Kinetic Modeling of Nitrate Reduction Catalyzed by ... - ACS Publications

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Kinetic Modeling of Nitrate Reduction Catalyzed by Pd−Cu Supported on Carbon Nanotubes Olívia Salomé G. P. Soares,† Xiaolei Fan,‡ José J. M. Ó rfaõ ,† Alexei A. Lapkin,*,‡ and Manuel Fernando R. Pereira*,† †

Laboratório de Catálise e Materiais (LCM), Laboratório Associado LSRE/LCM, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal ‡ School of Engineering, University of Warwick, Coventry CV4 7AL, U.K. ABSTRACT: New catalysts for liquid phase nitrate reduction were recently synthesized using multiwalled carbon nanotubes as a support. The most promising catalyst (1%Pd−1%Cu/CNT) was used in this kinetic study. The catalyst experimentally showed high selectivity to nitrogen, between 80 and 89%. A mechanistic model was applied to experimental data to show that the reason for high selectivity is due to the enhanced mass transfer near the catalyst surface promoted by the use of a structured support. The experimental selectivity is close to the maximum selectivity predicted for this catalytic system at the best operating conditions and in the absence of any mass transfer limitations.

1. INTRODUCTION The removal of nitrate from drinking water is necessary in order to protect the environment and human health from longterm pollution from excessive use of fertilizers in agriculture. The main harmful action of nitrate to human health is through conversion into nitrite in vivo. The latter ion may cause various diseases: blue baby syndrome, cancer, or hypertension. In 1989, Vorlop and Tacke1 reported for the first time that nitrate could be reduced to nitrogen over bimetallic catalysts in the presence of a reducing agent. The mechanism of catalytic nitrate reduction by bimetallic catalysts is believed to be associated with the formation of bimetallic active sites on the catalytic surface, which has been experimentally proven,2−5 and the redox reaction between the nitrate and the promoting metals (promoting metals are regenerated by hydrogen adsorbed on noble metal).6,7 Nitrate reduction can be described by consecutive and parallel reactions where nitrate is reduced to nitrite, which is then converted to nitrogen as the main product and ammonium as the undesired byproduct.8,9 This process is strongly influenced by several factors, such as operating conditions, composition, the nature of active metallic phases, and also by the catalyst support.4,10−13 When carbon materials are used as supports the monometallic catalysts were found to be inactive, making necessary the presence of a second metal in order to obtain high nitrate conversion.14 The bimetallic catalysts are usually composed of a noble metal, mainly Pd, Pt, or Rh, and a promoter metal such as Cu, Sn, Ag, Ni, Fe, or In on different supports (i.e., alumina,15,16 silica,17,18 titania,19,20 activated carbon,21,22 niobia,23 hydrotalcites,24,25 ceria,26,27 tin oxide,28,29 and polymers30,31). Among them, Pd−Cu seems to be the most effective catalytic system, but still inadequate in terms of selectivity to nitrogen, the latter being the main obstacle to commercial uptake of this technology.7,14,32,33 In the majority of studies on the reduction of nitrate with hydrogen, nitrite was found fully consumed at the completion of the reaction. However, the products distribution (N2, NH4+, © 2012 American Chemical Society

and NOx) varies depending on the catalytic system used. To avoid generation of secondary pollutants either in the liquid phase (NO2− and NH4+) or in the gas phase (NOx), it is important to understand key steps of the reaction mechanism controlling the selectivity of formation of ammonia and nitrogen. The problem of selectivity is understood to be related to diffusion limitations in the catalytic systems tested to date. The solution to the problem of internal diffusion resistance is currently sought by employing either new catalyst supports such as carbon nanotubes/nanofibers12,34,35 or new reactor types, such as catalytic membrane reactors.7,36 The problem of designing new catalysts with intrinsically high selectivity to nitrogen has been being investigated by numerous experimental studies.2,16,37−45 The two problems are intricately linked, as selectivity in the reaction depends simultaneously on the surface reaction mechanism and on the mass transfer, which affects the population of surface species. The interplay of reaction kinetics and mass transfer could be studied by detailed mechanistic modeling. However, very few models were developed for understanding the mechanism of catalytic reduction of nitrate. Recently we have published a mechanistic model of catalytic nitrate reduction, which was shown to be accurate for Pd−Cu/Al2O3 and Pd−Sn/Al2O3 bimetallic catalysts.46 In this study we used this model to describe kinetics and mass transfer phenomena in the reaction of nitrate reduction catalyzed by Pd−Cu supported onto carbon nanotubes. The results seem to confirm our earlier predictions of the importance of mass transfer near the solid catalyst surface on the reaction selectivity. Received: Revised: Accepted: Published: 4854

December 16, 2011 February 29, 2012 March 8, 2012 March 8, 2012 dx.doi.org/10.1021/ie202957v | Ind. Eng. Chem. Res. 2012, 51, 4854−4860

Industrial & Engineering Chemistry Research

Article

2. EXPERIMENTAL SECTION 2.1. Catalyst Preparation. A sample of commercial multiwalled carbon nanotubes Nanocyl-3100 (CNT) was used as a support. The bimetallic Pd−Cu catalyst was obtained by incipient wetness coimpregnation using aqueous solutions of the corresponding metal salts (PdCl2, Cu(NO3)2). The target amount of each metal was maintained constant at 1 wt %. After impregnation, the catalyst was dried at 373 K for 24 h, heat treated under a nitrogen flow at 473 K for 1 h and finally reduced at 373 K under hydrogen flow for 3 h. Under these preparation conditions the formation of alloys is avoided.12 2.2. Catalyst Characterization. Details of the structural characterization of catalysts from N2 adsorption isotherms obtained at 77 K, TEM, XPS, and ICP are described elsewhere.35 2.3. Kinetic Experiments. The reduction of nitrate was performed at atmospheric pressure using a semibatch reactor, equipped with a magnetic stirrer and a thermostatic jacket. The temperature was varied between 283 and 313 K. The reactor was filled with 790 mL of deionized water, and 400 mg of catalyst was added. Then, the magnetic stirrer rate was adjusted to 700 rpm, and the gas mixture of hydrogen and carbon dioxide (total flow rate = 200 cm3 (STP) min−1) was passed through the reactor for 15 min to remove air; the role of CO2 is that of a pH modifier (pH = 5.5). The hydrogen flow was varied in the experiments between 50 and 150 cm3 (STP) min−1. After that period 10 mL of a nitrate solution prepared from NaNO3 was added to the reactor in order to obtain an initial NO3− concentration in the range of 50−200 mg L−1 (0.823−3.23 mol m−3). Preliminary studies were carried out varying the stirring rate to check that under the selected conditions there was no external diffusional limitation. The concentrations of both nitrate and nitrite ions were followed by HPLC using a Hitachi Elite Lachrom apparatus equipped with a diode array detector. The stationary phase was a Hamilton PRP-X100 column (150 mm × 4.1 mm) working at room temperature, under isocratic conditions. The mobile phase was a 0.1 M solution of NaCl:CH3OH (45:55). The concentration of ammonium ions was determined by potentiometry using a convenient ion-selective electrode using a standard method. The error of ammonia concentration measurement was within 2%. The accuracy of ammonia determination by an ion-selective electrode was validated against ionic chromatography, using a Dionex IonPac CS12A column. A maximum random relative error between the ion selective electrode and the ionic chromatography of ±7% was observed. The systematic effect of Na+ on the electrodes was eliminated by using a Na-containing matrix for calibration. pH values were also measured. Catalyst performance was evaluated by calculating the nitrate conversion (XNO3−) and the selectivities to nitrite (SNO2−) and ammonium (SNH4+) as nNO− − nNO− 3i 3 XNO− = 3 nNO− (1) 3i SNO− = 2

SNH+ = 4

where nNO3−i is the initial amount of nitrate (mmol) and nNO3−, nNO2− and nNH4+ are the amounts of the respective species (mmol) at time t (min). The selectivity to nitrogen (SN2) was calculated by difference. 2.4. Model Development and Software. The mechanistic model was constructed on the basis of a literature mechanism, integrating liquid-phase equilibrium, effect of pH, mass transfer steps, and surface reactions. The tentative elementary surface reactions for the catalytic hydrogenation of NO3− over bimetallic catalysts were extracted on the basis of a critical discussion of the literature data; the reader is directed to ref 46. The formation of N2O has not been incorporated into the reaction mechanism proposed because no N2O formation was observed over the catalytic systems investigated in this study. The model was coded in MATLAB 7.11. The coupled differential equations describing the catalytic system of nitrates hydrogenation were integrated using MATLAB ODE15s for stiff systems. The function lsqnonlin in MATLAB 7.11 was used with lower and upper bounds for fitting the kinetic model with the experimental data to estimate the kinetics parameters. Initial values33,37 or guesses of each kinetic parameter and gasto-liquid mass transfer coefficient were needed for predicting the best estimates. To increase the accuracy of the developed model for generating independent results in practice, a cross-validation method was used to train the model and obtain model parameters based on the limited experimental validation data sets. The leave-one-out cross-validation method (LOOCV) was used in the present study to avoid wasting the experimental data. LOOCV method involves using a single datum from the available experimental data set as the validation data, and training the model on the remaining experimental data. The LOOCV evolutions were repeated such that each datum in the experimental data set was used once as the validation data and the mean squared error (MSELOOCV) was calculated for summarizing the errors.

3. RESULTS AND DISCUSSION 3.1. Catalysts Characterization. The catalyst was characterized in detail elsewhere35 and only a brief summary of the characterization data is shown in Table 1. Table 1. A Summary of Catalyst Characterization Data units

method

m2 g−1 m2 g−1 nm

N2 adsorption N2 adsorption HR-TEM ICP

XPS

3.2. Kinetic Experiments and Model Simulation. The developed mechanistic model was first fitted to the data obtained from the experiments of varying reaction temperature. Figure 1 shows changes in the concentrations of nitrate, nitrite, and ammonia versus time at three different temperatures. Analysis of Figure 1 shows that nitrate was continuously consumed over the reaction course. The nitrite concentrations increased slightly first and reached a maximum, then decreased gradually to zero at the end of the process. The ammonia

(2)

nNH+ 4

− nNO− nNO− 3i 3

value 320 301 2 0.95% Pd 1.08% Cu 0.88 Pd:Cu 0.89 Pd:Cu

metal distribution

nNO− 2 nNO− − nNO− 3i 3

parameter support BET surface area catalyst BET surface area node of particle size distribution metal distribution

(3) 4855

dx.doi.org/10.1021/ie202957v | Ind. Eng. Chem. Res. 2012, 51, 4854−4860


Article

activation energies of the elementary reactions are based on different metals. For instance, Oh et al.49 and Root et al.50 reported a value of 87.9 kJ mol−1 for N2 formation reaction k3

(N* + NO* → N2 ) on Rh; the values of Ea of the first step of ammonia formation were calculated by DFT method as 90.7 kJ mol−1 on Pt51 and ca. 115 kJ mol−1 on Ru;52−57 126 kJ mol−1 on Pt51 and 124 kJ mol−1 on Ru52,58 were reported for the consecutive hydrogenation of NH*. As shown in Figure 1, the reaction temperature has a significant effect on the catalytic reduction of nitrate. The rate of nitrate ion consumption and the rate of ammonia generation were found to increase with the increase in temperature. The reaction profiles of nitrite formation show no significant difference upon varying temperature in the range between 283 and 313 K. The calculated selectivities to nitrogen/ammonia were compared with the measured data as shown in Figure 2a. The simulated results coincide with the experimental study over the Pd−Cu_CNT catalyst. Ammonia formation is accelerated at elevated temperatures and therefore a relatively high selectivity to ammonia at high temperatures was predicted. The influence of temperature on selectivity can be explained by examining the population change of surface species with temperature. As proposed in the reaction mechanism,46 the formation of nitrogen via desorption-mediated reaction needs to pair two N-containing surface species, namely N* and NO*. The selectivity to N2 is therefore a function of the surface coverage of the two N-species on Pd monometallic sites. In the case of ammonia, its formation is controlled by the probability of pairing N* with the reductant (H*) on the Pd surface. As a result, the selectivity to N2 can be related to the ratio of the surface coverages of NO* to H* on Pd active sites (θNO*:θH*). It can be seen that the ratio of θNO* to θH* at different temperatures (Figure 2b) can be reflected by the trends of nitrogen selectivity in Figure 2a. The simulation results from the present model confirm the hypothesis of the effect of Nspecies-to-reductant (H) ratio on selectivity made by Prüsse and Sá.11,59 The fairly good selectivities to N2 (ca. 80% in the temperature-dependence experiments) observed for the Pd− Cu catalyst supported on carbon nanotubes can be related to the textural properties of the carbon nanotubes, that is, high mesoporous surface area (320 m2 g−1) and the absence of microporosity, which minimize mass transfer limitations and consequently the over-reduction of nitrite to ammonia.12 Previous work shows that the catalysts supported on carbon nanotubes are more selective to nitrogen than the catalysts supported on activated carbons (max 78% vs max 46%), which have an extensive microporosity.12 In the process of fitting the experimental data (obtained over the Pd−Cu_CNT catalysts) with the mechanistic model, the volumetric mass transfer coefficients were calculated to be up to two orders-ofmagnitude higher than those estimated in the modeling study of the catalysts employing conventional supports,46 indicating that the intensified rates of mass transfer in the catalytic reduction of nitrates were achieved by employing the fibrous support. The effect of H2 partial pressure was studied by varying the H2 flow-rate in the reducing gas feed stream. The kinetic parameters for the Pd−Cu_CNT catalyst (Table 2) were used to predict the effect of H2 partial pressure and of the initial nitrate concentration on the reaction. The model simulation results of reaction profiles and nitrogen selectivity coincide well

Figure 1. Fits of mechanistic model to the experimental data (variation of temperature) of the Pd−Cu_CNT catalyst (PTotal = 1 bar, PH2:PCO2 = 1:1). Symbols correspond to experimental data: circle, nitrate; square, nitrite; triangle, ammonia; lines denote model results.

concentration profiles are characteristic of a consecutive mechanism. This reflects the currently accepted mechanism in which nitrite is an intermediate product and ammonia is not formed directly from the nitrate. The kinetic rate constants were obtained from fitting the data of the temperature-dependence experiments. The kinetic parameters for the Pd−Cu_CNT catalyst were calculated based on Arrhenius equation and are shown in Table 2. The Table 2. Kinetic Parameters and Coefficient of Determination (R2) for Catalytic Reduction of Nitrates over the Pd−Cu_CNT Catalyst Calculated from Model Training elementary surface reactionsa kNO− 3

−# NO−# 3 ⎯⎯⎯⎯⎯⎯⎯→ NO2

rate laws

Ea (kJ mol−1)

ln(A/mol m−3 min−1)

R2

kNO3−

64.9

28.5

0.989

k1, k2

23.7

12.4

0.923

k3

52.6

24.6

0.977

k4

79.7

34.9

0.995

k5, k6

72.5

31.6

0.991

k1

NO− 2 * + H* → NO* k2

NO* + H* → N* k3

N* + NO* → N2 k4

N* + H* → NH* k5

NH* + H* → NH2* k6

NH2* + H* → NH3* a The number symbol (#) represents the Pd−Cu bimetallic site; the asterisk (∗) represents the Pd monometallic site.

cross-validation fitting process increased the accuracy of parameter estimation and also ensured that the model is independent of the data that were used to train the model for subsequent simulations. The mean squared errors, MSELOOCV, could be obtained for each component, i.e. nitrate, nitrite and ammonia, in the reaction system (MSELOOCV reflects the expected level of fit of the developed model to other data set). The kinetic parameters of the elementary reactions are few in the literature. For the first step of ammonia formation via N* k4

hydrogenation (N* + H* → NH*), the activation energy was obtained as 128 kJ mol−1 on Pd by DFT calculations.47,48 The predicted value obtained by the present model is 79.7 kJ mol−1, which is smaller than the literature data. Other reported 4856



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Figure 2. Model simulation results of the effect of temperature on (a) selectivities and (b) the ratio of surface NO to surface H on the Pd−Cu_CNT catalyst. Symbols correspond to experimental data: circle, nitrate; triangle, ammonia; lines denote model results.

Figure 3. Simulation results of the effect of H2 partial pressure on the reaction. (a) The mechanistic model fit to the experimental data obtained over the Pd−Cu_CNT catalyst. Symbols correspond to experimental data: circle, nitrate; square, nitrite; triangle, ammonia; lines denote model results. (b) Comparison of model predictions of nitrogen selectivity with experimental data for the Pd−Cu_CNT catalyst.

Figure 4. Model predictions of (a) variation of H* and N* surface coverages with H2 partial pressure and (b) the rates of nitrogen and ammonia formation for the Pd−Cu_CNT catalyst.

min) dropped by 10% (Figure 3b) after increasing H2 partial pressure from 0.25 to 0.75 atm, indicating the influence of fast ammonia formation on the distribution of products under H2 concentrated atmosphere. Experimental investigation with the Pd−Cu_CNT catalyst shows that the increase in the initial nitrate concentration has a positive influence on the selectivity to N2, which is consistent with the results from the experimental study with the Pd(5 wt %)−Cu(0.6 wt %)/AC catalyst by Mikami et al.60 and our earlier simulation results obtained with other catalytic systems46 (see Figure 5). It is reasonable to assume that the observed trend is correlated with the density of N-species on the surface becoming high with the increase in nitrate concentration (Figure 6), leading to the increased probability of combining N* with NO* to form N2 via the desorption-mediated reaction.

with the experimental data, as shown in Figure 3. In Figure 3a, one can see that the increase in the H2 partial pressure caused the faster reaction rates of nitrate consumption and ammonia formation. The mechanistic modeling revealed that a substantial increase in the H* surface population was caused by the increase in H2 partial pressure and no significant effect of H2 partial pressure on N* was found (Figure 4a). As described earlier, the consecutive hydrogenation of surface N is the reaction path to forming ammonia. Therefore, the increase in H* on monometallic Pd sites enhances the possibility of binding with N* thus favoring the steps of hydrogen addition to form ammonia. As can be seen in the model prediction of the reaction rates in Figure 4b, the rate of ammonia formation is enhanced considerably more than that of the nitrogen formation. The selectivity to N2 at the end of the reaction (300 4857



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Figure 5. Simulation results of the effect of initial NO3− concentration on the reaction over the Pd−Cu_CNT catalyst. (a) The mechanistic model fit to the experimental data. Symbols correspond to experimental data: circle, nitrate; square, nitrite; triangle, ammonia; lines denote model results. (b) Comparison of model predictions of N2 selectivity with the experimental data.

Figure 6. Model predictions of the effect of initial substrate concentration on (a) N* surface coverage and (b) the ratio of surface NO to surface H on the Pd−Cu_CNT catalyst.

L−1), the present catalytic system shows considerable progress in attaining a highly selective conversion of nitrate.

The best performance obtained with the current Pd− Cu_CNT catalyst was 89.1% N2-selectivity and 1.2 mg L−1 of NH4+ at the end of reaction under the conditions of T = 298 K, P = 1 atm (FH2 = FCO2 = 100 cm3 (STP) min−1), C0 = 200 mg L−1 (see Figure 5). Using the developed model we optimized process conditions for the maximum of selectivity to nitrogen. From the model prediction, under the optimal reaction conditions of reaction temperature 283 K and initial substrate concentration of 250 mg L−1, the selectivity to N2 can be pushed to ca. 94%. Further increase in initial concentration would result in higher selectivity; however, this would not have any practical significance. Another reason behind the apparent good performance of the synthesized Pd−Cu_CNT catalyst in terms of nitrogen selectivity is the use of the optimum Cu/Pd ratio (w/w) of 1.0. In a previous work12 it was found that the maximum activity is obtained for an atomic ratio of noble metal to copper close to 1 and that the selectivity to nitrogen increases with the atomic copper content in the bimetallic particles up to around 75%. Ilinitch et al.7 and Gao et al.20 conducted experimental studies with Pd−Cu/γ-Al2O3 and Pd−Cu/TiO2 catalysts, respectively, for investigating the effect of the Cu/Pd ratio on the catalytic performance in terms of NO3− conversion and N2 selectivity. They found that an optimum Cu/Pd ratio is in the range 0.5− 1.0. These experimental results are in good agreement with the simulation results obtained by Fan et al. based on simulation of the current mechanistic model.46 Although the obtained value of ammonia concentration, 1.2 mg L−1, exceeds the maximum allowed ammonia concentration in drinking water (0.5 mg

4. CONCLUSIONS Pd−Cu catalysts for nitrate reduction were recently synthesized on the basis of multiwalled carbon nanotube supports. As was hypothesized earlier and confirmed by experimental results and the results of the simulated mechanistic model, the markedly improved mass transfer at the catalyst surface in the case of the new carbon nanotubes-supported Pd−Cu catalysts resulted in a significant increase in the desired selectivity to nitrogen. Detailed kinetic study of the effects of reaction temperature, hydrogen flow rate, and partial pressure, and the initial nitrate concentration was undertaken and its results were also fitted by the detailed mechanistic model. There is an excellent agreement between experimental results and the model, confirming the validity of the current mechanistic understanding of the reaction. Both experimental and simulation results showed that the developed CNT supported Pd−Cu catalyst is promising in terms of maximizing N2-selectivity for catalytic reduction of nitrates because of the enhanced mass transfer achieved in this novel material.

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AUTHOR INFORMATION

Corresponding Author

*(M. F. R. Pereira) Tel.: +351 225 081468. Fax: +351 225 081449. E-mail: [email protected]. (A. A. Lapkin) Tel.: +44 24 76151101. Fax: +44 24 76418922. E-mail: A.Lapkin@warwick. ac.uk. 4858



Article

Notes

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 226347. OSGPS thanks Fundaçaõ para a Ciência e a Tecnologia (FCT) for the research fellowship BD/30328/2006.

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Qater using Pd−Cu on Active Carbon Support. Appl. Catal. B: Environ. 2003, 44, 79.

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