kinetic of methanol synthesis.pdf

8/11/2019 kinetic of methanol synthesis.pdf

1/12

Detailed kinetic modeling of methanol synthesis over a ternary copper catalyst

Maximilian Peter a, Matthias B. Fichtl a, Holger Ruland b, Stefan Kaluza b, Martin Muhler b, Olaf Hinrichsen a,

a Technische Universitt Mnchen, Catalysis Research Center and Chemistry Department, D-85748 Garching b. Mnchen, Germanyb Ruhr-Universitt Bochum, Laboratory of Industrial Chemistry, D-44780 Bochum, Germany

h i g h l i g h t s

" Kinetic modeling of methanol synthesis over a ternary Cu/ZnO/Al2O3catalyst.

" Comparison of differently detailed kinetic models." Sensitivity analysis of different parameters on the rate of methanol formation." Detailed presentation of structure sensitivity." Implementation of the dynamic catalyst behavior including morphology changes.

a r t i c l e i n f o

Article history:

Received 6 March 2012Received in revised form 13 June 2012Accepted 16 June 2012Available online 16 July 2012

Keywords:

Cu/ZnO/Al2O3catalystMicrokineticsModelingDynamic behaviorMethanol synthesisSensitivity analysisMorphology changes

a b s t r a c t

Three differently detailed kinetic models for methanol synthesis are derived for experimental data mea-sured over a ternary copper catalyst. Two global reactor models for reaction design, including a powerlaw and a LangmuirHinshelwoodHougenWatson approach, are presented. In addition a microkineticmodel is adapted to describe the whole experimental data and is used to discuss dynamical changesoccurring during methanol synthesis. The first global model based on power law kinetics is very preciselyin predicting the integral rates of methanol production. The power law requires the inclusion of a waterinhibition term to be applicable over the whole range of experiments. A semi-empirical LangmuirHin-

shelwoodHougenWatson model, taken from the literature, gives essentially the same results, evenupon extrapolation. The third model, a microkinetic model, was successfully fitted with only two vari-ables and is in reasonable agreement with the experimental data. For all models a sensitivity analysisshows the influencing parameters on the methanol production rate. The valid microkinetic model, how-ever, can give qualitative estimations of the structure sensitivity and dynamic behavior of methanol syn-thesis. The dynamic change of active sites and of site distribution of different copper low-index planesalong the reactor length is given and the inhibiting role of water, indicated by the power law and micr-okinetic model, is analyzed.

2012 Elsevier B.V. All rights reserved.

1. Introduction

Methanol counts among the most important basic chemicals inindustry and becomes more and more important as a chemical en-ergy carrier, i.e. as fuel for fuel cells[1,2]. Moreover, it is a promis-ing energy carrier, which can easily be handled by the existinggasoline infrastructure. Nowadays, methanol is commercially syn-thesized over Cu/ZnO/Al2O3 catalysts in a low-pressure (50100 bar) and low-temperature (473.15573.15 K) process. Threeoverall reactions describe the formation of methanol when a feedof CO2, CO and H2 is employed. Methanol can be formed viathe

highly exothermic hydrogenation of carbon monoxide and carbondioxide[2].

CO 2H2CH3OH DH298K 90:85 kJ=mol reaction 1

CO2 3H2CH3OH H2O DH298K 49:82 kJ=mol

reaction 2

In addition, carbon monoxide can be converted via the watergasshift reaction, which is under the mentioned conditions also exo-thermic and equilibrium-limited.

CO H2OCO2 H2 DH298K 41:03 kJ=mol reaction 3

It is generally accepted that methanol is primarily formed via thehydrogenation of carbon dioxide [35]. However, there is still a

1385-8947/$ - see front matter 2012 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.cej.2012.06.066

Corresponding author. Tel.: +49 89 289 13232; fax: +49 89 289 13513.

E-mail address: [email protected](O. Hinrichsen).

Chemical Engineering Journal 203 (2012) 480491

Contents lists available at SciVerse ScienceDirect

Chemical Engineering Journal

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c e j
http://dx.doi.org/10.1016/j.cej.2012.06.066mailto:[email protected]://dx.doi.org/10.1016/j.cej.2012.06.066http://www.sciencedirect.com/science/journal/13858947http://www.elsevier.com/locate/cejhttp://www.elsevier.com/locate/cejhttp://www.sciencedirect.com/science/journal/13858947http://dx.doi.org/10.1016/j.cej.2012.06.066mailto:[email protected]://dx.doi.org/10.1016/j.cej.2012.06.066


2/12

debate about the active site and the synergy effect of the differentcatalysts components. Generally, the activity in methanol synthesisis somehow proportional to the area of the metallic copper [68].The different effects of each component are discussed controver-sially in current literature. While aluminum oxide is believed toact as a structural promoter and reduces sintering effects, the inter-action of zinc oxide and copper are still to be investigated. A signif-icantly higher activity for a ternary copper zinc alumina catalyst isexhibited than for copper on alumina [9]. Several theories exist how

ZnO and Cu interact. Zinc oxide microcrystallites could stabilize astrain in the copper particles, leading to higher methanol produc-tion rates [10,11]. Another attempt to explain the synergy effectof ZnO and Cu is a CuZn alloy formation [1214]. Nakamuraet al.[15,16]describe the hydrogenation of carbon monoxide anddioxide over two different active sites, depending on the gas ap-plied. Methanol from carbon dioxide proceeds over copper zinc al-loys, which are formed by zinc dissolved into the copper particles,whereas carbon monoxide hydrogenation is catalyzed by CuOZn species [15,16]. Gas-dependent morphology changes of Cu onZnO have been found using in situ extended X-ray adsorption finestructure (EXAFS) measurements[17,18]. This was also referred aswetting/non-wetting behavior [17]. Depending on the reduction po-tential of the gas phase, the copper particles are spherical (oxidiz-

ing) or disk like (reducing), which was also visualized by in situTEM measurements[19,20]. Under reducing conditions a strongermetal surface interaction is found, which exhibits higher activityfor methanol synthesis[8,20]. Recently, a microkinetic model wasdeveloped by Grabow and Mavrikakis [21], based on density func-tional theory calculations on Cu(111). After fitting the data toexperimental values, they found that possibly a more open and par-tially oxidized Cu facet might be more suitable to represent the ac-tive site for methanol synthesis [21]. In their work only the exposedcopper (111) facets are responsible for the catalytic activity, butaccording to the authors the synergetic effect cannot be excludedby their findings.

Kinetic modeling is always a great subject in heterogeneouscatalysis[22,23]. Depending on the level of understanding for thecatalytic reaction, different approaches in engineering multiscalekinetic modeling can be applied [2428]. Global kinetic models,

i.e. in the simplest case power laws or LangmuirHinshelwoodHougenWatson models (LHHW) are mainly used for reactor de-sign and operation of chemical reactors[29]. On the other side,by introducing the surface chemistry modeling based on elemen-tary steps leads to microkinetic models[22,23,25].

In case of methanol synthesis, a variety of global kinetic modelswere postulated during the last decades. In the simplest case,power laws or LangmuirHinshelwoodHougenWatson models(LHHW) were used to describe the synthesis reaction. First, carbon

monoxide was believed to be the only carbon source to form meth-anol. Later on, also carbon dioxide was also considered[24,30].However, 14C-labeling experiments showed that carbon dioxideis the main carbon source in methanol synthesis, carbon monoxideis primarily converted by the watergas shift reaction[3]. VandenBussche and Froment[28]developed a kinetic model based on thisknowledge, also on pseudo-mechanistical basis, which will be dis-cussed later in more detail.

The first microkinetic model for methanol synthesis based onelementary steps was developed by Askgaard et al. [31]. It wasdeveloped fromresults obtained in surface science studies, propos-ing metallic copper as the active site. The microkinetic model forthe watergas shift reaction by Ovesen et al.[26,32,33]was incor-porated. Table 1 shows the proposedelementary reactions. The first

eight steps describe the watergas-shift reactionviaa redox mech-anism. Subsequently, methanol is formed viathe successive hydro-genationof adsorbed carbon dioxide. Thereby, the hydrogenation ofH2COOadsis considered as the rate-determining step for methanolsynthesis. The microkinetic model was successfully extrapolatedto industrial conditions[31]. However, Askgaard et al.[31]founda systematic deviation from measured data at high or low ratio ofpCO/pCO2. Ovesen et al. [27]eliminated most of the deviations ofthe model by Askgaard et al. [31]allowing the total copper areato depend on the reduction potential of the gas phase. The viareduction formed oxygen vacancies in the CuOZn interface influ-ence theparticleshape andincreasethe total number of activesites.The flatter the copper particles are, the higher the exposed surfacearea and therefore the number of active sites is. Structure sensitiv-ity on the different exposed copper low-index planes (11 1), (11 0),(10 0) was introduced. The gas atmosphere-dependent ratio of

Nomenclature

LatinA Arrhenius factor (according to model)A area (m2)D site density ()

E activation energy (J mol

1

)f fugacity, power law and microkinetic model (Pa)f fugacity, LHHW model (bar)f(c/c0) area of particular copper facet from Wulff construction

()DG Gibbs free energy (kJ mol1)DH enthalpy of reaction (kJ mol1)k reaction rate constant (according to model)K equilibrium constant (according to model)N number of active sites (mol)_n molar flow rate (mol s1)p partial pressure (Pa)p0 thermodynamic reference pressure (according to mod-

el)Par evaluated parameter in sensitivity analysis ()

PK parameter for equilibrium constant ()r reaction rate, microkinetic model (s1)r reactionrate, powerlaw andLHHW modelmol s1 kg

1cat

RMeOH integral reaction rate methanol, simulation or experi-mentmol s1 g1cat

R ideal gas constant (J mol1 K1)S sensitivity ()

T temperature (K)_V volumetric flow rate (Nml min1)var variation in sensitivity analysis ()z dimensionless reactor coordinate ()

Greeka power of driving force in methanol synthesis ()b equilibrium term ()e fraction of Cu(11 0) ()c/c0 relative surface contact free energy ()g fraction of Cu(10 0) ()u power of driving force in reverse watergas shift reac-

tion ()k stoichiometric coefficient ()H coverage ()

xcat catalyst weight (kg)n inhibition term (Pa)

M. Peter et al. / Chemical Engineering Journal 203 (2012) 480491 481


3/12

facets can be derived using the Wulff construction. The model mor-phology changes were incorporated into the microkinetic model byOvesen et al.[27].

This work compares three approaches at different theoreticalinput to model the methanol synthesis under industrial conditions

with respect to their applicability and validity. These approachesare namely a power law, a LHHW model and the dynamic microki-netic model by Ovesen et al.[27].

2. Experimental and computational section

The experiments were performed over an industrial ternary Cu/ZnO/Al2O3catalyst. The dry syngas consisted of carbon monoxide,carbon dioxide, hydrogen and nitrogen as an inert. It was dosed viamass flow controllers (Brooks, model 5850TRG). The followinggases of high purity were used: 20% CO2(99.997%) in H2(99.9999%), H2 (99.9999%), N2 (99.9999%), CO (99.998%). Theexperimental setup (Fig. 1) consists of four independent dosinglines, allowing a variation of the feed gas composition. Gas analysis

is carried out by two isotherm operating gas chromatographs (Shi-madzu 14A, Schimadzu 8A) with different columns. A Porapak Ncolumn (Supelco) was used to determine the concentrations of car-bon dioxide, water and methanol, whereas a Molsieve 5 column(Supelco) was incorporated to measure the effluent of carbon mon-oxide and nitrogen. Hydrogen was determined by the material bal-ance to yield 100%. The whole set-up was heated to 393 K in order

to avoid unwanted condensation of water or methanol. In order tooperate the reactor in an isothermal way and to avoid hot spots,about 0.2 g of the catalyst were mixed with 0.8 g SiC, which byitself did not exhibit any catalytic activity. The catalyst was groundinto a sieve fraction of 250355 lm to ensure a uniform distribu-tion over the catalyst bed and to avoid diffusion limitations.

Severe aging of the catalyst was performed before the kineticinvestigation in order to circumvent the initial formation period[9]. The following kinetic measurements exhibited further slightlydeactivation, which were carefully observed and as a good approx-imation assumed to be linear [9]. After the experiments the specificcopper metal surface area was measured ex situ to yield14:4 m2 g1cat by the N2O frontal chromatography method undermild reaction conditions, using a N2O/He mixture (1% N2O inHe (99.9999%)) [3436]. Experimental conditions ranged from 5to 60 bar and 463.15 K523.15 K, also varying the composition ofthe dry synthesis gas, yielding COx conversions of about 0.214%.The experimental conditions are summarized inTable 2. Experi-ments were carried out in three periods. In the first period, the res-idence time was varied at a total pressure of 60 bar in the wholetemperature range at standard feed conditions, yielding a set of28 experimental values. Second, the feed was varied in the rangeof 59.5 10% H2, 8 3% CO2, 6 3% CO and balance N2. This ledto a set of seven different synthesis gas compositions at a totalpressure of 60 bar and temperatures ranging from 463 to 523 K(28 data points). Finally, the total pressure of two feed gas compo-sitions (59.5% H2, 8% CO2, 6% CO, balance N2as well as 72% H2, 4%CO2, 10% CO, balance N2) was varied at different temperatures. Thisyielded additional 47 experimental data points, evaluated for thekinetic study. In each period, the standard feed was studied at a to-tal pressure of 60 bar and 100 Nml min1 while varying the tem-perature, in order to obtain reliable data for taking into accountthe deactivation of the catalyst as a function of time on stream.

In this work the fixed-bed reactor is modeled as an isothermalplug flow reactor:

d_nidz

X2j1

kijrjxcat; i 1;. . .;5 1

Hereby, _ni is the molar flow of a specific species along the dimen-sionless reactor coordinatez,k the respective stoichiometric coeffi-cient, rj the rate of methanol formation (2) or reverse watergasshift reaction (3) and xcat the mass of the catalyst (for global

Table 1

Elementary steps in methanol synthesis, accord-

ing to Ref.[31].

1 H2O (g) + H2O2 H2O+ OH+ H3 2OH H2O+ O4 OH+ O+ H5 2H H2(g) +26 CO (g) + CO7 CO+ O CO2+ 8 CO2 CO2(g) + 9 CO2+ H HCOO+

10 HCOO+ H H2COO+ 11 H2COO+ H H3CO+ O12 H3CO+ H CH3OH+ 13 CH3OH CH3OH (g) +

* surface site or adsorbed species, respectively.

Fig. 1. Experimental setup.

482 M. Peter et al. / Chemical Engineering Journal 203 (2012) 480491


4/12

models) or the number of active sites (microkinetic model) in kgcator mol, respectively. It is assumed that methanol is formed via car-bon dioxide hydrogenation (j= 1), whereas carbon monoxide canform carbon dioxidevia the watergas shift reaction (j= 2):

CO H2O$CO2 H2 $2H2

CH3OH H2O 2

This yields a set of five ordinary differential equations for the

reactive gas species in an isothermal plug flow reactor. Thoroughparameter estimations were performed based on the experimentaldata. The presented values were found using the Athena VisualStudio engineering software[37], with a build-in solver and fittingroutine. Weighted nonlinear least square routines, with a trust re-gion method, were selected to optimize the model parameters. Theobjective function included the integral rate of methanol and forthe global models also the rate of water formation, being derivedfrom a closed carbon balance. Gradients were calculated using aforward difference scheme; the objective function tolerance wasset to 1010. In case of Arrhenius constants, the parameters wereparameterized in the form of:

ki A exp

EiR

1

T

1

Tav 3with

A

A exp EiRTav

4

This parameterization was done at an average temperatureTavof 493.15 K. Thus, all parameter are reported in parameterizedand re-parameterized form. The goodness of the respective modelwas evaluated by the parity of the integral rate of methanol forma-tion and the coefficient of determination (R2). A local sensitivityanalysis [38] for all parameters was conducted by finite differencesin the form of a relative sensitivity coefficient:

S PariRMeOH

@RMeOH@Pari

limPari !0

RMeOH;varRMeOH;refRMeOH;refvar

5

Hereby Parirepresents the evaluated parameter, RMeOHthe integralrate of methanol synthesis and varthe variation of the respectiveparameter. Unless otherwise stated, the variation was chosen tobe 1% as suggested by Campbell [39]. For the microkinetic model,the reverse reaction rate constant is related to the forward reactionrate constant as follows:

ki

ki

Ki6

whereKiis the equilibrium constant of a specific elementary reac-tion as well as ki andk

i denote the reaction rate constants in theforward or reverse direction of each elementary step, respectively.Hence, a change of ki does not change the equilibrium constant,

assuring microscopic reversibility. Each parameter is varied oneby one. For significant sensitivities linearity for the change in RMeOH

was checked. Sensitivities are also cross-checked using MATLAB

R2010b (Mathworks Inc.).In global models, the chemical equilibrium is often accounted

for using an equilibrium term (1bi). Herebybirepresents the ap-proach to the chemical equilibrium for methanol synthesis or thereverse watergas shift reaction, respectively:

b1 bMeOH K1MeOHp

20fH2OfCH3 OH=f

3H2

fCO2 7

b2 bRWGS KWGSfH2OfCO=fH2fCO2 8

The equilibrium term becomes zero at the chemical equilib-rium. The equilibrium constants KMeOH and KWGS are calculatedby the means of statistical thermodynamics, explained by Ovesenet al.[26,33]and are used for all models discussed later. For globalmodels, the statistical thermodynamics of the gas phase are trans-formed into the following form:

Ki 10PK1;i

T PK2;i 9

The values are given inTable 3and for the WGS comparable tothe parameters found experimentally by Graaf et al.[40]. In case ofmethanol synthesis from CO2the overall equilibrium constant dif-

fers up to 17% compared to the results by Graaf et al. [40].The species are represented by their fugacities calculated by an

Athena built-in subroutine. A calculation according to SoaveRed-lichKwong[41,42]showed essentially the same results.

3. Results and discussion

Depending on the level of understanding of a catalytic process,differently detailed models can be used to describe the chemicalapparent kinetics.

3.1. Power law model

Usually, when there is little information on the mechanism of a

chemical reaction, power laws are used to describe reactive sys-tems[32], i.e.:

rMeOH AMeOH expEa;MeOH=RTfaH2

H2faCO2

CO2nfH2 O

aH2 O

nfCH3OHaCH3 OH 1 bMeOH

rRWGSARWGSexpEa;RWGS=RTfuH2

H2fuCO2

CO2n fH2O

uH2 OfuCO

CO 1 bRWGS

11

The reaction rates are reported in mol s1 kg1cat. In these equa-tions, all gaseous species are represented by their fugacity f (inPa) and a reaction ordera or u, respectively. Since the feed gasesused (see also Table 2), representing typical feed compositionsfor methanol synthesis, neither contained water nor methanol inmeasurable amounts, a termn (in Pa) was introduced to accountfor a possible inhibition by the products formed. This allows thereaction rate to be finite in the absence of methanol or water,respectively. An analysis without an inhibition of water showedunsatisfying results. In total, the set of 13 adjustable parameterscomprise ofAi,Ea,i,n and the reaction orders for the particular spe-cies in the respective reaction.Fig. 2shows the respective parity

Table 2

Experimental conditions.

Bed length 0.017 mBed diameter 0.008 mTemperature 463.15523.15 KVolume flow 100500 Nml min1

Pressure 560 barCatalyst Cu/ZnO/Al2O3

Catalyst weight 0.2003 gSiC dilution 0.7995 gSynthesis gas composition 59.5 10% H2, 8 3% CO2, 6 3% CO, balance N2

72% H2, 4% CO2, 10% CO, balance N2Standard feed 59.5% H2, 8% CO2, 6% CO, balance N2

Table 3

Thermodynamic data, adsorbed species on Cu(111) and Cu(100), adapted from Refs.

[26,27,31,32] . n are vibrational constants; the number in parentheses denotes the

degeneracy of a specific frequency.

PK1 PK2

MeOH 2980.8 10.346

WGS 2083.3 2.043



5/12

plot for the integral rate of methanol formation. A good agreementbetween measurement and experiment is obtained, also indicatedby anR2 value greater than 0.99.

The parameters are listed inTable 4. The number of parameteris reduced to nine, which is sufficient to describe the reaction rates.The reaction orders of methanol, carbon monoxide and water inthe reverse watergas shift reaction tend towards zero and weresubsequently omitted.

A sensitivity analysis shows that the most influencing parame-ters on the methanol rate are the order of hydrogen, the order ofwater and the order of carbon dioxide (Fig. 3). The variation ofaH2,aH2OandaCO2was set to 0.1% to ensure linearity in the changeofRMeOH. The sensitivity ofnscales withaH2O, which is mathemat-ically induced. The order of hydrogen in the reverse watergas shiftrate andn have a relatively high confidence interval, however, theirimpact on the rate of methanol formation is relative low (see alsoFig. 3). The equilibrium terms in the methanol synthesis (1-bMeOH)and reverse watergas shift (1-bRWGS) were evaluated for all reac-tion conditions at the reactor outlet. While (1-bMeOH) is alwaysclose to one (minimum 0.8, average 0.97, standard deviation0.05), the reverse watergas shift term mostly lies between 0.5and +0.5 (average 0.04, standard deviation 0.53). This meansthe reverse watergas shift reaction is closer to equilibrium. Froma power law kinetic approach, it is hard to distinguish between thedriving forces in the reverse watergas shift reaction. In our ap-proach we implemented the full equation for the reverse watergas shift reaction, comprising all fugacities of the components inreaction3. Upon such a procedure, the exponents of the fugacitiesof carbon dioxide, water and carbon monoxide become essentiallyzero. However, an implementation of justfCO2instead offH2yieldsessentially the same results, being slightly more inaccurate.

Finally, an implementation withoutfCO2andfH2gives also goodquantitative agreement between model and simulation, resultingin a R2 slightly less than for eitherfCO2or fH2. It has to be pointedout that hydrogen and carbon dioxide are almost identically trea-tedvia the equilibrium term and this term being far away from

one under our experimental conditions. Closer to equilibrium theadditional influence of a driving force term becomes less impor-tant, which makes the determination tough. However, in our mod-elingfH2turns out to optimally describe the experimental data.

3.2. LangmuirHinshelwooldHougenWatson model

Vanden Bussche and Froment[28]developed a kinetic modelwhere CO2is assumed to be the main carbon source in methanolsynthesis. The watergas shift reaction proceedsviaa redox mech-anism. In contrast to a widely used model by Graaf et al.[24], both

hydrogen and carbon dioxide adsorb on the same type of active

site. Adsorption of carbon dioxide further leads to carbonate struc-tures, which are hydrogenated via intermediates, i.e. formate ormethoxy species, yielding methanol in a final step. Under steadystate conditions they derived the following kinetic expressions:

rMeOH k0MeOHfCO2fH2 1 bMeOH=DEN

3 12

rRWGS kRWGSfCO2 1 bRWGS=DEN 13

DEN 1 KH2 O

K8K9KH2

fH2OfH2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffi

KH2fH2

q KH2OfH2 O 14

where k0MeOH is the lumped rate constant for methanol synthesis,kRWGS the rate constant for the reverse watergas shift reactionand the denominator DEN represents a typical adsorption term in

LHHW models being a function of fugacities and adsorption con-stants. The rates are reported in mol s1 kg1cat. The driving forcesfor the methanol synthesis are the fugacities of hydrogen and car-bon dioxide, respectively. For the reverse watergas shift reaction,

fCO2is the driving force, which is based on the theoretical mecha-nism. All parameter groups are calculated in Arrhenius form, yield-ing a set of ten variables:

Ki Aiexp EiRT

15

The values for the kinetic parameters underlay several pseudo-chemical constraints, formulated by Boudart and Djega-Mariadas-sou[22]: all frequency factors as well as Eifor the adsorption con-stants have to be positive. For the equilibrium adsorption constants,

the frequency factor representseS0=R

, thus (DS0

) has to remain po-sitive and should not exceed the entropy of the gas [28]. For the

0.0 2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

1.0x10-5

1.2x10-5

0.0

2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

1.0x10-5

1.2x10-5

RMeOH,simulation/(mol/s/g

cat)

RMeOH

, experimental / (mol/s/gcat

)

Fig. 2. Parity plot of the power law model.

Table 4

Estimated parameters of the power law model.

Parametera Value Asymptotic 95%confidence intervals

Re-parameterized

ln(AMeOH) 25.29 1.40 10.04Ea,MeOH 113.13 4.42 113,130ln(ARWGS) 15.90 3.30 7.36 10

9

Ea,RWGS 158.36 1.94 101 158,360

aCO2 0.55 7.19 102 aH2 1.25 1.26 10

1 aH2O 0.70 6.36 10

2 uH2 0.57 2.22 10

1 n 316.75 1.99 102

a Represents parameterized form.

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

H2

H2O

H2

kMeOH

CO2

Sensitiv

ity

Parameter

kwgs

Fig. 3. Sensitivity plot of the power law model.



6/12

kinetic constants,Eishould be negative within the definition above.

However, as k 0MeOH and KH2O

K8K9KH2are lumped parameters, they do not

underlay these restrictions.Table 5 shows the obtained parameters.As can be seen the activation energies satisfy the pseudo-chemicalrules by Boudart. The negative changes in entropy of hydrogen and

water adsorption (DS0

) are 13.7 and 99.4 J mol1

K1

, respec-tively. The corresponding gas entropies (S0) are 145 and207J mol1 K1 [28].

The parity between simulation and experiment is comparableto the power law model (Fig. 4). TheR2 value exceeds 0.98.

Fig. 5shows the result of the local sensitivity analysis. It can beseen that the highest sensitivity is obtained for the squared hydro-gen adsorption constant, followed by the rate constant of methanolsynthesis. The adsorption constant of water has the highest rela-tive confidence interval, but its relevance for the integral rate ofmethanol formation is very weak.

3.3. Microkinetic model

In order to examine the detailed microkinetics, the surface sci-ence based model of Ovesen was implemented [27]. Based on theirmodel for the watergas shift reaction[33], a model for methanolsynthesis was explored. Following a redox mechanism, where theeducts adsorb on the copper active sites and carbon dioxide isformed by oxidation of adsorbed carbon monoxide, a successivehydrogenation of carbon dioxide leads to methanol. Askgaardet al.[31]presented a model based on single crystal studies, whereonly Cu(111) facets are considered to be the active surface sites.Ovesen et al. [27] implemented the structure sensitivity of thereaction. The ratio of the exposed copper low-index planes de-

pends dynamically on the gaseous atmosphere, following the Wulffconstruction[27]. The gaseous species interact with the ZnOCuinterface, leading to oxygen vacancies which have an influence

on the particle morphology.H2 ZnOCuH2O ZnCu reaction 4

CO ZnOCuCO2 ZnCu reaction 5

In this work, the Wulff construction is represented using thesoftware package WinXMorph[43,44]. Values for the free energyof a free surface from Hansen et al. [19]under hydrogen atmo-sphere were used, likely being more accurate for syngas conditionswith a high ratio of hydrogen. The results are depicted inFig. 6.They are very similar to the published results by Ovesen et al.[27],where one of the (110) planes of the particle is attached tothe substrate. Differences are caused by the choice of the valuefor the free energy of a free surface, which changes the surface

plane distribution of the copper crystal.According to Ovesen et al.[27], the reduction potential and therelative surface contact free energy are related in the followingway:

c=c01

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiK1K2

pH2pCO

pCO2pH2 O

r

1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiK1K2

pH2pCO

pCO2pH2 O

r 16

whereK1and K2are the equilibrium constants of the reactions(4)and (5), respectively. The number of active sites Non the catalystscan then be calculated to be:

N N0Xi

fic=c0DiXj

fjc=c0fixDj 17

where fi(c/c0) is the surface area for (hkl) planes taken fromFig. 6andDi is the site density of that plane. N0is the number of activesites at fixed conditions, i.e. derived from N2O frontal chromatogra-phy or hydrogen temperature-programmed flow experiments. Theratio of an actual plane compared to the others can be calculatedstraightforward[27].

When comparing the published thermodynamic data[26,27,3133]for the different species inTable 1of the publishedwatergas shift and the methanol synthesis models, minor differ-ences of the data for the adsorbed species can be found. We ana-lyzed the data in some detail, especially the hydrogen ad- and

desorption in temperature-programmed flow experiments[45,46]. For those reactions, the data taken from the watergas

Table 5

Estimated parameters of the LHHW mode.


Re-parameterized

ln(AMeOH) 6.17 7.38 101 91,944

Ea,MeOH 72.16 1.13 101 72,161

ln(ARWGS) 7.27 4.04 101 4.70 1014

Ea,RWGS 168.31 2.94 101 168,310

ln(sqrt(AKH2)) 0.82 3.61 101 0.44EKH2 ln(AKH2O) 0.35 1.50 6.46 10

6

EKH2O 50.41 1.36 102 50,412

ln(AKH2O/K8/K9/KH2 ) 5.46 5.54 101 4.38 105

EKH2O/K8/K9/KH2 63.55 3.61 101 63,549


0.0 2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

1.0x10-5

1.2x10-5

0.0

2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

1.0x10-5

1.2x10-5


cat)

RMeOH


)

Fig. 4. Parity plot of the LHHW model.

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

KH2O(KH2)

1/2K

H2O(K

8K

9K

H2)-1k

MeOH

Sensitivity

Parameter

kwgs

Fig. 5. Sensitivity plot of the LHHW model.



7/12

shift publications [26,32,33] were found to be more accurate. How-ever, for the overall methanol synthesis reaction, both data setsyielded good results. Thus, we implemented them in terms of a

better description for our (additional) experiments. The rate formethanol synthesis and reverse watergas shift reaction wereimplemented according to Askgaard et al.[31]:

rMeOH;hkl k11K10hHCOOh

2H

h k11=K11hH3 COhO 18

rRWGS;hkl k7=K7hCO2h k7hCOhO 19

Hereby k i represents the kinetic rate constant of the specific reac-tion and Ki the equilibrium constant of a specific elementary step(see alsoTable 1), which can be calculated by the means of statisti-cal thermodynamics. These calculations were discussed in great de-tail [26,32,33]. hi is the fractional coverage of a specific species,whereas h is the fractional coverage of free active surface sites.

All coverages can be calculated directly by relating the reactionrates of the slow steps (steps2, 4, 7, 11 in Table 1)viaa steady-statesite balance for adsorbed hydroxyl groups and oxygen and the ac-tive site conservation law[31](see also Appendix).

According to Ovesen et al. [27], the overall reaction rate com-prises the sum of contributions from the net rate over a specificsurface, i.e. Cu(111), Cu(110) or Cu(100):

rMeOH grMeOH;100 erMeOH;110 1 g erMeOH;111 20

rRWGS grRWGS;100 erRWGS;110 1 g erRWGS;111 21

where gis the ratio of sites on Cu(100) relative to the overall activesites andeis the ratio of sites on Cu(11 0) relative to the overall ac-tive sites. From a site balance, the ratio of Cu(111) can be calcu-

lated. Expressions for the calculation ofg ande are given in theliterature[27]. The product of the equilibrium constants for reac-tions(4) and (5),and therefore the change in Gibbs free energy istaken from Vesborg et al.[20], being 3.8 kJ mol1. As mentioned be-fore, the examined catalyst exhibited a specific copper metal sur-face area of 14:4 m2 g1cat, which corresponds to 176lmol g

1cat N2O

consumed. This value was fixed at y/y0= 0.09, indicated by in situTEM measurements by Hansen et al. [19]for pure hydrogen atmo-spheres. This value seems reasonable since similar surface areaswhere measured by conducting N2O frontal chromatography andhydrogen temperature-programmed desorption [35,47]. For theparameter fitting, the only varying parameter is the rate constantof the slow step during methanol synthesis, namely the hydrogena-tion of H2COOads(step 11 in Table 1). Ovesen et al. [27]found the

rate constants for the rate-determining step on the different surfaceplanes to be related by:

kMeOH;110 : kMeOH;100 : kMeOH;111 125 : 4:875: 1 22

In our approach, these ratios have not been changed. As a result,this yields only two variables for the microkinetic model to de-scribe our experimental data, the Arrhenius factor and the activa-tion energy of step 11 in Table 1, being the rate-determiningstep. Hereby two Cu sites represent one active site [31]. When con-sidering each Cu site as an active site essentially the same model fit

can be derived, with a corresponding difference in k11. Microscopicreversibility was achieved by modeling the reverse rate constantk in terms of k+/K. The result of the parameter fitting, whereone of the copper (110) planes of the particle is attached to thesubstrate, is displayed in Fig. 7, indicating a good qualitative agree-ment between measured and calculated data. This is also con-firmed by a R2 > 0.95. We would like to point out thatconsidering either exclusively Cu(11 1), Cu(11 0) or Cu(100) as ac-tive sites does not lead to a sufficient model fit. The inclusion of thedifferent exposed surface sites is absolutely necessary for the mod-el to predict the reaction rates adequately.

The rate constant of methanol synthesis (Table 6) at the param-eterization temperature differs by a factor of 3 between the datadeduced from modeling single crystal experiments by Ovesen

et al.[27]However, the deviation is quite reasonable, consideringthe huge set of input data for the thermodynamic constants, thechoice ofc=c0fixand the uncertainty of the single crystal experi-ments[27]. Also, the coverage of intermediates may lead to a shiftin the Arrhenius parameters, as the model does not comprise anycoverage dependence. Modeling single steps of methanol synthesisunder ambient pressure suggest a coverage dependence, i.e. hydro-gen desorption or the reaction of carbon monoxide with pre-ad-sorbed oxygen[45,48]. The extracted parameters from our highpressure experiments can be seen as effective values at higher cov-erages compared to the UHV studies.

First, a sensitivity analysis of the static microkinetic model withexclusivelyCu(11 1),Cu(110) orCu(10 0)being exposed, aftera sep-aratefitting to the experimentaldata, wasconducted.When analyz-

ing all slow steps proposed, i.e. steps 2, 4, 7 and 11, only a few arefound to be rate-controlling at the given conditions. For all low-in-dex planes, thehydrogenation of H2COOads (step 11) is the rate-lim-iting step. Step 7 exhibits very slight sensitivities over Cu(11 1) andCu(110). This can also be observed when the ratio of the differentsurfaces is varied depending on the reduction potential of the reac-tive gas. The resultis depictedinFig.8.Ascanbeseen,step11israte-limiting over Cu(111), Cu(110) and Cu(100). Over Cu(111) andCu(110) step 7, i.e. the conversion of adsorbed carbon monoxideandoxygen to carbon dioxide, may slightly inhibit theintegralreac-tion rate to methanol. Theother investigatedsteps (steps 2 and 4) do

-1.0 -0.5 0.0 0.5 1.0

0

1

2

3

4

5

6

7

8

9

10

11

A/V2/3

y/y0

111

110

100

total

Fig. 6. Dimensionless surface area, calculated from Wulff construction, Cu(110)attached to the ZnO.

0.0 2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

1.0x10-5

1.2x10-5

0.0

2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

1.0x10-5

1.2x10-5


cat)

RMeOH


)

Fig. 7. Parity plot of the microkinetic model.



8/12

not exhibit any sensitivity on the integral rate of methanol forma-tion. The Cu(110) plane has the highest impact on the overall rate,followed by Cu(10 0). This can be explained by the Wulff construc-tion (Fig. 6). Under typical reaction conditions, the ratio of exposedfacets follows Cu(110) 6 Cu(100) < Cu(111) (see alsoFig. 10, de-scribed later). From single crystal studies it is known that Cu(11 1)shows the lowest rate constant for the hydrogenation of H2COOads(step 11). Thereactionrate constant over Cu(11 0) is approximately125 times higher than the one over Cu(111) and 26 times higherthan over Cu(100)[27]. Although the fraction of exposed Cu(110)is the lowest of all three low-index planes of the catalyst, it turnsout to display thehighest influence on theoverall reaction rate. Par-ticularly step 7 over Cu(111) may be inhibiting. This behavior is al-ways exhibited at lowpressures, as the watergas shiftreactionwillproceed in the reverse direction, forming water and carbon monox-ide. At a high CO-to-CO2 ratio andpressure, i.e. 2.4andP50 bar, themethanol production rate may(slightly) be limitedby the CO oxida-tion to CO2over Cu(111) (Fig. 8), indicating that this reaction willpreferably proceed over this surface plane. This behavior will bemore pronounced at higher conversions. The higher sensitivitiesfor step 7 over Cu(111) can be explained by the rate constant beingone magnitude higher than for the other surface planes.

In the following the sensitivities are examined in more detail.Fig. 9shows that Sk11,110drops significantly with increasing totalpressure, whereasSk11,100andSk11,111have the opposite tendency.The sensitivity ofSk11,111andSk11,100increase with increasing totalpressure. This can again be explained by the Wulff construction,

where the ratio of Cu(110) drops with increasing water content(see alsoFigs. 10 and 11, described later). The ratio of Cu(110) be-comes less along with the sensitivity of this plane. The inhibitingeffect of k7,111 andk7,110 becomes less at higher total pressures.While at lower total pressure the watergas shift reaction proceedsin the reverse direction, competing for the methanol synthesisreaction, at higher total pressure the inhibiting effect diminishes,as the watergas shift reaction tends towards equilibrium or evenproceeds in the forward direction to form carbon dioxide andhydrogen.

The valid microkinetic model is used to calculate the change inthe Cu surface area during the reaction. The drop in the Cu surface

area is mainly attributed to the amount of water produced in thereactor (Fig. 10). Furthermore, the morphology changes of the cop-per particles can now be calculated using the microkinetic model(Fig. 11). The relative amount e of Cu(110) sites drops instanta-neously when the reaction proceeds and water is produced.Cu(11 1) becomes the dominant facet and the amount g ofCu(10 0) sites rises along the reactor length.

3.4. Comparison of derived models

When comparing the three reaction models, it is obvious thatthe power law and LHHW models describe the methanol synthesisrate more accurately than the microkinetic model. However, nine(almost) freely varying parameters are fitted to describe the data.The microkinetic model has a lot of restrictions, but two adaptedparameters are sufficient to qualitatively describe our data andthe data derived by Graaf et al. [24,27,31]. The power law andLHHW models are comparable in their predictions, neverthelessthe LHHW model has some restrictions, which lead to a slight shiftfor some data points. The LHHW model has temperature-depen-dent adsorption terms and follows a redox mechanism[28], com-parable to the one proposed by Ovesen et al. [26,32,33], whichworks quite well for both models. Alternatively, the power law

Table 6

Estimated parameters of the microkinetic model.


Re-parameterized

ln(AMeOH,111) 6.03 3.59 102 1.93 1013

Ea,MeOH,111 100.71 3.58 100,710


-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.80.9

1.0

k7,110

Sensitivity

k11,100

k11,110

k11,111

Parameter

k7,111

Fig. 8. Sensitivity plot of the microkinetic model.

0 10 20 30 40 50 60

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9 k11,111

k11,110

k11,100

k7,110

k7,111

Sensitivity

Pressure / bar

Fig. 9. Pressure-dependenceon sensitivity,T= 463.15 K, standardfeed,_V 100 Nmlmin1.

0.00 0.25 0.50 0.75 1.00

0.00

0.05

0.10

0.15

0.20

molar fraction water

relative amount of active sites

Reactor length

Molarfractionwater/(%)

0.5

0.6

0.7

0.8

0.9

1.0

Relativeamountofactivesites

Fig. 10. Change in Cu surface area along the reactor length, accompanied by the

gas-phase water formation, standard feed, T= 463.15 K, p= 50bar,_V 100 Nml min1.



9/12

model without an inhibiting term for water cannot be fitted to theexperimental reaction rates, but performs well using the tempera-ture-independent empirical inhibition factor, which can be ex-plained by the microkinetic model. As water evolves in thereaction, the number of active sites drops significantly and thereaction slows down remarkably (see alsoFig. 10). This inhibitingeffect of water was also explained by Ovesen et al. [27]. Withoutsuch an inhibition term the methanol synthesis cannot be de-scribed adequately.

Fig. 12shows an extrapolation over a wide temperature range.The calculations have been performed until chemical equilibriumwas reached. Also upon extrapolation, the power law and LHHWmodel predictions are essentially equal. All three models exhibitthe highest reaction rate at about 553 K. Due to the thermody-namic influence at higher temperatures all models show a loss incatalytic activity. Besides quantitative reliability of the extrapola-tion, the microkinetic model proposes lower reaction rates at high-er temperatures compared to the global models. Moreover, thechange in the total amount of active sites is calculated (Fig. 12, de-noted by stars). Particularly the implementation of dynamic mor-phology changes leads to a more pronounced difference betweenthe microkinetic and global models. Further comparisons are givenin the appendix.

Interestingly the driving force for the reverse watergas shiftreaction is different for both global models. The LHHW model,

however, promotes carbon dioxide to be more critical than hydro-gen[28]. This may be more accurate for model extrapolations wayoutside our experimental window, i.e. at completely different reac-tion conditions. It was already stated that the power law worksquite well with either the partial pressure of carbon dioxide orhydrogen as driving force. The presented modeling approach didnot yield a reliable distinction, possibly due to the influence ofthe equilibrium. Hence, for the best model fit the partial pressureof hydrogen was chosen.

4. Conclusions

Three different models for describing data for the methanolsynthesis were presented. All models are found to be valid in theexperimental window and predict the rate of methanol synthesiscorrectly. The power law model, which has no restrictions concern-ing the model parameters and the pseudo-mechanistically Lang-muirHinshelwoodHougenWatson model yield a good fit tothe respective data. Care should be taken when a power law is usedto describe the measurements, as there are several possibilities tofit the data. The explicit microkinetic model can describe theexperiments adequately, even by varying only two parameters.

The global models are more accurate in predicting the kinetics inthe evaluated experimental parameter space and can thereforebe used for reactor modeling, i.e. concerning diffusion limitationsduring methanol synthesis, while the microkinetic model includesmorphology changes, which are very important during methanolsynthesis and are still subject of further investigations [19,20,27].Morphology changes can also be induced by adsorption of oxidiz-ing or reducing components, which change the surface free energyand may additionally change the strain in the copper particles.Additional high-pressure in situ studies have to be carried out toshow further synergy effects of ZnO and copper, i.e. zinc dissolvedin the copper particles or CuOZn species[15,16], which is pres-ently not included in the microkinetic model. Furthermore, re-search is recommended regarding the coverage-dependence of

the particular intermediates to improve the model and to study apossible inhibition of specific species.

Appendix A

Calculation of intermediates is taken from reference[31],tablesA1A4are taken from references [26,27,31,32];figures A1 to A4were not published before.

h1 1

ffiffiffiffiffiffiffiffiffiffiffifH2

K5p0

s K1

fH2Op0

K6fCOp0

1

K8

fCO2p0

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiK1fH2OK3p0

s b

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffib

2 4ac

p2a

!

K9K8


K5p0

s fCO2

p0

K9K10K5K8

fH2fCO2p20

1K13

fCH3 OHp0

1K12K13

fCH3OHp0

fH2K5p0

1=2

b


2 4ac

p2a

!2A:1

hHCOOK9K8


K5p0

s fCO2

p0h A:2

hH


K5p0

s h A:3

hH3CO 1

K12K13

fCH3 OHp0

fH2K5p0

1=2h

A:4

0.0 0.2 0.4 0.6 0.8 1.0

0.05

0.10

0.15

0.20

0.25

0.300.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

Fractionofparticularcopperfacets

Reactor length

Cu(100)

Cu(110)

Cu(111)

Fig. 11. Morphology change along the reactor length, standard feed, T= 463.15 K,p= 50bar, _V 100 Nml min1.

450 475 500 525 550 575 600 625

0.0

2.0x10-6

4.0x10-6

6.0x10-6

8.0x10-6

1.0x10-5

1.2x10-5

1.4x10-5

RMeOH

/(mol/s/g

cat)

Temperature / K

0.88

0.90

0.92

0.94

0.96

0.98

1.00 Relativeamountofactivesites,averaged

Fig. 12. Extrapolation of the kinetic models; experimental,j

power law model,dLHHW model, N microkinetic model, averaged relative amount of active sitesalong the reactor (second axis), dashed line represents integral rate of methanolformation at thermodynamic equilibrium; standard feed, p= 60bar,_V 100 Nml min1.



10/12

hO b


2 4ac

p2a

!2h A:5

hCO K6fCOp0

h A:6

hCO2 1

K8

fCO2

p0h A:7

Table A1

Kinetic data, adapted from Ref. [27].

Rate constanta A(s1) Ea(kJ mol1)

k2,111,k2,100 2.6 1014 114

k4,111,k4,100 2.3 108 99

k7,111 1.1 1013 72

k7,100 1.8 1013 87

k2,110 7.7 1012 91

k4,110 6.3 108 114k7,110 1.8 10

13 85

a Ovesen et al.[27]presented their values in the form of: k A expEa=RT).

Table A3

Thermodynamic data, adsorbed species on Cu(111) and Cu(10 0), adapted from Refs. [26,27,31,32]. v are vibrational constants; the number in parentheses denotes the

degeneracy of a specific frequency.

Species Vibrational parameters Ground state energy (kJ mol1)

CH3O* m= 400, 37(2), 360(3), 1020, 1150(2), 1460(3), 2840, 2940(2) cm1 300

CH3OH* m= 290, 36(2), 360(3), 750, 820, 1030, 1150(2), 1470(3), 2860, 2970(2), 3320cm1 413

CO2* m= 410, 14(2), 1343, 667(2), 2349cm1 463

H2COO

*

m= 405, 30(2), 400(3), 630, 960, 1090, 1220(2), 1420, 1480, 2920, 3000 cm

1

568H2O* m= 460, 21(2), 1600, 3370, 3370, 745 cm1 359

O* m= 391, 508(2) cm1 243OH* m= 280, 670(2), 3380 cm1 319H* m= 1291, 157(2) cm1 29a

HCOO* m= 322, 36(2), 400(3), 758, 1331, 1640, 2879, 1073, 1377 cm1 553CO* m= 330, 17(2), 2077cm1 181

a Extracted from our own experiments[45],original value 27 kJ mol1, well in the range of reported accuracy (3 kJ mol1 [27,32].* surface site or adsorbed species, respectively.

450 475 500 525

0.0

1.0x10-6

2.0x10-6

3.0x10-6

4.0x10-6

5.0x10-6

6.0x10-6

7.0x10-6

RMeOH

/(mol/s/g

cat)

Temperature / K

3 % CO

Fig. A1. Comparison of the kinetic models;experimental, j power law model, d

LHHW model, N microkinetic model, standard feed with varied CO content (3%),p= 60bar, _V 100 Nml min1.

450 475 500 525

0.0

1.0x10-6

2.0x10-6

3.0x10-6

4.0x10-6

5.0x10-6

6.0x10-6

RMeOH

/(mol/s/g

cat)

Temperature / K

6 % CO

Fig. A2. Comparison of the kinetic models;experimental, j power law model, d

LHHW model, N microkinetic model, standard feed with varied CO content (6%),p= 60bar, _V 100 Nml min1.

Table A2

Thermodynamic data, gas phase, adapted from Refs. [26,27,31,32]. vare vibrational constants;Iis the moment of inertia, sigma the symmetry number and B

the rotational constant.

Species Vibrational parameters Ground state energy(kJ mol1)

H2,g B= 60.8 cm1,r= 2,m= 4405cm1 35

CO2,g B= 0.39cm1,r= 2,m= 1340, 667(2), 2350 cm1 433

COg B= 1.90 cm1,r= 1,m= 2170cm1 132H2Og Iabc= 5.77 10

141 kg3 m6,r= 2,m= 1590, 3660, 3760 cm1 306CH3OHg Ia= 6.578 10

47 kg m2, Ib= 34 1047 kg m2, Ic= 35.3 10

47 kg m2,r= 3,m= 270, 1033, 1060, 1165, 1345,1477(2), 1455, 2844, 2960, 3000, 3681 cm1

343



11/12

with

a 2k7K6fCOp0

2 k11

K11K12K13

fCH3OHp0

fH2K5p0

1=2

k4K4


K5p0

s A:8

bk2

K2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiK1

K3K5

fH2fH2O

p20s k4 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiK1fH2O

K3p0s A:9

c 2 k7K7K8

fCO2p0

k2K1fH2Op0

2k11K9K10

K8

fCO2p0

fH2K5p0

3=2A:10

References

[1] , Silicon fire, Silicon Fire AG, 2011.[2] G.A. Olah, A. Goeppert, G.K.S. Prakash, Beyond Oil and Gas: The Methanol

Economy, second ed., Wiley-VCH, Weinheim, 2009.[3] G.C. Chinchen, P.J. Denny, D.G. Parker, M.S. Spencer, D.A. Whan, Mechanism of

methanol synthesis from CO2/CO/H2mixtures over copper/zinc oxide/aluminacatalysts: use of14C-labelled reactants, Appl. Catal. 30 (1987) 333338.

[4] G. Liu, D. Willcox, M. Garland, H.H. Kung,The role of CO2in methanol synthesison CuZn oxide: an isotope labeling study, J. Catal. 96 (1985) 251260.

[5] M. Muhler, E. Trnqvist, L.P. Nielsen, B.S. Clausen, H. Topse, On the role ofadsorbed atomic oxygen and CO2 in copper based methanol synthesiscatalysts, Catal. Lett. 25 (1994) 110.

[6] G.C. Chinchen, K.C. Waugh, D.A. Whan, The activity and state of the coppersurface in methanol synthesis catalysts, Appl. Catal. 25 (1986) 101107.

[7] W.X. Pan, R. Cao, D.L. Roberts, G.L. Griffin, Methanol synthesis activity of Cu/ZnO catalysts, J. Catal. 114 (1988) 440446.

[8] H. Wilmer, O. Hinrichsen, Dynamical changes in Cu/ZnO/Al2O3catalysts, Catal.Lett. 82 (2002) 117122.

[9] M. Kurtz, H. Wilmer, T. Genger, O. Hinrichsen, M. Muhler, Deactivation ofsupported copper catalystsfor methanol synthesis, Catal. Lett. 86 (2003) 7780.

[10] M.M. Gnter, T. Ressler, B. Bems, C. Bscher, T. Genger, O. Hinrichsen, M.Muhler, R. Schlgl, Implication of the microstructure of binary Cu/ZnOcatalysts for their catalytic activity in methanol synthesis, Catal. Lett. 71(2001) 3744.

[11] I. Kasatkin, P. Kurr, B. Kniep, A. Trunschke, R. Schlgl, Role of lattice strain anddefects in copper particles on the activity of Cu/ZnO/Al2O3 catalysts formethanol synthesis, Angew. Chem. Int. Ed. Engl. 46 (2007) 73247327.

[12] T. Fujitani, J. Nakamura, The effect of ZnO in methanol synthesis catalysts onCu dispersion and the specific activity, Catal. Lett. 56 (1998) 119124.

[13] N.-Y. Topse, H. Topse, FTIR studies of dynamic surface structural changes inCu-based methanol synthesis catalysts, J. Mol. Catal. 141 (1999) 95105.

[14] N.-Y. Topse, H. Topse, On the nature of surface structural changes in Cu/ZnOmethanol synthesis catalysts, Top. Catal. 8 (1999) 267270.

[15] Y. Choi, K. Futagami, T. Fujitani, J. Nakamura, The role of ZnO in Cu/ZnOmethanol synthesis catalysts morphology effect or active site model?, ApplCatal. 208 (2001) 163167.

[16] J. Nakamura, Y. Choi, T. Fujitani, On the issue of the active site and the role of

ZnO in Cu/ZnO methanol synthesis catalysts, Top. Catal. 22 (2003) 277285.[17] B.S. Clausen, J. Schitz, L. Grabaek, C.V. Ovesen, K.W. Jacobsen, J.K. Nrskov, H.Topse, Wetting/non-wetting phenomena during catalysis: evidence fromin situ on-line EXAFS studies of Cu-based catalyst, Top. Catal. 1 (1994) 367376.

[18] J.D. Grunwaldt, A.M. Molenbroek, N.Y. Topse, H. Topse, B.S. Clausen, In situinvestigations of structural changes in Cu/ZnO catalysts, J. Catal. 194 (2000)452460.

[19] P.L. Hansen, J.B. Wagner, S. Helveg, J.R. Rostrup-Nielsen, B.S. Clausen, H.Topse, Atom-resolved imaging of dynamic shape changes in supportedcopper nanocrystals, Science 295 (2002) 20532055.

[20] P.C.K. Vesborg, I. Chorkendorff, I. Knudsen, O. Balmes, J. Nerlov, A.M.Molenbroek, B.S. Clausen, S. Helveg, Transient behavior of Cu/ZnO-basedmethanol synthesis catalysts, J. Catal. 262 (2009) 6572.

[21] L.C. Grabow, M. Mavrikakis, Mechanism of methanol synthesis on Cu throughCO2and CO hydrogenation, ACS Catal. 1 (2011) 365384.

[22] M. Boudart, G. Djega-Mariadassou, Kinetics of Heterogeneous CatalyticReactions, Princeton University Press, Princeton, 1984.

[23] J.A. Dumesic, D.F. Rudd, L.M. Aparicio, J.E. Rekoske, A.A. Trevio, The

Microkinetics of Heterogeneous Catalysis, ACS Professional Reference Book,Washington, 1993.

450 475 500 525

0.0

1.0x10-6

2.0x10-6

3.0x10-6

4.0x10-6

5.0x10-6

6.0x10-6

7.0x10-6

RMeOH

/(mol/s/g

cat)

Temperature / K

9 % CO

Fig. A3. Comparison of the kinetic models;experimental,j power law model, dLHHW model, N microkinetic model, standard feed with varied CO content (9%),

p= 60bar, _V 100 Nml min1.

10 20 30 40 50 60

0.0

1.0x10-6

2.0x10-6

RMeOH

/(mol/s/g

cat)

Pressure / bar

Fig. A4. Comparisonof the kinetic models, total pressure variation; experimental,j power law model, d LHHW model, N microkinetic model, standard feed,T= 483 K, _V 100 Nml min1.

Table A4

Thermodynamic data, adsorbed species on Cu(110), adapted from Refs. [26,27,31,32].mare vibrational constants; the number in parentheses denotes the degeneracy of a specificfrequency.

Species Vibrational parameters Ground state energy (kJ mol1)

CH3O* m= 400, 37(2), 360(3), 1020, 1150(2), 1460(3), 2840, 2940(2) cm1 300

CH3OH* m= 290, 36(2), 360(3), 750, 820, 1030, 1150(2), 1470(3), 2860, 2970(2), 3320cm1 413

CO2* m= 410, 14(2), 1343, 667(2), 2349 cm1 463

H2COO* m= 405, 30(2), 400(3), 630, 960, 1090, 1220(2), 1420, 1480, 2920, 3000 cm1 568

H2O*

m= 460, 21(2), 1600, 3370, 3370, 745 cm1

365O* m= 391, 508(2)cm1 243OH* m= 280, 670(2), 3380 cm1 319H* m= 1291, 157(2) cm1 29a

HCOO* m= 322, 36(2), 400(3), 758, 1331, 1640, 2879, 1073, 1377 cm1 561CO* m= 342, 17(2), 2088 cm1 190

a Extracted from our own experiments[45],original value 27 kJ mol1, well in the range of reported accuracy (3 kJ mol1)[27,32].* surface site or adsorbed species, respectively.

http://www.siliconfire.com/http://www.siliconfire.com/


12/12

[24] G.H. Graaf, E.J. Stamhuis, A.A.C.M. Beenackers, Kinetics of low-pressuremethanol synthesis, Chem. Eng. Sci. 43 (1988) 31853195.

[25] O. Hinrichsen, Kinetic simulation of ammonia synthesis catalyzed byruthenium, Catal. Today 53 (1999) 177188.

[26] C.V. Ovesen, Kinetic Modeling of Reactions on Cu Surfaces, TechnicalUniversity of Denmark, 1992.

[27] C.V. Ovesen, B.S. Clausen, J. Schitz, P. Stoltze, H. Topse, J.K. Nrskov, Kineticimplications of dynamical changes in catalyst morphology during methanolsynthesis over Cu/ZnO catalysts, J. Catal. 168 (1997) 133142.

[28] K.M. Vanden Bussche, G.F. Froment, A Steady-state kinetic model for methanol

synthesis and the water gas shift reaction on a commercial Cu/ZnO/Al2O3catalyst, J. Catal. 161 (1996) 110.

[29] J. Solsvik, H.A. Jakobsen, Modeling of multicomponent mass diffusion inporous spherical pellets: application to steam methane reforming andmethanol synthesis, Chem. Eng. Sci. 66 (2011) 19862000.

[30] K. Klier, V. Chatikavanij, R.G. Herman, G.W. Simmons, Catalytic synthesis ofmethanol from CO/H2 IV. The effects of carbon dioxide, J. Catal. 74 (1982)343360.

[31] T.S. Askgaard, J.K. Nrskov, C.V. Ovesen, P. Stoltze, A kinetic model of methanolsynthesis, J. Catal. 156 (1995) 229242.

[32] C.V. Ovesen, B.S. Clausen, B.S. Hammershi, G. Steffensen, T. Askgaard, I.Chorkendorff, J.K. Nrskov, P.B. Rasmussen, P. Stoltze, P. Taylor, A microkineticanalysis of the watergas shift reaction under industrial conditions, J. Catal.158 (1996) 170180.

[33] C.V. Ovesen, P. Stoltze, J.K. Nrskov,C.T.Campbell, A kinetic modelof thewatergas shift reaction, J. Catal. 134 (1992) 445468.

[34] G.C. Chinchen, C.M. Hay, H.D. Vanderwell, K.C. Waugh, The measurement ofcopper surface areas by reactive frontal chromatography, J. Catal. 103 (1987)7986.

[35] O. Hinrichsen,T. Genger,M. Muhler,ChemisorptionofN2OandH2 forthesurfacedetermination of copper catalysts, Chem. Eng. Technol. 23 (2000) 956959.

[36] J.J.F. Scholten, J.A. Konvalinka, Reaction of nitrous oxide with copper surfaces,Trans. Faraday Soc. 65 (1969) 24652473.

[37] M. Caracotsios, Athena Visual Studio, .[38] H. Rabitz, M. Kramer, D. Dacol, Sensitivity analysis in chemical kinetics, Annu.

Rev. Phys. Chem. 34 (1983) 419461.[39] C.T. Campbell, Finding the rate-determining step in a mechanism: comparing

DeDonder relations with the Degree of Rate Control, J. Catal. 204 (2001)520524.

[40] G.H. Graaf, P.J.J.M. Sijtsema, E.J. Stamhuis, G.E.H. Joosten, Chemical equilibriumin methanol synthesis, Chem. Eng. Sci. 41 (1986) 28832890.

[41] T. Chang, R.W. Rousseau, J.K. Ferrell, Use of the Soave modification ofthe RedlichKwong equation of state for phase equilibrium calculations.

Systems containing methanol, Ind. Eng. Chem. Process Des. Dev. 22 (1983)462468.

[42] G. Soave, Equilibrium constants from a modified RedlichKwong equation ofstate, Chem. Eng. Sci. 27 (1972) 11971203.

[43] W. Kaminsky, WinXMorph: a computer program to draw crystal morphology,growth sectors and cross sections with export files in VRML V2.0 utf8-virtualreality format, J. Appl. Crystallogr. 38 (2005) 566567.

[44] W. Kaminsky, From CIF to virtual morphology using the WinXMorph program,J. Appl. Crystallogr. 40 (2007) 382385.

[45] M. Peter, J. Fendt, H. Wilmer, O. Hinrichsen, Modeling of temperature-programmed desorption (TPD) flow experiments from Cu/ZnO/Al2O3catalysts, Catal. Lett. 142 (2012) 547556.

[46] H. Wilmer, T. Genger, O. Hinrichsen, The interaction of hydrogen withalumina-supported copper catalysts: a temperature-programmedadsorption/temperature-programmed desorption/isotopic exchange reactionstudy, J. Catal. 215 (2003) 188198.

[47] M. Muhler, L.P. Nielsen, E. Trnqvist, B.S. Clausen, H. Topse, Temperature-programmed desorption of H2as a tool to determine metal surface areas of Cucatalysts, Catal. Lett. 14 (1992) 241249.

[48] M. Peter, J. Fendt, S. Pleintinger, O. Hinrichsen, On the interaction of carbonmonoxide and ternary Cu/ZnO/Al2O3 catalysts: modeling of dynamicmorphology changes and the influence on elementary step kinetics, Catal.Sci. Technol. (2012), http://dx.doi.org/10.1039/C2CY20189E.

http://www.athenavisual.com/http://dx.doi.org/10.1039/C2CY20189Ehttp://dx.doi.org/10.1039/C2CY20189Ehttp://www.athenavisual.com/

kinetic of methanol synthesis.pdf

Documents