Combustion of the Butanol Isomers:

Reaction Pathways at Elevated Pressures from Low-to-High Temperatures

Michael R. Harper, Mary Schnoor, Shamel Merchant, William H. Green*,

Kevin M. Van Geem, Bryan W. Weber, Chih-Jen Sung, Ivo Stranic, David F.

Davidson, and Ronald K. Hanson

MIT, U.Ghent, U.Conn., & Stanford

Primary Source of Funding: US DOE Combustion Energy Frontier Research Center

The Context & Challenge

• World is running out of light sweet crude… and benefits to using biofuels instead

• Dozens of alternative fuels proposed, how to assess which are worth pursuing?

• Several new combustion concepts, how to assess how they work with future fuels?

• Increasing regulation of emission species – need models with more chemistry

Goals/Philosophy of this Work

• Improve capability to predict performance of proposed new fuels– Faster, cheaper than exptlly testing all fuels– Butanol as a test case

• Can we build accurate models quickly? How?• Accuracy of predictions? How to validate models?

• “Right answers for the Right Reasons”: true rate coefficients, don’t force fits

Very Big Models: Need to Think Differently

• So many possible reactions and species!– Select ~350 species from ~30,000 considered.– Select ~7,000 reactions from ~106 considered.

• No way to determine all the numbers in the model experimentally…– …and impractical to compute them all accurately.– Most experiments do not conclusively determine any

number, instead constrain some combination.• Fuel performance and experiments are not

sensitive to most of these numbers…– …if those numbers are right order of magnitude

• Different experiments sensitive to different subsets of species and reactions.

Our Model Development Process• Computer assembles large kinetic model for particular

condition(s) using rough estimates of rate coefficients. (open source RMG software)– Start from model derived for other conditions, so appending new

reactions and species. – Automated identification of chemically activated product channels,

and computation of k(T,P).

• If sensitive to k derived from rough guess, recompute that k using quantum chemistry.– Generalize from quantum to improve rate rules.

• Iterate until not sensitive to rough estimates. • Compare with experiment.

– Big discrepancies? Look for bugs or typos.

• Match OK? Repeat for different conditions.

Many Experimental Data on Butanol Combustion/Oxidation/Pyrolysis

• Ignition Delays– Shock tube– Rapid compression machine (see poster T40)

• Flame Speeds– spherical and flat flames

• Speciated Data from:– MS sampling in premixed and diffusion flames– Flow reactors (pyrolysis & oxidation)– Jet-Stirred Reactors– Rapid Compression Facility – Next talk: Species time profiles in shock tube

We test our butanols model against all these types of experiments

9

MFC MFC

T1

T2

T3

T4

T5

T6

T7

T8

19

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1

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3

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10 10

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11 11

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18 20

22

VENT

Pyrolysis of the butanol isomers was conducted at the Laboratory for Chemical

Technology (Ghent)

1: butanol vessel, 2: water vessel, 3: electronic balance, 4: pump, 5: valve, 6: evaporator, 7: mixer,8: heater, 9: air, 10: pressure regulator, 11: mass flow controller, 12: nitrogen, 13: reactor,

14: nitrogen internal standard, 15: oven, 16: GC for formaldehyde and water, 17: GC for C5+, 18: cyclone, 19: condenser, 20: dehydrator, 21: GC for C4-, 22: data acquisition

7

0 20 40 60 80 100

0

10

20

30

40

50

60

70

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90

100

Experimental conversion [=] wt%

Pre

dict

ed c

onve

rsio

n [=

] w

t%

1-Butanoliso-Butanol2-Butanoltert-Butanol

The kinetic model’s predicted conversion agrees very well with the experimental pyrolysis

measurements

8

0 10 20 30 40 50 600

10

20

30

40

50

60

Experimental butene yield [=] wt%

Pre

dict

ed b

uten

e yi

eld

[=]

wt%

1-Butanol (1-Butene)iso-Butanol (iso-Butene)2-Butanol (1-Butene)2-Butanol (2-Butene)tert-Butanol (iso-Butene)

The kinetic model also predicts the pyrolysis product distribution well, including benzene and

small aromatics

0 5 10 15 20 250

5

10

15

20

25

Experimental ethylene yield [=] wt%

Pre

dict

ed e

thyl

ene

yiel

d [=

] w

t%

1-Butanoliso-Butanol2-Butanoltert-Butanol

CH2 CH2

0 5 10 150

5

10

15

Experimental butene yield [=] wt%

Pre

dict

ed b

uten

e yi

eld

[=]

wt%

1-Butanol (1-Butene)iso-Butanol (iso-Butene)2-Butanol (1-Butene)2-Butanol (2-Butene)tert-Butanol (iso-Butene)

CH2CH3

CH3CH3

CH3

CH2

CH3

0 0.5 1 1.5 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Experimental benzene yield [=] wt%

Pre

dict

ed b

enze

ne y

ield

[=

] w

t%

1-Butanoliso-Butanol2-Butanoltert-Butanol

9

Advanced Light Source allows direct detection of dozens of species including key radicals

Photoionization Molecular Beam Mass Spectrometry

Flames are analyzed with molecular beam time-of-flight mass spectrometry

Photoionization with tunable synchrotron-generated VUV photons allows identification of species

by mass by ionization energy

Experimental mole fraction profiles are compared with flame model predictions and reaction path and sensitivity analysis are performed

Advanced Light Source (ALS) Flame Data: Detailed Test of the Model’s Predictive Capabilities

Hansen, Harper,Green PCCP (submitted) Mole fraction profiles of the major species are

predicted accurately A more powerful test is provided by

comparing modeled and experimental profiles of intermediate species

Profiles have not been shifted Oßwald et al. flame data need to be

shifted for better agreement

Only a few of the many data traces shown here… most show good agreement

Oßwald, Güldenberg, Kohse-Höinghaus, Yang, Yuan, Qi, Combust.Flame (2011)158, 2

You learn more from discrepancies! C4H4 and C3H3 overpredicted

Sensitive to C4H5 Thermochemistry Simulations of the flames studied by ALS are sensitive to the enthalpy of

formation of i-C4H5 (CH2=CH-∙C=CH2 ∙CH2-CH=C=CH2) . None of the other available experimental data are sensitive to this number.

This radical’s enthalpy value was incorrect in the MIT database. Correcting to the accepted literature value largely resolved the discrepancy.

Now investigating origins of smaller discrepancies…

Focus on what is important! Most important fuel performance property:

ignition delay

• Gasoline “Octane Number”

• Diesel “Cetane Number”

• Small changes in fuel make big changes in ignition: sensitive to molecular structure!

• New engines under development are even more sensitive to ignition– Potential for big gains… but only if the fuel

ignition delay time matches engine requirements

0.6 0.65 0.7 0.75 0.8 0.8510

1

102

103

1000 K / T

Igni

tion

dela

y /

s

1-Butanol: 1% fuel, P ~ 1.3 bar

= 1.0

= 0.5

= 0.25

0.55 0.6 0.65 0.7 0.75 0.8 0.8510

1

102

103

1000 K / T

Igni

tion

dela

y /

s

2-Butanol: 1% fuel, P ~ 1.3 bar

= 1.0

= 0.5

= 0.25

0.6 0.65 0.7 0.75 0.8 0.8510

1

102

103

1000 K / T

Igni

tion

dela

y /

s

iso-Butanol: 1% fuel, P ~ 1.3 bar

= 1.0

= 0.5

= 0.25

0.5 0.6 0.7 0.8 0.910

1

102

103

1000 K / T

Igni

tion

dela

y /

s

tert-Butanol: 1% fuel, P ~ 1.3 bar

= 1.0

= 0.5

= 0.25

CH3 OH CH3

CH3

OH

OHCH3

CH3

CH3 CH3

CH3

OH

14

Model fairly accurate for high-T ignition delays

As we replace rough estimates with k’s from quantum, accuracy gradually improves.

Experiments:Stanford

“US meeting”=MIT model 4 months ago

Practical Engine Ignition: High Pressure and Low Temperature

10

100

1.15 1.2 1.25 1.3 1.35 1.4

Butanol/O2/N2, =1.0, PC=15 bar

sec-Butanoliso-Butanol

n-Butanol

tert-Butanol

Igni

tion

Del

ay, m

s

1000/TC, 1/K

O2 : N2 = 1 : 3.76

784

K

758

K

737

K

725

K

TC

0

5

10

15

20

25

30

35

-20 0 20 40 60 80

n-Butanol/O2/N2, =1.0, PC=15 bar

Pre

ssur

e, b

ar

Time, ms

O2 : N2 = 1 : 3.76

816

K839 K

Non-Reactive Case

n-butanol ignition is much faster than the other butanol isomers for T< 900 K

Measured by Bryan Weber & C.J. Sung (U.Conn.). Poster T40

Big Discrepancy: Model did not predict fast n-butanol ignition observed at T<900 K!

Model was built automaticallyusing computer “expert system”

Due to mistake in ratedatabase used by expert system, model wildlymis-estimated barrier forHO2 + C-H reactions.

With reasonable k for HO2 + butanol, model predicts ignition delay down to 800 K at 15 atm

Model was built automatically at MITusing computer “expert system”

Due to mistake in rate database used by expert system, model wildlymis-estimated barrier forHO2 + C-H reactions.

After correcting that big mistake, current CEFRCmodel is much closer……but still not quite rightat low T, high P.Work continues…

Model not capturing dependence on [O2] below 800 K: probably missing or mis-

estimating some peroxyl chemistry

1.1 1.2 1.3 1.4 1.5 1.6

101

102

1000 K / T

Igni

tion

dela

y / m

s

3.38% n-butanol, P = 15 bar

= 0.5Current = 1.0Current = 2.0Current

Exptl Data: U.Conn.

MIT model

Predictions Sensitive tochemically-activated R+O2 = QOOH:

-C4H8OH + O2 (+M) =

CH3CH(OOH)CH2CHOH

Summary• Kinetic models based on quantum chemistry + rate

estimates can be predictive for huge range of combustion/oxidation/pyrolysis experiments.– Big models can be built and refined pretty quickly.– Experimentalists + Modelers team very effective.– Useful for assessing proposed new fuels

• Big errors usually due to bugs, typos, holes in database. Experiments and team-mates great for catching them!

• P-dependence and chemical activation important for high-T, but also in peroxyl chemistry. More than 50% of k’s in model are significantly P-dependent.

• Starting to reach expected “factor of 2” small errors due to inaccuracies in rate coefficients and thermo. – May be difficult to significantly improve accuracy…but

calculations can be great guide for experiments to more precisely determine key parameters.

• Kinetics is starting to become a predictive science, possible to use in predictive design of new processes.