automotive catalytic converter refining

121

8 Automotive catalytic converter

8.1 Introduction

Today, the overwhelming majority of vehicles is equipped with internal combustionengines that use fossil derived fuels. This on-road traffic represents the main source ofpollutant emissions of carbon monoxide, volatile organic compounds (VOC, unburnthydrocarbons), and nitrogen oxides as is shown in Table 8.1. The initiative to introducelegislation for the limitation of exhaust gas emissions from vehicles was taken in Cali-fornia in 1966. At first, improvements of the internal combustion process were sufficientto meet the demands. The continuous decrease of the limits by American legislationrequired the aftertreatment of the exhaust gas by automotive catalytic converters intro-duced in 1976 [195]. In 1985, exhaust gas pollution limits were introduced in Europe,which consequently also led to the utilization of automotive catalytic converters.

NOx CO VOC SO2

Total emissions [1000 tons] 11932 40964 13807 9386Road traffic’s share in emissions 40% 56% 31% 4%

Table 8.1: Pollutant emissions in Europe in 1997 [196].

The most frequently used design of a catalytic converter is a monolithic structure (Fig.8.1), which is coated with a washcoat that supports the catalyst material. The devel-opment of such a catalytic converter is a complex process involving the optimizationof different physical and chemical parameters. Simple properties such as monolithlength, cell density and metal loading of the catalyst influence the performance of theconverter. Numerical simulation using detailed models for the transport and chem-ical processes are expected to accelerate the design and optimization of automotivecatalytic converters.

Figure 8.1: Sketch of a three-way catalytic converter; courtesy of J. Eberspacher GmbH& Co.

In this Chapter, the modeling and numerical simulation of steady-state and transientprocesses in automotive catalytic converters are discussed. First the focus is on a

122 8. AUTOMOTIVE CATALYTIC CONVERTER

typical three-way catalytic converters (3WCC) as commonly used in passenger cars.The computational tools, developed in this work, will be applied to study the steady-state behavior in the single channel of the catalytic monolith as well as the transientbehavior of the whole monolith. In the second part, a DeNOx catalyst is investigated.Here, the focus is on the application of different transport models. In particular, theeffect of simplified models for the description of washcoat diffusion and flow field willbe discussed.

8.2 Three-way catalyst

Today, three-way catalytic converters are used extensively to reduce the pollutant emis-sions of internal combustion engines. The role of the 3WCC is the complete oxidationof carbon monoxide, formed in the combustion process, and unburned hydrocarbons toharmless water and carbon dioxide14, and the reduction of nitrogen oxides to molecularnitrogen. These three functions of the catalytic converter can chemically be written as

CO + 12

O2 → CO2

CnHm + (n+m4) O2 → nCO2 + m

2H2O

CO + NO → CO2 + N2 .

The simultaneous conversion of all these harmful species can only be ensured if theexhaust gas composition is very close to the stoichiometric ratio. This requirementis technically controlled by the lambda-sensor and accompanying electronics for en-gine control [32]. Meanwhile, the performance of 3WCC is almost perfect after thecatalytic reaction is ignited. However, at low operating temperatures, below 570 K,almost no conversion of the pollutant emissions occurs. Therefore, current researchand development focuses on the reduction of this start-up period.

The majority of automotive catalytic converters have a monolithic structure madeof ceramics or metals. The walls of the monolith channels are coated with the wash-coat, usually alumina, that supports the noble metal such as platinum, palladium andrhodium. The monoliths consist of numerous parallel channels with a diameter of ap-proximately 1 mm to have a large catalytic surface area. For the design of a catalyticconverter, several chemical and physical properties of both the catalyst and the ex-haust gas must be considered, for instance the cell geometry (length and diameter ofthe channels, wall thickness), the composition of the noble metal and catalyst loading,the use of catalyst promoters, and the properties of the exhaust gas (temperature,velocity and chemical composition).

The experimental characterization of the catalytic performance of the converter istime-consuming and requires an expensive and complex experimental setup. Numericalsimulation offers an interesting alternative for the investigation of the catalytic activityof a converter as a function of external conditions. This method is also efficient inanalyzing the transient flow and thermal phenomena in the catalytic converter and can

14The effect of CO2 emissions on global warming is not a topic of this work.

8.2 Three-way catalyst 123

help to understand the complex interactions between the flow field and the catalyticsurface chemistry.

In recent years, several proposals were made for the numerical simulation of cat-alytic converters [197–202]. In most of these studies, a global model for the chemistrywas used, which neglected the complex network of chemical reactions on the catalyticsurface. An alternate approach is the description of the chemical reactions by a set ofelementary-like reaction steps describing the chemical processes on a molecular level asdiscussed in Chapter 3.2 and by Chatterjee et. al for 3WCC [87, 203]. This approachis superior to any fitted global kinetics because it can be used to predict the catalystbehavior at different external conditions. However, also the transport processes haveto be described and coupled to the chemical reactions in an accurate manner.

Detailed models for the chemistry and the transport processes are applied in thisstudy of a 3WCC. The numerical simulations are carried out using the computationaltools discussed in Chapter 4. The numerically predicted conversion of pollutants iscompared with experimentally derived data.

The catalyst investigated is a commercially available tree-way catalyst [87]. Thecatalyst contains 50 g/ft3 of rhodium and platinum metal with a Pt/Rh ratio of 5/1.The noble metals were impregnated on a ceria stabilized γ-alumina washcoat. Thewashcoat was supported by a cordierite monolith with a cell density of 62 cells per cm2

(400 cpsi) and a wall thickness of 0.165 mm.At first, the transport and chemistry in a single channel of the catalytic monolith

is discussed for steady-state conditions. Then the transient behavior of the 3WCC atreal operating conditions is described.

8.2.1 Steady state conditions

Experimental

The experimental determination of conversion as a function of temperature in thethree-way catalyst was conducted in an isothermal laboratory-scale tube reactor.15 Asample of 22 mm in diameter and 29 mm in length was taken from the catalyst for thisinvestigation. Table 8.2 summarizes the composition and the species concentrations ofthe exhaust gas sample. The oxygen concentration was varied in order to simulate astoichiometric, rich and lean exhaust gas mixture. The λox-values are defined as theinverse of the redox ratio [197,201,87]:

λox =XNO + 2XO2

XCO + 9XC3H6

. (8.1)

With this definition λox equals unity at stoichiometric conditions, and it is larger(smaller) than unity for lean (rich) conditions. The volumetric flow of the exhaustgas was 15 l/min at standard conditions (298.15 K). A uniform superficial velocity of1.35 m/s corresponds to this volumetric flow rate. The reactor was heated with a tubu-lar furnace with a heat rate of 100 K/h, which led to an almost isothermal sample. The

15The experiment was carried out by S. Kureti and O. Gorke at the University of Karlsruhe.


species concentration [vol.%]

nearly stoichiometric rich lean(λox=0.9) (λox=0.5) (λoxs=1.8)

CO 1.42 1.42 1.42O2 0.77 0.9 1.6C3H6 0.045 0.045 0.045NO 0.1 0.1 0.1N2 balance balance balance

Table 8.2: Composition of the simulated exhaust gas used in the experiment andsimulation.

Noble metal composition Pt/Rh, 5:1Noble metal loading 50 g/ft3

Active noble metal surface 0.247 m2/gSurface ratio Pt(s)/Rh(s) 3:1Noble metal dispersion 29.1%Catalyst surface/geometric surface (Fcat/geo) 70Channel diameter 1 mmChannel length 29 mmSuperficial velocity (STP) 1.35 m/sMean pore diameter (micropores) 12.29 nmPorosity (micropores) 27.9 %Tortuousity 3Washcoat thickness 100µmActive catalyst surface/washcoat volume 7.77·105 m−1

Table 8.3: Catalyst parameters used in the numerical simulation of the 3WCC.

maximum temperature gradient over the sample was reported to be 40 K caused by theexothermicity of the oxidation reactions. The exit species concentrations were mea-sured in 20 K steps. The steady state of the system was ensured at each temperaturestep.

The CO2 concentration in the inlet exhaust gas was zero due to experimental rea-sons. Tests have shown that there was no difference in conversion of CO, HC and NOin measurements whether or not CO2 was added.

After the experimental studies of the temperature-dependent conversion, the samplewas investigated with H2 chemisorption in order to obtain the properties of the activemetal phase. The active metal surface of the catalyst was 28 m2/g and the dispersion ofthe conditioned catalyst was 33%. The calculation of the ratio of active metal surfacearea and geometric surface area (Fcat/geo in Eq. 3.28) of the catalyst led to a valueof 70. The ratio of the platinum to rhodium surface was chosen to be 3:1 taken fromliterature [204] for a catalyst with a noble metal composition of Pt/Rh with 5:1, whichcorresponds to the catalyst used for the investigations.


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Figure 8.2: Conversion of C3H6, CO, and NO at lean conditions as function of temper-ature [87].

Numerical

It can be assumed that all channels of the monolith behave essentially alike because anisothermal sample was used in the experiment, and the inlet conditions (flow velocityand gas composition) did not vary over the various monolith channels. Therefore,only one channel needs to be analyzed. The single-channel is assumed to be a tube-likereactor with the flow inside this reactor being laminar. Hence, the single channel of themonolith can be modeled by the axisymmetric two-dimensional Navier-Stokes equationswith the axial and radial directions as spatial variables as discussed in Chapter 2.2.1.The flow field simulation is based on the CFD code FLUENT coupled with the chemistrymodule DETCHEM as described in Chapter 4.3.1.

The flow enters the computational domain with a known velocity, gas compositionand temperature given by the experimental conditions. A flat profile of the axialvelocity and a vanishing radial velocity are used in the simulation at the inlet boundary.At the reactor exit, an outlet boundary is applied at which values for all variables areextrapolated from the interior cells adjacent to the outlet. A structured grid is usedfor the simulation. The grid has to be very fine around the catalyst entrance and thecatalytic wall to resolve the flow field and also to determine the variations in the speciesconcentrations due to chemical reactions at the catalytic wall. The total number ofcomputational cells used for the single channel were 20 cells in radial direction and 72


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Figure 8.3: Conversion of C3H6, CO, and NO at nearly stoichiometric conditions asfunction of temperature [87].

cells in axial direction.

A detailed multi-step reaction mechanism is used to model the catalytic reactionsand to calculate the surface mass fluxes. The surface coverage of the species on thecatalytic material is also calculated as a function of the position in the channel. Themechanism includes only surface chemistry; gas phase chemistry can be neglected be-cause of the low pressure and temperature, and the short residence time.

The sample exhaust gas mixture is composed of C3H6, CO, NO, O2, N2 with con-centrations as given in Tab. 8.2. The surface reaction scheme consists of 61 reactionsteps among 27 chemical species, e.g., dissociative oxygen adsorption, non-dissociativeadsorption of C3H6, CO and NO, the formation steps of carbon dioxide, water andnitrogen, and desorption reactions for all species. Some activation energies (e.g., oxy-gen desorption) are coverage-dependent due to interactions between adsorbed species.It is assumed that all species are adsorbed competitively. The model also considersthe different adsorption sites (platinum or rhodium) on the metallic catalyst surface.However, on rhodium, surface reactions only between NO, CO, and O2 are consid-ered [49, 205]. The kinetic data of the mechanism were taken either from literature orfits to experimental data. Parts of the surface reaction mechanism were already usedfor numerical modeling of catalytic ignition (Chapter 7 and [45]), simulation of totaland partial oxidation of light hydrocarbons on platinum (Chapter 6 and [49]) and mod-eling the CO-O2 and NO-CO reactions on rhodium [205, 206]. A detailed description


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Figure 8.4: Conversion of C3H6, CO, and NO at rich conditions as function of temper-ature [87].

of the reaction mechanism can be found in reference [203]; the mechanism is given inTable B.5. In the simulation, the simplified washcoat model (Chapter 3.3.2) is usedwith CO as species that determines the effectiveness factor (Eq. 3.49).

Results and discussion

The experimental conditions are applied to the simulation. Table 8.3 summarizes theinput data for the simulation; the concentrations of the incoming exhaust gas arealready given in Tab. 8.2. The gas flows at a uniform velocity into the cylindric tube.The temperature is set to the furnace temperature.

In Fig. 8.2, the conversion data of CO, C3H6, and NO are shown as function oftemperature. The composition of the inlet gas mixture is lean. The conversion ofCO, C3H6, and NO starts at 570 K and increases up to 100% for CO and C3H6 at770 K and 670 K, respectively. The NO conversion shows a maximum at 630 K anddecreases at higher temperatures. The predicted conversion of all three species agreewell with the experimentally measured data. Especially the temperature behaviorof the NO conversion and the slow increase of the C3H6 conversion is well-predictedin the temperature range above 630 K. The simulation shows some CO conversionat temperatures lower than 570 K. This behavior is probably caused by the missingreactions for hydrocarbons on rhodium in the mechanism. Therefore C3H6 cannot


block the CO oxidation on Rh in contrast to the CO oxidation on Pt.

The conversion data as a function of the temperature for the nearly stoichiometricmixture are shown in Fig. 8.3. Again, conversion of CO, C3H6, and NO starts at 570 K,but increases more slowly with temperature compared to the lean mixture. Because ofthe insufficient amount of O2 in the mixture, CO conversion is not complete. Completeconversion of C3H6 is reached at 770 K, which indicates that C3H6 can compete withCO for O2. Also, for temperatures higher than 720 K, NO reduction is completed.Concerning the conversion of CO and NO and the competition between CO and C3H6

for O2, the simulation results agree well with the experimental data. Only C3H6 rateshows deviations between 610 K and 690 K. Compared to the experimental data, thepredicted conversion is too high.

The rich mixture, as shown in Fig. 8.4, contains 0.4 vol.% O2, which leads to amaximum of CO conversion of only 33%. The conversion of NO reaches 100% for tem-peratures higher than 800 K. In comparison with the results of the two other mixtures,the increase of the conversions of CO, C3H6 and NO with temperature is slower, inparticular for C3H6 conversion. Regarding the CO and NO conversion, the simulationsagree with the experimentally determined data. However, large deviations exist be-tween the predicted C3H6 conversion and the experimental data. These deviations canbe explained by the fact that in the rich regime a wider variety of surface species (e.g.,partial-oxidation products of C3H6) resides on the catalytic surface, which can reducethe oxygen coverage and lead to different reaction paths. In the reaction mechanismused in this study not all surface species possible are included. Further reactions andsurface species have to be included to improve the prediction of the C3H6 conversionat rich conditions.

In Fig. 8.5, the mass fractions of C3H6, CO, CO2, and NO for the lean mixture(Tab. 8.2) within the channel are shown for a temperature of 673 K . The input dataare as given in Tab. 8.3. The mass fraction profiles show that most of the propyleneis converted within the first centimeter. In this axial range, CO is almost completelyconverted. The NO conversion is limited to the first centimeter and vanishes furtherdownstream. This behavior can be explained by the surface coverages. The calculatedcoverages of the most relevant surface species on platinum and rhodium are shown asfunction of the axial position along the channel in Fig. 8.6. The coverages are definedin respect of the whole catalytic area, consisting of rhodium and platinum. A strongcoverage variation is revealed on the Pt surface at an axial position of 1.1 cm due tothe decreasing CO concentration in the gas phase. There, the surface state shifts froma mainly CO(s)-covered to an O(s)-covered state. During this transition the numberof free platinum sites, Pt(s), increases, which allows more NO to be adsorbed anddissociated. Where the surface finally reaches the O(s) covered state, the number offree platinum sites is decreased, and the equilibrium of NO dissociation is shifted toNO(s) resulting in a vanishing NO conversion.

The rhodium surface shifts from a N(s)-covered state to an O(s)-covered state.The O(s)-covered surface also prevents NO conversion on Rh. In front of the axialposition of 1 cm the surface is mainly covered with N(s) and active for NO conversion.With increasing reaction temperature, the transition point moves toward the channelentrance, which reduces the area that is active for NO conversion. In Fig. 8.2 the


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Figure 8.5: Mass fraction profiles in a single channel of the 3-way catalyst monolithat 673 K; lean conditions (λox=1.8) [87]. Different scales are used in axial and radialdirection for visual clarity.


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Figure 8.6: Pt and Rh surface coverages along the catalytic wall in a single-channel ofa 3-way catalyst monolith at 673K; lean conditions (λox=1.8) [87].

resulting decrease of NO conversion with increasing temperature is shown.A typical feature of the three-way converter is the λ-window behavior. The λ-

window is the narrow range around the stoichiometric air/fuel ratio, in which hy-drocarbons, CO, and NO are simultaneously converted with high efficiency. Fig. 8.7presents the predicted conversion for the catalytic converter tested for various exhaustgas compositions. The chosen temperature of 773 K is a typical catalyst inlet tem-perature for partial load [32]. The experimental data were already presented in theFigs. 8.2, 8.3, and 8.4. The simulation predicts the experimental data well in the leanregion. In the rich regime the CO conversion is also predicted well, while there aremajor deficiencies for C3H6 conversion as discussed above.

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Figure 8.7: Experimentally determined and numerically predicted conversion of C3H6,CO, and NO as function of the fuel/oxygen ratio (λox - window) at 773 K [87].


8.2.2 Transient conditions

The ultimate goal in numerical simulation of automotive catalytic converters is theprediction of exhaust gas emissions as function of time for varying inlet conditions, i.e.,the simulation of a driving cycle. Such a simulation must include the calculation of thetransient three-dimensional temperature-field of the monolithic solid structure of theconverter, which results from a complex interaction of a variety of physical and chemicalprocesses: (1) time-dependent and spatially-varying exhaust gas conditions (tempera-ture, species concentrations, velocity), (2) heat conduction with spatially varying andtemperature-dependent thermal conductivity in different solid media (ceramic or metalmonolith structure, insulation material, canning), (3) heat transport between exteriorconverter wall and the ambient air by conduction, convection, and thermal radiation,(4) axial and radial mass transport including pore diffusion in the washcoat in thesingle monolith channels, (5) the chemical reactions and heat release on the catalyst inthe single-channels, (6) heat transfer between fluid phase, catalyst/washcoat and solidconverter structure. Then, the integration over the chemical conversion in the singlechannels leads to the total conversion of the monolith as function of time.

The computer program DETCHEMMONOLITH [92, 93, 81], which has been discussedin Chapter 4.3.4, can be applied for the numerical simulation of the transient behaviorof automotive catalytic converters with a monolithic structure. All effects mentionedabove can be included.

As an example, a numerical simulation of the start-up period of a 3WCC is per-formed and discussed now. The hot exhaust gas is fed in the initially cold catalyticconverter, which heats up the converter and eventually ignites the catalytic reactionsleading to the conversion of the harmful pollutants. The converter including the mono-lithic structure, the fiber insulation, and the metal canning has a cylindric shape witha total diameter of ∼10 cm and a length of ∼20 cm. A sample exhaust gas composed ofC3H6, O2, CO, NO, and N2 flows at a spatially and temporally constant composition,temperature (673 K), and superficial velocity (3 m/s) in the initially cold (300 K) cat-alytic converter. The catalyst parameters are applied as in the investigation describedin Chapter 8.2.1 and [87]) again.

The heat transport model in the numerical simulation uses axial and radial varying,temperature-dependent thermal conductivity for all three solid materials (monolith,insulation, canning). The simplified washcoat model is applied, where the efficiencyfactor refers to the species CO. The chemistry model is based on the surface reactionscheme discussed above. Radiative and convective heat losses at the exterior boundaryof the converter are taken into account.

For example, Fig. 8.8 reveals the temperature profile in the solid converter structureand the CO mass fraction in one of the many single channels at three different timeperiods after starting the engine. Five seconds after start-up, only the front end of themonolith is already warmed up slightly, leading to little CO conversion. After 15 s, themaximum catalyst temperature already is significantly higher the the inlet gas tem-perature. Now, considerable chemical reactions occur; the exothermic CO oxidation isalmost complete, although only the front end of the converter is hot. The exothermicityof the oxidation reactions (CO and C3H8) lead to this peak temperature. At that time,


T (K)800767733700667633600567533500467433400367333300

YCO0.01420

t = 5 s

t = 15 s

t = 100 s

Figure 8.8: Two-dimensional temperature profiles of a 3WCC (left) and CO massfractions in a single channel located in the center of the monolith (right) at 5, 15, and100 s after start-up of the engine; the converter, 20 cm in length and 10 cm in diameterincluding insulation and canning, is initially is at 298 K; inlet gas conditions (spatialand temporal constant): u = 3 m/s, T= 673 K, vol.% of C3H6=0.00045, O2=0.016,CO=0.0142, NO=0.001, N2=0.96835.

conversion in the outer channels is still low (not shown), since the temperature is lowerdue to the heat loss into the ambient air. A large region of the converter has reachedits operating temperature after 100 s. However, the CO profile in a single-channel inthe center of the catalyst does not differ from the one at 15 s of operating time. Theseresults elucidate that the overall reaction rate of CO oxidation is already mass transferlimited.

Similar simulations have been carried out for temporally and spatially varying inletconditions [93]. The computation of an complete driving cycle for passenger vehiclesas given by European exhaust gas legislation [196] will take approximately 10 hoursCPU-time on Apple Mac G4 processors. In these simulations, between ten and twentyrepresentative channels are simulated at each time step in parallel manner, i.e., eachprocessor solves one channel.

8.3 DeNOx catalyst 133

8.3 DeNOx catalyst

In spite of the enormous achievements in the aftertreatment of exhaust gas emissions,the worldwide increasing number of vehicles represent a serious environmental problemdue to vehicles’ raw emissions, in particular, carbon dioxide, which has a strong impacton the greenhouse effect. A more efficient fuel consumption can be realized in Diesel andlean-operated engines, i.e., in excess of air (oxygen). Here, the problem is the formationof nitrogen oxides (NOx).

16 Because improvements of the combustion process itself arenot sufficient to meet future legislative limits, the development of a technique for theaftertreatment of NOx is a hot research topic today [207–209,88].

Aside from NOx reduction by plasma discharge techniques, all current concepts arebased on the utilization of catalysts. The demand of a reducing agent in the oxygen-rich mixture can be met by the addition of such a reducing agent, for instance ofhydrocarbons derived from the fuel (HC-SCR17 technique).

In this section, the HC-SCR on a Pt/Al2O3 catalyst is discussed using propylene asreducing agent [88,89]. In the experimental18 and numerical investigations, a monolithiccatalyst was used and isothermal and steady state conditions were applied. Therefore,only one single-channel needs to be analyzed again.

The numerical study also focuses on the application of various transport models forthe description of radial mass transport in the fluid (Chapter 2) and diffusion in thewashcoat (Chapter 3.3). Those models are compared to understand what kind of modelcomplexity is needed for an accurate description of the DeNOx catalytic converter.

In all cases simulated, a detailed surface reaction mechanism is used. This mecha-nism consists of 62 reactions among 20 surface species and the gas phase species C3H6,NO, NO2, N2O, N2, CO, CO2, H2, OH, H2O, and O2. The reaction kinetics is based onthe concept discussed in Chapter 3.2.2. For more detailed information on the reactionmechanism the reader is referred to [88, 89]. The mechanism is given in Table B.6.Gas-phase reactions are neglected because they are not significant at the conditions(temperature, pressure, residence time), at which the converter is operated.

8.3.1 HC-SCR with C3H6

In the experiment and simulation, a premixed gas, containing 500 ppm NO, 500 ppmC3H6, and 5 vol.% O2 in nitrogen dilution, flows at a superficial velocity of 0.633 m/s(standard conditions) in the channels of the monolithic structure. All the catalystparameters are given in Tab. 8.4.

Figure 8.9 presents the predicted and experimentally determined conversion ofC3H6, NOx, N2O, and NO2 as function of temperature and at different catalyst lengths.The simulation applies the plug-flow model (Chapters 2.2.3, 4.3.3 and Reference [88])with an additional mass transfer coefficient, which accounts for radial mass transport.Furthermore, the detailed washcoat model is used as discussed in Chapter 3.3.2.

In general, a good agreement between predicted and measured data could beachieved, the minor differences can also be caused by deviations in the washcoat thick-

16The 3WCC only works at stoichiometric conditions.17HC-SCR = hydrocarbon selective catalytic reduction18The experiments were carried out by E. Frank and W. Weisweiler at the University of Karlsruhe.


Monolith diameter 20 mmMonolith length 5 - 60 mmSingle-channel diameter 1 mmNumber of channels 201Catalytic material Pt/Al2O3

Pt-loading 1 weight-%Pt-dispersion 17%Active Pt-surface 0.22 m2/gActive catalyst / geometric surface (Fcat/geo) 60.44Mean diameter of the Pt-particles 6.5 nmBET-surface 27 m2/gPore volume 0.7257 cm3/gMean diameter of the micropores 4 nmPorosity of micropores 39.5%Mean diameter of the macropores 5.98 µmPorosity of macropores 10%Washcoat thickness 100µm

Table 8.4: Characteristic parameter of the catalyst used.

ness and catalyst dispersion in the different catalyst samples used. These parameterswere not experimentally determined for each single sample.

Although NO reduction at lean conditions can be achieved, most of NO (70-80%selectivity) is converted into N2O. The emission of laughing gas (N2O) is currently notlimited by legislation but is is known as harmful greenhouse gas, which really is theproblem with the commercial application of the HC-SCR/Pt system. The reason forthe observed selectivity is the total high coverage of the Pt-surface while N(s)-coverageis low. This favors the reaction NO(s) + N(s) → N2O and lowers the probabilityof N(s) recombination to molecular nitrogen. The selectivity to N2 formation couldbe increased if the use of promoters led to a higher activation energy for nitrogendesorption.

The correlation between conversion and surface coverage is revealed by Figs. 8.10and 8.11, in which the species profiles in the single channel and the surface coveragealong the channel wall inside the washcoat are presented. The results are based on asimulation using the Boundary-Layer model (Chapters 2.2.2 and 4.3.2) and the detailedwashcoat model (Chapter 3.3.2). The oxidation of C3H6 occurs within the first 2 cmchannel length. The reduction of NO is limited to the first 1.5 cm channel length,behind that, NO2 formation occurs. While the surface is mainly covered by CO atthe channel entrance, oxygen becomes the primary adsorbate downstream of an axialposition of 1.6 cm. Yet the oxygen poisoned surface is inactive for NO reduction andleads to NO oxidation instead. Hence, the width of the reaction zone of propyleneoxidation determines the zone, in which NOx can be destroyed.

The axial surface coverage at a certain washcoat depth, 0.45µm in Fig. 8.11, reallyis a simplification and does not describe the more complex picture of the interaction ofdiffusion and reaction inside the washcoat. Figure 8.12 presents the surface coverages as


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Figure 8.9: Comparison of experimentally determined and numerically predicted con-version as function of temperature and catalyst length (from top to bottom: 60 mm,15 mm, 5 mm). N2O and NO2 conversion means the fraction of that species formedreferring to the NOx conversion; NOx includes NO and NO2.


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Figure 8.10: Mole fraction distribution in a single monolith channel at 570 K; otherconditions as before.

0 1 2 3 4 5 6z [cm]

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rage

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Figure 8.11: Surface coverage along the catalytic channel wall inside the washcoat,0.45µm away from the fluid/washcoat boundary, at 570 K; other conditions as before.


function of axial and radial position inside the washcoat. The CO(s) and O(s) profilesreveal that the shift from CO(s) to O(s) coverage depends on the radial position insidethe only 100µm thick washcoat layer. Even at the washcoat entrance, a primarilyoxygen covered surface exists in 80µm washcoat depth. The other profiles reveal thatthere are axial and radial variations of the surface coverages and, hence, the reactionrates vary inside the washcoat.

8.3.2 Comparison of transport models

Now the application of different transport models of various complexity for modelingautomotive catalytic converters will be emphasized. In particular, there is a strongimpact of radial mass transport in the fluid and in the washcoat at high space velocitiesand at the catalyst entrance.

If radial mass transport is neglected in the fluid model, total conversion is over-predicted by the numerical simulation. For example, Fig. 8.13 compares a pure Plug-Flow simulation and a Plug-Flow simulation with mass transport coefficient and de-tailed washcoat model. At 5 mm channel length, which corresponds to a space velocityof 216000 h−1, propylene is already completely gone, which clearly contradicts experi-mental observation and the simulation with more complex transport models.

Because the gradients of the species concentration are relatively small at the condi-tions chosen, it is justified to use the PF model with mass transfer coefficient. Figure8.14 compares simulations using the BL model and the PF model with mass transfercoefficients, both simulations apply the same detailed washcoat model. There are novisible differences in the conversion data predicted. In contrast to that, a simulation us-ing the PF model without mass transfer coefficients fails to predict the right propyleneconversion as shown in Fig. 8.15.

In Chapter 3.3.2, a simple washcoat model based on an effectiveness factor wasdiscussed. In Fig. 8.16, two simulations are compared, which both use the PF-modelwith mass transport coefficients but different washcoat models: In the simple washcoatmodel, the effectiveness factor was determined for the propylene species because it is themost crucial reacting species as discussed above. The detailed washcoat model is basedon the solution of an additional one-dimensional reaction-diffusion equation for eachchemical species (Chapter 3.3.2). While the differences remain relatively small for thepredicted C3H6 conversion, the predicted NO2 formation considerably differs betweensimple and detailed model. This fact leads to a general problem when using effectivenessfactors: After light-off of C3H6 oxidation, the C3H6-consumption is primarily limitedby radial mass transport, while the NO oxidation is still controlled by chemical kinetics.In such situations, the simplified washcoat model cannot be applied.

Summarizing, it is difficult to a-priori judge whether or not simplified transportmodels can be applied. The application of the simplified model has to be checkedfrom case to case. If simplified models are accurate enough, their application can savetremendous computing time. For instance, the simplified washcoat model works finein the transient 3WCC simulations if the reference species for the effectiveness factoris chosen according to the species concentrations. As shown here, it fails in a similarsystem. Therefore, it is pointed out that the most detailed model should always be


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Figure 8.12: Surface coverage with CO(s), O(s), NO(s), N(s), N2O, and NO2 as func-tion of axial and radial position in the washcoat layer; conditions are as before; thefluid/washcoat boundary is at r= 0.


applied as a first step. Especially, if the simplified models involve unknown parameters,e.g., for the description of the reaction kinetics, it is very risky to fit these parametersto experimental data without having checked the accuracy of all the transport models.Otherwise the fitted parameters will compensate for the errors in the transport model,and conclusions drawn from the simulations are arbitrary.

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Figure 8.13: Impact of radial mass transport on conversion as function of temperatureat a catalyst length of 5 mm (left) and as function of axial position in the monolithicchannel at a temperature of 570 K; other conditions are chosen as before.

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Figure 8.14: Comparison of conversion as function of axial position using the BL modeland PF model with mass transfer coefficients (both simulations include the detailedwashcoat model) at a temperature of 570 K (left) and 800 K (right); other conditionsare chosen as before.


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Figure 8.15: Impact of the radial mass transport in the fluid phase. Comparisonof experimentally determined conversion vs. temperature with numerical simulationsusing the PF model with and without mass transfer coefficients (both simulationsinclude the detailed washcoat model); other conditions are chosen as before.

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Figure 8.16: Comparison of the washcoat models: Numerically predicted conversionvs. temperature for a 6 cm long catalyst (left) and vs. axial position at 570 K (right)for simulations using the PF model with mass transfer coefficients; other conditionsare chosen as before.

141

9 Conclusions

Experimental and numerical investigations were carried out to achieve a better under-standing of the interactions between mass and heat transport and homogeneous andheterogeneous chemical reactions in catalytic reactors. Gaseous chemically reactingflows with heterogeneous reactions on solid surfaces are considered. The objective ofthis research was the development of models and computer programs for the numericalsimulation of heterogeneous reactive flows.

In the experiments, catalytic reactors were studied for the partial and completeoxidation of light alkanes at short contact times. The noble metals rhodium and plat-inum served as catalyst. The conversion of the reactants and product selectivity weredetermined by gas chromatography and mass spectrometry; the latter technique canalso be used for transient measurements. The temperature was determined by thermo-couples. The reactor behavior was studied at varying conditions such as the flow rate,temperature, dilution by an inert gas, and hydrocarbon/oxygen ratio. The experimen-tal investigations served as basis for the development of reliable models. Experimentaldata from collaborating research groups were also used as further independent sourcesto judge the quality of the models applied.

A concept for modeling catalytic reactors was presented, in which all physical andchemical processes were described as detailed as possible. These models were combinedto understand and, eventually, optimize the behavior of the catalytic reactor. The flowfield was modeled by the multi-dimensional Navier-Stokes equations coupled with anenthalpy equation and one further governing equation for each chemical species. Themodel includes temperature- and composition-dependent transport coefficients. Thechemistry in the gas phase was described by elementary-reaction mechanisms. Theheterogeneous reactions of gas-phase species on the solid surfaces were described bymulti-step reaction mechanisms, which were also based on the elementary processes.

In most previous studies, the numerical simulation of catalytic reactors is basedon simplifying assumptions, either for the flow field or the chemistry. For instance,the flow field in tubular reactors is frequently assumed to be plug-flow-like, neglectingany effect of radial mass transport, or the chemical processes are described by globalreactions with kinetic data fit to limited experimental measurements. Those modelscan hardly be used for prediction or optimization of the reactor behavior.

In the concept presented in this work, the numerical simulations were based on themost accurate models available. For instance, a monolithic reactors with rectangular-shaped channel cross-sections were first modeled by a three-dimensional flow field sim-ulation. Then, the results of these detailed simulations were compared with those ofsimulations applying simpler models. This comparison allowed the potentials and lim-itations of the simplifying assumptions to be recognized. It was found that the channelflow in a catalytic monolith can be described by an axisymmetric flow field. Further-more, axial diffusion can be neglected at high flow rates leading to the Boundary-Layerequations. The simple Plug-Flow model failed to predict the reactor behavior cor-rectly. It was also shown that an extended Plug-Flow model, which includs a radialmass-transfer coefficient, works well for relatively slow reactions such as occurring inautomotive catalytic converters. However, this model cannot be used for very fast

142 9. CONCLUSIONS

reactions as in catalytic combustion monoliths, because no correlation was found toestimate proper mass-transfer coefficients.

In the models developed, the heterogeneous reactions of the gas-phase species oncatalytic surfaces are described by multi-step reaction mechanisms, based on the mole-cular processes at the gas-surface interface. The model takes the coverage dependenceof the reaction kinetics into account. The surface coverage with adsorbed species de-pends on the position in the reactor and, therefore, is computed at any position inthe reactor. This approach is superior to frequently used global reaction schemes, be-cause it permits the prediction of the reactor behavior at varying external conditions,and can eventually be used for optimization. The developed surface reaction mecha-nisms are based on the mean-field approximation, in which the surface is assumed tobe homogeneously covered on a microscopic scale, and the kinetic data are averagedover microscopic local inhomogeneities. A strategy was presented for the developmentof detailed surface reaction mechanisms under the mean-field approximation. Thelimitations of this approach were also discussed. The mean-field approximation, forinstance, fails if the concrete lateral interactions of the adsorbates including diffusionand recrystallization phenomena have to be explicitly taken into account. A more so-phisticated model is a Monte-Carlo (MC) simulation of the chemical processes on thesurface, coupled with the reactive flow. MC simulations have already been used forsimple reaction systems (e.g., CO oxidation on Pt) and flow configurations (e.g. 1Dstagnation point flows) [70]. However, MC simulations have not yet been coupled withmulti-dimensional flow configurations due to the tremendous computing time needed.

Further research on the heterogeneous kinetics shall focus on the determination ofreaction schemes and kinetic data at atmospheric pressure and on real catalysts tobridge the pressure and materials gap between most of the surface science experimentsand applied heterogeneous catalysis. High pressure scanning tunnel microscopy andnonlinear optic techniques such as sum frequency generation vibrational spectroscopy[5, 6] will be of great use on this path.

The surface of the solid catalyst support is frequently coated with a layer of highsurface area material (washcoat), in which the catalyst is dispersed. Transport ofchemical species inside the washcoat and the interaction of transport and chemicalreactions can be crucial in determining the behavior of the catalytic reactor. Twomodels were applied for the description for the chemical and transport processes inwashcoats. The simpler washcoat model calculates an effectiveness factor that limitsthe overall reaction rate. This model can be applied if the reaction rate of one chemicalspecies determines the whole process. In the more sophisticated model, a set of one-dimensional reaction-diffusion equations is solved in combination with the flow fielddescription. Both models were coupled with the equations for the description of thesurrounding flow field in the reactor and applied for the numerical prediction of conver-sion in automotive catalytic converters. The simpler model showed major deficiencies;the conversion was accurately predicted only for that species, on which the calculationof the effectiveness factor was based.

Several computational tools have been developed for the numerical simulationof reactive flows including heterogeneous chemical reactions. The software packageDETCHEM is a module for the application of multi-step reaction mechanisms in CFD

143

codes such as FLUENT. The code DETCHEMCHANNEL simulates the reactive flow incylindrical and annular channel with catalytic active walls based on a Boundary-Layermodel. DETCHEMMONOLITH was written for the numerical simulation of the tran-sient behavior of catalytic monoliths. In that code, the three-dimensional heat balanceequation for the monolithic structure is solved in combination of the simulation of thereactive flows in the single channels.

The new approach for modeling catalytic reactors and the computational tools wereapplied to investigate several catalytic reaction systems that have recently attractedstrong scientific and technological interest.

The catalytic partial oxidation of methane to synthesis gas on rhodium coatedmonoliths at short contact times is a promising route for natural gas conversion intomore useful chemicals. The numerical simulation of this autothermally operated reac-tor revealed fast variations of all transport properties at the catalyst entrance, wherethe initially cold methane/oxygen mixture enters a ∼1300 K hot monolith. The con-version of methane starts right at the catalyst entrance, where a strong competitionbetween partial and complete oxidation occurs. After 1 mm catalyst length, oxygen iscompletely consumed and steam reforming is the determining reaction path. Experi-mentally determined and numerically predicted conversion and selectivity data agreedvery well. The formation of synthesis gas was studied at varying conditions, e.g., thesyngas selectivity and methane conversion decrease with increasing flow rate. Homo-geneous gas-phase reactions become significant at pressures above 10 bar. They leadto a decrease in syngas selectivity and also increase the risks of flames. Light-off of thereactor was studied by a transient two-dimensional simulation of the whole monolith,which elucidated the impact of the temperature distribution in the monolithic structureon overall conversion. Reactor design and scale-up can benefit from the application ofthe models and computational tools developed.

The oxy-dehydrogenation of ethane to ethylene was investigated in platinum coatedmonoliths at contact times of ∼5 ms. The observed performance of the ethane reac-tor is the result of coupled heterogeneous and homogeneous chemical processes. Thevaluable ethylene product of this reactor results from both homogeneous and heteroge-neous dehydrogenation of ethane, the relative contributions of each depending on thereactor conditions. Heat is required to drive this highly endothermic dehydrogenation.It is concluded that this heat is provided through heterogeneous oxidation reactionsthat occur very near the front of the catalytic section of the reactor. Conditions thatresult in optimum reactor performance are most strongly characterized by a picture ofheterogeneous oxidation in conjunction with a relatively large contribution from homo-geneous ethane dehydrogenation. The addition of H2 to the inlet feed of this reactorresults in surface hydrogen oxidation at the expense of surface carbon oxidation, pro-ducing even more highly localized heat release into the gas-phase. Homogeneous ethanedehydrogenation is also enhanced, while heterogeneous decomposition and oxidationto CO and CO2 is diminished. The selectivity to ethylene increases because selectivityto CO and CO2 is decreased. Desorption of radical species does not play an importantrole in the initiation of homogeneous reactions under the conditions studied. Localizedheat release and high peak temperatures in the reactor are responsible for the shortresidence times required to initiate the homogeneous chemistry vital to the reactor

144 9. CONCLUSIONS

performance. As such, it is important to incorporate full heat and mass transport inthe simulation in order to capture the spatial distribution of homogeneous reactions,and an accurate wall temperature profile.

Further catalytic-supported gas-phase partial oxidation reactions of paraffines suchas propane, butane, pentane, and cyclohexane have recently attracted wide interestalso. The reactors used are noble-metal-coated monoliths and Pt/Rh wire gauzes.The homogeneous and heterogeneous chemical reactions in those systems are stronglysuperimposed by heat transfer effects, in particular by fast thermal quenching of theproducts, which leads to a highly non-equilibrium selectivity. The synthesis of olefins,aldehydes, and oxygenates by partial oxidation of these alkanes is not yet understood.The present work, that dealt with partial oxidation of the light alkanes methane andethane, also represents a first step towards a better understanding of the partial oxi-dation of the higher alkanes. Future work will also consider the more complex three-dimensional flow fields and the development of reliable reaction mechanisms for thosereactions. This modeling calls not only for heterogeneous reaction schemes for theoxidation of higher hydrocarbons but also for homogeneous gas-phase reaction mecha-nisms at fuel-rich conditions and low temperatures.

Light-off of heterogeneous reactions in the catalytic combustion of natural gas de-mands sophisticated techniques, because the catalytic reaction ignites at temperaturesabove 800 K. The ignition temperature can be lowered by adding hydrogen to theinlet gas, since hydrogen oxidation on the catalyst (Pt, Pd) happens almost at room-temperature. Hydrogen-assisted ignition of methane oxidation in a platinum coatedmonolith was numerically simulated by the solution of the transient three-dimensionaltemperature equations of the solid monolith structure coupled with detailed modelsfor the chemical reactions and flow field in the single channels of the monolith. It wasfound that, shortly after light-off, a large amount of methane is converted in the hotinner channels, while almost no methane is consumed in the colder exterior channels.

The investigated catalytic radiant burner is characterized by a more complex two-dimensional flow field and several modes of heat transfer, including external heat loss bythermal radiation, internal surface-to-surface radiation, and heat conduction throughsolid reactor walls. Therefore, several days of CPU time were needed for the numericalsimulation of the burner with the CFD code FLUENT coupled with DETCHEM. How-ever, the efforts were rewarded by a deeper insight into the interaction of flow, heattransfer, and chemistry in the burner. The predicted temperature, conversion and se-lectivity at the burner exit as well as the temperature of the radiative burner plateagreed well with the experimentally determined data. The computational tool cannow be used for burner design and optimization. Future work will also take gas-phasechemistry into account, which might be necessary to accurately describe the formationof pollutants such as CO.

The performance of automotive catalytic converters was investigated. Here, masstransport, heat transfer, and heterogeneous reaction kinetics are superimposed by dif-fusion of the reactive species in the pores of the washcoat. First, the simultaneousoxidation of carbon monoxide and propylene as a sample hydrocarbon, and the reduc-tion of nitrogen oxides was studied at steady-state conditions for a commercially usedthree-way catalyst. The numerical simulation of the behavior of a single channel of

145

the monolithic structure is based on a two-dimensional flow field description coupledwith a model for washcoat diffusion and a surface reaction mechanism on platinum andrhodium consisting of 65 reactions. Conversions of C3H6, CO, and NO were studiedas functions of temperature at lean, stoichiometric, and rich conditions. The model,for instance, explained the increase of NO conversion with increasing temperature atlow temperature and the decrease at higher temperatures by the variation of the sur-face coverage. A good agreement between experimentally determined and numericallypredicted conversion was achieved. Only in the rich regime, larger deviations wererecorded for the conversion of C3H6, which may be caused by partial oxidation stepsmissing in the reaction mechanism.

For the first time, a transient two-dimensional simulation of an automotive catalyticconverter was carried out with detailed models for the chemical reactions and the massand heat transport. The model and computer code can now be applied for the detailednumerical simulation of an entire driving cycle that is used by legislation for pollutantemission control of automotive vehicles. The implementation of models describingstorage features of the converter will be one of the next steps in transient simulationof automotive catalytic converters.

Different transport models were applied for the description of hydrocarbon selectivecatalytic reduction of nitrogen oxides (DeNOx catalyst). The comparison of numeri-cally predicted and experimentally derived conversion as function of temperature led toa model discretization. While the Boundary-Layer model and the extended Plug-Flowmodel, which includes a radial mass transfer coefficient, can be used as simplified mod-els for the flow field description, the pure Plug-Flow model fails, because it neglectsthe transport limitation by radial mass transfer. Furthermore, the simple washcoatmodel, based on an effectiveness factor, cannot be applied because it neglects that theoverall reaction is determined by the reaction rate of various species depending on theexternal conditions such as temperature.

While this research seems to have shed considerable light on the physical and chem-ical processes relevant to the performance of the catalytic reactors discussed, it is alsorecognized that chemical mechanisms based on a limited set of experimental data canbe neither unique nor complete in their description of the detailed kinetics. More ex-periments and kinetic mechanism development are required to achieve a comprehensiveset of rate coefficients. Nevertheless, a point seems to have been reached where opti-mization of catalytic reactors based on detailed models for the flow field, heat transfer,and reaction kinetics can be carried out. As a first step, homogeneous reaction sys-tems have recently been optimized. For instance, optimal temperature profiles led toan increase in the yields of ketene formation by homogeneous catalytic pyrolysis ofacetic acid [210], and of ethylene by homogeneous oxidative coupling of methane [211].Currently, the optimization code PRSQP by Schulz [212] is coupled with a model for atransient one-dimensional stagnation point flow on a catalytic active plate [213].

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157

A Gas-phase reaction mechanisms

In this section, the homogeneous reaction mechanisms are listed which are applied inthe Chpaters 5 and 6. Because gas phase chemistry is not significant in the applicationsof Chapter 7, the mechanisms used are not listed here; it is referred to the literatureas given in the text.

In the schemes, the symbol ⇀↽ denotes that the kinetic data of the reverse reactionare calculated using Eq. 3.10. The kinetic data are given according to Eq. 3.15. Theunits are: A [mol, cm, s], β [-], and Ea [kJ mol−1]. The lines beginning with the keywordsLow and Troe below the reaction equation refer to Troe parameters as described inReferences [37, 81] for the given reaction; the data behind the reaction equation referto k∞, behind Low to k0, and behind Troe to the Troe parameters a, T ∗∗∗, T ∗, and T ∗∗.Efficiency coefficients (ηik in Eq. 3.17) for third body reactions, i.e. M(*) is part of thereaction equation, are unity unless specified differently at the end of the table.

A.1 Partial oxidation of methane

This mechanism was developed for the numerical simulation of the homogeneous gas-phase reactions in partial oxidation of methane as described in Chapter 5, where theorigin of the mechanism is discussed.

Reaction A β Ea

1. O2 + H ⇀↽ OH + O 2.000 · 1014 0.0 70.32. H2 + O ⇀↽ OH + H 5.060 · 104 2.7 26.33. H2 + OH ⇀↽ H2O + H 1.000 · 108 1.6 13.84. OH + OH ⇀↽ H2O + O 1.500 · 109 1.1 0.45. H + O2 + M(1) ⇀↽ HO2 + M(1) 2.300 · 1018 −0.8 0.06. HO2 + H ⇀↽ OH + OH 1.500 · 1014 0.0 4.27. HO2 + H ⇀↽ H2 + O2 2.500 · 1013 0.0 2.98. HO2 + H ⇀↽ H2O + O 3.000 · 1013 0.0 7.29. HO2 + O ⇀↽ OH + O2 1.800 · 1013 0.0 −1.710. HO2 + OH ⇀↽ H2O + O2 6.000 · 1013 0.0 0.011. HO2 + HO2 ⇀↽ H2O2 + O2 2.500 · 1011 0.0 −5.212. OH + OH + M(1) ⇀↽ H2O2 + M(1) 3.250 · 1022 −2.0 0.013. H2O2 + H ⇀↽ H2 + HO2 1.700 · 1012 0.0 15.714. H2O2 + OH ⇀↽ H2O + HO2 5.400 · 1012 0.0 4.215. CO + OH ⇀↽ CO2 + H 6.000 · 106 1.5 −3.116. CO + HO2 ⇀↽ CO2 + OH 1.500 · 1014 0.0 98.717. CO + O + M(1) ⇀↽ CO2 + M(1) 7.100 · 1013 0.0 −19.018. CO + O2 ⇀↽ CO2 + O 2.500 · 1012 0.0 200.019. CHO + M(1) ⇀↽ CO + H + M(1) 7.100 · 1014 0.0 70.320. CHO + O2 ⇀↽ CO + HO2 3.000 · 1012 0.0 0.021. CH2O + M(1) ⇀↽ CHO + H + M(1) 5.000 · 1016 0.0 320.022. CH2O + H ⇀↽ CHO + H2 2.300 · 1010 1.1 13.723. CH2O + O ⇀↽ CHO + OH 4.150 · 1011 0.6 11.624. CH2O + OH ⇀↽ CHO + H2O 3.400 · 109 1.2 −1.925. CH2O + HO2 ⇀↽ CHO + H2O2 3.000 · 1012 0.0 54.726. CH2O + CH3 ⇀↽ CHO + CH4 1.000 · 1011 0.0 25.527. CH2O + O2 ⇀↽ CHO + HO2 6.000 · 1013 0.0 170.7

158 A. GAS-PHASE REACTION MECHANISMS

Reaction A β Ea

28. CH3 + O ⇀↽ CH2O + H 8.430 · 1013 0.0 0.029. CH4 + M(2) ⇀↽ CH3 + H + M(2) 2.400 · 1016 0.0 439.0

Low: 1.290 · 1018 0.0 380.0Troe: 0.000 1.350 · 1003 1.0 7830.0

30. CH3 + OH → CH3O + H 2.260 · 1014 0.0 64.831. CH3O + H → CH3 + OH 4.750 · 1016 −0.1 88.032. CH3 + O2 → CH2O + OH 3.300 · 1011 0.0 37.433. CH3 + HO2 ⇀↽ CH3O + OH 1.800 · 1013 0.0 0.034. CH3 + HO2 ⇀↽ CH4 + O2 3.600 · 1012 0.0 0.035. CH3 + CH3 → C2H4 + H2 1.000 · 1016 0.0 134.036. CH3 + CH3 + M(1) ⇀↽ C2H6 + M(1) 3.610 · 1013 0.0 0.0

Low: 3.630 · 1041 −7.0 11.6Troe: 0.620 7.300 · 1001 1180.0 0.0

37. CH3O + M(1) ⇀↽ CH2O + H + M(1) 5.000 · 1013 0.0 105.038. CH3O + H ⇀↽ CH2O + H2 1.800 · 1013 0.0 0.039. CH3O + O2 ⇀↽ CH2O + HO2 4.000 · 1010 0.0 8.940. CH3O + O ⇀↽ O2 + CH3 1.100 · 1013 0.0 0.041. CH4 + H ⇀↽ H2 + CH3 1.300 · 104 3.0 33.642. CH4 + O ⇀↽ OH + CH3 6.923 · 108 1.6 35.543. CH4 + OH ⇀↽ H2O + CH3 1.600 · 107 1.8 11.644. CH4 + HO2 ⇀↽ H2O2 + CH3 1.100 · 1013 0.0 103.145. C2H3 + M(1) ⇀↽ C2H2 + H + M(1) 1.900 · 1014 0.0 166.3

Low: 1.000 · 1042 −7.5 190.4Troe: 0.350 0.000 · 1000 0.0 0.0

46. C2H3 + OH ⇀↽ C2H2 + H2O 5.000 · 1013 0.0 0.047. C2H3 + H ⇀↽ C2H2 + H2 1.200 · 1013 0.0 0.048. C2H3 + O2 ⇀↽ CH2O + CHO 2.300 · 1012 0.0 −2.349. C2H4 + M(1) ⇀↽ C2H2 + H2 + M(1) 7.500 · 1017 0.0 332.050. C2H4 + M(1) ⇀↽ C2H3 + H + M(1) 0.850 · 1018 0.0 404.051. C2H4 + H ⇀↽ C2H3 + H2 0.540 · 1015 0.0 62.952. C2H4 + O ⇀↽ CHO + CH3 2.420 · 106 2.1 0.053. C2H4 + OH ⇀↽ C2H3 + H2O 2.200 · 1013 0.0 24.954. C2H4 + H + M(1) → C2H5 + M(1) 3.970 · 109 1.3 5.4

Low: 6.980 · 1018 0.0 3.2Troe: 0.760 4.000 · 1001 1025.0 0.0

55. C2H5 + M(1) → C2H4 + H + M(1) 8.200 · 1013 0.0 166.8Low: 3.400 · 1017 0.0 139.6Troe: 0.750 9.700 · 1001 1379.0 0.0

56. C2H5 + H ⇀↽ CH3 + CH3 3.000 · 1013 0.0 0.057. C2H5 + O2 ⇀↽ C2H4 + HO2 1.100 · 1010 0.0 −6.358. C2H5 + CH3 ⇀↽ C2H4 + CH4 1.140 · 1012 0.0 0.059. C2H5 + C2H5 ⇀↽ C2H4 + C2H6 1.400 · 1012 0.0 0.060. C2H6 + H ⇀↽ C2H5 + H2 1.400 · 109 1.5 31.161. C2H6 + O ⇀↽ C2H5 + OH 1.000 · 109 1.5 24.462. C2H6 + OH ⇀↽ C2H5 + H2O 7.200 · 106 2.0 3.663. C2H6 + HO2 ⇀↽ C2H5 + H2O2 1.700 · 1013 0.0 85.964. C2H6 + O2 ⇀↽ C2H5 + HO2 6.000 · 1013 0.0 217.065. C2H6 + CH3 ⇀↽ C2H5 + CH4 1.500 · 10−7 6.0 25.4

H2 H2O O2 N2 CO CO2 CH4

M(1) 1.00 6.50 0.40 0.40 0.75 1.50 3.00M(2) 1.00 6.50 0.40 0.40 0.75 1.50 0.66

A.2 Oxy-dehydrogenation of ethane 159

A.2 Oxy-dehydrogenation of ethane

This mechanism was developed for the numerical simulation of the homogeneous gas-phase recations in oxy-dehydrogenation as described in Chapter 6, where the origin ofthe mechanism [49,166] is discussed.

Reaction A β Ea

1. OH + H2 ⇀↽ H + H2O 2.140 · 108 1.5 14.42. O + OH ⇀↽ O2 + H 2.020 · 1014 −0.4 0.03. O + H2 ⇀↽ OH + H 5.060 · 104 2.7 26.34. H + O2 + M(1) ⇀↽ HO2 + M(1) 4.520 · 1013 0.0 0.0

Low: 1.050 · 1019 −1.2 0.05. H + O2 + H2 ⇀↽ HO2 + H2 4.520 · 1013 0.0 0.0

Low: 1.520 · 1019 −1.13 0.06. H + O2 + H2O ⇀↽ HO2 + H2O 4.520 · 1013 0.0 0.0

Low: 2.100 · 10223 −2.4 0.07. OH + HO2 ⇀↽ H2O + O2 2.130 · 1028 −4.8 14.68. OH + HO2 ⇀↽ H2O + O2 9.100 · 1014 0.0 45.99. H + HO2 ⇀↽ OH + OH 1.500 · 1014 0.0 4.210. H + HO2 ⇀↽ H2 + O2 8.450 · 1011 0.7 5.211. H + HO2 ⇀↽ O + H2O 3.010 · 1013 0.0 7.212. O + HO2 ⇀↽ O2 + OH 3.250 · 1013 0.0 0.013. OH + OH ⇀↽ O + H2O 3.570 · 104 2.4 −8.814. H + H + M(2) ⇀↽ H2 + M(2) 1.000 · 1018 −1.0 0.015. H + H + H2 ⇀↽ H2 + H2 9.200 · 1016 −0.6 0.016. H + H + H2O ⇀↽ H2 + H2O 6.000 · 1019 −1.2 0.017. H + OH + M(3) ⇀↽ H2O + M(3) 2.210 · 1022 −2.0 0.018. H + O + M(3) ⇀↽ OH + M(3) 4.710 · 1018 −1.0 0.019. O + O + M ⇀↽ O2 + M 1.890 · 1013 0.0 −7.520. HO2 + HO2 ⇀↽ H2O2 + O2 4.200 · 1014 0.0 50.121. HO2 + HO2 ⇀↽ H2O2 + O2 1.300 · 1011 0.0 −6.822. OH + OH + M ⇀↽ H2O2 + M 1.240 · 1014 −0.4 0.0

Low: 3.040 · 1030 −4.6 8.4Troe: 0.470 1.000 · 1002 2.0 · 1003 1.0 · 1015

23. H2O2 + H ⇀↽ HO2 + H2 1.980 · 106 2.0 10.224. H2O2 + H ⇀↽ OH + H2O 3.070 · 1013 0.0 17.625. H2O2 + O ⇀↽ OH + HO2 9.550 · 106 2.0 16.626. H2O2 + OH ⇀↽ H2O + HO2 2.400 · 100 4.0 −9.027. CH3 + CH3 + M(4) ⇀↽ C2H6 + M(4) 9.220 · 1016 −1.2 2.7

Low: 1.140 · 1036 −5.3 7.1Troe: 0.405 1.120 · 1003 69.6 1.0 · 1015

28. CH3 + H + M(4) ⇀↽ CH4 + M(4) 2.140 · 1015 −0.4 0.0Low: 3.31 · 1030 −4.0 8.8Troe: 0.000 1.0 · 10−15 1.0 · 10−15 40.0

29. CH4 + H ⇀↽ CH3 + H2 2.200 · 104 3.0 36.630. CH4 + OH ⇀↽ CH3 + H2O 4.190 · 106 2.0 10.731. CH4 + O ⇀↽ CH3 + OH 6.920 · 108 1.6 35.532. CH4 + HO2 ⇀↽ CH3 + H2O2 1.120 · 1013 0.0 103.133. CH3 + HO2 ⇀↽ CH3O + OH 7.000 · 1012 0.0 0.034. CH3 + HO2 ⇀↽ CH4 + O2 3.000 · 1012 0.0 0.035. CH3 + O ⇀↽ CH2O + H 8.000 · 1013 0.0 0.036. CH3 + O2 ⇀↽ CH3O + O 1.450 · 1013 0.0 122.237. CH3 + O2 ⇀↽ CH2O + OH 2.510 · 1011 0.0 61.338. CH3O + H ⇀↽ CH3 + OH 1.000 · 1014 0.0 0.039. CH3 + OH ⇀↽ 3-CH2 + H2O 3.000 · 106 2.0 10.540. CH3 + OH ⇀↽ CH2O + H2 5.480 · 1013 0.0 12.541. CH3 + OH ⇀↽ CH2O + H2 2.250 · 1013 0.0 18.042. CH3 + H ⇀↽ 3-CH2 + H2 9.000 · 1013 0.0 63.243. CH3 + M ⇀↽ 3-CH2 + H + M 1.960 · 1016 0.0 382.5

160 A. GAS-PHASE REACTION MECHANISMS

Reaction A β Ea

44. CH2O + H + M(5) ⇀↽ CH3O + M(5) 5.400 · 1011 0.5 10.9Low: 1.50 · 1030 −4.8 23.0Troe: 0.758 9.400 · 1001 1555 4200

45. CH3O + H ⇀↽ CH2O + H2 2.000 · 1013 0.0 0.046. CH3O + OH ⇀↽ CH2O + H2O 1.000 · 1013 0.0 0.047. CH3O + O ⇀↽ CH2O + OH 1.000 · 1013 0.0 0.048. CH3O + O2 ⇀↽ CH2O + HO2 6.300 · 1010 0.0 10.949. CH2O + OH ⇀↽ CHO + H2O 2.000 · 1013 0.0 0.050. CH2O + H ⇀↽ CH2O + H 2.000 · 1014 0.0 0.051. CH2O + O ⇀↽ CO2 + H + H 5.000 · 1013 0.0 0.052. CH2O + O ⇀↽ CO + OH + H 3.000 · 1013 0.0 0.053. CH2O + O2 ⇀↽ CO2 + H + OH 5.000 · 1012 0.0 0.054. CH2O + O2 ⇀↽ CO2 + H2O 3.000 · 1013 0.0 0.055. 3-CH2 + OH ⇀↽ CH2O + H 2.500 · 1013 0.0 0.056. 3-CH2 + CO2 ⇀↽ CH2O + CO 1.100 · 1011 0.0 4.257. 3-CH2 + O ⇀↽ CO + H + H 5.000 · 1013 0.0 0.058. 3-CH2 + O ⇀↽ CO + H2 3.000 · 1013 0.0 0.059. 3-CH2 + O2 ⇀↽ CH2O + O 3.290 · 1021 −3.3 12.060. 3-CH2 + O2 ⇀↽ CO2 + H + H 3.290 · 1021 −3.3 12.061. 3-CH2 + O2 ⇀↽ CO2 + H2 1.010 · 1021 −3.3 6.362. 3-CH2 + O2 ⇀↽ CO + H2O 7.280 · 1019 −2.5 7.663. 3-CH2 + O2 ⇀↽ CHO + OH 1.290 · 1020 −3.3 1.264. 3-CH2 + CH3 ⇀↽ C2H4 + H 4.000 · 1013 0.0 0.065. 3-CH2 + 3-CH2 ⇀↽ C2H2 + H + H 4.000 · 1013 0.0 0.066. 3-CH2 + HCCO ⇀↽ C2H3 + CO 3.000 · 1013 0.0 0.067. CH2O + OH ⇀↽ CHO + H2O 3.430 · 109 1.2 −1.968. CH2O + H ⇀↽ CHO + H2 2.190 · 108 1.8 12.669. CH2O + M ⇀↽ CHO + H + M 3.310 · 1016 0.0 338.970. CH2O + O ⇀↽ CHO + OH 1.800 · 1013 0.0 12.971. CHO + O2 ⇀↽ HO2 + CO 7.580 · 1012 0.0 1.772. CHO + M(6) ⇀↽ H + CO + M(6) 1.860 · 1017 −1.0 71.173. CHO + OH ⇀↽ H2O + CO 1.000 · 1014 0.0 0.074. CHO + H ⇀↽ CO + H2 1.190 · 1013 0.2 0.075. CHO + O ⇀↽ CO + OH 3.000 · 1013 0.0 0.076. CHO + O ⇀↽ CO2 + H 3.000 · 1013 0.0 0.077. CO + OH ⇀↽ CO2 + H 9.420 · 103 2.2 −9.878. CO + O + M ⇀↽ CO2 + M 6.170 · 1014 0.0 12.679. CO + O2 ⇀↽ CO2 + O 2.530 · 1012 0.0 199.580. CO + HO2 ⇀↽ CO2 + OH 5.800 · 1013 0.0 96.081. C2H6 + CH3 ⇀↽ C2H5 + CH4 5.500 · 10−1 4.0 34.782. C2H6 + H ⇀↽ C2H5 + H2 5.400 · 102 3.5 21.883. C2H6 + O ⇀↽ C2H5 + OH 3.000 · 107 2.0 21.484. C2H6 + OH ⇀↽ C2H5 + H2O 7.230 · 106 2.0 3.685. C2H5 + H ⇀↽ C2H4 + H2 1.250 · 1014 0.0 33.586. C2H5 + H ⇀↽ CH3 + CH3 3.000 · 1013 0.0 0.087. C2H5 + H ⇀↽ C2H6 7.000 · 1013 0.0 0.088. C2H5 + OH ⇀↽ C2H4 + H2O 4.000 · 1013 0.0 0.089. C2H5 + O ⇀↽ CH3 + CH2O 1.000 · 1014 0.0 0.090. C2H5 + HO2 ⇀↽ CH3 + CH2O + OH 3.000 · 1013 0.0 0.091. C2H5 + O2 ⇀↽ C2H4 + HO2 3.000 · 1020 −2.9 28.392. C2H5 + O2 ⇀↽ C2H4 + HO2 2.120 · 10−6 6.0 39.793. C2H4 + H ⇀↽ C2H3 + H2 3.360 · 10−7 6.0 7.194. C2H4 + OH ⇀↽ C2H3 + H2O 2.020 · 1013 0.0 24.895. C2H4 + O ⇀↽ CH3 + CHO 1.020 · 107 1.9 0.796. C2H4 + O ⇀↽ CH2CHO + H 3.390 · 106 1.9 0.797. C2H4 + CH3 ⇀↽ C2H3 + CH4 6.620 · 100 3.7 39.798. C2H4 + H + M(4) ⇀↽ C2H5 + M(4) 1.080 · 1012 0.5 7.6

Low: 1.11 · 1034 −5.0 18.5Troe: 1.00 1.0 · 10−15 95 200

99. C2H4 + M ⇀↽ C2H2 + H2 + M 1.800 · 1013 0.0 318.0Low: 1.5 · 1015 0.0 232.7

A.2 Oxy-dehydrogenation of ethane 161

Reaction A β Ea

100. C2H3 + H + M(5) ⇀↽ C2H4 + M(5) 6.100 · 1012 0.3 1.2Low: 9.80 · 1029 −3.86 13.9Troe: 0.782 2.080 · 1002 2663 6095

101. C2H3 + H ⇀↽ C2H2 + H2 4.000 · 1013 0.0 0.0102. C2H3 + O ⇀↽ CH2CO + H 3.000 · 1013 0.0 0.0103. C2H3 + O2 ⇀↽ CH2O + CHO 1.700 · 1029 −5.3 27.2104. C2H3 + O2 ⇀↽ CH2CHO + O 3.500 · 1014 −0.6 22.0105. C2H3 + O2 ⇀↽ C2H2 + HO2 2.120 · 10−6 6.0 39.7106. C2H3 + OH ⇀↽ C2H2 + H2O 2.000 · 1013 0.0 0.0107. C2H3 + CH3 ⇀↽ C2H2 + CH4 2.000 · 1013 0.0 0.0108. C2H3 + C2H3 ⇀↽ C2H4 + C2H2 1.450 · 1013 0.0 0.0109. C2H2 + OH ⇀↽ CH2CO + H 2.180 · 10−4 4.5 −4.2110. C2H2 + OH ⇀↽ CH2CO + H 2.000 · 1011 0.0 0.0111. C2H2 + OH ⇀↽ CH3 + CO 4.830 · 10−4 4.0 −8.4112. C2H2 + O ⇀↽ 3-CH2 + CO 6.120 · 106 2.0 7.9113. C2H2 + O ⇀↽ HCCO + H 1.430 · 107 2.0 7.9114. C2H2 + O2 ⇀↽ HCCO + OH 4.000 · 107 1.5 125.9115. C2H2 + H + M(4) ⇀↽ C2H3 + M(4) 3.110 · 1011 0.6 10.8

Low: 2.25 · 1040 −7.27 27.6Troe: 1.00 1.0 · 10−15 675 1.0 · 1015

116. CH2CHO + H ⇀↽ CH2CO + H2 4.000 · 1013 0.0 0.0117. CH2CHO + O ⇀↽ CH2O + CHO 1.000 · 1014 0.0 0.0118. CH2CHO + OH ⇀↽ CH2CO + H2O 3.000 · 1013 0.0 0.0119. CH2CHO + O2 ⇀↽ CH2O + CO + OH 3.000 · 1010 0.0 0.0120. CH2CHO + CH3 → C2H5 + CO + H 4.900 · 1014 −0.5 0.0121. CH2CHO ⇀↽ CH2CO + H 3.950 · 1038 −7.6 188.8122. CH2CO + O ⇀↽ CO2 + 3-CH2 1.750 · 1012 0.0 5.6123. CH2CO + H ⇀↽ CH3 + CO 7.000 · 1012 0.0 12.6124. CH2CO + H ⇀↽ HCCO + H2 2.000 · 1014 0.0 33.5125. CH2CO + O ⇀↽ HCCO + OH 1.000 · 1013 0.0 33.5126. CH2CO + OH ⇀↽ HCCO + H2O 1.000 · 1013 0.0 8.4127. 3-CH2 + CO + M(5) ⇀↽ CH2CO + M(5) 8.100 · 1011 0.5 18.9

Low: 1.90 · 1033 −5.11 29.7Troe: 0.591 2.750 · 1002 1226 5185.0

128. HCCO + O ⇀↽ H + CO + CO 8.000 · 1013 0.0 0.0129. HCCO + O2 ⇀↽ CHO + CO + O 2.500 · 108 1.0 0.0130. HCCO + O2 ⇀↽ CO2 + CHO 2.400 · 1011 0.0 −3.6131. HCCO + HCCO ⇀↽ C2H2 + CO + CO 1.000 · 1013 0.0 0.0

H2O H2 CH4 CO2 COM(1) 0.00 0.00 10.00 3.80 1.90M(2) 0.00 0.00 1.00 1.00 1.00M(3) 6.40 1.00 1.00 1.00 1.00M(4) 5.00 2.00 1.00 3.00 2.00M(5) 5.00 1.00 1.00 1.00 1.00M(6) 5.00 1.87 2.81 3.00 1.87

162 B. SURFACE REACTION MECHANISMS

B Surface reaction mechanisms

In this section, all heterogeneous reaction mechanisms are listed. The mechanisms aregiven application by application, even though the same reactions frequently occur indifferent schemes.

The development of detailed reaction mechanisms is a long process (Chapter 3.2.3).Mechanisms have continously to be revised after new experimental or theoretical inves-tigations. For instance, the reaction mechanism for the partial oxidation of methaneto synthesis gas, developed by Hickman and Schmidt in the early 1990s (Tab. B.1),was recently revised due to new experimental observations as discussed in Chapter 5;the revised mechanism is given in Tab. B.2.

The heterogeneous reaction schemes of hydrogen, carbon monoxide, light hydro-carbons, and oxygen on platinum catalysts has been becoming increasingly detailedin the last few years. For instance, the mechanism for complete oxidation of methaneon platinum, developed in 1995 (Tab. B.4), describes the decomposition of methaneon the catalyst in a very simple manner. The mechanism for oxy-dehydrogenation ofethane to ethylene, developed five years later (Tab. B.3), now includes more than adozen steps for methane decomposition and formation.

In the tables, the symbol ⇀↽ denotes that the kinetic data of the reverse reactionare calculated by Eq. 3.26. Species with the suffix (s) are adsorbed species. Thespecies named Pt(s) and Rh(s) denote uncovered surface sites available for adsorptionon platinum and rhodium, respectively.

The kinetic data are given according to Eq. 3.24. The units are: A [mol, cm, s], S0,β and µ [-], Ea and ε [kJ mol−1]. S0 denotes the initial (uncovered surface) stickingcoefficient. If the reaction kinetics exhibits an additional dependence on surface cov-erage (Eq. 3.24), the line under the reaction equation names the species, to which thedependence is referred, and gives the kinetic parameters µ and ε. µ and ε are zero forall other reactions.

B.1 Partial oxidation of methane on rhodium - I 163

B.1 Partial oxidation of methane on rhodium - I

This mechanism, developed by Hickman and Schmidt in 1993 [122], is used in thefirst numerical simulations of partial oxidation of methane on rhodium catalysts asdiscussed in Chapter 5).

Reaction A / S0 β /µ Ea / εI Adsorption1. H2 + Rh(s) + Rh(s) → H(s) + H(s) 8.400 · 1018 1.0 0.0

θRh(s) -1.02. O2 + Rh(s1) + Rh(s1) → O(s1) + O(s1) 1.300 · 1017 1.0 0.0

θRh(s1) -1.03. CH4 + Rh(s) + Rh(s) → CH3(s) + H(s) 1.100 · 1018 1.0 21.0

θRh(s) -1.04. H2O + Rh(s) → H2O(s) 4.600 · 109 1.0 0.05. CO + Rh(s) → CO(s) 1.200 · 1010 1.0 0.0II Desorption6. H(s) + H(s) → Rh(s) + Rh(s) + H2 3.000 · 1021 0.0 75.4

θH(s) -1.07. O(s1) + O(s1) → Rh(s1) + Rh(s1) + O2 3.000 · 1021 0.0 293.3

θO(s1) -1.08. H2O(s) → H2O + Rh(s) 1.000 · 1013 0.0 45.29. CO(s) → CO + Rh(s) 4.000 · 1013 0.0 132.4

θCO(s) 44.010. OH(s) → OH + Rh(s) 8.100 · 1011 0.0 142.5III Surface reactions11. O(s1) + H(s) → OH(s) + Rh(s1) 4.200 · 1021 0.0 83.812. OH(s) + Rh(s1) → O(s1) + H(s) 6.000 · 1021 0.0 21.013. H(s) + OH(s) → H2O(s) + Rh(s) 1.800 · 1026 0.0 33.514. H2O(s) + Rh(s) → H(s) + OH(s) 3.000 · 1023 0.0 155.015. OH(s) + OH(s) → H2O(s) + O(s) 2.400 · 1024 0.0 62.816. O(s) + Rh(s1) → Rh(s) + O(s1) 3.700 · 1021 0.0 0.017. C(s) + O(s1) → CO(s) + Rh(s1) 3.000 · 1022 0.0 62.818. CO(s) + Rh(s1) → C(s) + O(s1) 6.000 · 1019 0.0 167.619. CO(s) + O(s1) → CO2 + Rh(s) + Rh(s1) 6.000 · 1020 0.0 104.720. CH3(s) + Rh(s) → CH2(s) + H(s) 3.700 · 1021 0.0 0.021. CH2(s) + Rh(s) → CH(s) + H(s) 3.700 · 1021 0.0 0.022. CH(s) + Rh(s) → C(s) + H(s) 3.700 · 1021 0.0 0.0

The suffixes (s) and (s1) denote different adsorption sites, the latter one available for oxygen adsorption only.

Reaction steps 3. and 20.-22. represent only one recation in the original mechanism being first order in Rh(s). Splitting

up this reaction in an adsorption step, again being first order in Rh(s), and fast consecutive decomposition steps (20.-22.)

does not change any results.


B.2 Partial oxidation of methane on rhodium - II

This mechanism, developed in 2000 [142], is used for the more recent numerical simu-lation of partial oxidation of methane on rhodium catalysts (Chapter 5).

Reaction A / S0 β /µ Ea / εI Adsorption1. H2 + Rh(s) + Rh(s) → H(s) + H(s) S0 = 1.0 · 10−2 0.0 0.02. O2 + Rh(s) + Rh(s) → O(s) + O(s) S0 = 1.0 · 10−2 0.0 0.03. CH4 + Rh(s) → CH4(s) S0 = 8.0 · 10−3 0.0 0.04. H2O + Rh(s) → H2O(s) S0 = 1.0 · 10−1 0.0 0.05. CO2 + Rh(s) → CO2(s) S0 = 1.0 · 10−5 0.0 0.06. CO + Rh(s) → CO(s) S0 = 5.0 · 10−1 0.0 0.0II Desorption7. H(s) + H(s) → Rh(s) + Rh(s) + H2 3.000 · 1021 0.0 77.88. O(s) + O(s) → Rh(s) + Rh(s) + O2 1.300 · 1022 0.0 355.29. H2O(s) → H2O + Rh(s) 3.000 · 1013 0.0 45.010. CO(s) → CO + Rh(s) 3.500 · 1013 0.0 133.411. CO2(s) → CO2 + Rh(s) 1.000 · 1013 0.0 21.712. CH4(s) → CH4 + Rh(s) 1.000 · 1013 0.0 25.1III Surface reactions13. H(s) + O(s) → OH(s) + Rh(s) 5.000 · 1022 0.0 83.714. OH(s) + Rh(s) → H(s) + O(s) 3.000 · 1020 0.0 37.715. H(s) + OH(s) → H2O(s) + Rh(s) 3.000 · 1020 0.0 33.516. H2O(s) + Rh(s) → H(s) + OH(s) 5.000 · 1022 0.0 106.417. OH(s) + OH(s) → H2O(s) + O(s) 3.000 · 1021 0.0 100.818. H2O(s) + O(s) → OH(s) + OH(s) 3.000 · 1021 0.0 224.219. C(s) + O(s) → CO(s) + Rh(s) 3.000 · 1022 0.0 97.920. CO(s) + Rh(s) → C(s) + O(s) 2.500 · 1021 0.0 169.021. CO(s) + O(s) → CO2(s) + Rh(s) 1.400 · 1020 0.0 121.622. CO2(s) + Rh(s) → CO(s) + O(s) 3.000 · 1021 0.0 115.323. CH4(s) + Rh(s) → CH3(s) + H(s) 3.700 · 1021 0.0 61.024. CH3(s) + H(s) → CH4(s) + Rh(s) 3.700 · 1021 0.0 51.025. CH3(s) + Rh(s) → CH2(s) + H(s) 3.700 · 1024 0.0 103.026. CH2(s) + H(s) → CH3(s) + Rh(s) 3.700 · 1021 0.0 44.027. CH2(s) + Rh(s) → CH(s) + H(s) 3.700 · 1024 0.0 100.028. CH(s) + H(s) → CH2(s) + Rh(s) 3.700 · 1021 0.0 68.029. CH(s) + Rh(s) → C(s) + H(s) 3.700 · 1021 0.0 21.030. C(s) + H(s) → CH(s) + Rh(s) 3.700 · 1021 0.0 172.831. CH4(s) + O(s) → CH3(s) + OH(s) 1.700 · 1024 0.0 80.332. CH3(s) + OH(s) → CH4(s) + O(s) 3.700 · 1021 0.0 24.333. CH3(s) + O(s) → CH2(s) + OH(s) 3.700 · 1024 0.0 120.334. CH2(s) + OH(s) → CH3(s) + O(s) 3.700 · 1021 0.0 15.135. CH2(s) + O(s) → CH(s) + OH(s) 3.700 · 1024 0.0 158.436. CH(s) + OH(s) → CH2(s) + O(s) 3.700 · 1021 0.0 36.837. CH(s) + O(s) → C(s) + OH(s) 3.700 · 1021 0.0 30.138. C(s) + OH(s) → CH(s) + O(s) 3.700 · 1021 0.0 145.5

B.3 Oxy-dehydrogenation of ethane on platinum 165

B.3 Oxy-dehydrogenation of ethane on platinum

This mechanism is used for the numerical simulation of oxy-dehydrogenation of ethaneon platinum catalysts as described in Chapter 6. The development of the mechanismis discussed in [49].

Reaction A / S0 β /µ Ea / εI Adsorption1. H + Pt(s) → H(s) S0 = 1.0 · 10−0 0.0 0.02.. H2 + Pt(s) + Pt(s) → H(s) + H(s) S0 = 4.6 · 10−2 0.0 0.0

θPt(s) -1.03. H2 + C(s) → CH2(s) S0 = 4.0 · 10−2 0.0 29.7

θC(s) -4.64. O + Pt(s) → O(s) S0 = 1.0 · 10−0 0.0 0.05. O2 + Pt(s) + Pt(s) → O(s) + O(s) 1.891 · 1021 −0.5 0.06. OH + Pt(s) → OH(s) S0 = 1.0 · 10−0 0.0 0.07. H2O + Pt(s) → H2O(s) S0 = 7.5 · 10−1 0.0 0.08. CO + Pt(s) → CO(s) S0 = 8.4 · 10−1 0.0 0.09. CO2 + Pt(s) → CO2(s) S0 = 5.0 · 10−3 0.0 0.010. CH3 + Pt(s) → CH3(s) S0 = 1.0 · 10−0 0.0 0.011. CH4 + C(s) → CHCH3(s) S0 = 7.0 · 10−9 0.0 23.0

θC(s) -47.512. CH4 + Pt(s) + Pt(s) → CH3(s) + H(s) S0 = 9.0 · 10−4 0.0 72.213. CH4 + O(s) + Pt(s) → CH3(s) + OH(s) 5.000 · 1018 0.7 42.0

θO(s) -8.014. CH4 + OH(s) + Pt(s) → CH3(s) + H2O(s) S0 = 1.0 · 10−0 0.0 10.015. C2H2 + Pt(s) → C2H2(s) S0 = 5.0 · 10−2 0.0 0.016. C2H4 + Pt(s) → C2H4(s) S0 = 1.5 · 10−2 0.0 0.017. C2H5 + Pt(s) → C2H5(s) S0 = 1.0 · 10−0 0.0 0.018. C2H6 + Pt(s) + Pt(s) → C2H6(s) S0 = 1.5 · 10−2 0.0 0.0II Desorption19. H(s) → H + Pt(s) 6.000 · 1013 0.0 254.4

θH(s) 5.020. H(s) + H(s) → Pt(s) + Pt(s) + H2 3.700 · 1021 0.0 67.4

θH(s) 10.021. CH2(s) → C(s) + H2 7.690 · 1013 0.0 25.1

θC(s) -50.022. O(s) → O + Pt(s) 1.000 · 1013 0.0 358.8

θO(s) 94.123. O(s) + O(s) → Pt(s) + Pt(s) + O2 3.700 · 1021 0.0 227.4

θO(s) 188.324. OH(s) → OH + Pt(s) 5.000 · 1013 0.0 251.1

θO(s) 167.425. H2O(s) → H2O + Pt(s) 4.500 · 1012 0.0 41.826. CO(s) → CO + Pt(s) 2.500 · 1016 0.0 146.0

θCO(s) 33.027. CO2(s) → CO2 + Pt(s) 1.000 · 1013 0.0 27.128. CH3(s) → Pt(s) + CH3 1.000 · 1013 0.0 163.029. CH3(s) + H(s) → CH4 + Pt(s) + Pt(s) 1.500 · 1020 0.0 50.030. CH3(s) + H2O(s) → CH4 + OH(s) + Pt(s) 2.500 · 1020 0.0 23.0

θH(s) 50.031. CH3(s) + OH(s) → CH4 + O(s) + Pt(s) 3.700 · 1021 0.0 85.932. C2H2(s) → Pt(s) + C2H2 1.000 · 1012 0.0 58.633. C2H4(s) → Pt(s) + C2H4 1.000 · 1013 0.0 50.234. CHCH3(s) → C(s) + CH4 1.000 · 1010 0.0 25.5

θC(s) -47.535. C2H5(s) → Pt(s) + C2H5 1.000 · 1013 0.0 173.036. C2H6(s) → Pt(s) + Pt(s) + C2H6 1.000 · 1013 0.0 20.9


Reaction A / S0 β /µ Ea / εIII Surface reactions37. H(s) + O(s) → OH(s) + Pt(s) 1.280 · 1021 0.0 11.238. OH(s) + Pt(s) → H(s) + O(s) 7.390 · 1019 0.0 77.3

θO(s) 73.239. H(s) + OH(s) → H2O(s) + Pt(s) 2.040 · 1021 0.0 66.240. H2O(s) + Pt(s) → H(s) + OH(s) 1.150 · 1019 0.0 101.4

θO(s) -167.441. OH(s) + OH(s) → H2O(s) + O(s) 7.400 · 1020 0.0 74.042. H2O(s) + O(s) → OH(s) + OH(s) 1.000 · 1020 0.0 43.1

θO(s) -240.643. C(s) + O(s) → CO(s) + Pt(s) 3.700 · 1019 0.0 0.044. CO(s) + Pt(s) → C(s) + O(s) 3.700 · 1019 0.0 236.5

θCO(s) 33.045. CO(s) + O(s) → CO2(s) + Pt(s) 3.700 · 1019 0.0 117.6

θCO(s) 33.046. CO2(s) + Pt(s) → CO(s) + O(s) 3.700 · 1019 0.0 173.3

θO(s) -94.147. CO(s) + OH(s) → CO2(s) + H(s) 2.000 · 1019 0.0 38.7

θCO(s) 30.048. CO2(s) + H(s) → CO(s) + OH(s) 2.000 · 1019 0.0 28.349. CH3(s) + Pt(s) → CH2(s) + H(s) 1.262 · 1022 0.0 70.350. CH2(s) + H(s) → CH3(s) + Pt(s) 3.090 · 1022 0.0 0.0

θH(s) 2.851. CH2(s) + Pt(s) → CH(s) + H(s) 7.314 · 1022 0.0 58.9

θC(s) -50.052. CH(s) + H(s) → CH2(s) + Pt(s) 3.090 · 1022 0.0 0.0

θH(s) 2.853. CH(s) + Pt(s) → C(s) + H(s) 3.090 · 1022 0.0 0.0

θH(s) 2.854. C(s) + H(s) → CH(s) + Pt(s) 1.248 · 1022 0.0 138.055. C2H6(s) + O(s) → C2H5(s) + OH(s) + Pt(s) 3.700 · 1021 0.0 25.156. C2H5(s) + OH(s) + Pt(s) → C2H6(s) + O(s) 1.350 · 1030 0.0 77.457. C2H4(s) → CHCH3(s) 1.000 · 1013 0.0 83.358. CHCH3(s) → C2H4(s) 1.000 · 1013 0.0 75.359. C2H5(s) + H(s) → C2H6(s) 3.700 · 1021 0.0 41.860. C2H6(s) → C2H5(s) + H(s) 7.000 · 1012 0.0 57.761. CH3(s) + CH3(s) → C2H6(s) 1.000 · 1021 0.0 14.562. C2H6(s) → CH3(s) + CH3(s) 1.000 · 1013 0.0 89.063. C2H5(s) + Pt(s) → CHCH3(s) + H(s) 1.000 · 1022 0.0 54.464. CHCH3(s) + H(s) → C2H5(s) + Pt(s) 1.000 · 1021 0.0 29.365. CHCH3(s) + Pt(s) → CCH3(s) + H(s) 2.000 · 1022 0.0 99.166. CCH3(s) + H(s) → CHCH3(s) + Pt(s) 3.700 · 1021 0.0 75.367. CHCH3(s) + Pt(s) → CHCH2(s) + H(s) 3.700 · 1021 0.0 128.568. CHCH2(s) + H(s) → CHCH3(s) + Pt(s) 3.700 · 1021 0.0 57.369. C2H4(s) + Pt(s) → CHCH2(s) + H(s) 3.700 · 1021 0.0 112.770. CHCH2(s) + H(s) → C2H4(s) + Pt(s) 3.700 · 1021 0.0 33.571. CHCH2(s) + Pt(s) → CCH2(s) + H(s) 3.700 · 1021 0.0 121.372. CCH2(s) + H(s) → CHCH2(s) + Pt(s) 3.700 · 1021 0.0 51.773. CCH3(s) + Pt(s) → CH3(s) + C(s) 3.700 · 1021 0.0 46.9

θC(s) -50.074. CH3(s) + C(s) → CCH3(s) + Pt(s) 3.700 · 1021 0.0 46.075. C2H2(s) → CCH2(s) 1.000 · 1013 0.0 61.576. CCH2(s) → C2H2(s) 1.000 · 1013 0.0 4.277. CCH3(s) → CHCH2(s) 1.000 · 1013 0.0 176.078. CHCH2(s) → CCH3(s) 1.000 · 1013 0.0 128.679. C2H2(s) + Pt(s) → CCH(s) + H(s) 3.700 · 1021 0.0 133.580. CCH(s) + H(s) → C2H2(s) + Pt(s) 3.700 · 1021 0.0 66.981. CCH(s) + Pt(s) → CH(s) + C(s) 3.700 · 1021 0.0 125.182. CH(s) + C(s) → CCH(s) + Pt(s) 3.700 · 1021 0.0 121.3

B.4 Oxidation of hydrogen, carbon monoxide, and methane on platinum 167

B.4 Oxidation of hydrogen, carbon monoxide, and methaneon platinum

This mechanism is used for the numerical simulation of the catalytic combustion ofmethane on platinum catalysts as described in Chapter 7. The development of themechanism is discussed in [45,66].

Reaction A / S0 β /µ Ea / εI AdsorPtion1. H2 + Pt(s) + Pt(s) → H(s) + H(s) S0 = 4.6 · 10−2 0.0 0.0

θPt(s) -1.02. H + Pt(s) → H(s) S0 = 1.0 · 10−0 0.0 0.03. O2 + Pt(s) + Pt(s) → O(s) + O(s) 1.800 · 1021 −0.5 0.04. O2 + Pt(s) + Pt(s) → O(s) + O(s) S0 = 2.3 · 10−2 0.0 0.05. CH4 + Pt(s) + Pt(s) → CH3(s) + H(s) S0 = 1.0 · 10−2 0.0 0.0

θPt(s) 0.36. O + Pt(s) → O(s) S0 = 1.0 · 10−0 0.0 0.07. H2O + Pt(s) → H2O(s) S0 = 7.5 · 10−1 0.0 0.08. CO + Pt(s) → CO(s) S0 = 8.4 · 10−1 0.0 0.0

θPt(s) 1.09. OH + Pt(s) → OH(s) S0 = 1.0 · 10−0 0.0 0.0II DesorPtion10. H(s) + H(s) → Pt(s) + Pt(s) + H2 3.700 · 1021 0.0 67.4

θH(s) 6.011. O(s) + O(s) → Pt(s) + Pt(s) + O2 3.700 · 1021 0.0 213.0

θO(s) 60.012. H2O(s) → H2O + Pt(s) 1.000 · 1013 0.0 40.313. OH(s) → OH + Pt(s) 1.000 · 1013 0.0 192.814. CO(s) → CO + Pt(s) 1.000 · 1013 0.0 125.515. CO2(s) → CO2 + Pt(s) 1.000 · 1013 0.0 20.516. O(s) + H(s) ⇀↽ OH(s) + Pt(s) 3.700 · 1021 0.0 11.5III Surface reactions17. H(s) + OH(s) ⇀↽ H2O(s) + Pt(s) 3.700 · 1021 0.0 17.418. OH(s) + OH(s) ⇀↽ H2O(s) + O(s) 3.700 · 1021 0.0 48.219. CO(s) + O(s) → CO2(s) + Pt(s) 3.700 · 1021 0.0 105.020. C(s) + O(s) → CO(s) + Pt(s) 3.700 · 1021 0.0 62.821. CO(s) + Pt(s) → C(s) + O(s) 1.000 · 1018 0.0 184.022. CH3(s) + Pt(s) → CH2(s) + H(s) 3.700 · 1021 0.0 20.023. CH2(s) + Pt(s) → CH(s) + H(s) 3.700 · 1021 0.0 20.024. CH(s) + Pt(s) → C(s) + H(s) 3.700 · 1021 0.0 20.0

Reaction 3. and 4. represent alternative competing pathways.


B.5 Mechanism for a three-way catalyst

This mechanism is used for the numerical simulation of the chemical reactions in athree-way catalyst as described in Chapter 8.2. The propylene species presents thehydrocarbon species in the exhaust gas. The development of the mechanism is discussedin [203, 88]. While species adsorbed on platinum are denoted by the suffix (s), speciesadsorbed on rhodium are denoted by the suffix (Rh).

Reaction A / S0 β /µ Ea / εC3H6/CO - oxidation on Pt

I Adsorption1. O2 + Pt(s) + Pt(s) → O(s) + O(s) S0 = 7.0 · 10−2 0.0 0.02. C3H6 + Pt(s) + Pt(s) → C3H6(s) S0 = 9.8 · 10−1 0.0 0.03. C3H6 + O(s) + Pt(s) → C3H5(s) + OH(s) S0 = 5.0 · 10−2 0.0 0.0

θPt(s) -0.94. H2 + Pt(s) + Pt(s) → H(s) + H(s) S0 = 4.6 · 10−2 0.0 0.0

θPt(s) -1.05. H2O + Pt(s) → H2O(s) S0 = 7.5 · 10−1 0.0 0.06. CO2 + Pt(s) → CO2(s) S0 = 5.0 · 10−3 0.0 0.07. CO + Pt(s) → CO(s) S0 = 8.4 · 10−1 0.0 0.0II Desorption8. O(s) + O(s) → O2 + Pt(s) + Pt(s) 3.70 · 1021 0.0 232.2

θO(s) 90.09. C3H6(s) → C3H6 + Pt(s) + Pt(s) 1.00 · 1013 0.0 72.710. C3H5(s) + OH(s) → C3H6 + O(s) + Pt(s) 3.70 · 1021 0.0 31.011. H(s) + H(s) → H2 + Pt(s) + Pt(s) 3.70 · 1021 0.0 67.4

θH(s) 6.012. H2O(s) → Pt(s) + H2O 1.00 · 1013 0.0 40.313. CO(s) → CO + Pt(s) 1.00 · 1013 0.0 136.4

θCO(s) 33.014. CO2(s) → CO2 + Pt(s) 1.00 · 1013 0.0 27.1III Surface reactionsIIIa C3H5(s)-oxidation (global reaction)15. C3H5(s) + 5O(s) → 3C(s) + 5OH(s) 3.70 · 1021 0.0 95.0IIIb C3H6(s) -decomposition16. C3H6(s) → CC2H5(s) + H(s) 1.00 · 1013 0.0 75.417. CC2H5(s) + H(s) → C3H6(s) 3.70 · 1021 0.0 48.818. CC2H5(s) + Pt(s) → C2H3(s) + CH2(s) 3.70 · 1021 0.0 108.219. C2H3(s) + CH2(s) → CC2H5(s) + Pt(s) 3.70 · 1021 0.0 3.220. C2H3(s) + Pt(s) → CH3(s) + C(s) 3.70 · 1021 0.0 46.021. CH3(s) + C(s) → C2H3(s) + Pt(s) 3.70 · 1021 0.0 46.9IIIc CHx-decomposition22. CH3(s) + Pt(s) → CH2(s) + H(s) 1.26 · 1022 0.0 70.423. CH2(s) + H(s) → CH3(s) + Pt(s) 3.09 · 1022 0.0 0.024. CH2(s) + Pt(s) → CH(s) + H(s) 7.00 · 1022 0.0 59.225. CH(s) + H(s) → CH2(s) + Pt(s) 3.09 · 1022 0.0 0.026. CH(s) + Pt(s) → C(s) + H(s) 3.09 · 1022 0.0 0.027. C(s) + H(s) → CH(s) + Pt(s) 1.25 · 1022 0.0 138.0IIId C2Hx-oxidation28. C2H3(s) + O(s) → CH3CO(s) + Pt(s) 3.70 · 1019 0.0 62.329. CH3CO(s) + Pt(s) → C2H3(s) + O(s) 3.70 · 1021 0.0 196.7

θO(s) -45.030. CH3(s) + CO(s) → CH3CO(s) + Pt(s) 3.70 · 1021 0.0 82.931. CH3CO(s) + Pt(s) → CH3(s) + CO(s) 3.70 · 1021 0.0 0.0

θCO(s) -33.0

B.5 Mechanism for a three-way catalyst 169

Reaction A / S0 β /µ Ea / εIIIe CHx-oxidation32. CH3(s) + O(s) → CH2(s) + OH(s) 3.70 · 1021 0.0 36.633. CH2(s) + OH(s) → CH3(s) + O(s) 3.70 · 1021 0.0 25.134. CH2(s) + O(s) → CH(s) + OH(s) 3.70 · 1021 0.0 25.135. CH(s) + OH(s) → CH2(s) + O(s) 3.70 · 1021 0.0 25.236. CH(s) + O(s) → C(s) + OH(s) 3.70 · 1021 0.0 25.137. C(s) + OH(s) → CH(s) + O(s) 3.70 · 1021 0.0 224.8IIIf H, OH, H2O - reactions38. O(s) + H(s) → OH(s) + Pt(s) 3.70 · 1021 0.0 11.539. OH(s) + Pt(s) → O(s) + H(s) 5.77 · 1022 0.0 74.940. H(s) + OH(s) → H2O(s) + Pt(s) 3.70 · 1021 0.0 17.441. H2O(s) + Pt(s) → H(s) + OH(s) 3.66 · 1021 0.0 73.642. OH(s) + OH(s) → H2O(s) + O(s) 3.70 · 1021 0.0 48.243. H2O(s) + O(s) → OH(s) + OH(s) 2.35 · 1020 0.0 41.0IIIg CO-oxidation44. CO(s) + O(s) → CO2(s) + Pt(s) 3.70 · 1020 0.0 108.0

θCO(s) 33.0θNO(s) -90.0

45. CO2(s) + Pt(s) → CO(s) + O(s) 3.70 · 1021 0.0 165.1θO(s) -45.0

46. C(s) + O(s) → CO(s) + Pt(s) 3.70 · 1021 0.0 0.0θCO(s) -33.0

47. CO(s) + Pt(s) → C(s) + O(s) 3.70 · 1021 0.0 218.5θO(s) -45.0

NO - reduction on PtI Adsorption48. NO + Pt(s) → NO(s) 8.50 · 10−1 0.0 0.0II Desorption49. NO(s) → NO + Pt(s) 1.00 · 1016 0.0 140.050. N(s) + N(s) → N2 + Pt(s) + Pt(s) 3.70 · 1021 0.0 113.9

θCO(s) 75.0III NO - surface reactions51. NO(s) + Pt(s) → N(s) + O(s) 5.00 · 1020 0.0 107.8

θCO(s) -3.052. N(s) + O(s) → NO(s) + Pt(s) 3.70 · 1021 0.0 128.1

θO(s) 45.0

NO/CO - reactions on RhI Adsorption53. O2 + Rh(s) + Rh(s) → O(Rh) + O(Rh) 1.00 · 10−2 0.0 0.0

θRh(s) -1.054. CO + Rh(s) → CO(Rh) 5.00 · 10−1 0.0 0.055. NO + Rh(s) → NO(Rh) 5.00 · 10−1 0.0 0.0II Desorption56. O(Rh) + O(Rh) → O2 + Rh(s) + Rh(s) 3.00 · 1021 0.0 293.357. CO(Rh) → CO + Rh(s) 1.00 · 1014 0.0 132.3

θN(Rh) 41.9θCO(Rh) 18.8

58. NO(Rh) → NO + Rh(s) 5.00 · 1013 0.0 108.959. N(Rh) + N(Rh) → N2 + Rh(S) + Rh(S) 1.11 · 1019 0.0 136.9

θN(Rh) 16.7III NO/CO-surface reactions60. CO(Rh) + O(Rh) → CO2 + Rh(s) + Rh(s) 3.70 · 1020 0.0 59.961. NO(Rh) + Rh(s) → N(Rh) + O(Rh) 2.22 · 1022 0.0 79.5

Reaction 15. is a complex fast reaction that takes place if sufficient O(s) is available for Reaction 3. Reaction 15. isfirst order in O(s) and C3H5(s).


B.6 HC-SCR on Pt/Al2O3

This mechanism is used for the numerical simulation of the HC-SCR in a DeNOxcatalyst as described in Chapter 8.3. The development of the mechanism is discussedin [88,89].

Reaction A / S0 β /µ Ea / εC3H6 - oxidation

I Adsorption1. O2 + Pt(s) + Pt(s) → O(s) + O(s) S0 = 7.0 · 10−2 0.0 0.02. C3H6 + Pt(s) + Pt(s) → C3H6(s) S0 = 9.8 · 10−1 0.0 0.03. C3H6 + O(s) + Pt(s) → C3H5(s) + OH(s) S0 = 5.0 · 10−2 0.0 0.0

θPt(s) -0.94. H2 + Pt(s) + Pt(s) → H(s) + H(s) S0 = 4.6 · 10−2 0.0 0.0

θPt(s) -1.05. H2O + Pt(s) → H2O(s) S0 = 7.5 · 10−1 0.0 0.06. OH + Pt(s) → OH(s) S0 = 1.0 · 10−0 0.0 0.07. CO2 + Pt(s) → CO2(s) S0 = 5.0 · 10−3 0.0 0.08. CO + Pt(s) → CO(s) S0 = 8.4 · 10−1 0.0 0.0II Desorption9. O(s) + O(s) → O2 + Pt(s) + Pt(s) 3.70 · 1021 0.0 232.2

θO(s) 90.010. C3H6(s) → C3H6 + Pt(s) + Pt(s) 1.00 · 1013 0.0 72.711. C3H5(s) + OH(s) → C3H6 + O(s) + Pt(s) 3.70 · 1021 0.0 31.012. H(s) + H(s) → H2 + Pt(s) + Pt(s) 3.70 · 1021 0.0 67.4

θH(s) 6.013. H2O(s) → Pt(s) + H2O 1.00 · 1013 0.0 40.314. OH(s) → Pt(s) + OH 1.00 · 1013 0.0 251.415. CO(s) → CO + Pt(s) 1.00 · 1013 0.0 146.4

θCO(s) 33.016. CO2(s) → CO2 + Pt(s) 1.00 · 1013 0.0 27.1III Surface reactionsIIIa C3H5(s)-oxidation (global reaction)17. C3H5(s) + 5O(s) → 3C(s) + 5OH(s) 3.70 · 1021 0.0 95.0IIIb C3H6(s) -decomposition18. C3H6(s) → CC2H5(s) + H(s) 1.00 · 1013 0.0 75.419. CC2H5(s) + H(s) → C3H6(s) 3.70 · 1021 0.0 48.820. CC2H5(s) + Pt(s) → C2H3(s) + CH2(s) 3.70 · 1021 0.0 108.221. C2H3(s) + CH2(s) → CC2H5(s) + Pt(s) 3.70 · 1021 0.0 3.222. C2H3(s) + Pt(s) → CH3(s) + C(s) 3.70 · 1021 0.0 46.023. CH3(s) + C(s) → C2H3(s) + Pt(s) 3.70 · 1021 0.0 46.924. CH3(s) + Pt(s) → CH2(s) + H(s) 1.26 · 1022 0.0 70.425. CH2(s) + H(s) → CH3(s) + Pt(s) 3.09 · 1022 0.0 0.026. CH2(s) + Pt(s) → CH(s) + H(s) 7.00 · 1022 0.0 59.227. CH(s) + H(s) → CH2(s) + Pt(s) 3.09 · 1022 0.0 0.028. CH(s) + Pt(s) → C(s) + H(s) 3.09 · 1022 0.0 0.029. C(s) + H(s) → CH(s) + Pt(s) 1.25 · 1022 0.0 138.0IIIc CHx-decompositionIIId C2Hx-oxidation30. C2H3(s) + O(s) → CH3CO(s) + Pt(s) 3.70 · 1019 0.0 62.331. CH3CO(s) + Pt(s) → C2H3(s) + O(s) 3.70 · 1021 0.0 196.7

θO(s) -45.032. CH3(s) + CO(s) → CH3CO(s) + Pt(s) 3.70 · 1021 0.0 92.933. CH3CO(s) + Pt(s) → CH3(s) + CO(s) 3.70 · 1021 0.0 0.0

θCO(s) -33.0

B.6 HC-SCR on Pt/Al2O3 171

Reaction A / S0 β /µ Ea / εIIIe CHx-oxidation34. CH3(s) + O(s) → CH2(s) + OH(s) 3.70 · 1021 0.0 36.635. CH2(s) + OH(s) → CH3(s) + O(s) 3.70 · 1021 0.0 25.136. CH2(s) + O(s) → CH(s) + OH(s) 3.70 · 1021 0.0 25.137. CH(s) + OH(s) → CH2(s) + O(s) 3.70 · 1021 0.0 25.238. CH(s) + O(s) → C(s) + OH(s) 3.70 · 1021 0.0 25.139. C(s) + OH(s) → CH(s) + O(s) 3.70 · 1021 0.0 224.8IIIf H, OH, H2O reactions40. O(s) + H(s) → OH(s) + Pt(s) 3.70 · 1021 0.0 11.541. OH(s) + Pt(s) → O(s) + H(s) 5.77 · 1022 0.0 74.942. H(s) + OH(s) → H2O(s) + Pt(s) 3.70 · 1021 0.0 17.443. H2O(s) + Pt(s) → H(s) + OH(s) 3.66 · 1021 0.0 73.644. OH(s) + OH(s) → H2O(s) + O(s) 3.70 · 1021 0.0 48.245. H2O(s) + O(s) → OH(s) + OH(s) 2.35 · 1020 0.0 41.0IIIg CO-oxidation46. CO(s) + O(s) → CO2(s) + Pt(s) 1.00 · 1018 0.0 108.0

θCO(s) 33.0θNO(s) -90.0

47. CO2(s) + Pt(s) → CO(s) + O(s) 3.70 · 1021 0.0 155.1θO(s) -45.0

48. C(s) + O(s) → CO(s) + Pt(s) 3.70 · 1021 0.0 0.0θCO(s) -33.0

49. CO(s) + Pt(s) → C(s) + O(s) 3.70 · 1021 0.0 228.5θO(s) -45.0

NO - reduction and oxidationI Adsorption50. NO + Pt(s) → NO(s) 8.50 · 10−1 0.0 0.051. NO2 + Pt(s) → NO2(s) 9.00 · 10−1 0.0 0.052. N2O + Pt(s) → N2O(s) 2.50 · 10−2 0.0 0.0II Desorption53. NO(s) → NO + Pt(s) 1.00 · 1016 0.0 140.0

θO(s) 10.054. NO2(s) → NO2 + Pt(s) 1.00 · 1013 0.0 60.055. N(s) + N(s) → N2 + Pt(s) + Pt(s) 3.70 · 1021 0.0 113.9

θCO(s) 75.056. N2O(s) → N2O + Pt(s) 1.00 · 1013 0.0 54.4III NO-surface reactions57. NO + O(s) → NO2(s) 1.40 · 1010 0.5 97.5

θO(s) 45.058. NO2(s) → NO + O(s) 1.00 · 1013 0.0 98.759. NO(s) + Pt(s) → N(s) + O(s) 4.00 · 1021 0.0 107.8

θCO(s) -3.060. N(s) + O(s) → NO(s) + Pt(s) 3.70 · 1021 0.0 128.1

θO(s) 45.061. NO(s) + N(s) → N2O(s) + Pt(s) 1.00 · 1021 0.0 90.962. N2O(s) + Pt(s) → NO(s) + N(s) 3.70 · 1021 0.0 66.9

Reaction 17. is a complex fast reaction that takes place if sufficient O(s) is available for Reaction 3. Reaction 17. isfirst order in O(s) and C3H5(s).

Acknowledgments

First and foremost, I would like to thank Professor Jurgen Warnatz for his continuousscientific and personal support of my work. He is a great mentor, who not only pointedme in the right direction but also provided enough freedom for my own thoughts andcreativity.

The research described in this work is unthinkable without a strong research group,with which I enjoyed working. In particular, I would like to thank Steffen Tischerfor programming the boundary-layer code, Dr. Chrys Correa for programming themonolith code, and Daniel Chatterjee for modeling automotive catalytic converters.Renate Schwiedernoch set up the reactor for experimental investigations of short-contact-time catalysis in an impressively short time. I acknowledge Dr. Luba Maier’sdevelopment of reaction mechanisms and Oliver Grosshans’ work on optimization.For assistance in various software and hardware problems, I appreciate the help ofStefan Kleditzsch, Volker Karbach, Tillman Katzenmeier, and Markus Nullmeier.I also would like to thank my former colleagues Dr. Markus Wolf, Dr. ChristianTaut, and Dr. Ralf Kissel-Osterrieder. I thank Professor Frank Behrendt and Dr.Uwe Riedel for their long-lasting colleagueship. Ingrid Hellwig and Barbara Wernerare acknowledged for their supportive secretarial work. I would also like to thankall members of the research group Reactive Flows at the Interdisciplinary Centerfor Scientific Computing at Heidelberg University for their collaboration and a veryconducive working environment.

Professor Jurgen Wolfrum, Dr. Hans-Robert Volpp, and Dr. Uwe Metka (PCI,Heidelberg University) were very kind to provide the space and technical support forthe experimental study. I also like to thank them for a fruitful collaboration in theSonderforschungsbereich 359.

Professor Lanny D. Schmidt (University of Minnesota) introduced me with hisenthusiasm to the problems of short-contact-time reactors. I would like to thankhim and all his students for their hospitality and 15 wonderful months I spent asPostdoc in Minnesota in 1997/98. In particular, I would like to thank Dr. AshishBodke, Dr. Dimitri Iordanoglou, Prof. Keith Hohn, and Dr. Srinivas Tummala forour collaboration on catalytic partial oxidation. The friendship I shared with themand Jamease Kowlaczyck allowed me to survive the cold Minnesotan winter. I veryappreciate the still ongoing collaboration between our groups and thank JeremyRedenius and Ryan O’Connor for our collaboration on catalytic radiant burners andpartial oxidation of higher alkanes, respectively.

I have always been enjoying the meetings and discussions with Professor Robert J.Kee (Colorado School of Mines), who gave me numerous valuable advice. I veryappreciate the collaboration in catalytic combustion research with Professor Kee,Professor Laxminarayan L. Raja (Colorado School of Mines), and Professor RobertW. Dibble (University of California at Berkeley).

The investigation of oxy-dehydrogenation of ethane is based on a very fruitfulcollaboration with Dr. David K. Zerkle (Los Alamos National Laboratory). I wouldalso like to thank him for the nice summer of 1998 I spent in Los Alamos.

The experimental data for the studies on automotive catalytic converters wereprovided by Professor W. Weissweiler, Erik Frank, and Dr. Sven Kureti (Universityof Karlsruhe).

I thank Chrys, Steffen, Ryan, Jeremy, and Keith for proof-reading the manuscript.

The research presented in this work was funded by the Deutsche Forschungsge-meinschaft (Postdoctoral fellowship, SFB 359, Sachbeihilfen), ForschungsverbundVerbrennungskraftmaschinen, Bundesland Baden-Wurttemberg, US Department ofEnergy, Eberspacher GmbH & Co, and Conoco Inc. I very much appreciate thefinancial support.

I would like to thank my parents and my daughter Franziska for their love, and all myfriends for being there.

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