Fast and accurate CFD-model for NOx emission prediction during oxy-fuel combustion of natural gas using detailed chemical kinetics

Fuel 264 (2020) 116841

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Fast and accurate CFD-model for NOx emission prediction during oxy-fuel combustion of natural gas using detailed chemical kinetics

T

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C. Schlucknera, , C. Gabera, M. Landfahrera, M. Demuthb, C. Hochenauera a b

Graz University of Technology, Institute of Thermal Engineering, Inffeldgasse 25/B, 8010 Graz, Austria Messer Austria GmbH – Kompetenzzentrum Metallurgie, Industriestraße 5, 2352 Gumpoldskirchen, Austria

A R T I C LE I N FO

A B S T R A C T

Keywords: CFD NOx study Semi-industrial burner chamber OEC and oxy-fuel natural gas combustion Detailed combustion chemistry

Accurate prediction of NOx emission is essential in designing combustion devices and troubleshooting of existing ones. The aim of this work is to investigate the sensitivity of NOx formation during the combustion of natural gas with oxygen using a numerically inexpensive computational fluid dynamics model. To this end, an industrial jet burner was experimentally and numerically analysed for NOx formation at 600 kW controlled at 1320 and 1450 °C during the combustion of natural gas with pure oxygen and oxidizer mixtures containing up to 10% v nitrogen. Two mixture-fraction based and two classical species transport models were investigated for their ability to: (1) predict the flame shape and temperature, (2) calculate the OH and CH emissions driving the NOx formation, and (3) fast and accurately predict the NOx emissions during oxy-fuel combustion and during the presence of low nitrogen amounts in the oxidizer. The study shows that the widely-used steady-flamelet model fails to correctly predict the flame shape and temperature, due to a too low velocity difference between the oxidizer and the fuel. It is highlighted that only the partially-premixed steady flamelet model predicted the flame shape and the NOx formation rates adequately, fitting the experimental and numerical data closely. Moreover, the shear rate in the annular gap was identified as a crucial parameter for the applicability of mixture fractionbased models. Species transport models should be used for validity checks but they disqualify as fast-solving alternatives due to their high computational demand (EDC) or lack of detailed chemistry interaction (EDM).

1. Introduction The usage of fossil and alternative fuels (e.g., waste) has raised concerns on environmentally harmful pollutant emissions and carbon dioxide is the major greenhouse gas emitted during the combustion thereof. Some of the secondary combustion products are major pollutants, such as carbon monoxide, unburned hydrocarbons, soot, sulphur oxides and nitrogen oxides (NOx). In addition to the poisoning effect that NOx has on humans, they also cause photochemical smog, contribute to acid rain, cause ozone depletion and thus pose a detrimental health and environmental hazard [1]. These harmful pollutant emissions can potentially be minimized by the use of pure oxygen as oxidizer in combustion processes, e.g., in glass furnaces, in metallurgical plants or in the cement industry. The use of oxidizers containing oxygen mole fractions higher than 21% is termed oxygen enriched combustion (OEC), and oxy-fuel combustion refers to using pure oxygen. With them, pollutant emissions can be reduced and simultaneously the furnace efficiency can be increased significantly. Current research on combustion systems needs to focus on the

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prediction of pollutant formation in seminal burner technologies in order to minimize the anthropogenic footprint on the environment. In literature, many studies dealing with the optimization of industrial processes using the oxy-fuel technology focus on improving the quality of a product or increasing the throughput [2–6]. However, these optimizations often collide with environmental aspects, such as pollutant emission reduction, most prominently NOx emissions. In order to create both economically and environmentally optimized solutions, these industrial processes require fast and accurate numerical models that meet these contradictory requirements. Increasing optimization levels of these processes also call for models with high resolution in time and space. In order to model the NOx formation in turbulent combustion systems, numerous physical processes, including the fluid dynamics, the local mixing process, heat transfer, and chemical kinetics need to be described. Moreover, these processes are tightly coupled, which must be correctly modelled [7]. Computational fluid dynamics (CFD) models are perfectly suited for these tasks and thus, the present study focuses on computationally efficient CFD models that accurately predict NOx emissions during oxy-fuel combustion of natural gas with an industrial

Corresponding author. E-mail address: [email protected] (C. Schluckner).

https://doi.org/10.1016/j.fuel.2019.116841 Received 6 June 2019; Received in revised form 4 December 2019; Accepted 5 December 2019 Available online 21 December 2019 0016-2361/ © 2019 Elsevier Ltd. All rights reserved.

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Further low-NOx combustion methods were investigated by Hagihara et al. [18] and Helou et al. [19]. The first mentioned scrutinized a periodic oscillating combustion method for oxygen-enriched combustion systems on a bench-scale, which reduced the NOx emissions drastically. Helou et al. [19] developed a MILD (also known as flameless oxidation) combustion reactor for ultra-low NOx combustion of methane using air as oxidizer. NOx emissions could be reduced below 20 ppm v and simultaneously CO and unburned hydrocarbon emissions were minimized below 200 ppm v . Moreover, computationally extremely demanding large-eddy simulations revealed the occurrence of local extinction and flame detachment phenomena. In a recent study, Shakeel et al. [20] numerically investigated the oxy-fuel combustion of methane in a model gas turbine combustor. They used 3 global 3- to 7-step global reaction mechanisms and different weighted sum of gray gas radiation models to determine the best fit to their experimental data. Flame attachment to the burner nozzle and flame lift-off at high CO2 fractions in the oxidizer could be predicted with good accuracy. They used a RANS based turbulence model in combination with the eddy dissipation concept model, which required a high computational demand but NOx emissions were not investigated by the authors. Gaber et al. [21] scrutinized the possibility of thermochemical regeneration to improve the efficiency of natural-gas fired oxy-fuel furnaces by providing a seminal heat recovery technology applicable for oxy-fuel furnaces. They used hot oxy-fuel exhaust gases to reform the primary fuel into syngas and demonstrated stable long-term conversion and operation conditions. Moreover, the influence of up to 10% v nitrogen in the gas mixture was investigated, which only had a diluting effect on the process. However, NOx emissions were not investigated during their experiments. The presented literature review has shown that many studies investigate the use of oxygen as oxidizer for the combustion of fossil fuels. A majority thereof scrutinized the effective combustion of coal and possible pollutant emission reduction methods, especially for NOx. However, natural gas and methane have found less scientific attention in this context, although they are widely used in industrial processes, such as steel reheating, cement or glass furnaces. Additionally, the available numerical models mostly use computationally demanding approaches, which require elaborate computing resources. Anyway, in order to promote the development and advancement of environmentally friendly industrial-scale applications and sites, numerically inexpensive, detailed and accurate models need to be provided by the scientific community. This requirement is tackled in the present study.

jet burner. However, many numerical studies in literature focus on oxy-fuel combustion of coal and the resulting NOx formation rates. In [8,6], CFD simulations of a 100 kW furnace and a full-scale pulverised coal boiler were investigated, respectively. In both cases air and oxygen-enriched combustion (up to 29% v O2 in N2 ) was used as oxidizer and they found a good agreement of their numerical predictions to the experiment. A global 3-step chemical reaction mechanism was used for the combustion of coal and the NOx model used partial-equilibrium approaches for the radical formations. Karampinis et al. [9] numerically investigated co-firing of coal and biomass in a large-scale utility boiler using air as oxidizer. They found that a NOx emission reduction of up to 10% can be achieved by the co-firing strategy, due to the lower nitrogen content of the biomass fuel. In [10], besides air, oxygen-enhanced and oxy-fuel combustion of lignite was numerically investigated and the NO formation rates have been analysed locally and globally. They found a good agreement of their NOx emission prediction to their experimental data of the airfiring case and highlight the importance of numerical modelling for retrofit applications of full-scale boilers. Moreover, they predicted a reduction of NOx emissions under oxy-fuel firing conditions at a constant load of the boiler. Besides the presented literature on oxy-fuel combustion of coal with a focus on NOx emission prediction, other studies focus on heat transfer or char-conversion oxygen-enhanced and oxy-fuel combustion [11–14]. However, literature on oxy-fuel combustion of natural gas and the sensitivity of NOx emissions on the nitrogen content in the fuel or oxidizer is scarce. In an early study, Hedley et al. [15] investigated a semi-industrial scale test furnace combusting natural gas with oxygen. They examined the influence of fuel and oxidizer mixing on the flame characteristics and the formation of nitric oxides at high temperature and used the experimental data for the verification of their numerical model. In-flame gas sampling was done using a motorized probe system, which made local CO2, O2, NO and NOx measurements possible. For the conducted simulations, a global 4-step reaction mechanism for methane oxy-fuel combustion was used in combination with the Reynolds stress model and the NOx emissions were computed in a post-processing step. They could predict the furnace temperature with reasonable accuracy and their NOx emissions estimation differed at most by 20%. Moreover, they found a significant effect of the firing configuration on the temperature and NOx emissions, which was interpreted by increased internal recirculations which provoked increased prompt-NO formation. The interactions among soot, thermal radiation and NOx emissions in oxygen-enhanced (40% v O2 in N2) propane combustion was investigated by Wang et al. [16]. A detailed chemical mechanism including 122 species and 677 elementary reactions was included in their 3D CFD model of a lab-scale flame. They found that soot radiation decreases the flame temperature and the NOx emissions significantly, especially in the flame-tip region. Moreover, they highlight that soot prediction is sensitive to the surface growth mechanism and that soot formation is closely coupled with the flame temperature through soot radiation. A number of studies in the literature focus on methods to minimize NOx emissions during combustion. Awosope and Lockwood [17] numerically and experimentally investigated flameless oxidation (using air as oxidizer) as an effective method for the reduction of thermal NOx and for improving combustion efficiency in high temperature processes. They used a two parameter model (mixture fraction and progress variable) for the description of the high momentum flameless combustion and prompt and thermal NOx were calculated using a postprocessing step. Their modelling approach included a chemical reaction mechanism involving 72 reactions and 28 species and with it the NOx emissions could be predicted in good agreement to the experimental data. This model is a computationally efficient approach for high-momentum partially premixed combustion systems using air as oxidizer.

1.1. Objective of this paper The goal of this work is to present a computationally efficient CFDmodel for the prediction of NOx emissions during the combustion of natural gas with pure oxygen and mixtures containing up to 10% v N2 in oxygen. This range of nitrogen in oxygen is especially important for industrial high-temperature applications, since they can occur: (1) during the production of oxygen via industrial pressure-swing-adsorption (PSA) air-separation units, (2) as naturally varying amounts in natural gas, and (3) due to air-leakage into industrial combustion chambers due to their design or the underlying process. The model represents a semi-industrial burner chamber with an industrial co-axial jet burner designed for low-NOx oxy-fuel operation. In order to analyse different fast-solving alternatives, two mixture-fraction based and two classical species-transport models will be investigated that describe the combustion process within the burner chamber. Since NOx formation rates are significantly slower than combustion kinetics, they will be calculated in a post-processing step, based on the steady-state solutions of before mentioned models for burner chamber temperatures of 1320 and 1450 °C. The specific combustion model differences will be highlighted and the best-fitting approach will be selected and discussed. 2

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Fig. 1. Design and computational grid of the burner chamber.

radicals, which then further oxidize to NO. Prompt NO is weakly temperature dependent and the overall contribution to NO formation is below that of thermal NO [1].

1.2. NOx emissions during oxy-fuel combustion Accurate prediction of NOx emission is essential in designing combustion devices and troubleshooting of existing ones [22]. Numerical modelling can be used as a tool to investigate and improve the understanding of the complex processes involved [7]. NOx emissions mainly consist of nitric oxide (NO), and to a lesser degree nitrogen dioxide (NO2), as well as nitrous oxide (N2O). In case of oxy-fuel firing of natural gas, the main formation routes for NOx are the extended Zeldovich (thermal NO), the Fenimore (‘prompt NO’) and the N2O mechanism [1]. It is noted that thermal NO is the main source of NO in gaseous combustion systems, whereas NO in coal-fired systems is formed from the oxidation of nitrogen chemically bound in the fuel. Thus, NOx emissions of gaseous fuels are different from solid fuels. The underlying mechanisms occurring in gaseous combustion systems are discussed below.

1.5. N2O formation Another formation route of NO is through intermediate N2O, which is favoured under oxygen-rich conditions and elevated pressures. It can account for as much as 90% of the NOx emissions formed during combustion and its contribution and significance will thus be investigated in this study. Again, hydroxyl, oxygen and hydrogen radicals are involved in the reactions which requires a reliable prediction of these species. 2. Experimental and numerical setup 2.1. Burner chamber and experimental setup

1.3. Thermal NO mechanism

The experiments for this study were conducted on a natural-gas fired high-temperature burner chamber with a constant thermal input of 600 kW. In order to investigate the sensitivity of nitrogen oxide formation during oxy-fuel combustion, pure oxygen and mixtures containing up to 10% v nitrogen in oxygen were used as oxidizers. The oxygen used in the experiment was produced using an air-separation unit based on the Hampson-Linde cycle, with a technical purity rating of 99.5% v . The internal dimensions of the burner chamber are W × H × L = 1.25 m × 1.25 m × 4.5 m ; thermal insulation at all sides was done by a 0.2 m layer of refractory ceramic fibre with an average thermal conductivity of 0.23 Wm−1 K−1 (cf. Fig. 1a). A “Messer Oxipyr F” burner is placed at the centre plane at the front side, with a vertical offset of h= 0.525 m from the bottom of the chamber. Moreover, the burner tip is congruent with the inner front wall. The chamber temperature is controlled and monitored via 12 encapsulated Type B thermocouples (Tolerance class in temperature range 600–1700 °C: ± 0.25% × T ) above and along the burner axis with a inner front-wall distance a = 0.3 m ; the spacing between the thermocouples is 0.2 m except for the last, which is placed 0.4 m from TC11. All thermocouples are lowered 0.05 m from the top wall into the combustion chamber. In order to control the chamber temperature independently of the applied thermal input power of the burner, up to 19 water-cooled cooling lances (outer diameter 0.06 m ) can be inserted at the bottom of the chamber. The first lance is located at a distance of b = 0.3 m (cf. Fig. 1a) from the front inner wall and every lance has a spacing of 0.21 m between each other, except for the last, which has a 0.435 m spacing. The natural gas used in this study consisted of 96% v CH4, 3% v higher hydrocarbons (ethane and propane), 0.34% v CO2 and 0.66% v N2 This naturally occurring nitrogen content can lead to NOx formation aroused by the high temperatures of the oxy-fuel flame. In addition to pure

Thermal NO is formed by the oxidation of molecular nitrogen by oxygen and hydroxyl radicals [23]. The involved formation reactions (Eqs. (1)–(3)) are strongly temperature dependent and are thus eponymous for this mechanism.

O+ N2 ⇌ N+ NO

(1)

N+ O2 ⇌ O+ NO

(2)

N+ OH ⇌ H+ NO

(3)

The rate-limiting step of the given, extended Zeldovich mechanism is Eq. (1), characterized by the high activation energy required to break the strong N ^N triple bond (≈ 319 kJmol−1). Thus, the formation of Zeldovich NOx only becomes significant at temperatures greater than 1800 K and doubles for every 90 K temperature increase beyond 2200 K . The NO formation rates of Eqs. (1) and (2) are in the same order of magnitude, while reaction (3) becomes significant in near stoichiometric and fuel-rich mixtures. Temperature, residence time and oxygen radical concentrations are thus the major factors that influence thermal NO formation. 1.4. Prompt NO mechanism NO in fuel-rich flames can be formed proportionally to the carbon number of the fuel and is termed ’prompt NO’ and the experimentally determined NOx formation rate during the combustion of hydrocarbon fuels can exceed that produced only by the extended Zeldovich mechanism [24]. The formation occurs in a very rapid sequence of reactions with time scales similar to those of the main combustion reactions. The involved dissociation reactions of nitrogen are initiated by CH and CH2 radicals and result in the formation of HCN, NCN, H and NH 3

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equation.

Table 1 Oxidizer mixtures and equivalence ratios ϕ . O2 mole fraction

N2 mole fraction

Fuel

in the oxidizer

in the oxidizer

equivalence ratio ϕ

1 0.99 0.98 0.95 0.90

– 0.01 0.02 0.05 0.10

0.98467 0.98436 0.98405 0.98307 0.98129

2.3. Turbulence and flow model The flow regime within the burner, the furnace and the flue duct was turbulent and Mach numbers were below 0.3. The fluid can thus be assumed incompressible, which allows for using a double-precision pressure-based solver. The realizable k − ∊ turbulence model was used to close the Reynolds averaged Navier-Stokes (RANS) equations. In commercial CFD codes, three k − ∊ models have become established: standard, realizable and RNG models. They mainly differ in the modelling approach of generation and destruction terms in the transport equation for the dissipation rate ∊, calculation of the turbulent viscosity μt , and the turbulent Prandtl number [26]. Prieler et al. [27] and Mayr et al. [28] proved that the realizable k − ∊ model is robust and has found wide and satisfactory application in lab-scale and semi-industrial furnaces, such as the burner chamber investigated in the particular study. It predicts the spreading rates for axisymmetric jets and flows with strong streamline curvature better than the standard k − ∊ model. According to Prieler et al. [27], the flame shape of oxy-fuel flames is predicted identically with both the computationally inexpensive realizable k − ∊ and the more demanding Reynolds stress model (RSM). Since the focus of this study was to develop a fast and accurate numerical model for NOx emission formation during oxy-fuel combustion of natural gas, the two-equation realizable k − ∊ model was selected over the RSM model as best compromise between computational expense and accuracy.

oxygen, mixtures of 1, 2, 5 and 10% v N2 in O2 (see. Table 1 were used as oxidizers, which were realized by adding air to the oxygen. This was done to account for nitrogen contents that can occur during production of oxygen in industrial PSA air separation plants, through leakages or naturally occurring variations of the nitrogen content in the natural gas. Moreover, the investigations were done at two temperature levels: 1320 and 1450 °C. These temperature levels were determined by the peak temperature reading of any of the 12 installed thermocouples in the burner chamber. The furnace temperature was controlled by the mass flow rate of coolant through the cooling lances. The furnace was operated under fuel-lean conditions with an equivalence ratio corresponding to 3% v O2 in the dry flue gas. The particular equivalence ratios of the investigated oxidizer mixtures are given in Table 1. In order to prevent ambient air from leaking into the burner chamber, a positive pressure in the furnace of 20 Pa was held constant. Gas analysis and NOx emission measurement was done at a gas analysis port in the flue duct (schematically depicted as a green dot in Fig. 1a). Exhaust gases, such as, CO2, CO and O2 were continuously measured using an “Emerson MLT 3T” gas analyser. The sampling ranges were: CO: 2500–10, 000 ppm v , CO2: 30 − 100% v and O2: 5 − 100% v . NOx emissions were monitored using a “Testo 350 – Flue gas analyzer” with a measurement range for NO: 0–4000 ppm v and NO2: 0 − 500 ppm v . The flue gas was filtered and dried before entering the gas sampling line. All experimental data is thus given on a dry base.

2.3.1. Boundary conditions As described in Section 2.1, natural gas was used as fuel in this study. However, for the simulations, the natural gas mixture was simplified to be only a mixture of methane and nitrogen. The fuel composition was thus 99.34% v CH4 and 0.66% v N2. The used oxidizer mixtures and equivalence ratios were identical to the experiment, as given in Table 1. The fuel lean operation is representative for industrial applications in order to prevent carbon monoxide-rich exhaust gases. Both the fuel and oxidizer inlet were set as mass flow inlets and entered the computational domain at ambient temperatures of 25 °C and the turbulent intensity was set to 10%. It is noted that the turbulent intensity at the inlet does not affect the NOx formation. The flue duct outlet was modelled as a pressure outlet. A convective heat flux boundary condition was applied to the outer walls of the burner chamber in order to model their losses caused by natural convection. Thus, a heat transfer coefficient of 10 Wm−2 K−1 and a free stream temperature of 25 °C was used. The emissivity of the inner walls was set to ∊ = 0.6; the average material parameters of the refractory ceramic fibre were set according to the manufacturer specifications for the investigated temperature range (1320 − 1450 °C ): heat conductivity λ = 0.23 Wm−1 K−1, specific heat capacity c p = 871 Jkg−1 K−1 and density ρ = 2719 kgm−3. Furthermore, an appropriate boundary condition for the heat flux dissipated by the cooling lances had to be defined. Again, a convective boundary condition was used to avoid numerical instabilities aroused by the density differences of about three orders of magnitude between flue gas and water. The free stream temperature was set to 27 °C, which calculated from an arithmetic average of coolant inlet and outlet temperatures measured during the experiments. The outer surface emissivity of the cooling lances was set to 0.8 and the required heat transfer coefficient was adapted to the two temperature levels (1320 and 1450 °C) in accordance to the experiment. Similar to the refractory material, constant material properties for the cooling lances (steel) were used: and λ = 16.27 Wm−1 K−1, c p = 502.5 Jkg−1 K−1 ρ = 8030 kgm−3 .

2.2. Computational domain and numerical setup The numerical simulations were conducted with the commercial computational fluid dynamics (CFD) code ANSYS Fluent R19.0 [25]. For the steady-state simulations, the furnace had to be discretized for the applied finite-volume method of the used CFD code. Cell type and size were selected carefully in regions of high gradients in velocity, temperature and mixture fraction. Thus, a finer mesh with low growth rates was used in the regions of the burner tip and the annular gap between fuel and oxidant (cf. Fig. 1b). The domain was mainly discretized with hexahedrons and wedge elements due to their good numerical properties. Tetrahedrons were used in the transition area between the burner chamber and the flue duct and in some parts of the flue duct insulation. The mesh consisted of 1.38 million cells with a minimum orthogonal quality of 0.09 and a maximum aspect ratio of 51. The simulations were performed on a 6-core CPU (Intel Core i7-3930 K, 3.2 GHz , 32 GB RAM) and a converged steady-state solution was reached after 3 days computing time. Convergence was determined by low residuals and constant monitor values for the thermocouple and flue gas outlet temperatures. The residuals for energy, species and radiation equations had to fall below 10−6 , whereas those of the remaining equations had to fall below 10−3 to render a simulation convergent. A SIMPLE scheme was used for the pressure-velocity coupling and a second order scheme was applied for pressure interpolation. Gradients were calculated by a Green-Gauß node-based method, since it minimizes numerical diffusion on tetragonal regions within the mesh. Momentum, energy, turbulence and species transport (i.e., combustion) equations were spatially discretized by second order upwind schemes, whereas a first order upwind scheme was used for the radiation

2.3.2. Applied combustion models and reaction mechanism In order to accurately predict NOx emissions, a multi-step multicomponent reaction mechanism is required that includes oxygen, 4

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used for the EDM simulations, whereas for the EDC, the multi-step skeletal25 [29] mechanism was used to describe the chemical kinetics during combustion. As it was proposed by [36], the specific heat of the flue gas mixture needs to be corrected when radical formation is neglected by a combustion model. By doing so, the huge temperature overshoot that would occur during oxy-fuel combustion is artificially damped and more realistic flame temperatures can be predicted. Thus, in the present study the specific heat data of the flue gas was adapted according to [36] for the EDM simulations; for all other simulations where radical formation was incorporated, the material data was not modified. Moreover, with the EDC model, 17 additional species transport equations have thus to be solved, which makes it computationally more demanding than all the other models.

hydrogen and hydroxyl radicals, since these act as reactants in the NOx formation mechanisms. Thus, in this study the detailed chemical kinetics described by the skeletal25 [29] mechanism were used to model an oxy-fuel methane flame. The mechanism considers 25 reversible, homogeneous reactions among 17 species and was thoroughly scrutinized for its validity and applicability in oxy-fuel combustion cases using the mixture fraction-based steady flamelet model [30,28,31,32,3,4]. It is noted that Prieler et al. [31,27] scrutinized applicable reaction mechanisms, including the elaborate GRI-Mech3.0 [33] and the widely accepted smooke46 [34] mechanism for the successful use during oxy-fuel combustion of natural gas. However, they both failed to predict the oxy-fuel methane flame and were thus neglected in the present study. Besides an adequate reaction mechanism, an appropriate combustion model is a crucial parameter in modelling turbulent combustion processes. The focus of this study was to create a detailed model with low computational expense. For non-premixed combustion, that is, the fuel and oxidant are spatially separated before combustion, the mixture fraction based models simplify the complex combustion process to a mixing problem. Therefore, the difficulties associated with closing nonlinear mean reaction rates are avoided. Once mixed, the chemistry can be modelled as being near chemical equilibrium with the steady laminar flamelet model (SFM) or the partially-premixed steady diffusion flamelet model (PPSFM). They offer this possibility for non-adiabatic cases at moderate computational cost. The SFM model is based on the laminar diffusion flamelet theory, wherein a turbulent flame is represented by a number of one-dimensional laminar counterflow diffusion flame elements, called flamelets ([35]). The flamelets can be calculated with detailed chemical kinetics, and the data describing the thermodynamic state of the flue gas is stored in look-up tables. In contrast to classical species transport models (e.g., the eddy dissipation model (EDM) or the eddy dissipation concept (EDC) model), the SFM solves one transport equation for the mean mixture fraction and one equation for the mean mixture fraction variance to describe the combustion chemistry. Non-equilibrium effects are considered by the scalar dissipation field, which is calculated form the turbulence field and the mixture fraction variance. Generally, the SFM unites the benefits of classical species transport models, such as the EDM and the EDC because they allow detailed chemistry to be implemented with comparatively low computational demand. However, Mayr et al. [28] pointed out that the strain rate between the fuel and oxidizer stream is a crucial parameter for the usability of the steady flamelet model. If the strain rate is too low, the SFM fails to predict the flame shape correctly. The investigated burner in the present study has a velocity ratio between the oxidizer and the fuel close to unity, which was identified by Mayr et al. [28] as critical for the SFM. The mixing and combustion process is thus very likely to be predicted incorrectly, which opens the need for another combustion model. Alternative approaches have been proposed and discussed by Awosope and Lockwood [17] to account for finite rate chemistry in diffusion flames embodying partially premixed flames. With this approach, the principles of the SFM are combined with a reaction progress variable to describe the resulting partially premixed combustion caused by the high inlet momenta of reactant jets under diffusion flame conditions. Thus, the partially-premixed steady diffusion flamelet model (PPSFM) was chosen as a fast-solving alternative to the SFM traditionally used in oxy-fuel cases. Moreover, by the introduction of the mean reaction progress c the mixing of both the oxidizer and fuel can be described in more detail, especially in such cases where the SFM fails. Thus, the PPSFM was investigated in the present study for its applicability in oxyfuel combustion cases with critically low strain rates between the fuel and oxidizer flow. Additionally to the fast-solving mixture fraction models, simulations were carried out with the eddy dissipation model (EDM) and the eddy dissipation concept (EDC) model, to provide numerical results of classical species-transport models. A global 2-step reaction mechanism was

2.3.3. Radiation model In conventional air-fired combustion, spectral dependence of the flue gas is considered by a gray-gas approximation, since nitrogen is the main component in the flue gas. In the case of oxygen-enriched and oxy-fuel combustion a different model approach is needed to describe the radiative properties of the flue gas due to their elevated amounts of water vapour and carbon dioxide. Combustion gases, such as H2O, CO2, CO and SO2 absorb and emit radiation only over certain wave lengths, known as absorption bands. Between these bands, the gases do not absorb or emit radiation and a wide range of models are available to calculate these absorption bands. The flue gas resulting from oxy-fuel combustion renders the gray-gas approximation insufficient, and requires a non-gray gas approach with respect to the absorption bands of H2O and CO2. To this end, a weighted sum of gray gases model (WSGGM) was used in this study to create a reliable and fast-solving CFD-model. The calculation of the radiative heat transfer was then performed with the discrete ordinates (DO) model with WSGGM parameters by Smith et al. [37] for the determination of the flue gas absorption coefficients. For the mean beam length of the present semiindustrial scale furnace (0.99 m) , this approach was found applicable by Prieler et al. [31,30,38,39] despite the higher concentrations of CO2 and H2O under oxygen enriched conditions. An angular discretization of 4 × 4 was chosen for each octant in the DO model, which solves the radiative transport equation for a finite number of polar and azimuthal angles θ and φ , respectively. This resulted in a direction number of 128, which showed an optimum balance between computational effort and accuracy. 2.3.4. NOx pollutant model In general, three numerical approaches to predict NOx emissions in turbulent non-premixed flames are common: 1) A direct prediction of NOx emissions with the main combustion calculation using the transported PDF or the EDC model; 2) The use of a tabulated flamelet approach and 3) Using NOx post-processing tools decoupled from the main combustion and flow field calculations [1]. The direct NOx calculation using the transported PDF or EDC approach is computationally expensive (despite the employment of an ISAT algorithm) and thus is not feasible for engineering applications. The NOx pollutant formation reactions are much slower than the combustion reactions and fluid flow time scales and the NOx concentrations in the flue gas are too small to influence the overall combustion and heat release. Thus, NOx models are commonly decoupled from the generalized combustion models and their formation and breakdown is computed in a post-processing step using the temperature and species concentrations from converged CFD cases [26,16,7,40,17]. This approach is computationally much more efficient than the in situ calculation and is thus used in this study. To predict the NOx emissions, transport equations for NO and N2O are solved, taking advection, diffusion, production and consumption of these species into account. Since the NOx emissions are post-processed on a converged combustion solution, it is evident that appropriate turbulence, chemistry and radiation models must be employed to gain 5

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increasing burner chamber length, which indicates that the predicted temperature distribution by the models is accurate. This error corre−1 ,T sponds to a maximum relative error er of 4.4% (er = (Tsim − Texp) Tsim in K) at a temperature level of 1320 °C, which is representative for the good fit and validity of the simulations. The relative errors at a temperature level of 1450 °C depicted in Fig. 2(b) show a maximum value of 7.5% at TC 1 for the EDC model. All other models resulted in a closer fit to the experimental data, again with decreasing errors with increasing chamber length. The best fit was calculated at TC 11 and 12 independent of the underlying modelling approach. Thus, each model is capable to predict the overall temperature distribution within the burner chamber with acceptable accuracy and the results can be used for further scrutiny. This in-depth investigation is performed for the local temperature distribution along a vertical cross-section along the burner axis, as depicted in Fig. 3. The results of the fast-solving, mixture fraction-based models are shown in Fig. 3(a) (SFM) and 3(c) (PPSFM). In Fig. 3(a), the position of the installed thermocouples, the flue gas exhaust duct and a detail of the mixing zone downstream the burner tip are schematically given. In oxy-fuel operation, the steady-flamelet model predicts a very thin, hot mixing area originating from the burner annular gap with a length of 5 − 10 × dBurner . The subsequent heat release is predicted to occur further downstream (starting at ≈ 20 × dBurner from the burner tip), separated by a comparably cold transition region, which seems physically unlikely and originates from the underlying modelling approach. The maximum temperature in the mixing area is predicted to 2450 °C , whereas the maximum temperature further downstream is 1880 °C. Mayr et al. [28] argued in their study that the velocity fraction or rather the shear rate between the oxidizer and fuel stream is a crucial parameter for the successful use of the steady-flamelet approach. The applied burner in this study has a velocity fraction close to 1, which led to a misprediction of the flame shape in the study of Mayr et al. [28]. The presented results in Fig. 4 underline that only minor differences in the local strain rate at the burner tip region result from the application of different combustion models (e.g., PPSFM and SFM). However, the improvement in flame shape and temperature prediction and distribution can be attributed to the additional reaction progress variable incorporated in the PPSFM model, which thus makes it better suited for burners with outlet velocity ratios in the range of unity. However, a burner is often designed for a specific purpose, e.g., high momentum for good mixing inside the combustion chamber, adapted momentum for uniform temperature and heat flux distribution onto slabs/billets in the furnace or the minimization of pollutant formation, such as NOx. With the investigated burner, all of the before-mentioned positive characteristics were tried to be met which resulted in this critical outlet velocity fraction. Therefore, another fast-solving numerical model had to be applied to provide an alternative to predict the flame shape: the partially-premixed steady flamelet model. It is noted that for all investigated models, both the fuel and oxidizer were geometrically separated before entering the burner chamber. Thus, no partial-premixing occurred prior to entering the burner chamber, but due to the high momentum of both streams, a partial pre-mixing can be postulated (as demonstrated by [17]) to occur downstream the burner tip prior to combustion. To this end, Fig. 3(c) shows the predicted temperature distribution of the PPSFM model. It can be noted that the introduction of the mean reaction progress c¯ significantly impacts the mixing area downstream the burner tip. In contrast to the SFM, combustion and thus heat release does not start directly at the burner tip and the reaction zone forms more pronounced in radial direction. Peak temperatures occur in this mixing zone where they reach the adiabatic flame limit, and they drop to 1870 °C in the burnout zone. Moreover, a low-temperature transition region between the burner tip and the burnout zone is not predicted by the PPSFM, and is thus an improvement over the steady-flamelet model. The results of the classical species transport models are shown in Figs. 3(b) (EDM) and 3(d) (EDC). It needs to be mentioned that only the

adequate results. Moreover, the required radical concentrations, determining the NO formation rates will be directly computed in this study by means of the applied skeletal reaction mechanism [29] and will thus deliver more accurate results than equilibrium or partialequilibrium approaches for radical formation. Nitric oxide emissions cannot be predicted from the mean values of temperature and species concentrations alone, because of the large temporal fluctuations of the state variables in turbulent flows. A computationally inexpensive method for modelling the mean turbulent reaction rate is based on the PDF approach, which applies only to the NOx transport equations and is independent from the used combustion model. A beta-PDF for temperature was used in this study to model the turbulence interaction. It is evident from Table 1 that in this study all three formation pathways will be equally important, since high flame temperatures can be expected by the used oxidizer compositions and equivalence ratios [1]. However, this analysis indicates that great care has to be taken during model selection and, realistically, under most circumstances, NOx variation trends can be accurately predicted, but quantitative results cannot be pinpointed. 3. Results In a first step, the different combustion models were validated against data, gained from experiments conducted in the burner chamber. The duration of an experimental test run was restricted to a maximum duration of 8 h due to safety regulations. However, this duration was sufficient to gain reliable steady-state experimental results for the investigated parameters in this study, such as temperature distribution along the burner axis and NOx emission formation. As described in Section 2.1, two distinct temperature levels at a constant fuel input of 600 kW were investigated: 1320 and 1450 °C. These temperature levels were controlled by the mass flow and the according heat flux from the burner chamber to the cooling lances. The cooling water mass flow was regulated by a speed-controlled pump and thus, the maximum burner chamber temperature was subject to a certain hysteresis. Fig. 2(a) shows the temperature evolution along the burner axis during oxy-fuel operation at the temperature level of 1320 °C . The column groups (TC 1 − 12 ) represent the thermocouples along the burner axis (cf. Fig. 1a and 3(a) for the TC positions). The first bar in every group shows the experimental data, whereas the second, third, fourth and fifth column are the numerical results for the SFM, PPSFM, EDM and EDC models, respectively. Considering only the experimental results, a parabolic temperature increment is noticeable with increasing TC number and burner chamber length, respectively. The trend is representative for the combustion reaction progress and the flame length. The maximum was reached at TC 11 with a temperature of 1329 °C, overshooting the targeted maximum temperature by only 9 K or 0.7% . The cooling strategy is thus capable to accurately adjust the temperature of the burner chamber. Moreover, the used jet burner creates a relatively uniform temperature profile with a maximum temperature spread of 83 K along the burner axis. The numerical values for all investigated models show results in good accordance to the experiment. In general, the temperature trend along the burner axis corresponds to the experimental data. However, in the front section of the burner chamber (cf. TC’s 1 − 6 ), higher temperatures were calculated by all models. This can be attributed to the fact that the burner chamber features a maintenance door in the front, which interrupts the insulation and thus leads to higher heat losses in this region. In this joint the ceramic fibre does not overlap and thus results in lower temperatures in the front. However, in the simulations this door was neglected and the burner chamber is uniformly and ideally insulated (λ = const .), which leads to higher temperatures in the front section. However, all models predict the temperature distribution along the burner axis with high accuracy, with a maximum absolute deviation of 70 °C at TC 1 for the EDC model. The deviation decreases with 6

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Fig. 2. Temperature trend and relative errors along the burner axis at the top-wall measurement points at oxy-fuel conditions (cf. Fig. 1(a) for TC position description).

obtained [36]. For a better understanding of these numerical adaptations, the specific heat differences between the adapted EDM and the standard fluent properties are given in Fig. 5. The data depicts a strong Cp change above 2000 K for the major reactant and product species. Thus, a temperature increase above 3000 K is artificially inhibited, due to the strong exponential specific heat increase of combustion products. Furthermore, the temperature contours in Fig. 3(b) show a more steady flame shape evolution than the mixture fraction-based models.

EDM can be considered as a fast-solving model for NOx predictions, whereas the EDC model is extremely computationally demanding. The underlying global 2-step reaction mechanism of the EDM lacks the ability to predict radical formation (e.g., hydroxyl OH or methylidyne CH) or dissociation reactions and thus tends to overestimate the combustion temperature. However, in this study the specific heat of the flue gas was adapted for oxy-fuel combustion so that this temperature overshoot could not occur and a more realistic temperature field can be

Fig. 3. Temperature contours along a vertical cross-section at the burner axis for the investigated combustion models at 1320 °C and oxy-fuel conditions. 7

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Fig. 4. Local strain rate for the mixture-fraction based models at 1320 °C and oxy-fuel conditions.

burner tip, with high OH mole fractions at the beginning of the flame near the outer surface. Moreover, elevated OH mole fractions were predicted in the mixing zone of fuel and oxidant, representing stoichiometric equivalence ratio. An asymmetric distribution resulted from the geometric design of the burner chamber and its cooling system, respectively. The PPSFM predicts a more continuous OH trend in this mixing zone, in contrast to the SFM, showing a more discontinuous OH trend. The OH trend predicted by the EDC model lacks the distinctive outer OH envelope and does not feature high mole fractions originating from the burner tip. The reaction zone starts about 7 × dBurner downstream from the burner tip, aroused by the high fuel and oxidant momentum. Internal flue gas recirculation zones stabilize the flame and prohibit a lift-off. Generally, the lateral spreading of the EDC flame is lower than calculated for the mixture fraction-based models. The CO trend shown in Figs. 6(b), (d) and (f) complete the analysis of the oxy-fuel flame shape. Similarly to the OH trends, the CO trends of the mixture fraction-based models are resembling each other. However, the PPSFM predicts maximum values in the mixing zone near downstream the burner tip in contrast to the SFM. Maximum CO emissions by the EDC model were predicted in the centre of the flame and 0.043 mol mol−1 CO on a dry base were calculated in the outlet. Thus, an incomplete burnout was predicted by the EDC aroused by the high burner chamber temperatures, which however, were not measured during the experiment, since the flue gas was cooled in the flue gas duct which shifted the equilibrium significantly towards CO2 conversion. The flame shape and length is qualitatively shown by the contours and the OH = 0.01 mol mol−1 iso-line in Fig. 6, but a straightforward comparison and evaluation of the spatial results needs to be done. This evaluation is presented in Fig. 7 for a temperature level of 1320 °C during oxy-fuel operation (lines) and for 10% v N2 in O2 (markers) as oxidizer. In this figure, the flame radius (lateral spreading) and the flame length at the cross-section along the burner axis are shown, wherein the origin of the axis of ordinates was set to the burner axis. The mixture fraction-based models predict a 60% longer flame compared to the EDC model (≈ 1 m ). The maximum flame radius was 0.15 m for the SFM, slightly less (0.14 m ) for the PPSFM and 0.08 m for the EDC model during oxy-fuel combustion. By the addition of nitrogen to the oxidizer, the OH radical formation becomes less intense and thus the position of the 1% v OH threshold shifts upstream towards the burner tip, which is accompanied by a simultaneous decrease in the flame radius. An exact measurement of the flame length involves complex measurements, which were not applicable with non-invasive methods. Thus, these calculated lengths and radii can be used as indicative values which need to be compared to experimental data since the threshold value of 1% v OH was arbitrarily chosen by the authors. However, the presented analysis reflects that different combustion models deliver distinctively different flame shapes and lengths. This needs to be taken into account during the model selection phase for any numerical study involving combustion reactions. The main parameters for NOx formation, such as temperature and OH radical formation, were scrutinized in the preceding paragraphs.

Fig. 5. Species heat capacity differences between the adapted (for EDM) and standard ANSYS Fluent properties.

The maximum temperature was again predicted in the annular burner gap (2710 °C) and the temperature further downstream was 2100 °C. When the specific heat of the flue gas was unmodified, a maximum flame temperature of 3810 °C was predicted, which is physically not achievable with the given boundary conditions. As stated in Section 2, the skeletal25 mechanism [29] was also used for the EDC simulations. The computational expense of these simulations was four times higher than that of mixture fraction-based models. The EDC model can thus not be taken into account as a fast-solving alternative, but its results have to be viewed as a reference. Fig. 3(d) shows that the contours resemble a combination of the PPSFM and the EDM results. The flame is not attached to the burner tip and the maximum temperatures were calculated near the adiabatic flame temperature (2948 °C) . The contours show a convex shape at the beginning of reaction zone, contrary to a concave slope of the eddy-dissipation model. The discussed model-specific in-flame differences do not significantly affect the overall temperature distribution in the burner chamber, as shown in Fig. 2. However, peak temperatures and radical formation have a major influence on the NOx formation rates, which will be discussed later. The flame shape is often characterised by the spatial distribution of the hydroxyl radicals and their availability affects the formation of NOx emissions [41–44]. Thus, in a next step, the OH and CO contours will be analysed along a vertical cross-section at the burner axis. The left side of Fig. 6 shows the spatial OH radical prediction and an arbitrarilychosen iso-line of OH = 0.01 mol mol−1 of the three models using the detailed skeletal25 reaction mechanism [29]. The trend of the 1% v OH iso-line was used to analyse the flame shape and length, since Fig. 6 illustrates that this threshold value can be used to represent the geometric border of the OH envelope. It is noted that no plot will be shown for the EDM results, since radical formation can not be predicted by the global 2-step mechanism employed by this model. The mixture-based models (cf. Fig. 6a and 6c) show very similar results, whereas the EDC model (Fig. 6(e)) differs distinctively from them, showing a shorter, locally more intense reaction zone wherein peak OH mole fractions occur. The simulation results for the mixturefraction based models show a drop-like envelope originating from the 8

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Fig. 6. OH (left) and CO (right) contours for three models along a vertical cross-section at the burner axis for different combustion models at 1320 °C (oxyfuel).

chamber, (2) uniform temperature distribution in the burner chamber, and (3) low NOx emissions. Despite oxy-fuel combustion and the corresponding high maximum flame temperature, very low NOx emissions were achieved over the entire oxidizer spectrum. NOx emissions at 1320 °C were 246, 595, 955 and 1324 ppm v on a dry basis for 0, 2, 5 and 10% v N2 in O2, respectively. A temperature increase to 1450 °C led to marginally higher NOx emissions (1370 ppm v ) with 10% v N2 in O2. It needs to be mentioned, that the NOx emissions during oxy-fuel operation (246 ppm v ) originate from the available nitrogen content in the natural gas (0.66% v ) used during the experiment, since the burner chamber was operated under a controlled positive gauge pressure of 20 Pa . Thus, NOx emissions resulting from air leakage into the system were not possible in the experiment and this naturally occurring nitrogen content in the natural gas contributes to thermal NOx formation since it is not chemically bound by hydrocarbons. This result is an important finding and underlines that NOx emissions cannot be completely eliminated during the combustion of natural gas, despite the use of pure oxygen as oxidizer. As it was mentioned in Section 2, the NOx emission calculations in the CFD simulations were done in a post-processing step, using the temperature and species concentrations from the converged numerical data. The calculated emissions based on the SFM simulations are shown in Fig. 8(a), wherein huge deviations across the entire operating points are evident. The steady-flamelet approach in combination with the critical outlet velocity fraction of the used burner leads to a significantly too low flame peak temperature, which is responsible for the underestimation of the NOx emissions across the entire nitrogen bandwidth in the oxidizer. However, the general trend for higher NOx emissions with rising burner chamber temperature are predicted accordingly by the model, disregarding the huge overall errors. Fig. 8(b) shows that the NOx emissions can be predicted with the eddy-dissipation model, based on two chemical global reactions omitting detailed radical formation. This result, however, is based on partial-equilibrium approaches to calculate the required O, OH and CH radical formation rates required for thermal and prompt NOx formation rates. The assumption of partial-equilibrium can be justified by a reduction in the importance of radical overshoots at higher flame

Fig. 7. Position of the 1 % v OH iso-surface predicted by the SFM, PPSFM and EDC model for oxy-fuel and 10 % v N2 in O2 as oxidizer.

Thus, in the following sections, the experimental and numerical results of the NOx investigations will be presented. The experiments were conducted for 1320 and 1450 °C under fuel-lean conditions with an equivalence ratio corresponding to 3% v O2 in the dry flue gas. As given in Table 1, five distinct oxidizer mixtures were used for the NOx formation investigations, which represent possible oxidizer compositions resulting from industrial PSA air-separation units or possible air-leaking into the burner chamber. It is noted that classical air-separation units using the liquefaction of gases produce high-purity oxygen and are thus not affected by this discussion. However, due to the design of the air control valve, which was used to mix air and pure oxygen to the required composition, the minimum experimentally realizable nitrogen content in the oxidizer at 600 kW was 2% v in O2. Thus, for 1% v N2 in O2 only numerical results are available. Moreover, every operating point was confirmed and validated by three independent, succeeding experiments wherein steady-state conditions were verified by constant temperature, gas analysis and pollutant emission readings. Fig. 8 presents the comparison of experimentally determined NOx emissions (markers) and numerical data (lines) for the four combustion models investigated in this study. The experimental data underlines the success of the previously described design attempt of the applied burner: (1) combine good mixing of the flue gases in the burner 9

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Fig. 8. NOx emissions at 1320 and 1450 °C .

Fig. 9. Thermal NO formation rates and NOx emissions along a vertical cross-section at the burner axis for two combustion models at 1320 °C (10% v N2 in O2).

formation rates, and under-prediction of the flame temperature or marginally higher nitrogen contents in the fuel as provided by the gas suppliers’ composition analysis. The NOx emission prediction based on the steady-state results of the PPSFM (cf. Fig. 8(c)) is significantly improved compared to the SFMbased predictions. Similar to the results of the EDM, for nitrogen contents below 5% v in O2, NOx emissions are underestimated and above this nitrogen content, slightly overestimated. However, the quality of these results are of higher value, since the underlying combustion mechanism directly calculates the formation of O, OH, CH and several further species (e.g., CH2O, CH3, HCO, etc.) which adds to the

temperature and the O and OH concentrations are computed according to empiric Arrhenius-type rate expressions [45,46]. This method must be used, when instantaneous radical concentrations are not available due to the chosen species transport model. The presented data reflects the good fit between experiment and simulation and underlines that this approach is a valid alternative for a fast-solving model to predict NOx emissions. However, it needs to be mentioned that the specific heat capacity of the flue gas mixture was numerically adapted to numerically account for radical formation and thus limit the maximum reachable temperature [36]. Highest deviations occurred when using pure oxygen as oxidizer, which can be attributed to too low radical 10

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classical species-transport models were investigated. Two mixture-fraction based models and two classical species transport models were investigated for their ability to: (1) predict the flame shape and temperature, (2) calculate the OH and CH radical emissions responsible for NOx formation, and (3) fast an accurately predict the NOx emissions during oxy-fuel combustion and during the addition of up to 10% v N2 in O2. The simulations showed that the widely-used steady-flamelet model is not able to correctly predict the flame shape and temperature, due to a velocity fraction between the oxidizer and the fuel near unity. Moreover, by modifying the flue gas specific heat, the numerically efficient eddy-dissipation model reached realistic flame temperatures but it cannot not calculate the required radical formation. However, these radicals can be calculated using a partial-equilibrium formulation and thus NOx emissions could be predicted in good agreement to the experiment. Nevertheless, this model lacks the ability to describe detailed combustion reaction pathways and can therefore not be counted as an adequate solution to the postulated problem. The EDC model is the computationally most demanding model and the calculated results (flame shape and maximum temperature) differ from the mixture-fraction based models. The simulations conducted with the EDC model result in an overestimation of the NOx emissions, which are caused by the high maximum flame temperature. However, the EDC model in combination with the skeletal25 [29] mechanism is computationally extremely demanding and thus leads to high computing times. This in turn disqualifies the EDC as a potential candidate for a fast-solving model to predict NOx emissions and thus renders the findings as reference results. Last, the second mixture-fraction based model, the PPSFM provides a significantly better flame prediction than the steady-flamelet model with comparably low computing times. A good fit between experimental and numerical NOx emissions could be achieved with the model, but they are slightly underestimated below 2% v N2 in oxygen as oxidizer. This model represents the best trade-off between accuracy and computational efficiency and thus turned out to fulfil the problem description. However, these simulation results underline that the prediction of NOx emissions is extremely dependent on reliable combustion models accompanied by realistic local temperature predictions. For oxy-fuel combustion, these local maximum flame temperatures are both numerically and experimentally difficult to determine, which still needs to be accomplished by the scientific community. Concluding, this study showed that the partially-premixed steady flamelet model offers accurate flame shape and NOx predictions in combination with a low computational expense and is thus not only suitable for semi-industrial investigations, but also for industrial-size applications, for both retro-fit and the development of steel reheating, glass or any other industrial high-temperature furnaces.

complexity and significance of the approach. In Fig. 8(d), the significantly overestimated results for the NOx predictions based on the EDC combustion simulations are shown. It is again highlighted, that for the EDC simulations, the skeletal25 [29] mechanism was used and thus the instantaneous radical concentrations of O and OH were used, in contrast to the simplified approach applied in the EDM simulations (cf. Fig. 8(b)). The overestimation is based on a too high flame temperature in combination with an intense OH radical zone within the flame. Generally, a parabolic NOx trend is calculated with increasing N2 content in the oxidizer. However, the NOx emissions are overestimated by a factor of ≈ 3. The underlying NO formation rates are shown in Figs. 9(a) and 9(c), in order to investigate the source of NOx production. It is noted that the thermal NO formation rate was 1.5 orders of magnitude higher than the prompt NO formation rate, since local fuel-rich zones could be avoided with the applied burner. Thus, only the thermal NO formation rates are presented in the figure. Figs. 9(a) and 9(c) show distinctly different simulation results for the location of the NO emissions. In the PPSFM, the production of NO is concentrated near the burner tip, which corresponds to the maximum flame temperature (cf. Fig. 3(c)). Despite reaching almost the adiabatic flame temperature with this model in this region, the production of NO focuses on a locally very small spot with low residence time due to the high momentum. The temperature evolution predicted by the EDC model is more gradual and thus the thermal NO production occurs further downstream the burner tip. NO is predicted with the EDC model in a locally bigger area, but with lower magnitude, compared to the PPSFM results. The resulting NOx emissions (sum of thermal, prompt and N2O intermediate) for the models discussed above are shown in Fig. 9(b) and (d). On the one hand, the comparably low flame temperature calculated by the PPSFM results in NOx emissions as they were measured during the experiment. On the other hand, the gradual temperature increase provided by the EDC base simulation results in local NO peaks and ultimately to the overshoot given in Fig. 8(d). However, the best fit between the experiment and simulation could be achieved with the PPSFM and with it, fast (computing time of the PPSFM was 1/4 of the EDC model) and accurate NOx emission predictions can be done for oxy-fuel combustion of natural gas with up to 10% v N2 in O2. 4. Summary and Conclusion This work presented the development and critical analysis of a fast and accurate model to predict NOx emissions during oxy-fuel combustion of natural gas. The model represented a semi-industrial hightemperature burner chamber operated at 1320 and 1450 °C at a constant natural gas (including naturally occurring nitrogen) input of 600 kW and a fuel-lean equivalence ratio corresponding to 3% v O2 in the dry flue gas. The oxidizer mixtures consisted of pure oxygen and contained up to 10% v N2 in O2, as they can occur during the production of oxygen via pressure-swing-adsorption air-separation units. Moreover, air leakage into industrial burner chambers cannot always be prevented and the investigated oxidizer mixtures can represent these variable leakage rates. All experimental data required for the validation of the numerical model were taken from in-house tests using a specifically designed oxy-fuel jet burner. The conducted experiments showed a gradual, parabolic increase in the NOx emissions with increasing nitrogen content in the oxidizer with maxima at 10%v N2 in oxygen of 1324 and 1370 ppm v NOx (at 3% v dry O2 reference) at 1320 and 1450 °C, respectively. As it was the focus of this study, a fast-solving computational fluid dynamics (CFD) model, based on the finite-volume method, representing the experimental setup, was developed to scrutinize NOx formation. Steady-state simulations including a skeletal mechanism applicable for oxy-fuel combustion were conducted and the NOx emissions were calculated in a post-processing step. Two mixture fraction-based models were investigated to reduce the computational expense and additionally two

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements Funding of this work has been provided by the Austrian Research Promotion Agency (FFG) and the European Regional Development Fund (ERDF) in the course of the project ‘Multifuel High Temperature Oxygen Applications’ (grant Project Nos., 865595, eCall 14625282 and 872149, eCall 22662407), which is gratefully acknowledged by the authors. References [1] Baukal C. Oxygen-enhanced combustion. Industrial combustion. 2nd ed.CRC Press; 2013. ISBN 978-1-4398-6228-5 978-1-4398-6230-8.

11

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[2] Landfahrer M, Prieler R, Mayr B, Gerhardter H, Zmek T, Klarner J, et al. Characterization of the temperature distribution on steel tubes for different operating conditions in a reheating furnace using CFD and three different measuring methods. Appl Therm Eng 2018;133:39–48. https://doi.org/10.1016/j. applthermaleng.2017.12.098. ISSN 1359-4311. [3] Mayr B, Prieler R, Demuth M, Moderer L, Hochenauer C. CFD modelling and performance increase of a pusher type reheating furnace using oxy-fuel burners. Energy Proc 2017;120(Suppl. C):462–8. https://doi.org/10.1016/j.egypro.2017.07.222. ISSN 1876-6102. [4] Prieler R, Mayr B, Demuth M, Holleis B, Hochenauer C. Numerical analysis of the transient heating of steel billets and the combustion process under air-fired and oxygen enriched conditions. Appl Therm Eng 2016;103:252–63. https://doi.org/10. 1016/j.applthermaleng.2016.04.091. ISSN 13594311. [5] Schluckner C, Gaber C, Demuth M, Forstinger S, Prieler R, Hochenauer C. CFDmodel to predict the local and time-dependent scale formation of steels in air- and oxygen enriched combustion atmospheres. Appl Therm Eng 2018;143:822–35. https://doi.org/10.1016/j.applthermaleng.2018.08.010. ISSN 1359-4311. [6] Al-Abbas AH, Naser J, Dodds D. CFD modelling of air-fired and oxy-fuel combustion in a large-scale furnace at Loy Yang A brown coal power station. Fuel 2012;102:646–65. https://doi.org/10.1016/j.fuel.2012.06.028. ISSN 0016-2361. [7] Hill SC, Douglas Smoot L. Modeling of nitrogen oxides formation and destruction in combustion systems. Prog Energy Combust Sci 2000;26(4):417–58. https://doi.org/ 10.1016/S0360-1285(00)00011-3. ISSN 0360-1285. [8] Al-Abbas AH, Naser J. Effect of chemical reaction mechanisms and NOx modeling on air-fired and oxy-fuel combustion of lignite in a 100-kW furnace. Energy Fuels 2012;26(6):3329–48. https://doi.org/10.1021/ef300403a. ISSN 0887-0624, 15205029. [9] Karampinis E, Nikolopoulos N, Nikolopoulos A, Grammelis P, Kakaras E. Numerical investigation Greek lignite/cardoon co-firing in a tangentially fired furnace. Appl Energy 2012;97:514–24. https://doi.org/10.1016/j.apenergy.2011.12.032. ISSN 0306-2619. [10] Nikolopoulos N, Nikolopoulos A, Karampinis E, Grammelis P, Kakaras E. Numerical investigation of the oxy-fuel combustion in large scale boilers adopting the ECOScrub technology. Fuel 2011;90(1):198–214. https://doi.org/10.1016/j.fuel.2010. 08.007. ISSN 0016-2361. [11] Tan Y, Croiset E, Douglas MA, Thambimuthu KV. Combustion characteristics of coal in a mixture of oxygen and recycled flue gas. Fuel 2006;85(4):507–12. https://doi. org/10.1016/j.fuel.2005.08.010. ISSN 0016–2361. [12] Liu H, Zailani R, Gibbs BM. Comparisons of pulverized coal combustion in air and in mixtures of O2/CO2. Fuel 2005;84(7):833–40. https://doi.org/10.1016/j.fuel. 2004.11.018. ISSN 0016-2361. [13] Nozaki T, Takano S-I, Kiga T, Omata K, Kimura N. Analysis of the flame formed during oxidation of pulverized coal by an O2/CO2 mixture. Energy 1997;22(2):199–205. https://doi.org/10.1016/S0360-5442(96)00143-0. ISSN 0360-5442. [14] Andersson K, Normann F, Johnsson F, Leckner B. NO emission during oxy-fuel combustion of lignite. Indus Eng Chem Res 2008;47(6):1835–45. https://doi.org/ 10.1021/ie0711832. ISSN 0888-5885. [15] Hedley JT, Pourkashanian M, Williams A, Yap LT. NOx formation in large-scale oxyfuel flames. Combust Sci Technol 1995;108(4-6):311–22. https://doi.org/10.1080/ 00102209508960404. ISSN 0010-2202, 1563-521X. [16] Wang L, Haworth DC, Turns SR, Modest MF, soot Interactions among, radiation thermal. and NOx emissions in oxygen-enriched turbulent nonpremixed flames: a computational fluid dynamics modeling study. Combust Flame 2005;141(1):170–9. https://doi.org/10.1016/j.combustflame.2004.12.015. ISSN 0010-2180. [17] Awosope IO, Lockwood FC. Prediction of combustion and NOx emission characteristics of flameless oxidation combustion 28; 2005. [18] Hagihara Y, Haneji T, Yamamoto Y, Iino K. Ultra-low NOx oxygen-enriched combustion system using oscillation combustion method. Energy Proc 2017;120(Suppl. C):189–96. https://doi.org/10.1016/j.egypro.2017.07.164. ISSN 1876-6102. [19] Helou IE, Foale J, Giusti A, Sidey JAM, Mastorakos E. Experimental and numerical investigation of an ultra-low NOx methane reactor. Energy Proc 2017;120(Suppl. C):214–21. https://doi.org/10.1016/j.egypro.2017.07.167. ISSN 1876-6102. [20] Shakeel MR, Sanusi YS, Mokheimer EMA. Numerical modeling of oxy-methane combustion in a model gas turbine combustor. Appl Energy 2018;228:68–81. https://doi.org/10.1016/j.apenergy.2018.06.071. ISSN 0306-2619. [21] Gaber C, Demuth M, Prieler R, Schluckner C, Schroettner H, Fitzek H, et al. Experimental investigation of thermochemical regeneration using oxy-fuel exhaust gases. Appl Energy 2019;236:1115–24. https://doi.org/10.1016/j.apenergy.2018. 12.046. ISSN 0306-2619. [22] Sung CJ, Law CK. Dominant chemistry and physical factors affecting no formation

[23] [24]

[25] [26] [27]

[28]

[29] [30]

[31]

[32]

[33] [34]

[35] [36]

[37] [38]

[39]

[40] [41]

[42]

[43]

[44]

[45]

[46]

12

and control in oxy-fuel burning. Symp (Int) Combust 1998;27(1):1411–8. https:// doi.org/10.1016/S0082-0784(98)80547-X. ISSN 0082-0784. Zeldovich Y. The oxidation of nitrogen in combustion and explosions. Acta Physicochim U.S.S.R 1946;21:577–628. Fenimore C. Reactions of fuel-nitrogen in rich flame gases. Combust Flame 1976;26:249–56. https://doi.org/10.1016/0010-2180(76)90075-4. ISSN 00102180. ANSYS Inc. ANSYS FLUENT User’s Guide V19.0. Canonsburg, PA; 2017. ANSYS Inc. ANSYS FLUENT Theory Guide V19.0. Canonsburg, PA; 2017. Prieler R, Demuth M, Spoljaric D, Hochenauer C. Numerical investigation of the steady flamelet approach under different combustion environments. Fuel 2015;140(Suppl. C):731–43. https://doi.org/10.1016/j.fuel.2014.10.006. ISSN 0016-2361. Mayr B, Prieler R, Demuth M, Hochenauer C. The usability and limits of the steady flamelet approach in oxy-fuel combustions. Energy 2015;90(Part 2):1478–89. https://doi.org/10.1016/j.energy.2015.06.103. ISSN 0360-5442. Peeters TWJ. Numerical modeling of turbulent natural-gas diffusion flames [Ph.D. thesis]. TU Delft; 1995. Mayr B, Prieler R, Demuth M, Potesser M, Hochenauer C. CFD and experimental analysis of a 115kW natural gas fired lab-scale furnace under oxy-fuel and air-fuel conditions. Fuel 2015;159(Suppl. C):864–75. https://doi.org/10.1016/j.fuel.2015. 07.051. ISSN 0016-2361. Prieler R, Demuth M, Spoljaric D, Hochenauer C. Evaluation of a steady flamelet approach for use in oxy-fuel combustion. Fuel 2014;118(Suppl. C):55–68. https:// doi.org/10.1016/j.fuel.2013.10.052. ISSN 0016-2361. Prieler R, Mayr B, Viehböck D, Demuth M, Hochenauer C. Sensitivity analysis of skeletal reaction mechanisms for use in CFD simulation of oxygen enhanced combustion systems. J Energy Inst ISSN 1743-9671.https://doi.org/10.1016/j.joei. 2017.02.004. Smith GP, Golden DM, Frenklach M, Moriarty NW, Eiteneer B, Goldenberg M, et al. GRI-Mech 3.0.http://combustion.berkeley.edu/gri-mech/version30/text30.html. Smooke M, Puri I, Seshadri K. A comparison between numerical calculations and experimental measurements of the structure of a counterflow diffusion flame burning diluted methane in diluted air. 21st Symposium (International) on Combustion/The Combustion Institute 1986:1783–92. Peters N. Laminar diffusion flamelet models in non-premixed turbulent combustion. Prog Energy Combust Sci 1984;10(3):319–39. Rose JW, Cooper JR, Spiers HM. Technical data on fuel. British National Committee, World Energy Conference Edinburgh: Distributed by Scottish Academic Press, London. ISBN 978-0-7073-0129-7, oCLC: 3230621; 1977. Smith TF, Shen ZF, Friedman JN. Evaluation of coefficients for the weighted sum of gray gases model. J Heat Transfer 1982;104(4):602–8. Yin C, Rosendahl LA, Kr SK. Chemistry and radiation in oxy-fuel combustion: a computational fluid dynamics modeling study. Fuel 2011;90(7):2519–29. https:// doi.org/10.1016/j.fuel.2011.03.023. ISSN 0016-2361. Yin C, Johansen LCR, Rosendahl LA, Kr SK. New weighted sum of gray gases model applicable to computational fluid dynamics (CFD) modeling of oxy-fuel combustion: derivation, validation, and implementation. Energy Fuels 2010;24(12):6275–82. https://doi.org/10.1021/ef101211p. ISSN 0887–0624. Mackrory AJ. A mechanistic investigation of nitrogen evolution in pulverized coal oxy-fuel combustion [Ph.D. thesis]. Provo: Brigham Young University; 2008. Pfadler S, Beyrau F, Leipertz A. Flame front detection and characterization using conditioned particle image velocimetry (CPIV). Opt Express 2007;15(23):15444. https://doi.org/10.1364/OE.15.015444. ISSN 1094-4087. Tanahashi M, Murakami S, Choi G-M, Fukuchi Y, Miyauchi T. Simultaneous CH-OH PLIF and stereoscopic PIV measurements of turbulent premixed flames. Proc Combust Inst 2005;30(1):1665–72. https://doi.org/10.1016/j.proci.2004.08.270. ISSN 1540-7489. De Leo M, Saveliev A, Kennedy LA, Zelepouga SA. OH and CH luminescence in opposed flow methane oxy-flames. Combust Flame 2007;149(4):435–47. ISSN 0010–2180, DOI: 10.1016/j.combustflame.2007.01.008. Li ZS, Kiefer J, Zetterberg J, Linvin M, Leipertz A, Bai XS, et al. Development of improved PLIF CH detection using an Alexandrite laser for single-shot investigation of turbulent and lean flames. Proc Combust Inst 2007;31(1):727–35. https://doi. org/10.1016/j.proci.2006.08.015. ISSN 1540-7489. Drake M, Correa S, Pitz R, Shyy W, Fenimore C. Superequilibrium and thermal nitric oxide formation in turbulent diffusion flames. Combust Flame 1987;69(3):347–65. https://doi.org/10.1016/0010-2180(87)90126-X. ISSN 00102180. Westbrook CK. Chemical kinetic modeling of hydrocarbon combustion. Prog Energy Combust Sci 1984;10(1):57. https://doi.org/10.1016/0360-1285(84)90118-7.