Ceramic foams as structured catalyst inserts in gas–particle filters for gas reactions—Effect of backmixing

Applied Catalysis A: General 357 (2009) 85–92

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Ceramic foams as structured catalyst inserts in gas–particle filters for gas reactions—Effect of backmixing Sebastian Zuercher, Kyra Pabst, Georg Schaub * Engler-Bunte-Institut, Bereich Gas, Erdo¨l und Kohle, Universita¨t Karlsruhe (TH), Engler-Bunte-Ring 1, D-76131 Karlsruhe, Germany

A R T I C L E I N F O

A B S T R A C T

Article history: Received 14 November 2008 Received in revised form 12 January 2009 Accepted 12 January 2009 Available online 19 January 2009

Ceramic open cell foams have recently been proposed as a new catalyst structure to benefit from its characteristic properties like low pressure drop at relatively high specific surface and enhancement of heat and mass transfer. This study focuses on the application of an impregnated ceramic foam inside a gas–particle filter element to create a multifunctional reactor configuration. Selective catalytic NO reduction (SCR) and C3H6 oxidation over a V2O5–WO3–TiO2 catalyst are used as model reactions. Experiments were performed in two different reactor configurations (axial flow in cylindrical foam, radial flow in foam ring) and at typical conditions of filtration in flue gas cleaning (low gas velocity, low concentrations) and compared with data from a particle fixed bed as reference structure. Experiments at various temperatures (150–340 8C) and modified residence times (0.02–0.07 g s/cm3) in foams show no deviation from particles in the axial configuration. Conversion in the radial flow configuration, however, is significantly lower for both reactions. Given the low gas velocities due to a higher cross-sectional area and a shorter length of the catalyst in flow direction, backmixing is presumed to be the cause. For closer examination, experiments were performed to quantify the foam backmixing behaviour by measuring the residence time distribution (RTD). Combining the results, it was possible to establish mixed flow models in agreement with the data, thus suggesting backmixing to be the cause of decreasing conversion. The results of the kinetic study show no effect on the performance comparing fixed bed configurations of particles and foams. Ceramic foams appear well suited as structured catalyst inserts in gas–particle filters, given their low pressure drop and permeability in all flow directions. ß 2009 Elsevier B.V. All rights reserved.

Keywords: Open cell foam Catalytic gas cleaning SCR VOC oxidation Multifunctional filter reactor Residence time distribution Dispersion Mathematical modelling

1. Introduction Multifunctional reactors allow for carrying out simultaneously other functions besides a chemical reaction, which should lead to overall process improvements. For example, a filter reactor for simultaneous catalytic removal of gaseous pollutants and particle filtration (fuel ash, injected sorbents) has been developed and discussed in the open literature. Especially applications for off gas cleaning at stationary sources like NOx [1], SO2 [2] or organic compounds (VOC) [3,4] removal, syngas purification [5] but also diesel exhaust treatment of mobile units are of interest. For solidcatalyzed reactions, high-temperature ceramic filter candles seem well suited because of their high voidage (up to 60% (v/v)), mechanical stability and high filtration efficiency. Bag filters are limited in operating temperature because of their thermochemical instability. Two different methods to integrate the catalyst inside the filter are possible. The first one is to use the ceramic filter

* Corresponding author. Tel.: +49 721 6082961; fax: +49 721 606172. E-mail address: [email protected] (G. Schaub). 0926-860X/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.apcata.2009.01.020

medium itself to be impregnated with the catalyst, and the second one to place an impregnated open cell foam structure in the void space inside the filter element. Both methods are described in the open literature, concerning preparation [6,7], reaction kinetics [8,9], catalyst selection [4] and filtration performance [10]. However, the present study focuses on reaction engineering and modelling aspects of the multifunctional filter reactor for NOx removal. A ceramic open cell foam coated with a catalyst is placed inside a ceramic filter candle, as shown in Fig. 1, which should allow sufficiently high NOx conversion via selective catalytic reduction (SCR) to fulfill environmental requirements. The foam samples used for studying reaction kinetics are coated with V2O5– WO3–TiO2 as catalytic material, being the most common SCR catalyst and rather well understood. TiO2 (anatase configuration) represents the washcoat to increase the outer surface but also has the ability to improve the catalyst performance compared to other materials. While V2O5 is known as the active component, WO3 provides a broadening of the temperature window and inhibits deactivation at low sulfur concentrations in the feed. Further information can be found in reviews by Janssen [11] and Busca et al. [12].

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Nomenclature A Bo C Ca Dm Dax d H kj K L mcat N p rj Re Sv t T u V˙ n V w Xi yi z

cross-sectional area (m2) Bodenstein number molar concentration (mol m3) Carberry number ((Cbulk  Cinterphase)/Cbulk) molecular diffusion cefficient (m2 s1) dispersion coefficient in flow direction (m2 s1) diameter (mm) height of catalyst structure (mm) rate constant of reaction j (m3)(1 + a + b) kg mol(a + b) s1 sorption constant (m3 mol1) length of catalytic bed (mm) catalyst mass (g) number of moles, number of tank reactors pressure (mbar) reaction rate of reaction j (mol kg1 s1) Reynolds number (udchar/n) specific geometric surface (m1) time (s) temperature (K) superfical gas velocity (see Table 2) (cm s1) volume flow at 0 8C, 1013 bar (m3 s1) volume (m3) mass fraction conversion of component i volume fraction of component i (ppmv, %(v/v)) differential length in flow direction (m)

Greek symbols difference porosity (% (v/v)) square error modified residence time (g s cm3) stoichiometric coefficient (mol) density (g cm3) variance (s2) Weisz number (1st order assumed: ki,effd2/Deff)

D e j tmod n r s2 c

Subscripts act active calc calculated cat catalyst char characteristic eff effective exp experimental ext exterior (outer) geo geometric in entering the system out leaving the system surf surface Rigid open cell foams, sometimes called sponges in the open literature, have recently been identified as potential catalyst carrier structures and therefore have been investigated intensively. Made from ceramic or metal materials, foams consist of interconnected struts forming cells, which can communicate between each other through windows (see Fig. 3). The pore count expressed in ppi (pores per linear inch) is the main geometric characteristic of foams, but can

Fig. 1. Multifunctional reactor configuration in a gas–particle filter for simultaneous NOx removal and particle filtration as investigated in this study.

only be considered as an orientation. For exact proportions and structural measurements, intensive characterisation is necessary. Ceramic foams typically possess a high void fraction of 75–85% (v/v) and a pore count between 5 and 100 ppi. Because of their irregular but highly porous structure, open cell foams provide very lowpressure drop and fair specific surface. Although honeycombs even have a lower Dp/Sv ratio, foams provide permeability in all flow directions through their tortuous pores and are assumed to cause turbulent flow patterns in cells and around struts. This enhances transport phenomena in comparison to honeycombs due to convective (heat and mass) and radiative (heat) transfer. This may qualify solid foams as a catalyst structure alternative to fixed bed or honeycomb configurations for complex applications in reactor design. Various studies about physical characterisation [13,14], mass [13] and heat transfer [15], pressure drop [16] and applicability [17,18] of foams can be found in the literature. For the low gas velocities and geometries typical of gas–particle filtration (1–5 cm/s, change in flow direction) limited information is available on the performance of catalytic foams. Therefore, the present study aims at elucidating kinetic effects related to the characteristic flow situation inside the multifunctional filter reactor. 2. Experimental 2.1. General procedure The ring-shaped foam sample, which can be placed in the tubular space inside a ceramic candle was investigated as well as a

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Table 1 Relevant values of the different carrier structures used in experiments for kinetic investigations (different pore densities neglected). Particles Material Pore count (ppi) dp or dwindow (mm) t (mm) Sv (m1)

e

Fig. 2. Schematic representation of the different structures investigated in this study and the logical approach (from left: fixed bed of particles, foam for axial flow, foam for radial flow).

particle fixed bed configuration and a cylindrically shaped foam. By comparison, the influence of structural and configurational differences on reaction characteristics can be determined as shown in Fig. 2. Two kinds of experiments can be distinguished: (i) experiments of reaction kinetics in all the structures and configurations and (ii) measurement of residence time distribution (RTD) in foam structures and glass bead packings. Two model reactions were used for the kinetic studies: selective catalytic reduction of NO with NH3 and oxidation of propene with oxygen. Experiments were performed in lab-scale integral reactors. The data achieved allow kinetic studies by using mathematical methods. With an appropriate model, the findings of the RTD measurements can be combined with the kinetic analysis to quantify potential effects of low-velocity conditions on reactor performance. 2.2. Catalyst and support materials The preparation of the catalytic foams and particles in the present study was performed by a multistep wet impregnation method. The suspended (TiO2 in 2-propanol) or dissolved (V2O5 or WO3 in H2O) precursors are put on the supports separately. This way the mass fraction of every single component can be determined by weighing. The contents of V2O5 and WO3 are especially chosen higher (see Table 2) than in industrial application, in order to minimise the fluctuation related to the mass of catalyst and to ensure crystalline vanadia species. As a result of BET surface measurements and reflecting electron microscopy the catalyst layer is 12–40-mm thick and highly porous. Details of the preparation procedure are given in a previous paper [9]. The coating of monolithic foams with active material represents a major problem and is for itself a broad topic. In addition to common wet methods of monolith preparation [19], the

douter (mm) dinterior (mm) H (mm) A (cm2) L (mm) a

– 350–500 – 7200 0.4 15 3a 78 1.9 78

Foam (axial)

30 930 393 1460 0.75 15 – 48 2.0 48

Mullite 20 1187 590 1291

Foam (radial)

30 930 393 1460 0.75 40 10 20 25.1 15

45 638 230 2162

Thermocouple placed inside fixed bed.

deposition of catalyst material by supercritical organometallic CO2 solutions [20] and flame spray pyrolysis [21] has been proposed recently. Three different shapes of catalyst supports were investigated in particular: (i) cylindrical foams in a tubular reactor to be compared with (ii) particles as a typical fixed bed configuration, and (iii) a ring-shaped foam configuration to be placed in the void space of a ceramic filter candle (see Figs. 2 and 3 and Table 1). While the shape of the foam ring is defined by the volume inside an ceramic filter candle, the shapes of the ceramic supports inside the tubular reactor are chosen to accomplish desired conditions for lab-scale kinetic investigations (i.e. high gas velocity or sufficiently high number of pores along reactor diameter). The ring geometry was selected in order to allow the forced change of flow direction (Fig. 1) without major deviation from ideal radial flow. The particles are derived from crushed and sieved foams. Because of the limited information about structural properties provided by the manufacturer the foams were characterised intensively. By a BET method very low inner surface area is detected (<1 m2/g). Hgporosimetry is used to identify the pore diameter distribution and void fraction of the foam. The specific geometric surface is determined by MRI showing comparable small values due to the high voidage [14]. By the analysis of digital images (optical magnification of 25) of foam slices (thickness 4–6 mm) it is possible to statistically evaluate geometric measurements of windows and struts [13]. The characteristic values of the three carrier supports investigated are listed in Table 1. 2.3. Experimental procedures Experiments for studying the catalytic reactions were performed in two different lab-scale reactors, a tubular reactor for

Fig. 3. REM pictures (left: foam structure with relevant geometric attributes; upper center: catalyst layer; lower center: TiO2 (spheres) and V2O5 (needles) inside catalyst layer; right: carrier structures investigated in this study, impregnated with catalyst).

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Fig. 4. Flow scheme of the test rig with axial and radial reactors.

axial flow structures and a specially designed ring-shaped reactor for the radial flow foam. Both are installed in the test rig shown in Fig. 4, in which inlet and outlet concentrations of NO, NH3, N2O, NO2, H2O, C3H6, CO, CO2 are measured by FTIR-spectroscopy. The reactor can be bypassed to analyse the inlet flow. Gas flow and composition are adjusted by mass flow controllers. A pressure indicator connected to different parts of the test rig allows to measure the pressure drop along the reactors or the pressure in the analyser. While the pressure drop in the tubular reactor is measured and taken into account in calculations, it is low and therefore neglected in the radial configuration. The reactors are assumed to be isothermal given the negligible heat production due to the low reactant concentrations. Experiments were performed at the same residence times for each model reaction. Accordingly, inlet volume flows (and thus gas velocities) were adjusted to the catalyst mass fraction of the foam samples. Table 2 shows a list of experimental conditions. For measuring residence time distributions, a test rig was developed that generates a pulse signal of a tracer (Ar) in a carrier fluid (N2) flowing continuously through either foams or glass beads

For the mathematical description of the catalytic reaction systems examined, three different models are applied: plug flow reactor model, dispersion model, tanks-in-series model. The conversion of each model compound (NO, C3H6) is used as a characteristic value. Assuming a constant volume flow, conversion can be calculated from measured concentration values (Eq. (1)). Xi ¼

C i;in  C i;out C i;in

(1)

Axial

Radial

0.21/0.53 4.0/5.7 21.7/33.0

0.5–2.1 3.4–6.7 20.4–87.9

X @C i ¼ divðu  C i Þ þ divðDi  gradC i Þ þ nij r j rcat @t j

Particles

1.0 6.3 67.0

2.4. Data analysis

For describing a SCR reactor, a one-dimensional, pseudohomogenous model is used. Thus, starting from the general differential mass balance containing mass flows of convection, diffusion and reaction (Eq. (2)), simplifications are made leading to a system of ordinary differential equations.

Table 2 Values of different catalyst structures and experimental conditions.

mcat (g) wcat (% (w/w)) rcat (kg/m3) wTiO2 =V2 O5 =WO3 (% (w/w))

placed in a steel tube (dint = 16 mm, L = 400 mm). The concentration of Ar was monitored continuously (Dt <100 ms) with a thermal conductivity detector. The volume of the tracer pulse was chosen to be small compared to the tube volume (Vtracer = 3 ml <0.01 Vtube). Experiments were performed with different foam or glass bead fillings and velocities equivalent to the reaction experiments. Experimental errors increased with higher velocities because of the shorter average residence time.

Foam

66/11/23

(2)

Assuming steady state, isothermal conditions and plug flow behaviour (no dispersion in flow direction), the mass balance can be simplified to the following equation (Eq. (3)).

yNO,in (ppmv) yNH3 ;in (ppmv) yO2 ;in (% (v/v)) T (8C) tmod (g s/cm3) u (cm/s)

10.4–36.1

500 500 3 150–320 0.02/0.035/0.05 3.0–13.1

yC3 H6 ;in (ppmv) yO2 ;in (% (v/v)) T (8C) tmod (g s/cm3) u (cm/s)

– – – – –

300 3 220–340 0.035/0.05/0.07 2.2–5.3 0.3–1.0

0.5–3.8

˙

Vð p;TÞ mcat mcat ¼ mTiO2 þ mV2 O5 þ mWO3 ; rcat ¼ mVcat ; t mod ¼ Vð0 ˙  C; 1 atmÞ; u ¼ A .

dC i 1 X ¼ n rr u j ij j cat dz

(3)

where rcat is the catalyst density defined as the ratio of the active mass of the catalyst and the reactor volume occupied by the catalyst and u as gas velocity at reaction conditions. The latter is constant for axial configuration while in radial configuration it

S. Zuercher et al. / Applied Catalysis A: General 357 (2009) 85–92

the NO reduction is described by Eq. (14).

increases with decreasing flow area. axial : u ¼ ua ¼ const: r out radial : u ¼ uðrÞ ¼ uout  r

(4) (5)

The reaction rate rj describes the rate of change of one component over time and is based on the active mass of the catalyst. rj ¼

1



dNi

(6)

ni j mcat  dt

If dispersion in flow direction cannot be ruled out, the general mass balance (Eq. (2)) cannot be simplified as described above. There is an analytical solution only for first order reactions [22], which is the reason for selecting the propene oxidation as a model reaction, in addition to NO reduction. For the solution, the boundary conditions of Danckwerts (Eqs. (7) and (8)) are used considering dispersion in flow direction at the inlet and no reaction (no concentration gradients) and convection dominating transport at the outlet of the reactor (Eq. (9)). The Bodenstein number is defined by Eq. (11) with Dax determined through residence time distribution experiments (see Section 3.3).   dC i u  C i;in ¼ u  C i ð0Þ  Dax  (7) dz 0   dC i ¼0 dz L

X i;calc ¼ 1  ¼1

(8)

Ci C i;in 2

Bo ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4  k j  t mod 1þ Bo

(10)

uL Dax

(11)

For NO reduction, with an order different from one (Eq. (14)), the general mass balance with dispersion in flow direction cannot be solved analytically. Solving the 2nd order nonlinear inhomogeneous differential equation numerically is complex. Approaches with a substitution method using MATLAB and a prepared solver implemented in COMSOL showed no definite results and were therefore not considered as appropriate procedure. As an alternative, it is possible to describe the system with a tanks-in-series model. If backmixing is low (Bo > 50 for dispersion model), the tanks-in-series model is equivalent to the dispersion model, if half the Bodenstein number is used as number of tanks. The conversion can be calculated by Eq. (12). X i;calc ¼ 1 

C NH3  C 0:22 1 þ K NH3 C NH3 O2

(14)

Oxidation of ammonia is described by a simple first order reaction rate (Eq. (15)). The oxidation of propene is also considered to be of first order as the dependence of oxygen can be neglected due to a large excess. The temperature dependence is described by the Arrhenius law (Eq. (16)) which is used to determine the frequency factor k0,j and the activation energy EA,j of the reactions considered. r j ¼ k j ðTÞ  C i

j ¼ NH3 -Ox:; C3 H6 -Ox:;

  EA; j k j ðTÞ ¼ k0; j  exp  RT

i ¼ NH3 ; C3 H6

(15)

(16)

For modelling the residence time experiments, the tracer signal is represented as a pulse function. This leads to an outlet signal which can be directly transformed to a function for the residence time distribution. The characteristic backmixing in the reactor is calculated using the dispersion model by regarding the difference of the whole system and the bypass (see example in Fig. 5). The difference of the variances is made dimensionless, dividing by the mean residence time. For low backmixing (Bo > 50), a direct correlation between this dimensionless variance and the Bodenstein number exists (Eq. (17)) [25]. Using the definition of the Bodenstein number (Eq. (11)), the dispersion coefficient in flow direction is calculated.

Ds 2 2 t¯

¼

s 2total  s 2bypass 2 ðt¯total  t¯bypass Þ

¼

2 Bo

(17)

ð1 þ a j Þ  expða j =2  BoÞ  ð1  a j Þ  expða j =2  BoÞ (9)

aj ¼

0:67 r SCR ¼ kSCR ðTÞ  CNO 

Ds 2Q ¼

4  a j  expð1=2  BoÞ 2

89

Ci 1 ¼1 N C i;in ð1 þ ðk j  t mod Þ=NÞ

The models presented in this section are treated numerically with MATLAB routines. The calculation of the conversion using the plug flow model is done in MATLAB by using a differential equation solver (ode23s, modified 2nd order Rosenbrock equation). For each temperature, kinetic parameter values are determined by a nonlinear least-square regression algorithm. An Arrhenius-type regression using the calculated kj(T) values determines the activation energy and the frequency factor for the reaction. For the NO reduction, the parameter values are determined considering conversion of NO and NH3 up to 300 8C. The parameter values of the NH3-oxidation are taken from a previous study [9]. The mean relative error is calculated from the sum of the deviations of calculated and experimental conversion divided by

(12)

with N

Bo 2

(13)

The SCR reaction system is represented by two reactions of the components NO and NH3. The reaction rate is based on a mixed Eley-Rideal power law equation by Lintz and Turek [23], with the use of power values by Hackel [9]. As a result, the reaction rate for

Fig. 5. Residence time distribution of the bypass and the whole system (left), schematic of the sampling points (right), curves calculated with equation by Danckwerts [24], example: 30 ppi foam filling measured at u = 2 cm/s.

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90

Table 3 Kinetic parameters determined by regression analysis (SCR, C3H6-oxidation) or taken from a previous study (NH3-oxidation [9]).

SCR NH3 oxidation C3H6 oxidation a

Fig. 6. Conversion of NO (gray) and C3H6 (black) measured in particle fixed bed configuration at different modified residence times (operating conditions see Table 2; curves calculated with plug flow model (PFR)).

the number of data points used for the regression procedure. P

j¼

t

nexp jXð mod ; TÞcalc

 Xðt mod ; TÞexp =Xðt mod ; TÞcalc j nexp

(18)

3. Results and discussion

EA (kJ/mol)

k0 (m3/(kg s)a

KNH3 (m3/mol)

j (%)

51.4 160.0 64.5

3.94  106 2.04  1011 3.19  107

4.63  103

6.7 4.1

(m3)1.89/(kg mol0.89 s) for SCR.

configuration concerning criteria generally used (Carberry Ca < 1.3  105, Weisz-Prater C  0.6 and Bodenstein Bo > 100, Table 4). This allows the determination of intrinsic kinetic parameters. Experiments with foams as catalyst support were performed at the very same conditions as with a particle fixed bed. Because of the preparation method, the weight fraction of catalyst and therefore the layer thickness varied slightly. In order to achieve the same modified residence times, the gas velocitiy was adjusted in foam experiments (see Table 2). The results show only a negligible difference in NO conversion measured (see Fig. 7). Temperatures above 300 8C are not shown in the figure because complete conversion is achieved at very low residence times. An analogous behaviour was found for the oxidation of propene. As a result of these findings, kinetic parameters could be determined by regression analysis with data from both foam and particle fixed bed experiments using the plug flow model (see Table 3). A comparison with calculated rates derived from the literature shows that parameter values are in the range of earlier findings (compare, for example [23,26,27] for SCR and [26,28] for propene oxidation).

3.1. Catalytic conversion in axial configuration (foam and particles) 3.2. Catalytic conversion in radial configuration (foam) The conversion of the model compounds NO and C3H6 measured in the fixed bed configuration at different residence times and temperatures is shown in Fig. 6. Complete conversion can be achieved at around 300 8C for NO and 400 8C for C3H6, respectively. Above 300 8C the rate of the simultaneous NH3 oxidation becomes significant and causes decreased NO conversion. Curves are calculated with the plug flow model described above. This seems justified because no mass transfer limitations or backmixing occurs in the fixed bed

Foam experiments in radial configuration exhibit the conditions inside a ceramic filter (low gas velocities due to the higher cross-sectional area, change of flow direction). Firstly, experiments were performed to examine the possible influence of the pore density. As a result, no significant effect could be found in the experimental NO conversion (see Fig. 8). The absence of an effect of higher surface, thinner catalyst layer or hydrodynamic diameter may be explained by the lack of transfer limitation.

Fig. 7. Measured NO conversion with () foam (axial) and (*) particle fixed bed vs. modified residence time for different temperatures (from bottom 150, 180, 220, 260, 300 8C), curves calculated with plug flow model (PFR), parameter values: Table 3.

Fig. 8. Measured NO conversion vs. modified residence time in radial foams of different pore densities at (from bottom) 150, 180, 260, 300 8C (: 20 ppi; ^: 30 ppi; &: 45 ppi, curves calculated with plug flow model (PFR) validated in axial configuration), parameter values: Table 3.

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91

3.3. Residence time distribution measurements

Fig. 9. Propene conversion vs. modified residence time, measured in radial configuration (*), curves: plug flow model (PFR) validated in axial configuration at (from bottom) 220, 250, 280, 310, 340 8C. Parameter values: Table 3.

Secondly, the experimental results of the radial configuration are compared with the results of the axial configuration represented by calculated curves using the plug flow model and the kinetic parameters mentioned before. Measured conversion values in the radial configuration are slightly higher at low temperatures (<180 8C) but systematically lower at high temperatures (>260 8C) and therefore high conversion. The latter may be attributable to backmixing caused by diffusion along concentration gradients (given the low gas velocities applied) or the forced change of flow direction (possibly leading to uneven flow through the structure). Experiments with reverse flow (from inside to outer surface) showed identical results, indicating that the forced change of flow direction may be less significant. Measured conversion values of the propene oxidation in radial configuration experiments exhibit an analogous effect (Fig. 9). Calculated curves using kinetic parameters determined from axial experiments (Table 3) exhibit higher conversion values than those measured in the radial configuration.

As no effect on integral conversion was found by comparing axial foams and particle fixed beds of the same geometry, but a decrease of conversion comparing axial and (low velocity) radial configuration residence time distribution measurements were performed to identify possible backmixing effects in foam and particle bed structures. No data of residence time distribution experiments with foams at similar conditions can be found in the literature. Therefore, glass beads with a similar characteristic diameter (d = 1.4 mm) were chosen for comparison. For packings of spherical particles, many data and empirical correlations are known. The correlation of Wen and Fan [29] (Eq. (19)) is used in the present study to give a first quantification of dispersion coefficients for solid foams assuming that the dependency is similar in the narrow range of low Reynolds numbers. The results of the residence time distribution experiments are summarised in Fig. 10. The experimental results show that dispersion coefficients in solid foams are generally higher than in glass bead packings. For foams with a pore density of 30 and 45 ppi, nearly identical results are found. The data is used to determine the parameter values for the given correlation (Eq. (19)). 20 ppi foams show the lowest Dax values of the tested foams, but still higher than in glass bead packings. Understanding the different behaviour of the different pore-size foams will require further investigation.

(19) 3.4. Reactor modelling including backmixing The reactor for SCR was calculated using the tanks-in-series model whereas for propene oxidation a dispersion model was applied as described in Section 2.4. For both models, the dispersion coefficient for a reaction temperature of 300 8C is extrapolated from the conditions applied in residence time distribution measurements (20 8C), in analogy to a molecular diffusion coefficient (Eq. (20)). The Bodenstein number is calculated using the values listed in Table 4. Applying these models and the intrinsic kinetic parameters from the axial experiments, mixed flow reactor models are created, which represent the experimental data well, as shown in Fig. 11. Dm ðTÞ ¼ Dm ðT 0 Þ 



T T0

1:75

(20)

The calculated conversion of NO and C3H6 is decreasing with increasing backmixing behaviour. The plug flow model without any dispersion in flow direction represents the experimental data of the axial configuration (see Section 3.1). The perfectly mixed

Table 4 Values for determination of dispersion coefficients at 300 8C and 1 bar.

a

Fig. 10. Dispersion coefficient vs. dimensionless Reynolds number as comparison of backmixing behaviour of foams (&: 20 ppi; *: 30 ppi; : 45 ppi) and (^) fixed bed of glass beads (d = 1.4 mm), curves calculated with empirical correlation (Eq. (19), see text), T = 20 8C.

dchar (mm) L (cm) Dm (m2/s) u (m/s) Dax (m2/s) Bo a

Particles (axial)

Foam (radial)

500 5 7.5  105 0.22 3.782  105 291

1323 1.5 7.5  105 8.12  103 2.521  105 4.83

For foams dchar = dw + t, according to [30].

92

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model) describe the experimental data of the low-velocity radial configuration well. Effects of the forced change of flow direction in the ring-shaped foam sample could not be singled out. Conversion experiments in axial configuration under conditions of significant backmixing could not be performed due to insufficient flexibility in foam and reactor geometry. Overall, the application of foams as structured catalyst inside a filter candle is shown to be attractive. The approach used in this study may offer opportunities for studying other applications, e.g. in cases where intrinsic reaction kinetics are known. Future work will include experimental and mathematical investigations of Ptcoated foams for the simultaneous oxidation of VOC and NOx reduction inside a catalytic filter. Especially the effect of the presence of VOC on SCR kinetics and the reaction pathways of NH3 and NO over Pt catalyst will be of interest. On the basis of kinetic calculations, the optimum operating conditions (temperature, mcat, etc.) of this configuration should be estimated. Acknowledgments Fig. 11. Comparison of calculated and experimental data of NO reduction (gray, filled symbols, tmod = 0.035 g s/cm3) and C3H6 oxidation (black, empty symbols, tmod = 0.07 g s/cm3) at different levels of dispersion. (*) axial, (&) radial configuration; (—) ideal plug flow (Bo = 1) (- -) mixed flow (Bo = 5.37 doe C3H6 oxidation, NCSTR = 3 for SCR) (. . .) perfectly mixed flow (Bo ! 0 or N = 1).

flow model (tanks-in-series model, N = 1) shows the maximum effect that can be caused by backmixing. The mixed flow models represent the experimental data of the low-velocity radial foams well, using dispersion coefficients from the own separate experiments. Although the dispersion coefficient is typically low and almost only dependent on diffusion (see Table 4 and Eq. (19)) this procedure represents the effect of backmixing in foams inside a filter candle, i.e. at typical low-velocity conditions. This approach may be generally applied to other gas reactions with catalytic foams. 4. Conclusion and outlook Experiments in a lab-scale tubular reactor were found to be suitable for studies of intrinsic reaction kinetics in the present case. In these experiments, the structure of the catalyst support (particles, foam) showed no effect on conversion for the model reactions chosen (NO reduction via SCR, propene oxidation). Experiments with foams in radial configuration, as applied in gas filtration with typically low gas velocities, showed systematically lower conversion values. Here, backmixing by the forced change of flow direction and diffusion becoming effective due to low gas velocities is assumed to be the cause. Mathematical reactor models could be established for the two model reactions, assuming a perfect plug flow for the high velocity conditions used in axial flow experiments. Kinetic parameters were determined by regression analysis showing a good agreement with experimental results. A procedure was developed to measure residence time distributions at a wide range of velocities and in different structures (glass beads, foams). Using the dispersion model, the effect of backmixing could be quantified. The dispersion coefficient found in foam experiments is generally higher than those in glass bead packings. The empirical correlation by Wen and Fan [29] was applied to represent the relationship found between dispersion coefficient and Reynolds numbers. The effect of pore size in foams on backmixing behaviour will need further investigation. Combining these findings, reactor models that take into account the backmixing represented as dispersion in flow direction were established. Mixed flow models (tanks-in-series and dispersion

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