Chemical Structures of Premixed iso-butanol Flames

Transcription

1 Paper # 070RK-0060 Topic: Laminar Flames 8 th U. S. National Combustion Meeting Organized by the Western States Section of the Combustion Institute and hosted by the University of Utah May 19-22, 2013 Chemical Structures of Premixed iso-butanol Flames N. Hansen, 1 S.S. Merchant, 2 M.R. Harper, 2 W. H. Green 2 1 Combustion Research Facility, Sandia National Laboratories, Livermore, CA Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA The combustion chemistry of iso-butanol was investigated by determining the chemical composition of premixed, laminar low-pressure flames and by an automatically generated combustion chemistry model. The flames, which were stabilized on a flatflame burner under a reduced pressure of 15 to 30 Torr, were quantitatively analyzed by flame-sampling molecular-beam mass spectrometry. The new set of data consists of isomer-resolved mole fraction profiles for more than 40 species in each of the four flames and provides a comprehensive benchmark for testing of high-temperature oxidation chemistry model for iso-butanol. Temperature profiles were measured using OH laser-induced fluorescence. The kinetic model, which has been extensively tested against other experimental data (ignition delay times, profiles in jet-stirred reactor and flow reactor) shows impressive capabilities for predicting the new flame data presented here. Predictions of the C 2 H 4 O, C 3 H 6 O, and C 4 H 8 O enol-aldehyde-ketone isomers were significantly improved compared to the earlier work by Hansen et al. [Phys. Chem. Chem. Phys. 13 (2011) ] on similar n- butanol flames. A reaction path analysis identified prominent fuel-consumption oxidation sequences, and some significant differences with previous published models are highlighted. 1. Introduction This presentation focuses on the assessment of the predictive capabilities of an automatically generated mechanism for iso-butanol combustion. We report new experimental data on the high-temperature oxidation of iso-butanol in the form of isomer-resolved species mole fraction profiles in four different low-pressure premixed flames and provide a general description of the newly developed reaction set and its performance. The extensive experimental dataset provides new opportunities to test the detailed chemical mechanisms used to predict the chemical structures of the iso-butanol flames. This presentation is based on a paper which has recently been submitted to Combust. Flame and is currently under review (Hansen et al., 2013). It is a continuation of our earlier experimental and modeling study of n-butanol flames (Hansen et al., 2011). Interest in the combustion chemistry of iso-butanol results from global concerns over energy security and environmental problems. It is known that alcohols show significant potential to be additives or even alternatives to conventional, petroleum-based gasoline. For example, in many countries, bio-derived ethanol is already a component of reformulated gasoline (Agrawal, 2007; Kohse-Höinghaus et al., 2010). Compared to the traditionally used ethanol, higher alcohols possess several advantages, such as a lower hygroscopicity, vapor pressure, and corrosivity, full compatibility with existing engines, and a higher energy density (Dürre, 2007; Wallner and Miers, 2009). In particular, n-butanol (n- C 4 H 9 OH, CH 3 CH 2 CH 2 CH 2 OH) has received recent attention in the transportation energy sector and considerable research efforts are aimed at the production of n-butanol and the fundamental understanding of its combustion chemistry. The isomeric iso-butanol [i-c 4 H 9 OH, (CH 3 ) 2 CHCH 2 OH] has a higher octane number than the n-isomer and consequently there is also interest in utilizing the iso-form as a neat fuel or additive in combustion engines. So-far only a few different chemistry models and experimental data exist on the combustion of iso-butanol: The model by Grana et al. (Grana et al., 2010) was developed to reproduce the experimentally determined chemical structure of a counterflow non-premixed flame and its results were later compared to shock tube measurements of ignition delay times (Stranic et al., 2012) and by Frassoldati and co-workers (Frassoldati et al., 2012) to the premixed low-pressure flame structure reported by Oßwald et al. (Oßwald et al., 2011). The model reported by Moss et al. was assembled to describe the oxidation of iso-butanol at high temperatures behind reflected shock waves (Moss et al., 2008). The model s results were also compared against species profiles in a jet-stirred reactor (Togbe et al., 2010) and flame speed measurements (Veloo and Egolfopoulos, 2011). A third model was reported by Sarathy et al. (Sarathy et al., 2012), who tested it

2 against premixed flat flame species profiles (Oßwald et al., 2011), premixed laminar flame velocity (Liu et al., 2011; Veloo and Egolfopoulos, 2011), rapid compression machine and shock tube ignition delay (Stranic et al., 2012), and jetstirred reactor species profiles (Togbe et al., 2010). Yasunaga et al. used that model to describe the pyrolysis and oxidation of iso-butanol behind reflected shock waves (Yasunaga et al., 2012). All findings of this study are described and discussed with respect to the earlier modeling studies of Sarathy et al. (Sarathy et al., 2012) and Frassoldati et al. (Frassoldati et al., 2012) who both tested their models against the earlier premixed flame data from Oßwald et al. (Oßwald et al., 2011). 2. Methods 2.1 Experimental Procedures The isomer-resolved chemical structures of four low-pressure iso-butanol flames are determined using flame-sampling molecular-beam mass spectrometry (MBMS) in combination with photoionization (PI) by tunable synchrotron-generated vacuum-ultraviolet (VUV) radiation (Cool et al., 2005a). The experiment is carried out at the Chemical Dynamics Beamline at the Advanced Light Source (ALS) at the Lawrence Berkeley National Laboratory (LBNL). The four premixed laminar low-pressure iso-butanol/o 2 /Ar flames are stabilized on a 6-cm-diameter stainless steel McKenna burner under the following conditions: Flame 1: φ= % Ar 3.5% i-butanol 24.1% H % O 2 p=15 Torr v=132.8 cm/s Flame 2: φ= % Ar 7.1% i-butanol 42.9% O 2 p=15 Torr v=96.1 cm/s Flame 3: φ= % Ar 10.0% i-butanol 40.0% O 2 p=30 Torr v=48.0 cm/s Flame 4: φ= % Ar 5.0% i-butanol 25.0% O 2 p=30torr v=40.0 cm/s Details of the flame-sampling PI-MBMS technique and the custom-built mass spectrometer have been published elsewhere (Cool et al., 2005a; Hansen et al., 2009; Taatjes et al., 2008). In short: Flame gases are sampled through a ~0.3-mm orifice of a quartz sampling cone on the flow axis of the flat-flame burner. A skimmer of 2.0-mm aperture collimates the sampled gases to form a molecular beam, which passes into the ionization region of the mass spectrometer, where it is crossed by the quasi-continuous synchrotron-generated VUV light. The resulting photoions are mass-selected by pulsed-extraction time-of-flight (TOF) mass spectrometry and detected using a multi-channel plate. The burner is mounted on a translational stage which allows movements with high precision towards different sampling position within the flame. Species are identified both by mass-to-charge (m/z) ratios and by photoionization efficiency (PIE) curves from photon-energy scans at fixed burner positions. In total, more than 40 species are identified in each flame and subsequently their mole fraction profiles are determined as a function of distance from the burner. The data analysis procedures for determining mole fraction profiles from the photoion signals were described elsewhere and are not repeated here (Cool et al., 2005b; Oßwald et al., 2007). Possible sources for errors for individual mole-fraction profiles include errors in the mass discrimination factors, the respective cross section data and/or measurement, and the degree to which the target signal can be separated from potential overlaps caused by parent-ion fragmentation and dissociative ionization of higher-mass species. Typically, the uncertainties of the mole fractions are larger for larger m/z ratios because potentially more isomers and near-equal mass species have to be separated. The accuracies of the mole fraction profiles are estimated to be within 20% for the major species, but uncertainties may be as large as a factor of two for intermediates with unknown photoionization cross section. However, it has been shown that this level of experimental accuracy is usually sufficient for testing kinetic models, since with current techniques one seldom can predict rate coefficients to better than a factor of two in accuracy. And, it should be kept in mind that when comparing modeled and experimental mole fractions, peak shapes and positions can be instructive as well. It is estimated that the experimental spatial location has an absolute accuracy of within ±0.5 mm. Flame 4 seemed to be slightly attached to the cone and the experimental profiles are shifted by 3.0 mm. The flame temperatures are measured with OH laser-induced fluorescence (LIF) (Hansen et al., 2011). The temperature profiles are recorded in the absence of the quartz sampling probe, smoothed and subsequently used as input for the model calculations. We estimate the uncertainties in temperature profiles to be ±150 K in the postflame and reaction zones, and somewhat larger in the preheat zones where the OH concentrations diminish and the temperature gradients steepen. 2.2 Combustion Chemistry Modeling The iso-butanol kinetic model presented in this study is created using the automatic Reaction Mechanism Generator (RMG) (Harper et al., 2011). As a first estimate, the parameters (thermochemistry of species, transport parameter for species and rate coefficient for all reactions) needed in the mechanism are generated based on group additivity schemes (i.e. Benson group additivity for thermochemistry). The parameters which are found sensitive are later refined using quantum chemistry. Please note that we do not adjust any of parameters to fit the experimental data. 2

3 For the current kinetic model we utilize the Seed Mechanism feature of the RMG, where a user defined list of species, reactions and rate coefficients is read in by the software and included in the initial core of the reaction mechanism. The RMG then creates reactions for each species of that list with the new molecule of interest, e.g. iso-butanol, using the RMG s reaction templates. This way, the algorithm generates additional species which will be included in the core depending on their significance (as determined by a user defined tolerance). This process is continued for given temperatures, pressures, and species concentrations of interest until a user defined conversion criterion is met. A CHEMKIN compatible input file is created for the final kinetic model. The current model for iso-butanol combustion utilizes the extensively tested model of Merchant et al. (Merchant et al., 2012) as a seed mechanism and extends it to include the flame conditions given above. The pressure dependent kinetics for this mechanism is estimated using the Modified Strong Collision (MSC) theory. An earlier n-butanol model overestimated the mole fraction of several enols in low pressure premixed flames (Hansen et al., 2011). Specifically, mole fractions for the propenol-propanal pair showed the greatest discrepancy between model and experiment. In order to have an improved prediction for enols and their corresponding aldehydes, the current kinetic model includes unimolecular enol-aldehyde isomerization, catalyzed enol-aldehyde isomerization by acid, water and alcohol and H-assisted reactions. A preliminary flux analysis showed that the H-assisted reaction is the dominant pathway for isomerization. In order to improve the model s capabilities, detailed quantum chemistry calculations and a kinetic study are performed in the course of this work on the propenol+h propanal+h reaction on the C 3 H 7 O potential energy surface. The complete model will be published in Combust. Flame very soon. The flame simulations are performed using the Premixed Burner reactor model available in CHEMKIN-MFC (2010). For the simulations the thermal diffusion (Soret) effect is included and the mixture-averaged transport formalism is assumed. The initial grid is uniformly spaced using 200 grid points. The maximum number of grid points is set to 1000 and the adaptive grid control tolerance based on solution gradient and curvature is set to 0.2, which was found to be a reasonable compromise between accuracy and CPU time. 3. Results and Discussion In this section, we compare selected experimentally determined mole-fraction profiles with the modeled mole-fraction profiles and discuss the quality and the predictive capability of the automatically generated combustion chemistry model. For the following discussion, we consider the modeling results to be predictive when they are within a factor of 2-3 of the experimental data. We will show that based on this criteria, the agreement between experimental and modeling results is very good including peak magnitude, position, and shape. It is obviously beyond the scope of this presentation to show all experimental profiles and we will focus on only a few. It is worth pointing out that for most species profiles similar levels of agreement are observed for all flames studied here. For a few species, agreement between experiment and model results is found to be good for some flames, while larger discrepancies, which are still within the expected uncertainties, are observed for the other flames. The automatically generated model predicts the overall fuelconsumption quite accurately, as shown by the agreement Figure 1: Experimental (symbols) and modeled (lines) mole fraction profiles for iso-butanol, iso-butanal, and the sum of the butenol isomers in Flame 1. To allow visual comparison, the experimental and modeled profiles for the fuel (iso-butanol) are multiplied by 0.2. Figure 2: Experimental (symbols) and modeled (lines) mole fraction profiles for the C 3 H 6 O isomers propen-1-ol, allylalcohol, acetone, and propanal in Flame 3. Only the sum of allylalcohol and acetone is shown, because the species contributions are not separated experimentally. between experimental and modeled mole fractions of iso-butanol, as shown for example in Fig. 1 for Flame 1. Through oxidation of the initially formed C 4 H 9 O radicals, iso-butanal (i-c 4 H 8 O), 2-methyl-prop-1-en-1-ol (C 4 H 8 Oenol1), or 2- methyl-prop-2-en-1-ol (C 4 H 8 Oenol2) can be formed. Because the latter two are not distinguished experimentally, they 3

4 are here discussed as butenol isomers. Separated mole fraction profiles as function of distance from the burner for the iso-butanal and the sum of the butenol isomers and their comparisons to the modeling predictions are also shown in Fig. 1. A very good agreement is obvious, thus demonstrating the predictive capabilities of the automatically generated combustion chemistry model. Other products of the oxidation of the C 4 H 9 O radicals include propen-1-ol (C 3 H 6 Oenol1), allylalcohol (C 3 H 6 Oenol2), iso-butene, propene, and formaldehyde. Experimental and modeled mole fraction profiles of the first two intermediates are shown in Fig. 2 together with the profiles of their isomers acetone and propanal. We are unable to separate the contributions from acetone and allylalcohol, due to very similar ionization thresholds. Therefore only their combined contributions are shown here. Nevertheless, the modeling results indicate that the majority of this predicted mole fraction is from acetone. Many more species profiles will be discussed in the presentation and all experimental data will be made publically available soon. Here, we would like to summarize that the model s predictive capabilities are very good for many species mole fraction profiles; however, discrepancies between experimental and modeled data appear to be larger than the expected errors for several species. Notably, the model underpredicts the mole fractions of 1,3-butadiene by about a factor of 10, which is outside the expected experimental error. This issue remains unresolved and a better description of the chemistry of 1,3-butadiene formation and consumption seems warranted. Such an agreement between experimental and modeled data allows for an accurate reaction path analysis that identifies the dominant mechanistic pathways converting iso-butanol to the oxidation products. Because the reaction paths depend on the pressure, the temperature profile, and on the concentration of radical species like H, O, and OH, it is expected that the reaction paths will be different for the different flame conditions discussed in this paper. However, our results show that for all analyzed iso-butanol flames the same set of reactions is responsible for almost all of the fuel oxidation. This set includes the H-abstraction reactions from C-H bonds with H, O, and OH forming various C 4 H 9 O isomers and the unimolecular decompositions of iso-butanol through simple C-C bond fission. Only the contributions of each reaction vary between the different flame conditions. We therefore do not discuss the reaction paths in all four flames in great detail; instead only the results of Flame 3, which are shown in Fig. 3, are discussed exemplarily. As can be seen, close to the burner surface the fuel consumption is dominated by H-abstraction reactions by OH radicals, forming the α-, β-, and γ-c 4 H 9 O radicals. Abstraction reactions from the OH group are found not to be important, largely because of the O-H bond strength. Further away from the burner surface, in the hightemperature region of the flame, unimolecular dissociation reactions of the fuel become important consumption pathways. The weakest bond in the molecule is the (CH 3 ) 2 CH-CH 2 OH bond, while the breaking of the C-O bond is found to be not competitive. Reaction via a four-center transition state, forming iso-butene and water, is also found to be not important. Figure 3: The ten largest rates of fuel consumption in Flame 3 as predicted by the current model and the models of Sarathy et al. and Frassoldati et al.. More details are provided in the text. In order to compare our modeling results with the work from Sarathy et al. (Sarathy et al., 2012) and Frassoldati et al. (Frassoldati et al., 2012), we use their models and the conditions of Flame 3 to predict the reaction pathways. As can be seen in Fig. 3, earlier models are only in qualitative agreement with the present work. For example, the model of Sarathy et al. (Sarathy et al., 2012) predicts the H + iso-butanol reactions to be most important, while unimolecular dissociation and reactions of iso-butanol with OH are less significant. According to their modeling results, also the water-elimination 4

5 reaction contributes to the fuel-consumption. Contributions of the latter reaction are even more pronounced in the modeling results of Frassoldati et al. (Frassoldati et al., 2012) who also found the H 2 -elimination reaction forming isobutanal to be a non-negligible fuel-consumption pathway. The different predictions of the three models clearly show the needs for more certainty with respect to the data to further constrain the model calculations and for use of accurate rate coefficients for H-abstraction and unimolecular dissociation reactions of iso-butanol. In the current model, the rate coefficients for H-abstraction from iso-butanol and for the unimolecular dissociation reaction of iso-butanol come from detailed quantum calculations Furthermore, the relative rates for formation and consumption of the α-, β-, and γ-c 4 H 9 O radicals will be discussed in the presentation. According to the modeling results, these radicals are mainly consumed via oxidation reactions with O 2 (or HO 2 ) and β-scission reactions. It is worth noting that the β-scission reactions of the C 4 H 9 O radicals to form propen-1- ol (C 3 H 6 Oenol1), allylalcohol (C 3 H 6 Oenol2), and iso-butene are identified to be the main formation pathways for these species. The fact that experimental and modeled mole fraction profiles agree within the given error limits adds confidence to the model s description of the C 4 H 9 O formation and consumption steps. 4. Conclusions The results of this study and of the previous work by Hansen et al. suggest that alcohol combustion chemistry in lowpressure premixed flames may be modeled successfully using an automatically generated reaction mechanism. The current model shows impressive capabilities for predicting the chemical structure of four different iso-butanol flames. A reaction path analysis identifies the fuel-consumption pathways and under the conditions studied here, iso-butanol is consumed through H-abstraction reactions by H, O, and OH. Unimolecular C-C bond fission reactions become competitive to the H-abstraction reactions only at the higher-temperature regions of the flames. Subsequent fast β- scission reactions of the C 4 H 9 O radicals and oxidation with O 2 and HO 2 govern the formation of C 4 H 8 O and C 3 H 6 O isomers as well as other smaller intermediates, with their mole fraction profiles being predicted quite accurately. To resolve disagreements with previously published combustion models, strongly accurate data and more reliable rates for i- butanol consumption reactions are necessary. Acknowledgements The work is supported by the U.S. Department of Energy, Office of Basic Energy Sciences under the Energy Frontier Research Center for Combustion Science (Grant No. DE-SC ). The authors thank P. Oβwald, K. Kohse- Höinghaus, and F. Qi for sharing their temperature and speciation data. The measurements are performed within the "Flame Team" collaboration at the Advanced Light Source (ALS), Lawrence Berkeley National Laboratory, Berkeley, USA, and we thank the students and postdocs for the help with the data acquisition. The experiments have profited from the expert technical assistance of Paul Fugazzi. The Advanced Light Source is supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH Sandia is a multi-program laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the National Nuclear Security Administration under contract DE-AC04-94-AL Disclaimer This is a work-in-progress paper and is not intended to be an archival publication. References CHEMKIN-MFC, MFC 5.0 (2010). Reaction Design, San Diego. Agrawal, A. K. (2007). Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines. Progr. Energy Combust. Sci. 33: Cool, T. A., McIlroy, A., Qi, F., Westmoreland, P. R., Poisson, L., Peterka, D. S. and Ahmed, M. (2005a). Photoionization mass spectrometer for studies of flame chemistry with a synchrotron light source. Rev. Sci. Instrum. 76. Cool, T. A., Nakajima, K., Taatjes, C. A., McIlroy, A., Westmoreland, P. R., Law, M. E. and Morel, A. (2005b). Studies of a fuel-rich propane flame with photoionization mass spectrometry. Proc. Combust. Inst. 30: Dürre, P. (2007). Biobutanol: An attractive biofuel. Biotechnol. J. 2: Frassoldati, A., Grana, R., Faravelli, T., Ranzi, E., Oßwald, P. and Kohse-Höinghaus, K. (2012). Detailed kinetic modeling of the combustion of the four butanol isomers in premixed low-pressure flames. Combust. Flame 159:

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