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Computer-Aided Construction of Chemical Kinetic Models
Computer-Aided Construction of Chemical Kinetic Models
ComputerAided Construction of Chemical
Towards Predictive Chemistry
William H. Green
MIT Dept. of Chemical Engineering
DOE Combustion Meeting
Potomac, MD, May 2014
1 Why do we want predictive fuel chemistry
• Historically, Fuels have changed pretty slowly… Engines evolved faster,
using moreorless static fuels. Fuel was a constant, not a variable…
• But now, fuels are changing
– To reduce greenhouse gas, laws require renewable fuels
– Conventional light sweet petroleum is getting more more expensive
– Possible to make many future fuels with synthetic biology chemistry
• If fuel is a variable, we need to be able to predict how engine+fuel system
depends on fuel composition, not just on engine operating conditions.
– Much faster to explore new concepts on computer than by building prototype
engines and synthesizing hundreds of barrels of a new fuel for engine tests
• Scientifically, we don’t really understand something until we can make
quantitative predictions…and discrepancies between those predictions and
experiments reveal opportunities to learn something new
2 Challenge: Lots of alternative fuels to evaluate
Not practical to synthesize test every possible future fuel in every possible
…rapid, easy predictions are valuable and needed
3 Automated Predictive Chemical Kinetics:
The Concept Motivation
• Rapid, Easy Predictions are valuable needed
– Assess alternative fuels feedstocks
– Design new processes and engines
• Hydrocarbon/Fuel chemistry is complicated
– many reactions, species: need to automate
4 Fuel Chemistry models are very big
• Many chemical intermediates between fuel and CO .
• Multiple competing pathways
• Nonlinear, sometimes nonmonotonic kinetics
• Real fuels (gasoline, jet, diesel) have many chemical species
– So even initial conditions are complicated
• Models built by hand: 1,000 species, 5,000 reactions
– Human tries to include just the more important reactions.
• Models built by computer: 500 species, 10,000 reactions
– RMG software typically considers 30,000 species and 100,000 reactions
• most of them are found to be numerically negligible
5 We need this black box
and solves the
Similar issues for fuelformation chemistry e.g. in refinery
6 Commercial software can solve
detailed kinetic simulations…
…if one can supply the fuel’s
Diff. Eq. solver
dY/dt = …
7 How we construct fuel chemistry models
Diff. Eq. solver
dY/dt = …
8 Chemical Kinetic Modeling Challenges
• Identify all important reactions species
– But not unimportant species reactions: how to distinguish
• Estimate all reaction rate coefficients (and properties,
e.g. thermochemistry) to sufficient accuracy.
– We use Functional Group extrapolations Quantum Chemistry
• Large models pose numerical and computer problems
– Very challenging for humans to handle, interpret, debug…
…SO WE TRY TO AUTOMATE EVERYTHING
We build on prior efforts by combustion research community, e.g.
Comprehensive Chemical Kinetics 35 (1997)
Advances in Chemical Engineering 32 (2007)
9 RMG algorithm: Faster pathways
explored further, growing the model
“Current Model” inside.
RMG decides whether
or not to add species to
Final model typically
500 species, 8000 rxns
OpenSource RMG software.
10 Kinetic Model Predictions Rely on
Quantum Chemistry for Thermo, Rates
• Functional Group approximation
– Compute a few examples of each reaction type with quantum, then use same
barrier, A factor for analogous reactions.
• Most of our calculations at CBSQB3 level
– Geometries, Vibrational Frequencies from DFT
– Single point energies at stationary points at higher level
– Extrapolation to Basis Set Limit
• Recent calculations use F12 methods
– Explicit dependence on distance between every pair of electrons
– Much faster basis set convergence
• Most calculations rely on common approximations
– RigidRotor HarmonicOscillator approximation
– Conventional Transition State Theory (dividing surface at saddle point)
– Simple corrections for internal rotors and tunneling
– Modified Strong Collision approx. for k(T,P)
Are computed thermo, rates accurate enough
Testing Accuracy of Model Predictions vs.
Experiment: CEFRC data on Butanols
With collaborations from
other institutes like
Univ. Ghent, NIST.
Rare Situation where detailed data available at many different conditions
12 We used RMG to build a mechanism for
butanol pyrolysis and combustion.
Four isomers, very different octane numbers.
Octane number = 86 Octane number = 98 Octane number = 100
More reactive Less reactive
RMG considered about 30,000 possible species, selected
• 372 chemical species
• 8,723 reactions
Important k’s with quantum chemistry
Shamel S. Merchant, E.F. Zanoelo, R.L. Speth,
M.R. Harper, K.M. Van Geem and William H. Green,
Combustion Flame (2013)
13 Model predicted butenes yield from
butanols pyrolysis accurately
T 1000 K
P 2 bar
Van Geem et al.
0 5 10 15
Experimental butene yield = wt
Predicted butene yield = wtThe kinetic model also quantitatively predicts
formation of aromatics from butanols
(via rather complicated reaction sequences)
Data from K. Van Geem, Ghent
pyrolysis of isobutanol 1000 K, 2 atm, 2 seconds
15 When you change reaction conditions, need
to add reactions, compute rate coefficients
Predictions of 1000 K pyrolysis model for 1467 K
H O concentration
16 Microsecond H O formation at 1400 K
sensitive to different reactions than
longtime product formation at 1000 K.
Early Times 10 sec
So… computed improved estimates of bond scissions
dehydrations based on quantum chemistry…. Quantum calcs for k(T,P) sensitive at
1467 K significantly changes predictions
H O concentration
18 Using quantum calcs (rather than rough estimates by
analogy) for most sensitive high T reaction k(T,P)
improves predictions of shock tube pyrolysis:
It is always a good idea to do the quantum calcs for the
Sensitive numbers in the model
H O concentration
Experimental data: Stranic et al., Combust. Flame (2012).
See also RosadoReyes et al., J. Phys. Chem. A (2013).
19 Advanced Light Source allows quantification of
dozens of species including key radicals in flames
• Flames are analyzed with molecular
beam timeofflight mass
• Photoionization with tunable
photons allows identification of
• by mass
• by ionization energy
Data measured by Nils Hansen (Sandia)
at Advanced Light Source (LBNL).
Hansen et al. PCCP (2012)
Hansen et al. Combust. Flame (2013)
20 Often only a few smallmolecule reactions are P
dependent. But in MBMS flames, Many Rates
B. M. Wong, D. M. Matheu, and W.
H. Green. J. Phys. Chem. A 2003,
increasing pressure dependence
107, p. 62066211.
increasing pressure dependence Enols sensitive to Hatomcatalyzed chemically
activated ketoenol tautomerization
“Chemicallyactivated” = “product reacts faster than
Chemical activation is a major complication in automated
reaction generation: keep track of “wellskipping” reactions,
Instead of 2 possible products, 10 are formed.
Instead of 2 Transition States, must compute 16 TS’s.
22 Speciation profiles confirm predictive
capabilities: nbutanol flame
• Major species are predicted accurately
• A more powerful test is provided by
comparing modeled and experimental
profiles of intermediate species
• Originally one significant deviation, due
to typo in thermo for one radical (C H )
Dozens of additional species traces, variety of flames: all show comparably good agreement.
For isobutanol we worked in predictive mode, with similar level of agreement with expt.
Significant uncertainties due to calibrations, uncertainties in T(z), boundary conditions at burner, probe
23 Our model predicted Hansen’s MBMS
measurements on isobutanol very well
(see Hansen et al., Combust. Flame 2013)
However, Experimental Data consistent
with a model prediction does NOT prove
the model is correct
24 Although they all match MBMS data, Literature
Models for isoButanol Flame Significantly Differ
MIT Model (Green group) Mi KA lano UST Model Model (Fr (Sarathy assoldati et et al al.). )
Many parameters in detailed kinetic models:
just because it matches experiment does not
mean it is the truth
Slide from Nils Hansen Flame Speed predictions look good – but are
they accurate enough What accuracy needed
not completely consistent,
involve tricky extrapolation to
zero strain. Error bars
may be underestimated
Model very sensitive to
HCO + H O = H + CO + H O
This k alone leads to
uncertainties in predicted
flame speed comparable
to deviations on this plot.
Data from Veloo Egolfopoulos (343 K) , and W. Liu … C.K. Law (353 K), both in Proc Combust Inst (2011).
26 Model quantitatively predicts highT ignition
delays for all butanol isomers conditions
Stranic et al., Combust. Flame, 2012, 159 (2), 516527.
27 Model also predicts lowT ignition delays
in air at conventional conditions fairly
well, and the dependence of t on fuel
In these RCM experiments
by Weber CJ Sung, P N :O ratio is
held fixed (as in air), and so O is also
Most expts are done this way.
Then changes in f are mostly changing
28 We don’t know everything: model completely
mispredicts O sensitivity of lowT ignition
delay of butanols
29 Early stages of ignition: positive feedback
versus damping (OH loss channels)
Low T ignition.
So we have
30 Discrepancy Led To Discovery of New
Type of Reaction of Peroxides, Dominant
at Low T, longer t (JSR or liquid phase)
Jalan et al.,
J. Am. Chem. Soc. (2013)
31 It’s not just butanols…Green group
recently made predictive models for
many other fuels where fewer data are
• Diisopropyl Ketone (large collaboration with Sandia etc.)
– See Allen et al., Combust. Flame (2014)
• Ethylamine (Buesser et al., about to be submitted)
• JP10 (in collaboration with Ghent, Aerodyne)
• Decane mixtures w/isooctane toluene (surrogate jet fuel)
• Cineole (in collaboration with Sandia)
• Pentanol (in collaboration with Xi’an, Ghent)
• IsoPentanol (in collaboration with Ghent)
• Thioethers Thiols (organosulfur) pyrolysis
• Phenyldodecane pyrolysis
• Kinetic models based on quantum chemistry + rate estimates are
predictive for huge range of combustion/oxidation/pyrolysis
– Big models can be built and refined pretty quickly.
– Experimentalists + Theorists team very effective.
• Quick to identify and resolve discrepancies, whether due to model or expt.
– Method useful for assessing proposed new biofuels
• We are working to extend method to more complicated fuels,
accelerate process of building models, add more heteroatom
• This approach is great for quickly seeing what is predicted by
current knowledge of combustion chemistry.
• Sometimes discover serious Discrepancies.
• Discrepancies motivate ongoing work to improve on current
knowledge, and to develop better methods.
Most of the theoretical methods and computer programs described here,
and this whole approach, were developed with funding over many years
from this DOE Basic Energy Sciences Chemical Physics program managed by
Mark Pederson Wade Sisk.
The modeling work reported here was done primarily by MIT student Shamel Merchant, with
help from other members of Green group.
This work benefited greatly from collaborations with other groups supported by DOE Basic
Energy Sciences: Stephen J. Klippenstein, Ronald K. Hanson, ChihJen Sung, Donald G. Truhlar,
Fokion N. Egolfopoulos, Chung K. Law, Nils Hansen
We also gratefully acknowledge our collaborators Kevin Van Geem Guy Marin at U. Ghent,
Richard West at Northeastern U., and C. Franklin Goldsmith at Brown U.