Oxidative Photomineralization of Dichloroacetic Acid in an Externally

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Ind. Eng. Chem. Res. 2010, 49, 6722–6734

Oxidative Photomineralization of Dichloroacetic Acid in an Externally-Irradiated Rectangular Bubble Tank Reactor: Computational Fluid Dynamics Modeling and Experimental Verification Studies Francisco J. Trujillo, Tomasz Safinski, and Adesoji A. Adesina* Reactor Engineering & Technology Group, School of Chemical Sciences & Engineering, The UniVersity of New South Wales, Sydney, NSW 2052, Australia

The effect of antecedent factors on the performance of an aerated tank photoreactor containing externally irradiated suspended titania particles has been carried out using the oxidative photomineralisation of dichloroacetic acid (DCA) as the model reaction. The phase hold-up contours, velocity distribution profiles, and the spatial variation of the incident radiative flux as well as the local volumetric rate of photon absorption (LVPRA) inside the reactor were obtained from computational fluid dynamics (CFD) simulation based on the simultaneous solution of the Navier-Stokes equation (NSE) and radiation transport equation (RTE). The species modeling equation (SME) for the oxidative decomposition of DCA to HCl and CO2 was then coupled to the NSE and RTE to determine the influence of catalyst loading, air superficial velocity, pollutant concentration, and radiation intensity on the reactor performance. The SME utilized the intrinsic kinetic expression provided by Zalazar et al. [Chem. Eng. Sci. 2005, 60, 5240-5254] with rate parameters secured from a preliminary fit of our experimental data. The good agreement between numerical results and empirical data for practically all predictor variables suggest that CFD modeling is a reliable and valid tool for the design and evaluation of the new photoreactor system and may in fact be used as a surrogate for subsequent optimization studies. Within the range of variables examined, it is evident that although reaction rate initially increased with air flow rate, a “plateau” was attained after about 30 L min-1. The rate also exhibited a maximum at a catalyst loading of about 2.5 g L-1 while a characteristic Langmuirtype dependency on DCA concentration was observed. However, reaction rate varied only linearly with light intensity indicating the absence of deleterious hole-electron recombination at the relatively low values (20-80 W m-2) employed. 1. Introduction Photocatalysis has emerged as one of the most promising advanced technologies for wastewater detoxification.2,3 A variety of reactor types has been employed in pilot-scale studies requiring solar light application. These may be classified according to the method by which solar light is received and the mode of catalyst support.4 From a hydrodynamic standpoint, a system with suspended catalyst particles has better mixing and mass transport properties as well as superior exposure of the catalyst surface area to light exposure than immobilized catalyst in a fixed bed photoreactor. Fixed-bed catalyst systems, however, eliminate the need for downstream solid-liquid separation, and many authors have reported that photocatalysis in laboratory immobilized catalyst reactors also offers comparable degradation rates for organic pollutants to those obtained in slurry photoreactors.5-7 The double skin sheet reactor (DSSR) described by Dillert et al.8 for large-scale solar water treatment studies can be tilted to the desired orientation to maximize exposure to light depending on the latitude of the location (34° in Rabat, Morocco; 43° at Bilbao, Spain). It is essentially a flat, transparent Plexiglas box with internal channels containing a suspension of the photocatalyst maintained in circulation by a centrifugal pump.8 The liquid feed to the reactor is presaturated with air or oxygen, and consequently, the availability of oxygen to the catalyst surface may be limited by the amount of dissolved oxygen.4 However, using an aerated reactor system, the continuous * To whom correspondence should be addressed. E-mail: a.adesina@ unsw.edu.au.

bubbling of the oxidizing air into the liquid phase ensures that the dissolved oxygen concentration is at (or near) equilibrium value, and thus, the reaction rate would be kinetically controlled since gas flow may be adjusted to guarantee minimum gas-liquid mass transfer resistance. Oxygen acts as the primary electron acceptor and hence helps to improve photoefficiency due to reduced hole-electron recombination. Additionally, the superoxide anion produced from the electron uptake of absorbed liquid phase oxygen4 may be protonated to give hydroperoxyl radicals which are also highly reactive (oxidation potential ) 1.70 V) and may attack adsorbed or fluid phase organic molecules.9 Even so, the design of a multiphase photoreactor is significantly more complex than the single-phase system; nevertheless, the associated higher (2 orders of magnitude) interphase mass transport in an aerated reactor10,11 is advantageous while the induced solid recirculation within the vessel also improves catalyst exposure to light and hence enhanced photoreactor performance. In the present work, an externally illuminated aerated rectangular tank photoreactor has been proposed because of its suitability for solar utilization and the performance is analyzed via computational fluid dynamics (CFD) as part of the progression toward process optimization. The multiphase flow hydrodynamics is described by the Eulerian-Eulerian (EE) model.12 The light intensity distribution inside the reactor may be obtained by solving the radiation transport equation (RTE). The discrete ordinate (DO) method was used to solve the RTE since it is an integral-differential equation. The DO approach has been previously employed to investigate the radiation profile in cylindrical bubble column

10.1021/ie901364z  2010 American Chemical Society Published on Web 01/19/2010

Ind. Eng. Chem. Res., Vol. 49, No. 15, 2010

Figure 1. Reactor configuration with a (1) UV light source (2) etched Pyrex diffuser, (3) temperature control jacket, and (4) sintered stainless steel gas distributor.

photocatalytic reactors13,14 because it can handle complex situations ranging from surface-to-surface radiation to participating radiation media. Dichloroacetic acid (DCA) was used as the model pollutant due to the consensus on the mechanism for its oxidative photomineralization15 and the generalized nature of the rate expression provided by Zalazar and co-workers.1 The DCA degradation profile was obtained by solving the unsteady-state species transport equation coupled to the hydrodynamic and light intensity distribution patterns using the commercial package FLUENT 6.2. 2. Reactor Description Figure 1 illustrates the basic features of the setup. The reactor is a rectangular-shaped stainless steel vessel of dimensions 240 × 140 mm (base) and 130 mm height. The diameter and the length of the AVP06C Primarc medium pressure mercury lamp (Australian UV Manufacturing Pty Ltd., Sydney) are 20 and 150 mm, respectively. The lamp is encased in a parabolic reflector that directs the radiation toward the reactor. The lamp-reflector unit is placed and centered 160 mm vertically above the reactor. A Pyrex filter with a cut off wavelength of 315 nm was used to provide

Figure 2. Radiation spectrum distribution after passing the filter.

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mainly near-UV radiation. Additionally, the lid of the reactor was made from Pyrex glass sand-blasted on the internal side in order to act as a diffuser. This was done so that solution of the RTE can be made more tractable with boundary conditions that consider the light entering the reactor as uniformly diffused. Figure 2 shows the radiation distribution provided by the lamp after passing through the filter. The figure shows only the wavelengths lower than 400 nm since the higher wavelengths are not absorbed by titania. This radiation profile was calculated from the spectral distribution of the output power of the lamp (supplied by the manufacturer) and the transmission coefficient of the filter-diffuser as a function of wavelength. The peak at 365 nm accounts for 84% of the total radiation. The reactor was operated in the semibatch mode (batch in liquid and continuous in gas). The air was prehumidified in a separate packed contactor to minimize the water loss by evaporation. The air flow rate was controlled and monitored with a Selas Flo-Scope rotameter. Aldrich titania (>99% anatase) was used as the photocatalyst (0.05-3.0 g L-1). 3. Mathematical Formulation Unlike conventional multiphase reactors, the design and analysis of heterogeneous photocatalytic reacting systems requires the solution of the mass, momentum, and radiation transport for the selected geometry. The coupling of these equations unveils important nonlinear interactive phenomena such as hydrodynamically enhanced light intensity distribution within the liquid phase due to the presence of reflecting gas bubbles3,16 and, hence, improved performance of the aerated photoreactor over a single-phase system in which the flowing liquid has been presaturated with the oxidizing gas before entering the reactor. 3.1. Hydrodynamics. In order to determine the phase holdup distribution and velocity profiles within the vessel, the Eulerian-Eulerian approach was used to solve the set of transport equations representing the system. This model assumes that the phases present in the system behave like a continuum, such that the conservation of mass and momentum is satisfied by each phase individually, and the associated volume-average transport equations reflect the interpenetration of phases resulting from the interfacial forces.17 The Reynolds averaged NavierStokes equations (RANS) were then used to account for turbulence. In this procedure, the instantaneous value of any

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variable is decomposed into a time-averaged value and a fluctuating component.12 The continuity equation for the kth phase is given by ∂ (R F ) + ∇ · (RkFkb V k) ) 0 ∂t k k

(1)

while the momentum equation is expressed as ∂ (R F b V ) + ∇ · (RkFkb V kb V k) ) -Rk∇p + ∇ · (Rkτ¯ k) + ∂t k k k Fk(2) RkFkg + b where b Fk denotes the interphase momentum exchange term. In this model only the drag force is taken into account as interphase exchange. Drag forces and buoyancy (gravity) are the two main components of the vertical force balance.18 For a bubble swarm, the drag force per unit volume may be estimated from19 3CD Fk ) FD ) RGFL (V b -b V L)| b VG - b V L| 4dB G

(3)

CD is the drag coefficient, dB is the bubble diameter, and the bracketed quantity b Vr ) |V bG - b VL| is the relative or slip velocity. The subscripts L and G stand for the gas and liquid phases respectively. A constant bubble diameter of dB ) 3 mm was used in this simulation and the drag coefficient function was calculated from the Schiller-Naumann equations:20,21

{

0.687 )/Re Re e 1000 CD ) 24(1 + 0.15Re 0.44 Re g 1000

}

(4)

The relative Reynolds number is obtained from Re )

FL | b V r |dB µL

(5)

The granular Eulerian model, which takes the same form of eq 2 but with an additional term for the solid phase pressure (∇ps) was used to solve the momentum equation for the solid titania particles. In this approach, the solid-phase stresses are derived from the kinetic theory of granular flow, which is an extension of the classic kinetic theory of gases to dense particle flow22 developed by making an analogy between the particle-particle collision and the random thermal motion of molecules in a gas.23 The granular temperature is directly proportional to the kinetic energy of the random motion of the particles. The liquid-solid momentum exchange coefficient was calculated with the Wen and Yu model24 which is appropriate for dilute systems. For the bulk viscosity, the expression developed by Lun et al.25 was used. The turbulence field in the continuous liquid phase was modeled using the standard k-ε model26 supplemented with additional terms that take into account the interfacial turbulent momentum transfer as proposed by Simonin and Viollet.27 The average fluctuating terms were computed using 2 τ¯ L ) µt,L(∇V bL + ∇V bLT) - (kL + µt,L∇V bL)I 3

(6)

where the turbulent viscosity of the liquid phase µt,L may be estimated from µt,L ) FLCµ

kL2 εL

(7)

The dispersed gas phase was treated as laminar flow.18,28,29 A steady-state solution of the continuity, momentum, k, and ε

equations was procured and used to obtain the time-average hydrodynamic representation of the system. The density and viscosity of the liquid and gas phases were assumed constant. For the boundary conditions, the gas distributor (sparger) plate was modeled using a uniform gas surface source.13,30 It was necessary to model the inlet as a smaller area than the actual sparger plate since otherwise severe convergence problems could occur as reported by Michele.31 The width and depth of the plate was reduced by 6 and 8 mm, respectively, and the resulting smaller inlet region was centered in the middle of the sparger region. The rise velocity of air bubbles may be assumed constant in the size interval 2-6 mm and determined from the Harmathy equation as 0.25 m s-1.29 The volume fraction at the inlet, RG0, is related to the superficial gas velocity and the bubble rise velocity Vb via12 RG0 )

Vs As Vb Am

(8)

where the area correction factor As/Am ) 1.09. As is the area of the sparger, and Am is the modeled area used to ensure numerical convergence. Since the liquid is the stationary phase, an inlet water velocity of zero was used while the hydraulic diameter () 77.1 mm) and 10% turbulent intensity was assumed for the k-ε turbulence model. The particles volume fraction at the inlet is calculated with the equation: RS )

CS (1 - RG) FS

(9)

Where, CS is the concentration of solids, and FS is the density of the particles. The titania particle diameter size was assumed equal to 10 µm. The literature reports average particle diameters of the water suspension agglomerated in the range 0.3-1 µm.32,33 However, attempts to simulate particle sizes equal to or less than 1 µm failed to converge. The top surface of the hydrodynamic domain was fixed at a height of 50 mm, and it was modeled as a nonshear wall. This automatically set the normal gas and liquid velocities to zero. Thus, in order to represent the escaping bubbles, a sink was defined for all the computational cells attached to the top surface as proposed by Ranade:12 SG ) -ABRGBWGBFG /VC

(10)

where AB is the area of the bottom surface of the computational cell attached to the top surface, VC is the volume of the top cell, and WGB and RGB are the normal velocity of gas bubbles and gas volume fraction of the computational cells lying below the computational cell attached to the top surface. With this arrangement, only the mixture gas-liquid region is solved and there is no need to model the gas disengaging region of the reactor. The computational domain was meshed (using GAMBIT) with volume elements of 2.5 mm × 2.5 mm × 2.5 mm. The mesh at the top of the gas-liquid mixture was finely refined (starting at 0.25 mm and then growing by a factor of 1.49) given that most of the light is absorbed within the first few millimeters of the multiphase mixture. The model was solved with FLUENT 6.2 using an extension of the SIMPLE algorithm (phase-coupledSIMPLE). 3.2. Radiation Modeling. This aspect of the modeling provides insight into the light intensity distribution within the reactor as well as information on the local volumetric rate of photon absorption, LVRPA. The radiation transport equation


for a scattering and absorbing medium over the direction of the light propagation, Ω, is34 dIλ(s, Ω) ) -[σλ + κλ]Iλ(s, Ω) + ds σλ Φ(Ω′ f Ω)Iλ(s, Ω′)dw′(11) 4π 4π

∫

where Iλ is the radiation intensity, σλ is the scattering coefficient, κλ is the absorption coefficient, w′ is the solid angle in the scattered direction Ω′, Φ(Ω′ f Ω) is the phase function, and s is the longitudinal magnitude on the incident direction of propagation Ω. The absorption coefficient κλ was calculated using κλ ) 0.1κ *C λ s(1 - RG)

(12)

where κ*λ is the specific absorption coefficient, RG is the gas holdup, and Cs is solid (catalyst particles) concentration. The volumetric scattering coefficient, σλ, is also estimated from σλ ) σλ,P + σλ,B

(13)

The terms σλ,P and σλ,B account for the contribution of the scattering coefficient by the particles and bubbles,3 respectively: σλ,P ) 0.1σ*C λ s(1 - RG) σλ,B ) FλπR2Ns

(14)

where σ *λ is the specific scattering coefficient, Fλ is the hemispherical reflectivity of the bubbles, R is the radius of the bubbles, and Ns is the number of bubbles per unit volume. Ns can be easily correlated with the gas hold-up: Ns )

RG Vbub

(15)

where Vbub is the volume of a single bubble and the gas hold-up profile (RG) has been obtained from the hydrodynamics modeling described in the previous section. The scattering phase function Φ(Ω′ f Ω) represents the directional distribution of the scattered radiation. It may be physically interpreted as the scattered intensity in a direction divided by the intensity that would be scattered in that direction if the scattering were isotropic. We have, however, assumed that the system in this study is isotropic, i.e. Φ(Ω′ f Ω) ) 1, since most of the scattering in a gas-sparged reactor is caused by particles as suggested by Zalazar and co-workers.1 The walls of the reactor are made of polished stainless steel but the reflectivity at the walls was simplified as diffuse. The emissivity was estimated to be 0.8 based on previous actinometric experiments by comparing the absorbed radiation with black and silver walls.16 The UV light enters the reactor from the top after passing a UVA filter; as a result, the light may be deemed diffused after passing through the filter (because the quartz reactor lid-cover has been finely sand-blasted on the inner, light emergent side). Strictly speaking, eq 11 should be solved for each one of the nine discrete wavelength intervals displayed in Figure 2; hence, the incident radiation and the local volumetric rate of photon absorption (LVRPA), eaλ, can be calculated for each of the wavelength intervals as34 Gλ )

∫

I dw 4π λ,w

(16)

eaλ

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) κλGλ

(17)

The LVRPA for the discrete wavelength spectrum is given by1 ea )

∫

λb

λa

λb

κλGλ )

∑e

a λ

(18)

λa

However, a pseudo-mono-chromatic approximation to eq 18 may be obtained14,34 if weighted optical properties of the medium, such as absorption coefficient (κ *λ*), scattering coefficient (σ *λ*), and useful wavelength (λ*), are employed; thus λb

ψ* )

∑ ψ*F λ

(19)

λ

λa

The radiation spectrum distribution after passing the filter (cf. Figure 2) is used as the weighting factor (Fλ) where ψ* and ψ*λ are the average and wavelength-specific optical property respectively. In this case, the LVRPA can be estimated by combining eqs 16 and 18 as follows: ea ) κ*λ*Gλ* ) κ*λ*

∫I 4π

λ*,w

dw

(20)

Equation 11 was solved using the DO method, where the radiation field is divided into a discrete number of directions. The RTE was solved for each of these directions. FLUENT uses a conservative variant of the DO model called the finite-volume scheme.35,36 The number of discrete directions is important for the solution of the RTE equation. An insufficient number of control angles can cause the “ray effect” due to radiation concentration along discrete directions.36 Even so, accuracy is not guaranteed as a result of “control angle overhang”. To minimize the control angle overhang, structured 3D-hexahedral grids were meshed37 using 288-discrete directions with a pixelation of 4 × 4. 3.3. Species Transport Modeling. Having obtained the spatial variation for gas hold-up, velocity profile, light intensity distribution, and the local volumetric photon absorption within the reactor, we can now proceed to more accurately describe and predict spatiotemporal reactant composition profile within the reactor as well as the impact of key operating variables on reactor performance. In an aqueous solution with pH > 2, dichloroacetic acid (DCA) exists in the anionic form, CHCl2COO- (DCA-), since its pKa ) 1.29 at 298 K,38 viz DCA(aq) T DCA-(aq) + H+(aq)

(21)

The anion adsorbs onto TiO2 by forming a bidentate complex with both oxygen atoms of the carboxylate group attached to the surface at pH < 4. Nevertheless, adsorption occurs over a wide range of pH with the monodentate surface species observed at pH g 5; as a result, the rate of DCA photomineralisation was found to be insensitive to pH.15 Indeed, the photocatalytic degradation of DCA may also proceed by direct attack of photogenerated positively charged holes in an acidic medium even when oxygen is not present.37-39 The aerobic photocatalytic oxidation reaction yields CO2 and HCl as given by UV, TiO2

CHCl2 - COOH + O2 98 2CO2 + 2HCl

(22)

Zalazar et al.1 have proposed a rate expression which accounts for both electron-hole primary quantum yield and the local volumetric rate of photon absorption as

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RDCA- ) -

[

φλ,Peaλ φλ,Peaλ

1 1 1 1 + + kobs 2 2 4 2C C C DCA O2 s

]

1/2

(23)

where φλ,P is the electron-hole primary quantum yield, eaλ is the local volumetric rate of photon absorption (LVRPA), and Cs is the catalyst loading, while CO2 and CDCA- are the concentrations of oxygen and DCA-, respectively. Upon

Figure 3. Gas volume fraction distribution in the reactor for Q ) 30 L min-1.

Figure 4. Solid volume fraction distribution in the reactor for Q ) 30 L min-1.

complete mineralization, 2 mol HCl will be produced per mole of DCA- reacted, thus RHCl )

[

2φλ,Peλa

a 1 1 φλ,Peλ 1 1 + + kobs 2 2 4 C 2CDCA- CO2 s

]

1/2

(24)

Although the primary quantum yield φλ,P is wavelengthdependent, a wavelength-weighted primary quantum yield,


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Figure 5. Concentration of particles, CS, in the reactor for Q ) 30 L min-1.

Figure 6. Titania particles residual distribution for Q ) 30 L min-1.

[φ], may be defined for the spectral window over which photocatalysis occurs; thus

∫ φ e dλ ∫ e dλ λ2

[φ] )

λ1

a λ,P λ

λ2 a λ1 λ

(25)

Consequently, for the polychromatic photocatalytic oxidation of DCA, eq 25 may be readily introduced into eq 24 to obtain the rate of the dichloroacetate anion disappearance. The transient local concentration for DCA and HCl may be predicted, by solving the reaction-diffusion equation for each ith species: ∂ (R C ) + ∇ · (RLb V LCi,L) ) -∇ · RLb J i,L + RLRi,L + ∂t L i,L (m ˙ GiLj - m ˙ LjGi)(26)

where Ri,L is the net rate of production or disappearance of the homogeneous species i (DCA or HCl) by chemical reaction in the liquid phase, L; RL is the volume fraction of the liquid phase; and m ˙ GiLj is the mass transfer rate between species i and j from the liquid to the gas phase. In this case, only the disappearance of DCA and formation of HCl in liquid phase are modeled. The transfer of species between liquid and gas phases is neglected due to the low volatility of DCA and HCl at room temperature. The liquid oxygen concentration is assumed to be constant due to the rapid equilibration with the gas phase oxygen for continuous aeration at atmospheric pressure. The concentration of dissolved oxygen was calculated as 0.2716 mol m-3 using Henry’s law and assuming normal air composition (21% O2) and at 25 °C. The associated concentration of DCA anion (DCA-) was estimated using the equilibrium dissociation constant, Ka, given by

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Ka )

CDCA-CH+ CDCA

(27)

where, Ka is 10-pKa with pKa ) 1.48 for dichloroacetic acid. 4. Results and Discussion The homogeneous dispersion of gas bubbles through the liquid phase is evidenced in Figure 3 where the small reduction seen near the reactor wall is probably due to the higher

Figure 7. Liquid phase velocity vector in the reactor for Q ) 30 L min-1.

Figure 8. Solid phase velocity vectors in the reactor for Q ) 30 L min-1.

concentration of titania particles at the wall as confirmed by Figure 4. The latter also shows that the solid-phase holdup is essentially uniform throughout the tank. In fact, Figure 5 plots the solid particle distribution profile within the reactor. It is apparent that the concentration of solids is constant consistent with Figure 6 which shows that the residual distribution of Cs exhibits a perfect bell shape centered at practically nil residual. Figures 7 and 8 display the velocity vectors of the solid and liquid phases respectively. It shows that the solid particles are


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Figure 9. Liquid phase velocity vector in the reactor for Q ) 30 L min-1 modeling only the gas and liquid phases.

Figure 10. LVRPA for Q ) 30 L min-1, CS ) 1 g L-1 using the polychromatic model (cf. eq 16-18) and a radiation intensity of 100 W m-2.

moving at the same velocity as the liquid phase. Figure 9 shows that the liquid phase velocity vectors when the solid phase is neglected is essentially identical to the profiles in Figure 8. It seems that at the relatively low solid content used in slurry photocatalytic reactors it is reasonable to assume that the liquid and solid phases may be considered as a homogeneous medium. Similar observations have been reported by Montante et al.33 Therefore, the modeling of the solid phase can be neglected

saving computational resources. In what follows, only the liquid and gas phases are modeled and it is assumed that the solid concentration, Cs is constant. Figures 10 and 11 show the LVRPA calculated with the polychromatic (cf. eqs 16-18) and pseudo-mono-chromatic (cf. eqs 19 and 20) modeling approaches, respectively. The two modeling results are nearly identical thus justifying the use of the pseudo-mono-chromatic approximation in subsequent treat-

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Figure 11. LVRPA for Q ) 30 L min-1, CS ) 1 g/L, and a radiation intensity of 100 W m-2 using the pseudo-mono-chromatic approximation (cf. eqs 19 and 20).

ment. The similarity obtained with both approaches is primarily due to the fact that the filtered radiation is almost monochromatic (84% at λ ) 365 nm, cf. eq 2). The CFD modeling of the reaction system was conducted in three stages. First, a time-averaged hydrodynamic representation of the system was obtained by solving the steady-state continuity, momentum, and k-ε transport equations for the gas and liquid phases. The solid phase modeling was neglected since the solids may be assumed to be homogenously distributed in the liquid phase and moving at the same velocity as the liquid phase. Second, the radiation distribution and the local volumetric rate of photon absorption, LVPRA, were obtained by solving the RTE. The gas hold-up distribution obtained from the hydrodynamic modeling was then used to calculate the absorption and scattering coefficient distributions (cf. eqs 12-14). Finally, the DCA degradation profile was obtained by solving the unsteady state species transport equations using the timeaveraged hydrodynamics and the LVRPA. As may be seen from Figure 12, the DCA concentration was overestimated by using the set of kinetic constants proposed by Zalazar and co-workers.1 However, by minimizing the rootmean-square percentage error between our experimental data (cf. Table 1) and CFD calculated values using the Newton method, we obtained optimal estimates of

CFD-predicted DCA transient concentration for several other runs. The difference in the parameter estimates between our study and those of Zalazar et al. ([φ] ) 0.53 ( 0.08 mol Einstein-1; kobs ) 4.43 ( 0.80 × 10-9 mol s g2 m-9) may be attributed to the difference in photoreactor design. In our system, the reaction medium was externally illuminated from the top of the gas-liquid mixture interface which is characterized by high liquid velocities and a turbulent behavior caused by the bursting of the bubbles. On the other hand, Zalazar et al.1 used a cylindrical reactor where the light enters through two flat circular quartz windows directly to the solid-liquid mixture. In this case, the hydrodynamics in the near wall region is affected by the wall where lower mixing and therefore higher mass transfer resistance are expected compared with the highly turbulent interface in the present photoreactor. This effect is especially important since most of the radiation is absorbed only

[φ] ) 0.12 ( 0.015 mol Einstein-1 kobs ) 3.62 ( 0.453 × 10-7 mol s g2 m-9 Figure 13 which plots the experimental DCA concentration (from separate runs different from those provided in Table 1) against the corresponding CFD values illustrates the good agreement between simulation and empirical knowledge and hence, credence in the reliability of computerized multiphase hydrodynamic modeling. Figure 14 shows the experimental and

Figure 12. Representative DCA concentration vs time. Comparison between Zalazar1 and the new proposed value of the kinetic constants [φ] and kobs. Experimental conditions are in Table 1.

Ind. Eng. Chem. Res., Vol. 49, No. 15, 2010 a

Table 1. Experimental Setup

Changing Air Flow Rate run number

Q (L min-1)

CoDCA (ppm)

CS (g L-1)

L (W m-2)

1 2 3

15 30 45

156 161 162

1.67 1.67 1.67

29 29 29

Changing Catalyst Loading run number

Q (L min-1)

CoDCA (ppm)

CS (g L-1)

L (W m-2)

4 5 6 7 1 8 9

15 15 15 15 15 15 15

166 153 154 125 156 160 177

0.05 0.25 0.50 1.00 1.67 2.00 2.50

29 29 29 29 29 29 29

Changing DCA Concentration run number

Q (L min-1)

CoDCA (ppm)

CS (g L-1)

L (W m-2)

10 11 7 12 13

15 15 15 15 15

72 104 125 159 235

1 1 1 1 1

29 29 29 29 29

run number

Q (L min-1)

CoDCA (ppm)

CS (g L-1)

L (W m-2)

12 14 15

15 15 15

159 164 165

1 1 1

29 67 80

Changing Lamp Power

a Q, air flow rate (L min-1); CoDCA, initial concentration of DCA (ppm); CS, solids concentration (g L-1); L, radiation intensity entering the reactor and emitted by the lamp (W m-2).

within the first few millimeters as may be seen in Figure 15. The difference in reactor design is probably responsible for the 2 orders

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of magnitude increase in the kobs for our reactor over that of Zalazar and co-workers; however, they used a lamp with superior wavelength-weighted average primary quantum yield, [φ]. The integral reaction rate during the first 2 h of degradation was numerically calculated from the experimental data in order to analyze the effect of the gas flow rate, catalyst concentration, initial concentration of the DCA, and light intensity on the overall reaction rate. Figure 16 shows that the reaction rate increases by increasing the gas flow rate probably due to better mixing obtained at higher gas flow rates. Figure 17 shows that the reaction rate increases by increasing the catalyst concentration in the studied range. This may be ascribed to the increase in the reactive surface area rather to a higher light absorption because, as seen in Figure 15, the light is efficiently absorbed even at very low catalyst concentrations. Finally, Figures 18 and 19 show that the reaction rates increased with the initial concentration of DCA and the light intensity. 5. Conclusions The reaction kinetics in a new externally irradiated aerated photoreactor was successfully described by CFD simulation. The model was carried out in three stages: First, a time-averaged hydrodynamic representation of the system was obtained by solving the steady-state continuity, momentum, k and ε transport equations for the gas and liquid phases. Second, the radiation distribution and the local volumetric rate of photon absorption were obtained by solving the radiation transport. Finally, the DCA degradation profile was obtained by solving the unsteady state species transport equations using the time averaged hydrodynamics and the previously obtained LVRPA. A new set of kinetic parameters were proposed since the DCA concentrations were overestimated by using the constants proposed by Zalazar and co-workers. The difference arose as a

Figure 13. Model predictions vs experimental values for DCA photocatalytic degradation and histogram of error frequency distribution. Root mean square percentage error of the model ) 0.56%.

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Figure 14. DCA concentration vs time during the photocatalytic degradation for experimental runs 1, 5, 8, 11, 14, and 15. (•) Experimental data. (-) CFD calculation. Experimental conditions are in Table 1.

Figure 15. LVRPA versus the distance from the top of the mixture liquid interface toward the bottom of the reactor for Q ) 30 L min-1 and a radiation intensity of 29 W m-2.

result of dissimilarities in both reactor design and lamp type. The faster degradation in the aerated externally illuminated photoreactor was explained by the increased turbulence, hydrodynamically enhanced light distribution within the reactor vessel, and higher interphase mass transfer rates. This effect is important given that most of the radiation is absorbed only in

Figure 16. Integral averaged reaction rate after 2 h vs gas flow rate. (•) Experimental data. (0) CFD calculation.

the first few millimeters from the top of the gas-liquid interface. The model predicted that the overall reaction rate increases by increasing the gas flow rate, catalyst concentration (in the range 0.5-2.5 g L-1), DCA initial concentration, and light intensity. This new proposed photoreactor can be a faster alternative to


Figure 17. Integral averaged reaction rate after 2 h vs CS (concentration of solid catalytic particles). (•) Experimental data. (0) CFD calculation.

Figure 18. Integral averaged reaction rate after 2 h vs initial DCA concentration. (•) Experimental data. (0) CFD calculation.

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Fλ ) fraction of the total lamp power at a wavelength λ g ) gravity (m s-2) Gλ ) radiant flux or incident radiation (W m-2 or Einstein s-1 m-2) I ) unit tensor Iλ ) radiation intensity (W m-2 sr-1) b Jil ) flux of species i on the liquid phase kL ) turbulent kinetic energy of the liquid phase (m2 s-2) kobs ) kinetic constant (mol s g2 m-9) Ka ) DCA acid dissociation constant L ) radiation intensity entering the reactor and emitted by the lamp (W m-2) Ns ) number of bubbles per unit volume p ) Pressure (Pa) ps ) solid pressure (Pa) Q ) air flow rate (L min-1) R ) radius of the bubbles (m) RDCA- ) rate of ions DCA- disappearance (mole m-3 s-1) RHCl ) rate of HCl formation (mole m-3 s-1) Ril ) net rate of production or disappearance of the species i in phase l (mol m-3 s-1) s ) longitudinal magnitude on the incident direction of propagation Ω (m) t ) time (s) SG ) sink term to account for the escape of bubbles (kg m-2 s-1) b VG ) velocity vector of the gas phase (m s-1) b Vk ) velocity vector of the k phase (m s-1) b VL ) velocity vector of the liquid phase (m s-1) b Vr ) slip velocity vector (m s-1) VC ) volume of the top cell (m3) VG0 ) velocity of the gas at the inlet (m s-1) Vb ) rise velocity of the bubbles (m s-1) Vbub ) volume of a bubble (m3) Vs ) gas superficial velocity (m s-1) w ) solid angle between the radiation direction Ω and the normal wall direction (sr) w′ ) solid angle in the scattered direction Ω′ (sr) WGB ) normal gas velocity of the cells attached below the top computational cells (m s-1) Y ) moles of DCA Yil ) mass composition of species i in the liquid phase x ) distance Greek Symbols

Figure 19. Integral averaged reaction rate after 2 h vs lamp filtered radiation intensity entering the reactor. (•) Experimental data. (0) CFD calculation.

the degradation of pollutants especially if solar application is to be implemented. Nomenclature AB ) area of the bottom surface of the top computational cell (m2) Am ) modeled sparger area (m2) As ) sparger area (m2) Cµ ) constants of the turbulence model CS ) concentration of solid particles (g L-1) CO2 ) concentration of oxygen (mol m-3) CDCA- ) concentrations of DCA- (mole m-3) dB ) diameter of the bubbles (m) eaλ ) local volumetric rate of photon absorption (LVRPA) (Einstein m-3 s-1) b Fk ) interphase momentum exchange force (N m-3) FD ) drag force (N m-3)

R ) volume fraction RG ) volume fraction of the gas RG0 ) volume fraction of the gas at the inlet RGB ) gas volume fraction of the cells attached below of the top computational cells Rk ) volume fraction of the kth phase RS ) volume fraction of the solid particle phase Rl ) volume fraction of the liquid phase εL ) turbulent energy dissipation rate of the liquid phase (m2 s-3) φλ,P ) electron-hole primary quantum yield [φ] ) wavelength weighted average primary quantum yield Φ(Ω′ f Ω) ) scattering phase function κλ ) absorption coefficient at λ (m-1) κ *λ* ) weighted average absorption coefficient (m-1) λ ) radiation wavelength (nm) λ* ) weighted average or useful wavelength (nm) FG ) density of the air phase (kg m-3) FL ) density of the liquid phase (kg m-3) Fk ) density of the k phase (kg m-3) FS ) density of the solid particles (kg m-3) Fλ ) hemispherical reflectivity

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σλ ) scattering coefficient (m-1) σλ,P ) scattering coefficient by the particles (m-1) σλ,B ) scattering coefficient by the bubbles (m-1) σ *λ* ) weighted average scattering coefficient (m-1) µt,L ) turbulent viscosity of the liquid phase (kg m-1 s-1) jτk ) stress-strain tensor pf the k phase (N m-2) jτL ) stress-strain tensor of the liquid phase (N m-2) Ω ) direction of the radiation propagation (unit vector) Ω′ ) direction of the scattered radiation (unit vector) ψ *λ ) wavelength specific optical property ψ* ) weight average optical property Subscripts k ) phase G ) gas phase L ) liquid phase n ) normal direction v ) volume (volume integral) λ ) wavelength Superscripts T ) transpose

Literature Cited (1) Zalazar, C. S.; Romero, R. L.; Martin, C. A.; Cassano, A. E. Photocatalytic intrinsic reaction kinetics I: Mineralization of dichloroacetic acid. Chem. Eng. Sci. 2005, 60 (19), 5240–5254. (2) Alfano, O. M.; Bahnemann, D.; Cassano, A. E.; Dillert, R.; Goslich, R. Photocatalysis in water environments using artificial and solar light. Catal. Today 2000, 58 (2-3), 199–230. (3) Trujillo, F. J.; Safinski, T.; Adesina, A. A. CFD analysis of the radiation distribution in a new immobilized catalyst bubble column externally-illuminated photoreactor. J. Sol. Eng. 2007, 129, 27–36. (4) Adesina, A. A. Industrial exploitation of photocatalysis: progess, perspectives and prospects. Catal. SurV. Asia 2004, 8 (4), 265–273. (5) Guillard, C.; Disdier, J.; Monnet, C.; Dussaud, J.; Malato, S.; Blanco, J.; Maldonado, M. I.; Herrmann, J.-M. Solar efficiency of a new deposited titania photocatalyst: chlorophenol, pesticide and dye removal applications. Appl. Catal. B: EnViron. 2003, 46 (2), 319–332. (6) Guillard, C.; Lachheb, H.; Houas, A.; Ksibi, M.; Elaloui, E.; Herrmann, J.-M. Influence of Chemical Structure of Dyes, of pH and of inorganic salts on their photocatalytic degradation by TiO2 comparison of the efficiency of powder and supported TiO2. J. Photochem. Photobiol. A: Chem. 2003, 158, 27–36. (7) Noorjahan, M.; Pratap Reddy, M.; Durga Kumari, V.; Lavedrine, B.; Boule, P.; Subrahmanyam, M. Photocatalytic degradation of H-acid over a novel TiO2 thin film fixed bed reactor and in aqueous suspensions*1. J. Photochem. Photobiol. A: Chem. 2003, 156 (1-3), 179–187. (8) Dillert, R.; Cassano, A. E.; Goslich, R.; Bahnemann, D. Large scale studies in solar catalytic wastewater treatment. Catal. Today 1999, 54 (23), 267–282. (9) Pareek, V. K.; Adesina, A. A. Analysis of Photocatalytically Reactive Systems: Kinetic Modeling and REactor Design via Computational Fluid Dynamics. In Handbook of Photochemistry and Photobiology; Nalwa, H. S., Ed.; American Scientific Publishers: Stevenson Ranch, CA, 2003; Vol. 1, pp 345-412. (10) Kantarci, N.; Borak, F.; Ulgen, K. O. Bubble column reactors. Process Biochem. 2005, 40 (7), 2263–2283. (11) Deckwer, W.-D. On the mechanism of heat transfer in bubble column reactors. Chem. Eng. Sci. 1980, 35 (6), 1341–1346. (12) Ranade, V. V. Computational Flow Modeling for Chemical Reactor Engineering, 1st ed.; Academic Press: New York, 2002; Vol. 5, p 452. (13) Pareek, V. K.; Cox, S. J.; Brungs, M. P.; Young, B.; Adesina, A. A. Computational fluid dynamic (CFD) simulation of a pilot-scale annular bubble column photocatalytic reactor. Chem. Eng. Sci. 2003, 58 (3-6), 859–865. (14) Romero, R. L.; Alfano, O. M.; Cassano, A. E. Cylindrical photocatalytic reactors. Radiation adsorption and scattering effects produced by suspended fine particles in an annular space. Ind. Eng. Chem. Res. 1997, 36, 3094–3109. (15) Bahnemann, D. W.; Hilgendorff, M.; Memming, R. Charge Carrier Dynamics at TiO2 Particles: Reactivity of Free and Trapped Holes. J. Phys. Chem. 1997, 101 (21), 4265–4275.

(16) Trujillo, F. J.; Lee, I. A.-L.; Hsu, C.-H.; Safinski, T.; Adesina, A. A. Hydrodynamically-Enhanced Light intensity distribution in an externally irradiated novel aerated photoreactor: CFD simulation and experimental studies. Int. J. Chem. Reactor Eng. 2008, 6, Article A58. (17) Lakehal, D. On the modelling of multiphase turbulent flows for environmental and hydrodynamic applications. Int. J. Multiphase Flow 2002, 25 (5), 823–863. (18) Pfleger, D.; Gomes, S.; Gilbert, N.; Wagner, H.-G. Hydrodynamic simulations of laboratory scale bubble columns fundamental studies of the Eulerian-Eulerian modelling approach. Chem. Eng. Sci. 1999, 54 (21), 5091– 5099. (19) Joshi, J. B. Computational flow modelling and design of bubble column reactors. Chem. Eng. Sci. 2001, 56 (21-22), 5893–5933. (20) Clift, R.; Grace, J. R.; Weber, M. E. Bubbles, Drops and Particles; Academic Press: New York, San Francisco, London, 1978. (21) Fluent Inc., FLUENT 6.2 user’s guide. Fluent Inc.: Lebanon, NH, 2005; Vol. 11. (22) Huilin, L.; Yuronga, H.; Gidaspow, D. Hydrodynamic modelling of binary mixture in a gas bubbling fluidized bed using the kinetic theory of granular flow. Chem. Eng. Sci. 2003, 58 (7), 1197–1205. (23) Ding, J.; Gidaspow, D. A bubbling fluidization model using kinetic theory of granular flow. AIChE J. 1990, 36 (4), 523–538. (24) Wen, C.-Y.; Yu, Y. H. Mechanics of Fluidization. Chem. Eng. Progr. Symp. Ser. 1966, 62, 100–111. (25) Lun, C. K. K.; Savage, S. B.; Jeffrey, D. J.; Chepurniy, N. Kinetic Theories for Granular Flow: Inelastic Particles in Couette Flow and Slightly Inelastic Particles in a General Flow Field. J. Fluid Mech. 1984, 140, 223–256. (26) Launder, B. E.; Spalding, D. B. The numerical computation of turbulent flows. Comput. Methods Appl. Mech. Eng. 1974, 3, 269–289. (27) Simonin, O.; Viollet, P. L. Prediction of an Oxigen Droplet Pulverization in a Compressible Subsonic Coflowing hydrogen Flow. In Numerical Methods for Multiphase Flow; Celik, I., Hughes, D., Crowe, C. T., Lankford, D., Eds.; The American Society of Mechanical Engineers: Toronto, Canada, 1990; Vol. FED91, pp 73-82. (28) Pfleger, D.; Becker, S. Modelling and simulation of the dynamic flow behaviour in a bubble column. Chem. Eng. Sci. 2001, 56 (4), 1737– 1747. (29) Krishna, R.; van Baten, J. M.; Urseanu, M. I. Three-phase Eulerian simulations of bubble column reactors operating in the churn-turbulent regime: a scale up strategy. Chem. Eng. Sci. 2000, 55 (16), 3275–3286. (30) Sanyal, J.; Vasquez, S.; Roy, S.; Dudukovic, M. P. Numerical simulation of gas-liquid dynamics in cylindrical bubble column reactors. Chem. Eng. Sci. 1999, 54 (21), 5071–5083. (31) Michele, V. CFD Modeling and measurement of liquid flow structure and phase holdup in two and three phase-phase bubble columns. Ph.D. Thesis, Technical University of Braunshweig, Braunshweig, Germany, 2002. (32) Cabrera, M. I.; Alfano, O. M.; Cassano, A. E. Absorption and scattering coefficients of titanium dioxide particulate suspensions in water. J. Phys. Chem. 1996, 100, 20043–20050. (33) Montante, G.; Paglianti, A.; Magelli, F. Experimental analysis and computational modelling of gas-liquid stirred vessels. Chem. Eng. Res. DeV. 2007, 225, 647–653. (34) Cassano, A. E.; Martin, C. A.; Brandi, R. J.; Alfano, O. M. Photoreactor analysis and design: fundamentals and applications. Ind. Eng. Chem. Res. 1995, 34, 2155–2201. (35) Murthy, J. Y.; Mathur, S. R. Finite volume method for radiative heat transfer using unstructured meshes. J. Thermophys. Heat Transfer 1998, 12 (3), 313–321. (36) Raithby, G. D. Evaluation of Discretization Errors in Finite-Volume Radiant Heat Transfer Predictions. Numer. Heat Transfer, Part B 1999, 36, 241–264. (37) Murthy, J. Y.; Mathur, S. R. Radiative Heat Transfer in Axisymmetric Geometries Using an Unstructured Finite-Volume Method. Numer. Heat Transfer, Part B 1998, 33, 397–416. (38) Bahnemann, D.; Kholuiskaya, S. N.; Dillert, R.; Kulak, A. I.; Kokorin, A. I. Photodestruction of dichloroacetic acid catalyzed by nanosized TiO2 particles. Appl. Catal., B: EnViron. 2002, 36 (2), 161–169. (39) Lindner, M.; Bahnemann, D.; Hirthe, B.; Gribler, W.-D., Solar Water Detoxification: Novel TiO2 Powder as Highly Active Photocalysts. In Solar Energy 1995; Stine, W. B., Tanake, T., Claridge, D. E., Eds.; The American Society of Mechanical Engineering: New York, 1995; Vol. 1, pp 399-408.

ReceiVed for reView August 30, 2009 ReVised manuscript receiVed December 12, 2009 Accepted December 21, 2009 IE901364Z

Oxidative Photomineralization of Dichloroacetic Acid in an Externally

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