Computational Study of NOx Formation at Conditions Relevant to Gas

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Computational Study of NOx Formation at Conditions Relevant to Gas Turbine Operation, Part I Jeffrey Santner, Sheikh F. Ahmed, Tanvir I. Farouk, and Frederick L. Dryer Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b00420 • Publication Date (Web): 30 Jun 2016 Downloaded from http://pubs.acs.org on July 6, 2016

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Computational Study of NOx Formation at Conditions Relevant to Gas Turbine Operation, Part I Jeffrey Santner1, Sheikh F. Ahmed2, Tanvir Farouk2, Frederick L. Dryer1* 1

Department of Mechanical and Aerospace Engineering, Princeton University,

Princeton, NJ 08544, USA 2

Department of Mechanical Engineering, University of South Carolina,

Columbia, SC 29208, USA *

Corresponding Author Email: [email protected]

ABSTRACT: Along with other nitrogen oxides, nitric oxide (NO) is a regulated air pollutant that is primarily produced as a result of combustion processes. This pollutant is produced in every combustion system that utilizes oxidizer mixtures containing N2 and/or fuels that contain organically bound nitrogen. In order to design combustors to minimize NO emissions, high fidelity computational models for NO production and NO/NO2 interconversion are required. To improve model performance and understanding of NO production pathways, a computational parametric study is performed to investigate the effects of fuel chemistry, reaction temperature history, and inert gas dilution on NO production in methane and ethylene combustion. Predictions using popular models from

ACS Paragon Plus Environment

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the literature are compared for premixed laminar flame conditions as well as against previously reported stirred reactor measurements. Differences in the predictions and their relationships to the NOx sub-model and hydrocarbon chemistry are investigated. We find that significant differences in rich flame conditions occur as a result of differences in the CH kinetic pathways and parameters within the hydrocarbon models, while NO production in lean and stoichiometric flames is sensitive to differences in the assumed Zeldovich (thermal) NO sub-model parameters. Due to uncertainties in the spectrum of timescales and the degree of mixing in stirred reactors, JSR data do not provide sufficient constraints on results to resolve the existing model differences. Well defined time history evolution NOx formation data are shown to be critical for developing improved characterization of the relative importance of the different sub mechanisms on NOx formation at gas turbine conditions.

KEYWORDS: Zeldovich, Fenimore, NOx, Combustion Targets, Premixed Flames

1. INTRODUCTION Oxides of nitrogen (NOx) are a major pollutant class emitted by combustion processes. The components of concern are nitric oxide (NO) and nitrogen dioxide (NO2). Together, NO and NO2 cause asthma, emphysema, bronchitis, and other respiratory illness, while reacting in the atmosphere to cause ground-level ozone, another respiratory irritant[1]. Nitrous oxide (N2O) and other nitrogen containing species are generally not included in this pollutant class, but also raise concerns in terms of pollutant interactions producing ozone, other secondary pollutants, and as greenhouse gases. The United States Environmental Protection Agency (EPA) limits NOx produced by aircraft and


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reciprocating engines, and limits decrease each time they are renewed, with the goal of improving public health. Thus, engine manufacturers require accurate models for NOx emissions in order to create high efficiency, cost effective designs that meet regulations. Nitric oxide is formed in hydrocarbon flames through four major mechanisms. NO is formed at high temperatures, almost exclusively in burned gases through the extended Zeldovich mechanism, reactions N1, N6, and N7 (see Table 1) 1. The remaining three routes depend on high radical concentrations within the fuel combustion region itself. The “Fenimore mechanism”

2

involves reactions of CH and CH2 radicals with N2,

forming compounds that readily produce N and NH, and then form NO through N4, N5, and N6. The “NNH mechanism” proceeds through N8, which then forms NO through N3. Finally, the “N2O mechanism” proceeds through N9 and the production of NO through N4 and N5. There have been a substantial number of efforts directed towards accurately modeling NO formation in combustion systems, for example, those of the Gas Research Institute (GRI)

3-5

, Konnov et al.6-8, Klippenstein et al. 9, and Glarborg et al.

10-13

.

However, the models generated in these works all attribute quantitatively different amounts of NOx formation, because of differences in mechanistic frameworks, target data used for model refinement, and reaction rate parameters for the NOx-related and hydrocarbon submodel components. In a very recent paper, Lipardi et al.

14

report a linear uncertainty analysis associated

with the kinetic and thermodynamic parameters on predicting NO in the exhaust gas of freely propagating laminar flames as a function of adiabatic flame temperature, equivalence ratio and dilution for atmospheric pressure, laminar methane and propane


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5

flames. Their analysis showed that for GRI

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and San Diego Mech

15

, the nitric oxide

predictions in the post flame region could have uncertainties in the order of 50 – 60%. The present work which summarizes efforts conducted over several years complements these results by utilizing freely propagating laminar flame predictions for methane and ethylene flames to demonstrate differences in model predictions and their variance on NO production associated with each of the recognized mechanistic pathways as a results of the coupling of different hydrocarbon flame and NOx sub model assumptions commonly used in industry. We compare the post flame NOx formation for GRI-Mech 2.11, GRIMech 3.0, San Diego Mech, and Aramco Mech

16

hydrocarbon oxidation models and

consider three different NOx sub models, that found in GRI-Mech 3.0, that of Konnov 7, and the recent result from Klippenstein et al 9. A large body of work investigating NO formation and consumption in stirred reactors (e.g.

17-21

) has been created over many years of investigation. These experiments have

been used to develop models of NO chemistry at high temperature in simple 0-D systems. Recently, measurements of NO formation in a coaxial turbulent jet diffusion 22, partially premixed counter flow diffusion flames 23 and premixed flames 24-28 have been performed at atmospheric and lower pressures. The studies involving premixed flames had been considering stabilized flame where the stabilization is attained by the burner via heat transfer

26-28

or by a stagnation flat plate

24, 25

via hydrodynamic means. This work

contributes new insights in terms improving the relationship of compositionaltemperature-time history on NO formation. Furthermore, development of laser diagnostics has allowed for non-intrusive measurement of NO-relevant species in complex flame geometries. Comparisons with such flame data are not emphasized here,


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as a number of such comparisons already appear in the literature

22-28

.

Here, we

emphasize comparisons of the more recent NO models against stirred reactor measurements frequently utilized by industry in developing computational engineering design tools.

2. FUEL EFFECTS – NO PRODUCTION IN METHANE AND ETHYLENE Nitric oxide production in methane and ethylene flames is compared in order to demonstrate impacts on pollutant formation due to chemical and thermal fuel effects. These particular fuels are chosen because they are simple, small fuel molecules present in natural gas, as well as being key intermediate species produced in the combustion of larger carbon number fuels. The NOx production from the various models tested were calculated from planar, adiabatic flame simulations with multi-component transport and Soret diffusion using the PREMIX code 29. A minimum of 1000 grid points was imposed in the freely propagating flame calculations to ensure convergence and grid independency. As a result of a higher adiabatic flame temperature, the Zeldovich NO production rate in ethylene/air flames is much higher than for methane/air flames at all equivalence ratios (Figure 1). Though the post flame residence times for the same distance downstream are much larger for methane than for ethylene flames (due to higher flame speed of ethylene), the significant differences in flame temperature have the larger impact on the downstream NO spatial predictions.


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2.1. Chemistry Effects at Constant Flame Temperature.

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The dominant effect of

flame temperature disparities can be removed by diluting the ethylene-air flame with excess argon such that, at each equivalence ratio, flames of each fuel have nearly the same adiabatic flame temperature and N2 content (see Table 2 for mixture compositions). Figure 2 presents NO contributions through each of the four NO sub-mechanisms noted above: Zeldovich, Fenimore, NNH, and N2O. Each sub-mechanism contribution is resolved by the removal of one or two key reactions that selectively control each mode of NO production. For example, the key reactions in GRI Mech 3.0

5

for each sub-

mechanism are: N1 for the Zeldovich mechanism, N2 for Fenimore, N3 for NNH-derived NO, and N4-N5 for the N2O mechanism. Figure 2 shows the contribution from each of the four NO sub-mechanisms in methane/air and diluted ethylene flames at 1 atm over a range of equivalence ratios. For lean flames, NO is produced mostly via the NNH pathway, while the Zeldovich pathway is important for stoichiometric flames and the Fenimore pathway dominates for rich flames. After adjusting the flame temperatures of the two systems to be the same, the NO predictions from the Zeldovich mechanism for flames of each fuel with identical equivalence ratios are nearly the same. Remaining differences likely exist because of small differences in flame temperature and O2 mole fractions. Methane flames produce significantly more Fenimore NO than diluted ethylene flames. The key Fenimore reaction (N2) is an extremely temperature sensitive (high activation energy) reaction. Compared to methane flames, the peak mole fraction of CH is larger for diluted ethylene flames, but it occurs at a lower temperature location leading to a lower reaction rate of CH+N2 (see fig. 3). Thus, Fenimore NO production in methane flames is


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larger compared to diluted ethylene flames of the same equivalence ratio and flame temperature. Diluted ethylene flames produce more NO through the NNH sub-mechanism than methane flames, and this is especially important at stoichiometric conditions. This difference results from the stronger branching and higher H/O/OH radical content of ethylene flames, leading to more NNH production through N8 and a higher rate of the key NNH reaction, N3. Finally, methane flames produce more NO from the N2O sub-mechanism than diluted ethylene flames, but in all cases production through this mechanism is only a small portion of the total NO produced.

3. EFFECTS OF EXCESS DILUTION OF METHANE FLAMES BY NITROGEN A common method of NO reduction is to burn the fuel in vitiated air to reduce the flame temperature. The effects of vitiation on methane/air flames with excess N2 are investigated presently. Figure 4 shows the NO mole fraction in the flame structure over a range of equivalence ratio and excess N2 dilution in the spatial coordinate (left) and temperature coordinate (right). In spatial coordinates (left), Figure 4 demonstrates a reduction in flame-zone NO as well as Zeldovich (post-flame) NO. The lean and stoichiometric flames produce a small amount of NO in the flame, while the rich flames produce significant in-flame NO. In the burned gas, the stoichiometric flame produces a large amount of NO via the Zeldovich mechanism. Plotting in temperature coordinates (Figure 4, right) offers further information on the effects of dilution. The majority of the NO produced in stoichiometric flames occurs in the post flame zone, indicated by the


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vertical behavior of the NO vs. temperature plot. However, post-flame NO vanishes when the stoichiometric flame temperature is brought below 1900 K through dilution. Similarly, the undiluted lean flame temperature is below 1900 K, such that the post-flame NO formation rate is zero even though there is excess O2 in the burned gas. Additionally, the major in-flame Fenimore NO pathway is inactive for lean flames due to the very low CH concentration. Hence NO production for lean flames is very low. For the lean and stoichiometric flames, NO content is solely a function of temperature within the flame structure, while NO formation continues in the post-flame region when flame temperatures exceed 1900 K. The rich flames demonstrate a different behavior, as the O2 and O radical content in the burned gas is too low to produce NO through the Zeldovich sub-mechanism. Instead, a sharp rise in NO content is seen slightly below the adiabatic flame temperature, and the NO content stabilizes or decreases slightly in the post flame zone. As shown previously, NO formation in rich flames is mainly produced through the Fenimore mechanism. This production rate peaks before complete combustion occurs, while the CH radical is present. Fenimore NO formation is effectively halted with strong N2 dilution to a flame temperature below 1700 K due to the previously discussed high activation energy and temperature sensitivity of CH+N2↔HCN+N. Unlike the lean and stoichiometric flame cases, the NO profile in rich flames depends on more than the temperature distribution within the flame. Early in the flame, NO forms slowly through the Zeldovich and NNH mechanisms. These pathways are relatively weak in rich flames, so they are sensitive to changes in residence time and O2 content caused by N2 dilution, and the NO profile within the flame is strongly affected by dilution. Later


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in the flame, there is a drastic increase in NO formation rate. This behavior is a direct consequence of the high activation energy of reaction N2. As temperature rises within the flame, CH is produced from CH2 and consumed by reactions with major species (H2O and O2) and H. At temperatures where reaction N2 is fast, only a small quantity of CH remains, but it quickly reacts to form NO through the Fenimore mechanism. By reducing the flame temperature below 1700 K, NO produced in rich methane flames can be reduced significantly.

4. DIFFERENCES MODELS

BETWEEN

COMMONLY

USED

CHEMICAL

Particularly in rich flames, production of NO is dependent on predictions of CH radicals, which vary greatly among the presently available hydrocarbon models. Kinetic models also vary in their mechanistic structure and relevant rate parameters for Fenimore NO production. Here we investigate three popular models: GRI Mech 3.0 5, the Konnov 0.6 model 7, and Klippenstein et al. 9. While other models exist, it is these models that have been the focus of the stationary gas turbine community. 4.1. Effects of the Hydrocarbon Model. Understanding Fenimore NO is important for low-NO rich-quench-lean combustor configurations. The CH radical reacts with N2 to form HCN 5 or NCN 7, which are then oxidized through various pathways to form NO. Thus, NO formation in rich flames is dependent on the highly uncertain parameters for CH formation and destruction. Figure 5 (left) shows that there is a factor of 7 among the peak CH mole fractions predicted by the various models for the same stoichiometric methane/air flame. The temperature at which peak CH concentration occurs also varies, strongly affecting the rate of reaction N2. Although the NOx submodel is the same for


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four of the six traces in Figure 5 (right), there is over a factor of 6 difference in predicted NO concentration due to the selected hydrocarbon model. In general, CH is formed from CH2 via H abstraction, which is then consumed in reactions with H2O, H, CH4, and O2. However, the specific elementary reaction constructs of each model differ. The San Diego model

15

16

model does not include the CH+H and CH+CH4 pathways, while the Aramco includes an additional CH+O2 pathway. These mechanistic differences

contribute to the variation in the predictions shown in Figure 5. The models investigated also have significant disparities for the reaction rates governing CH production and destruction, as shown in Figure 6. The reaction rates used for CH2+H↔CH+H2, one of the two major reactions producing CH, are not consistent with those recommended in the literature30-34, varying among each other by over a factor of 10 at 2000 K. The rates for the major CH consumption pathway, CH+O2↔HCO+O, also vary greatly among the models. Furthermore, the models do not include other product channels for CH+O2, although they may be significant. One cannot evaluate which rate parameterization for CH+O2↔HCO+O

is most accurate, as direct rate

measurements of CH+O2 have only been performed over a small temperature range and do not isolate the different product channels

35, 36

. Finally, the reaction rates for the

second-most important CH consumption pathway, CH+H2O↔CH2O+H, vary by over a factor of 5 at 2000 K. None agree with the recent recommendation of Bergeat et al.

37

at

low temperature. The large uncertainties and variations in the hydrocarbon submodels lead to large variations in NO emission predictions for rich conditions. Presently, it is difficult to determine which reaction rate parameterizations are “correct”. New


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experiments and reaction rate computations are needed to reduce uncertainties in the prediction of CH in flames. 4.2. Effects of the NO Model. The two recent methane/NO models, GRI Mech 3.0

5

and the Konnov model 7, and the recent N2 chemistry model of Klippenstein et al. 9, all differ substantially in terms of important NO model parameters. Under stoichiometric conditions, the Konnov model predicts the most NO formed, followed by GRI Mech 3.0, and then Klippenstein et al. This is due to differences primarily in the Zeldovich mechanism descriptions (N1, N6, N7). At typical flame temperatures (denoted by a vertical line in Figure 7 at 2200 K), the reaction rate for N2+O↔NO+N (N1) is 40% faster in the Konnov model than in Klippenstein et al., and the reaction rate for N+O2↔NO+O (N6) is ~20% slower in the model of Klippenstein et al. than in either of the other models. By substituting these three Zeldovich mechanisms into the same overall chemical model, zero-dimensional simulations show that differences among the Zeldovich mechanisms in these three models cause a significant change in NO production in the burned gas from a stoichiometric methane-air flame at 1 atm. The NO production rate, d[NO]/dt, in these models differs by a factor of ~1.5 – a difference of 45 ppm after 1 ms or 250 ppm after 100 ms. Klippenstein et al. 9 take their Zeldovich reaction rates from the evaluations of Baulch et al. 38, 39 for N1 and N6. Abian et al. 40 recently found that the forward and reverse rates for this reaction were inconsistent in Baulch et al.38, 39, however Klippenstein et al.9 only uses the reverse rate – the forward rate is calculated consistently from thermochemistry. The rate for N7 is taken from Miller et al.

41

, which cites Flower et al.

measurements above 2400 K and Howard and Smith

43


42

for

for measurements below 515 K.

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Zeldovich reactions in GRI Mech 3.0 are taken from Baulch et al. 44 for N6, a fit of high temperature experimental data

45-47

for N1, and a fit to experimental data for N7.

However, N7 was then revised by a factor of 0.59 in the model optimization process performed to generate GRI Mech 3.0. The Konnov 0.6 model used here

7

employs the

same Zeldovich mechanism as Konnov 0.5 6 (models older than version 0.5 are no longer accessible

48

). Although citations cannot be found, Konnov appears to take N1 from

measurements by Thielen and Roth

47

and N6 from Baulch et al.

44

. Although the

reaction rate parameters in these models vary, it is difficult to determine which is the most accurate, as the measurement recommendations (Figure 7) claim uncertainties from 10%

45

to ~20%

42, 45, 46

to 40%

46

. These experimental uncertainties, combined with

experimental disagreement and lack of measurements over a large temperature range, make it difficult to further constrain the Zeldovich mechanism. More experiments with well-defined boundary conditions and accurate higher order theoretical rate predictions are needed for this critical NO sub-model. For rich conditions, GRI Mech 3.0 predicts higher NO emissions than the Konnov model due to large differences in the Fenimore route, in addition to disagreements in CH production and consumption. In GRI-Mech 3.0, CH reacts with N2 to form HCN, which forms NO through various pathways, while in the Konnov model, CH reacts with N2 to form NCN, which then forms HCN, which creates NO. Pathways involving NCN chemistry have recently emerged as the more likely route 7. Additionally, there are major differences in reaction rates converting HCN to NO between the two models, and the Konnov model includes additional pathways from HCN to NO involving C2N2. These


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differences in mechanistic structure and reaction rates cause large uncertainties in the predictions of NO for rich flame conditions.

5. COMPARISON WITH EXPERIMENTAL MEASUREMENTS Flame measurements that can be found in the literature at pressures greater than 1 atm typically do not provide in-flame resolution, only reporting exhaust concentration. Such measurements are complicated by the long post-flame residence times needed to study Zeldovich NO. In many of these experiments, uncertain temporal temperature histories necessitate complex reactor network approaches (e.g.49). Relatively recent measurements of NO formation have been performed in diffusion

22, 23

and premixed flames

24-27

at

atmospheric pressure. These works include thorough comparisons to many of the presently available models, and we have no new insights to add based upon further analyses of these works to those already noted above. Rather, this section focuses on comparisons with and reanalysis of stirred reactor experiments. Although there has been significant work on the effects of nitrogen chemistry on hydrocarbon ignition processes at lower temperatures in stirred reactors

17-19

(and flow reactors

50-52

), here we focus

specifically on the production of NO, which typically occurs in high temperature stirred reactors 20, 53, 54. Due to imperfect mixing and the possibility of flame stabilization, jet stirred reactors with highly reactive mixtures are typically simulated using a series of adiabatic and/or isothermal perfectly stirred reactors (PSRs) and plug flow reactors (PSRs) (e.g.

20, 53-55

).

Therefore, chemical model predictions strongly depend on the physical model – e.g. configuration and residence times of reactors in the network. In most cases, there is no objective method to determine these parameters, and they are typically identified only by


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choosing the physical model that best reproduces the measurements for the selected chemical model. Such circular reasoning makes chemical model refinements using such datasets extremely difficult. Indeed, the uncertainty associated with the physical model prevents accurate investigation of chemical model uncertainty. Figure 8 shows comparisons among the predictions using various chemical models and the measurements of Steele et al. 53, 54. For each chemical model, two physical models are used. The first is an adiabatic PSR, and the second is an adiabatic PSR followed by an isothermal PFR, where the PSR represents 80% of the residence time and the PFR represents 20%. These two physical models are the extremes given by Steele et al., with a claim that the most accurate physical model lies somewhere between these extremes. Predictions from GRI Mech 3.0 5 reproduce the measurements reasonably well, but this is not surprising as GRI Mech 3.0 was optimized to reproduce the measurements from Steele et al. at 1 atm with a 603 K inlet temperature and 3.4 ms residence time (top right, Figure 8). The model from Konnov

7

predicts more NO formation in most cases, likely

due to the faster Zeldovich route, as these experiments are all fuel-lean. Hydrocarbon models

16, 56

with the nitrogen chemistry of GRI Mech 3.0 predict more NO than GRI

Mech 3.0. This is likely due to changes in the O/H/OH radical pool for these lean conditions. Comparisons of model predictions to the measurements of Shuman

20

are shown in

Figure 9. One would prefer modeling each individual experiment using the data provided for pressure, flow rate, equivalence ratio, inlet temperature, and gas temperature. However, this gives poor results due to the scatter in the experimental parameters. In order to continuously model the measurements, the following simplifications were


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applied. With a heated inlet, the equivalence ratio used does not depend on residence time, so the average value of 0.625 was used, although the equivalence ratio in experiments ranges from 0.58 to 0.74. With an unheated inlet, the equivalence ratio was varied from 0.66 to 0.8 in order to maintain gas temperature, and a linear correlation of equivalence ratio with residence time was used. The inlet temperature was 573 K for the heated cases, and is given by a linear correlation with residence time computed in the present study for the unheated cases (323 – 432 K). In order to demonstrate effects of physical model uncertainty, Figure 9 shows predictions both for an isothermal and adiabatic PSR, as the actual system should be modeled as an adiabatic PSR followed by an isothermal PSR. The temperature of the isothermal PSR ranges from 1800 – 1938 K, and is given as a function of residence time in equations 3.4 – 3.5 from Shuman 20. The simulation results show a significant difference between isothermal and adiabatic predictions except at short residence times with the unheated inlet. This is likely caused by the differences of up to 100 K in reactor temperature between the two physical models. When interpreting these simulations, short residence times should be closer to adiabatic results (dashed line), while long residence times should be closer to isothermal results (solid line), as there is less opportunity for heat loss with short residence times. With the exception of GRI Mech 3.0 at short residence times, all simulations over-predict NO production. Substitution of the hydrocarbon model in GRI Mech 3.0 with that in San Diego Mech increases NO production slightly, indicating some hydrocarbon model dependence. However, the hydrocarbon model dependence is weaker than and opposed to the dependence seen in Figure 5, as these experiments are sensitive to Zeldovich, not


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Fenimore chemistry. Simulations from Konnov v0.6 predict the most NO, likely due to its faster Zeldovich chemistry, as discussed previously.

6. CONCLUSIONS The effects of fuel chemistry on NO emissions have been investigated through a case study on methane and ethylene flames. We find that, besides strong flame temperature effects, NO emissions are enhanced by high concentrations of CH through the Fenimore route, and high concentrations of H and O through the NNH route. It was found that dilution to flame temperatures below 1900 K greatly reduces post-flame Zeldovich NO in lean and stoichiometric flames, while dilution below 1700 K also strongly reduces Fenimore NO in rich flames. Differences in predictions from various models were investigated. The hydrocarbon portion of the model affects NO production through the Fenimore route, which is highly sensitive to the peak content and location of CH radicals within the flame structure. Hydrocarbon models differ significantly in their description of CH chemistry. Nitrogen chemistry models also differ in descriptions of Zeldovich and Fenimore chemistry, causing large differences in NO production at the exit of a combustor. These findings are similar to recent premixed stagnation flame experiments

23, 24

which show large

discrepancies between model predictions, especially at rich conditions. Furthermore, those measurements could not be accurately predicted by any single model over the entire range of conditions. In light of the large differences among the models, much of the agreement with experiments must be due to fortuitous cancellation of errors, model optimization, or experiments that are insensitive to specific elementary reactions.


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Model validation with stirred reactor experiments is confounded by the effects of physical model uncertainty. The degree of mixing within these reactors is unknown, and the effect of this physical model uncertainty on NO prediction is similar to the scatter among chemical models with consistent physical models. Furthermore, presently available stirred reactor experiments focus on lean conditions, offering no constraining information on Fenimore NO production pathways. Experiments with well-defined physical parameters over a wider range of equivalence ratios are needed to improve the state of NO modeling. Such experiments, when performed with a variety of fuels from those that do not produce Fenimore NO (e.g. syngas, H2) and hydrocarbon fuels, could greatly constrain models of NO production.

7. Acknowledgements Funding support from the National Energy Technology Laboratory of the US Department of Energy, through University Turbine Systems Research (UTSR) Program (Award Number DE-FE0012005), as well as Siemens Power Generation (Award Number DEFC26-05NT42644-SUB24B) is gratefully acknowledged.


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Tables and Figures Table 1. Reaction numbers for nitrogen-containing reactions used in this work. N + NO ↔ N2 + O CH + N2 ↔ HCN + N NNH + O ↔ NH + NO NH + NO ↔ N2O + H N2O + O ↔ 2NO N + O2 ↔ NO + O N + OH ↔ NO + H N2+H(+M)↔NNH(+M) N2+O(+M)↔N2O(+M)

N1 N2 N3 N4 N5 N6 N7 N8 N9


Page 19 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 2. Mixture composition used for investigations of chemical effects. φ 0.7 1 Methane 1.3 0.7 Ethylene 1 1.3

Fuel 0.068 0.095 0.120 0.040 0.055 0.067

O2 0.196 0.190 0.185 0.173 0.164 0.155

N2 0.736 0.715 0.695 0.736 0.715 0.695


Argon 0.000 0.000 0.000 0.050 0.066 0.083

Tf (K) 1837 2208 2057 1843 2147 2031

10000

Exit NO mole fraction (ppm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 32

2400 2200

1000

2000 1800

100

1600 1400

10

1

Methane

1200

Ethylene

1000 800 600

0.1 0.5

1

1.5

Adiabatic Flame Temperature (K)

Energy & Fuels

2

Equivalence Ratio

Figure 1. Adiabatic flame temperature (dashed lines) and NO mole fraction 5 cm downstream from the flame (solid lines) for methane/air and ethylene/air flames. Simulations performed at 1 atm and 300 K using GRI Mech 3.0 5.


Page 21 of 32

1.E-04

3.E-05

8.E-05

Mole Fraction NO

Mole Fraction NO

Fenimore NO

Zeldovich NO ϕ=0.7 ϕ=1.0 ϕ=1.3

4.E-05

2.E-05

1.E-05

6.E-05

4.E-05

2.E-05

0.E+00

0.E+00 0

0.2

0.4

0.6

0.8

0

1

0.05

Position (cm)

0.1

0.15

0.2

0.25

0.3

0.4

0.5

Position (cm)

2.E-05

8.E-06

N2 O ba sed NO

NNH ba sed NO

Methane/air Diluted Ethylene

6.E-06

Mole Fraction NO

Mole Fraction NO

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

4.E-06

2.E-06

1.E-05

0.E+00

0.E+00 0

0.2

0.4

0.6

Position (cm)

0.8

1

0

0.1

0.2

Position (cm)

Figure 2. NO mole fraction caused by each NO submechanism for methane/air flames and argon-diluted ethylene flames at nearly the same adiabatic flame temperature, over a range of equivalence ratios. All calculations use GRI Mech 3.0 5 at 1 atm.


Energy & Fuels

1.5E-05

CH Mole Fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 32

Methane Diluted Ethylene

1.0E-05

φ = 1.3 φ = 1.0

5.0E-06

0.0E+00 1400

1600

1800

2000

Temperature (K)

Figure 3. CH mole fraction within the stoichiometric and rich flame structures at 1 atm, calculated using GRI Mech 3.05. Mixture conditions are shown in Table 2.


Page 23 of 32

6

6

Methane/'Air' 1 atm ϕ=0.7

5

Mole Fraction NO (ppm)


5 4 3 2 1

Metha ne/'Air' 1 atm ϕ=0.7

4

80% N2 in 'air' 81% N2 in 'air'

3

82% N2 in 'air' 2 1

0

0

Position (cm)

Temperature (K)

80


Methane/Air 1 atm ϕ=1.0

100



70 60 50 40 30 20

80

80% N2 in 81% N2 in 82% N2 in 83% N2 in 84% N2 in 85% N2 in

60

40

'air' 'air' 'air' 'air' 'air' 'air'

20 10 0

0

Position (cm)

Temperature (K)

80


100

Metha ne/Air 1 atm ϕ=1.3 80% N2 in 81% N2 in 82% N2 in 83% N2 in 84% N2 in 85% N2 in

70


120


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

80 60 40 20

60 50 40 30

'air' 'air' 'air' 'air' 'air' 'air'

20 10

0

0 0

0.2

0.4

0.6

Position (cm)

0.8

1

500

1000

1500

Temperature (K)

2000

Figure 4. NO profiles as a function of position (left) and temperature (right) within the flame for three equivalence ratios. The unburned mixture contains methane, nitrogen, and oxygen, with the fraction of N2 in the N2+O2 mixture shown in the legend. All calculations use GRI Mech 3.0 5 at 1 atm.


Energy & Fuels

8

6

120

Methane/Air ϕ=1.3 1 atm

5 4 3 2


7

Mole Fraction CH (ppm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 32

GRI 2.11 GRI 3.0 San Diego USC Aramco Konnov

1600

80 60

GRI 2.11 San Diego Aramco

GRI 3.0 USC Konnov

40 20

1 0 1400

100

Methane/Air ϕ=1.3 1 atm

1800

Temperature (K)

2000

0 1400

1600

1800

Temperature (K)

2000

Figure 5. CH and NO mole fraction predictions using USC Mech 56, San Diego Mech 15, GRI 2.11 3, GRI 3.0 5, and Aramco Mech 1.3 16. The GRI 3.0 NOx submodel was added to models that do not include nitrogen chemistry (San Diego, USC, Aramco).


Page 25 of 32

1E+15

Rate coefficient (cm 3 mol-1 s-1 )

CH 2 +H=CH+H 2 Rate coefficient (cm 3 mol-1 s-1 )

1E+14

1E+13

GRI Mech 3.0 San Diego USC and GRI 2.11 Aramco Mech Konnov Measurements

1E+12

1E+11

CH+O2 =HCO+O

5E+13

GRI Mech 3.0 San Diego USC, Aramco, GRI 2.11 Konnov Rohrig et al. - all products Markus et al. - all products 5E+12

0

0.5

1

1.5

2

0

0.5

1000/T (K-1 )

1

1.5

1000/T (K-1 )

1E+14

Rate coefficient (cm 3 mol-1 s-1 )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

CH+H 2 O=CH 2 O+H

1E+13

San Diego and Konnov

1E+12

USC and GRI 3.0 Aramco and GRI 2.11 Bergeat et al. 1E+11 0

0.5

1

1.5

2

1000/T (K-1 )

Figure 6. Reaction rates governing CH concentration from the models investigated. Rates from the models are compared against experiments and calculations30-37.


2

Energy & Fuels

R1: N2+O=NO+N

R6: N+O2 =NO+O Normalized Reaction Rate (-)

Normalized Reaction Rate (-)

1

Konnov GRI 3.0 Klippenstein et al. Michael and Lim Thielen and Roth Davidson and Hanson 0.1 0.25

0.75

1000/T (K-1)

1

Konnov GRI 3.0 Klippenstein et al. 0.1 0.25

1.25

0.75

1.25

1000/T (K-1 )

1E+14

R7: N+OH=NO+H Reaction Rate (cm 3 mol-1 s-1 )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 32

Konnov GRI 3.0 Klippenstein et al. Flower et al. Howard and Smith

1E+13 0.25

1.25

2.25

1000/T (K-1 )

Figure 7. Reaction rates in the Zeldovich sub-mechanism from the models of interest 5, 7, 9

and recommendations from direct measurements 42, 43, 45-47. The vertical dashed line

indicates the adiabatic flame temperature of a stoichiometric methane-air flame at 1 atm.


Page 27 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Figure 8. NO produced in a stirred reactor at equivalence ratios of 0.48 – 0.7 53, 54 compared with simulations. Solid lines indicate adiabatic perfectly stirred reactor (PSR) computations, while dotted lines indicate simulations where gas travels from an adiabatic PSR to an isothermal plug flow reactor (PFR). Chemical models that don’t contain nitrogen chemistry 16, 56 have been supplemented with the nitrogen chemistry of GRI Mech 3.0 5.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 9. NO produced in a stirred reactor 20 compared with simulations from various models. Solid lines indicate simulations from an isothermal reactor, dashed lines indicate an adiabatic reactor. The isothermal reactor temperature and inlet temperatures are given as continuous functions of residence time, achieved from fitting the measurements. Simulations utilize an equivalence ratio of 0.625 for the heated inlet and a linear function of residence time for the unheated inlet, to mimic experiments where the equivalence ratio varies from 0.58 to 0.8.


Page 28 of 32

Page 29 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

References 1. Miller, J.; Bowman, C., Mechanism and modeling of nitrogen chemistry in combustion. Progress in Energy and Combustion Science 1989, 15, 287 - 338. 2. Fenimore, C. P., Formation of nitric oxide in premixed hydrocarbon flames. Symposium (International) on Combustion 1971, 13, 373 - 380. 3. Frenklach, M.; Wang, H.; Goldenberg, M.; Smith, G.; Golden, D.; Bowman, C.; Hanson, R.; Gradiner, W.; Lissanski, V. GRI-Mech: An optimized detailed chemical reaction mechanism for methane combustion; Gas Research Institute: 1995. 4. Frenklach, M.; Wang, H.; Yu, C.; Goldenberg, M.; Bowman, C.; Hanson, R.; Davidson, D.; Chang, E.; Smith, G.; Golden, D.; Gardiner, W.; Lissanski, V. http://www.me.berkeley.edu/gri_mech/ 5. Smith, G.; Golden, D.; Frencklach, M.; Moriarty, N.; Eiteneer, B.; Goldenberg, M.; Bowman, C.; Hanson, R.; Song, S.; Gardiner, W.; Lissanski, V.; Qin, Z. GRI-Mech 3.0. http://www.me.berkeley.edu/gri_mech/ 6. Coppens, V.; De Ruyck, J.; Konnov, A., The effects of composition on burning velocity and ntiric oxide formation in lamiar premixed flames of CH4 + H2 + O2 + N2. Combustion and Flame 2007, 149, 409 - 417. 7. Konnov, A. A., Implementation of the NCN pathway of prompt-NO formation in the detailed reaction mechanism. Combustion and Flame 2009, 156, (11), 2093-2105. 8. Konnov, A.; Dyakov, I.; Knyazkov, D.; Korobeinichev, O., Formation and destruction of nitric oxide in NO doped premixed flames of C2H4, C2H6, and C3H8 at atmospheric pressure. Energy and Fuels 2010, 24, 4833 - 4840. 9. Klippenstein, S.; Harding, L.; Glarborg, P.; Miller, J., The role of NNH in NO formation and control. Combustion and Flame 2011, 158, 774 - 789. 10. Glarborg, P.; Dam-Johansen, K.; Miller, J.; Kee, R.; Coltrin, M., Modeling the thermal DeNOx process in flow reactors: Surface effects and nitrous oxide formation. International Journal of Chemical Kinetics 1994, 26, 421 - 436. 11. Glarborg, P.; Alzueta, M.; Dam-Johansen, K.; Miller, J., Kinetic modeling of hydrocarbon/nitric oxide interactions in a flow reactor. Combustion and Flame 1998, 115, 1- 27. 12. Rasmussen, C. L.; Hansen, J.; Marshall, P.; Glarborg, P., Experimental measurements and kinetic modeling of CO/H2/O2/NOx conversion at high pressure. International Journal of Chemical Kinetics 2008, 40, 454-480. 13. Tian, Z.; Li, Y.; Zhang, L.; Glarborg, P.; Qi, F., An experimental and kinetic modeling study of premixed NH3/CH4/O2/Ar flames at low pressure. Combustion and Flame 2009, 156, 1413 - 1426. 14. Lipardi, A.; Bergthorson, J.; Bourque, G., NOx emissions modeling and uncertainity from exhaust-gas dilueted flames. Journal of Engineering for Gas Turbines and Power 2016, 138, 051506-1 - 10. 15. Chemical-kinetic mechanisms for combustion applications: San Diego Mech. http://combustion.ucsd.edu 16. Metcalfe, W.; Burke, S. M.; Ahmed, S. S.; Curran, H. J., A hierarchical and comparative kinetic modeling study of C1-C2 hydrocarbon and oxygenated fuels. International Journal of Chemical Kinetics 2013, 45, 638 - 675.


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17. Daguat, P.; Mathieu, O.; Nicolle, A.; Dayman, G., Experimental study and detailed kinetic modeling of the mutual sensitization of the oxidation of nitric oxide, ethylene and ethane Combustion Science and Technology 2005, 117, 1767 - 91. 18. Dubreuil, A.; Foucher, F.; Mounai m-Rousselle, C.; Dayman, G.; Daguat, P., HCCI combustion: Effect of NO in EGR. Proceedings of the Combustion Institute 2007, 31, 2879 - 2886. 19. Moreac, G.; Daguat, P.; Roesler, J.; Cathonnet, M., Nitric oxide interactions with hydrocarbon oxidation in a jet-stirred reactor at 10 atm. Combustion and Flame 2006, 145, 512 - 520. 20. Shuman, T. NOx and Co formation in lean-premixed methane-air combusiton in a jet-stirred reactor operated at elevated pressure. University of Washington, Seattle, 2000. 21. Steele, R. NOx and N2O formation in lean-premixed jet stirred reactors operated from 1 to 7 atm. Ph.D. Thesis, University of Washington, Seattle, 1995. 22. Kang, Y.; Wang, Q.; Lu, X.; Wan, H.; Ji, X.; Wang, H.; Guo, Q.; Yan, J.; Zhou, J., Experimental and numerical study on NOx and CO emission characteristics of diemethyl ether/air jet diffusion flame. Applied Energy 2015, 149, (1), 204 - 224. 23. Ravikrishna, R.; Laurendeau, N., Laser-induced fluorescence measurements and modeling of nitric oxide in methane-air and ethane-air counterflow diffusion flames. Combustion and Flame 2000, 120, 372 - 382. 24. Chung, G.; Akih-Kumgeh, B.; Watson, G.; Bergthorson, J., NOx formation and flame velocity profiles of iso-and n-isomers of butane and butanol. Proceedings of the Combustion Institute 2013, 34, 831 - 838. 25. Watson, G.; Munzar, J.; Bergthorson, J., Diagnostics and modeling of stagnation flames for the validation of thermochemical combustion models for NOx predictions. Energy and Fuels 2013, 27, 7031 - 7043. 26. Konnov, A.; Alvarez, G.; Rybitskaya, I.; Ruyck, J., The effects of enrichment by carbon monoxide on adiabatic burning velocity and nitric oxide formation in methane flames. Combustion Science and Technology 2008, 181, 117 - 135. 27. Konnov, A.; Dyakov, I.; Ruyck, J., Nitric oxide formation in premixed flames of H2+CO+CO2 and air. Proceedings of the Combustion Institute 2002, 29, 2171 - 2177. 28. Lamoureux, N.; Merhubi, H.; Pillier, L.; Persis, S.; Desgroux, P., Modeling of NO formation in low pressure premixed flames. Combustion and Flame 2016, 163, 557 - 575. 29. Kee, R. J.; Grcar, J. F.; Smooke, M. D.; Miller, J. A. A FORTRAN Program for Modeling Steady Laminar One-dimensional Premixed Flames; SAND85-8240; Sandia National Laboratories: Livermore, 1985. 30. Bohland, T.; Temps, F., Direct determination of the rate constant for the reaction CH2 + H = CH + H2. Berichte Der Bunsen-Gesellschaft Fuer Phys Chemie 1984, 88, (459 - 461). 31. Bohland, T.; Temps, F., A direct study of the reactions of CH2(X 3B1) radicals with H and D atoms. Journal of Physical Chemistry 1987, 91, 1205 - 1209. 32. Boullart, W.; Peeters, J., Production distributions of the C2H2 + O and HCCO + H reactions. Rate constant of CH2(X 3B1) + H. Journal of Physical Chemistry 1992, 96, 9810 - 9816. 33. Devriendt, K.; Poppel, M.; Boullart, W.; Peeters, J., Kinetic investigation of the CH2(X3B1) + H → CH(X2II) + H2 reaction in the temperature range 400 K < T < 1000K. Journal of Physical Chemistry 1995, 99, 16953 - 16961.


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50. Ano, T.; Dryer, F., Effect of dimethyl ether, NOx and ethane on CH4 oxidation: High pressure, intermediate-temperature experiments and modeling. Symposium (International) on Combustion 1998, 27, 397 - 404. 51. Bromly, J.; Barnes, F.; Mandyczewsky, R.; Edwards, T.; Haynes, B., An experimental investigation of the mutually sensitised oxidation of nitric oxide and nbutane. Symposium (International) on Combustion 1992, 24, 899 - 907. 52. Stein, Y.; Held, T.; Dryer, F., Reactions studies on n-butane and oxygen system and 12.5 atmospheres and 500 - 800 K In Fall Meeting of the Western States Section of the Combustion Institute, Livermore, CA, 1992. 53. Steele, R.; Malte, P.; Nicol, D.; Kramlich, J., NOx and N2O in lean-premixed jet stirred flames. Combustion and Flame 1995, 100, 440 - 449. 54. Steele, R.; Jarrett, A.; Malte, P.; Tonouchi, J.; Nicol, D., Variables affecting NOx formation in lean premixed combustion. Journal of Engineering for Gas Turbines and Power 1997, 119, 102 - 107. 55. Bengtsson, K.; Benz, P.; Scharen, R.; Frouzakis, C., NyOx formation in lean premixed combution of methabe in high pressure jet-stirred reactor. Symposium (International) on Combustion 1998, 27, 1393 - 1399. 56. Wang, H.; You, X.; Joshi, A. V.; Davis, S. G.; Laskin, A.; Egolfopoulos, F.; Law, C. K. "USC Mech Version II. High-Temperature Combustion Reaction Model of H2/CO/C1-C4 Compounds" http://ignis.usc.edu/USC_Mech_II.htm


Computational Study of NOx Formation at Conditions Relevant to Gas

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