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Chemical Engineering Journal 172 (2011) 8495

Contents lists available at ScienceDirect

Chemical Engineering Journal

journa l homepage: www.elsevier .com/locate /ce j

CFD modelling ofhydrodynamics and degradation kinetics in an annular slurryphotocatalytic reactor for wastewater treatment

Nana Qia,b, Hu Zhang b,, BoJin b,c,d, Kai Zhang a,e,

a State Key Lab ofHeavyOil Processing, ChinaUniversity ofPetroleum, Beijing102249, Chinab School ofChemical Engineering, The University ofAdelaide, Adelaide, SA 5005,Australiac School ofEarth andEnvironmental Sciences, TheUniversity ofAdelaide, Adelaide, SA 5005,AustraliadAustralianWater Quality Centre, SAWater Corporation, Bolivar, SA 5110,Australiae National Engineering Lab for Biomass Power GenerationEquipment, NorthChina Electric PowerUniversity, Beijing 102206, China

a r t i c l e i n f o

Article history:

Received 20 November 2010

Received in revised form 17 May 2011

Accepted 17 May 2011

Keywords:

Annular slurry photocatalysis reactor

Water treatment

Computational fluid dynamics

Hydrodynamics

Degradation kinetics

a b s t r a c t

Photocatalytic degradation process has been recognized as a low-cost, environmental friendly and sus-

tainable technology for water and wastewater treatment. As a key carrier ofthe photocatalytic process,

the semi-conduct photoreactor has been employed in many studies. Analysis and modelling ofhydrody-

namics and degradation kinetics in the three phase system can provide useful information for process

design, operation and optimization. We have modelled the hydrodynamics and degradation kinetics in

an annular photocatalytic bubble column reactor using an Eulerian multi-fluid approach. All the results

(gas holdup, fluid flow patterns and organic concentrations) were evaluated against experimental data

from our lab or literature reports. The local information on gas holdup, solid particle concentration, light

intensity as well as organic pollutant concentration has been obtained. The simulation results reveal that

the degradation rate oforganic pollutants is controlled by the local hydrodynamics and light intensity.

By combining CFD and degradation kinetics models, the mechanisms of governing the photocatalytic

reaction can be determined. The computational models will also help optimize reactor design to improve

the hydrodynamics and light intensity distribution and provide guided information for scale-up.

2011 Elsevier B.V. All rights reserved.

1. Introduction

Around 4 billion people around the worldhaveno or little access

to clean and sanitized water supply and millions ofpeople have

lost their lives due to severe waterborne diseases [1]. Figures are

expected to grow in the near future because of the increasing

industrial practices and water contamination by harmful organic

substances. Organicpollutants in wastewater from many manufac-

turing industries, such as textile and paper industries, are toxic and

biologically non-biodegradable [2], which cannot be removed by

commonly used biological treatment process. These contaminants

can be removed using conventional treatment processes, such as

adsorption onto granular activated carbon, reverse osmosis and air

Corresponding author at: School of Chemical Engineering, The University of

Adelaide, SA 5005, Australian, Tel.: +61 8 83033810; fax: +61 8 83034373. Corresponding author at: National Engineering Lab for Biomass Power Genera-

tion Equipment, North China Electric Power University, Beijing 102206, China.

Tel.: +86 10 61772413.

E-mail addresses: [email protected] (H. Zhang), [email protected]

(K. Zhang).

stripping [1,3,4]. Most ofthese processes are highly costly and none

are environmental-friendly. Photocatalytic oxidation (PCO), how-

ever, has been recognized as an effectiveprocess in the degradation

and mineralization ofa wide variety ofpriority pollutants in water

and wastewater [5,6], as well as a green sustainable process by

producing environmentally harmless compounds (carbon dioxide,

water and mineral acids) after degradation [7]. During the process,

oxygen reactive species are generated on the surface oftitanium

dioxide (TiO2) particles, and these oxidative species can degrade

nearly all organic pollutants.

The overall photocatalytic reactions taking place in a semi-

conduct reactor can be divided into five independent steps: mass

transfer of the organic contaminants in the liquid phase to the

TiO2 surface; adsorption of the contaminants onto the photon-

activated TiO2 surface; photocatalytic reaction for the adsorbed

phase on the TiO2 surface; desorption ofthe intermediate(s) from

the TiO2 surface; and mass transfer of the intermediate(s) from

the interface region to the bulk fluid [8,9]. Great efforts have been

made to improve the performance for above steps, including syn-

thesis of new catalysts [10,11] and optimization of an annular

slurry photoreactor system [12]; design ofphotocatalytic reactor

[13]; and optimization ofoperational parameters [10]. It has been

1385-8947/$ see front matter 2011 Elsevier B.V. All rights reserved.

doi:10.1016/j.cej.2011.05.068
http://dx.doi.org/10.1016/j.cej.2011.05.068http://dx.doi.org/10.1016/j.cej.2011.05.068http://dx.doi.org/10.1016/j.cej.2011.05.068http://www.sciencedirect.com/science/journal/13858947http://www.elsevier.com/locate/cejmailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.cej.2011.05.068http://dx.doi.org/10.1016/j.cej.2011.05.068mailto:[email protected]:[email protected]://www.elsevier.com/locate/cejhttp://www.sciencedirect.com/science/journal/13858947http://dx.doi.org/10.1016/j.cej.2011.05.068


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N.Qi et al. / Chemical Engineering Journal 172 (2011) 8495 85

Nomenclature

a specific area (m1)

A constant

CD drag force coefficient

CL lift force coefficientCTD turbulent diffusion coeffitcient

C1 parameters in the standard k model

C2 parameters in the standard k modelC parameters in the standard k modeld diameter (m)

D kinematic diffusivity in the liquid phase (m2 s1)

Eo Etvs number

F total interfacial force (N)g gravity acceleration (m s2)G spectral incident radiation (Wm2)

Ib blackbody emission intensity (Wm2 sr1)

Io radiation intensity leaving the boundary

(W m2 sr1)

I spectral radiation intensity (Wm2 sr1)

k turbulence kinetic energy per unit mass (m2 s2)

ko,ls liquid solidorganicmass transfer coefficient (m s1)

K apparent reaction rate constant (s1)Ka absorption coefficient (m

1)

Ks scattering coefficient (m1)

M Morton numberP pressure (Pa)

qr radiative flux (W)

r position vector (m)

Re Reynolds numbers director vector (m)

S radiation intensity source term (W m2 sr1)

ScT turbulence Schmidt number

So,s reaction source oforganic in solid phase (s1)t time (s)

T local absolute temperature (K)

u velocity vector ofgas or liquid (ms1

)ug superficial gas velocity (m s1)Y mass fraction

Greek letters

volume fraction turbulence dissipation rate (m2 s3) viscosity (Pas) light frequency (Hz) density (kgm3) surface tension (Nm1)t turbulent Prandtl numbertl liquid turbulent Schmidt numberk turbulence model constant for the k equation

k turbulence model constant interphase masstransfersource termoforganicpol-

lutants per unit volume (kgm3) in-scattering phase function

s(x,y,z,t) solid particle concentration solid angle (sr)

Superscripts

D drag

L liftT turbulent dispersion

Subscripts

b bubble

c catalyst

g gas-phase

i gas, liquid or solid phase

j other phase

l liquid-phase

o organicp particle

ref reference

s solid-phase

t turbulence

demonstrated that there are a number of systemic factors which

could affect the oxidation rates and efficiency ofthe photocatalytic

system, including TiO2 loading [1,10,11] pH [10,11,14,15], tem-

perature [1], dissolved oxygen [1,10,11], contaminants and their

loading [1,10,11], light wavelength [1] and light intensity [1,16].

Kinetics studies are often conducted to assemble the above opera-tional parameters into a simple model for scalingup andoptimizing

photoreactor system. However, Emeline et al. [17] argued that

the kinetic data alone does not establish the actual mechanism of

photocatalytic reaction, which must be supported by other evi-

dences outside the field ofkinetics. These evidences include an

understanding of the hydrodynamics and the radiation intensity

distributions in the multiphase photoreactors. This requirement

becomes essential for further scale-up and optimization.

Chong et al. [10] have reported a promising photocatalytic pro-

cess by using a newly synthesized titania impregnated kaolinite

nano-photocatalyst in an annular slurry photoreactor. Congo Red,

which is an organic substance often used as a surrogate for organic

pollutants was completely degraded after 4 h irradiation. Chemical

oxygen demand (COD) was reduced to 20% in the system in 2 h. Itwas noticed that the degradation rate was relatively fast and then

became gradually slow. The significance ofhydrodynamics inside

the slurry reactor has also been experimentally demonstrated by

providing agitation and controlled aeration [10]. The role ofhydro-

dynamics in the process, however, remains unclear as complicated

interactions exist between ranges ofoperational parameters.

Computational fluid dynamics (CFD) techniques have been

widely used in stirred reactors [18], bubble columns [1921], flu-

idized beds [22] and loop reactor [23,24]. CFD models have been

developed to address the hydrodynamics in the photocatalytic

bubble column reactors [19,25]. However, most studies are only

focused on hydrodynamics while kinetics models have not been

included. In this study, the radiationintensitydistributionand reac-

tion kinetics models were combined with CFD models to elucidate

the degradation mechanism in the photocatalytic system.

2. Mathematicmodels

2.1. Hydrodynamicmodels

Three-dimensional transient CFD models were developed to

compute the local hydrodynamics of the gasliquidsolid three-

phase annular photocatalytic bubble column reactor. An Eulerian

multi-fluid approach was adopted to describe the flow behaviours

of each phase. Fluid is considered to be the continuous phase,

whereas gas bubbles and solid particles are assigned to the dis-

persed phases. Based on the principles of conservation of mass


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86 N.Qi et al. / Chemical Engineering Journal 172 (2011) 8495

and momentum, the continuity and momentumequations for each

phase in a photocatalytic bubble column reactor are given below:

(ii)

t+ (ii ui) = 0 (1)

where, and u stand for the volume fraction, density and velocityvector, respectively. The subscript i represents gas, liquid or solid

phase.

(ii ui)t

+ (ii ui ui) = iPi + (ii( ui + ( ui)T

))

+ Fi + ii g (2)

where P, , and gare the pressure, viscosity and gravity accelera-tion, respectively. Fi is the interfacial forces acting on phase i dueto the presence ofthe other phase,j.

2.1.1. Interface models

Virtual mass force is ignored in comparison with drag, buoyancy

and turbulent dispersion forces [2628]. Accordingly, the interfa-

cial forces ofgreat significance are assumed to be drag force, lift

force and interphase turbulent dispersion force in this study:

Fi = FDi + FLi + FTi (3)

where FDi

, FLi

and FTi

are the interfacial forces due to drag force, lift

force, turbulent dispersion, respectively.

The drag component of the gasliquid interfacial force term is

given as:

FDlg =3

4

CD,lgdg

l gug ul (ug ul) (4)

where CD is drag force coefficient. The Grace relation is chosen

for the drag force coefficient because bubbles are experimentally

observed to be spherical and dispersed. The drag force coefficient

[29,30] is:

CD =

4

3

gdbu2T

l (5)

wheredb stands for the mean bubble diameter, is the differencein density between the liquid and gas phases, and uT is the bubble

terminal rise velocity. It can calculated as follows:

uT =lldb

M0.149(J 0.857) (6)

In the above expression, M, the Morton number related to the

fluid property, is defined by:

M =4lg

2l3

(7)

where is surface tension andJis given by:

J= 0.94H0.751 2 < H 59.3 (8a)

J= 3.42H0.441 H > 59.3 (8b)

In Eqs. (8a) and (8b), H is expressed as:

H =4

3EoM0.149

lref

0.14(9)

where ref is the molecular viscosity oftap water under a refer-ence temperature and pressure [29] and Eo, the Etvs number, is

defined as:

Eo =gd2

b

(10)

The lift force, FLi

, exerting on the liquid phase from the gas phase

is given by:

FLlg = CLl g(ug ul) ( ul) (11a)

The turbulent dispersion force, FTi

, is calcuated by the model of

Lopez de Bertodano [31]:

FTlg = CTDCDtltl

gg

ll (12a)

where, CTD is the momentum transfer coefficient for the inter-

phase drag force, CD is the drag coefficient as described in Eq. (5),

tl stands for turbulent viscosity, and tl is the liquid turbulentSchmidt number. g and l are the gas and liquid phase volume

fractions, respectively.

In the liquidsolid system, the drag component of the

liquidsolid interfacial force is similar to that ofgasliquid:

FDls =3

4

CD,lsds

lsus ul (us ul) (13)

The drag coefficient exerted by the solid phase on the liquid

phase, CD,ls is obtained by the Schiller Naumann drag model [30]:

CD,ls= max24Re (1 + 0.15Re

0.687

),0.44

(14)

where

Re =lds

us ull

(15)

The lift force, FLi

, is determined by:

FLls = CLls(us ul) ( ul) (11b)

where CL is a non-dimensional lift coefficient, and it is assumed to

a constant, the value is listed in Table 1 [32].

The turbulent dispersion force, FTi

, for liquidsolid interaction is

similar to that ofgasliquid:

FT

ls= C

TDCD

tl

tls

s

l

l (12b)

All items are explained in Eq. (12a), except that s is the solidvolume fraction.

2.1.2. Turblence model

The standard k model for single phase flows has beenextended for the three phase flows for simulating the turbulence

in the present study, which can be described as:

t(llkl) + (ll ulkl) =

l

l +

tlk

kl

+ lPl

lll (16)

t(lll) + (llull) = ll + tl

l

+l

lkl

(C1pl C2ll) (17)

where C1, C2,k, and are parametersin the standardkmodeland the values are listed in Table 1. The liquid phase turbulent vis-

cosity is calculated based on the Sato enhanced turbulence model

[33]:

tl = tl,s + tl,b (18)

where tl,s is the conventional shear-induced turbulent viscosity,which is obtained by the standard k model as:

tl,s = Clk2

(19)


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Table 1

Constantsused in the CFD models.

CL C1 C2 C k D (m2 s1) ScT ko,ls (m s

1) ds (m) db (m)

0.5 1.45 1.9 0.05 1.0 1.3 4.4 1010 0.9 2.0 104 3.5 106 2 103

where C is a constant, which is listed in Table 1. tl,b is a bubble-

induced component ofturbulent viscosity given by:

tl,b = C,bl gdbug ul (20)

andthe particle-induced componentofturbulentviscositygiven

by:

tl,p = C,plsdsus ul (21)

The gas and solid phase turbulence is modelled using a zero

equation model, in which gas turbulent viscosity is proportional to

liquid phase turbulent viscosity [27,30]:

tg =gl

tlt

(22a)

ts =sl

tst

(22b)

wheretis a turbulentPrandtl number relating the dispersed phasekinematic eddy viscositytgandtl to the continuous phase kine-

matic eddy viscositytl.

2.2. Radiation transport equation

The radiation transport equation (RTE) is employed to charac-

terize the light intensity distribution inside the photoreactor. The

robust form of the RTE was first published by Spadoni et al. [34],

while it was more recently applied to the photocatalytic system

[35].

A radiation balance is made along a given direction ofprop-

agation together with the appropriate boundary conditions [34].

Assumptions are made to simplify the simulation: shadowing,

reflection, and refraction effects have little influence on the finaldistribution [25]. The spectral radiative transfer equation (RTE) can

be written as:

dI(r, s)

ds= (Ka + Ks)I(r, s) + KaIb(v, T)

+Ks4

4

I(r, s)(s s

) d + S (23)

where rand s are the position and director vector, respectively;Kaand Ks represent wavelength-averaged absorption and scattering

coefficient, which were calculated from the equations proposed by

Pareek [13] as shown in Eq. (24a); v is the light frequency; s is the

photon path length; Ib is blackbody emission intensity (which is

negligible whenoperated at ambienttemperature)and I is spectral

radiation intensity which depends on position (r) and direction (s);Tis local absolute temperature; is solid angle; is in-scatteringphase function; and S is the radiation intensity source term.

Ka = 0.2758 Wcat (24a)

Ks = 3.598 Wcat (24b)

where Wcat is the catalyst loading of6 g/dm3.

The RTE is a first order integro-differential equation for I in a

fixed direction, s. To solve this equation within a domain, a bound-

ary conditionfor I is required. The diffusely emitting and reflecting

opaque boundary is defined as:

I(rw, s) = (rw)Ib(v, T) +w(rw)

ns


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Fig. 1. Schematic diagram ofthe photoreactor used in the simulation.

Base on the assumptions,the transportequationsfor the organic

pollutants are described as:

t(llYo,l) + (llYo,l ul) = [l(lDo,l +

T,lScT,l

)(Yo,l)]

So,l o,ls (29a)

t(ssYo,s) = So,s + o,ls (29b)

where Yis the mass fraction oforganic pollutants in liquid or solid

phase; D is the kinematic diffusivity ofCongo Red in water, which

is listedin Table 1 [20];t is the turbulence viscosityofeach phase;ScT is the turbulence Schmidt number, the value is listed in Table 1

[30]; S is the user specified masssource termoforganic species; and

is the interphase mass transfer source term oforganic pollutantsper unit volume; The subscript l, s means the liquid and solid phase,

respectively; o denotes organic species.

The balance for species in each phase is given blow:

Yo,l + Yw,l = 1 (30a)

Yo,s + Yc,s = 1 (30b)

where the subscriptw andcmeans water and catalyst, respectively.

The reaction source is defined as:

S = K Y (31a)

Although the Langmuir-Hinshelwood (L-H) model is used for

most kinetics studies in the photomineralization, the pseudo-

first-order model could be more applicable when the organics

Fig. 2. Gas phase velocity vectors in a vertical plane ofthe photocatalytic reactor.

concentration is low. The above equation is employed to describe

the loss oforganic species in the solid phase due to chemical reac-

tion. K is the apparent reaction rate constant, which is a function

of local incident radiation G(x,y,z) [39] and solid particle concen-

tration s(x,y,z,t). As there is no suitable expression for the rateconstant Kin the Chongs photocatalytic system [10], Kis assumed

to be proportional to the incident radiation and solid particle con-

centration:

K=AG(x, y, z)s(x, y, z, t) (31b)

where A is a constant; G(x,y,z) is gained by integrating Iwhich is

computed from Eq. (28) and s(x,y,z,t) is updated from the CFDcalculation from Eq. (1).

The interphase mass transfer between the liquid and solidphase

is defined as:

o,ls = ko,ls as (s Y s Yo,s) (31c)

where ko,ls is the liquid solid organic mass transfer coefficient, the

value is listed in Table 1 (see details in [40]); Yo,s is the saturatedmass fraction in solid phase, which can be calculated from the

adsorption isotherm; and as is photocatalyst specific area, equal

to 6s/ds, s is the volume fraction ofsolid particle and ds is theparticle diameter.

Because the organic pollutant concentration is quite low, its

degradation reaction and interphase mass transfer are neglected

for the continuity equations (Eq.(1)) andthe momentum equations

Eq. (2) [30].


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3. Numerical procedure

3.1. Geometry and grid generation

As can be seen in Fig.1, a conical bottomed and internally irradi-

ated photocatalytic reactor withUV light being placed in the central

core axis ofthe reactor is used [10]. The reactor has a diameter of

0.149 m and a height of0.05 m.The initial slurry height is 0.0322 m,

which will be used for all simulations in this work. The details

for the experimental equipment characteristics can be found in

Ref. [10]. An unstructured numerical grid has been implemented

with a total number of278,235 elements, after a grid independent

investigation ofthree levels ofgrids.

3.2. Initial and boundary conditions

In this work, the bubble size is treated to be uniformly dis-

tributed in the multiphase reactor. As (d1.50 uglg0.5/) 1 in our

study (whered0 is the diameterofthe orificeandugis the superficial

gas velocity at the distribution plate, the size ofbubbles produced

at the orifice db is calculated by Chen et al. [41]:

db = 2.9d0gl

1/3(32)

The calculated result ofthe bubble diameter db at the orifice is

listed in Table 1.

Thegas inlet is providedat a uniform inlet gas velocity according

to the experiment. In order to prevent a significant liquid loss by

introducing air into the reactor, an inlet air velocity from zero to its

final value over the first 2.5 real seconds is used [42]. At the gas out-

let, an outlet condition at the top ofthe reactor is defined based on

the proposal from Padial et al. [42], which was successfully adopted

by Michele and Hempel [43]: the periphery area is defined as the

opening boundary where only liquid can leave the computation

domain; the inner area is defined as the degassingboundarywhere

only gas can leavethe computation domain; the solid is kept within

the reactor [30,44]. Along the reactor walls and the UV light tube,gas and solid phase are treated as free-slip, while for liquid phase

no-slip boundary condition is applied [45]. Initially, the reactor is

filledwith liquidand the solidparticles distribute uniformly within

the reactor, and the liquid and particles are stationary. The den-

sity and diameter ofphotocatalysts are 4507 kg m3 and 3.5m,

respectively. The initial organic concentration is 40 mg/dm3.

In order to solve the RTE described above sufficient boundary

conditions need to be defined, which are source boundary condi-

tion and wall properties. A line source model is used to determine

the source boundary condition [13]. The inner quartz wall through

which the UV radiation passes into the reacting media is assumed

has an emissivity of1. While the outer wall made ofstainless steel

was assumed has an emissivity of0.1 [30].

3.3. Numerical procedure

The simulations are carried out in a platform ofANSYS CFX

12.0 software [30]. The high resolution scheme were selected as

the iteration scheme to numerically solve the mass and momen-

tum equations (Eqs. (1), (2)), while the implicit first order upwind

scheme were selected to numerically solve the turbulence equa-

tions (Eqs. (16), (17)) and mass transfer equations (Eq. (29a)),

respectively [46,47]. Two different time steps are applied for vari-

ables changingwith time.When calculating the distribution for gas

bubbles and solids, a time step of0.01 s was used, while for mass

transfer between liquid and solid phase and chemical reaction of

theorganicspecies,alargetime step,0.1 s,was applied.A maximum

residual convergent target of1 104 was set for all simulations.

Fig. 3. liquidphase velocity vectors in a vertical plane ofthe photocatalytic reactor.

4. Results and discussion

4.1. Flow patterns

Hydrodynamics, transport, and mixing properties depend

strongly on the prevailing flow pattern in bubble columns [48].

Three flow patterns, i.e., bubbly flow, transitional flow, and tur-

bulent flow patterns, have been observed according to the upward

movement ofthe bubble swarm in the column [21]. The gas- and

liquid-phase velocity vectors within the column are presented in

Fig. 2 when the simulations reach steady state. It can be seen from

Fig. 2, at a superficial gas velocity of0.036 m s1, corresponding to

10 dm3/min ofair flow rate, the gas bubble swarm starts to swing

towards the column wallin the middle partofthe reactor and move

towards the central tube at the gas outlet. There are no vorticesformed in the column while the movement pathways ofgas bub-

bles are skewed. The gas bubbles move relatively slower in the

central part in the middle ofthe reactor and in the region close

to the reactor wall in the upper part.

The liquid phase is carried upward by the bubble swarm and

flows downward in the spiral manner between the central bubble

swarm and column wall. The liquid downward produces several

vortices as can be seen from the liquid vectors in a vertical plane

(Fig. 3). It can be noticed that the vortices in the left and right are

not symmetric, which is due to the gas bubbles move upward in a

spiral manner. And these asymmetric vortices can be captured by

the three-dimensional simulation. In the current geometric config-

uration at a gas flow rate of 10 dm3/min, three vortices are found

in the left and right, respectively. Two large vortices are located in


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the upper and lower part of the reactor, while the middle one is

relatively small and away from the centre.

The simulation results are qualitatively in agreement with the

three-dimensional flow structuresexaminedby Chenet al. [49] and

Qi et al. [21] in bubble column reactors.The flow pattern falls within

the regime between the homogeneous bubbly flow and the transi-

tional flow [21], where liquid starts to form small local circulations

while there are no vortices in the gas phase.

4.2. Distribution ofgas bubbles and solid particles

The gas flow pattern has an impact on the gas holdup distri-

bution in the reactor, while the gas velocity plays a significant

role as well. Gas bubbles along the movement pathways rise faster

than those in other regions, which means less bubbles are trapped

inside the reactor. An asymmetric gas holdup distribution profile is

obtained from the three dimensional simulation (Fig. 4a). It can be

seen that the highest gas holdup regions on the right side ofFig. 4a

are close to the light tube, however these regions as shown in the

black regions in Fig.4a are discontinuously presented in the vertical

plane. The discontinuous regions are due to the spiral movement

ofgas bubble swarm [5052]. In the right side, a relative high gas

holdup region is located along the movement pathways ofgas bub-

ble swarm. Interestingly, there is quitelow gas holdup in the regionclose to the central tube in the upper part of the reactor and this

region may potentially decrease the degrading efficiency ofthe col-

umn. In the left side of Fig. 4a, a completely different gas holdup

distribution is presented. In the middle ofthe reactor, few gas bub-

bles are trapped inside this region. However, gas holdup is much

higher in the upper part ofthe reactor.

The gas holdup at an axial position of0.2 m as referred in Fig. 4a

is plotted along the radial direction in Fig. 4b. It can be seen that

at higher gas velocity (0.054 m s1), a sharp drop in gas holdup is

seen in the central region; while the gas holdup values increase in

the middle ofthe reactor between the central light tube and the

reactor wall. The increase can be explained by bubbles trapped in

the liquid vortex illustrated in Fig. 3. By decreasing the gas bubble

velocity,the gas holdup becomes morehomogeneous while the fewgas bubbles are trapped inside the reactor.

In contrast to the heterogeneous distribution of gas bubbles

inside the reactor, the solid catalysts are distributed uniformly in

themajorityofthe reactor, except in the lower andupper part ofthe

reactor (Fig. 5a), which is corresponding to the higher gas holdup

region (Fig. 4a). The lower concentration of solid catalyst in this

region may decrease the degradation efficiency. By reducing the

aeration rate to one tenth ofthe original value, the solids distribu-

tion is homogeneously in the reactor (Fig. 5b). Because at low gas

flow velocities, the gas bubbles move up in a homogeneous regime,

and gas hold up in the reactor is ignorable. In this context, the solid

distribution is not affected by the gas bubble movement. The com-

parison between thehigher andlower flow rates can be further seen

from Fig. 5c, where the solids concentration is plotted along theaxial direction at the radial point ofr/R= 0.27. It can be found that

smaller gas inlet velocity, but greater than particle terminal set-

tling velocity,can makemore uniformlydistributed photocatalysts,

which will be ofbenefit to the organic degradation process. How-

ever,lower gas flow rates can leadto agreat reductionin gas holdup

or dissolved oxygen concentration. Furthermore, Chong et al. [10]

estimated the minimum aeration rate was 5 dm3/min (0.018 m s1)

for the critical solids loading of6 g/dm3. Given the photocatalysts

solution is quite dilute, a higher flow rate can be used to keep all

particles in homogeneous suspension and then a lower flow rate

is used to maintain the homogeneous suspension. However as the

dissolved oxygen plays an important role in TiO2 photocatalytic

reaction to assure sufficient electron scavengers present to trap the

excited conduction-band electron from recombination[10] an opti-

Fig. 4. Volume fraction profiles ofgas phase. (a) Simulated gas holdup. (b) Radial

profile ofgas holdup under different gas inlet velocities (z=0.2 m).

mized gas flow rate should be used to counteract negative effects

ofheterogeneous solid distribution.

4.3. Radiation intensity

The gas holdup and catalyst distribution affect the degrada-

tion oforganic pollutants, while the light intensity distribution


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Fig. 5. Volume fraction profiles ofsolid phase. (a) Normalized solid concentration (ug=0.036 m s1). (b) Normalized solid concentration (ug=0.0036 m s

1). (c) Normalized

solid concentration under different air inlet velocity (r/R= 0.27).

inside the reactor plays a significant role in the degradation as

well. The light incident radiation profile in the reactor (Fig. 6) can

be obtained by solving the radiation transport equation (Eq. (28))

in CFX 12.0. As the solid particles are nearly uniformly distributed

insidethe reactor (Fig.5b) andthe concentration ofparticlesis quite

low, the adsorption and scattering coefficients are assumed to be

proportionalto the catalyst loading [13]. The incident radiationdis-

tribution is characterized by a decrease radially from a maximum

near the UV light tube to a minimum at the outer wall ofthe reac-

tor, which is in good agreement with reports by Pareek et al. [13].

Because the distribution is nearly identical along the axial and cir-

cumferential directions, a simple expression ofG related to rcan be

derived as

G(r) =11

(r/0.0745)2

(33)

The sharp drop in incident radiation away from the central light

tube leads to a rapid decrease in degradation efficiency in the pho-

toreactor. A better design ofphotoreactor is required to achieve

uniform distribution ofthe light incident radiation, such as intro-

ducing multiple lamps in the reactor [13]. This problem can be also

alleviated by increasing the total irradiated surface area ofcatalyst


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1.00.80.60.40.20.0

0

100

200

300

400

500

RadiationIn

tensity(Wm

-2)

r/R (-)

light tube

Fig. 6. Radial profile ofradiation intensity.

per unit volume, for example, utilization ofslurry photocatalytic

reactor [11].

4.4. Degradation oforganicpollutants

The organic degradation process can be described in Fig. 7. The

organic molecules are first transferred to the boundary ofthe cat-

alyst particles surface by convective flow, and then are adsorbed

onto the surface of the catalyst particles. Those molecules are

finally degraded through the photocatalysis. The degraded prod-

ucts are released from the catalyst surface and transferred back

to the bulk liquid stream or into the gas stream. Overall degrada-

tion rate depends on the transfer rate ofthe pollutant molecules by

mass-transfer mechanisms, and the degradation rate. The rate of

mass transfer has been characterized by CFD simulations and the

detailed information on the gas holdup or oxygen concentration,

catalyst concentration, and incident radiation has been obtained.

The rate ofdegradation,however, requires thedegradationkinetics.

Currently there is no kinetics model available for this photocat-

alytic reactor. Two simplified models have been used in this work

as described below.

The first case is to simulate the organic degradation under the

same experimental conditions [10]. In this case,the organic species

are adsorbed onto the catalyst surface for half an hour before the

light source is introduced.The photocatalytic reaction occurs on the

surface ofTiO2 nanoparticles where enough photons from the light

source are received (So,l = 0 in Eq. (29a)). New organic molecules

are constantly adsorbed onto the catalyst surface as soon as the

molecules are consumed during degradation process. After 4 h, the

distribution of organic concentration in the liquid phase is pre-

sented in Fig.8a. It can be seen that most oforganic molecules have

beendepletedin the regions close to the light tube.Bycloselyexam-

ining the change in organic concentration at the axial position of

0.2 m, theorganic molecules in the area close to the central tube are

degraded faster than those in the area close to the reactor wall. This

can be due to much higher incident radiation in the central region.

However,the concentration oforganic pollutants increasessteadily

along the radial direction and reaches a plateau when approach-

ing the reactor wall region (Fig. 8b), resulting from a combination

of incident radiation and particle concentration. Although Fig. 6

shows that the incident radiation has a gradual decline away from

the central tube, andthe absolute reductionrate seems to be minor.

While the solidconcentration at a gas flow rate of10 dm3/min has asteady increase away from the central tube (Fig. 5c) and more solid

particlescan result in increasing surface area ofoxidation platform,

contributing to the degradation oforganic pollutants in the region

awayfrom the central tube.The results demonstrate thatthe hydro-

dynamics andthe incidentradiationdistributions in the multiphase

photoreactors should be integrated with photocatalytical kinetics.

Thus, we can have a better understanding ofthe degradation pro-

cess. Herein, we compared the simulated averaged global organic

concentration values with experimental data, as shown in Fig. 8c.

At a high solid loading, the degradation trend is well predicted. A

minor deviation between the simulation results and experimen-

tal data was found in the initial degradation process at a low solid

loading. The deviation is reduced and becomes negligible after 4 h.

In the early stage of photocatalytic reaction when the organicspecies is adsorbed onto the catalyst surface, our simulation is

based on the assumption that the reaction rate is much faster than

(a)

(c)

(b)

is the pollutant molecules, is the degraded products, is the catalysts and the big circle represent the

solid particles.

(d) (e) (f)

Fig. 7. The process ofpollutant degradation. (a) Pollutant molecule in the bulk flow; (b) pollutant molecule is transferred to the catalyst surface; (c) pollutant molecule is

absorbed onto the catalyst surface; (d) molecule is reacted in the presence ofcatalyst; (e) pollutant molecule is fullyconvertedto products; (f) products are released into the

main stream.


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Fig. 8. Normalized concentration oforganic in liquid phase. (a) Effect ofTiO2/K photocatalyst loading on organic degradation rate. (b) Simulated organic normalized con-

centration. (c) Radial distribution oforganic normalized concentration at axial position of0.2 m. (d) Simulated organic degradation rate at solid loading of6 g dm3 (case

2).

the transfer process. In such a case, there is no accumulation of

organic pollutants in the solid phase, i.e. (/t)(ssYo,s) = 0. There-fore, So,s =o,ls, or the amount of pollutants is transferred from

liquid to solid ( in the Eq. (29a)) should be equal to that which

is degraded in Eq. (31a). The degradation curve presented in Fig. 8d

shows that the degradation process is much faster than the first

case. 70% of organic pollutants have been mineralized in 2 h. By

doubling the incident radiation ofthe original light source, 90% of


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organic pollutants have been degraded within less than one hour,

(data not shown). Our results revealed that the fluid dynamics and

incident radiation distribution greatly affect the degradation pro-

cess. We can see the mass transfer from liquid to solid phase is akey

controlling mechanism in this photocatalyst reaction. Therefore,

further design on improving the fluid dynamics, incident radiation

distribution andmass transfer will be required basedon the current

model.

The photocatalytic degradation process involves a range of

factors: incident radiation, contaminants and their concentra-

tion, dissolved oxygen, temperature, pH and catalysts loading.

Traditionally, a lump kinetic model has been used to interpret

experimental data without consideration of dynamic changes in

liquid holdup, incident radiation, catalyst distribution and organic

pollutant concentration. This model may not reveal the actual

governing mechanism for photocatalytic degradation. The above

two cases are referred to two possible degradation mechanisms

in the photo-assisted degradation process. The combined CFD

and kinetic models can be used for investigating more possi-

ble mechanisms. By comparing the predictions and experimental

results, the actual mechanism ofthe photocatalytic reactionmay be

determined.

5. Conclusion

Decontamination of polluted waters has become increasingly

important and photocatalysis by titania impregnated kaolin-

ite nano-photocatalyst in an annular slurry photoreactor is

attractive as an environmentally friendly and effective process.

Hydrodynamic characteristics, incident radiation distribution and

degradation kinetics were simulated by employment of3D tran-

sient CFD models in an annular photocatalytic bubble column

reactor using an Eulerian multi-fluid approach. Simulation results

of gas holdup, liquid flow patterns as well as organic concentra-

tions in the liquid phase are in good agreement with corresponding

experimental measurements from our lab or literature reports. The

results revealed that organic degradation rate is highly dependenton the local fluid hydrodynamics and incident radiation distribu-

tion.

The 3D transient CFD models coupled with the kinetics model

are able to capture the global fluid dynamics and light distri-

bution in the three-phase photocatalytic bubble column reactor,

and provide more detailed local information on the gas holdup,

solids concentration as well as organic pollutants concentra-

tion. Two possible controlling mechanisms for photocatalytic

degradation of organic pollutants are investigated. The results

demonstrate that CFD combined with kinetics models can be

applied to elucidate the governing mechanisms in the process.

These models can be used for optimizing the design to accel-

erate the degradation process, as well as facilitating scale-up

strategies.

Acknowledgements

Financial support from the National Natural Science Founda-

tion ofChina (No 51076043) and the Major State Basic Research

Development Program ofChina (973 Program, 2009CB219801) is

gratefully acknowledged. Ms. Qi would like to acknowledge finan-

cial support from the China Scholarship Council (CSC) and the

University ofAdelaide, Australia. We thank for Dr Meng Nan Chong

and the co-workers research progress generatedin Water Environ-

ment Biotechnology Laboratory at the University ofAdelaide. We

would like to acknowledge Dr Kenneth Davey for his proof-reading

the manuscript.

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