A Refined Global Reaction Mechanism for Gently Preheated MILD

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A refined global reaction mechanism for lowly preheated MILD combustion of methane Yaojie Tu, Wenming Yang, and Hao Liu Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b01666 • Publication Date (Web): 09 Aug 2017 Downloaded from http://pubs.acs.org on August 13, 2017

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A refined global reaction mechanism for lowly preheated MILD combustion of methane

Yaojie Tu 1,2, Wenming Yang* 1,2, Hao Liu** 3 1

Sembcorp-NUS Corporate Laboratory, National University of Singapore, Singapore 117576,

Singapore 2

Department of Mechanical Engineering, Faculty of Engineering, National University of

Singapore, Singapore 117576, Singapore 3

State Key Laboratory of Coal Combustion, School of Energy and Power Engineering,

Huazhong University of Science and Technology, Wuhan 430074, PR China

Abstract Moderate or intense low-oxygen dilution (MILD) combustion is a promising technology for simultaneously reducing NOX emission and improving thermal efficiency. To apply MILD combustion in industrial production, computational fluid dynamics (CFD) modeling plays an efficient role in design and optimization. To reduce the computational time while maintaining the predictive accuracy, a valid global reaction mechanism (GRM) is necessary. In this paper, an accurate and robust GRM was proposed for methane under MILD combustion conditions, especially without the condition of highly preheated air. The adequacy of the proposed GRM was firstly compared with experimental data from a bench-scale MILD combustion furnace, where ambient temperature air was used. Subsequently, experimental data from an industrial-scale MILD combustion furnace, where air was slightly preheated to 130 °C, was employed to further validate its capability for practical application. As compared to the previous GRMs, the present proposed GRM exhibits improved predictive accuracy in terms of flame temperature, oxygen concentration and carbon monoxide concentration, for both bench-scale and industrial-scale MILD combustion cases. With growing interest being focused on MILD combustion using low temperature or even ambient temperature air, the present proposed GRM is expected to be adopted by CFD users to design and optimize MILD combustion processes with reliable results and less computational time. 1

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Keywords: MILD combustion; methane combustion; global reaction mechanism; CFD modeling; low temperature air

1. Introduction The direct combustion of natural gas serves as an important energy source in electricity generation, transportation and industrial heating in many countries. In order to provide a solution to use natural gas more efficiently and environmentally friendly, moderate or intense low-oxygen dilution (MILD) combustion has attracted lots of attention recently, owing to its potential in reducing NOX emission while maintaining high thermal efficiency during combustion 1. To achieve MILD combustion, the essential condition is to establish a strong flue gas recirculation inside the furnace. Through the flue gas recirculation, combustion takes place in a low oxygen condition where fuel oxidation rate is suppressed and the reaction zone is enlarged 2. Consequently, peak combustion temperature can be reduced, which subsequently leads to the inhibition of thermal NOX formation. More importantly, the recirculated flue gas improves the ignition of fuel under such diluted atmosphere, and the enhanced internal residence time by flue gas recirculation has a positive effect on the fuel burnout, as long as a reasonable flow dynamics is formed 3. Under the MILD combustion regime, the heat transfer behavior is also different from that under conventional combustion, since uniform heat flux together with improved net heat transfer amount on furnace walls have been reported 4, 5. According to the description in the above paragraph, it can be known that MILD combustion is very different from conventional combustion. The essential reason behind this is from the different flow structures inside the combustion furnaces between the two combustion modes. Specifically, the establishing of MILD combustion requires strong internal flue gas recirculation, which plays a predominant role on both diluting and preheating the fresh reactants. It has been indicated that the MILD combustion behaviors can be affected significantly by burner configurations

6-9

. Furthermore, Li et al.

10

has suggested a critical momentum ratio

between initial air and fuel below which MILD combustion regime cannot be established. Therefore, when adopting the MILD combustion technology, the conventional combustion burner needs to be replaced with or retrofitted into a MILD combustion burner. Sometimes it is also suggested to change the furnace geometry to obtain an even better performance 11. To this end, computational fluid dynamics (CFD) modeling is considered as an efficient tool for predicting and designing purposes, superior to experimental approaches in acquiring the detailed flow and combustion information inside the furnace. However, the reliability of CFD modeling results

2


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is highly dependent on the choice of combustion model and reaction mechanism. It has been reported that the “mixed-is-burned” model is not suitable to describe the MILD combustion process due to slower reaction rate. Instead, the finite-rate combustion model, such as eddy-dispassion-concept (EDC) model, needs to be taken into consideration 12, 13. For reaction mechanisms, the detailed chemistry schemes are accurate; however, they require much more computational cost than simplified schemes. Especially, for industrial combustion furnaces, the CFD modeling with detailed reaction mechanisms (DRMs) could be a very time-expensive task. Therefore, under these situations, interest is always focused on the global combustion characteristics, such as heat transfer (related to gas temperature, flow rate and flow velocity, etc.), fuel burnout (represented by CO emission and relevant to O2 distribution) and NO emission. These global combustion characteristics can be acquired even with the aid of simplified chemistry, such as a global reaction mechanism (GRM). To this end, a plethora of work has been carried out to obtain simplified reaction mechanisms for methane oxidation under different operating conditions 14-21. So far, there are two most widely received GRMs for methane oxidation, namely the Westbrook and Dryer (WD) scheme

15

and the Jones and Lindstedt (JL) scheme

14

. However, both of them were proposed for

methane/air mixture under conventional combustion. Based on these works, several studies were performed to provide optimized kinetic parameters for methane combustion under specific operating conditions. For example, Anderson et al.

22

modified the kinetic constants of both WD and JL mechanisms to implement oxy-fuel

combustion. They found the optimized WD scheme could improve the predictions of gas temperature and CO concentration in the post flame zone, while the optimized JL scheme predicted a better CO presence in the flame zone, compared to the original schemes. On modeling the Sandia Flame D, Kim et al.

19

compared the

performances of several GRMs originated from JL scheme in terms of temperature, CO and H2 distributions. The optimum one was then applied to model an industrial-scale furnace firing under MILD combustion mode with oxidant temperature of 1573 K. Even though general agreement was obtained between the predictions and measurements, the CO was still found overestimated in the upper furnace while underestimated in the lower furnace. Later, Wang et al.

20

compared the feasibilities of different GRMs derived from both WD and JL

schemes on simulating a jet flame in a hot and diluted coflow, which was previously developed by Dally et al. 23 to emulate MILD combustion regime. The fuel consisted of methane and hydrogen in equal volume fraction was issued into a coflow with a temperature of 1300 K. All of the considered GRMs were tested against the experimental measurements as well as numerical results from DRM. They found the WD-based GRMs were able to predict a better methane concentration over the JL-based GRMs, and they further modified the kinetic

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constants of WD mechanism for modeling methane combustion in hot and diluted oxidation conditions. It is worth noting that, in coal MILD combustion, Saha et al.

24

adopted the refined WD GRM of Wang et al.

20

to

describe the oxidation of volatiles. In this regard, the above GRMs for methane in some degree can also be translated to other similar hydrocarbon fuels. MILD combustion used to be applied in reheating furnaces with highly preheated air (typically higher than 1000 °C)

25

. This condition limits the further application of MILD combustion in other fields

26

. Nowadays, it

has been well established that high temperature air preheating is not the essential condition for achieving MILD combustion

27-30

. This motivates more and more interest to focus on the MILD combustion characteristics with

lowly preheated or even non-preheated air. However, it has been indicated that lower air preheating temperature tends to generate a reduced peak combustion temperature as well as a retarded main reaction zone inside a MILD combustion furnace

31

. More importantly, considering the ignition point of gaseous fuels, which are

typically in the range of 400 K to 1000 K, reducing the air preheating temperature can have a negative influence on the fuel ignition stability, thus affecting the CO formation and heat release. On the other hand, according to Ye et al. 32, under MILD combustion conditions, the ignition delay may need to be controlled to avoid premature ignition. If the ignition delay is too short, there may be insufficient mixing with the exhaust products, which can also have a negative effect on stability. Therefore, it is necessary to provide a valid GRM for CFD modeling of such circumstances. However, up to now, the studies on low temperature air assisted MILD combustion are still insufficient, not to mention the optimization of valid GRM for CFD modeling of such process. To address the above-mentioned problems, in this paper, experimental and CFD numerical work have been carried out in both bench-scale and industrial-scale furnaces, respectively. The objective of the present work is to propose a valid and robust GRM for methane under MILD combustion without highly preheated air. To help the readers better understand the present work, a summary of the subsequent structure of the paper is given as follows. In section 2.1, the experimental facilities are firstly presented for both bench-scale and industrial-scale MILD combustion furnaces, and then in section 2.2 the simulation method is introduced. In section 3, the detailed modeling results are compared with experimental data from the two experiments. Specifically, in section 3.1, the adequacy of previous GRMs is examined based on the bench-scale experimental case; in section 3.2, the GRM which performs the best in section 3.1 is selected for further optimization, and the optimization process is presented; in section 3.3, the validity of the final optimized GRM is examined with experimental data collected from the industrial-scale MILD combustion furnace. With these demonstrations, conclusions are then drawn in section 4.

4


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2. Experimental and numerical details 2.1 Experimental facilities 2.1.1 Bench-scale MILD combustion furnace A 20 kW bench-scale MILD combustion furnace (BS-MCF) has been established in State Key Laboratory of Coal Combustion (SKLCC), Huazhong University of Science and Technology (HUST) to study the formation mechanisms of CO and NO under methane MILD combustion without air preheating

28, 33

. Figure 1 shows the

schematic diagrams of the furnace and the burner. The BS-MCF is similar to the one built in the University of Adelaide 31, which has the inlet and outlet placed on the same plane. However, it has a smaller cross sectional size (250 × 250 mm) and total height (550 mm). Measuring ports are distributed every 25 mm from the sidewall to the centerline at five axial locations, i.e. x = 135 mm, 225 mm, 315 mm, 405 mm and 495 mm, to detect gas temperature and major species concentrations (O2, CO and NO). Four exhausted gas outlet are located evenly on the furnace top wall. The burner is consisted of a central fuel nozzle, a coaxial air nozzle and a bluff body between the fuel and air, as indicated in Figure 1c. Table 1 summarizes the major operational conditions of the experiment (see the BS-MCF column). During the experiment, the furnace was operated at a fixed fuel input of 9.5 kW. Ambient temperature air and fuel were delivered with the excess air ratio being 1.25. At the burner exit, the velocities of fuel and air were 9.32 m/s and 22.38 m/s, respectively. R type (Pt-Pt-13% Rh) thermocouples were used to measure the gas temperature, whose uncertainty is 0.25%. While for gas species measurement (O2, CO and NO), Kane 9106 flue gas analyzer was employed. The accuracies and range of this analyzer for various species are as follows: O2 (±0.1%, 0-25%), CO (±20 ppm, 0-400 ppm; ±5%, 401-2000 ppm; ±10%, 2000-10000 ppm) and NO (±5ppm, 0-100 ppm; ±5%, 100-5000 ppm). During the experiment, the normalized spatial temperature variation was reported to be 8.4% 28, which is lower than the critical value of 15% for judging the occurrence of MILD combustion regime provided by Kumar et al. 34. On the other hand, after switching from conventional firing mode, there was no visible flames inside the whole furnace and the CO and NO emissions at the exit were quite low. Therefore, it is believed MILD combustion has been established in this furnace. 2.1.2 Industrial-scale MILD combustion furnace Experiment has also been performed in a 1 MW industrial-scale MILD combustion furnace (IS-MCF), which was used to investigate the feasibility of using low temperature air (130 °C) for MILD combustion in large-scale furnaces 30. According to the schematic diagram of the IS-MCF shown in Figure 2, the furnace has a

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dimension of 4000 × 1800 × 1500 mm in length, width and height, respectively. The burner is consisted of a central partially swirled fuel nozzle and two laterally distributed air nozzles. Four measuring ports are designed on the furnace sidewalls for measuring gas temperature, O2, CO and NO volume fractions. Furthermore, on the back wall of the furnace a digital camera was used to record the flame images. Table 1 summarizes the major operational conditions of the experiment (see the IS-MCF column). The burner was operated at a fuel input of 300 kW, and the overall excess air ratio was maintained at 1.15. After the combustion became steady, gas temperature and compositions were detected with K type thermocouples (Ni-Cr/Ni-Al) and TESTO 360 flue gas analyzer. The uncertainty K type thermocouple is 0.75%, and the uncertainty as well as range of species measurements are estimated to be: O2 (±1.2%, 0-21%), CO (±2%, 0-10000 ppm) and NO (±2.8%, 0-3000 ppm). According to the experimental measurements, maximum temperature and NO emission were reduced remarkably and the flame turned invisible when the burner was switched from conventional mode to MILD mode. In addition, CO emission was found as low as that measured under conventional firing mode. In our previous study30, the flow field information was obtained by CFD modeling, and the maximum recirculation ratio was found close to 4, which is higher than the critical value of 2.5 for methane MILD combustion provided by Wünning et al. 3. Based on these observations, the MILD combustion regime was considered to be realized in this larger facility.

2.2 Numerical details 2.2.1 Numerical models The commercial software ANSYS FLUENT 16 was adopted as CFD modeling tool

35

. The steady

Reynolds-averaged Navier-Stokes (RANS) equations were calculated together with chemical reactions. SIMPLE algorithm was selected for coupling velocity and pressure calculation. The eddy dissipation concept (EDC) combustion model, which has already been proved adequate for modeling MILD combustion, was adopted to solve the chemical reactions

36, 37

. The in situ adaptive tabulation (ISAT) method was employed to

accelerate the convergence of CFD simulation 38. To improve the prediction accuracy of radiation heat transfer, the discrete ordinate (DO) radiation model was chosen instead of P1 model. The weighed sum of gray gas (WSGG) model was taken to calculate the total gas phase emissivity coefficient as a function of temperature and concentration 39. However, the turbulent models were different between the two MCFs. As for the BS-MCF, the standard k−ε turbulent model was selected to simulate the flow dynamics. To improve the prediction accuracy

of round jets, the constant Cε1 of the standard k−ε turbulent model was modified from 1.44 to 1.6 considering 6


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the round jets 40, 41. While for the IS- MCF, to account for the swirling effects of the fuel stream, realizable k−ε

turbulent model was selected instead of standard k−ε turbulent model. 2.2.2 Reaction mechanisms To gain a better insight into the combustion details, CFD modeling was conducted preliminarily with DRM for the BS-MCF. The predicted results together with experimental measurements will be used for validating the performances of different GRMs. Specifically, the well-known DRM of GRI-Mech 3.0 was coupled in FLUENT as Chemkin format, which contains 54 species and 325 elementary reactions 42. The first considered GRM for methane is the original WD (ORWD) mechanism, which was developed by Westbrook and Dryer 15. It is consisted of three irreversible reactions listed below. ‫ܪܥ‬ସ + 1.5ܱଶ → ‫ ܱܥ‬+ 2‫ܪ‬ଶ ܱ

(R1)

‫ ܱܥ‬+ 0.5ܱଶ → ‫ܱܥ‬ଶ

(R2)

‫ܱܥ‬ଶ → ‫ ܱܥ‬+ 0.5ܱଶ

(R3)

The ORWD mechanism assumes methane is firstly oxidized into CO, and then CO is further oxidized into CO2. To properly reproduce the heat reaction and [CO]/[CO2] equilibrium, a reaction of CO2 dissociation is considered. Moreover, this multistep mechanism is directly available for users in the FLUENT code for modeling natural gas and air mixture combustion. The second considered GRM for methane is the one modified by Wang et al. herein referred to as WRWD for short. It

20

based on ORWD, and is

consists of the same reactions as ORWD. However, the kinetic

parameters of each single reaction are altered in order to improve the modeling accuracy of methane oxidation under hot and diluted conditions, namely high temperature MILD combustion regime. The third considered GRM for methane is the original JL (ORJL) mechanism, which was proposed by Jones and Lindstedt 14, for modeling premixed and non-premixed flames. The JL reactions are listed below. ‫ܪܥ‬ସ + 0.5ܱଶ → ‫ ܱܥ‬+ 2‫ܪ‬ଶ

(R4)

‫ܪܥ‬ସ + ‫ܪ‬ଶ ܱ → ‫ ܱܥ‬+ 3‫ܪ‬ଶ

(R5)

‫ ܱܥ‬+ ‫ܪ‬ଶ ܱ → ‫ܱܥ‬ଶ + ‫ܪ‬ଶ

(R6)

‫ܱܥ‬ଶ + ‫ܪ‬ଶ → ‫ ܱܥ‬+ ‫ܪ‬ଶ ܱ

(R7)

‫ܪ‬ଶ + 0.5ܱଶ → ‫ܪ‬ଶ ܱ

(R8)

‫ܪ‬ଶ ܱ → ‫ܪ‬ଶ + 0.5ܱଶ

(R9)

The ORJL scheme contains two methane oxidation reactions with O2 and H2O, respectively, namely R4

7


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and R5, generating the only two products of CO and H2. Subsequently, CO and H2 are further oxidized with H2O and O2 through R6 and R8, respectively. To better control the reaction rates of CO and H2, the reverse water shift reaction and H2O decomposition are also included as R7 and R9. The reaction rates of R7 and R9 are calculated by using the equilibrium constants, which can be obtained by calculating the standard-state Gibbs free energy 43. The fourth considered GRM for methane is the one modified by Kim et al.

19

based on the ORJL scheme,

which was proposed to model methane MILD combustion with high temperature air. It has the same reactions as ORJL while the kinetic parameters are varied. In the present work, this mechanism is herein referred to as KRJL for short. Table 2 lists the detailed kinetic parameters for the above-mentioned four GRMs. These four schemes were considered with a preliminary aim to evaluate the validity of the existing GRMs on modeling methane MILD combustion without highly preheated air. 2.2.3 Computational grids Owing to the symmetrical geometry, the computational domain of the BS-MCF can be reduced from three-dimensions to two-dimensions. By comparing the modeling results of the experimental case, the differences between the three-dimensional and two-dimensional geometries were reported to be less than 10% and acceptable 28, 33. Therefore, in this work, the BS-MCF was modeled by a two-dimensional geometry to save the computational time when implementing DRM. Note that, in order to maintain the same cross-sectional areas of the modeled MCF and the real one, the diameter of the two-dimensional cylindrical furnace was increased from 125 mm to 141 mm. The grid independence was examined using four various grids with cell numbers of 10,000, 20,000, 30,000 and 40,000. All the grids were composed of fully structured cells with enhanced density in the vicinity of the burner region. When the cell number is larger than 10,000, the discrepancies of predicted maximum temperature and CO mole concentration are within 1K and 0.01%, respectively. However, the computational time increases linearly with the cell number. Therefore, to save the computational cost while maintaining sufficient predictive accuracy, a fine grid consisted of about 20,000 cells was adopted for modeling, as shown in Figure 3. As for the IS-MCF, three-dimensional geometry was considered due to the relatively complex configuration of burner. To enhance the converging stability, the main-combustion chamber was meshed with fully structured cells. Several size functions have been applied in the burner and combustion regions to control the cell size increment. Unstructured cells were used for the part between the burner and pre-combustion

8


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chamber. This resulted in a slightly increased calculation instability, thus smaller relaxation factors were considered during calculation. Figure 4 displays the meshed grid for the IS-MCF. It consists of about 800,000 cells and has successfully been used for modeling previously 30.

3. Results and discussion 3.1 Performance of different existing GRMs on modeling bench-scale experiment In the bench-scale experiment, gas temperature and major species volume fractions (O2 and CO) were measured at five axial locations. Figure 5 compares the experimental measurements and calculated profiles for gas temperature, O2 volume fraction and CO fraction. As for the DRM, except for the over-predicted CO concentration in the central furnace at x = 315 mm and 405 mm, it gives generally reasonable agreement for gas temperature and species concentration predictions with comparison to the measured data. However, for the two original schemes, ORWD mechanism produces a higher temperature outside the central jet in the upstream furnace, while a lower temperature in the downstream central region. Simultaneously, ORWD mechanism shows a relatively higher O2 concentration at the lower end of the furnace. As for ORJL mechanism, it predicts a larger temperature value in the central part of the furnace (225 mm ~ 405 mm), and underestimates the O2 level than other cases. After applying the previously modified schemes, the modeling accuracy seems to be improved. In particular, WRWD mechanism obtains a much better prediction on both temperature and O2 as compared to ORWD. More distinctively, the CO profiles resemble those predicted by DRM in the upper four measuring locations. As for the KRJL mechanism, it ameliorates the O2 prediction as compared to ORJL, but the improvement in temperature and CO are not obvious. To quantify the differences of the simulation results between various GRMs, the relative error (RE) is extracted for temperature, O2 and CO at all the measuring points in Figure 6. Herein, RE is defined as: RE = ቚ

௏಴ಷವ ି௏ಶ೉ು ௏ಶ೉ು

ቚ

(1)

where ܸ஼ி஽ and ܸா௑௉ are the predicted value by CFD modeling and measured value in experiment, respectively. As can be observed from Figure 6, for temperature, the errors are existed mainly in the furnace center; while for species, the largest errors are shifting outward to the furnace sidewalls. In specific, the RE for temperature is most pronounced with the two JL schemes at the upper four measuring locations. Moreover, the two JL schemes together with the ORWD mechanism present a higher RE for O2 as compared to DRM and WRWD mechanism, especially at the upper four measuring locations. For CO prediction, larger REs are noticed at r = 0 mm, 100 mm and 125 mm for all five axial measuring locations. This is because there is very little CO 9


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detected in the central and outer furnace regions, so that the denominator in Eq. (1) is relatively small. However, at r = 25 mm, 50 mm and 75 mm, the DRM shows a generally smaller RE than all the GRMs in predicting CO. This is because CO consumption under MILD combustion regime is mainly controlled by the elementary reaction of OH + CO → H + CO2 33. In other words, the accurate prediction of CO is highly dependent on the radical pools. Hence, it would be a challenge for the GRMs to generate good CO prediction since OH free radical is not included. For this reason, the justification of the different GRMs will be more emphasized by comparing temperature and O2. By jointly considering the REs of temperature and O2 at different locations in Figure 6, the WRWD mechanism is found to produce generally smaller REs for both temperature and O2 than other three GRMs in the BS-MCF. Especially at the upper four measuring locations, the WRWD mechanism even exhibits very similar predictive accuracy with the DRM. To further understand the performance of the four existing GRMs, Figure 7 displays the calculated spatial distributions of gas temperature, O2 volume fraction and CO volume fraction by both DRM and different GRMs. For DRM, the high temperature zone or main reaction zone is located in the central downstream furnace. Consequently, a small low O2 region can be recognized in the same place. Besides, the CO shows a typical “/\” shape of non-premixed diffusion flames indicating the strong reaction layer. While for GRMs, ORWD mechanism shifts the high temperature zone to the side regions in the upstream furnace, and leads to much higher O2 in the lower part of the furnace. However, CO can hardly be observed inside the whole furnace, which is caused by the slower CO formation rate and faster CO destruction rate. When replaced by WRWD mechanism, predictions of both temperature and O2 concentration are improved. However, it results in a “π” shaped CO profile with largely over-predicted peak value. In the JL schemes, both ORJL and KRJL mechanisms predict an enlarged high temperature zone in the downstream furnace, which is believed to be resulted from faster fuel consumption rate according to the significantly lower presences of O2 and CO. It is also important to see the differences in predicting the fuel consumption among the considered GRMs. For this purpose, Figure 8 plots the radial CH4 mole fraction profiles at the five measuring locations with different GRMs. Since no experimental data is available for comparison, the predicted results by DRM are then extracted for reference. As can be observed, near the burner exit, namely x = 135 mm, all the GRMs show a similar CH4 prediction as the DRM does. However, in the subsequent locations, differences can be noticed between WD and JL schemes. In specific, the two WD schemes show a slower consumption rate of CH4 in the central region of the furnace, while the two JL schemes behave much faster in consuming CH4. More noticeably, the ORWD mechanism has the lowest CH4 consumption rate that the CH4 mole fraction is still found as high as

10


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3% at the end of the furnace. Based on the above comparisons, it can be deduced that the original GRMs, neither WD nor JL scheme, can be continued to be used for the present application conditions, which are under MILD combustion with ambient temperature air. After applying the modified schemes provided by previous researchers

19, 20

, the

modeling accuracy can be improved. Between these two GRMs, the improvement is more pronounced for the WD scheme, where the WRWD mechanism yields a better prediction of not only temperature but also O2 distribution. In spite of the high CO RE in the downstream furnace, the WRWD mechanism produces generally similar CO distributions as DRM does, and can be regarded as the optimum one among the considered four existing GRMs. However, it is worth noting that, there still exists a large potential for further refining the WRWD mechanism towards more accurate modeling results, especially for CO formation and oxidation in the downstream furnace. Therefore, in the subsequent section, efforts will be dedicated to refining the WRWD mechanism by adjusting the kinetic parameters of the reactions. Since the major modeling discrepancy between WRWD and DRM lies in CO formation, the optimization of WRWD mechanism will be performed based on comparisons of CO distributions, in terms of its peak value and its peak location. Specifically, the targets of the optimization should be at: 1) reducing CO peak value in the downstream furnace; 2) making CO peak location shifting towards the furnace center.

3.2 Proposal of refined GRM for bench-scale experimental case For a certain chemical reaction, its reaction rate constant can be calculated by Arrhenius’ law, as expressed below 44. ݇ = ‫ܣ‬eିாೌ/(ோ்)

(2)

where ݇ is the rate constant, ‫ ܣ‬is the pre-exponential factor, ‫ܧ‬௔ is the activation energy for the reaction, ܶ is the absolute temperature, and ܴ is the universal gas constant. In modeling chemical reactions, ‫ ܣ‬and ‫ܧ‬௔ are the two most important parameters that are needed. In particular, ‫ ܣ‬denotes the collision frequency in the correct orientation, while ‫ܧ‬௔ defines the minimum energy required to start the reaction. Recently, Evans et al. 45

proposed a new method for defining the non-premixed MILD combustion regime as a function of initial and

final temperatures, and the effective activation energy (‫ܧ‬௘௙௙ ) of chemical reaction. The method distinguished MILD combustion regime from autoignitive flames. However, the ‫ܧ‬௘௙௙ required for this method is based on equivalent one-step reaction. Westbrook and Dryer have reported that the variation of ‫ܧ‬௔ in a reasonable range only affected the computed flame thickness 15.Considering the absence of experimental flame thickness data, in this study the values of ‫ܧ‬௔ are not altered, and modification is only made to ‫ܣ‬. 11


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Figure 9 presents the predicted CO profiles at different measuring locations by modifying ‫ ܣ‬of R1 ( ‫ܪܥ‬ସ + 1.5ܱଶ → ‫ ܱܥ‬+ 2‫ܪ‬ଶ ܱ ), R2 ( ‫ ܱܥ‬+ 0.5ܱଶ → ‫ܱܥ‬ଶ ) and R3 ( ‫ܱܥ‬ଶ → ‫ ܱܥ‬+ 0.5ܱଶ ), respectively. For comparison, the experimental data is also shown. As can be observed, when increasing ‫ ܣ‬of R1, CO peak value is promoted with its peak location moving towards the furnace center gradually. This is caused by the enhanced oxidation of CH4 into CO. When increasing ‫ ܣ‬of R2, CO peak value is found reduced, and the CO peak location shifts towards the furnace center. This is because of the accelerated CO oxidation rate which facilities the CO consumption in both central fuel jet and outer region. It is interesting to notice that changing ‫ ܣ‬of R3 seems to have no significant impact on either CO peak value or peak location at all measuring locations. Even when R3 is removed from the mechanism, difference can be hardly observed. According to the CO variations with changes of R1 to R3, it can be deduced that, ‫ ܣ‬value of R1 and R2 should be increased to improve the CO formation in the upstream and CO consumption in the downstream furnace, respectively. This would necessarily make the CO peak location shift towards furnace center. From Figure 9, it can be noticed that when ‫ ܣ‬of R2 is modified from 2.24 × 106 to 2.24 × 107, CO prediction agrees with the measured data in the downstream furnace very well. Therefore, refinement of R1 is subsequently carried out under the condition that ‫ ܣ‬of R2 equals to 2.24 × 107. After a series of CFD modeling by adjusting ‫ ܣ‬of R1, it appears that the prediction of CO can be significantly improved than that with WRWD mechanism when ‫ ܣ‬of R1 is modified to 1.25 × 1012. In Table 3, the detailed reaction kinetic parameters are shown for the refined GRM. The short term of PRWD is used for the present refined WD mechanism. Figure 10 shows the comparisons of gas temperature and major species concentrations between experimental measurements and numerical predictions respectively with DRM, WRWD mechanism and PRWD mechanism. With comparison to WRWD mechanism, the PRWD mechanism improves the temperature predictions in the downstream of the furnace, and results in a more similar temperature profiles with the DRM at the last two axial locations. For oxygen profiles, the PRWD mechanism shows a distinctive improvement in the central furnace at x = 495 mm. Consequently, the largely overestimated CO by WRWD mechanism is found reduced and agreeing with the measured data well. Moreover, with the PRWD mechanism, the peak CO values at different axial locations decrease, simultaneously, the location of peak CO moves inward. These two changes fulfill the two targets of the optimization process. In all, the PRWD mechanism demonstrates a higher capability to refine the modeling results in terms of both gas temperature and species than the WRWD mechanism. In addition, when referring to the DRM, it can be noticed that the PRWD mechanism brings quite similar results, especially in the lower part of the furnace.

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According to Figure 9, R3 seems unimportant for CO formation in the frame of WD mechanism. However, the influences of R3 on gas temperature and O2 distributions are not known. For this reason, in Figure 10, the results obtained by PRWD mechanism without R3 are also plotted. It shows that the modeling results are basically the same no matter whether R3 is contained or not in the PRWD mechanism. This is because R3, which represents the decomposition of CO2, takes place under the condition of high temperature. While in most of the furnace, the gas temperature is lower than 1700 K, thus the contribution of R3 to CO formation is minimized. Therefore, it indicates that R3 can be neglected in the PRWD mechanism for modeling the present BS-MCF with ambient temperature air. Figure 11 further compares the spatial distributions of gas temperature and major species calculated by DRM, WRWD mechanism and PRWD mechanism (with R3), respectively. It can be seen that PRWD mechanism behaves very similarly to the DRM. Specifically, with PRWD mechanism, the local high temperature and low O2 region in the central lower furnace is captured. More distinctively, the “/\” shape of the CO special distributions is well reproduced by PRWD mechanism with comparison to DRM. For global reaction mechanism, another challenge is to predict the combustion behaviors near the burner region. In the WD scheme, CO is regarded as the only intermediate species for CH4 oxidation, thus it would be significant to examine the CO formation in the vicinity of the burner. In Figure 10, even though the CO peak value is under-predicted by the PRWD mechanism at x = 135 mm, the radial location of the CO peak is much closer to that predicted by DRM. Therefore, the PRWD mechanism is likely to generate a more accurate reaction zone than the WRWD mechanism. On the other hand, according to Figure 10, the PRWD mechanism exhibits a lower CO distribution throughout the whole furnace as compared to the WRWD mechanism, which indicates the larger CO consumption rate of PRWD mechanism. In this regard, the PRWD mechanism would generate a generally smaller reaction timescale than the WRWD mechanism. In other words, the predominant role of chemical kinetics on the MILD combustion regime will be over-predicted by the WRWD mechanism.

3.3 Performances of refined GRM on modeling industrial-scale experimental case In order to extend the application and test the robustness of PRWD mechanism proposed in the above section, CFD numerical simulation of natural gas MILD combustion in the IS-MCF (see Figure 2) with low temperature air has been conducted. In Figure 12, comparisons are made between experimental measurements and numerical predictions with WRWD mechanism and PRWD mechanism (with R3), in terms of radial gas temperature, O2 volume fraction and CO volume fraction. In general, WRWD mechanism produces a quite similar prediction as PRWD mechanism does on temperature and O2 volume fraction, while an obvious

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difference can be noticed in the CO predictions. Specifically, PRWD mechanism succeeds in reproducing the CO peak value and location close to experiment in the upstream furnace, however WRWD mechanism generates a bimodal CO profile with significantly over-predicted CO peak value at the upper measuring location (x = 900 mm). The large amount of CO mainly results from the lack of O2 in the central region, and it reflects the slower heat release rate which then leads to the lower temperature peak in the upstream furnace as compared to the case with PRWD mechanism. While in the downstream furnace, the two profiles baisically overlap with each other with overall agreement to the experimental measurement. It should be noted herein that, the MILD combustion burner used in the IS-MCF is a an unique design which has a partially swirling fuel nozzle located in the center of the pre-combustion chamber and two inclined air pipes inserted on the front wall of the main-combustion chamber 30. Both the swirling effect of fuel and the inclined injecting of air can make the mixing of air and fuel take place earlier with comparison to the case with parallel burner inlets. According to the temperature profiles in Figure 12a (x = 900 mm), the experimental measurement indictaes a high uniformity of temperature distribution in the upstream furnace, while the numerical prediction with with WRWD or PRWD mechanism shows noticable temperature peak and velleys in the central region of the furnace. There are several possible reasons for this discrepancy: 1) The burner used is very complex in geometry, which is able to cuase strong swirling and reversing flow in the region near the burner exit, where is also the location of the upper measuring point. Therefore, it would be also a challenge to accurately reproduce the flow structure with current turbulent models. Even though the realizable k−ε turbulent model has been adopted to account for the swirling and reversing flow inside the furnace, its capability for such complicated flow dynamics still needs to be further verified. 2) For the present experiment, ambient temperature air is used. However, there is no thermal shielding measures being taken for the thermocouples, hence the measured temperature peaks can be significantly reduced due to heat dispassion from convection and radiation heat transfer with surrounding cold flow. 3) The K type thermocouples used in the industrial scale furnace have a larger uncertainty than the R type thermocouples used in the bench scale furnace. When gas temperature exceeds 1400 K, the errors in temperature measurement will increase significantly. In order to have a deeper insight into the modeling differences between the two GRMs, Figure 13 and Figure 14 display the spatial distributions of velocity, gas temperature, O2 volume fraction and CO volume fraction on the central horizontal palne (y = 0 mm) and vertical plane (z = 0 mm). Dispite the differences in

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kinetic parameters between the two GRMs, the distributions of velocity, gas temperature and oxygen concentration on both horizontal and vertical planes are found very similar. Whereas obvious differences are noticed for CO distribution on either vertical or horizental plane. In specific, CO formation is found remarkably enhanced in the upstream furnace by WRWD mechanism, in both pre-combustion chamber and main-combustion chamber. This subsequently results in a retarded CO consumption, and in turn it produces a slightly longer high temperature region (see Figure 13b) and low oxygen region (see Figure 13c) in the axial direction. Note that, there are some distinctive differences in geometrial configuration between the BS-MCF and IS-MCF. In specific, the reactants are injected through straight pipes in the BS-MCF, while the fuel is injected through partially swirling nozzles in the IS-MCF. Moreover, the secondary air pipes are installed with an inclined angle of 5 degrees to the axial centerline in the IS-MCF. As a result, the fuel consumption happens in the more downward location inside the BS-MCF as compared to the IS-MCF. In turn, CO formation is overestimated by WRWD mechanism in the downstream of the BS-MCF, while in the upstream of the IS-MCF. According to the spatial distributions of gas temperature and major species in Figure 13 and Figure 14, respectively, differences derived by the two reaction mechanisms of WRWD and PRWD mainly lie in CO formation, which has been also revealed in Figure 10. Therefore, accurate prediction of the CO concentration would be the key cretiria on selecting the mechanisms. As already shown in Figure 12, PRWD mechanism greatly improves the CO prediction, in both peak value and its radial location. This indicates that PRWD mechanism can be further extended for application in IS-MCF under circumstances of low air preheating.

4. Conclusions Due to the different CO formation mechanisms, the previous GRMs for modeling methane high temperature air MILD combustion cannot be further adopted under the condition of low temperature air MILD combustion. As compared to JL scheme, WD scheme shows a higher potential for better prediction. The major differences among the GRMs lie in the prediction of CO distribution, in terms of its peak value and the location of its peak value. When modeling low temperature air assisted MILD combustion, the reaction rates of R1 (‫ܪܥ‬ସ + 1.5ܱଶ → ‫ ܱܥ‬+ 2‫ܪ‬ଶ ܱ) and R2 (‫ ܱܥ‬+ 0.5ܱଶ → ‫ܱܥ‬ଶ ) both need to be improved to accurately reproduce the gas temperature, O2 and CO distributions. However, R3 (‫ܱܥ‬ଶ → ‫ ܱܥ‬+ 0.5ܱଶ ) was found to have a minor impact on the global combustion behaviors, and can be neglected due to lower combustion temperature under MILD combustion condition. As compared to the previously existing GRMs, the present proposed GRM remarkably improves the

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predictions of gas temperature and major species concentrations, not only in bench-scale furnace, but also in industrial-scale furnace. Considering the fact that more and more research interest has been shifted to low temperature air assisted MILD combustion, it is highly expected that the present proposed GRM can be adopted for modeling such process by CFD users in the future.

Acknowledgements The authors acknowledge the financial support by the National Research Foundation of Singapore, Sembcorp Industries Ltd and National University of Singapore under the Sembcorp-NUS Corporate Laboratory.

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Figures

Figure 1. Schematic diagrams of bench-scale MILD combustion experimental facility 28.

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Figure 2. Schematic diagrams of industrial-scale MILD combustion experimental facility 30.

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Figure 3. Two-dimensional mesh of the bench-scale MILD combustion furnace (BS-MCF): (a) overall domain, (b) burner region.

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(a) Overall furnace

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Figure 4. Three-dimensional mesh of the industrial-scale MILD combustion furnace (IS-MCF).

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Figure 5. Comparisons of (top) gas temperature, (middle) dry oxygen volume fraction and (bottom) dry carbon monoxide volume fraction between experimental measurements and numerical predictions by different reaction mechanisms in BS-MCF.

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125

16

8

4 25

WRWD

12 8 4

0

25

r (mm)

ORWD

20

12

4 0

x=405mm

x=405mm

CH4 (%)

x=135mm 16

CH4 (%)

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

Energy & Fuels

50

75

100

r (mm)

ORJL

125

0

0

25

50

KRJL

Figure 8. CH4 profiles at five measuring locations from CFD simulations by different reaction mechanisms in BS-MCF.

25


75

r (mm)

100

125

Energy & Fuels

x=225mm

x=405mm

x=495mm 15.0k

12.0k

12.0k

12.0k

12.0k

12.0k

9.0k 6.0k

9.0k 6.0k

9.0k 6.0k

9.0k 6.0k

3.0k

3.0k

3.0k

3.0k

0.0

0.0

0.0

0.0

0

25

50

75

100

125

0

25

50

75

100

125

0

25

r (mm)

EXP

75

100

125

0

x=225mm

6.0k

25

50

75

100

0.0

125

5E+12

x=315mm

x=405mm

12.0k

12.0k

12.0k

6.0k

6.0k

9.0k 6.0k

3.0k

3.0k

3.0k

3.0k

0.0

0.0

0.0

0.0

0

25

50

75

100

125

0

25

r (mm)

50

75

100

125

0

25

r (mm)

75

100

125

x=225mm

0

25

50

75

100

125

0.0

9E+06

x=315mm

12.0k

0.0

0

25

50

75

100

125

3.0k 0.0

0

r (mm)

25

50

75

100

125

6.0k 3.0k 0.0

0

25

r (mm) EXP

Remove

50

75

100

125

9.0k 6.0k 3.0k 0.0

r (mm)

0

25

50

75

100

125

100

125

9.0k 6.0k 3.0k 0.0

0

r (mm) 1.1E+11

1.1E+10

CO (ppm)

12.0k

CO (ppm)

12.0k

CO (ppm)

12.0k

CO (ppm)

12.0k

9.0k

125

75

x=495mm 15.0k

3.0k

50

x=405mm 15.0k

6.0k

25

2.24E+07

15.0k

6.0k

0

r (mm)

15.0k

9.0k

100

6.0k

15.0k

9.0k

125

9.0k

r (mm)

5.5E+06

2.24E+06

100

3.0k

r (mm)

EXP

x=135mm

50

CO (ppm)

12.0k

CO (ppm)

12.0k

CO (ppm)

15.0k

CO (ppm)

15.0k

6.0k

75

x=495mm

15.0k

9.0k

50

1E+13

15.0k

9.0k

25

r (mm)

15.0k

9.0k

0

r (mm)

1E+12

5E+11

9.0k

3.0k

r (mm)

1E+11

x=135mm

50

CO (ppm)

15.0k

CO (ppm)

15.0k

CO (ppm)

15.0k

r (mm)

CO (ppm)

x=315mm

15.0k

CO (ppm)

CO (ppm)

x=135mm

CO (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

Page 26 of 34

1.1E+12

25

50

75

r (mm) 1.1E+13

Figure 9. Effects of pre-exponential factor of (top) R1, (middle) R2 and (bottom) R3 on CO distributions at five different axial locations in BS-MCF.

26


Page 27 of 34

x=225mm

x=315mm

x=405mm

x=495mm

2100

2100

1800

1800

1800

1500

1500

1500

1500

1500

900 600

900 600

0

25

50

75

100

300

125

900 600

0

25

r (mm)

50

75

100

300

125

0

25

x=225mm

16

16

16

O2 (%)

20

O2 (%)

20

8

900

50

75

100

300

125

12 8

x=315mm

8

0 0

0 0

0 0

125

25

50

75

100

125

25

r (mm)

300

125

50

75

0

100

125

25

50

75

100

0 0

125

12.0k

0.0 0

25

50

75

100

125

0.0 0

r (mm)

3.0k 25

50

75

100

125

0.0 0

DRM

9.0k 6.0k 3.0k

25

r (mm) EXP

CO (ppm)

12.0k

CO (ppm)

12.0k

CO (ppm)

12.0k

CO (ppm)

12.0k

3.0k

50

75

100

125

0.0 0

r (mm) WRWD

50

125

100

125

9.0k 6.0k 3.0k

25

50

75

100

125

0.0 0

25

50

r (mm) PRWD (with R3)

75

x=409mm

x=405mm

15.0k

3.0k

25

r (mm)

15.0k

6.0k

100

4

x=315mm

6.0k

125

8

15.0k

6.0k

100

12

r (mm)

9.0k

75

16

15.0k

9.0k

50

x=495mm

20

15.0k

9.0k

25

r (mm)

x=405mm

r (mm)

x=225mm

x=135mm

100

8

0 0

100

75

12

4

75

50

16

12

4

r (mm)

25

20

4 50

900

r (mm)

4 25

1200 600

0

r (mm)

20

12

1200 600

r (mm)

x=135mm

O2 (%)

1200

O2 (%)

300

1200

O2 (%)

1200

T (K)

2100

1800

T (K)

2100

1800

T (K)

2100

T (K)

T (K)

x=135mm

CO (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

Energy & Fuels

75

r (mm)

PRWD (without R3)

Figure 10. Comparisons of (top) gas temperature, (middle) dry oxygen volume fraction and (bottom) dry carbon monoxide volume fraction between experimental measurements and numerical predictions using detailed and different WD mechanisms in BS-MCF.

27


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

T (K)

DRM

WRWD

Page 28 of 34

PRWD

(a) Gas temperature field O2 (%)

DRM

WRWD

PRWD

(b) O2 volume fraction (dry basis) CO (ppm)

DRM

WRWD

PRWD

(c) CO volume fraction (dry basis)

Figure 11. Spatial distributions of (top) gas temperature, (middle) oxygen volume fraction and (bottom) dry carbon monoxide volume fraction from CFD simulations using detailed and different WD mechanisms in BS-MCF.

28


Page 29 of 34

Gas temperature

O2 volume fraction (dry basis)

1200 800 400 -0.9

CO volume fraction (dry basis)

20

20000

16

16000 CO (ppm)

1600 O2 (%)

Temperature (K)

2000

12 8 4

-0.6

-0.3

0.0

0.3

0.6

0 -0.9

0.9

12000 8000 4000

-0.6

-0.3

Z (m)

0.0

0.3

0.6

0 -0.9

0.9

-0.6

-0.3

Z (m)

0.0

0.3

0.6

0.9

Z (m)

(a) Comparison of modeling results at x=900 mm Gas temperature 2000

20000

1200 800

12 8 4

-0.6

-0.3

0.0 Z (m)

0.3

0.6

0.9

EXP PRWD WRWD

16000 CO (ppm)

16

1600

400 -0.9

CO volume fraction (dry basis)

O2 volume fraction (dry basis)

20

O2 (%)

Temperature (K)

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

Energy & Fuels

12000 8000 4000

0 -0.9

-0.6

-0.3

0.0

0.3

0.6

0.9

Z (m)

0 -0.9

-0.6

-0.3

0.0

0.3

0.6

0.9

Z (m)

(b) Comparison of modeling results at x=3100 mm

Figure 12. Comparisons of gas temperature, O2 volume fraction and CO volume fraction between experimental measurements and CFD predictions in IS-MCF.

29


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

V (m/s)

WRWD

WRWD

PRWD

PRWD

(a) Velocity O (%) 2

Page 30 of 34

T (K)

(b) Temperature WRWD

WRWD

PRWD

PRWD

CO (ppm)

(d) Carbon monoxide mole fraction (dry basis)

(c) Oxygen mole fraction (dry basis)

Figure 13. Comparisons of CFD modeling results between WRWD and PRWD mechanisms on central horizontal (y = 0 mm) plane in IS-MCF.

30


Page 31 of 34

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

V (m/s)

T (K) WRWD

WRWD

PRWD

PRWD

(a) Velocity

(b) Temperature

O (%)

CO (ppm)

2

WRWD

WRWD

PRWD

PRWD

(c) Oxygen mole fraction (dry basis)

(d) Carbon monoxide mole fraction (dry basis)

Figure 14. Comparisons of CFD modeling results between WRWD and PRWD mechanisms on central vertical (z = 0 mm) plane in IS-MCF.

31


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

Tables Table 1. Operational conditions for bench-scale and industrial-scale MILD combustion experiments. BS-MCF

IS-MCF

Flow rate (m3/h)

0.95

32

Temperature (°C)

20

20

Composition (vol.%)

CH4=99.5, N2=0.5

CH4=97, N2=3

Velocity (m/s)

9.32

2.4

Flow rate (m3/h)

11.3 (at ambient temperature)

340 (at ambient temperature)

Temperature (°C)

20

130

Composition (vol.%)

O2=21, N2=79

O2=21, N2=79

Velocity (m/s)

22.38

23.5

Overall excess air coefficient

1.25

1.15

Fuel

Air

32


Page 32 of 34

Page 33 of 34

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. Reaction kinetic parameters of four different existing GRMs for methane combustion. Global mechanism

Reaction ID

A

β

EA

Reaction order

ORWD

R1

2.80×109

0

2.03×108

[CH4]0.3[O2]1.3

R2

3.98×1014

0

1.7×108

[CO][O2]0.25][H2O]0.5

R3

5.00×108

0

1.7×108

[CO2]

R1

5.01×1011

0

2.00×108

[CH4]0.7[O2]0.8

R2

2.24×106

0

4.18×107

[CO][O2]0.25][H2O]0.5

R3

1.10×1013

-0.97

3.28×108

[CO2][O2]0.25][H2O]0.5

R4

4.40×1011

0

1.26×108

[CH4]0.5[O2]1.25

R5

3.00×108

0

1.26×108

[CH4][H2O]

R6

2.75×109

0

8.37×107

[CO][H2O]

R7

6.71×1010

0

1.14×108

[CO2][H2]

R8

2.50×1016

-1

1.67×108

[H2]0.5[O2]2.25[H2O]

R9

2.51×1014

0

3.98×108

[H2O]

R4

4.40×1011

0

1.26×108

[CH4]0.5[O2]1.25

R5

3.00×108

0

1.26×108

[CH4][H2O]

R6

2.75×109

0

8.37×107

[CO][H2O]

R7

6.71×1010

0

1.14×108

[CO2][H2]

R8

5.69×1011

0

1.46×108

[H2][O2]0.5

R9

2.51×1014

0

3.98×108

[H2O]

WRWD

ORJL

KRJL

33


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

Page 34 of 34

Table 3. Reaction kinetic parameters of the PRWD mechanism for methane MILD combustion. Reaction ID

A

β

EA

Reaction order

R1

1.25×1012

0

2.00×108

[CH4]0.7[O2]0.8

R2

2.24×107

0

4.18×107

[CO][O2]0.25][H2O]0.5

R3

1.10×1013

-0.97

3.28×108

[CO2][O2]0.25][H2O]0.5

34


A Refined Global Reaction Mechanism for Gently Preheated MILD

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