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
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Combustion of the Butanol Isomers: Reaction Pathways at Elevated Pressures from Low-to-High Temperatures Michael R. Harper, Mary Schnoor, Shamel Merchant,
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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
• 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
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Pyrolysis of the butanol isomers was conducted at the Laboratory for Chemical
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.