Detailed chemistry models for butanols based on ab initio rate coefficients, and comparisons with experimental data

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Ronald Oliver
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1 Detailed chemistry models for butanols based on ab initio rate coefficients, and comparisons with experimental data William H. Green, Michael Harper, Mary Schnoor, & Shamel Merchant CEFRC Annual Meeting August 16, 2011

2 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

3 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 ~10 6 considered. Even more for low T ignition chemistry 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.

4 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 estimate, 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.

5 So far, no adjustment of parameters to match experiments Main Goal: Assess how accurately we can predict chemistry of new fuels and which experiments would be most helpful Too many parameters in butanols model: many different parameter sets could match all the existing experiments Considering making adjustments in future: C0-C4 values from Fundamental Fuels thrust Butanol decomposition rates to match NIST expts Total butanol + OH rate to match Stanford data

6 Many Experimental Data on Butanol Combustion/Oxidation/Pyrolysis Ignition Delays Shock tube Rapid compression machine 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 Species time profiles in shock tube Single-pulse shock tube We test our butanols model against all these experiments

7 Pyrolysis of the butanol isomers was conducted by collaborator Van Geem (Ghent) VENT 3 7 T1 6 T T3 T MFC MFC T5 T T7 T : 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

8 Predicted conversion [=] wt% The kinetic model s predicted conversion agrees very well with the experimental pyrolysis measurements Butanol iso-butanol 2-Butanol tert-butanol Experimental conversion [=] wt% 8

9 Predicted butene yield [=] [=] wt% Predicted benzene yield [=] wt% Predicted ethylene yield [=] wt% The kinetic model also predicts the pyrolysis product distribution well, including benzene and small aromatics Butanol (1-Butene) 25 1-Butanol iso-butanol (iso-butene) 1.8 iso-butanol 1-Butanol 2-Butanol (1-Butene) iso-butanol Butanol 2-Butanol (2-Butene) 20 tert-butanol 2-Butanol tert-butanol 1.4 (iso-butene) tert-butanol H 2 C CH 2 H 3 C CH 2 CH 3 H 3 C CH Experimental butene yield Experimental [=] wt% benzene ethylene yield [=] wt% H 2 C CH 3 9

$spectrometry Photoionization with tunable synchrotron-generated VUV photons allows identification of species by mass by ionization energy Experimental mole fraction profiles are compared with model$

10 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 model predictions Expts by Nils Hansen at ALS

11 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

12 You learn more from discrepancies! C 4 H 4 and C 3 H 3 overpredicted Sensitive to C 4 H 5 Thermochemistry Simulations of the flames studied by ALS are sensitive to the enthalpy of formation of i-c 4 H 5 (CH 2 =CH- C=CH 2 CH 2 -CH=C=CH 2 ). 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

13 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

14 Ignition delay / s Ignition delay / s Ignition delay / s Ignition delay / s Model fairly accurate for high-t ignition delays 1-Butanol: 1% fuel, P ~ 1.3 bar 2-Butanol: 1% fuel, P ~ 1.3 bar 10 3 CH 3 H 3 C OH H 3 C 10 3 OH 10 2 = = 1.0 = 0.5 = 0.5 = iso-butanol: % fuel, K / TP ~ 1.3 bar = tert-butanol: % K fuel, / T P ~ 1.3 bar 10 3 HO CH OH H 3 C CH 3 CH 3 CH = = 1.0 = 0.5 = Stanford Data = K / T = K / T

15 As we replace rough estimates with k s from quantum, accuracy gradually improving. Experiments: Stanford US meeting = MIT model 5 months ago

16 Big Discrepancy from Last Year: Model did not predict fast n-butanol ignition <900 K! 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 HO 2 + C-H reactions.

17 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 HO 2 + 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

18 Ignition delay / ms Model not capturing dependence on [O 2 ] below 800 K: probably missing or misestimating some peroxyl chemistry 3.38% n-butanol, P = 15 bar Exptl Data: U.Conn MIT model 10 1 = 0.5 Current = 1.0 Current = 2.0 Current K / T Predictions Sensitive to chemically-activated R+O 2 = QOOH: g-c 4 H 8 OH + O 2 (+M) = CH 3 CH(OOH)CH 2 CHOH

19 Summary: Technical Capability Big comprehensive detailed combustion chemistry models can be built pretty quickly. Current capability: ~500 species, ~10,000 rxns Current database: C,H,O + some Sulfur, Nitrogen Weak in aromatic ring forming/breaking chemistry P-dependence and chemical activation important for high-t, maybe also in peroxyl chemistry. More than 50% of k s in model are significantly P-dependent. Comparison with experiments is only semi-automatic, takes longer than generating the models. Technical issues with solvers for some experimental configurations. Have not yet run the models in engine simulators.

20 Summary: Accuracy Predictions from kinetic models based on quantum chemistry + rate estimates, without any adjustment to match experiment, are fairly accurate for huge range of combustion/oxidation/pyrolysis experiments. Big errors usually due to bugs, typos, holes in database. Big errors appear to be mostly eradicated in butanols case. But probably more big errors lurking in other parts of databases. Experiments and team-mates great for catching errors! As we eliminate the big errors, starting to reach expected factor of 2 small errors due to inaccuracies in rate coefficients and thermo derived from quantum chemistry. May be difficult to significantly improve accuracy but calculations can be great guide for experiments to more precisely determine key parameters. Significantly less accuracy at low T (< 800 K). Peroxyl chemistry very complicated, and maybe not completely understood.

21 Big Picture 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 Should be able to use the models to help guide experiments. We are doing this a little, but not yet routine. Kinetics is starting to become a predictive science, possible to use in predictive design of new fuels and engines.

Combustion Chemistry of a New Biofuel: Butanol

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