2011 US Combustion Meeting - Kinetic Modeling of Methyl Formate Oxidation

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

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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



•Improve some estimates

•thermochemistry data from quantum calculations

•decomposition kinetics of fuel molecule and radicals

6




•Use improved RMG to build new kinetic models

•use 3 different ‘seed’ mechanisms from the literature

7





•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





•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.

AB

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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

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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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“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


16


Semi-empirical PM3






GRI-Mech



k(T,P) calculated by Master Equation solution in RMG (+3000)ME

ME

ME

ME

150

10

100

4000


16


Semi-empirical PM3






GRI-Mech



Radicals calculated by Hydrogen Bond Increment methodHBI

HBI

HBI


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ME

ME

150

10

100

4000


16


Semi-empirical PM3






GRI-Mech



Radicals calculated by Hydrogen Bond Increment methodHBI

HBI

HBI


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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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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0.688625 global optimum

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0.883070764 correl0.689325 optimum0.688625 global optimum

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ɸ=0.5 ɸ=1 ɸ=2

18

Phi = 20 p scaling



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•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


0.688625 global optimum2.690715605

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20

Scale ignition delay times by P50.69

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0.688625 global optimum2.946534131

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0.982344879 correl0.64875 optimum


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21

Phi = 0.50.688625 p scaling


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•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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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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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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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