7th CLEERS Workshop Notes by John Hoard, Ford

June 16-17, 2004

Summary

The two day workshop was held at Detroit Diesel.  Presentations are to be posted on the CLEERS web site in the near future, http://www.cleers.org/.

 

DPFs are being modeled and tested.  The deposition of soot in a filter is controlled by the Peclet number – the ratio of inertial to diffusion forces on a particle.  Different operating characteristics change the penetration into the filter wall, and the density/permeability of the soot cake. The process of understanding DPFs is very rapid, and many papers are being published.  If we already understood it, there would be few papers!

 

The pressure drop, filtration, and wash coating capability are strong functions of DPF pore size and size distribution, as well as cell density and wall thickness.  These same parameters also affect the strength.  Substrates suitable for catalyst coating are much weaker than uncatalyzed designs.  A narrow pore size distribution is highly desired. A clear understanding of material properties along with flow and temperature distribution is needed in order to assure long life.  Thermal stress is a major issue.  Catalyst coatings affect physical properties, and it is the coated filter that must survive.

 

System models are being developed to include DOC, DPF, and NOx catalysts.  Manufacturers would like a nice set of data to be provided by suppliers; the data would be put into models to allow system design.  Unfortunately, it may not be readily possible to have a simple data set to describe a complex, history dependent device like and LNT or a DPF.

 

Chalmers and GM R&D have developed a global kinetic LNT model for a model Ba/Pt/alumina catalyst.  They have also done a commercial catalyst but those results can’t be shared.

 

ORNL has tested different LNT regeneration strategies on an engine and reports the speciation of HC, CO, and H2.  Extensive instrumentation has generated useful data.

 

The LNT Subgroup is developing a standardized test protocol for LNTs, intended to get enough data to calibrate a kinetic model in a reasonable length of time.

 

LLNL is starting some interesting quantum chemical computations for cases related to LNTs.

 

ORNL is doing DRIFTS and other testing on model LNT catalysts to find mechanisms information.  Nitrate and carbonate are the dominant storage species.  There are interaction with PGM and supports.  Water is also important.  Key deactivation mechanisms include sintering of PGM and Adsorbates, and apparently coverage of PGM sites by Adsorbates.  Kinetic models of the lean storage are being developed.

 

A reformer can generate H2 and CO from fuel onboard.  These reductants can reduce the required temperature for deSOx and deNOx, potentially improving durability and efficiency.

 

There is continuing work toward HC-SCR catalysts.  Many years of research has led to better understanding of mechanisms, but the important chemistry is strongly dependent on what conditions (HC species, temperature, water, etc) are being run.  Adding H2 to HC greatly improves low temperature NOx conversion and widens the conversion window.  GM has a DOE program that is using combinatorial methods and finding improved HC-SCR catalysts.

 

ORNL has done careful analysis of exhaust in a urea SCR system, looking into urea decomposition and reaction products.  At low temperature, you might make a lot of nitrates.  At higher temperatures, you might make some deposits.  There is very large storage of urea-derived components in the SCR catalyst; a control system must model this to avoid large NH3 break through.

 

Ford is continuing development of a diesel SCR SUV.  Emission cycle results are approaching Tier 2 Bin 5 at low mileage.  Work continues, and will be followed by durability testing.

 

NREL is working with AVL to include SCR catalysts in the FIRE code.  JohnsonMatthey and University of Michigan also have SCR models.

 

Opening

Walter Putz, Senior VP Engineering, Detroit Diesel

 

This is an important time to talk about simulation for diesel aftertreatment.  New standards are forcing major developments for cars and trucks.  In the truck business, life cycle cost is critical.  We have to control aftertreatment cost and also usage costs.  The engine-aftertreatment system is a complex system and must be handled together – you can’t just put a DPF on an unmodified engine.  Simulation tools are an important part of the design and development process.

 

Workshop Overview

Dick Blint, GM Research and Stuart Daw, ORNL

 

CLEERS is Crosscut Lean Exhaust Emissions Reduction Simulation.  “Crosscut” comes from the parent DOE Diesel Crosscut Team.  CLEERS is to foster communication and collaboration – not to write software.

 

Several forums have been developed including these workshops.  Common terminology and standards are being developed.

 

The CLEERS team leadership is Blint GMR, Daw ORNL, Sisken DDC, Harold Kung (NW Univ), Singh DOE.

 

There are subgroups for DPF, LNT, and SCR.  This emphasis came from input by the users. The Focus Groups each have 5 or more Crosscut members, plus sponsored participants such as catalyst companies.  The Groups hold monthly net meetings.  6-7 engine mfrs, 1-2 universities, 1-2 National Labs are a typical Group makeup.

 

Kinetics of the catalysts is a key issue to be studied.  Controlled lab data is needed to develop the kinetic models.  ORNL is developing a set of shared data.

 

There is a web site that compiles information and articles.  There are now over 500 citations on the web site.  There is a quarterly newsletter.

 

All Soot Deposits Are Not Created Equal: Variations on a Theme of Peclet Number

Mansour Masoudi, Delphi

 

At the last CLEERS meeting, George Muntean reported variations in soot properties according to deposition conditions. It usually takes 2 years for a new finding to get widespread attention and be utilized in industrial applications. 

Filter pressure drop is a system in itself:

  • Filter attributes (size, length, permeability, area, inlet and outlet coefficient, segment size and cement thickness, ….)
  • Exhaust flow attributes (flow rate, temperature)
  • Soot attributes (engine emission rte, structure of deposit – dense, porous,…)

 

You can map soot mass emission rate as a function of speed, load, EGR, temperature …but in many cases we don’t have this data yet.

 

You need to understand and quantify the roles of all three of these attributes.  Most papers just give a plausible explanation, but you really need to quantify things.  This paper will deal only with the soot structure issue.

 

You can write the equation for dP; there are SAE papers that show it.  To have a good understanding, you have to know soot density and permeability.

 

The flow through the filter has an enormous effect on soot density and permeability.  High flow (>7-8 cm/sec normal velocity at the filter wall) means the soot particles tend toward ballistic impact deposit.  At low flow rates, deposition is driven more by diffusion and less by particle inertia. The latter gives a lower density, more permeable soot deposit and thus lower dP for the same total soot mass.

 

How do you quantify this? Equations were shown to estimate mean free path (MFP) of gas molecule; Knudsen number (relative significant of mean free path to diameter of PM), Stoke-Cunningham factor, and Kubawara function.  These allow estimation of the soot permeability.  The difference between a leaf in the wind versus soot in gas flow is the relation between MFP and particle size.  The Stroke Cunningham factor embodies this effect.

 

The Peclet number is ratio of inertial force to diffusion force.  Soot layer porosity is a function of Peclet number.  The density of the soot layer is then related to the porosity.

 

Thus, two major characteristics of the soot layer – density and permeability – can be identified. You can now plot these parameters vs Peclet number.  Around Pe=1, there is a strong increase in density and decrease in permeability.  In other words, if deposition velocity is large you get dense layers with low permeability.  Pe is much more important to permeability than temperature.

 

With these two values, you can calculate dP knowing the filter characteristics.  Note that dP alone does not provide a reliable estimate of PM mass, unless you know the conditions under which soot was deposited.

 

SAE 2003-01-0842 gave particle layer packing density vs Pe.  This matched the theory well, both for bare and catalyzed DPF.  The presence of the washcoat has little effect on soot deposit; deposit occurs before catalysis.

 

A slide showed good agreement between dP calculated using the above analysis; it cam very close to the test data.

 

You could implement the equations in a controller to predict the current soot loading in real time.  Delphi did this, and a graph showed good agreement with the integral value versus a measured total mass.  This kind of estimate can be used in the deSoot algorithms for triggering and/or controlling heat release during deSoot.

 

Discussion

Of course, the estimation of soot in the DPF only works at low temperatures where soot is not oxidizing.  For a real system, you will also have to have a regeneration model.

 

Does soot get “stickier” when hotter? The effect of temperature is in the analysis – i.e. in MFP. The model as presented does not make any assumption about SOF-related sticking etc.

 

If you have a loose layer, then go to high flow does it change the soot layer that was already there? The model assumes you don’t, but we don’t have hard data.  On the other hand, the model seems to agree well with data so maybe the assumption is not too bad.

 

Modeling DPFs

Prof. Athanasios Konstandopoulos, Aerosol Particle Technology Laboratory, CERTH/CPERI

 

The Center studies fine particles as suspensions in gas media – either as pollutants or intentional (catalysts in industrial processes).

 

Their lab has 24 full time engineers and scientists, all funded by industry contracts and grants.

 

To make the DPF system work, you need a DPF simulation tool for a variety of users, and also algorithms such as virtual sensors for aftertreatment management and control.  Different users (substrate mfrs, coaters, engine makers,…) have different uses for the models.

 

There are a number of DPF configurations – Cd, SiC, sintered metal, fibrous ceramic….   This is like a Cambrian explosion – lots of diversity that will eventually move toward a single winning version.

 

An interesting graph shows number of simulation papers versus time from 1980 to now.  We are about ˝ way through sigmoid shaped curve typical of new technology development.  We are right about the peaks of the publication rate!  The curve predicts that by 2012 there will be little new publication – either the technology goes away or else you understand it by then!

 

There are multiple scales of DPF systems – microns pore size, wall thickness, entire filter, and entire exhaust system.  We have to understand all these scales.  You can’t run a single model that includes the finest scale over the full volume in a reasonable computational time.  So, you use different models for different purposes.

 

At the nano/micro scale you use Monte Carlo, particle dynamics, etc.

 

At meso scale, unit cell filtration codes, single and multi channel DPF simulation (WALTER), and exhaust line simulator (ELVIS).

 

It is important to avoid short cuts that use heuristic models – they are not general and cause problems later.  Each level must be mathematical!

 

At the macro scale, couple to CFD and FEA with user defined functions, to calculate stress, temperature field, etc.

 

Diesel aggregates have a fractal structure – soot is not spherical!  The fractal overlap seems to be around 0.174.  Recently, extensive data was taken of effective density versus mobility diameter for different engines and speed/loads.  As diameter increases, density drops.  The fractal dimension also varies with size, running about 2.4 on average. 

 

The normalized size distribution is remarkably similar over a range of engines.  Sigma is about 1.89.  This is handy for instrumentation and for modeling.  The shape can be explained by a model of oxidative fragmentation.  That model says the larger a particle gets, the more likely it will break.  The fragmentation must be random in order to fit the data (i.e., soot does not break in half or some other constant fraction, but rather at a random location).

 

The models of DPFs include wall scale, channel scale, and DPF scale features.  (See slide for more detail).   Current commercial models of the walls have gas temperature but not surface temperature considered; that’s a future enhancement.

 

The micro scale is done with Lattice Boltzmann.  You need a reconstruction of the wall structure; they make digital models from pictures.  This can be done with several algorithms for granular media, with a library or grain types, packing, and resulting structure.  This can also be done for fibers.  The porous structures we use have a lot of wasted porosity; regions the flow will not really go through.  The comparison of model to test results is quite good.

 

Next, there are algorithms for catalyst coatings – both uniform and non-uniform.  Coaters sometimes think there is such a thing as a uniform coating!  A uniform coating, however, reduces the cross section of the flow path.  Non uniform coating depends on coating method.  Coating is usually by dip-and-blow.  Here, the catalyst is removed where the flow velocity is high.  The different methods can give very different permeability versus loading and fraction of pores filled.

 

Coating can also be done on a filtration method.  This gives good results.

 

The dynamics of soot filtration are being modeled also.  Initial deposition is in the wall, followed by a cake development.  Layer characteristics can be predicted.

 

DPFs are very good at collecting small particles; if you see small PM downstream, they have condensed after the filter.

 

Soot deposits first at the high flow locations.  That area plugs first and the flow redistributes to cleaner areas.  You have to feed back soot deposit into the flow field calculation.

 

Soot cake personality is characterized by the Pe number.  You get more wall deposition at higher Pe.

 

Ice particle growth on a window is very similar to soot deposition growth dynamics!

 

Soot cake geometry can become non-convex at low Pe number.

 

A quenched diffusion flame burner was used to provide reference soot distributions.  With this, we learned that input PM size affects the soot cake density; larger PM makes looser cake.  We don’t see this so much in engines because the soot input size is more constant than in this bench experiment.

 

The soot microstructure changes as you oxidize the soot.  This has been modeled (series of slides shown).  Density is linear with oxidation rate.  The model agrees well with regeneration experiments.

 

At the DPF level, there are practical issues such as the glue thermal characteristics in segmented filters.  If the glue is a good insulator, there are larger temperature gradients in the catalyst.  You can get regeneration of some segments without other regenerating.  Yu can get partial regeneration when the temperature is non uniform.

 

As others have said, partial regeneration destroys the correlation between dP and soot mass.

 

They are now trying to use hooks in CFD codes to put the DPF models in. These codes can predict soot deposition geometry and thermal distribution.

 

A DOC in front helps the temperature for regeneration on the DPF.

 

The ash distribution (SAE 2003-01-1963) along the length depends on the shear on the ash layer, and the ash shear strength.

 

You have to know the origin and pathways of ash. It can be by pyrolysis on the filter, or combustion upstream.  Ash can re-entrain at high shear.  Ash stickiness changes depending on its history. More work is needed to characterize ash!

 

Future emission control systems will be very complex, whether you like it or not.  You will have multiple catalysts/DPF units, sensors and virtual sensors, and control algorithms.  Models will have to be made faster to use in vehicle ECU.  Fiat paper at FISITA F2004V068 showed DPF control algorithms based on this work.  Their paper shows the value of modeling!

 

Discussion

The regeneration oxidation model shown in these slides is very simple. We have more complex models but no time today to show it.  NO2 and O2 mechanisms are both important.  A 2-layer model is used: some soot in contact with catalyst, some not, so there is an effectiveness factor.  HC or wetness of soot is also important.  We don’t see an effect of wet soot; it becomes dry by the time it is hot enough to react.  You might get some added heat release from Pt oxidation of the desorbing HC.

 

Relationship Between Pressure Drop and Pore Microstructure for DPF from Experimental and Capillary Pore Modeling.

Greg Merkel, Corning

 

You can show dP versus flow at various soot loadings; generally a parabolic fit.  Or, you can look at dP versus soot loading at various flow conditions.

 

dP affected by

  • Length
  • diameter
  • plug length
  • channel width
  • wwall thickness
  • soot in pores
  • pore microstructure
  • soot cake

 

We are particularly interested in soot penetration into wall.  How much gets in, how deep does it penetrate, location within the pores (uniform coating or local?), and packing density in pores.

 

Slide shows 6 different pore structures developed.  These have different dP characteristics – initial dP, slope of initial dP versus soot loading.

 

The simplest case is a filter that only has a surface cake, no penetration. Useful filters, though, have an initial steeper dP slope.  Some filters have a low initial slope that seems to be building a uniform coating on the pore walls, followed by the steeper and then the shallower slope.

 

Each of the above three cases is modeled as two overlaying slabs with different thickness and permeability.

 

The flow is treated with an approximation of randomly oriented capillary pores.  The Hagen-Poiseuille equation relates dP to capillary geometry.  You sum over all pores to get an equation for the wall.  Permeability can then be estimated, and is linear in porosity and quadratic in pore diameter.   K=C phi d2.

 

This model was used to compare to literature data.  The agreement is pretty good to porous glass  and MgO ceramics of known characteristics.  Agreement is also pretty good for cordierite (Cd).

 

The soot deposit permeability also has to be modeled.  The Konstandopoulos method was used.  Soot cake porosity is typically >90%.

 

These are combined then to calculate filter dP.  Assumptions for following slides are listed; high SV, 180K.

 

For the case of a soot cake forming without permeation into the wall a low slope versus loading is predicted. For the case where soot uniformly fills surface and near surface pores you get a steeper slope. For the case where soot deposits uniformly coat the capillary walls you get very little pressure increase until the model begins to close the entire pore.

 

One can then compare modeled dP to experimental data with Cd DPFs.

  • Case 1 – 50% porosity, pore d50=8 micrometers: the excremental data is closet to the “soot cake on wall” model.  You get to the soot cake phase very early.
  • Case 2 – 70% porosity, d50-16: Still looks like soot cake build.  If soot does get into the surface pores, it must be quite porous.
  • Case 3 -  50%, d50 30 but wide pore size distribution: Now we see low slope, steep slope, then low slope again.  This seems to be coating pores, then plugging pores, then soot cake.
  • Cake 4 – 50% 12 micron pores: Steep initial followed by lower slope. The initial slope corresponds to a very low porosity in the pores, ~80%.

 

How do you control that initial slope?  A study done with 110 Cd filters having various structures.  38-57% porosity, different pore size and distribution.  These generated a wide range of dP slopes.  The Konstandopoulos model was used to generate permeabilities and coefficients, and these were fit to the experimental variables.

 

Pore size distribution defined by the diameter of 10, 50, and 90 percentile. Also, d90-d10 is width of distribution.  You can also normalize the parameters:  (d90-d10)/d50 etc.  These parameters were part of the dP data fitting.

 

Permeability of a clean wall correlates well (r2 = 0.87) with d502.

 

A soot filled wall permeability is dominated by (d50-d10)/d50.  The d502 term is now minor.  High porosity and narrow pore size distribution maximize permeability.  (d50-d10)/d50 seems to be related to whether the flow path seems like large pores connected by small necks, or more like a constant width channels.  A large value is more-connected.  Narrow necks increase the likelihood of dense soot packing in the pore necks.

 

Summary:

Pore microstructure is important

A random oriented capillary model matches data

It has been extended to the two layer wall + soot cake model

Experiments show desired pore size distribution characteristics.

 

Discussion

Prof K: interesting work.  What “pore size” means is a matter of definition.  There are solid reasons for the data shown.  Permeability always scales with a characteristic size squared and a constant around 1.6.  Deposition is dominated by stagnation regions.  It is a matter of style if you prefer to model flow in pores versus flow around objects.  We think it is easier in denser media to model flow around objects.  the sharp regions of slope are related to percolation mechanisms.  I don’t think the flow acceleration mechanism is really likely; the flow can move to another channel instead of accelerating.  The high slope is related to the penetration depth.  The best filter is the one that keeps the filter wall separate from the soot cake – but that is the worst if you want to get catalyst and soot into contact!  Of course, the capillary pore model is only a rough approximation.

 

The pore distributions are based on mercury porosimetry, so they are volume related.

 

In real filters the pores have a lot of interconnection, while your model has none.  How can it match the data as well as it does? I don’t really know! It does seem surprising that such a simple model fits the data so well.  Perhaps it is because the porosimetry only measures the “accessible” pores.

 

We are also now doing image analysis of real filters.

Regeneration Characteristics of DPFs Under Transient Exhaust Conditions

Tariq Shamin, U Mich Dearborn

 

Most available DPF regeneration data is at steady state.  In the real world, regeneration will have to occur under transient driving conditions.  Can we regenerate at lower temperature by some form of modulation.

 

Their model is based on Bisset (like everyone else’s!).  This is a fairly simple model.  A Corning EX-80DPF was used, 100 cpsi, 188 x 152 mm, 50% porosity.

 

The model’s predicted temperature matched published data pretty well for a SS regeneration.

 

Using the model several cases were evaluated.  If inlet temperature is sinusoidally varied +/-50C at 0.01 Hz, the Toutlet varies less than the Tbed.  Wall temperature is not symmetric around the mean value.  dP also varies sinusoidally.

 

Exhaust flow rate modulation does not affect the filter temperature but strongly affects dP.

 

For a soot loaded filter at Tinitial 510C (below light off), +/-50C inlet temperature modulation at 0.01 Hz you get more temperature rise and faster regeneration.  At 0.1 Hz, the effect is damped out and it looks more like SS.

 

For higher temperature, 500C – close to soot ignition temperature, you see larger effect of modulation.

 

Modulation of exhaust flow rate has major dP effect, and small improvement in regeneration.

 

For a CDPF at 250C modulation has a larger effect of enhancing soot regeneration.  At 300C (regeneration will go by itself), modulation increases regeneration completeness and speed.

 

Discussion

This arises because chemical reaction rates are nonlinear: for the same average T, modulation increases the net reaction rate.  Low frequency is necessary since thermal mass will prevent fast modulation from having an effect.

 

Did you normalize for the total energy input? Is there an advantage in regeneration versus total energy? For soot regeneration, you see accumulated effect.

 

What would you have to change in your model to reduce the effect of frequency?  Is it a material property, a soot property, or….? I don’t know if it is mass or heat transport limited.  It seems likely it is the heat capacity or thermal mass effect.

 

Physical Durability of Cd Ceramic Filter

Suresh Gulati, Research Fellow and Consultant, Corning

 

DPF requirements:

  • Filter efficiency
  • Soot capacity
  • Ash storage
  • Low dP
  • Low fuel penalty
  • High heat capacity
  • High regeneration efficiency
  • Durability

 

Durability:

  • Key requirements
  • Key physical properties
  • Strength vs porosity
  • vs geometry
  • Model for isostatic strength
  • OxiCat vs DPF
  • Model for regeneration stresses
  • Allowable stress for safe regeneration

 

LD needs something like 200K miles durability.  You can’t have (large scale) cracks, although a flow through catalyst substrate can.  HC needs 435K miles.

 

Stresses and relevant strength characteristics:

  • Coating and calcining – tensile
  • Packaging – compressive
  • Heat treat mat – compressive & tensile
  • Degreening – tensile
  • Regeneration - tensile

 

Physical properties

  • Compressive strength
  • Isostatic
  • Modulus of rupture
  • Fatigue behavior
  • Elastic modulus  (Emod)
  • Thermal expansion

 

Maximum stress occurs somewhere in the center, not at the ends where the plugs are. Property measurements focus on unplugged section.

 

A flex test measures bending strength – axial, direction of flow – or face plane – tangential.

 

You can do a crush test in 3 directions.

 

A 3D isostatic test pressurizes in all directions.  It finds what stress can be applied during canning. A 2D isostatic tests can also be used to predict canning.

 

The focus is on isostatic and bending strengths.

 

Strength measurements are usually made in a short time, while real parts have loads applied for years.  So, the service load must be lower than the ultimate strength measured.

 

Web thickness and porosity each affect strength. Wall crush strength in the axial direction drops exponentially with porosity.  Thus, 35% porosity gives about 3000 psi, while 50% porosity has about 1500 psi strength, about half.

 

Web strength is 17000 exp(-4.9/P) where P is porosity.  Isostatic strength is 2.8 (web strength) (t/L), t= wall thickness, L = cell width.

 

Ceramics are weaker in tension than in compression.  Canning is more compressive, thermal stress is tensile.

 

The honeycombs also have a ceramic skin; this improves canning strength significantly.  The inner part is called the matrix. During isostatic tests (or canning) the stress in the skin is much higher than in the matrix.  Even so, the matrix is where you see failure first because it is so much weaker than the skin material.  For 900/1, stress skin / stress matrix =11.  For 400/6.5, 6.6.  Typically, canning loads are lower than strength by factors of >2.5.

 

DPF vs DOC

  • Higher Tpeak
  • More porous
  • Lower strength
  • Larger temperature Trange
  • Large axial T gradient
  • More T cycling
  • Outer zone temperature is lower
  • High fatigue loading
  • DPF is about 5 times heavier than catalyst

 

Thermal expansion: up to about 200C, the material actually shrinks.   There is only 140 ppm length change 0-400C. This grows to 1150 ppm at 800C.  axial and circumferential growth rates are different. If the center gets too much hotter than the outside, then the skin is put into tension and you may get a ring off crack.

 

This depends on Tpeak, CTE, Tgradients, how many cycles over lifetime.

 

A simple model allows input of measured temperatures. A linear T difference (hot center, cooler outside) crates maximum stress in the middle.  When there is a T difference along the axis, you get an added stress.  You have to add these two stresses.

 

The stress also depends on the aspect ratio, L/D.  Longer DPFs experience more stress.

 

A table compares EX-80 to RC, 100/17 vs 200/19.  Measured temperature profiles were put into the model. The radial T variation causes high stress.  Highest stress is in the middle (between inlet and outlet).  A 6” filter would have about half the stress of a 12” one.

 

To avoid cracks, the highest stress must not exceed the statistical lowest strength. To allow less than 0.1% failure probability, allowable stress is 0.18 So or about 130 psi.  Either you need a stronger product, or you reduce the thermal stress by regeneration more frequently.

 

For a 3 g/L soot load, stress is about 217 psi.  7.0 č 412.  If you want to go to 1 in a million failures, you need even lower stress. (these numbers are for RC).

 

Regeneration stress parameter isx defined as Emod x (length change center vs edges).  This correlates pretty well with regeneration stress.

 

Summary:

Thermal cracks can be avoided by decreasing both coated Emod and CTE by 40%..  In addition, allowable stress can be increased about 15% by reducing variability of MOR in the skin region.

 

Discussion

This argues you need to couple CFD to FEA codes in the long run.  Are we close? It is important to get exhaust flow well distributed.  Low flow at the OD contributes to larger T differences.  Soot should also be evenly distributed.  The inlet cone needs to be a diffuser.  We could also imagine a cell geometry that helps flow distribution.

 

Doesn’t a DPF self-adjust the flow top an extent?  High flow channels soot up faster, so the flow should gradually redistribute to other cells. It might help to direct more flow to the OD – this reduces the dT.  Mass and temperature distribution don’t necessarily go together. It is also harder to regenerate the OD cells.

 

The T gradient can be larger is the Tpeak is lower – since the CTE grows at higher temperature.

 

Have you looked to see what happens to the ceramic with fatigue aging? Yes; we see a slow crack growth due to stress corrosion at high humidity – near the OD.

 

Controlled regeneration is not really an issue.  The only major issue is an uncontrolled regeneration, where you start regeneration then drop to idle.

Discrete Particle Modeling of DPF

Mark Stewart, PNNL

 

Model development started this year.  We are modeling flight and deposition of individual particles.

 

There are multiple length scales of interest in a DPF.  We are looking at the finest scale, discrete particles.  In the long run, this work should lead to characteristics o\at pore and channel scale – we don’t think you would routinely use such a small scale model.

 

PNNL started with a 3D digital map of filter microstructure.  Corning EX80 was cut and imaged.  Images were combined to give a 3D model.

 

Flow is solved with Lattice Boltzman.  Time step is 3 x 10-8.

 

Initially we assume that all PM acts the same as 100 nm particles.  The PM motion is from gas velocity with Brownian motion superimposed.  Particles that hit a wall always stick.  For now there is no re-entrainment of oxidation.

 

As PM deposits, the flow field gets an added flow resistance.

 

Preliminary results:  An example condition was run, taking 88 ours to run on 64 Itanium2 processors at 1.5 GHz.  About 1 million particles introduced, about 14K escaped.  Typically 3K in flight at any time.  The model illustrates deep bed filtration transitioning to cake filtration.  There is little change in flow deep into the wall.  On the surface, the high velocity areas move as deposits build.  The bulk of gas flow follows a few major channels. (note: this is EX80, a wide pore size distribution – these flow channels are a reason for narrow pore distribution)

 

The model makes reasonable predictions of porosity at two conditions.  Qualitative agreement is good between photos of real soot and the model predictions.

 

A method is being developed to run experiments for validation.  A single channel filter is placed in exhaust.  Gas is drawn out of the channel, so the outside walls are the filter medium.  You can then analyze the soot cake without cutting up the sample.

 

Next steps: compare data to projections – dP, capture efficiency, deposit location and structure.  Then, apply to other substrate materials.

 

 

Discussion

How reproducible are these calculations?  You have “random” motion.  So far, we have not changed the random seed, so it runs the same each time.

 

What factors go into the Darcy resistance when a particle hits the wall?  So far, we are using a simple linear relation between the number of particles in a volume and the Darcy resistance. A future step will be more rigorous.

 

EX-80 has a broad pore size distribution.  Have you tried anything else?  Not yet but we plan to.  We would be very happy if someone gave us the 3D model!  We plan to digitize other images of the same EX-80 (different portions) to see if the results come out the same.

 

Prof K noted that you would prefer to have a model to build the filter geometry rather than the laborious method of cutting up a piece.

 

Simulation of a Coupled DOC and a DPF model

Tony Triano, Mich Tech University

 

Research sponsored by John Deere and DOE.

 

JMI CRT is DOC + DPF, intended to convert NO to NO2 and use NO2 to regenerate the soot.

 

The DOC model has routines to calculate exhaust properties, dP, gas phase energy conservation and species conservation.  The resulting values are input to a DPF model.  The DPF model calculates exhaust properties, velocity field, inlet temperature, regeneration chemistry, wall temperature, dP and filter effectiveness.

 

The DOC model is in SAE 2003-01-3176.  It includes kinetics, reaction rates, mass and energy conservation.  Reaction rates are assumed first order in both reaction species and oxygen.  All chemical reactions occur at the walls, and at wall temperature.

 

NO conversion to NO2 versus temperature is well modeled against a set of experimental data.

 

The DPF model is like Prof K’s; DPF only, no catalyst in it.  The code has been licensed to GM and U-Wisc Madison.  The NO2 +C reaction was added.

 

The DOC is  JMI part, 10.5 x 6”, 300 cpsi.  DPF: 200 cpsi, 52% porosity, 10.5 x 12”. Tested on John Deere engine.

 

Including the NO2 in the model predicts much better deSoot; this agrees well with the model.  NO2/C ratio needs to be high enough for CRT to work well.

 

An issue: if you have a soot cake and are oxidizing in the wall, are you still accumulating on the soot cake?

Discussions

On an engine, it is hard to tell is you are getting oxidation at the same rate as deposition – the mass balance is obscured by CO/CO2 in engine exhaust.  You will need some lab data too.

 

CRT will be more of a problem when you have lower FG NOx.

 

How do you know you have repeatable data? The filter was baked in air for a long time to start clean.  We have not run a lot of repeat yet.

 

Wisconsin Aftertreatment Model

Andrea Strzelec, U Wisc

 

Sponsored by GM.

 

A system model is needed, including engine exhaust and thermal models, a soot emission model, and an aftertreatment model.

 

We used a 1D engine model of a 4.9L Isuzu engine.  The model was calibrated against 8 Mode California cycle.  Fuel step transients and thermal outputs were reasonable.

 

The exhaust thermal model is 1D in gas flow and 2D in heat transfer.  Flow pulsations and the resulting heat transfer effects are included.  It was created in WAVE.  The model was validated at 18 SS operating conditions.  Gas stream temperatures were within 1%, and wall temperatures within 3%.

 

The engine soot emission model is a neural network and physical model. See SAE 2003-01-3227.

 

The DPF model is based on MTU’s adaptation of Prof K’s model, 2003-01-0841.  Input data from WAVE through Simulink.

 

There will also be a model for DPNR – DPF plus LNT models.  For now the kinetics are separate but will be integrated later.

 

The component models can be combined into a system model.  Practical issues include selection of a reasonable time step, and synchronizing the various models.  Computational demands are also an issue.

 

3 SS and 2 transient cases were run to test the model.  Results are preliminary but look reasonable.  Work is continuing.

 

Discussion

How long does it take to run 350 sec?  25 hours on 1 Pentium 4 3 GHz PC. The bottleneck is the neural net. There are several ideas to speed it up.

 

Catalyst Performance Maps – Needs and Perspectives from an Engine Manufacturer

Houshun Zhang, DDC (“DaimlerChrysler NAFTA Truck Business Unit”!!)

 

The paradigm is that suppliers will provide a map of component behavior.  An example is a turbocharger, where performance maps are provided that manufacturers can use for performance simulations.  We would like a similar sort of map for catalyst/DPF function. The map must include the necessary input values over the useful range.  One does not need to know details of the catalyst if you have an adequate model.

 

For aftertreatment models in system controls and strategy development we use 1D and 2D models. For real time control in the product we use 0D models.  3D typically requires more computation time and more detailed knowledge; we typically rely on universities or suppliers for those models.

 

National Labs and universities can assist in developing more physically based maps, and detailed micro kinetics.  Suppliers can map and provide standardized format data.

 

Each catalyst has its own unique map.  Further, each kind of catalyst (DOC, DPF, LNT, SCR…) has its own map criteria.

 

An example of what is wanted for a DPF:

 

  • DPF map is useful for model calibrations – tuning to engine data
  • System integration parameters – dP, soot loading, emission reduction, matching engine to catalyst and catalyst to engine.
  • Maps should have a generic format.  Each company can interface to its own model set.

 

The map must

  • Cover range of SV, T, soot loading, NOx/PM ratio, species emissions associated with catalyzed filters
  • Have a simplified form
    • Normalized dP
    • Normalized flow rate or T
    • Normalized soot loading
  • Include things like heat capacity, dP vs soot loading, kinetic rates…

 

This maybe a task for the DPF Focus Group to coordinate!

 

A map might include

  • Soot loading versus time for various temperature and flow
  • Normalized dP versus soot loading for various exhaust flow rate
  • Kinetics of NO to NO2 oxidation, NO2+C reaction, C+O2 reaction, etc. – or enough data so we can calibrate a kinetic model

 

The LNT team has made progress on maps; can the DPF team do so too?

 

Discussion

Prof. K: In systems that are path dependents, you can’t describe by a simple map like a turbo map.  You have to have an intrinsically dynamic model, not a static map.  Perhaps what is meant is a minimum set of performance data, from which true dynamic models can be calibrated.  It might better be called a protocol rather than a map.

 

We don’t have reliable handbook values of things like permeability.  Further, since models are not standardized, the same permeability in different models will give different results.

 

Prof K: you need to get more of the basics nailed down so there is less to be calibrated from data.  As you do so, you will need less and less calibration data.

 

You need to develop a very detailed test protocol.  The whole thermodynamics is involved: eat and mass transfer, chemistry, …

 

George Muntean (PNNL): the “map” is intended to be a simplification that lets you bypass detail consideration of some variables.  For instance, a turbo could be calculated from geometry – but not easily.  At this point, we haven’t even decided what we want in a DPF model.  Is it just pressure-flow?  Do you need to know detailed soot cake characteristic?  How do you short cut the data process to make it economic and timely?  Because DPFs have a history effect things get more complex.  Perhaps the map should be rate of change rather than current value based.  The discussion is still very open.

 

 

Global Kinetic LNT Model

Louissa Olsson, Chalmers

 

With GM R&D

 

Model is a storage and shrinking core type model.  There are rates for BaCO3 to Ba(NO3)2 and vice versa.  The model includes recognition that some Ba sites never participate.

 

Reaction set: (see slides for better detail)

Storage

BaCO3 + NO2

BaCO3 + NO – forms nitrites

Regeneration

BaNO3 + c3h6

NOx + CO

 

PGM:

NO + O2 č NO2

c3h6 + O2 č CO2 + H2O

NO + c3h6 č N2, CO2, H2O

 

Experiment sequence: 

NO to NO2 over Pt/alumina.  T ramp 25-500C, 8% O2 and 600 ppm NO

NOx storage on Pt/Rh/Ba/alumina – 320C with long lean period. 8% O2 (0 rich), 900 ppm c3h6, varying [NO].

 

The NO oxidation rate kinetics were calculated from the above data.  As expected, above 350C or so thermodynamics defines concentrations of NO and NO2.  Below that, by the reaction kinetics. The resulting model matches data well.

 

When Ba is present, the ability of Pt to oxidize NO is reduced.

 

Similarly, the model matches the experiment data well for the NOx storage, including the NOx spike on regeneration. The experiment was repeated at different temperatures and [NO].  The model predicts barium nitrite and nitrate in agreement with FTIR data.

 

The model was modified to include the inactive Ba core because our experiment did not use all the Ba.  Fit was good.

 

The model was validated against another data set with 4% O2 rather than 8%.  Good prediction.

 

Summary:

A global LNT map of a model catalyst was developed and matches experimental data.  NO oxidation was investigated with a Pt/alumina catalyst.

 

Discussion

How important is the NO storage in comparison to NO2? Only important initially, when you don’t get much NO to NO2 oxidation at low temperature.  It also happens at the start of the adsorption.

 

The Ba nitrite should be unstable >260C.  Why do you see so much?  We should see more at low temperature.  In real catalysts, you may oxidize the nitrite to nitrate.

 

There was neither water nor CO2 in this test data.  It has been done, but is not released at this time.

 

There is work indicating a diffusion limitation of barium carbonate to nitrate – there is a molar volume changes so there is a diffusion limitation. We did not look into that.

 

What determines the break trough peak?  It gets larger at higher temperature.  It gets smaller with lower O2.  The model does include exothermic heating.

 

A recent paper discussed the stability of barium nitrate in the presence of CO2.  You can’t get the nitrate made without being on a surface.  Shouldn’t the pure compound limitations be relevant when you get into the bulk of Ba? I would not think delta-G should be positive at these temperatures for nitration of Ba in the presence of CO2.  The surface of the Ba will still form nitrate.  The nitrate is not bulk though.

 

Engine/Dyno Testing of In-Cylinder LNT Regeneration Strategies

Katey Lenox, ORNL

 

Regeneration strategies were investigated for 3 fuels, 2 strategies, one MECA catalyst at 300C.  Extensive instrumentation was used to characterize the operation.  A paper will be presented at the SAE Fall meeting.

 

Mercedes 1.7L CIDI Common Rail engine with full control.  SpaciMS used with 6 capillaries. .  TEOM, Celesco, LII, and AVL smoke meter were used to look at PM.

 

The strategies:

1.        Delayed extended main (DEM) injection

2.        Post injection

3.        Added EGR to get LTC before regeneration.

 

A DOC and LNT are used (no DPF).  Exhaust was analyzed at engine out, after DOC, and after LNT.

 

FTIR concentrations (L/R cycle average) of HC cracking products (methane etc) drop with later delayed main timing, but not with porst injection.

 

Post injection gives higher total HC and a larger range of HC species than delayed extended main.

 

PM increases quite a bit with DEM strategy but not with post injection.

 

BP15 and ECD-1 are likely commercial fuels.  DECSE is a research fuel – chosen because it is different!  DEM makes more CO/H2 and lower total HC with all 3 fuels.  DEM also gets better LNT catalyst conversion (60 versus 45%).  The operating conditions were chosen to get ~50% NOx conversion so differences would be more apparent; other timings could get much higher conversion.

 

The relative significance of CO/H2 varies with temperature: toluene works better at high temperature, H2 at low temperature.

 

With DEM, H2 peak at DOC out is about the same as engine out.  H2 drops very early in the LNT, and is gone by half way through the catalyst.  The cycle is 57/3 L/R.  CO is present still at the catalyst outlet.

 

A deSOx was performed. DeSOx was done by heating to a high temperature (700C) by engine control strategy then going rich 6-8 seconds.  Temperature rises, so you go back lean to control temperature.  Total rich time was 6-8 minutes.  There was probably a lot of soot but htat wasn’t measured – juts trying to get sulfur out without destroying the catalyst.

 

With the sulfated catalyst, L/R cycle average NOx conversion improves as you make the rich lambda richer, but you reach a plateau where going richer no longer helped.  This seems to be a mass transfer limit in the deNOx regeneration.

 

Next, looked at regeneration in a LTC operation mode.  They started lean, then transitioned to LTC by adjusting injection timing and EGR.  Then you put in a rich pulse, and then go back to lean.  The regeneration is very similar to the DEM strategy; you still get soot during the rich pulse.  LTC does give low NOx.

 

Plans:

  • Look at other temperatures
  • More investigation of LTC regeneration
  • Rapid sulfation/deSOx
  • Examine CLEERS model catalysts and other MECA catalysts
  • Share results and coordinate plans through CLEERS LNT group.

 

Detailed questions should be directed to Shean Huff.

 

Discussion

300C was the LNT inlet temperature. Didn’t the Temp change with strategy? They did control it; details in the SAEpaper.

 

We think the DOC was not in very good condition.

 

We are working on a better calibration and interpretation of the UEGO rich lambda data.  The values reported are “indicated” from the UEGO.

 

The emission data from SpaciMS should have 5-10 Hz time resolution.  Why doesn’t the O2 go to zero at the LNT outlet?  If you are really rich, there seems to be too much O2 for the CO and HC that made it out.  It might also have been O2 storage.

 

LNT Standard Test Protocol Discussion

Stuart Daw, ORNL

 

The second day started with a discussion that included several LNT Focus Group members by audio.

 

The current proposal is a test protocol that would give enough information on a degreened catalyst to permit extraction of kinetic constants to be used in an LNT model.  Note that sulfation and aging effects are not included at this time; they will come later.

 

A subgroup has been leading the protocol development: Dick Blint (GM), Ed Jobson (Volvo/Mack, Neal Currier (Cummins) and Daw.  Also, reps from Delphi, Engelhard, JMI, 3M, ArvinMeritor, and Tenneco.

 

The protocol includes 72 runs including cleaning steps, and is intended to take less than 2 weeks to run (on an automated stand).  Simulated gas is used including water, CO2, O2, NO and NO2.  “Fast” and “Slow” cycles are used to get short and long time kinetic data.  Degreening procedure is defined by the catalyst supplier.  A difficulty is that some suppliers consider the degreening process to be confidential.

 

Three temperature ranges might be used depending on the intended application

  • LD diesel 150-400C
  • HD diesel 300-500C
  • GDI 250-500C

The protocol tests five temperature spread across the applicable range.

 

Regeneration is done with two levels of reductant for the fast cycle, and one level for the long cycle.  The reductant can be H2/CO and/or liquid: diesel (toluene/dodecane)/gasoline (toluene,isooctane). There are still issues about how to do the liquid reductant tests: mixing, vaporization etc.  It is intended to have fast L/R and R/L transitions – that is also a hard problem.

 

20 mil thermocouples are used at entrance, center and exit of the brick.  Exit gas composition measurement includes NO, NO2, H2, CO, O2, N2O, HCN, NH3, total HC.  Sampling rate is 2-5 Hz for the fast cycles, 1 Hz for the slow cycles.  A blank reactor test is to be included so you can characterize the instrument dynamic response.

 

There is a lot of variation between different labs in terms of instrumentation and time resolution.  It may be necessary to compromise on sampling rates.

 

### offer time resolved data to CLEERS.

 

Remaining Protocol questions:

  1. O2 inlet procedure during regeneration (off/on, diesel versus gasoline)
  2. O2 storage measurements?
  3. Formula for Pmax (amount of reductant per cycle), accounting for stored NOx, inlet + stored O2
  4. Liquid injection procedure
  5. Sampling rate
  6. L/R transition method
  7. Procedure for instrument response calibration

 

Stan Roth – why not just do 150-550C for all catalysts?  The variation in Pmax is intended to show sensitivity to reductant amount; you want to have levels that are actually different.  Also, you should define the degreening procedure so it is consistent.

 

Ming Wei – what about SV measurements?  We think SV is fairly easy to model; the table includes 30K and 50K.

 

Stan Roth - a MD diesel with 2X LNT size will approach 150K SV! Is the model good enough to predict from data at 50K SV.  Rather than 15-30-50K why not 30-50-and something higher?  125K will look pretty good!

 

To get good kinetic data at high SV you need fast instrument response.  You can get into mass transfer issues also, that don’t show up at lower SV.

 

Dick Blint – this procedure probably reflects pretty much where industry people are testing today.

 

We also need to develop how the data will be organized and presented so it is useful and accessible.

 

ORNL plans to run the method.  They may use a production GDI catalyst  (VW GDI from Umicore?) since it is available without proprietary issues.  There are also some model catalysts to be tested. The first trial should be run in August.

 

Stuart Daw said that the data in this protocol, combined with previous experience and modeling know how, allow you to model a catalyst.  Without the prior knowledge, this would not be enough data.

 

Quantum Chemistry Calculations of NOx Species on a NOx Trap Surface

Bill Pitz, LLNL

 

There are a lot of basic questions to be answered – how are nitrites and nitrates attached to the surface?  How does the carbonate affect it?  Ab Initio molecular dynamics are used to examine these kinds of issues.  We can predict binding energies and what the species geometry is.

 

Car-Parrinello molecular dynamics is used on the LLNL MCR massively parallel computer, up to 2000 processors.

 

Cluster calculations (18 atoms) and surface calculations (infinite slab) are used.  We find surface calculations to be faster, but perhaps clusters are more interesting to represent things like steps on particles.

 

Others have done some NO2 on BaO calculations.  We wanted to see if we get the same answer. We got the same result as Broqvist (2004).  We find ionic bonding of the NO2. N’s sit ab ove O’s.

 

We also did a surface calculation with 4 layers of 16 atoms each.  Boundary conditions make it appear infinite. This calculation did not show an electron moving from the surface – i.e., not ionic bonding.  Again we see the N’s over O’s.  This is very recent work, not really finished yet.

 

It is not obvious which calculation is more likely to be relevant.  The calculations take 10’s of hours on this very fast computer.

 

We also looked at a barium carbonate surface.  Peden and Muntean are doing related experiments  at PNNL.  The surface looks like CO3 laying flat above the Ba plane.  The next step will be to put NOx on this surface.  BaCO3 and BaO surfaces are very different; it is amazing they can transform back and forth!

 

In another case, a BaO surface was loaded with NO2 and the surface taken to 300K (most literature is at 0K).  The NOx tends to bend over from the N-down initial position.

 

Summary: first time we know that a BaCO3 surface is calculated, or that BaO-NO2 done at non-0 temperature.

 

Plans:

  • Put NO2 on the BaCO3 surface
  • Look at NO desorption in the presence of exhaust products (CO/H2)
  • Use it to look at cases with PNNL experiments
  • Compute SO2 adsorption

 

Discussion

Will try to get the movies available on a web site; not yet approved to release.

 

Have you been able to determine the interaction of the nitrites on the surface?  To some extent – we can look at electron interactions and so on.  We can compare to FTIR data, but haven’t done so yet.

 

Will you be looking at temperature effects? We hope to but not yet.

 

Will you later throw in some Pt? Yes, eventually.  We have the tool and we are starting with relatively easy things.  It should be fairly easy to put in a Pt atom.

 

It is hard to interpret either calculations or data to say if bonding is ionic or not! We can calculate bond strengths and vibrational spectra.

 

It would be interesting to look at bonding as a function of the number of NO2s. Yes; some papers show a configuration of N-down and N-up pairs; we see that at low temperature but not at 300K.

 

There are probably cluster size effects. Yes; we have not looked in to that yet.  It matters where the NO2 sits on the cluster – step, edge, etc.

 

Water is so important; can you look at how water influences the environment?  All these oxides at hydroxylated, for instance. It shouldn’t be that hard for us to do so; we should try it.

 

DRIFTS Studies of LNT Model Sorbents

Todd Toops, ORNL

 

The objective is to understand what is happening on the LNT surface.  Model catalysts were studied 150-400C to identify key reaction steps, with water and CO2 present.  Regeneration effects and thermal deactivation effects have also been studied.

 

The focus has been on K based LNT: Pt/K/alumina.  We have also started looking at Ba based catalysts. 

 

DRIFTS has been used as well as physisorption and chemisorption, and MS to look at effluents.

 

DRIFTS is in-situ up to 500C with up to 10% water.

 

NO3- and CO2- dominate on Pt/K/alumina.  When water is present, we do not see nitrate storage on the alumina as we did dry.  Water favors nitrate formation in favor of carbonate.

 

The saturated stored NOx species is the same at all temperatures, but the amount stored changes with temperature.

 

If you look at smaller time scale where NOx is not saturated, we do not see NO adsorption on K/alumina.  Nitrites observed when Pt is poor oxidizer of NO; these are eventually converted to nitrates at higher saturation.  NO only adsorbs near Pt sites, but only at low temperature (at higher temperature Pt converts NO to NO2 for adsorption.

 

NO2 adsorption: With NO and O2, K/alumina does not form nitrites – everything storing is a nitrate.  One route is NO č NO2 on Pt and spill over to K.  Alternatively, gaseous NO2 can form nitrates readily.  There can be NO2 adsorption on Ba sites through disproportionation but this is not shown in experimental data (yet!).

 

NO2 never adsorbs as a nitrite, only as nitrate.

 

Water is important to regeneration.  Starting with a saturated Pt/K/alumina trap, water inhibits the regeneration especially <200C.  Water inhibits nitrate reduction.  Without water, 20 seconds removes all nitrate.  With water, only 21% removal after 20 minutes.

 

We need to start looking at the species evolution to understand regeneration.  That experiment is beginning.

 

Thermal deactivation has been studied on K and Ba base catalysts.  Key mechanisms are sintering of Pt and Adsorbates; noble metal masking by Adsorbate.  The deSOx cycle on ORNL’s dyno got to 780C or so.

 

A reactor allows flow through or chemisorption experiments; they can measure surface areas in situ.  Ran 15 hr at various temperatures with cycling lambda (9 min lean, 1 min rich).  Water and CO2 present at all times.  NOx storage at 250C was checked after high temperature exposure.  H2/O2 titration was used to find Pt surface area, and BET for total surface area.

 

K based: They found surface area was affected above 850C; this is alumina phase transition. Pt size steadily increases, with a leap at 900C.  Effective NOx capacity decreases steadily – not directly linked to either surface area or Pt area.  After 900C aging, ˝ of storage capacity is gone.

 

Ba based: Larger drop in surface area.  Pt size increase smaller with only a minor decrease between 760 and 900. This suggests Ba is covering some Pt.  Pt area decreases more than Pt size would suggest.  Storage has an initial decrease, then relatively stable until it dies at 900C.

 

Storage rates were measured after the aging.  The initial rate is not dramatically lower after 900C than after lower temperature aging.

 

Summary:

  • DRIFTS gives insight to storage mechanisms – near Pt, nitrate, water important
  • Water is important
  • Thermal deactivation: K loses 50% storage after 900C, Ba loses 90%

 

Plans:

  • More thermal deactivation experiments
  • More on regeneration chemistry in flow through reactor
  • Sulfur based deactivation mechanisms

 

Discussion

Ran NO2 adsorption at room temperature; it takes a long time, more than 30 minutes.  Usually I ran over night to be sure it is fully loaded.  That is when you see the alumina effect without water; with water the process is much faster.

 

Have you tried separating Pt/alumina and Ba/alumina and varying the particle size to change “intimacy”?  Chalmers tried physical mixtures of Pt and Ba – didn’t work.  You seem to need them very close together.

 

Modeling Lean Phase Kinetics for NO and NO2 in LNTs

Kalyana Chakravarthy, ORNL

 

LNT models can be based on elementary kinetics – but it is difficult especially if you can’t look at the surface composition in detail on a proprietary catalyst.  Or, you can do global kinetics but there is a risk t is less broadly applicable.  We tried a global model.  We can include mass transfer, bulk versus surface diffusion, reaction time delays, etc.

 

We want to predict NO and NO2 adsorption based on experimental observations and known phenomenology.

 

Key observations:

  • At least two routes to adsorption: disproportionation (without noble metal), and spill over (Pt adsorption then movement to adsorbent).  The details of the spill over mechanism are not clear at all.
  • Catalytic activity diminishes with time during storage.  This might be due to nitrate saturation, but may also be due to forming PtO and blocking activity.
  • NO2 and NO3 detected by drifts
  • Peroxides detected with Raman
  • Equilibrium NO2/NO on the surface is different than in the gas phase.

 

The global model formulation is shown on a slide (I can’t type that fast!).  4 reversible reactions (8 rate parameters) are used for NOx-Ba interaction.  Two steps are needed to retain the basic features: a fast step and a slower step.

 

The fast reaction must involve release of NO, since experimentally we see NO immediately on NO2 exposure.

 

The spill over mechanism is still quite fuzzy.  We assume the sites right next to Pt are nitrated very quickly.  Once those sites are filled, species have to migrate on the surface.  So you get a fast initial adsorption followed by a slower adsorption rate.  Wee assume an exponential drop in rate with coverage.  We think spill over does not happen below 250C.

 

We model NO < = > NO2 over Pt.  the rate of this reaction gradually decreases with time; based on the Chalmers (Olsson, 2004) data we think this is oxidation of the Pt over time.

 

We assume disproportionation and spill over are independent.  Pt passivation is by NO2 + Pt č PtO + NO.  O2 gas concentration is not calculated – there is lots of O2 around.  The model is constrained to measured isotherm.  Equilibrium includes material heterogeneity and complexity.

 

The sorbent shifts NO2/NO equilibrium toward NO.  The NOx storage isotherm is measured.

 

The model is compared to some data sets with good matching.

 

Evidence of the Pt oxidation is that [NO] has a peak in time when the input NOx is mainly NO2.  You quickly get some Pt passivation.

 

The passivation behavior is important.  The Pt at the front of the LNT may get close to saturation even in 30-60 seconds.  Experimentally, you have to saturate the whole catalyst to measure the kinetic effect.

 

NO to NO2 conversion cannot be modeled by a simple first order reaction rate.  A slide shows relative activity versus time for lean mix with either NO or NO2 input.

 

Summary:

  • Qualitative behavior of LNTs appear similar
  • A global model appears feasible
  • Model limitations
    • Better at high temperature >300C
    • Requires measurement of an isotherm
    • Effect of exhaust species (like O2 concentration, CO2, water) still need to be incorporated.  They are in the gas used to get the model constants but not explicitly modeled.

 

Plans:

  • Axial distribution of NOx storage should be measured with SpaciMS
  • Pt passivation should be independently verified
  • Temperature variations of model parameters: kinetic versus mass transfer effects
  • Create a regeneration model

 

Discussion

There is obviously something that slows down the reaction – shrinking core, bulk diffusion, Pt passivation, …?  The model has a deactivation factor – any of these mechanisms might be what causes the need for this term.  The passivation is a function of time, not degree of saturation.

 

Another possible reason for the slow down is that there can be many different sorts of Ba sites – perhaps the easier sites fill first and then it gats harder to store.

 

We find the Pt deactivation faster is there is Ba than if you have only Pt/alumina.  O2 oxidation of Pt is much slower than with NO2, we think.

 

We think the CLEERS protocol will generate enough data to calibrate this model.

 

Advantages of Using ArvinMeritor’s Plasma Fuel Reformer for LNT deSOx

Navon Khadiya, ArvinMeritor

 

H2 and CO are very good reductants for LNTs, both deNOx and deSOx.  The plasma fuel reformer is a generator of H2 and CO.  Preliminary results of deSOx will be shown.

 

The device uses electrical power to generate a plasma to do the main reforming; a catalyst is used to maximize [H2].  A fuel rich mixture is treated to get ideally 24% H2 and 26% CO.  The actual hardware (without catalyst) processes diesel fuel into 6-8% H2 with very little soot.  With catalyst, [H2] increases to 23%, still very low soot.

 

The plasma unit can start very fast.  There is about an 8 second rise time in measured [H2] (catalyst plus plasma).

 

You can rapidly cycle on and off.  Data shown for 3000 cycles; the same as fresh [H2] response.

 

Engine tests were run to test LNT deSOx benefits of reformate.  “Low temperature” LNT used in dual leg configuration.  The catalyst can reportedly be deSOxed at 550C with normal means.  This system delivered reformate while the LNT was of line.  This was compared to the same system with diesel fuel injected in the “dead” leg for regeneration.

 

The catalysts were sulfated at 260C with 35 ppm SO2.  Performance measured on ESC Mode 7 before and after sulfation, and again after deSOx.

 

100% recovery of activity was obtained after deSOx at 510C with diesel in 6.3 minutes.  At 450C, diesel gave only 8% recovery.  With reformate, 400C gave full recovery in 6.3 minutes.  At 350c, 100% in 7.3 minutes.

 

### add a reformer to the LNT/SCR cost picture

 

Clearly, reformate is able to deSOx at temperatures more than 100C lower than diesel fuel.

 

There is significant work on S effect over TWCs, including H2S formation. By comparison, there is little modeling of LNT sulfur effects.

 

A simple global model was proposed here.  When H2 and CO are present, there are added deSOx pathways: H2S, COS.  Pt catalyzes the H2 + SO2 č H2S reaction.  Pt also thought to influence the decomposition of sulfated adsorbents.

 

H2/CO may permit deNOx as low as 100-120C on Pt and Pd.

 

Summary:

  • Reformer is fast start with high [H2]
  • DeSOx was possible more than 100C lower than with diesel fuel
  • Reduced deSOx temperature will reduce thermal damage to LNTs and improved durability.

 

Discussion

The performance of the catalyst in the reformer deSOx tests was poorer than in the diesel deSOx. The LNT had been damaged by diesel deSOx before the reformer tests.

 

The deSOx temperatures listed are catalyst inlet.  Bed temperature was not measured (they are doing so now).  Outlet temperatures were 100-140c higher than inlet in all cases.  There was some leakage (1-3%) past the shut off valve. For diesel fuel, regeneration lambda was ~0.7; the reformer was somewhat richer.  If the bed temperatures aren’t lower, you will still get damage!  The outlet temperature was not so high as to indicate catalyst damaging bed temperatures.

 

There is some good sulfation data on LNTs in the SAE literature.  That is probably more relevant than the TWC kinetics.

 

Do you see PtS being made?  No; we see it from literature but not in our data.

 

Fundamental Aspects of Lean Burn HC SCR

Robbie Burch, Queen’s University

 

Many millions of dollars have been spent on research…. and we don’t really know much yet!

 

There are a number of mechanistic studies.  See slides!  Organo-nitrogen species are involved on some catalysts.  On others, inorganic nitrogen species are important. With enough patience, you can find almost any species you want – but are they relevant?  What are the simplest possible explanations that cover the data?

 

HC-SCR of PGM metals

Factors:

  • Metal choice
  • Support choice
  • Reductant choice
  • Temperature
  • Gas composition

 

The conclusions you reach depend on the experiment you do!  For instance, propene adsorbs strongly on Pt.  Pt is in a reduced state all the time.  Propane does not adsorb on Pt; Pt is oxidized, and NO goes to NO2 rather then alumina.  The NO2 can move to another site and get reduced there.  The mechanism is quite different even on the same catalyst.  Octane acts a lot like propene, juts at a higher temperature.  N2O formation is an issue.

 

Evidence for direct NO decomposition mechanism at high temperatures

You can get this under lean conditions.  A temporal analysis of products (TAP) reactor was used.  It has good transient time resolution while analyzing the surface.  A pulse of NO can decompose, but subsequent pulses are less decomposed; Pt gets oxidized.  You need a reductant to keep the catalyst functioning. 

 

Propene is effective in reducing the Pt.  On the other hand, oxygen completely kills the CO/NO reaction.

 

A Japanese group looked at single crystals, and reduce CO/NO or CO/O2 at 400C.  In either case, CO2 comes from COads + Oads.   This is evidence for NO dissociation.

 

Evidence for organo-nitrate species at lower temperature.

Good FTIR evidence for cyanates etc.  However, this species is changing over very long times (100s of minutes). 

 

On another catalyst, -CN seen on the order of minutes.  But, water was not present and there was no CO2 in the inlet gas.  Isocyanates react very rapidly with water.  With water, you see HNCO and NH3 in the gas phase.

 

It is hard to resolve which species are really the important reaction intermediates.

 

Real world effects

Exhaust temperatures in driving vary.  Low temperature is important.  Here, H2 is a good reductant.  But, you get mostly N2O.

 

Evidence for decomposition and N2O at low temperatures

NO decomp was seen over Pt at 100C in the TAP.  But, you need a reductant to keep the Pt from oxidizing.  You never see N2O, unlike the steady state experiments.  If you add H2, you see N2 formation and not N2O initially, but N2O appears after several pulses as the oxygen coverage increases.

 

Conclusions: you can decompose NO on a reduced Pt surface.  You do not form N2O on a reduced Pt surface.

 

SSITKA experiments were run to look into mechanisms.  Here, an isotopic tracer gas is used, and the transient is isotopic rather than concentration.

 

These show that N2 is formed from adsorbed + gas phase NO.  On the other hand, N2O is almost all from gas phase NO.  This suggests different mechanisms.  Looking at how the concentrations change in time can suggest the number of intermediates that exist.

 

This method suggests an (NO)2 dimer intermediate.

 

Density function theory of the formation of N2O on Pt

Can the modeling help explain the data?

 

The most favored N2O mechanism still has a barrier of 1.78 eV; defects etc may lower to <1.  On the other hand the dimer mechanism at a step has a barrier of only 23 kCal/mole.  The dimer mechanism becomes dominant on partially oxidized surface.

 

H2 effects with Ag catalysts

Adding H2 to a propane-NO mix improves both peak NOx conversion and lower temperature activity.  H2 is also good in combination with octane.  H2 also broadens the temperature window where HC is partially oxidized.  The NOx conversion light off is about the same for H2 with a wide range of HC reductants.

 

H2 also enhances formation of NO2 – even going above equilibrium at 800C.  This is surprising since NO2 poisons the Ag NO activity.  One theory is that H2 encourages formation of Ag clusters.  There is some controversy: is it Ag-aluminate, Ag+ ions, clusters???   UV evidence for clusters is not conclusive.

 

Replacing H2 with CO causes NO activity to slowly decline at low temperature.  Activity recovers immediately when H2 comes back on.  We also ran H2/CO mix.  It seems like CO does nothing at low temperature (not inhibiting, just not working).

 

Another issue is that H2 may decrease nitrate inhibition.

 

It is pretty clear you don’t want metallic Ag; you want to prepare ionic Ag on the surface.

 

Ag catalyst is interesting: low temperature conversion, no N2O. 

 

Catalyst scientists are falling into the surface science trap: the right experiment is very hard, so they do the experiment they can do to get it published.  We need to go after a real problem.

 

Discussion

Is H2 also that important on other catalysts? We find Ag unique in that.  We have tried Cu, zeolites, etc.  Ag is not easy to oxidize; the oxide decomposes above 150-200C or so.  You have to form something like a nitrate to activate the HC.  Once you activate it, it can stick to the surface where it will eventually react with NOx.

 

Is there an optimal H2/HC ratio? There seems to be some storage effect.

 

High Throughput Program for the Discovery of NOx Reduction Catalysts

Dick Blint, GMR

 

There won’t be any publications soon, and we won’t show any new materials… because we can’t show it!  We believe that there are better HC-SCR catalysts than people have found so far.  We are looking for them.

 

Partners are Engelhard, GM, and Accelerys (software).

 

Combinatorial methods are used to look for new materials.  Parallel synthesis and multiple sample reactors are used along with informatics.  Hundreds of samples per month are evaluated.  Engelhard makes the supported catalysts.  GM does the lab reactor testing. Data is put into a database to allow searching and analysis.  Engine and vehicle testing are in the future.  HC-SCR is the target, not decomposition or urea.

 

If a product is developed, Engelhard will be able to sell to anyone.

 

Over 3000 materials evaluated; something around 10% have been interesting enough to look at further.

 

Future combustion regimes such as HCCI may permit us to be happy with 40-50% NOx conversion, and this seems possible with HC-SCR.  Low temperature activity improvement is needed; perhaps both low and high temperature materials would be used.

 

Engelhard makes a range of materials and runs the combinatorial testing.  Promising materials are evaluated in more detail at GM, followed by another down select. The information also feeds back into the discovery phase of the program.

 

GM’s reactor evaluations use diesel fuel.

 

Criteria for selection of interesting candidates is peak conversion >70-80% (have moved to at least 150%) of the benchmark standard catalyst, a wider temperature window, or unexpected positive performance.  Disqualifiers are N2O, HCN, NH3, excessive adsorption or poisoning.

 

All compositions are binary, tertiary or higher.

 

All samples are tested degreened and lightly aged. On the GM reactor a temperature sweep is run.  At the peak conversion temperature, the NO/NO2 ratio is swept.  Several different reductants are tested.  Another temperature sweep is then done at optimal NOx ratio and reductant conditions.  Sulfur effects are next if still interesting.

 

We have correlated the Engelhard bench to the GM bench and to the engine.

 

Informatics is important. Instead of sparse data, you have a ton of data – composition, synthesis, and analysis info.  You have to extract the right info to decide about scale up and further testing.  The database is set up with secure connections so all participants have secure access.  You have to have tools to get your instrument data into the data base.

 

Trend analysis is a part of the learning. This is not mechanistic learning but rather statistical analysis.  What preparation and composition steps gave good performance?  Can we predict a better one, then make it and try.

 

Summary:

  • Methods are in place and being used
  • Finding interesting candidate materials
  • Trend analysis has started and is beginning to show results.
  • There is reasonable likelihood of finding a useful HC-SCR catalyst and we expect to find some in the remaining 1-1/2 years of the project.

 

Discussion

Do you think DOE action is what lead to this?  Would it have happened without them? Yes.  The start up cost of informatics and so on would have kept people from getting it started.  You probably could  buy the software now; Accelerys owns the software and is allowed to market it.  National Labs get a certain number of paid licenses.

 

Have you tried genetic techniques for the trend analysis?  Yes.  Initial test data sparsely populates a large data space.  With that data you can do genetic or neural routines to search for interesting regions.

 

Experimental Speciation of Urea SCR Exhaust

John Storey, ORNL

 

The original LEP CRADA for this work started back in 1992.  This work is nearly the last of that project, although there is still some fuel funding to continue part of this work.

 

SNL and LANL were also involved in this CRADA.  The goal was to develop and characterize improved urea SCR catalysts. ORNL tested full scale catalysts on the engine.

 

Urea decomposition begins at 80C and melting at 135C; there is no gas phase urea.  A chemical isomer is ammonium isocyanates.  For aftertreatment we use 32.5% in water because that is the minimum freezing point.  Decomposition is endothermic.

 

What might happen to urea in hot exhaust?  A slide shows several possible paths.  You might get biuret, cyanic acid, ammelide, ammeline, cyanuric acid, melamine, cyanamide, dicyanimide, or ammonium nitrate.  Cyanuric acid and melamine make an H bonded solid that we have observed in exhaust at higher temperatures.  We have seen all of these compounds at some level at some time.

 

Analysis is by FTIR, photoacoustic analyzer for NH3 and N2O, and filter analyses.  A two stage filter gets PM on the first, while a second stage can gather some gas phase components.  Impingers were also used.

 

For light duty works, around 250C, we don’t see the above compounds.

 

The exhaust system has  a  DOC plus urea SCR.  Urea is injected after the DOC.  The urea injection is by air atomizing nozzle.  The atomizing air was humidified before use to improve metering and measuring accuracy.

 

The engine was operated without EGR.  Temperature was varied by changing load, so NOx input rises with temperature.

 

We wanted to measure urea decomposition before the catalyst.  The initial work used a 6” SCR catalyst brick; this was later changed to 3” to reduce storage time delay effects.

 

It is very hard to measure ammonia upstream of the SCR.  We wanted to see if the urea had decomposed.  The trouble is that if you collect any urea on the sample filter, then you see ammonia even at very low filter temperature.  If you run the filter cooler, you get condensation.  It would be better to use an in situ ammonia measurement but we don’t have one that works well.

 

You expect to see urea decompose to ammonia and isocyanic acid, and the acid hydrolyzes to another ammonia and a CO2.

 

You would also like the NOx going in to the SCR to be 50% NO2.  The DOC tended to convert NO more to NO2 at higher temperature, and less than 50% at low temperature.

 

We got 65% NOx conversion at 262C, dropping to ~20% at 155C – although at the lowest temperature it may be storage or conversion to ammonium nitrate rather than real conversion.

 

Nitrate is as much as 20% of the total NOx at 210C.  It is all particle phase, probably ammonium nitrate or nitric acid.  This is not made over the DOC.  HONO was very low after the SCR.

 

There was significant reductant storage.  You can see this in that turning off the urea and increasing the load (raises NOx and temperature) causes large ammonia desorption.  NO conversion continues for a while.  This could be a problem in a vehicle system.

 

Another slide showed 165C accumulation followed by a burn off at 190C.  There is a complex result of NO, NO2, and NH3 concentration.  We think isocyanic acid is stored in the catalyst.  As temperature increases, you get SCR on the surface and ammonium nitrate decomposing into NH3 and HNO3.  At higher temperature NH3 SCR stops as you exhaust the storage, and NO2 increases faster than NO.

 

Modeling needs to include all these reactions and storage effects.  It seems hard to incorporate into a real time model.  At low temperatures, you can assume no gas phase urea decomposition. (current models assume you are getting NH3 in the inlet!)  We need to understand the nitrate formation, storage, release, reactions.  How does rapid warmup (sudden acceleration) affect this?

 

Plans:

  • Sample raw unfiltered exhaust into an impinger to look at urea decomposition upstream of the catalyst
  • Look for surface trapped species – maybe washing the catalyst?  Repeat with longer monoliths.
  • Look at low temperature storage and release – use NO2 break through as an indicator o “fill”; redo the temperature ramp experiments; repeat the ramps while continuing to inject urea.
  • Compare urea to NH3 injection at the low temperatures.

 

Questions:

  • Can we identify isocyanic acid?
  • Does nitrate break through below 175C?
  • Is nitrate emitted during transients with normal urea injection?  What are the implications to DPF and overall systems?

 

Discussion

Have you thought of using the SpaciMS? NH3 is right next to water so it is hard to measure in MS.  Also, NH3 tends to plug the capillaries.

 

At high temperature, the SCR catalyst works well but you make the various side products (the downstream clean up cat gets rid of them).  At low temperature, we don’t see the same products but we do see the nitrates.

 

Urea distribution is probably important.  In our low temperature case, think of it as spray drying – the urea probably arrives as a fine dried powder

 

At the low temperature, even the 3” brick took ~2 hours to stabilize.

 

Modeling Effects of Exhaust Gas Speciation on Efficiency of HC SCR

Bob Weber, TIAX

 

Or, microkinetrics of lean NOx catalysis.

 

You have to be careful not to over simplify the models.  Global models are appropriate for steady state chemical plants, but vehicles are virtually never steady state.  Global models will capture lab data but may not be so useful for real systems.

 

How complex a model do you need to capture the behavior of HC-SCR catalysts?  How do you construct the model in a way that is extensible for future enhancements?  How do you get enough data to calibrate and validate the model?

 

To build a simple model with as few parameters as possible, we start by ignoring S and aging effects. The model includes oxidation of HC, CO, H2 and reduction and oxidation of NO and NO2.  Rate equations are expressed.  The rate parameters are expected to look like they conform to transition state theory and our knowledge of bond strengths.  Parameter values can be guided by thermodynamic consistency. 

 

A software package has been built that allows you to build the models. You select reactions and define the reactor.  We can use more than one experiment for parameter fitting.

 

A syngas SCR model over Ag catalyst was presented as a simple case.  We find that the surface is largely covered by oxygen; other sites are at the ppm levels, making them hard to see in IR studies.

 

The butane model an be added.  The previous parameters were not changed when butane was added.  Experience shows that several of the reactions listed are in fact insignificant.

 

You could next add a decane model.  This uses a joining with the CH4 network.

 

Without having calibrated the model to any data set, you get a predicted NOx conversion with peak temperature and characteristics that are reasonable.  There are certainly enough parameters you could use to fit the data, but mostly you don’t need to since standard values give reasonable answers. In fact, you only need to calibrate a few large-effect parameters.  Calorimetry and other supporting experiments can be used for calibration too.  Quantum Chemistry may help.

 

DFT was combined with this modeling approach to ask what would happen is you have a few atoms of S under the surface of Pt(111) you decrease the heat of adsorption of oxygen near the sulfur, moving TPD to lower temperatures.

 

Models of this sort are an important part of the discovery-testing-using cycle.  Model reduction is necessary if you are going to put the model into your powertrain controller in real time.

 

You want to choose models that have “optimal sloppiness”!

Discussion

What you are doing sounds a lot like combustion codes.  It turned out there were a lot of rates we don’t know.  How do you fill your database with meaningful rates?  Easy; hire us!  Parameters come from chemical intuition of bond strength, transition state theory, and thermodynamic consistency. The hardest part is a plausible reaction network, but the literature is full of good suggestions.

 

We would generally suggest using a reduced model to couple to a CFD code rather than both detailed models at once.

 

If your end result is a neural net model of a model, why not just use the neural net in the first place? You could do that.  But, you are limited to what you measured.  With a more physical model an extrapolation is more rational.  Thus, we can tune the neural net beyond measured data.  Also, you can use literature data for some inputs.

Ford/DOE SCR Program Update

Christine Lambert, Ford

 

The program started 3 years ago, and has one year left.  It is part of the DOE Ultra Clean Fuels program.  We target 2007 Tier 2 Bin 5 standards, 0.07 NOx and .1 gm/mi PM.  ExxonMobil and FEV are subcontractors.

 

An F250 diesel truck is the test bed.  Durability testing is scheduled to go 120K equivalent miles, starting July 2004.

 

To hit Bin 5 we need >90% NOx conversion.  We are using CDPF + urea-SCR.

 

Models have been used to predict system performance, NOx, FE, and direction for vehicle system design. Models are also used to help diagnose problems.  An example is the demonstration that you need a fast warmup strategy to meet FTP NOx emissions.

 

The vehicle exhaust has a small upstream DOC, another DOC where it fits, then the SCR, then CDPF.  Urea is added after the DOCs.  The SCR is 2X engine displacement volume.

 

the vehicle took about 150 seconds to get to 150C with a base calibration.  With a fast warmup strategy we would expect to meet Bin 8 NOx level.

 

Urea mixing is important.  We use a spray deflector to get acceptable mixing in the length available.  you have about 12” to mix in a 4” diameter pipe.

 

NO2/NO ratio is important.  You have to pick the right DOC to get about the right ratio over the temperature range.

 

SCR catalysts are also good HC adsorbers.  When you adsorb HC you lose NOx activity.   Heating to 500C for 10 minutes regains NOx conversion.

 

ExxonMobil is working on a demonstration of a urea co-fueling system.  A vehicle and fuel dispenser have been modified and are being tested, including cold conditions.  Durability and reliability testing are underway.  Some antifreezes have been tried, but so far all are HC based and poison the SCR.  Instead, electric heating is required for very cold weather.

 

Plans:

  • Complete rapid warming strategy to meet Bin 5
  • Choose final catalysts
  • Develop aging cycle
  • NOx and NH3 sensor development
  • Complete urea infrastructure investigation.

 

Discussion

What aging cycle will you use?  What is your control strategy?  We have control strategies for both urea injection and DPF regeneration during the driving cycles.  We stay lean at all times.  Sulfur and HC can be removed from the SCR with an active heating, lean strategy.

 

Is Ford developing the sensors? NGK NOx sensors; we are looking at several potential NH3 sensors.

 

Anything special for cold starts? Yes, rapid warmup strategy. We reduced thermal mass, increased exhaust temperature by injection and EGR calibration.  An ensemble of many items is needed to get enough heat.

 

Have you experienced issue like Storey mentioned? If we get the SCR warm enough fast enough we should not have big issues.  Do you get repeatable data on FTPs? The FTP data is better if you have some NH3 stored in it.  We do track the NH3 storage in the strategy.

 

Do you have a cost target?  Yes, $4 per kW – a challenging target!  We see a path to get clost to that target but we are not there yet.

 

SCR Performance Modeling Using AVL FIRE CFD Code

Matt Thornton, NREL

 

FIRE is AVL’s CFD code.  It is used for flow distribution, can handle spray into exhaust, catalyst kinetics, A/F mixture distribution. A fuel cell model is being developed.

 

Modeling issues:

  • Urea spray and mixing
  • Decomposition and hydrolysis issues
  • Catalytic reactions including undesired ones
  • Various catalyst candidates.

 

Urea spray is modeled. Urea decomposition was modeled with CHEMKIN in the pipe upstream of the SCR.  The catalyst is modeled with global lumped chemistry.  The catalyst is modeled with an Eley Riddel mechanism.

 

For the catalyst model, see SAE 2003-01-0845.

 

Exhaust geometry is defined, and input boundary conditions like exhaust mass flow and inlet temperature.  Catalyst substrate, cell density etc are defined.  Urea injector design is specified along with urea injected quantity.  Parameters specify washcoat activity.

 

This model is to be used to support several DOE fuels research programs.  Three of the APBF-DEC programs are looking at LNT and one at SCR.  The latter is an SWRI HD SCR/DPF project.

 

The SCR project is using a Caterpillar C12 12L engine.  early results have shown very low emissions.  Zero hours hit 0.24 gm/hp-hr NOx, about equal to the 2010 levels.  Access to the engine controls is limited.  The system is being aged, now at 200 of 6000 planned test hours.

 

 Split emission control system is being used, 2 DPFs followed by 2 SCRs, followed by a single doc.  EGR is taken after the DPF to have clean EGR.

 

We want to do a system validations and compare engine data using the CFD code.  This will set parameters.  Then, we will use the model to look at system optimization.

 

Summary:

  • Fire code model created
  • To be used for optimization

 

Discussion

Modeling an NH3 SCR deNOx Catalyst: Model development and Validation

David Bergeal, JMI Royston

 

We do mainly global reaction modeling.  We also do CFD but generally not together with chemistry.

 

Lab reactor experiments and adsorption studies.  We use this data to calibrate the model and validate against engine data.

 

The model is 1D heat and mass transport, surface reactions, convection and heat conduction.

 

The model was described in SAE 2003-01-????.

 

The model shown as a vanadium catalyst.  The model uses NH3, not urea.

 

3 NH3 reactions are in the model.  In addition, 3 NH3 oxidation reactions (2 going to NO) are included.  NO oxidation is not observed on these catalysts.

 

The coverage term is given by a Temkin isotherm.  The desorption energy changes with the NH3 coverage: it is less easy to adsorb more if you have high coverage.

 

NH3 adsorption was measured and modeled.  First, adsorb at about 200C, then ramp the temperature to about 350C and watch desorption. This gives information on adsorption and desorption energy.

 

NH3 oxidation was measured with NH3-O2 mixtures.

 

Next, measure NO conversion; NO+NO2 conversion; NO2 conversion versus temperature.  100-200 experiments are needed for each new formulation.

 

Once you build up this data, we calibrate the model and then compare to engine data.

 

Slides show effect of catalyst volume on the HD 13 mode test.  Also, model versus data for cold start.

 

The DOC can over-oxidize NO, creating too large an NO2/NO ratio and causing a dip in NOx conversion versus temperature.  System optimization suggests a smaller DOC to avoid this problem.  But, you have to trade low temperature performance and HC/CO control.

 

The model can also investigate different urea injection strategies.

 

The model projections match engine data pretty well.  The model is useful for predictions of catalyst volume, cell density, injection rate, test cycles, DOC catalyst and its properties.

 

Discussion

You model NH3 coverage.  The surface coverage lags behind considerably.  Did you account for this?  Yes – but only algebraic, not differential equation.  It doesn’t seem like that should work – you ought to need a differential equation.  Somebody said they thought it did …. some confusion here!

 

The model is a single channel catalyst model – we assume good flow distribution etc.  You have to use the CFD to get flow distribution.

 

Why did you choose vanadium catalyst? It is popular in Europe.  Are the kinetics different than a Cu-ZSM5? I don’t know first hand.

 

1D Catalyst Modeling and its Application to Urea SCR Devices

Chris Depcik, U of Michigan

 

Work supported by Ford (Baker, Lambert, Laing, Johnson), TACOM, and EPA.

 

Why a 1D model?  3D is much harder and runs much slower.  1D models are faster, use calibrated global kinetics, and have easier input boundary conditions.

 

A slide gave 1D catalyst modeling history.  There was some discrepancy between the 1982 and 1992 equations, so he re-derived the equations from the conservative Euler equations.  Constant density is assumed, allowing independent solution of the energy equation.  It also eliminates the sped of sound from the equations.  Constant velocity is also assumed; eliminates the need for a momentum conservation equation.  The resulting formulation is the same a Harmed in 1972.  Some terms are commonly eliminated.

 

For bulk gas temperature, equations were also re-derived. People usually neglect the time derivative.

 

Surface species are gas species at the surface, and also surface coverage of intermediates.  Also re-derived. These equations allow for all possible catalyst formulations.

 

Surface temperature is like the literature with the addition of a volume heating terms and a pseudo-2D heat transfer network.

 

A slide listed the model terms and assumptions. The model runs at approximately real time on a PC.

 

This general model is applied to a particular catalyst:  different chemical species, diffusion properties etc can be in a table.  The different catalysts have different reaction mechanisms.  That reaction scheme is a separate subroutine.  It has a graphical user interface.

 

It was applied to urea SCR.  A 3-reaction mechanism was postulated.  Arrenhius constants were fit to some vehicle data.

 

The model and derivations are available in his dissertation and on the web. Preprints of the published paper can be made available.

 

Discussion

1D is strictly useful only for uniform flow distribution at front of monolith. You could divide the catalyst into a few segments and solve the 1D model a few times.

 

There have been other important modeling contributions between 1982 and 1994!

 

You seem to be driven more by computational convenience than by valid computations!  When temperature varies, density must change too.

 

Dr. Christopher Depcik’s comments

 

John,

 

I was looking at your notes on the web for my part in the CLEERS conference and I noticed that you included the discussion after my presentation.  However, you did not include any rebuttal that I had to those questions...

 

For instance,

 

I mentioned at the beginning of the slide that it was a brief synopsis of the history of catalyst modeling.  Of course there have been important contributions between 1982 and 1994, but in terms of the general model for catalyst that everyone tends to use, there was no significant change to the traditional model equations.

 

Yes, I was driven by computational convenience, however I mentioned in the conference that I have done the full compressible flow simulations and it is not practical to do for optimization over a full transient FTP cycle. Also, in order to get the model equations that everyone uses for catalyst modeling (see Oh and Cavendish, 1982 for instance) from the governing equations of fluid flow (Euler), you have to make similar assumptions that I did.

 

When looking at the comments as-is, it appears that I did not do my homework in regards to catalyst modeling, however that is anything but the case.  I am in the process of publishing a complete description of my work that should enlighten those who had comments on my work.  I don't know if you were able to catch my rebuttal comments at the time, so I thought I'd expand on them here.

 

Sincerely,

 

Chris Depcik

 

 

 

 

 

 

John Hoard

Staff Technical Specialist

Ford Motor Company Research and Advanced

Phone (313) 594-1316

FAX (313) 594-2963

Email jhoard@ford.com