Fluid Dynamics and Kinetic Aspects of Syngas Combustion

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1 Fluid Dynamics and Kinetic Aspects of Syngas Combustion A. Cuoci, A. Frassoldati, T. Faravelli, E. Ranzi!"#$%&"'()&*+,"+-."'"/$0+1$&(%"$2"+(+3)4(4)(%"$+-."'"/$+5+6*2"&(/)"/*+,"+1"2$)*0+3789:+ Abstract Aim of this paper is to present and discuss an effective methodology for the prediction of pollutants in combustion systems fed with syngas by using computational fluid dynamics (CFD) and detailed chemical kinetics. CFD can be successfully applied to the study of combustion devices, but its computational cost significantly increases with the number of chemical species (typically as NS 2 or NS 3 ). Even with the continuous increase of computer power, it is still unfeasible to directly couple fluid dynamics and detailed kinetics, especially when considering the typical dimensions of the computational grids used for industrial applications. However, if the main interest is the characterization of combustion systems in terms of pollutant emissions, it is possible to follow a different approach. In fact pollutant species affect only marginally the main combustion process and consequently do not influence the overall temperature and flow field. This consideration allows to post-process the CFD results with a detailed kinetic mechanism with a newly-conceived numerical tool, called Kinetic Post-Processor (KPP), able to predict the formation of different pollutants. A syngas jet flame has been simulated using the commercial code FLUENT and then postprocessed with a detailed kinetic scheme by means of the KPP. The successful prediction of NOx supports the use of this tool to improve the design of new burners with a particular attention to pollutants formation. 1. Introduction Synthesis gas (syngas) is a mixture of hydrogen ad carbon monoxide and it can be obtained from natural gas, coal, petroleum, biomass and even from organic wastes [1]. Syngas offers the opportunity to furnish a broad range of chemicals. Almost all hydrogen gas, whose demand from industry is constantly growing, is manufactured from syngas. The methanol synthesis and the Fischer-Tropsch synthesis remain the largest consumer of syngas. Syngas is also the principal source of carbon monoxide, which is used in a great number of carbonylation reactions. However syngas is also an increasing source of environmentally clean fuels and is a potentially major fuel for the production of essentially pollution-free energy. The use of syngas as fuel presents many advantages: its cost is generally lower than other fuels and it can be used for a clean combustion with a sensible reduction in pollutant emissions. A direct application of syngas as fuel that promises to increase is its use for Integrated Gasification Combined Cycle (IGCC) units for the generation of electricity from coal, petroleum coke or heavy residuals. In the next 10 years the amount of syngas employed in this manner may approach that used for other specific purposes. The syngas can be used as a supplemental fuel to reduce the consumption of fuels more expensive, such as pulverized coal and fuel oil [2]. Furthermore, it has the potential to be used as a reburning fuel to reduce NOx emissions. The design of combustion systems using syngas as fuel can take advantage of CDF in order to optimize their efficiency to respect the increasingly stringent limitations of pollutants emission. The modeling of such systems is a very difficult task: in practical combustion devices the flow is turbulent and involves strong interactions between fluid mixing and chemical reactions. Moreover the computational cost significantly increases with the number of chemical species. It is still unfeasible to directly couple fluid dynamics and detailed kinetics, especially when considering the typical dimensions of the computational grids used for industrial applications. In this work a different approach to partially overcome IV6.1

2 ;<&.+1((&")4+*)+-*'=>?&"*)+ these problem is proposed. Pollutant species affect only marginally the main combustion process and consequently do not influence the overall temperature and flow field. This consideration allows to post-process the CFD results [3] [4] (usually obtained using a simplified kinetic scheme) and accurately predict the formation of different pollutants ( such as NOx, CO, unburned hydrocarbons, PAH and soot). In order to show the validity of this tool a syngas turbulent jet flame was simulated and post-processed. 2. Experiment The flame studied in this work has been experimentally investigated by Barlow et al. [5]. The compositional measurements have been conducted at Sandia National Laboratories, Livermore, California; velocity measurements were obtained at ETH Zurich, Switzerland. The jet flame is unconfined and the syngas composition is 40% CO, 30% H 2, 30% N 2 by volume. The burner has a central conduct constructed from straight tubing with squared-off ends with an internal diameter of 4.58 mm. The thick wall of the tubing (~0.88 mm) allows a small recirculation zone that helps to stabilize the flame. The jet fuel velocity is ~76 m/s and its temperature 292K; the co-flow air conditions are 0.70 m/s velocity and 292K temperature. Experimentally the turbulent diffusion jet flame is fully attached to the nozzle. 3. Flame modeling The flame was simulated with the commercial CFD code FLUENT 6.2. Only one half of the physical domain has been considered due to the axial symmetry of the system and steadystate conditions have been considered. The computational domain included only the combustion chamber; the boundary conditions at the inlets were evaluated, both for the central jet and for the air co-flow, by the simulation of pipe flows with the same mean velocity. The adopted 2D grid was structured and non uniform, with high resolution in the flame region close to the inlets. The domain was resolved by 320x130 control volumes in a cylindrical coordinate system. For the spatial resolution the Second-Order Upwind Scheme was adopted. The segregated implicit solver was used with the SIMPLE procedure for the pressure-velocity coupling. For the pressure interpolation the PRESTO! (PREssure Staggering Options) algorithm was used. Turbulence was modeled by the RANS approach, using the closure model. However it is well known that model poorly predicts the velocity field in round jets: in particular it tends to overestimate the spreading rate and the decay rate [6]. Also in this case model over-predicts the diffusion of the central jet and underestimates the axial velocity on the centerline. In order to solve this problem the modification suggested by McGuirk and Rodi was adopted: the parameter -!B in model was corrected from 1.44 to 1.60 [7, 8]. Using this correction the model results are satisfactory and it can be used in order to avoid the additional computational expense of the Reynolds Stress Model (RSM) [9]. The radiative heat transfer of the unconfined flame was calculated with the DO model. The description of the turbulence-chemistry interactions represents one of the most difficult tasks in turbulent combustion; it is necessary adopt a model to describe the chemical reactions. In this work three different models have been applied: the Eddy Dissipation (ED) model [10], the Eddy Dissipation Concept (EDC) model [11, 12] and the Steady Laminar Flamelets (SLF) model [13, 14]. Here we presents only their main features. C,,D+!"??"#$&"*)+'*,(2E The idea of this model is to consider that chemistry does not play any explicit role while turbulent motions control the reaction rate. For the simple F # %G $$%! 1# %" 6 the fuel burning rate is estimated from fuel &! F, oxidizer &! G and products &! 6 mean mass fractions and from a turbulent mixing time & '"H : IV6.2

3 3&$2"$)+I(/&"*)+*J+&.(+-*'=>?&"*)+3n?&"&>&(+ 1 )! &! min, G &, 6 * ' )! &! min, G &, 6 * KF + 8(,! & F L - + 8(,! & F L - &. % 1# % % 1# % / '"H where 8 and L are two model constants. In this equation the reaction rate is limited by the deficient mean species. C,,D+!"??"#$&"*)+-*)/(#&+'*,(2E+In a turbulent environment, combustion takes place where there is a molecular mixing, that is at the small turbulence scales. According to the EDC model, the chemical reactions occur only in the J")(+?&%>/&>%(?. These fine structures are treated as a perfectly stirred reactors (PSR) with a residence time " M and mass fraction # M. Based on the mass transfer between the J")(+?&%>/&>%(? and their surroundings, the mean reaction term becomes: (1 2 0 * 0 K" + &" 3& " 2 *( ) 3 ( ) 1/ 2 1/ 4 ) 4 * ) 4' * 2* , ' /. / where $ is the density, % & the volume fraction of the fine the turbulent kinetic energy and N the laminar kinematic viscosity. The basic assumption is that chemical reactions are quenched if the characteristic chemical times for limiting species are longer than " M. I&($,D+ 2$'")$%+ J2$'(2(&?+ '*,(2E The main hypothesis of this approach is that the instantaneous thermo-chemical state of the fluid is related to a conserved scalar quantity known as the mixture fraction. In this way the species transport equations can be reduced to a transport equation for the mixture fraction and one for its variance. The temperature and thermo-chemical variables can be extracted from a flamelets library. The steady flamelets library was obtained by means of the PREPDF software and stored in look-up tables describing the dependence of flamelets on the scalar dissipation rate and the mixture fraction. A library of 20 different Strained Laminar Flamelets with strain rate up to about 1000 s -1 was used in CFD calculations. 4. The Kinetic Post-Processor The Kinetic Post-Processor (KPP) operates assuming the temperature and flow fields as predicted by the CFD and it solves mass balances in the cells with detailed chemistry at fixed temperature. Two major features make this approach advantageous over the direct coupling of detailed kinetics and CFD. The first is the possibility to lump and group several cells and the latter is to fix the temperature inside the cells. CFD solution provides detailed flow, composition and thermal fields allowing the identification of the zones where the detail of the description can be reduced. The fixed temperature inside the cells reduces the high non linearity of the system, mainly related to the reaction rates and to the coupling between mass and energy balances. The Kinetic Post-processor solves the mass balances over each computational cell, accounting for convection, diffusion and chemical reaction terms. The large non-linear algebraic system resulting from the previous steps is initially solved with successive substitutions, using CFD results as a first-guess. Only when residuals reach sufficiently low values, a modified global Newton method is applied to reach the final solution. The Newton method solves the main diagonal structures and approximates extradiagonal elements. This approach allows to save the memory allocation which is the real bottleneck of this very large system. Newton method involves the solution of a linear system with coefficient matrix given by the Jacobian matrix. It has to be clearly underlined that high attention is required in the convergence check, due to the trace amounts of pollutant components. Additional details on the Kinetic Post-Processor are reported elsewhere [15, 16]. IV6.3

4 5. Kinetic schemes ;<&.+1((&")4+*)+-*'=>?&"*)+ The kinetic scheme adopted for the ED simulation is very simple and consists of only three reactions (this model can t manage complex reaction mechanisms). On the contrary the kinetic scheme used in the EDC and SLF simulations is more accurate and consists of 13 chemical species and about 50 reactions [17]. Such model can significantly improve the predictions of temperature and compositional fields, but is unable to characterize the flame in terms of pollutant formation. Finally the KPP can work with a very detailed kinetic scheme [18] which allows to predict NO x formation and consumption in the flame, but is too large and computationally expensive for its direct application in the fluid dynamics simulations. 6. CFD results Figure 1 shows a comparison between experimental and computed profiles of temperature and mass fractions using three different combustion models. The prediction of the flow field (not shown) is satisfactory although the model tends to over-estimate the decay rate of the fuel jet. The ED model gives unsatisfactory predictions, both for the temperature and compositional fields. The SLF model tends to over-estimate the flame temperature and this fact represent a serious problem for the correct prediction of pollutant species with the KPP. The better agreement with experimental measurements is obtained for the EDC model: the temperature and compositional profiles can be consider very satisfactory. CO 2 is generally over-estimated while H 2 O and CO are predicted more precisely. F"4E+B+ 7('#(%$&>%(+$),+/*'#*?"&"*)+#%*J"2(?+$2*)4+&.(+$H"?+$&+,"JJ(%()&+,"?&$)/(?+J%*'+&.(+ =>%)(%+?>%J$/(+")+%$,"$2+,"%(/&"*)+O,+"?+&.(+")&(%)$2+,"$'(&(%+*J+&.(+)*PP2(0+H+"?+&.(+ $H"$2+,"?&$)/(+J%*'+&.(+)*PP2(+*>&2(&QR+---+C!-+'*,( C!+'*,( I9F+'*,(2+ IV6.4

5 7. Kinetic Post-Processor results 3&$2"$)+I(/&"*)+*J+&.(+-*'=>?&"*)+3n?&"&>&(+ A good prediction of the temperature field is obviously a necessary condition for the correct application of the KPP: the reliability of the KPP results in terms of pollutant predictions is strongly dependent on the completeness and consistency of the original CFD simulation. The Kinetic Post-Processor was applied to the flame simulated with EDC model using a detailed kinetic scheme with 52 chemical species and more than 200 reactions. This kinetic scheme allows to predict NOx formation and consumption in the flame. Figure 2 shows the comparison of measurements and predictions of NO for three different radial sections. The agreement is satisfactory, but in the very fuel rich-region. As reported by Barlow and Carter [19], a realistic target for the agreement between NOx measurements and predictions is 520%6530%, mainly due to uncertainties in measurements, boundary conditions and model assumptions. Experimental uncertainties are larger for very fuel-rich and for lean conditions at T<1000 K. The shape of the NO profile in flame is correctly reproduced at the different distances from the burner surface; the absolute value is slightly underestimated at high distances from fuel inlet because of the high chemical time scale of nitrogen chemistry, much greater than residence time in the fine structures, as already observed also by Magnussen [12]. Figure 3 shows the thermal field as predicted by the CFD code and the NO mass fraction field calculated by the Kinetic Post-Processor. It is possible to observe the effect of local temperature hot spots on the formation of NO in the flame zone. F"4E+;+ SG+'$??+J%$/&"*)+#%*J"2(?+$2*)4+&.(+/()&%(2")(+$),+$&+,"JJ(%()&+,"?&$)/(?+J%*'+&.(+ =>%)(%+?>%J$/(+")+%$,"$2+,"%(/&"*)+O,+"?+&.(+")&(%)$2+,"$'(&(%+*J+&.(+)*PP2(0+H+"?+&.(+ $H"$2+,"?&$)/(+J%*'+&.(+)*PP2(+*>&2(&Q+ Temperature (K) NO mass fraction F"4E+T+ 7('#(%$&>%(+$),+SG+'$??+J%$/&"*)+J"(2,?++ 8. Conclusions A turbulent non-premixed jet flame fed with syngas has been computed using different combustion models; the best agreement with measurements has been obtained with the EDC model. The results obtained for flow and thermal fields from the fluid dynamics were satisfactory. The CFD simulation was post-processed by using a newly-conceived Kinetic Post-Processor (KPP) using a detailed kinetic scheme to predict the formation of pollutants. The computed results have been validated in terms of NOx formation and the agreement with IV6.5

6 ;<&.+1((&")4+*)+-*'=>?&"*)+ experimental measurements is good. The proposed approach seems very encouraging as a solution to manage the complexity involved in the modeling of turbulent reactive flows using detailed kinetic models and can be applied for the design of practical combustion systems. Of course, the reliability of model predictions is strongly dependent on the completeness and consistency of the original CFD simulation. 9. References 1. Wender, I., K($/&"*)?+*J+?D)&.(?"?+4$?E Fuel Processing Technology 48:189 (1996). 2. Wu, K.T., et al., I&>,D+ *J+?D)4$?+ /*AJ"%")4+ $),+ %(=>%)")4+ ")+ $+ /*$2+ J"%(,+ =*"2(%E Fuel, : p Faravelli, T., et al., Computers and Chemical Engineering, : p Frassoldati, A., et al., Chemical Engineering Science, : p Barlow, R.S., et al., I$),"$UC7VAW>%"/.+ -GUV;US;+ F2$'(+!$&$+ A+ K(2($?(+ BEBE Sandia National Laboratories, Wilcox, D.C., 7>%=>2()/(+'*,(2")4+J*%+-F!. 2004: 2nd Edition, DCW Industries, Inc. 7. Hossain, M., Jones, J.C., Malalasekera, W., F2*X0+7>%=E+$),+-*'=>?&"*) 67:217 (2001). 8. Dally, B.B., D.F. Fletcher, and A.R. Masri, -*'=>?&E+7.(*%D+1*,(2")4, : p Zucca, A., 1*,(22")4+*J+&>%=>2()/(A/.('"?&%D+")&(%$/&"*)+$),+?**&+J*%'$&"*)+")+&>%=>2()&+ J2$'(?E Tesi di Dottorato di Ricerca in Ingegneria Chimica, Politecnico di Torino, Magnussen, B.F. and B.H. Hiertager, On mathematical modeling of turbulent combustion, 6%*/+-*'=>?&+3)?&+16:719 (1976). 11. Magnussen, B.F., G)+ &.(+?&%>/&>%(+ *J+ &>%=>2()/(+ $),+ $+ 4()(%$2"P(,+ C,,D+!"??"#$&"*)+ -*)/(#&+J*%+/.('"/$2+K($/&"*)?+")+&>%=>2()&+J2*XE Nineteenth AIAA Aerospace Science Meeting, St. Louis, Missouri, Magnussen, B.F., 1*,(2")4+*J+6*22>&$)&+J*%'$&"*)+")+4$?+&>%=")(+/*'=>?&*%+X"&.+?#(/"$2+ ('#.$?"?+ *)+?**&+ J*%'$&"*)+ $),+ /*'=>?&"*)E Eighteenth International Congress on Combustion Engines, International Council on Combustion Engines, Tianjin, China, Peters, N., 7>%=>2()&+-*'=>?&"*). 2000, Cambridge: Cambridge University Press. 14. Libby, P.A. and Williams, F.A., 7>%=>2()&+ K($/&")4+ F2*X?. 1994, London: Academic Press. 15. Frassoldati, A. et al., 6*?&A6%*/(??")4+*J+-F!+-*'#>&$&"*)?+X"&.+,(&$"2(,+@")(&"/?E 28th Meeting of the Italian Section of The Combustion Institute, 3-6 July 2005, Naples. p.iv Frassoldati, A., et al., G#&"'$2+,(?"4)+*J+=>%)(%?+$),+J>%)$/(?+X"&.+-F!+?"'>2$&"*)?+$),+,(&$"2(,+@")(&"/?E Sixth International Symposium on high Temperature Air Combustion and Gasification, Faravelli, T., Frassoldati, A., and Eliseo Ranzi, Y")(&"/+1*,(2")4+*J+1>&>$2+3)&(%$/&"*)?+ ")+SGAVD,%*/$%=*)+9*X+7('#(%$&>%(+GH",$&"*), Combustion and Flame0 132/1-2 pp (2003). 19. Barlow, R.S., et al., Combustion and Flame, : p. 4. IV6.6