combustion of the butanol isomers: reaction pathways at elevated pressures from low-to-high...
TRANSCRIPT
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
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17
21
16
1
2
3
3
4
4
5
5
6
6
7
8
5
5
10 10
12
11 11
13
14
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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
80
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.