Effect of Nozzle Arrangement on Venturi Scrubber Performance

Ind. Eng. Chem. Res. 1999, 38, 4889-4900

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Effect of Nozzle Arrangement on Venturi Scrubber Performance Nochur V. Ananthanarayanan and Shekar Viswanathan1 Department of Chemical and Environmental Engineering, National University of Singapore, 10, Kent Ridge Crescent, Singapore 119260, Singapore

The effect of nozzle arrangement on flux distribution is studied in a rectangular, pilot-scale, Pease-Anthony-type Venturi scrubber. The annular, two-phase, heterogeneous, threedimensional gas-liquid flow inside the scrubber is modeled using a commercial computational fluid dynamic (CFD) package, FLUENT. The comparison of predicted liquid drop concentration shows good agreement with experimental data. The model predicts the fraction of liquid flowing as film on the walls reasonably well. Visualization of flux patterns studied using four typical nozzle configurations indicate that the nonuniformity in flux distribution increases when the nozzle-to-nozzle distance is greater than 10% of the width of the side on which the nozzles are placed. An analysis of the effect of multiple jet penetration lengths on liquid flux distribution yielded a comparable distribution at 10-45% less liquid than uniform penetration for a particular nozzle configuration. This would lead to significant improvements in scrubber performance by achieving comparable collection efficiency at a lower pressure drop. Introducton Venturi scrubbers are one of the most important devices used to control the emission of particulate and gaseous pollutants. They are wet scrubbing units that have a converging section, throat, and a diverging section (Figure 1). The operation of a Venturi scrubber involves liquid atomization and acceleration by a highspeed air stream. The gas stream (normally air) containing particles is accelerated through the scrubber. Liquid is injected into the system either as film along the walls or as jets through a system of nozzles. The former is called the wetted-wall approach and the latter is termed as the Pease-Anthony mode. The point of liquid atomization (location of source) inside the unit depends on the injection arrangement, nozzle size, and liquid-to-gas ratio. This liquid is atomized into fine droplets and accelerated by the high-velocity gas stream. The discrete liquid droplets distribute inside the system under the influence of the turbulent gas stream. The drops move both axially (predominantly due to momentum) and radially (initially due to liquid momentum and later due to convective diffusion). When the injected drops reach the wall, they form a film on the wall and hence flow at less than drop velocity. This film plays a negligible role in the cleaning process and causes twophase frictional pressure losses. The complicated liquid flow pattern inside the Venturi scrubber dictates the magnitude of particle collection efficiency and warrants accurate simulation. Recent studies on Pease-Anthony-type scrubbers have established the liquid flux distribution as a function of key operating and design variables.1-4 These variables include throat gas velocity, liquid-to-gas ratio, nozzle diameter, and throat cross-sectional area. However, the nozzle arrangement was fixed in these studies. The system of nozzles used to inject the liquid could be arranged in different ways and would affect the initial distribution of the source liquid. Mayinger and Lehner5 * To whom correspondence should be addressed. E-mail: [email protected]. Tel.: 65-8744309. Fax: 65- 8725483.

Figure 1. Venturi scrubber used for simulation.

conducted experiments on a pilot-scale scrubber by first injecting the liquid through five rows of nozzles along the throat and then restricting the liquid to the first three rows. They reported higher collection efficiencylower pressure drop, and hence better performance, for five-row injection as compared to three-row injection at similar operating conditions. Hence, the nozzle arrangement may affect the liquid flux distribution and performance in Venturi scrubbers. Though industrial personnel have used different configurations, a systematic evaluation of the effect of nozzle arrangement on liquid distribution has not been done to date. A threedimensional model would be required to study this

10.1021/ie9902131 CCC: $18.00 © 1999 American Chemical Society Published on Web 11/13/1999

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Ind. Eng. Chem. Res., Vol. 38, No. 12, 1999

effect. Over the recent years, as computing costs have decreased, computational fluid dynamics (CFD) has found widespread applications, particularly in the simulation of complicated flows. These include applications such as spray drying involving the motion of discrete droplets in a continuous gas stream similar to that in Venturi scrubbers.6-14 In these studies, considerable effort has been made toward predicting the complicated gas flow pattern accurately as this dictates the discretephase motion. However, applications describing CFD codes using droplet spray data are limited. Hence, the objective of this work is to perform a CFD simulation of the annular, discrete, two-phase flow occurring in a rectangular, Pease-Anthony-type Venturi scrubber using a commercial code. The CFD model is validated with experimental data and is then applied to study the effect of nozzle arrangement on liquid flux distribution. CFD Model The scrubber chosen for simulation is shown in Figure 1. A commercial CFD package, FLUENT/UNS (version 4.2), is used to perform the numerical analysis of the two-phase flow. Flow symmetry is assumed along the width (X direction) and the depth (Z direction) of the scrubber. Hence, only one-fourth of the scrubber volume is modeled. The grid for the chosen geometry is set up using an available built-in packagesGeomesh. Because modeling of turbulent flow properties is the most unreliable element of computational fluid dynamics, an appropriate turbulence model must be chosen.15 Successful computations of turbulent flows require some consideration during the mesh generation.16 Because of the strong interaction of the mean flow and turbulence, the numerical results for turbulent flows tend to be more susceptible to grid dependency than those for laminar flows. Hence, it is recommended to resolve, with sufficiently fine meshes, the regions where the mean flow changes rapidly. Moreover, the flow is wall-bounded (especially in the diffuser) and requires sufficient resolution of the grid in the near-wall region, depending on the wall function applied in the turbulence model. To satisfy the above requirements, a sufficiently dense hybrid mesh with thin prism layers at the wall and tetrahedral cells at all other locations is used for the simulation. Simulation of Gas Flow without Spray The gas flow inside the scrubber is highly turbulent. The turbulence inside the unit determines droplet mixing in the two-phase flow. Hence, it is an important prerequisite to predict the single-phase gas flow as accurately as possible. In this work, the renormalization group (RNG) k- turbulent model is used to simulate the gas flow. The RNG model is derived by a more rigorous statistical technique and is particularly beneficial for separated flows and recirculating flows16 (e.g., backward- or forward-facing steps, sudden expansions, diffusers, bluff bodies, and lift devices at a high incidence) that may occur in Venturi scrubbers. The control volume for the simulation includes two wall planes and two symmetry planes. There are two approaches to modeling the near-wall region, namely, the wall-function approach and the near-wall modeling approach. In the first approach, the viscous sublayer is not resolved and semiempirical formulas are used to “bridge” this inner viscosity-affected region between the

wall and the fully turbulent region. In the latter approach, the viscosity-affected region is resolved with a mesh all the way to the wall, including the viscous sublayer. The wall function approach is economical, robust, reasonably accurate and is a practical option for industrial flow simulations.16 The flow in the diffuser is wall-bounded and the near-wall flows could be subject to severe pressure gradients. This model uses the nonequilibrium wall functions that take into account the pressure gradient effects. The swirl-dominated flow option is employed to account for the effect of mild swirls on the turbulence. The velocity inlet to the scrubber is set using the velocity-inlet boundary condition. The pressure-outlet boundary condition is used at the end of the diffuser where the cross-sectional area gradually decreases with a possibility of backflow. The turbulence model requires parameters that estimate k and at the inlet and the outlet. The values at the inlet are important and are sensitive to the flow modeling. The values set at the outlet must be realistic and take into account any backflow into the system. The turbulent intensity and hydraulic diameter approach is used to specify k and at the inlet and outlet of the unit. The equivalent diameter of the Venturi scrubber is used as the hydraulic diameter and the turbulent intensity is calculated using the relation for fully developed flow,16 namely,

turbulent intensity, I ) 0.16(NRE)-0.125 The pressure at the outlet of the diffuser is set at atmospheric pressure. The gas flow inside the scrubber is validated with the axial pressure drop data for three different gas velocities. It can be seen from Figure 2 that the model predictions are in good agreement with the experimental values. The sharp peaks in the predicted pressure drop values are representative of losses at sudden bends encountered at the interface of the throat with the converging and diverging sections. The experimental pressure drop data indicate some pressure recovery in the throat that could be due to some form of backflow in the system or represent a systematic measurement error. However, the model even with a sufficiently dense mesh did not capture this effect. Further investigation and validation with experimental data could help establish the model validity in this region. Because no other experimental measurements such as velocity vectors and turbulent kinetic energy (k) are available on the single-phase flow, further validation of the model predictions is not possible. However, the ability of CFD to predict similar complicated single-phase flows has been shown by many researchers.7,9-12 This coupled with the fact that the Venturi geometry is simple and the flow does not encounter any additional turbulence-inducing factors such as small openings or swirl vanes the model validation based on the above predictions could be considered reasonable. Simulation of Two-Phase Flow The discrete-phase model of FLUENT/UNS is used to study the droplet behavior. The volume of the discrete phase in comparison to that of the gas phase is very little and is close to 0.2%. The droplet size varies from 40 to 150 µm for the operating conditions simulated. These two factors make the discrete-phase model suitable for studying the two-phase flow. A Eulerian (con-

Ind. Eng. Chem. Res., Vol. 38, No. 12, 1999 4891

Figure 2. Comparison of predicted and experimental axial pressure drop for gas-only flow.

tinuous-phase)-Lagrangian (discrete-phase) approach is employed by this model. Liquid is injected through a series of nozzles perpendicular to the gas stream at the throat inlet. The liquid jet penetrates into the gas core and is atomized into drops of varying size by the high velocity air stream. In this model, a simplified method is used to simulate the atomization process. The point at which the liquid jet atomizes depends on the degree of penetration and is a function of the liquid injection velocity. On the basis of the operating conditions, the point of atomization could be calculated by17

FjVj l* ) 0.1145 do FGVGth This point is used to locate the source drops. The drops are assumed to be monodisperse with an average drop size calculated from Boll’s equation:18

42200 + 5776 Dd,avg )

(GL)

VGth1.602

1.932

The initial liquid inertia is difficult to characterize accurately because of the instantaneous and heterogeneous (uncontrolled) nature of the atomization process at highly turbulent conditions. Most investigations in liquid distribution modeling in Venturi scrubbers assume the initial drop velocity to be zero or very low at the point of atomization (penetration length) for crossstream liquid injection. Recent studies on jet penetration by cross-stream liquid injection predict jet acceleration in the range of 0.2-5 m/s for conditions of interest in Venturi scrubbers.19,20 Hence for simplicity, the averagesized drops in this model are located at the penetration length with a velocity of 1 m/s in the direction of injection. When “group injection” is used, the mass flow from each nozzle is divided into 10 streams emerging from a face with cross-sectional area like that of the nozzle. Because the streams represent droplets, which accelerate very quickly after atomization, the use of a low initial velocity would not contribute to any significant error. The droplet trajectories are calculated using a Lagrangian formulation that includes the discretephase inertia, hydrodynamic drag, and force of gravity. As both the phases are at ambient conditions, there is

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no heat or mass transfer between the discrete and the continuous phase and the droplets are defined in the model as inert. The dispersion of droplets due to turbulence in the gas phase is predicted by using stochastic particle tracking.16 FLUENT/UNS predicts the turbulent dispersion of particles by integrating the trajectory equations for individual particles, using the instantaneous fluid velocity along the particle path during integration. The integral time, which is the time spent in turbulent motion, can be approximated as 16

k T L ) CL where CL is to be determined and is not well-known. The value of CL is calculated by estimating the mean fluctuation velocity on any plane (isotropy) by16

u j′ )

x2k3

It has been established that the Peclet number ranges from 100 to 130 for flows in Venturi scrubbers.2,3,21 In this model, the value of CL corresponds to a Peclet number of 100. The eddy diffusivity of the gas is calculated from the Peclet number as follows:2

EG )

VGD NPE

The product of the Prandtl mixing length, lg, and the mean fluctuation velocity gives the gas eddy diffusivity. Hence, the Prandtl mixing length is estimated from3

lg )

EG u j′

The characteristic lifetime of the eddy is taken to be constant in the model and is given by3

τe ) 2TL )

lg u j′

On the basis of the above equations, the value of CL was estimated as 8. The model requires two other parameters that control the time integration of the particle trajectory equations, namely, the length scale, used to set the time step for integration within each control volume, set as 0.001 m; and the maximum number of time steps used to abort trajectory calculations when the droplet never exits the flow domain, set as 10 000. A large number of particle trajectories must be computed to account for the random effects of turbulence on the droplet dispersion. This is done by setting the number of tries in the stochastic model, which defines the number of random trajectories computed. Values of 75, 100, and 150 were used initially for the “number of tries”. No significant change in the dispersion characteristics was found, indicating a good statistical representation of the spread of the droplets due to turbulence. The “number of tries” was set as 100 in the present model. The number of tries divides the mass flow rate of each stream in the group injection so that an equal mass flow of droplets follows each stochastic trajectory. The boundary condition at the wall allows the droplets to escape from the flow domain as it reaches the wall.

The droplets move with considerable momentum inside the scrubber and this affects the continuous phase. The impact of the discrete phase on the continuous phase is accounted by coupling the continuous- and discretephase calculations. In the present model, this two-way coupling is accomplished by solving the discrete-phase equations after five continuous-phase iterations until the solutions in both phases converge. Simulations use nozzles of size 2.1 mm arranged in triangular form (see Figure 7). Values compared for liquid distribution are normalized with the average concentration across the axial plane. All comparisons are made at the end of the throat. Changes from these conditions are stated explicitly. The predicted liquid distribution is compared with experimental data at four different operating conditions. Figure 3 shows the comparison of dimensionless liquid flux at a distance of 2.5 cm from the sidewall (z ) 2.54). The predicted values are found to be in good agreement with the experimental data. Figure 4 shows contours of the liquid concentration in a plane along the entire throat for another operating condition. It can be seen from this figure that the liquid spread increases with distance along the throat. Data from the CFD simulation shows a maximum dimensionless flux of 2.75 on an axial plane at 25% the throat length. This value decreased by almost 38% to 1.7 at the end of the throat, showing a significant improvement in the uniformity in liquid flux distribution. This trend is in agreement with experimental observations of Haller et al.22 on a Pease-Anthony-type cylindrical Venturi. Because of unavailability of such data for a rectangular Venturi, independent validation of the flux distribution along the throat could not be done. Liquid drops that reach the wall form a film that does not participate in the cleaning process but increases frictional losses. Hence, it is important to estimate the film flow. Figure 5 shows the fraction of film flowing at the throat end as a function of the liquid-to-gas ratio for two throat gas velocities. A reasonably good match between the experimental and predicted values indicates the model’s ability to predict film flows. Film flow measurements and predictions are complicated issues in Venturi scrubber research. Deposition and entrainment mechanisms significantly affect the quantity of film flow. A detailed study made by A. H. Govan23 on deposition and entrainment to predict film flow show that entrainment correlations are derived assuming equilibrium flow (deposition and entrainment rates are assumed equal) and are used for nonequilibrium conditions. In this work, the amount of liquid on the walls is computed by taking a mass average of the liquid concentration on the walls that has escaped from the point of injection. The model does not take into account reatomization (entrainment) from the walls. A userdefined subroutine would be required to incorporate liquid entrainment in the model and this would complicate the calculations. Moreover, simplifying assumptions such as uniform drop size and negligible drop breakup and coalescence may have an impact on the film flow predictions. Hence, detailed liquid atomization and entrainment processes may be required for accurate prediction of film flow. The film flow in the diffuser is difficult to predict as a significant amount of reatomization is expected at the throat exit. Koehler et al.24 studied the gas-borne liquid flow in a cylindrical pilotscale Venturi scrubber and proposed values for entrain-


Figure 3. Comparison of predicted and experimental liquid flux distribution in a pilot scrubber.

ment ranging from 4 to 40% of the film flowing at the throat end by fitting experimental data to model predictions at specific operating conditions. These experiments were done with axial and wetted wall injection methods and the results for Pease-Anthony-type scrubbers could be somewhat different. Incorporation of such entrainment data into the model would only improve the film flow predictions. However, it can be seen from the work

by Viswanathan et al.21,25,26 that film flow does not vary significantly along the length of the scrubber. The model assumes a single drop size, initial droplet inertia at the point of atomization, complete atomization at one point, and absence of re-entrainment at the walls. Though simplified, the CFD model shows predictions of liquid distribution and film flow that match the experimental data well. More importantly, it shows a

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Figure 4. Liquid concentration distribution (kg/m3) on a plane along the throat: VGth ) 61 m/s and L/G ) 1.20 m3/1000 m3.

very good trend of the nonuniformity in liquid distribution as a function of operating variables. Figure 6 shows the variation in liquid distribution as a function of liquid loading for the same gas velocity. The liquid distribution varies from near-wall to good spread to near-center as the liquid rate increases as indicated by experimental studies.21 Moreover, the CFD model is three-dimensional and takes into account the nozzle arrangement that could also affect the liquid distribution. Because the collection efficiency in a Venturi scrubber is a strong function of the degree of uniformity in liquid distribution, this model could prove useful in assessing and improving the degree of liquid utilization in Venturi scrubbers. Effect of Nozzle Arrangement Pease-Anthony-type Venturi scrubbers that employ a system of nozzles to inject the liquid into the gas stream are common in the industry. The cleaning efficiency in such scrubbers, which is a function of the uniformity of liquid distribution within the scrubber, is sensitive to many parameters such as throat gas velocity, liquid-to-gas ratio, nozzle diameter, nozzle type, throat aspect ratio, throat length, and the nozzle arrangement. Many researchers1-4,21,22,24,27,28 have studied the independent effect of all of these parameters on flux distribution, with the exception of nozzle arrangement. However, the nozzle arrangement may have significant effects on the flux distribution as this directly determines the location of the liquid source inside the scrubber. The validated CFD model is used to study the effect of nozzle arrangement on the liquid flux distribution. Typically, nozzles are located on the longest side of the throat (Z direction). Four nozzle configurations that may be encountered in industrial applications were chosen for this study. These include (1) single file in which the nozzles are arranged in one straight line; (2) triangular configuration in which the nozzles in the second row are at the midpoint of two nozzles in the

Figure 5. Experimental vs predicted fraction of total injected liquid flowing as film on the walls at the throat end.


Figure 6. Visualization of liquid flux distribution on an axial plane at the throat end as a function of liquid loading: VGth ) 76.2 m/s.

first row and vice-versa; (3) double file in which the nozzles are arranged in two identical straight lines; (4) triple file in which nozzles are arranged in three straight lines. Apart from the effect of nozzle arrangement, the effect of multiple penetration on liquid distribution is also examined. The concept of “multiple penetration” involves splitting the injected liquid into two different regions inside the throat with the intention to improve the throat coverage. The nozzles are arranged along the depth (Z direction) on both sides (X direction) of the scrubber. Multiple rows are arranged along the length (Y direction) of the scrubber. The control volume for the model accounts for only one-half the depth (symmetry along Z) of the scrubber. The different nozzle configurations are shown schematically in Figure 7 on a plane representing the depth of the scrubber within the control volume. Figure 8 compares the simulated liquid distribution for the first four configurations. No significant change in the flux distribution is observed between the single-, triangular-, and double-row arrangements. It is expected that the flux distribution for these conditions would essentially remain constant as long as the distance between the nozzles remains small. The fourth configuration employs three rows to arrange the nozzles with the nozzles placed further apart. From Figure 8, it is clear that the triple row configuration shows an increase in the nonuniformity in liquid distribution. Increasing nozzleto-nozzle distance causes nonuniformity in liquid distribution and increases the number of rows to accommodate a fixed number of nozzles. It is clear from previous discussions that the liquid spreads better with distance in the throat. Therefore, the location of the nozzles further from the throat inlet also decreases the uniformity of flux distribution and hence would result in reduced collection efficiency. However, Mayinger and

Figure 7. Schematic representation of typical industrial nozzle configurations studied.

Lehner5 reported that a five-row injection performed better than a three-row injection for similar operating conditions. This difference could be attributed to the gas-liquid flow mechanism inside the unit. Mayinger

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Figure 8. Comparison of liquid flux contours for four nozzle configurations: VGth ) 76.2 m/s and L/G ) 1.20 m3/1000 m3.

and Lehner used a system of nozzles to inject the liquid which “did not disintegrate directly into droplets but into a large number of sheet-like structures”.5 Drop formation was observed only toward the throat end when some of these “lamellar-shaped sheets”5 disintegrated to smaller structures. Hence, the five-row injection provided a more uniform curtain of liquid (more surface area) along the throat for the gas to sweep through in comparison to three-row injection. The gasliquid flow in this case could be interpreted as slug flow where as the present work attempts to model annular two-phase flow that is characteristic of Venturi scrubbers. Moreover, because details pertinent to liquid flux distribution are not provided, it is difficult to analyze and compare their work in this study. Figure 9 shows the effect of nozzle-to-nozzle distance on the uniformity of flux distribution. For nozzle-tonozzle distance between 5 and 10% of the nozzle-side width the average concentration in the cells containing

Figure 9. Effect of nozzle-to-nozzle distance on the uniformity of liquid flux distribution: VGth ) 76.2 m/s and L/G ) 1.20 m3/ 1000 m3.


Figure 10. Comparison of liquid distribution by uniform and multiple penetration (by using different nozzle diameters) for a fixed L/G ratio: (VGth ) 76.2 m/s and L/G ) 0.75 m3/1000 m3).

droplets does not change appreciably. Increase in the nozzle-to-nozzle distance beyond 10% of the nozzle-side width shows a 50% increase in the average concentration, indicating a high degree of nonuniformity in liquid distribution. From this analysis, it is clear that it is beneficial to keep the nozzle-to-nozzle distance to a minimum. The effect of multiple penetration on liquid flux distribution is studied using the triangular arrangement. Multiple jet penetration is achieved in two ways. In the first case, the nozzles are of different diameters. The purpose is to ensure variable penetration for the same liquid load through each nozzle. The top row of nozzles is 1 mm and the bottom row of nozzles is 1.5 mm. The regular arrangement used in the experimental setup is a fixed nozzle size of 2.1 mm and gives uniform penetration. Figure 10 shows the liquid flux contours at the throat end for a liquid-to-gas ratio of 0.75 m3/ 1000 m3 by employing uniform and multiple penetration. From the figure, it is clear that the liquid distribution improves by using nozzles of different sizes rather than one size. For instance, by analysis of Figure 8 (triangular), it can be seen that the flux distribution using a L/G ratio of 0.75 m3/1000 m3 and multiple size nozzles appears close to that of 1.20 m3/1000 m3 of liquid with a single-size nozzle. In the second case, multiple penetration is induced in the system with a fixed nozzle diameter (2.1 mm) by varying the liquid load through different sets of nozzles. A simulation run was done using a liquid rate of 0.78 m3/1000 m3 for the first row of nozzles and 0.30 m3 /1000 m3 for the second. A simulation was also done with the total liquid load (1.08 m3/1000 m3) divided equally among all nozzles (uniform penetration). A comparison of the results in Figure 11 shows an improvement in the uniformity of liquid distribution for multiple penetration by using varying liquid loads. Further analysis indicates that multiple penetration with a total liquid load of 1.08 m3/1000 m3 (Figure 11) provides a more uniform distribution than

that of 1.20 m3/1000 m3 (Figure 8, Triangular) of liquid injected uniformly among all the nozzles. It can also be seen that the flux distribution for an L/G of 1.08 and 1.20 by uniform penetration is not very different. This can be expected as the penetration length traverses an optimum range at these conditions. However, studies show that the collection efficiency, which is dictated by the uniformity of liquid distribution, is highest for an L/G of 1.20.1 Hence, the case of multiple penetration with an L/G of 1.08 could be compared with uniform penetration using an L/G of 1.20 for collection efficiency on a conservative basis. It has been established recently that jet penetration is the most important factor affecting liquid distribution in Pease-Anthony scrubbers. This has been represented by a dimensionless term containing key design and operating variables and is called the Venturi number.1 The Venturi number is defined as

Venturi number, VN )

(

L R0 Z G* d0 nj

)

Table 1 gives a summary of Venturi numbers calculated for different operating conditions studied. For multiple penetration conditions, an average Venturi number can be used for comparison. This is obtained by taking a weighted average of the individual Venturi numbers based on the corresponding fraction of the total liquid used. From the values in Table 1 and previous discussions, it is clear that similar flux distributions have comparable Venturi numbers. Thus, the Venturi number is representative of the flux distribution, irrespective of multiple or uniform penetration. It has been proven that Venturi numbers between 1 × 10-3 and 1.5 × 10-3 give the most uniform liquid distribution.1 From the detailed derivation, it can be seen that the Venturi number is proportionally related to the jet penetration length as1

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Figure 11. Comparison of liquid distribution by uniform and multiple penetration (by using different liquid rates) for a fixed L/G ratio: (VGth ) 76.2 m/s and L/G ) 1.08 m3/1000 m3). Table 1. Tabulation of Venturi Numbers for Different Operating Conditions Studied by Employing Multiple and Uniform Penetration L/G VGth (m3/1000 m3) (m/s) 0.4 0.75 0.75 1.08 1.08 1.2

penetration

76.2 76.2 76.2 76.2 76.2 76.2

Venturi variable used number (multiple penetration) × 1000

multiple uniform multiple multiple uniform uniform

[

nozzle diameter nozzle diameter liquid rate

0.745 0.736 1.378 1.313 1.153 1.27

]

l* 0.1145 Fj L R0 Z ) 16 R0 π FG G* d0 nj for rectangular scrubbers ) 4 × 0.1145

[

]

Fj L R0 Z FG G* d0 nj for cylindrical scrubbers

The constant of proportionality contains the ratio of the liquid-to-gas density that would change for systems operating with fluids that differ in densities from airwater systems. However, the other two constants would remain the same as they are either obtained from geometry considerations or are inherent in the jet penetration correlation from which the derivation is made. Hence, the optimum values of Venturi number for systems other than air-water systems could be linearly related by the ratio of the liquid and gas densities with air-water system and is defined as

Venturi number, VN )

(

L R0 Z G* d0 nj

)

air-water

×

(Fj/FG)system (Fj/FG)air-water

Therefore, the Venturi number is both comprehensive and versatile in characterizing the uniformity in liquid distribution for Pease-Anthony-type scrubbers.

Thus, the variables to achieve multiple penetration (nozzle diameters or the liquid rates) could be calculated using the Venturi number such that the average number falls within the optimum range. However, a combination of a high Venturi number (high penetration with most of the liquid flowing at the center) and a low Venturi number (low penetration with a large quantity of liquid flowing on the walls) could give an average value within the optimum range and produce a distribution that is far from uniform. Hence, there are limits on the upper and lower bound for penetration independent of the method employed to achieve multiple penetration. To estimate these limits, simulation studies were done for different degrees of penetration by varying the liquid load through the nozzles with the total liquid load remaining constant. From the results in Figure 12, it is clear that the degree of nonuniformity increases with increasing extremities in multiple penetration. This analysis yielded 75 and 25% the throat radius as the upper and lower bounds for multiple penetration, respectively. This would restrict the Venturi numbers that constitute the average value for multiple penetration between 0.5 × 10-3 and 1.7 × 10-3 for air-water systems. A comparison of the operating conditions at comparable Venturi numbers from Table 1 indicates that multiple penetration gives an equivalent flux distribution at lower liquid rates. It also suggests that multiple penetration is capable of producing a comparable flux distribution at 10-45% less liquid than uniform penetration. Because the pressure drop increases with the liquid rate, an improvement of the liquid distribution at lower liquid rates would improve the scrubber performance significantly. This concept could be used effectively in designing new scrubbers and improving the performance of existing scrubbers. Future Work The present CFD model uses a simplified version of droplet atomization. Simulation of the column breakup


Figure 12. Effect of degree of multiple penetration on the uniformity of liquid flux distribution. Notation: 75-25 implies penetration to 75% the throat radius by nozzles in the first row and 25% the throat radius by nozzles in the second row).

and jet atomization is very complicated as it involves free surface flows. The predictions of the model could be improved by providing more accurate information on the spatial mass and velocity of the drops as they atomize. This would require the atomization process to be included into the FLUENT/UNS model through userdefined subroutines. The effect of drop size distribution could be studied for the same scrubber by incorporating distributions similar to Rosin-Ramler for the drop size spectrum. The effect of heat and mass transfer on the liquid distribution, like evaporation, could be studied by choosing appropriate models that are available in FLUENT/UNS. The equations solved by the CFD model for the pilot- and industrial-scale units are similar. Hence, if identical (scaled) inlet conditions and Reynolds number are maintained in geometrically similar industrial units, the model, taking into account heat transfer, gravitational, and buoyancy effects, could predict the behavior of industrial-scale units to the same degree of accuracy as the pilot unit.29 This would result in a comprehensive CFD model for large-scale industrial units. Conclusions A simplified CFD model is developed to predict the liquid flux distribution for a pilot-scale, rectangular Pease-Anthony Venturi scrubber using a commercial packagesFLUENT. The model, though simplified, shows results of flux distribution and film flow that are in good agreement with the experimental data. A systematic study of the effect of nozzle arrangement on flux distribution was made using four different configurations. This study indicates that it is beneficial to keep the nozzle-to-nozzle distance to a minimum in such a way that the injected liquid jets do not interact. The liquid flux distribution was found to be essentially the same for configurations with nozzle-to-nozzle distance between 5 and 10% of the nozzle-side width. The nonuniformity in flux distribution increased when the nozzle-to-nozzle distance exceeded 10% of the nozzle-

side width. Multiple jet penetration achieved by changing the liquid rate or the nozzle diameter gave a comparable flux distribution at 10-45% lower liquid than uniform penetration, which is commonly practiced. This is expected to improve the scrubber performance significantly. The Venturi number concept can be used to calculate the optimum nozzle diameters or the liquid rates for obtaining the most uniform liquid distribution with multiple penetration. The proposed CFD model could be used to study, design, and improve large-scale industrial units. Acknowledgment The authors would like to thank Dr. K. R. Kumar from the Institute of High Performance Computing, Singapore, for providing resources and reviewing this work. Notation CL ) time scale constant D ) diameter, m d0 ) orifice diameter, mm G ) gas flow rate, 1000 m3/s k ) turbulent kinetic energy, m2/s2 L ) liquid flow rate, m3/s l* ) jet penetration length at which the droplets form, mm NPE ) Peclet number NRE ) Reynolds number R0 ) radius of the throat, mm V ) velocity, m/s X ) width of the scrubber, m Y ) length along the axis, m Z ) depth of the scrubber, m x, y, z ) rectangular coordinates, m Greek Symbols ) turbulent dissipation rate, m2/s3 F ) density, kg/m3 µ ) fluid viscosity, kg/m s

4900


Subscripts d ) drop G ) gas j ) jet or liquid th ) throat

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Received for review March 24, 1999 Revised manuscript received September 8, 1999 Accepted September 16, 1999 IE9902131

Effect of Nozzle Arrangement on Venturi Scrubber Performance

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