Catalytic Wet Oxidation in Three-Phase Moving-Bed Reactors

Article pubs.acs.org/IECR

Catalytic Wet Oxidation in Three-Phase Moving-Bed Reactors: Modeling Framework and Simulations for On-Stream Replacement of a Deactivating Catalyst Ion Iliuta and Faïçal Larachi* Department of Chemical Engineering, Laval University, Québec, G1 V 0A6, Canada S Supporting Information *

ABSTRACT: Phenol wet oxidation over deactivating catalysts in three-phase moving-bed reactors was simulated by formulating and solving a two-scale, nonisothermal, non-steady-state model to highlight the strength of on-stream catalyst replacement, in comparison to catalyst-batch fixed-bed reactors. Simulation results indicate that three-phase moving-bed reactors offer a promising alternative to fixed-bed reactors. The autonomy of fixed-bed reactors is limited due to severe reduction of catalyst activity, while in moving-bed reactor configurations, the decline of pollutant conversion is reduced with increased solid velocity, to compensate for the decrease in catalyst activity loss. The fixed-bed reactor operates in non-steady-state mode, because of the continuous decline of catalyst activity while moving-bed reactors evolve to steady-state operation after a transient period. Decreasing the reactor feed phenol concentration and increasing liquid residence time in the reactor and feed temperature are the best ways to oppose rapid deactivation of catalyst in moving-bed reactors.

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INTRODUCTION Water is becoming an increasingly coveted commodity, so regional and planetary policies are being implemented for the safeguarding and economical exploitation of water resources and for the efficient treatment of contaminated industrial and domestic water effluents. Wastewater management is a perennial problem facing in the first place the industries. Because of the reinforcement of regulations for the quality control of effluents, the manufacturing industries must minimize their emissions of organic and inorganic wastes using efficient routes via the socalled “atom economy” concept, which consists of maximizing atom utilization from the raw materials to the final products.1,2 In addition, it is vital to develop highly efficient processes with the ability to destroy the hazardous xenobiotic pollutants that are contained in the residual waste effluents. Wastewater produced in many industrial processes contains organic compounds in the range of few hundred ppm to a few thousand ppm, which cannot be addressed economically via incineration or biological processes. Within this niche of concentrations and toxicity, catalytic wet oxidation (CWO) is vouched as the most suitable disposal routes. CWO is a pollutantdestructive wastewater technology based on catalytic oxidative breakdown of water-soluble organics. Recourse to solid catalysts offers a practical technological alternative to noncatalytic or homogeneously catalyzed routes,3,4 because treatments can be carried out under relatively mild temperature and pressure, at notably shorter residence times within compact installations; besides, catalysts, in principle, can be easily recovered, regenerated, and reused. Laboratory tests reveal that CWO is so versatile that wastewaters containing a broad spectrum of organic and/or inorganic pollutants including carbon, oxygen, nitrogen, halogen, sulfur and phosphorus-bearing molecules are tackled efficiently. However, despite its success in laboratory testing, industrial-scale application of the CWO process is limited,5,6 mainly because of a lack of stable catalytic performances over © 2012 American Chemical Society

sufficiently long periods of time. It has been demonstrated that catalyst deactivation can take place because of active-phase leachoff, the formation of carbonaceous deposits, catalyst sintering, metal oxidation, inactive metal, or metal oxide deposition.7,8 Given the potential of CWO, over the last three decades, a great deal of research has been conducted on various aspects of process including catalyst development and deactivation, CWO reaction pathways and kinetics, and effects on total organic carbon (TOC) removal of temperature, pressure, and pH. In contrast, the literature still remains short about remediation steps for catalyst deactivation and operation windows in which deactivation of the catalyst is minimized and high selectivity is maintained. For example, there is a lack of information about the types of three-phase reactors most suitable for implementing catalytic wet oxidation in the presence of deactivating catalysts.9 Therefore, it is vital to explore novel reactor configurations for handling CWO while accounting for the complex chemical kinetics, interphase and intraparticle heat and mass transport phenomena, thermodynamics, flow patterns and hydrodynamics. This contribution is offered in this direction and continues our endeavor10,11 to develop conceptual models for the prediction and simulation of catalytic wet air oxidation multiphase reactors. In this work, we investigate the applicability and effectiveness of three-phase moving-bed reactors for CWO under catalyst deactivation conditions. The study attempts to set the CWO reactor modeling by integrating the complex physical and chemical processes at the catalyst and reactor levels. Therefore, a two-scale, nonisothermal, non-steady-state model was developed to account for a relatively detailed gas−liquid dynamics Received: Revised: Accepted: Published: 370

October 2, 2012 December 9, 2012 December 10, 2012 December 10, 2012 dx.doi.org/10.1021/ie302690p | Ind. Eng. Chem. Res. 2013, 52, 370−383

Industrial & Engineering Chemistry Research

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simulations, the behavior of a three-phase moving-bed reactor for CWO, which we will compare to a standard fixed-bed reactor.

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THREE-PHASE MOVING BED REACTOR MODEL Reaction Network. Catalytic oxidation of aqueous phenol over a deactivating MnO2/CeO2 oxide catalyst8,12 (deactivation induced by formation of carbonaceous deposits on active sites) shows that, apart from the main reaction, where phenol is converted to total inorganic carbon, other lump reactions are also possible. Figure 1 shows a multiple deactivation−reaction network describing phenol CWO reaction.8 The network, in which all the intervening species are grouped into four carbon lumps, is represented by a sequence of elementary steps according to the Langmuir−Hinshelwood−Hougen−Watson

Figure 1. Deactivation−reaction network for phenol CWO with the formation of carbonaceous foulant.8

that was associated with the deactivation-reaction network kinetics, the descriptions of thermodynamics and thermal effects, and the impact of on-stream catalyst replacement on the moving-bed-reactor performances. We will analyze, through

Figure 2. Time-dependent phenol conversion for different values of interstitial solid velocity: (a) H = 1 m, vsS = 0.0015 m/s, vsg = 0.0283 m/s; (b) H = 2 m, vsS = 0.003 m/s, vsg = 0.04 m/s. 371

dx.doi.org/10.1021/ie302690p | Ind. Eng. Chem. Res. 2013, 52, 370−383


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approach. Chemisorbed phenol (lump A) is converted to chemisorbed oxidation intermediate lump B, which, in turn, breaks down into oxidation end-product lump C (total inorganic carbon). Lumped species A, B, and C can instantaneously adsorb or desorb. Concomitant with these steps, a foulant (lump W) is formed by a series of complex polymerization reactions between lumps A and B and is adsorbed irreversibly on the catalyst surface progressively blocking access to the active sites. The Langmuir−Hinshelwood−Hougen−Watson kinetic model developed by Hamoudi et al.,8 based on this deactivation− reaction network, was used to describe the evolution of the various carbon lumps, as well as the catalyst activity decline during phenol wet oxidation:8 rA =

k 2K1αCA 1 + K1CA + K3C B + K3′CC

(1)

rB =

(k 2K1CA − k′2 K3C B)α 1 + K1CA + K3C B + K ′3 CC

(2)

(k4K1CA + k′4 K3C B)α dα = dt 1 + K1CA + K3C B + K ′3 CC

(3)

−

Reactor Scale Model. A co-current unidirectional downward gas−liquid viscous Newtonian flow through a porous moving/fixed bed with constant bed void fraction and singlesized catalyst particles is considered. The catalyst bulk flow is downward and driven by gravity with catalyst grains partially wetted by the liquid film. The solid pellets within the reactor were assumed to move steadily in a close-packing fashion. The gas is ideal and the liquid is incompressible. Moreover, the following general assumptions are made in developing the modeling framework: (i) the organic pollutants are nonvolatile, keeping the oxidation reaction solely in the liquid phase; (ii) the catalyst particles are internally completely wet; (iii) whereas diffusion of reactants inside the catalyst particle occurs in the liquid; (iv) no correction of effective diffusivity is made to account for catalyst progressive deactivation due to carbonaceous deposits; (v) water evaporation is described using mass and energy conservations with interfacial water vapor pressure equated to saturated vapor pressure. The catalyst migration time being relatively large, compared to liquid mean residence time in the reactor, the three-phase moving-bed reactor can be viewed as a pseudo-two-phase (gas− liquid) flow fixed-bed system. The ratio between interstitial liquid and solid velocities is at least of the order of 600. Consequently, the non-steady-state flow model is based on the macroscopic volume-average forms of the transport equations for multiphase systems developed for fixed-bed reactors.13 The general model equations consist of volume conservation, continuity and momentum balance equations for both gas and liquid, species conservation equations in the liquid and gas, and the overall gas + liquid + solid energy (heat) balance: Conservation of volume: εS + εg = ε

Figure 3. Time-dependent axial distribution of catalyst deactivation function for different values of interstitial solid velocity (H = 2 m, vsS = 0.003 m/s, vsg = 0.04 m/s, r/rp = 0.934): (a) us = 0 m/s; (b) us = 2 × 10−5 m/s; (c) us = 10−4 m/s.

(4)

Gas and liquid momentum balance equations:

Gas and liquid continuity equations: ∂ ∂ (εg ρg ) + (εg ρg ug ) = 0 ∂t ∂z

(5)

∂ ∂ (εSρS ) + (εSρS uS) = 0 ∂t ∂z

(6)

∂ ∂ (ρg εg ug ) + ug (ρg εg ug ) ∂z ∂t 2 ∂ u ∂P g = εg μge 2 − εg + εg ρg g − Fg S − Fgs ∂z ∂z 372

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Figure 4. (a) Phenol conversion vs time at different values of catalyst particle diameter. (b) Effectiveness factor vs catalyst particle diameter at different reaction times (H = 1 m, vsS = 0.0015 m/s, vsg = 0.0283 m/s, us = 2 × 10−5 m/s).

∂ ∂ (ρ εSuS) + uS (ρS εSuS) ∂t S ∂z εη − εS ∂ 2u ∂P = εSμSe 2S − εS + εSρS g + e [Fg S + Fgs] − FSs ∂z εg ∂z

∂(εSC B, S) ∂t

+

∂(εSuSC B, S)

∂z ∂C B, p ∂ 2(εSC B, S) = DS − DB,effp 2 ∂r ∂z

(8)

∂(εSC H2O, S)

Species balance in liquid phase with axial dispersion:

∂t ∂(εSCA, S) ∂t

+

∂(εSuSCA, S)

∂z ∂CA, p ∂ 2(εSCA, S) eff = DS − DA, p 2 ∂r ∂z

= DS as(1 − ε)ηe r = rp

∂z

∂ 2(εSC H2O, S) 2

− DHeff2O, p

∂C H2O, p

− (kga)H2O (PHsat2O − PH2O, g )

(9) 373

(10)

∂(εSuSC H2O, S)

+

∂z

as(1 − ε)ηe r = rp

∂r 1 RT


(11)


Industrial & Engineering Chemistry Research ∂(εSCO2, S) ∂t = DS

+

∂(εSuSCO2, S) ∂z

∂ 2(εSCO2, S) ∂z

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2

− DOeff2, p

∂CO2, p ∂r


⎛ PO , g ⎞ 2 + (kSa)O2 ⎜⎜ − CO2, S⎟⎟ ⎝ HeO2RT ⎠

(12)

Species balance in (plug-flow) gas phase: PO2, g ⎞ ∂ ⎛ PO2, g ⎞ ∂⎛ ⎜εg ⎟+ ⎜ug εg ⎟ RT ⎠ ∂t ⎝ RT ⎠ ∂z ⎝ ⎛ PO , g ⎞ ∂CO2, p 2 = −(kSa)O2 ⎜⎜ − CO2, S⎟⎟ − DOeff2, p ∂r ⎝ HeO2RT ⎠

r = rp

× as(1 − ε)(1 − ηe)

(13)

∂ ∂ (εg PH2O, g ) + (ug εg PH2O, g ) = (kga)H2O (PHsat2O − PH2O, g ) ∂t ∂z (14)

Overall heat balance equation with plug-flow solids: (εSρS cpS + εg ρg cpg + (1 − ε)ρp cps)

∂T ∂t

+ (εSρS uScpS + εg ρg ug cpg + (1 − ε)ρp uscps) = εSλSeff ,z

∂Tp ∂ 2T − λpeff 2 ∂r ∂z

× (PHsat2O − PH2O, g )

∂T ∂z

as(1 − ε) − (kga)H2O r = rp

1 ΔHev ,H2O RT

(15)

Gas and liquid velocities at the inlet are specified in Dirichlettype boundary conditions. At the outlet, an open boundary condition was used,14 which implies, except for pressure, zero gradients for all flow variables normal to the outflow boundary. The liquid holdup at the reactor inlet was calculated assuming ∂ug/∂z = ∂uS /∂z = 0 and combining eqs 7 and 8: Fg S ⎡ Fgs ⎡ εη − εS ⎤ εη − εS ⎤ F ⎢1 + e ⎥+ ⎢1 + e ⎥ − Ss + (ρS − ρg )g = 0 εg ⎣ εS ⎦ εg ⎣ εS ⎦ εS

(16)

The initial and boundary conditions for eqs 9−15 are as follows: t = 0, z > 0

Pj , g = P jin, g ,

Cj , S = Cjin, S

t > 0, z = 0

Pj , g |z = 0 = P jin, g ,

uSεSCjin, S = uSεSCj , S|z = 0+ − DS εS

T = T in

(17)

∂Cj , S ∂z

z = 0+

Figure 5. Catalyst deactivation function as function of time and space for different values of wetting efficiency (H = 1 m, vsS = 0.0015 m/s, vsg = 0.0283 m/s, us = 2 × 10−5 m/s, r/rp = 0.934): (a) ηe = 0.65, (b) ηe = 0.85, and (c) ηe = 1.0.

(18)

t > 0, z = 0 (uSεSρS cpS + ug εg ρg cpg + us(1 − ε)ρp cps)T in= (uSεSρS cpS + ug εg ρg cpg + us(1 − ε)ρp cps)T |z = 0+ ∂T − εSλSeff ,z ∂z z = 0+

t > 0, z = H

∂Cj ,S ∂z

= 0, z=H

∂T ∂z

=0 z=H

(20)

Pellet-Scale Model. Description of the simultaneous transport across, and consumption within, each catalyst particle

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Figure 6. Phenol conversion versus time for different values of wetting efficiency (H = 1 m, vsS = 0.0015 m/s, vsg = 0.0283 m/s, us = 2 × 10−5 m/s).

requires simulation of time and space mapping of species concentrations and temperature, as well as catalyst activity loss rate −∂α/∂t. To accomplish this task, and assuming spherical grain symmetry, the catalyst particle-scale mass and heat balance equations to be solved are εp

∂Cj , p ∂t

∂Cj , p ⎞ 1 ∂⎛ = 2 ⎜r 2Djeff, p + usεp ⎟ + υjrjρp ∂z ∂r ⎠ r ∂r ⎝

(1 − εp)

t = 0, z > 0, r > 0

+ us(εpρS cpS + ρp cps) ∂z ∂t ⎛ ⎞ ∂Tp 1 ∂ = λpeff 2 ⎜r 2 ⎟ + ( −ΔHr ,A )rAρp r ∂r ⎝ ∂r ⎠

∂r

∂CO2, p ∂r

∂Tp ∂r

Cj , p = Cj , S |z = 0+

=0

(27)

α=1 (28)

Cj , p = Cinj , S

α=1

Tp = T in

Reactor-Scale Closure Laws. In the momentum balance described by eqs 7 and 8, a set of constitutive equations is required for the interfacial drag forces. The assumption of bed partial wetting entrains that the gas-phase drag has contributions due to effects located at the gas−liquid (Fg S ) and gas− solid (Fgs) interfaces. Similarly, the resultant of the forces exerted on the liquid phase involves two components: (i) the drag force (FSs ) experienced by the liquid due to shear stress nearby the liquid−solid boundary, and (ii) the gas−liquid interfacial drag due to slip between fluids (Fg S ). Assuming a trickle flow regime, the double slit analogy provides satisfactory approximations for the liquid−solid, gas−solid, and gas−liquid drag forces:15

(23)

= kSs , j(Cj , S − Cj , s)ηe r = rp

j = A, B, H 2O DOeff2, p

∂r

=0

(29)

The corresponding boundary and initial conditions are given as ∂Cj , p

∂Cj , p

(22)

∂Tp

Djeff, p

r = rp

Tp = T |z = 0+

1 + K1CA, p + K3C B, p + K3′CC , p

t > 0, z > 0, r = rp

= αgs(T ps − T )(1 − ηe) + αSs(T ps − T )ηe

t > 0, z = 0, r > 0

∂(Ctα) ∂(Ctα) + us(1 − εp) ∂t ∂z (k4K1CA, p + k4′K3C B, p)αCt

(εpρS cpS + ρp cps)

∂r

t > 0, z > 0, r = 0 (21)

∂Tp

∂Tp

(26)

∂Cj , p

j = A, B, H 2O, O2

=−

−λpeff

(24)

⎧ ⎡ a 2(1 − ε)2 μ η ⎤ ⎪⎛ E ⎞ S e⎥ 2⎢ s 1⎟ ⎜ ⎨ FSs = ηe⎪ Cw 3 ⎝ ⎠ ⎢⎣ ⎥⎦ εS ⎩ 36

= kSs ,O2(CO2, S − CO2, s)ηe r = rp

⎛ PO , g ⎞ + kgs ,O2⎜ 2 − HeO2CO2, s⎟(1 − ηe) ⎝ RT ⎠

+

(25) 375

⎫ ⎡ a (1 − ε) ⎤ ⎪ E2 ⎥ρS |vs S|⎬vs SεS (1 + ψg S)Cwi⎢ s 3 ⎪ 6 εS ⎦ ⎣ ⎭

(30)



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Figure 7. (a) Phenol conversion vs time at different values of superficial liquid velocity. (b) Effectiveness factor versus superficial liquid velocity at different reaction times. (c) Reactor temperature versus dimensionless axial distance for different values of superficial liquid velocity after 25 h (H = 1 m, vsg = 0.0283 m/s, us = 2 × 10−5 m/s). 2 2 ⎧ ⎪⎛ E ⎞ ⎡ as (1 − ε) μg ⎤ ⎥ Fgs = (1 − ηe)⎨⎜ 1 ⎟Cw 2⎢ 3 ⎥⎦ ⎪⎝ 36 ⎠ ⎢⎣ ε ⎩

+

To represent the gas−liquid relative motion intervening in Fg S , the effect of slip between gas and liquid is taken into account by means of a velocity ug,i at the gas−liquid interface derived in the work of Iliuta et al.15 and estimated from the double slit model as

⎫ ⎪ ⎛ E2 ⎞ ⎡ as(1 − ε) ⎤ ⎟C ⎢ ρ | | v ⎥ g sg ⎬vsg ε wi 3 ⎝6⎠ ⎣ ⎦ ⎪ ε ⎭

⎜

⎛ 72 ⎞ ⎡ 1 ⎛ ΔP ⎞ εS ⎜− + ρS g ⎟εS ug , i = ⎜ ⎟ 2 2 2 ⎢ ⎠ ⎝ E1 ⎠ (1 − ε) as ηe μS ⎣ 2 ⎝ H

(31)

⎤ ⎛ ΔP ⎞ + ⎜− + ρg g ⎟(εηe − εS)⎥ ⎝ H ⎠ ⎦

⎧ as2(1 − ε)2 μg ⎪⎛ E ⎞ ⎛E ⎞ 2 1 + ⎜ 2 ⎟Cwi(1 + ψg S) Fg S = ηe⎨⎜ ⎟Cw 2 ⎪⎝ 36 ⎠ ⎝6⎠ [ε − (εS /ηe)] εg ⎩ ×

⎛ εS ⎞ ⎜ ⎟⎟ug , i − − ε v ⎜ sg ηe ⎠ [ε − (εS /ηe)]2 εg ⎝ as(1 − ε)ρg

⎡ ⎛ ε⎞ ⎤ × ⎢vsg − ⎜⎜ε − S ⎟⎟ug , i ⎥εg ⎢⎣ ηe ⎠ ⎥⎦ ⎝

(33)

In eqs 15 and 19, the condition us = 0 designates the solids stationary (or fixed bed) interstitial velocity and us ≠ 0 is the average solid interstitial velocity in the case of three-phase moving-bed reactor. Model Parameters. The correlations for mass- and heattransfer coefficients developed for two-phase downflow fixedbed reactors (listed in Table S1 in the Supporting Information) were considered appropriate in moving-bed reactors, considering

⎫ ⎪ ⎬ ⎪ ⎭

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Figure 8. Steady-state catalyst deactivation function versus dimensionless axial distance for different values of superficial liquid velocity (H = 1 m, vsg = 0.0283 m/s, us = 2 × 10−5 m/s, r/rp = 0.934).

the very low ratio between interstitial solid and liquid velocities, where the moving-bed reactor can be viewed as a pseudo-twophase flow fixed-bed system. The Ergun single-phase flow parameters can be set either by measurements of single-phase pressure drops16 or estimated from literature correlations.17 Regarding the interaction parameter (ψg S ), which accounts for the interaction between the gas and liquid, it is bounded within the range 1−10 in two-phase downflow fixed-bed reactor: under no interactions (trickle flow regime), the value is close to unity, whereas, at high gas-to-liquid ratios and high-pressure conditions, it can be as high as 10. This interaction function can be estimated using a correlation that was proposed by Iliuta et al.15 The diffusivity of each component in the liquid phase was calculated using the Wilke−4Chang method.18 The effective diffusion coefficients were evaluated assuming a tortuosity factor of 3. The correlations for the viscosity of the gas mixture, the thermal conductivity of the gas and liquid mixture, the effective thermal conductivity of the catalyst particles, and the α-phase effective thermal conductivity in the axial direction are presented in Appendix 1, which is given in the Supporting Information.8,18,22−28 The extent of backmixing in the liquid phase was quantified in terms of axial dispersion coefficient, evaluated using a comprehensive Bodenstein number correlation.19 The wetting efficiency was evaluated using the neural network correlation developed by Larachi et al.20 The kinetic parameters of the catalytic wet oxidation of phenol were taken from Hamoudi et al.8 and are summarized in Table S2 in the Supporting Information. Operating Conditions. The characteristics of the porous MnO2/CeO2 catalyst particles, the design and geometric parameters and the operating conditions are listed in Table S3 in the Supporting Information. With water being the overwhelming compound and the phenol concentration being relatively small, the physical properties specific to water are used for the liquid phase. Moreover, because of the moderate severity of the simulated oxidation (0.5 MPa and 80 °C) and dilute water conditions, it is not necessary to use detailed equations of state (EOSs) for the fluid phases. Fugacity and activity coefficients, as well as the compressibility factor for oxygen and water, are all assigned

values of unity. Raoult’s and Henry’s laws are assumed to be valid for water and oxygen, respectively. Method of Solution. To solve the system of partial differential equations (PDEs) given above, we discretized in space and solved the resulting set of ordinary differential equations (ODEs). The spatial discretization is performed using the standard cell-centered finite difference scheme, at the reactor level (60 grid points), and the method of orthogonal collocation, at the catalyst particles level. The number of collocation points specified for the catalyst particles was restricted to 8. The GEAR integration method for stiff differential equations was employed to integrate the time derivatives. The relative error tolerance for time integration in the present simulations is set at 10−7 for each time step. Fortran 77 software on an Intel Core 2 Duo Processor E7500 was used to generate the numerical platform.

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RESULTS AND DISCUSSION A series of simulations was first carried out to investigate the performance of the phenol CWO process for different solid velocity values. The potential and limits of the moving-bed reactor under different operating conditions then were investigated. For the base case, the characteristics of the porous MnO2/CeO2 catalyst particles and the operating conditions are listed in Table S3 in the Supporting Information. Because the outer shell of the catalyst surface (r/rp > 0.8) is most sensitive to deactivation (large catalyst particle diameter), our analysis is restricted to this thin peripheral shell. For the operating conditions simulated in this study, phenol CWO is likely to be a liquid-reactant-limited process,21 taking into account that the ratio between liquid reactant flux (phenol) and gas reactant flux (oxygen) to catalyst particles is small (γ < 1). Figure 2 shows the time-dependent phenol conversion at different values of solid interstitial velocity in two reactor configurations operated under high-concentration phenol-containing wastewater conditions (phenolic carbon concentration = 0.06 mol carbon/L). The wetting efficiency is ca. 65% (reactor 1, see Figure 2a) and 80% (reactor 2, see Figure 2b), so that the bed does operate with partially wetted catalyst particles. Under fixed-bed 377



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Figure 9. (a) Phenol conversion versus time for different values of superficial gas velocity. (b) Effectiveness factor versus dimensionless axial distance for different values of superficial gas velocity after 25 h. (c) Reactor temperature versus dimensionless axial distance for different values of superficial gas velocity after 25 h (H = 1 m, vsS = 0.0015 m/s, us = 2 × 10−5 m/s).

nonsteady state due to a continuous decline of catalyst activity, moving-bed reactors, in contrast, have a tendency to attain steady state after a transition period. The model predicts a higher conversion of phenol for movingbed reactors when the catalyst particle diameter is reduced (see Figure 4a). The reduction of catalyst particle diameter improves the wetting efficiency, thus ameliorating the transport of liquid reactant to the catalyst. In addition, a decrease in catalyst particle diameter promotes larger values of the effectiveness factor (Figure 4b), which reflect into larger values of phenol CWO reaction rate and phenol conversion, alike. The increase of the effectiveness factor and the decrease of catalyst activity with time are counterbalanced, and, as a result, the performance of the moving-bed reactors is not significantly affected as time progresses. Because phenol catalytic wet oxidation is liquid-reactantlimited, an increase in wetting efficiency provides faster transport of the liquid reactant to the catalyst. In addition, as catalyst wetting improves, the decline in time of activity of the MnO2/CeO2 catalyst is attenuated (Figure 5). As a result, the model predicts

conditions (us = 0), phenol conversion rapidly declines, because of a severe reduction of catalyst activity, especially in the entrance region of the bed, as exemplified in the catalyst activity profiles shown in Figure 3a. The autonomy of the fixed-bed reactor configurations would be ca. 1 day before the catalyst gets severely “coked”, after which point spent catalyst burnoff and regenerability become important issues for long-term exploitation of the catalyst. On the other side, under movingbed conditions (us > 0), the time decay of phenol conversion is reduced by increased solid velocity substantially compensating for the decrease of catalyst activity loss, as exemplified in Figures 3b and 3c. The decline in phenol conversion becomes virtually marginal at us ≥ 2 × 10−5 m/s in both moving-bed configurations, under high-concentration phenol-containing wastewater conditions. Figure 3 shows that after a transition period, which shrinks as solid velocity increases, the activity function of the MnO2/CeO2 catalyst will remain constant and, consequently, the moving-bed reactor attains a steady state (Figure 2). Therefore, if fixed-bed reactors operate in 378



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Figure 10. Steady-state catalyst deactivation function versus dimensionless axial distance for different values of superficial gas velocity (H = 1 m, vsS = 0.0015 m/s, us = 2 × 10−5 m/s, r/rp = 0.934).

Figure 11. Steady-state catalyst deactivation function versus dimensionless axial distance for different values of the feed temperature (H = 1 m, vsS = 0.0015 m/s, vsg = 0.0283 m/s, us = 2 × 10−5 m/s, r/rp = 0.934).

better performance of moving-bed reactors as wetting efficiency improves (Figure 6). Increasing the superficial liquid velocity reduces phenol conversion (Figure 7a), despite an increase of catalyst wetting efficiency and effectiveness factor (Figure 7b), and temperature in the reactor (Figure 7c). The moving-bed reactor performance decreases as the superficial liquid velocity increases, because of the lower liquid phase residence time and lower catalyst activity (Figure 8). Catalyst deactivation at higher superficial liquid velocity due to extension of carbonaceous deposits results into a lower reaction rate and, consequently, a lower phenol conversion. Increasing superficial gas velocity similarly reduces phenol conversion (Figure 9a), even if the effectiveness factor is

higher (Figure 9b). The moving-bed reactor performance decreases as the superficial gas velocity increases, because of the lower gas phase residence time, lower temperature (Figure 9c), and lower catalyst activity (Figure 10). Lower temperature in the catalyst bed at high gas velocities is the result of amplification of heat removal due to water evaporation. An option to compensate catalyst deactivation in the movingbed reactor is to increase the reactor feed temperature (see Figure 11). For the reactor operation conditions of Figure 11, the increase of catalyst activity at the reactor exit with 60% requires an increase in feed temperature from 80 °C °C to 110 °C. The moving-bed reactor performance increases as the 379



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Figure 12. (a) Phenol conversion versus time for different values of feed temperature. (b) Effectiveness factor versus dimensionless axial distance for different values of feed temperature after 25 h. (c) Reactor temperature versus dimensionless axial distance for different values of feed temperature after 25 h (H = 1 m, vsS = 0.0015 m/s, vsg = 0.0283 m/s, us = 2 × 10−5 m/s).

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CONCLUSION Phenol catalytic wet oxidation (CWO) over deactivating catalysts in moving-bed/fixed-bed three-phase reactors was simulated using a two-scale, nonisothermal, non-steady-state model to account for gas−liquid hydrodynamics coupled with a deactivation−reaction network kinetics and descriptions of thermodynamics and thermal effects. This study is the first of its type to address phenomenological modeling of phenol CWO in three-phase moving-bed reactors as a promising alternative to fixed-bed reactors with deactivating catalysts. Other issues about catalyst handling outside the reactor when using the moving-bed option were not considered. Continuous catalyst regeneration strategies, such as bypassing catalyst particles moving downward, because of gravity, in a moving bed can be found elsewhere.29 The simulation results indicate that liquid-reactant limited reactions such as phenol CWO are better performed in movingbed reactors rather than fixed-bed reactors. The autonomy of fixed-bed reactors is limited because of the inevitable reduction of catalyst activity, whereas in moving-bed reactors, the time

feed reactor temperature increases (Figure 12a), because of the higher reaction rate and higher catalyst activity. However, the increase in phenol conversion is limited by the decrease of the effectiveness factor (Figure 12b) and the increase of reactor temperature difference (Figure 12c). The temperature gradient (Tin − Te) in the catalyst bed increases as the feed temperature increases, because of additional water evaporation, but certainly the water evaporation process does not significantly influence the performance of the moving-bed reactor. An additional alternative to diminish catalyst activity loss is through decreasing the phenol concentration in the reactor feed. For wastewaters with small phenolic carbon concentration, the decline in catalyst activity along the moving-bed reactor is virtually marginal (Figure 13). As in the case of fixed-bed reactors (Figure 14), to treat highconcentration phenol-containing wastewaters a priori, dilution before the inlet into the CWO moving-bed reactor is suggested. 380



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Figure 13. Steady-state catalyst deactivation function versus dimensionless axial distance for different values of phenolic carbon concentration (H = 1 m, vsS = 0.0015 m/s, vsg = 0.0283 m/s, us = 2 × 10−5 m/s, r/rp = 0.934).

Figure 14. Catalyst deactivation function versus dimensionless axial distance for different values of phenolic carbon concentration − fixed-bed configuration (H = 1 m, vsS = 0.0015 m/s, vsg = 0.0283 m/s, r/rp = 0.934, t = 25 h).

The moving-bed configuration, such as that observed in the on-stream catalyst replacement (OCR) technology that has been proven and operated by ChevronTexaco in hydrodemetallization of heavy petroleum residua or the Institut Français du Pétrole three-phase moving beds could be a viable option for CWO to counterbalance the compulsory shutdowns of packed beds due to deactivation.30,31 Obviously, moving-bed technology is more prohibitive than fixed-bed technology and can be justified only if important capital savings can be accomplished through reactor size reduction and savings on catalyst amounts, because CWO does not yield marketable products but treated waters. We hope the present effort will shed some light to devise experimental

decay of phenol conversion is reduced by the increase in the solid velocity (us), to compensate for the decrease in catalyst activity loss. The decline in phenol conversion in moving-bed reactors operated under high-concentration phenol-containing wastewater conditions is virtually marginal for us ≥ 2 × 10−5 m/s. The fixed-bed reactor operates in non-steady-state mode due to a continuous decline of catalyst activity, whereas moving-bed reactors have a tendency to operate in steady-state mode after a period of transition. Decreasing the phenol concentrationin the reactor feed and increasing liquid residence time and feed temperature are found to be the best ways to diminish catalyst deactivation in moving-bed reactors. 381



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Greek Letters

studies to test the value of this new type of reactor in the realm of CWO.

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α = deactivation function αgs = gas-particle heat transfer coefficient, J/(m2 s K) αSs = gas-particle heat-transfer coefficient, J/(m2 s K) ΔHev = enthalpy of evaporation, J/mol ΔHr = reaction enthalpy, J/mol εg = gas holdup εS = liquid holdup εp = particle internal porosity γ = ratio between the liquid reactant flux and gas reactant flux to the catalyst particle η = effectiveness factor ηe = external wetting efficiency λα = thermal conductivity of α-phase, J/(m s K) λα,j = thermal conductivity of species j in α-phase, J/(m s K) eff λα,z = axial effective thermal conductivity of α-phase, J/(m s K) λeff p = effective thermal conductivity of catalyst particles, J/(m s K) μα = dynamic viscosity of α-phase, kg/(m s) ρα = density of α-phase, kg/m3 ρp = particle density, kg/mp3 ρsc = catalyst bed density, kg/m3 ψg S = gas−liquid interaction factor

ASSOCIATED CONTENT

S Supporting Information *

Appendix 1. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: (418) 656-2131, Ext. 3566. Fax: (418) 656-5993. E-mails: [email protected] (I.I.), [email protected] (F.L.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Support from the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chair “Green Processes for Cleaner and Sustainable Energy” and the FQRNT-funded “Center in Green Chemistry & Catalysis” (CVCC) is gratefully acknowledged.

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NOTATION as = bed specific surface area, m2/m3solid cpα = specific heat capacity of α-phase, J/(kg K) Cj = concentration of lump j, kmol/m3 CjinS = j-component concentration in feed stream, kmol/m3 Cp,j = heat capacity of species j, J/(mol K) Ct = mole total sites concentration, kmol/ms3 Ctα = mole total active sites concentration, kmol/ms3 dp = particle diameter, mm, μm 2 Deff j,p = effective diffusivity of species j inside the particle, m /s DS = liquid axial dispersion coefficient, m2/s E1, E2 = Ergun constants Fg S = gas−liquid drag force, N/m3 Fgs = gas−solid drag force, N/m3 FSs = liquid−solid drag force, N/m3 H = bed height, m Hej = Henry constant, mS 3/mg 3 k, k′ = lumped rate constants, mol/(kg min) kga = gas side volumetric gas−liquid mass transfer, 1/s kgs = gas−solid mass transfer coefficient, m/s kSa = liquid side volumetric gas−liquid mass transfer, 1/s kSs = liquid−solid mass transfer coefficient, m/s K, K′ = adsorption equilibrium constants, m3/kmol Mj = molecular mass of species j, kg/kmol P = reactor pressure, Pa Pj = partial pressure of species j, Pa rj = reaction rate, kmol/(kgcatalyst s) r = radial position within catalyst particle, m rp = radius of catalyst particle, m t = time, s T = temperature, K Tc = critical temperature, K uα = average interstitial velocity of α-phase, m/s vsα = superficial velocity of α-phase, m/s X = phenol conversion yj = mole fraction of species j in gas phase z = longitudinal coordinate, m

Subscripts/Superscripts

A = phenolic carbon, A lump B = carbon of oxidation intermediates, B lump C = carbon of fully mineralized products, C lump e = reactor exit g = gas phase in = reactor inlet S = liquid phase p = catalyst particle s = solid phase, surface of the catalyst particle

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Catalytic Wet Oxidation in Three-Phase Moving-Bed Reactors

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