2011 us combustion meeting - kinetic modeling of methyl formate oxidation
DESCRIPTION
A kinetic model for methyl formate oxidation is generated using our open-source Reaction Mechanism Gen- erator (RMG) software, supplemented with high level quantum calculations and transition state theory (TST). New rate coefficients are calculated for the decomposition pathways of methyl formate, methoxy-formyl (CH3 OC · O), and formyloxy-methyl (C · H2 OCHO), and hydrogen abstractions from methyl formate by H and methyl radicals. We compare the predictions to experimental data including previously unpublished shock tube ignition delays over a wide range of T and P, as well as atmospheric-pressure laminar burning velocities and low-pressure flame species profiles from the literature. Using RMG we investigate the effect of changing the small molecule (C0−C1) “seed mechanism” and show that predictions of all the experiments are sensi- tive to these reactions. Until the small molecule chemistry is resolved it is impossible to have a conclusive mechanism for the fuel molecule oxidation.TRANSCRIPT
Massachusetts Institute of Technology
Kinetic Modeling of Methyl Formate OxidationRichard H West, C Franklin Goldsmith, Michael R Harper, William H Green, Laurent Catoire, Nabiha Chaumeix
1a06
We would like to model kinetics of biofuel combustion predictively.
•Automatically built models are more extensible than hand-curated models, and easier to update.
Hand curated models Automatically built models
Interpretation tool Design tool
Specific to validated fuels, T, P Extensible to new fuels, T, P
Chosen sub-models may be inconsistent with new data
Model can be rebuilt with new data, adding all new pathways
Accurate (where validated) Improving...
2
Three steps towards predictive kinetic models of biofuels.
•Develop method to build kinetic models automatically.
•Collect or generate data and rules suitable for biofuels.
•Perform experiments to validate and check understanding.
3
Three steps towards predictive kinetic models of biofuels.
•Develop method to build kinetic models automatically.
•Collect or generate data and rules suitable for biofuels.
•Perform experiments to validate and check understanding.
⇌���facebook.com/rmg.mitr m g . s o u rc e f o r g e . n e t
Reaction Mechanism Generator
•free and open source software
•version 3.3 released last month
3
Methyl Formate is a useful moleculeto study for biofuel modeling.
•Biodiesel consists of methyl esters.
•Methyl formate is the smallest methyl ester.
•Rules that can predict methyl formatecan likely predict other esters.
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Methyl Formate
Biodiesel
Diesel
4
Overview of this study
•Use RMG to build a kinetic model
5
Overview of this study
•Use RMG to build a kinetic model
•Improve some estimates
•thermochemistry data from quantum calculations
•decomposition kinetics of fuel molecule and radicals
6
Overview of this study
•Use RMG to build a kinetic model
•Improve some estimates
•Use improved RMG to build new kinetic models
•use 3 different ‘seed’ mechanisms from the literature
7
Overview of this study
•Use RMG to build a kinetic model
•Improve some estimates
•Use improved RMG to build new kinetic models
•Compare with experiments
•new dilute shock tube ignition delay times
•flame speed and low-P flame profile from Dooley et al. (Dryer group at Princeton).
8
Overview of this study
•Use RMG to build a kinetic model
•Improve some estimates
•Use improved RMG to build new kinetic models
•Compare with experiments
•Conclude
•choice of small-molecule seed mechanism (C0-C1 sub-model) affects all other conclusions
9
RMG generates secondary chemistry of byproducts, exploring paths with high flux.
•Reaction family templates generate all possible reactions.
•‘Edge’ species with highest flux are moved to ‘core’.
•Expansion continues until tolerance reached.
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Thermochemistry for ~200 small molecules from high-level QCI//DFT calculations
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• conformer search:CBS-QB3
• geometry and frequencies:B3LYP/6-311++G(d,p)
• scan for hindered rotors:B3LYP/6-31+G(d,p)
• electronic energy:RQCISD(T)/CBS extrapolated from RQCISD(T)/cc-pVTZ and RQCISD(T)/cc-pVQZ
11
Same QM methods used to calculate PES of methylformate & radical decomposition
•High-Pressure rate coefficients calculated with TST:
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Methyl Formate PESFormyloxymethyl and Methyloxyformyl PES
12
kcal/mol
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60
30
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CH4 + CO2
kcal/mol
30
20
10
0
-10
40
-20
O OO O
OO
OO
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O
OH +
CO + CH3O·
CH3 + CO2
80.8
-27.2
67.5
9.60.0
Pressure-dependence calculated in RMG using reservoir state method
•RMG explores additional pathways and solves full Master Equation for chemically activated and fall-off reaction rates
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Methyl Formate PESFormyloxymethyl and Methyloxyformyl PES
13
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O OO O
“Seed” mechanism placed in RMG’s core at start of mechanism generation
•Used for trusted rates of small-molecule chemistry.
•Rate expressions used without modification.
•Additional reactions generated by RMG.
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Seed Mechanism 1
Seed Mechanism 2
14
“GRI”
•GRI-Mech 3.0
•(Methane
oxidation)
•Smith (1999)
Three Seed Mechanisms were taken from published literature.
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“Glarborg”
•Glarborg group’s “Master Mechanism”
•C0-C1 only
•Lopez (2009)
“Dooley”•Dryer group’s mechanism
•Used in methyl formate study
•C0-C1 only•Dooley (2010)
15
The models are built using a hierarchy of data sources.
16
QCISD(T)//B3LYP database
Semi-empirical PM3
Seed MechanismGRI / Glarborg / Dooley
Methyl-formatecalculations
Glarborg Master Mechanism
Rule-based estimates
Benson group estimates
GRI-Mech
Group-based estimates
Kinetics Thermochemistry Transport
150
10
100
4000
The models are built using a hierarchy of data sources.
16
QCISD(T)//B3LYP database
Semi-empirical PM3
Seed MechanismGRI / Glarborg / Dooley
Methyl-formatecalculations
Glarborg Master Mechanism
Rule-based estimates
Benson group estimates
GRI-Mech
Group-based estimates
Kinetics Thermochemistry Transport
k(T,P) calculated by Master Equation solution in RMG (+3000)ME
ME
ME
ME
150
10
100
4000
The models are built using a hierarchy of data sources.
16
QCISD(T)//B3LYP database
Semi-empirical PM3
Seed MechanismGRI / Glarborg / Dooley
Methyl-formatecalculations
Glarborg Master Mechanism
Rule-based estimates
Benson group estimates
GRI-Mech
Group-based estimates
Kinetics Thermochemistry Transport
Radicals calculated by Hydrogen Bond Increment methodHBI
HBI
HBI
k(T,P) calculated by Master Equation solution in RMG (+3000)ME
ME
ME
ME
150
10
100
4000
The models are built using a hierarchy of data sources.
16
QCISD(T)//B3LYP database
Semi-empirical PM3
Seed MechanismGRI / Glarborg / Dooley
Methyl-formatecalculations
Glarborg Master Mechanism
Rule-based estimates
Benson group estimates
GRI-Mech
Group-based estimates
Kinetics Thermochemistry Transport
Radicals calculated by Hydrogen Bond Increment methodHBI
HBI
HBI
k(T,P) calculated by Master Equation solution in RMG (+3000)ME
ME
ME
ME Constant across all models in this work
150
10
100
4000
Shock tube experiments performed by CNRS in Orleans.
•99% Argon
•Equivalence ratios ɸ = 0.5, 1, 2
•Ignition defined as half of peak OH* emission
•P5 = 151 – 1446 kPa
•T5 = 1268 – 1812 K
High Pressure Low Pressure
P transducers
UV photomultiplier
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Pressure
- OH* emission
delay
17
Unscaled ignition delay times (IDT) are hard to interpret plotted vs 1/T
Phi = 10 p scaling
0.908841851 correl0.7425 optimum
0.688625 global optimum2.690715605
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Phi = 20 p scaling
0.89880299 correl0.64875 optimum
0.688625 global optimum
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Phi = 0.50 p scaling
0.883070764 correl0.689325 optimum0.688625 global optimum
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ɸ=0.5 ɸ=1 ɸ=2
18
Phi = 20 p scaling
0.89880299 correl0.64875 optimum
0.688625 global optimum
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*+++),)%)$-)
)**+,-#./0#123,456,78#.+94:*4;#
Phi = 10 p scaling
0.908841851 correl0.7425 optimum
0.688625 global optimum2.690715605
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()
*+++),)%)$-)
)**+,-#./0#123,456,78#.+94:*4;#
Phi = 0.50 p scaling
0.883070764 correl0.689325 optimum0.688625 global optimum
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()
*+++),)%)$-)
)**+,-#./0#123,456,78#.+94:*4;#
ɸ=0.5 ɸ=1 ɸ=2
Unscaled ignition delay times (IDT) are hard to interpret plotted vs 1/T
•Each experiment performed at a different P5 pressure.
•Simulate each experiment with the corresponding T5, P5.
19
Scale ignition delay times by P5α
to improve correlation with 1/T
Phi = 10 p scaling
0.908841851 correl0.7425 optimum
0.688625 global optimum2.690715605
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()
*+++),)%)$-)
)*+,-./,01#
Phi = 20 p scaling
0.89880299 correl0.64875 optimum
0.688625 global optimum
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()
*+++),)%)$-)
)*+,-./,01#
Phi = 0.50 p scaling
0.883070764 correl0.689325 optimum0.688625 global optimum
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()
*+++),)%)$-)
)*+,-./,01#
ɸ=0.5 ɸ=1 ɸ=2
•Each experiment performed at a different P5 pressure.
20
Scale ignition delay times by P50.69
to maximize correlation with 1/T
Phi = 10.688625 p scaling
0.982161783 correl0.7425 optimum
0.688625 global optimum2.946534131
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()!*
+%,-.(/0123
4///353%3$+3
)*+,-./,01#
Phi = 20.688625 p scaling
0.982344879 correl0.64875 optimum
0.688625 global optimum
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()!*
+%,-.(/0123
4///353%3$+3
)*+,-./,01#
Phi = 0.50.688625 p scaling
0.982027468 correl0.689325 optimum0.688625 global optimum
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()!*
+%,-.(/0123
4///353%3$+3
)*+,-./,01#
ɸ=0.5 ɸ=1 ɸ=2
21
Phi = 0.50.688625 p scaling
0.982027468 correl0.689325 optimum0.688625 global optimum
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()!*
+%,-.(/0123
4///353%3$+3
)**+,-#./0#.+123*24##567,289,:;#
Phi = 10.688625 p scaling
0.982161783 correl0.7425 optimum
0.688625 global optimum2.946534131
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()!*
+%,-.(/0123
4///353%3$+3
)**+,-#./0#.+123*24##567,289,:;#
Phi = 20.688625 p scaling
0.982344879 correl0.64875 optimum
0.688625 global optimum
!""#
!"""#
!""""#
"$%"# "$&"# "$'"# "$("#
!"#$%&'()!*
+%,-.(/0123
4///353%3$+3
)**+,-#./0#.+123*24##567,289,:;#
ɸ=0.5 ɸ=1 ɸ=2
•All too fast
•Glarborg seed fastest
•Dooley seed slowest
•Good agreement
•Glarborg seed slowest
•Dooley seed fastest
•All too slow
•Glarborg seed slowest
•GRI-Mech seed fastest
Shock tube ignition delays predicted OK, but vary with ɸ and seed mechanism.
22
!"#!"$
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,-./01/2'3445',67'3445'811-49'3445':;<4/=>4?@'A811-49B'(!%!C'811-49'A(!%!C'>154-'
Laminar flame speeds all over-predicted, and also vary with seed mechanism.
•Experimental data and model from Dooley et al. (2010) Int. J. Chem. Kinet.
23
Seed mechanism is mostly responsible for over-predicted flame speed.
•For model with GRI seed mechanism, the10 most sensitive reactions are all from the GRI seed.
24
Reac%on Sensi%vityH.0+0O20=0O0+0OH 0.22OH0+0CO0=0H.0+0CO2 0.15H.0+0HO20=0O20+0H2 90.09H.0+0HO20=0OH0+0OH 0.09H.0+0O20+0H2O0=0HO20+0H2O 90.08HC.O0+0H2O0=0H.0+0CO0+0H2O 0.08HC.O0+0m0=0H.0+0CO0+0m 0.07HC.O0+0O20=0HO20+0CO 90.07HO20+0CH30=0OH0+0CH3O. 0.06H.0+0HC.O0=0H20+0CO 90.05
Sensitivity of flame speed with respect to reaction A factor
!"#!"$
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'2/#345'
67)$+"-,%/,'8"9.'
,-.'/001'
2340567089':;<<=0>?'(!%!@'
Minor species profiles poorly predicted in low-pressure flame. Seed has some effect.
•Low pressure (22 torr) stoichiometric (ɸ=1) flame data and temperature profile from Dooley et al. (2010) Comb. Flame.
•Overpredicted C2H4 in model with Glarborg seedis due to missing decomposition pathway.
!"#$
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01234536*078*9551:;*)<=:3>?:@A*
!"#
$%&'($')*'&'(+''*%&'($')*,&'($'#*,&%($'#*-&'($'#*-&%($'#*#&'($'#*#&%($'#*)&'($'#*
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CH3 mole fraction C2H4 mole fraction
25
Pressure-dependent ethyl formate reactions important to C2H4 profile
•Replacing high-pressure-limit ethyl formate reactions with chemically activated pressure-dependent network increases prediction of C2H4 and C2H2 concentrations.
C2H2 mole fractionC2H4 mole fraction
26
Acknowledgments
•C Franklin Goldsmith
•Michael R Harper
•William H Green
•Laurent Catoire
•Nabiha Chaumeix
•Stephen Dooley
27
Contributions
•Improved tools to automatically build kinetic models.
•Calculated new data for methyl esters.
•New shock tube ignition delay data for methyl formate.
•Information in hard-to-read fonts is better remembered than easier to read information (Diemand-Yauman et al., Cognition, 2011)
28
Contributions
•Improved tools to automatically build kinetic models.
•Calculated new data for methyl esters.
•New shock tube ignition delay data for methyl formate.
•Choice of C0-C1 kinetic model affects all other conclusions. Hard to “validate” other parts of model when this remains unsure.
29