Shock Tube Measurements and Kinetic Study of Methyl Acetate Ignition

Mar 30, 2015 - Sensitivity analysis and reaction pathway analysis were conducted on the basis of the models that give reasonable predictions, i.e., th...
0 downloads 6 Views 3MB Size
Article pubs.acs.org/EF

Shock Tube Measurements and Kinetic Study of Methyl Acetate Ignition Zihang Zhang, Erjiang Hu,* Cheng Peng, Xin Meng, Yizhen Chen, and Zuohua Huang* State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, People’s Republic of China S Supporting Information *

ABSTRACT: Using a shock tube facility, ignition delay times of methyl acetate (MA) were measured covering the temperature range of 1120−1760 K, equivalence ratio range of 0.5−2.0, pressure range of 1.2−10 atm, and fuel mole fraction range of 0.5− 2.0%. An Arrhenius correlation for all of the measured data was obtained through multiple linear regression. The correlation was further compared to the literature correlation [Akih-Kumgeh, B.; Bergthorson, J. M. Combust. Flame 2011, 158 (6), 1037−1048.], and discrepancy was found between the two studies. Four existing chemical kinetic models were used to simulate the measurements, and results suggested that the Princeton model and the Yang model gave better predictions on current ignition data compared to the Westbrook model and the Akih-Kumgeh model. By updating the base model of the Princeton model, a combined model was obtained, and it gave the best prediction on current experimental data among all available models. Sensitivity analysis and reaction pathway analysis were conducted on the basis of the models that give reasonable predictions, i.e., the Princeton model, the combined model, and the Yang model, to gain a further insight into the ignition process of MA and make a comprehensive comparison on different kinetic models. performed, mainly including the laminar flame speeds,24 diffusive extinction limits,12 intermediate species during the pyrolysis and oxidation process,23,25−28 and reaction rate constants.29 However, few studies focused on the ignition delay times of MA, which are essential parameters for the construction and validation of a chemical kinetic model. AkihKumgeh and Bergthorson22 carried out MA ignition delay times measurement in a shock tube with limited conditions, which is the only available ignition data of MA reported in the literature so far. Recently, Yang et al.23 developed a chemical kinetic model for MA, and they used their model to validate against these only available ignition data. While their model gives poor prediction on these ignition data, they pointed out that the ignition data of MA are still lacking and more investigations on MA ignition are necessary. In this study, ignition delay times of MA/O2/AR mixtures were measured behind reflected shock waves at a wide range of experimental conditions. Several of the existing MA chemical kinetic models were used to simulate the experimental data. Kinetic study was carried out based on different models to gain a further insight into the MA ignition process, and the discrepancy between different models has been discussed in detail.

1. INTRODUCTION Motivated by the urgent needs in protecting the atmosphere and reducing the excessive consumption in petroleum, biodiesel is attracting more and more researchers’ interest in the world due to its clean combustion characteristics, low greenhouse effect, and renewability.1,2 The biodiesel, which can be easily produced from the transesterification of animal fats and vegetable oils, is regarded as one of the most important alternative fuels and has been widely used in many different applications, such as internal combustion engines and boilers. Up to now, extensive studies have been reported on the combustion properties of biodiesel and its main components, i.e., long-chain fatty acid methyl esters.3−11 To have a thorough understanding on biodiesel and its main components, studies on small esters are also fundamental. Small esters are the intermediate products during the combustion process of biodiesel and large esters. Therefore, having a comprehensive understanding of their combustion property is beneficial to understand the combustion process of biodiesel and large esters. Moreover, constructing reliable chemical kinetic models for small esters is vital for developing biodiesel and large ester models. In addition, large esters with long carbon chains exhibit similar combustion property compared to long-chain alkanes;12 thus, investigations on small esters are needed to have an indepth comprehension on the specific combustion kinetics induced by the ester functional group. Literature studies on small esters are primarily focused on methyl formate (MF) and methyl butanoate (MB),13−21 while the investigations on methyl acetate (MA) and methyl propanoate (MP) are extremely lacking. Among the four smallest methyl esters, MA was reported to have the lowest reactivity in the literature;22,23 thus, investigations on the unique combustion kinetics of MA are of great significance. Up to now, limited investigations on MA combustion have been © 2015 American Chemical Society

2. EXPERIMENTAL SETUP The shock tube facility consists of a driver section and a driven section, which was separated by two PET (polyester terephthalate) diaphragms. The diameter of the shock tube and the lengths of the driver section and the driven section were 11.5 cm, 2 m, and 7.3 m, respectively. Four fast response pressure transducers (PCB 113B26) were mounted on the sidewall along the end part of the driven section, Received: February 9, 2015 Revised: March 29, 2015 Published: March 30, 2015 2719

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels with a fixed distance of 300 mm. The time intervals between the adjacent transducers were recorded by three time counters (FLUKE, PM6690), which were triggered by these sidewall pressure transducers when the incident shock wave passed through. The endwall incident shock velocity was then calculated by extrapolating these time intervals through a linear method. A pressure transducer (PCB 113B03) and a photomultiplier (Hamamtsu, CR 131) were mounted on the endwall to detect the endwall pressure and OH* chemiluminescence, respectively. All of these data were recorded in a digital recorder (Yokogawa DL750). Further detailed description on the shock tube can be found in the previous publication.30 The onset of ignition here was defined as the time by extrapolating the peak slope of the OH radical chemiluminescence curve to the zero line. The ignition delay time was defined as the time interval between the arrival time of the incident shock wave at the endwall and the onset of ignition, as shown in Figure 1. The ignition temperature was

Figure 1. Definition of ignition delay times. obtained through Gaseq.31 The maximum temperature uncertainty was estimated to be 18 K, and the ignition delay time uncertainty was generally less than 17%. The detailed uncertainty analysis method can be seen in the attachment in the previous publication;30 thus, it was not displayed here for concision. Nitrogen and helium, both with a purity of 99.99%, were used for driver gases in this experiment. The components of the reactant mixture, i.e., MA, O2, and AR, have respective purities of 99.9%, 99.99%, and 99.99%. Detailed experimental conditions are listed in Table 1.

Table 1. Compositions of MA/O2/Ar Mixtures mixtures

ϕ

MA/%

O2/%

Ar/%

p/atm

1 2 3 4 5

0.5 1.0 2.0 1.0 1.0

1 1 1 0.5 2

7 3.5 1.75 1.75 7

92 95.5 97.25 97.75 91

1.2, 5, 10 1.2, 5, 10 1.2, 5, 10 5 5

Figure 2. Measured and fitted ignition delay times of MA.

fitting, a correlation of MA ignition delay times as a function of XMA, XO2, p, and T was obtained as follows: τ = 2.85 × 10−4XMA 0.256 ± 0.089XO2−0.764 ± 0.044p−0.508 ± 0.028

3. RESULTS AND DISCUSSION 3.1. Ignition Delay Time Measurements. Figure 2 shows the measured ignition delay time of MA at different conditions. As expected, ignition delay times increase with a decrease in pressure, an increase in equivalence ratio, or a decrease in MA concentration, as shown in Figure 2a−c, respectively. It can be seen that the tested mixtures generally exhibit strong Arrhenius dependence on the temperature; thus, through linear regression

× exp(37.13 ± 0.63/R uT ) (R2 = 0.975)

(1)

where τ is the measured ignition delay time in microseconds, XMA and XO2 respectively are the mole fraction of MA and O2, and p and T respectively are the pressure in atmospheres and 2720

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels

Figure 3. Comparison between the measured ignition delay times and the predicted values using the Akih-Kumgeh model and the Westbrook model.

temperature in kelvin. Ru, which stands for the universal gas constant, has a value of 1.986 × 10−3 kcal/(mol·K). The regression coefficient R2 has the value of 0.975, indicating a good agreement between the correlation and the measurements. Equation 1 is only applicable to the current experimental ranges, i.e., the temperature range of 1120− 1760 K, equivalence ratio range of 0.5−2.0, pressure range of 1.2−10 atm, and fuel mole fraction range of 0.5−2.0%. To make a rigorous comparison, current correlation was further compared to the only literature correlation for MA ignition, which was obtained by Akih-Kumgeh and Bergthorson:22

the fuel concentration. Generally, the two correlations give agreement in activation energy but discrepancy in ignition delay times. The difference in the initial tested conditions in the two studies may be partly responsible for the observed discrepancy. 3.2. Ignition Delay Time Simulations. The calculations on the ignition delay time were performed through Senkin codes32 in the Chemkin II33 package, using the constant volume homogeneous reactor. Considering the nonideal effects induced by the interaction between the shock wave and boundary layer, a 4%/ms pressure rise was taken into account in the simulations.30 Totally five chemical kinetic models were used to simulate the experimental data of MA, and a brief introduction to these models is shown as follows: The Yang model: The Yang model was developed by Yang et al.23 in 2014, and it contains 83 species and 592 reactions. The rate constants of small molecular species were obtained through literature and their own calculation or estimation. The rate constants of MA decomposition reaction were obtained through the modification on literature values. The rate constants of subsequent decomposition reactions of MA radicals and H-abstraction reactions were determined by high-level ab initio and RRKM master equation calculations. The Princeton model: The Princeton model, which contains 1105 species and 4681 reactions, was constructed by Diévart et al.12 in 2012. It contains a core C0−C4 model, which was updated in accordance with the nC5_49 model34 developed by

τ = 4.5 × 10−5ϕ−0.28 ± 0.13D0.99 ± 0.09p−0.74 ± 0.07 exp(38.2 ± 1.5/R uT )

(2)

where ϕ and D respectively stand for the equivalence ratio and the dilution ratio. Detailed discrepancy between the two correlations can be observed in Figure 2. From Figure 2a, it can be seen that, at relatively low pressure, the correlated values of Akih-Kumgeh and Bergthorson are generally higher than current measurements under all equivalence ratios. From Figure 2b, it is observed that the two studies exhibit good agreement at relatively high pressures. Figure 2c gives the comparison between the two correlations at different fuel concentrations; it can be seen that the ignition delay times tested by Akih-Kumgeh and Bergthorson are more sensitive on 2721

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels

Figure 4. Comparison between the measured ignition delay times and the predicted values using the Princeton model and the combined model.

Princeton model slightly overpredicts the ignition delay times for all tested mixtures, especially for rich mixtures. The combined model, which was obtained through attaching the Princeton MA submodel to the Aramco Mech1.3 model, gives precise prediction for all tested conditions. As the two models have the identical MA submodel, the discrepancies between the two models are completely induced by the differences in small molecular reactions, indicating that the small molecular reactions play important roles in the process of MA ignition. Figure 5 gives the comparison between the measured data and the predicted values of the Yang model. The Yang model gives a good prediction on lean and stoichiometric mixtures ignition, but it underestimates the mixture reactivity at the equivalence ratio of 2.0 and different fuel concentrations. Generally, the combined model performs the best predictive ability on current ignition data among all five models. The simulation results of the Westbrook model and the Akih-Kumgeh model are extremely deviated from the measured ignition delay times. Though discrepancy also exists, the Princeton model and the Yang model give better predictions compared to the Westbrook model and the Akih-Kumgeh model. Thus, the following kinetic analysis was conducted on the basis of the Yang model, the Princeton model, and the combined model as they give better simulation results compared to the other two models. 3.3. Sensitivity Analysis. Sensitivity analysis was performed to find out the difference in the reactions that play the important roles during the ignition process simulated by the

the Curran group in 2010 and several methyl ester submodels including a MA submodel. The detailed oxidation chemistry for MA in this model was taken from that of Dooley et al.17 The combined model: The Aramco Mech1.3 model,35 which was developed by Curran’s group, is a newly developed model for C1−C4 species that has been optimized and validated extensively. By attaching the Princeton MA submodel to this model, a combined model contains 279 species and 1607 reactions were obtained. The Westbrook model: The Westbrook model24 contains 411 species and 2612 reactions. Totally eight ester submodels, namely, MF, MA, MP, MB, ethyl formate (EF), ethyl acetate (EA), ethyl propanoate (EP), and methyl isobutanoate, are included in this model. The MA submodel in this model was developed through a similar approach.28 The Akih-Kumgeh model: This model contains 75 species and 490 reactions. The base model is the USC Mech 2.0 model.36 Most of the rate constants in the MA submodel were adopted on the basis of the propane kinetic data proposed by Tsang37 through analogy. Figures 3−5 give the comparison between the measured data and the simulated values using the aforementioned chemical kinetic models. Figure 3 manifests that the Westbrook model underestimates the tested ignition delay times exceedingly at all tested conditions. While the Akih-Kumgeh model gives an underestimate for lean and stoichiometric mixtures, it gives a reasonable prediction for rich mixtures. Figure 4 shows that the 2722

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels

Figure 5. Comparison between the measured ignition delay times and the predicted values using the Yang model.

1300 K are CH3 + HO2 ⇔ CH4 + O2, MA + H ⇔ MAMJ (CH3COOCH2) + H2, and CH3 + CH3(+M) ⇔ C2H6(+M), as they consume reactive radicals and produce stable molecules instead. However, apart from reaction H + O2 ⇔ O + OH, the sensitivity coefficients of the other previously mentioned reactions decrease sharply when temperature is increased to 1600 K. Instead, reactions CO + OH ⇔ CO2 + H and MA + H ⇔ MA2J (CH2COOCH3) + H2, which are not very sensitive at low temperature, play dominant roles during the ignition process at high temperature. Figure 6b gives the most sensitive reactions in the combined model. The results are quite similar to that of the Princeton model as the top five promoting reactions and the top three inhibiting reactions at 1300 K are the same between the two models. Similarly, the sensitivity coefficients of these reactions at 1600 K are also much smaller than that of 1300 K. As the MA submodel in the combined model is taken from the Princeton model, the discrepancies between the predicted values of the two models are mainly induced by the discrepancies between the small molecular reactions in each model. Among all of the reactions exhibited in Figure 6a, totally five reactions, respectively CH2O + HO2 ⇔ HCO + H2O2, CO + OH ⇔ CO2 + H, HO2 + OH ⇔ H2O + O2, CH2CO + H ⇔ HCCO + H2, and CH3 + CH3(+M) ⇔ C2H6(+M), have different rate coefficients between the two models. To make a brief comparison, rate coefficients of these reactions in the two models were depicted together. The rate coefficients as a

Yang model, the Princeton model, and the combined model, respectively. The following equation is used to define the sensitivity coefficient: S=

τ(2.0ki) − τ(0.5ki) 1.5τ(ki)

(3)

where τ is the calculated ignition delay time and ki stands for the rate constant of the ith reaction. A negative sensitivity coefficient indicates a promoting effect on the overall reactivity and vice versa. Figure 6 exhibits the reactions with the highest sensitivity coefficients during the ignition process at the condition of temperatures of 1300 and 1600 K, pressure of 1.2 atm, and equivalence ratio of 1.0 calculated by the three models, respectively. From Figure 6a, it can be seen that, in the Princeton model, reaction H + O2 ⇔ O + OH has the highest promotion effect on MA ignition, as it has the highest negative sensitivity coefficient both at high and low temperatures. At low temperature, the fuel decomposition reaction, MA ⇔ CH3CO2 + CH3, is most sensitive among all of the fuel specific reactions and it has a strong acceleration effect on global ignition. Other promoting reactions are generally small molecular reactions. Among them, reactions CH3 + HO2 ⇔ CH3O + OH, CH2O + CH3 ⇔ HCO + CH4, and CH2O + HO2 ⇔ HCO + H2O2 exhibit relative higher negative sensitivity coefficients. The top three inhibiting reactions at 2723

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels

Figure 6. Sensitivity analysis for MA ignition using the Princeton model, the combined model, and the Yang model.

promoting reactions at 1300 K are still H + O2 ⇔ O + OH, MA ⇔ CH3CO2 + CH3, CH3 + HO2 ⇔ CH3O + OH, and CH2O + CH3 ⇔ HCO + CH4, their sensitivity coefficients do not decrease sharply at 1600 K. On the contrary, the fuel decomposition reaction MA ⇔ CH3CO2 + CH3 is even more sensitive at 1600 K. The reaction CH3 + HO2 ⇔ CH4 + O2, which produces two stable molecules and competes with the important promoting reaction CH3 + HO2 ⇔ CH3O + OH, acts as the most inhibiting reaction. The methyl combination reaction, CH3 + CH3(+M) ⇔ C2H6(+M), also has a high positive sensitivity coefficient. The methanol elimination reaction MA ⇔ CH3OH + CH2CO has the largest inhibiting effect among all of the fuel specific reactions. Different from the Princeton model and the combined model, no H-abstraction reaction was found to be very sensitive during the MA ignition process simulated by the Yang model, indicating that fuel

function of temperature of the ahead four reactions were illustrated in Figure 7, and the pressure dependence of the rate coefficient for CH3 + CH3(+M) ⇔ C2H6(+M) were illustrated in Figure 8. It is observed that the rate coefficients of the same reaction in the two models are approximate to each other. By substituting the rate coefficients in the Princeton model for the corresponding rate coefficients in the combined model, negligible change was observed for the predicted values of the Princeton model. Though these five reactions are more sensitive in the ignition process of MA, modification only on these reactions is not enough to improve the predictive ability of the Princeton model, indicating that the discrepancy between the two models is induced by the global effect of more small molecular reactions. From Figure 6c, it can be seen that sensitive reactions of the Yang model are quite different from those of the Princeton model and the combined model. Though the top four 2724

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels

adjacent to the carbonyl (97.7 kcal/mol) is comparable to that of the C−H bond in the CH3O group (98.5 kcal/mol), as shown in ref 22. The majority of MA2J further decomposes through the β-scission reaction and produces CH2CO and CH3 O. Only a small portion of MA2J goes through isomerization reaction and forms MAMJ. The other main product of H-abstraction reactions, MAMJ, exclusively decomposes through the β-scission reaction and produces CH3CO and CH2O. At 1300 K, about 28.1% of MA is consumed through fuel decomposition reactions. Among them, the reaction MA ⇔ CH3COO + CH3 has the highest branching ratio because it just needs to break the weak β C−O bond with a low bond energy of 87.3 kcal/mol. The other decomposition pathway MA ⇔ CH3OCO + CH3 contributes to a negligible branching ratio because the C−C bond energy (94.3 kcal/mol) it overcomes is much higher than the β C−O bond. At 1600 K, the branching ratio of H-abstraction reactions decreases to 57% and the fuel is still dominantly attacked by the H radical. The branching ratio of fuel decomposition reactions increases to 42%, indicating that more fuel are consumed through fuel decomposition reactions at high temperature. This is reasonable because the fuel decomposition reactions generally have high activation energy and tend to proceed at high temperature. Figure 9b exhibits the consuming paths of MA simulated by the combined model. As the two models have the identical MA submodel, the branching ratio of each pathway in the combined model is very similar to that of the Princeton model, which implies that the change in the base model does not affect the primary consumption paths of MA greatly. The reaction pathway analysis of the Yang model is shown in Figure 9c. Generally, the reaction paths calculated by the Yang model are quite different from that of the Princeton model and the combined model. In the Yang model, MA is also dominantly consumed through H-abstraction reactions at 1300 K with a total branching ratio of 66.8%. However, MA is dominantly attacked by the CH3 radical, with a large portion of 46.4%. The H-abstraction reactions by the H radical and OH radical only contribute to 18.7% MA consumption in total. This is quite different from the reaction paths calculated by the Princeton model and the combined model, where the H radical and OH radical totally contribute to about 60% fuel consumption. The major product is MA2J with a larger portion of 36.2%, and the minor product is MAMJ with a smaller portion of 30.6%. MAMJ mainly undergoes the β-scission reaction and produces CH3CO and CH2O. However, MA2J also mainly decomposes to CH3CO and CH2O in the Yang model. A similar phenomenon can also be observed in the reaction path analysis for MA combustion in the flat low pressure flame calculated by Yang et al.23 This is quite different from the reaction path simulated by the Princeton model and the combined model. In these models, MA2J is dominantly decomposed to CH2CO and CH3O. About 31.8% of MA is consumed through fuel decomposition reactions at 1300 K. Reaction MA ⇔ CH3COO + CH3 and reaction MA ⇔ CH3OCO + CH3 contribute to 19.3% and 1.1% MA consumption, respectively. Beside these two decomposition pathways mentioned earlier, the methanol elimination reaction MA ⇔ CH3OH + CH2CO, which is absent from the Princeton model and the combined model, has been added in the Yang model. As reported in the previous literature,22,23 the methanol elimination reaction plays an important role in the consumption of methyl esters. About 11.4% MA destructs

Figure 7. Temperature dependence of the rate coefficients for four small molecular reactions in the Princeton model and the combined model (where, for example, 1E15 represents 1 × 1015).

Figure 8. Pressure dependence of the rate coefficients for CH3 + CH3(+M) ⇔ C2H6(+M) in the Princeton model and the combined model (where, for example, 1E14 represents 1 × 1014).

decomposition reactions are more sensitive in the Yang model than in the Princeton model and the combined model. 3.4. Reaction Pathway Analysis. To gain a further insight into the ignition process of MA and the difference between different models, reaction pathway analysis was carried out at the conditions of 1% MA concentration, pressure of 1.2 atm, temperatures of 1300 and 1600 K, and equivalence ratios of 1.0 and 20% fuel consumption. The results are illustrated in Figure 9. The result of reaction pathway analysis of the Princeton model is shown in Figure 9a. At 1300 K, MA is dominantly consumed through H-abstraction reactions, with a total percentage of 71%. Among all of the attacking radicals, H radical contributes to 42.2% fuel consumption as it has the highest reactivity. Other free radicals, such as OH, CH3, and O, consume 15.6%, 10.8%, and 2.4% MA, respectively. The product s of H -abstract ion reactio ns, i.e., MA2J (CH2COOCH3) and MAMJ (CH3COOCH2), possess the identical branching ratio, as the rate coefficients of the Habstraction reactions from the CH3 group adjacent to the carbonyl and the CH3O group attacked by the same radical were analogized to the same rate coefficients. This is reasonable because the bond energy of the C−H bond in the CH3 group 2725

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels

Figure 9. Reaction pathway analysis for MA ignition using the Princeton model, the combined model, and the Yang model.

through methanol elimination reaction at 1300 K in the Yang model. The proportion of total consumption through fuel decomposition reactions also increases with an increase in temperature in the Yang model, as 31.8% and 54.2% of MA are consumed through decomposition reactions at 1300 and 1600 K, respectively.

mole fraction range of 0.5−2.0%. Conclusions of this study are summarized as follows: (1) Through the multiple regression analysis, an Arrhenius correlation for the measured ignition delay times of MA as a function of XMA, XO2, p, and T was obtained. The correlation was further compared to the literature correlation for MA ignition fitted by Akih-Kumgeh and Bergthorson, and discrepancy was found between the two studies. (2) Simulations were performed based on the existing MA models. Result shows that the Westbrook model and the AkihKumgeh model give poor predictions on current measurement, while the predicted values of the Princeton model and the Yang

4. CONCLUSION Ignition delay times of MA were measured using a shock tube within the temperature range of 1120−1760 K, equivalence ratio range of 0.5−2.0, pressure range of 1.2−10 atm, and fuel 2726

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels

(8) HadjAli, K.; Crochet, M.; Vanhove, G.; Ribaucour, M.; Minetti, R. A study of the low temperature autoignition of methyl esters. Proc. Combust. Inst. 2009, 32 (1), 239−246. (9) Wang, W.; Oehlschlaeger, M. A. A shock tube study of methyl decanoate autoignition at elevated pressures. Combust. Flame 2012, 159 (2), 476−481. (10) Haylett, D. R.; Davidson, D. F.; Hanson, R. K. Ignition delay times of low-vapor-pressure fuels measured using an aerosol shock tube. Combust. Flame 2012, 159 (2), 552−561. (11) Campbell, M. F.; Davidson, D. F.; Hanson, R. K.; Westbrook, C. K. Ignition delay times of methyl oleate and methyl linoleate behind reflected shock waves. Proc. Combust. Inst. 2013, 34 (1), 419−425. (12) Diévart, P.; Won, S. H.; Gong, J.; Dooley, S.; Ju, Y. A comparative study of the chemical kinetic characteristics of small methyl esters in diffusion flame extinction. Proc. Combust. Inst. 2013, 34 (1), 821−829. (13) Akih-Kumgeh, B.; Bergthorson, J. M. Shock Tube Study of Methyl Formate Ignition. Energy Fuels 2009, 24 (1), 396−403. (14) Lin, K. C.; Lai, J. Y. W.; Violi, A. The role of the methyl ester moiety in biodiesel combustion: A kinetic modeling comparison of methyl butanoate and n-butane. Fuel 2012, 92 (1), 16−26. (15) Walton, S. M.; Karwat, D. M.; Teini, P. D.; Gorny, A. M.; Wooldridge, M. S. Speciation studies of methyl butanoate ignition. Fuel 2011, 90 (5), 1796−1804. (16) Metcalfe, W. K.; Simmie, J. M.; Curran, H. J. Ab Initio Chemical Kinetics of Methyl Formate Decomposition: The Simplest Model Biodiesel. J. Phys. Chem. A 2010, 114 (17), 5478−5484. (17) Dooley, S.; Burke, M.; Chaos, M.; Stein, Y.; Dryer, F.; Zhukov, V. P.; Finch, O.; Simmie, J.; Curran, H. Methyl formate oxidation: Speciation data, laminar burning velocities, ignition delay times, and a validated chemical kinetic model. Int. J. Chem. Kinet. 2010, 42 (9), 527−549. (18) Fisher, E. M.; Pitz, W. J.; Curran, H. J.; Westbrook, C. K. Detailed chemical kinetic mechanisms for combustion of oxygenated fuels. Proc. Combust. Inst. 2000, 28 (2), 1579−1586. (19) Gaïl, S.; Thomson, M. J.; Sarathy, S. M.; Syed, S. A.; Dagaut, P.; Diévart, P.; Marchese, A. J.; Dryer, F. L. A wide-ranging kinetic modeling study of methyl butanoate combustion. Proc. Combust. Inst. 2007, 31 (1), 305−311. (20) Dooley, S.; Curran, H.; Simmie, J. Autoignition measurements and a validated kinetic model for the biodiesel surrogate, methyl butanoate. Combust. Flame 2008, 153 (1), 2−32. (21) Huynh, L. K.; Lin, K. C.; Violi, A. Kinetic modeling of methyl butanoate in shock tube. J. Phys. Chem. A 2008, 112 (51), 13470− 13480. (22) Akih-Kumgeh, B.; Bergthorson, J. M. Structure-reactivity trends of C1−C4 alkanoic acid methyl esters. Combust. Flame 2011, 158 (6), 1037−1048. (23) Yang, X.; Felsmann, D.; Kurimoto, N.; Krüger, J.; Wada, T.; Tan, T.; Carter, E. A.; Kohse-Höinghaus, K.; Ju, Y. Kinetic studies of methyl acetate pyrolysis and oxidation in a flow reactor and a lowpressure flat flame using molecular-beam mass spectrometry. Proc. Combust. Inst. 2015, 35 (1), 491−498. (24) Wang, Y. L.; Lee, D. J.; Westbrook, C. K.; Egolfopoulos, F. N.; Tsotsis, T. T. Oxidation of small alkyl esters in flames. Combust. Flame 2014, 161 (3), 810−817. (25) Dagaut, P.; Smoucovit, N.; Cathonnet, M. Methyl Acetate Oxidation in a JSR: Experimental and Detailed Kinetic Modeling Study. Combust. Sci. Technol. 1997, 127 (1−6), 275−291. (26) Osswald, P.; Struckmeier, U.; Kasper, T.; Kohse-Höinghaus, K.; Wang, J.; Cool, T. A.; Hansen, N.; Westmoreland, P. R. IsomerSpecific Fuel Destruction Pathways in Rich Flames of Methyl Acetate and Ethyl Formate and Consequences for the Combustion Chemistry of Esters. J. Phys. Chem. A 2007, 111 (19), 4093−4101. (27) Farooq, A.; Davidson, D. F.; Hanson, R. K.; Huynh, L. K.; Violi, A. An experimental and computational study of methyl ester decomposition pathways using shock tubes. Proc. Combust. Inst. 2009, 32 (1), 247−253.

model are reasonable. By updating the small molecular species base model of the Princeton model, a combined model was obtained and it gives better prediction on current data compared to the existing models. (3) Sensitivity analysis shows that the reactions with high sensitivity in the Princeton model and the combined model include small molecular reactions, fuel decomposition reactions, and H-abstraction reactions. However, only small molecular reactions and fuel decomposition reactions were found to be very sensitive in the Yang model. Reaction pathway analysis shows that the reaction pathways calculated by the Princeton model and the combined model are quite different from that of the Yang model.



ASSOCIATED CONTENT

S Supporting Information *

Measured ignition delay times of methyl acetate and the mechanism files of the combined MA model. This material is available free of charge via the Internet at http://pubs.acs.org.



AUTHOR INFORMATION

Corresponding Authors

*(E.H.) Tel.: 86-29-82665075. Fax: 86-29-82668789. E-mail: [email protected]. *(Z.H.) Tel.: 86-29-82665075. Fax: 86-29-82668789. E-mail: [email protected]. Notes

The authors declare no competing financial interest.



ACKNOWLEDGMENTS This study is supported by the National Natural Science Foundation of China (Grant 51306144), the National Basic Research Program (Grant 2013CB228406), and the State Key Laboratory of Engines at Tianjin University (Grant SKLE201502). The support from the Fundamental Research Funds for the Central Universities is also appreciated. We also thank Prof. Westbrook for providing the latest ester model.



REFERENCES

(1) Hill, J.; Nelson, E.; Tilman, D.; Polasky, S.; Tiffany, D. Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels. Proc. Natl. Acad. Sci. U. S. A. 2006, 103 (30), 11206−11210. (2) Ma, F.; Hanna, M. A. Biodiesel production: A review. Bioresour. Technol. 1999, 70 (1), 1−15. (3) Sarathy, S.; Thomson, M.; Pitz, W.; Lu, T. An experimental and kinetic modeling study of methyl decanoate combustion. Proc. Combust. Inst. 2011, 33 (1), 399−405. (4) Seshadri, K.; Lu, T.; Herbinet, O.; Humer, S.; Niemann, U.; Pitz, W. J.; Seiser, R.; Law, C. K. Experimental and kinetic modeling study of extinction and ignition of methyl decanoate in laminar nonpremixed flows. Proc. Combust. Inst. 2009, 32 (1), 1067−1074. (5) Dayma, G.; Sarathy, S.; Togbé, C.; Yeung, C.; Thomson, M.; Dagaut, P. Experimental and kinetic modeling of methyl octanoate oxidation in an opposed-flow diffusion flame and a jet-stirred reactor. Proc. Combust. Inst. 2011, 33 (1), 1037−1043. (6) Togbe, C.; Dayma, G.; Mze-Ahmed, A.; Dagaut, P. Experimental and modeling study of the kinetics of oxidation of simple biodiesel− biobutanol surrogates: Methyl octanoate−butanol mixtures. Energy Fuels 2010, 24 (7), 3906−3916. (7) Glaude, P. A.; Herbinet, O.; Bax, S.; Biet, J.; Warth, V.; BattinLeclerc, F. Modeling of the oxidation of methyl estersValidation for methyl hexanoate, methyl heptanoate, and methyl decanoate in a jetstirred reactor. Combust. Flame 2010, 157 (11), 2035−2050. 2727

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728

Article

Energy & Fuels (28) Westbrook, C. K.; Pitz, W. J.; Westmoreland, P. R.; Dryer, F. L.; Chaos, M.; Osswald, P.; Kohse-Höinghaus, K.; Cool, T. A.; Wang, J.; Yang, B.; Hansen, N.; Kasper, T. A detailed chemical kinetic reaction mechanism for oxidation of four small alkyl esters in laminar premixed flames. Proc. Combust. Inst. 2009, 32 (1), 221−228. (29) Lam, K.-Y.; Davidson, D. F.; Hanson, R. K. High-Temperature Measurements of the Reactions of OH with Small Methyl Esters: Methyl Formate, Methyl Acetate, Methyl Propanoate, and Methyl Butanoate. J. Phys. Chem. A 2012, 116 (50), 12229−12241. (30) Zhang, Z.; Hu, E.; Pan, L.; Chen, Y.; Gong, J.; Huang, Z. ShockTube Measurements and Kinetic Modeling Study of Methyl Propanoate Ignition. Energy Fuels 2014, 28 (11), 7194−7202. (31) Morley, C. GasEq−Calculation Methods used. http://www. gaseq.co.uk/. (32) Lutz, A. E.; Kee, R. J.; Miller, J. A. SENKIN: A FORTRAN program for predicting homogeneous gas phase chemical kinetics with sensitivity analysis, Sandia National Laboratories Report SAND878248; Sandia National Laboratories: Albuquerque, NM, USA, 1988. (33) Robert, J. Chemkin-II: A Fortran chemical kinetics package for the analysis of gas-phase chemical kinetics, Sandia National Laboratories Report SAND89-8009B; Sandia National Laboratories: Albuquerque, NM, USA, 1989. (34) Healy, D.; Kalitan, D. M.; Aul, C. J.; Petersen, E. L.; Bourque, G.; Curran, H. J. Oxidation of C1−C5 Alkane Quinternary Natural Gas Mixtures at High Pressures. Energy Fuels 2010, 24 (3), 1521− 1528. (35) Metcalfe, W. K.; Burke, S. M.; Ahmed, S. S.; Curran, H. J. A hierarchical and comparative kinetic modeling study of C1− C2 hydrocarbon and oxygenated fuels. Int. J. Chem. Kinet. 2013, 45 (10), 638−675. (36) Wang, H.; You, X.; Joshi, A.; Davis, S.; Laskin, A.; Egolfopoulos, F.; Law, C. USC Mech Version II. High-Temperature Combustion Reaction Model of H2/CO/C1−C4 Compounds, http://ignis.usc.edu/ USC_Mech_II.htm, 2007. (37) Tsang, W. Chemical Kinetic Data Base for Combustion Chemistry. Part 3: Propane. J. Phys. Chem. Ref. Data 1988, 17 (2), 887−951.

2728

DOI: 10.1021/acs.energyfuels.5b00316 Energy Fuels 2015, 29, 2719−2728