. A large number of tracers (>10,000) was needed to account for the random effects of turbulence and ensure statistically stable results. The Time Scale Constant in the Radom Walk Model was adjusted to accommodate significant turbulence level differences in discreet regions of the tank. The CFD-predicted flow pattern compared well with lab-scale experiments. The predicted mean RTD was consistent with the bulk reactor turn-over time