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
Pyrolysis of the butanol isomers was
conducted at the Laboratory for Chemical
Technology (Ghent)
5
6
4
1
8
VENT
3
7
T1
6
T2
18
T3
19
20
21
13
5
4
T4
5
2
3
MFC
MFC
11
11
10
T5
5
10
T6
17
T7
9
12
14
T8
16
22
15
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
The kinetic model’s predicted conversion agrees
very well with the experimental pyrolysis
measurements
100
Predicted conversion [=] wt%
90
80
70
1-Butanol
iso-Butanol
2-Butanol
tert-Butanol
60
50
40
30
20
10
0
0
20
40
60
80
Experimental conversion [=] wt%
100
8
The kinetic model also predicts the pyrolysis
product distribution well, including benzene and
small aromatics
40
10
Predicted benzene yield [=] wt%
50
H2C
2
25
1-Butanol (1-Butene)
1-Butanol
1.8
1-Butanol
iso-Butanol (iso-Butene)iso-Butanol
iso-Butanol
2-Butanol (1-Butene)
2-Butanol
1.6
2-Butanol
20
2-Butanol (2-Butene)
tert-Butanol
tert-Butanol 1.4
(iso-Butene) tert-Butanol
30
20
5
10
Predicted ethylene yield [=] wt%
Predicted butene yield [=] wt%
60
15
CH2
CH2
H3C
1.2
15
1
H3C
CH3
0.810
0.6
0.4 5
H2C
CH3
0.2
0
0
10
CH3
00
20
40
60
5 0 0 30
10
15
50.5
1050 1
15
1.520
Experimental butene yield
[=] wt% benzene
Experimental
ethylene yield
Experimental
yield [=]
[=]wt%
wt%
252
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
Model
fairly accurate for high-T ignition delays
1-Butanol: 1% fuel, P ~ 1.3 bar
2-Butanol: 1% fuel, P ~ 1.3 bar
Ignition delay / s
10
H3C
OH
2
 = 1.0
10
 = 1.0
 = 0.25
 = 0.25
1
0.65
0.7
0.75
0.8
iso-Butanol:1000
1% fuel,
P
~
1.3
bar
K/T
10
0.55
0.85
HO
0.6
0.65
0.7
0.75
0.8
1000
K
/
T
tert-Butanol: 1% fuel, P ~ 1.3 bar
0.85
OH
CH3
3
10
CH3
2
 = 1.0
10
H3C
CH3
CH3
2
 = 1.0
10
 = 0.5
 = 0.5
 = 0.25
 = 0.25
1
10
2
10
 = 0.5
Ignition delay / s
Ignition delay / s
10
OH
 = 0.5
1
10
0.6
3
CH3
H3C
3
10
Ignition delay / s
3
1
0.6
0.65
0.7
0.75
1000 K / T
0.8
0.85
10
0.5
0.6
0.7
1000 K / T
0.8
14
0.9
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
35
Butanol/O2/N2, =1.0, PC=15 bar
n-Butanol/O2/N2, =1.0, PC=15 bar
tert-Butanol
iso-Butanol
sec-Butanol
n-Butanol
100
Ignition Delay, ms
725 K
737 K
839 K
758 K
Pressure, bar
TC
816 K
25
784 K
30
20
15
10
Non-Reactive Case
5
O2 : N2 = 1 : 3.76
O2 : N2 = 1 : 3.76
10
0
-20
0
20
40
Time, ms
60
80
1.15
1.2
1.25
1.3
1.35
1.4
1000/TC, 1/K
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
automatically
using computer “expert
system”
Due to mistake in rate
database used by expert
system, model wildly
mis-estimated barrier for
HO2 + 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 MIT
using computer “expert
system”
Due to mistake in rate
database used by expert
system, model wildly
mis-estimated barrier for
HO2 + C-H reactions.
After correcting that big
mistake, current CEFRC
model is much closer…
…but still not quite right
at low T, high P.
Work continues…
Model not capturing dependence on [O2]
below 800 K: probably missing or misestimating some peroxyl chemistry
3.38% n-butanol, P = 15 bar
Ignition delay / ms
Exptl Data:
U.Conn.
2
10
MIT model
 = 0.5
Current
 = 1.0
Current
 = 2.0
Current
1
10
1.1
1.2
1.3
1.4
1000 K / T
1.5
1.6
Predictions Sensitive to
chemically-activated
R+O2 = QOOH:
g-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.