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.

Microencapsulation is a widespread technology that has many applications, like the protection and controlledrelease
of active ingredients in the medical and cosmetics industries, or the fabrication of fragranced fabrics in the
textile industry.
This work focuses on the emulsification step of an interfacial polymerization microencapsulation process. Firstly,
an emulsion is prepared that comprises a population of droplets. This dispersed phase contains a monomer. In a
second step, another monomer, which is soluble in the continuous phase, is added to the system to begin the
reaction at the interface of droplets.
In industry, microencapsulation by interfacial polymerization is usually performed in stirred-tank reactors, where
both the emulsification and encapsulation steps are carried out. But this process is very costly in energy due to
the power input necessary for the generation of a fine dispersion, as well as the time needed to get the right drop
size distribution. Moreover, the characteristics of the final product, such as the particle size distribution with
respect to the target size, and the membrane thickness and structure, are not necessarily well controlled. These
characteristics are strongly dependant on the hydrodynamic conditions of the different steps. In particular, it is
crucial to control the drop size of the emulsion in order to control the microcapsule size distribution resulting from
this process.
In this study, the emulsification process is carried out using Sulzer SMX mixers. Such mixers are usually
employed for the dispersion of viscous liquids in the laminar flow regime. However, it is demonstrated in this study
that they are also well adapted for liquid-liquid dispersion in turbulent flow.
The influence of the dispersed phase concentration, the flow velocity and the number of mixing elements on the
drop size distribution under various turbulent flow conditions is investigated. The drop size distribution is
characterized in terms of the mean surface-volume drop diameter and standard deviation, which are measured
with a laser diffraction device. The emulsions are cyclohexane-in-water stabilised with Tween 80, which are the
same fluids involved in the system chosen for the encapsulation process.
A correlation of the Sauter mean diameter with the Weber number and the Reynolds number is proposed for the
flow rate range studied and compared with the correlation given by Streiff (1977) for SMV Sulzer mixers at low
energy input.
The dispersion process in turbulent flow is governed by the ratio of the stress forces outside the drop to the
surface forces at the interface of the drop. The external stress forces are the turbulent drag forces on the drop
surface created by local velocity differences, which are promoted by turbulent eddies. In this case, the smallest
drop size corresponds to the microscale of turbulence and the size can be correlated with the specific energy
dissipation in the mixer. The specific energy can be determined from flowrate and pressure drop through the static
mixer.
Since the correlations available to calculate the pressure drop in SMX mixers are valid for single phase Newtonian
fluid flow, the pressure drop of the liquid-liquid flow is measured in this study and used to calculate the specific
energy dissipation. A correlation of the maximum drop diameter with specific energy dissipation is proposed and
compared with that given by Hinze (1955) for isotropic turbulent flow.
Finally, the minimum number of mixer elements required to obtain a stable drop diameter is given for different
hydrodynamic conditions and dispersed phase concentrations.
The work carried out has enabled the emulsification conditions in the static mixer to be optimized, which should
allow the encapsulation process to be performed in the best conditions. Moreover, the SMX static mixers show
good performance for emulsification in turbulent flow in terms of droplet size and energy consumption compared
with the conventional stirred-tank reactor.

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