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Modelling the combustion of pulverized biomass in an industrial combustion test furnace
Fuel 86 (2007) 1959–1965 www.fuelfirst.com
Modelling the combustion of pulverized biomass in an industrial combustion test furnace L. Ma *, J.M. Jones, M. Pourkashanian, A. Williams Energy and Resources Research Institute/Centre for Computational Fluid Dynamics, School of Process, Environmental and Materials Engineering, University of Leeds, LS2 9JT, UK Received 27 April 2006; received in revised form 11 December 2006; accepted 13 December 2006 Available online 19 January 2007
Abstract A CFD model that simulates the combustion of biomass in existing pf coal fired furnaces has been developed and model results for the combustion of a typical wood in a 1 MW industrial test facility have been presented. The model is primarily based on coal combustion submodels using an Eulerian–Lagrangian frame of reference. Biomass specific constants that define the submodels have been investigated and employed in the simulation. In particular, potassium release during biomass combustion and the formation of NOx have been simulated. Numerical predictions have been compared with some experimental measurements that have been taken and reasonably good agreement has been achieved. 2006 Elsevier Ltd. All rights reserved. Keywords: Biomass combustion; CFD model; Potassium release; NOx formation
1. Introduction The use of biomass as a fuel in existing coal fired power systems has been considered as an important step in reducing environmental emissions. Currently, co-firing coal with a limited amount of biomass, typically 2–20%, has been widely implemented in large-scale plants [1–3]. With smaller amount of biomass, the coal and biomass are milled together and both enter the same burner. With larger amounts, the biomass is milled separately and enters the furnace in a dedicated burner with other burners operating on coal. Industrial tests on firing pure biomass have been carried out in order to predict any problems that may rise when this is happening in large-scale plants. CFD modelling techniques for biomass combustion however still face significant challenges. One of the primary reasons is the diversity of the biomass fuels and the lack of knowledge of their combustion properties [3]. The presence *
Corresponding author. Tel.: +44 113 343 2481; fax: +44 113 246 7310. E-mail address: [email protected] (L. Ma).
0016-2361/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2006.12.019
of potassium in the biomass has become a significant issue since it is implicated in slagging, fouling and corrosion to the combustion system [4]. However, the detailed mechanisms of the potassium release during combustion and reaction in the combustion gases are still not well known. Modelling NOx formation is different from that of coal and the presence of alkali metals in the fuel can add to the complexity [5]. There are numerous other issues that prove to be problematic, such as the large particle sizes and the significant irregularity in shapes [6]. At the moment, there are a very limited number of numerical simulations of pulverized biomass combustion using detailed combustion models and they are focused on biomass co-firing [7,8]. In this paper, we present a CFD model and some of the model predictions for the firing of pure pulverized biomass in an industrial combustion test facility. The release of potassium and the formation of NOx have been simulated. Model predictions have been compared with the measurement data that were collected during the combustion test performed on the furnace.
1960
L. Ma et al. / Fuel 86 (2007) 1959–1965
2. The fuel and the combustion test facility The fuel that employed is a wood with thermal and chemical properties listed in Table 1. The fuel as received is in a wide range of particle shapes, typically with a fraction of flat chip and cylindrical shapes also with some small spherical particles. The aspect ratio of the particles received is usually very high, typically in the magnitude of 10. The sizes of the particles are also larger than that of typical pf coal with a particle size range of 90 lm–1500 lm. If represented by the Rosin-Rammler particle size distribution then the spread parameter is about 1.56 with a mean size of about 332 lm. Experimental investigations have been carried out in the 1 MW pulverized fuel combustion test facility (CTF) situated at the Power Technology Centre, UK. The CTF consists of a single burner and a combustion chamber with an over-fire air facility which is shown in Fig. 1. The combustor was a three-staged, wall-fired, low-NOx swirl burner of generic design widely used in large-scale power utilities. The furnace is fully refractory-lined and cooled with recirculating water. The CTF is originally designed for performing coal combustion test that replicates the time– temperature history of a coal particle entering a full-scale wall-fired furnace. In this investigation, wood has been used as the fuel and the measurements performed provide necessary boundary conditions and the validation data Table 1 Chemical and thermo physical properties of the fuel (%wt ar) Ultimate C H O Cl K N S
Proximate 47.8 5.9 37.6 0.02 0.26 0.17 0.026
Moisture VM FC Ash Physical property Density, kg/m3 Specific heat, J/kg K
10.1 69.4 18.0 2.5 550 1670
for the CFD model. In the combustion trial pre-ground wood was fed to the burner with the primary air at the ambient temperature. The main combustion air was indirectly heated prior to be split between the secondary, tertiary registers and the over-fire air has been introduced at the upper vertical section of the furnace. Flue gas temperature and composition have been recorded including NOx and CO2 contents via furnace ports. This wood combustion model has been previously validated [8] and was used with little change here. 3. The CFD model When biomass particles are injected into the furnace they quickly heat up at a rate in the order of 103–104 K/s. The particle will then experience four distinctive processes, namely drying, volatile release, volatile combustion and char combustion. It is noted that for those particles with a large aspect ratio and a significant irregular shape will experience significant differences in heating rate at different parts of the particle. In an extreme case, the different combustion stages may coexist within a single particle. 3.1. Particle transport In the framework of the Eulerian–Lagrangian modelling approach, the movements of the biomass particles in the furnace are numerically tracked according to the particle momentum equation. In most cases, aerodynamic drag is the dominant force, particularly for those small fuel particles that possess a high surface-volume ratio. For a spherical particle the drag is often obtained experimentally and it is usually expressed as a function of the particle Reynolds number [9]. In order to take into considerations the aerodynamic effect of the irregularity of the biomass particle shape, a shape factor was introduced defined as the ratio of the surface area of an equivalent spherical particle and the real particle surface area [8,10], so that empirical formulae can be employed to calculated the drag on the non-spherical particles. There are numerous other ways of calculating drag coefficient for irregular particles and inevitably, there are also debates on the accuracy of any of the formulations developed to cover the vast variety of particle shapes [11]. 3.2. Particle heating and drying The heating of a particle is governed by the following equation of heat balance: cp;p
Fig. 1. Computational mesh employed.
dT p ¼ Qc þ Qr Qv dt
ð1Þ
where Tp refers to the temperature of the particle, cp,p is the specific heat of particle, Qc, Qr and Qv are the heat transfer due to convection, radiation and vapourization of the particle. The convection heat transfer to the particle is calculated based on the Reynolds number of the particle and
L. Ma et al. / Fuel 86 (2007) 1959–1965
the Prandtl number of the continuous phase [12]. For radiation heat transfer we used the P1 radiation model. The drying process is controlled by the temperature of the particle and the concentration of the water vapour in the gas around the particle. It is assumed that the rate of vapourization is governed by the diffusion process that is driven by the gradient of the water vapour concentration between the surface of the particle and the bulk gas. Further, it is assumed that the partial pressure of the vapour at the surface of the particle is equal to the saturated water vapour pressure at the temperature of the particle and is not influenced by the dissolved salts. Therefore, the molar flux of water vapour, Nw, is [12]: p p N w ¼ k w sat X w ð2Þ RT RT p where p is the pressure of the bulk gas and psat is the saturated water vapour pressure. Xw is the molar fraction of the water vapour in the bulk gas and R is the gas constant. The mass transfer coefficient, kw, is calculated from a Nusselt correlation as follows: kwd p 1=2 ¼ 2:0 þ 0:6Red Sc1=3 Dw
ð3Þ
where dp is the diameter of the particle, Dw is the diffusion coefficient of water vapour in the bulk gas, Red is the particle Reynolds number and Sc is the Schmidt number. It is assumed that the wood particle starts to give off moisture at a relatively low temperature of about 25 C with a very low vapourization rate controlled by its saturation pressure. The rate of vapourization will increase with increase in the particle temperature until the particle reaches water boiling point where all the heat received would be used for vapourizing the moisture. 3.3. Devolatilisation and potassium release Pyrolysis of biomass typically starts to give off volatiles at a temperature of 160–300 C [13,14]. Therefore, we assume that the devolatilisation temperature of the wood commences at about 200 C and a single rate temperature dependent devolatilisation model expressed in an Arrhenius form has been employed. The data for the kinetic rate of the volatile release is calculated using the network program, FG-BioMass [15], which involves the use of a model that accounts for the decomposition of the fuel matrix during particle heat-up. The values obtained for the pre-exponential factor and the kinetic energy for wood are A = 6 · 1013 1/s and E = 2.5 · 108 J/kg mol. The values used in this model may be subject to debate since there is a considerable diversity in the values of kinetic data in the literature. The exact mechanism of potassium release during biomass combustion is still uncertain. Experiments suggest that the release of potassium in a combustion environment leads to KOH and KCl in the vapour phase. Some loss of potassium occurs during devolatilisation and the chloride in the
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fuel has been cited as a shuttle for potassium between the biomass particle and deposit in the combustor [16] or emitted as an aerosol [17]. The hydroxides, formed in the drying stage can increase the volatility of the potassium due to the high vapour pressure [16,17]. Fuels with a high chlorine content, such as straw, tend to form KCl while fuels with a low chlorine content, such as wood, mostly form of KOH. In contrast, silica can efficiently capture the potassium in the ash and so prevent potassium evaporation [16]. Experiments suggested that the release of K during devolatilisation may occur over two temperature stages [18–21]. At a temperature about 300–500 C, a small amount of potassium is released whilst a significant amount is released at a higher temperature of 700–1000 C. However, most experiments of potassium release have been performed under low heating rate and this raises some uncertainty about how well the findings extrapolate to the high temperature and high heating rates that exist in the situation of pf combustion. Since the chlorine content of the wood investigated here is low, we assume that the release of potassium in the fuel during devolatilisation rapidly forms KOH (or K2H2O2 in the gas phase). In the calculation, only potassium evolution during devolatilisation has been considered and the rate is assumed to be the same as that of the overall volatile release. Potassium is the most readily vapourized metal present and has much greater influence than the other metal components and therefore they were not taken into consideration. Any potassium chloride present is converted to potassium hydroxide by the following overall reaction involving dissociation and reaction with water decomposition intermediates: KCl þ H2 O ) KOH þ HCl
ð4Þ
It is recognized that potassium is also released during the biomass char combustion and it may be in the form of potassium hydroxide, potassium chloride or other potassium compounds [22] and this is still a subject of current investigation. 3.4. Volatile combustion and NOx formation Biomass volatiles consist mostly of CO, CO2, H2O, with CH4, H2 and other inorganic products [1,2]. Although the details of the compositions of the biomass volatile may be obtained through chemical analysis techniques, uncertainty remains for high temperature volatiles. The composition of the volatiles that has been used in the simulation was estimated based on the data in Table 1 from the proximate, ultimate, and the ash analyses of the fuel. During volatile combustion, the organic part of the volatiles is oxidised to form CO2 and water in the gas stream whilst the inorganic vapour may experience other transformations, such as sulphations, etc. For the modelling of oxidation of the hydrocarbon species, a global one step reaction mechanism has been employed: Volatiles ðHCÞ þ 1:1O2 ¼ CO2 þ 0:9H2 O
ð5Þ
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The effect of the turbulence on the reaction has been modeled with the Eddy Dissipation turbulence-chemistry interaction model and the RNG-k-e turbulence model. NO can be formed by the thermal mechanism or from fuel NO in an analogous way to coal [22]. However, in biomass fuel-N can exist both as heterocyclic compounds and as amino acid groups in proteins. Hence, NO can be formed from the former via the HCN intermediate by the De Soete fuel-N mechanism [12]. The protein nitrogen can be converted [23] into both HCN and NH3 which react to N-intermediates and ultimately into NO and other stable combustion products. Again this can be described by the De Soete mechanism, and the only difference between these routes is the choice of kinetic constants. The protein nitrogen content of wood has been estimated to be in the range of 70–90 wt% of the total nitrogen [23] and therefore a combination of HCN and NH3 routes has been considered. 3.5. Char combustion In the case of biomass char, the burnout rate is more complicated as it is affected not only by the composition of the biomass fuel but also by the shape and size of the particles. The biomass char is much more reactive than coal char for a number of reasons. Unlike coal particles that soften and tend to become spherical, biomass particles mainly keep their original irregular shape during devolatilisation. Often the process leads to a partially activated char with a high surface area and this results in a larger oxygen flux into the biomass char particles and contributes to a much higher char combustion rate than spherical coal particles. The structural disorder may also lead to a higher reactivity of biomass in the later stages of combustion [24]. The catalytic effect of metals in the biomass char may also increase the reactivity of the char [25]. However, it is generally believed that at high temperature char combustion in pf furnaces, where a very fast chemistry exists, the catalytic effect whilst still present is not as significant as it is in low temperature combustion [8]. Experiments have shown that the lingnin content maintains the particle shape until the majority of combustion has taken place [26] and it was assumed that fragmentation did not take place. These analyses are basically qualitative and an advanced char combustions model for biomass is yet to be developed. Therefore, here we model char combustion using Smith’s intrinsic model as before [8] and increased the reaction rate by a factor of 4 in order to represent the high burning rate of the biomass char particles [24].
tion of the particle tracks in the furnace which in turn influences the overall combustion of the fuel. The fuel particles are fed in at the entrance plane of the burner with the flow of primary air and this allows the evolution of the particle concentration profile to be simulated. As a result, model predictions show a clear stratified particle concentration at the exit plane of the burner into the combustion chamber generated by the swirling flow inside the burner. 4. Results A number of simulations have been performed and Fig. 2 shows the typical trajectories of the biomass particles predicted in the furnace. The plot shows the situation at about 3 s after the particles are released into the furnace, when the majority of them have not yet fully penetrated the furnace. The typical particle residence time calculated is about 5 s while some of the particles may stay over 10 s. The trajectories in the plot are colored by the mass of the particle and thus we can observe that particles lose mass rapidly just after the front of the burner and this is where the main drying and devolatilisation proceeds. When they have reached the vertical chamber of the furnace most of the particles have become much lighter but a significant number of particles are observed to fall to the bottom of the furnace due to gravity. These particles consist of both large sized particles and the particles that have not completely devolatilised. The prediction is consistent with the experimental observations which showed that the larger
3.6. Computational mesh and boundary conditions The computational domain employed 300,000 computational cells as shown in Fig. 1. Special attention has been paid on the meshing of the burner and the nearby regions since the accurate modelling of the fluid and particle flows inside the burner is considered to be crucial to the predic-
Fig. 2. Predicted tracks of particle colored by particle mass (kg).
L. Ma et al. / Fuel 86 (2007) 1959–1965
biomass particles tend to fall to the bottom, many near the burner, while others followed the gas flow field out through the furnace. Fig. 3 shows the heating up and the mass losses of three typical wood particles entering the furnace, namely, 150 lm, 320 lm and 640 lm. Particles heat up very quickly to about 1500 K in less than 0.25 s after being injected at ambient temperature. During this time the particles dry, then lose volatiles followed by char combustion. Char particle combustion is relatively slow, and as combustion proceeds the reactivity of the char particles decrease resulting in a drop in particle temperature and a slow decrease in the particle mass. Small particles are heated much quicker and release volatiles earlier than large particles. At present, the model accounts for potassium release during devolatilisation although some is retained in the char from which it is slowly lost by evaporation. Further validation of the model was obtained and it is found that experimental measurements of CO were less than 20 ppmv which justified the use of the single step reaction. Fig. 4 shows the simulated
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contours of KOH concentration in the vertical symmetric plane of the furnace. A KOH concentration of 7 ppmv is predicted in the exit plane of the furnace. One of the characteristics of biomass is its relatively low heating value compared with that of the coal and consequently the overall temperature of the furnace is lower than that of the coal combustion. Fig. 5 shows numerically predicted contours of the gas temperature. A temperature of about 1000 C was obtained at the exit of the furnace which is in good agreement with the measured data, and the errors are within 10% of experiment data. The predicted carbon dioxide is 13 mol% at the furnace exit, which
Fig. 3. Typical temperatures and mass losses of wood particles as a function of residence time. Fig. 5. Predicted contours of gas temperature (C).
Fig. 4. Predicted contours of potassium concentration (mol/mol).
Fig. 6. Predicted NO formation in the furnace through the NH3 route (mol/mol).
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L. Ma et al. / Fuel 86 (2007) 1959–1965
is also in good agreement with the measurements with an error of about 15%. Both the HCN and the NH3 route have been considered for the NOx formation and Fig. 6 shows the predicted NO concentrations through NH3 route. At the exit of the furnace a NO concentration of 280 ppmv has been predicted using the NH3 mechanism only. When using the NCH mechanism alone, the calculated NO was 90 ppmv. However, using a combination of the two, on a 1:3 basis (this means 1 NO from NH3 and 3 NO from HCN), yields a predicted concentration of 137 ppmv NO which is similar to the 140 ppmv found experimentally. This is in consistent with the protein content of 70–90% of the total nitrogen content. 5. Discussion Irregularity in the shape of the biomass particle causes substantial complexity for numerical simulation, particularly for those of large aspect ratios. A particle shape factor of 0.25 has been used based on measurements of the fuel samples. The particle shape has a significant influence on the particle trajectories and the residence time, as well as the ash deposition characteristics. Calculations show that if the aspect ratio of the particle is ignored then much more particle material ends up in the bottom ash. The introduction of the shape factor to some extent represents the effect of the aspect ratio on the motion of the particle, but the details, such as the possible tumbling of the particle during their penetrating the furnace are not calculated exactly. In addition, when the particle size becomes large, then the heating up of the particles will be influenced by the heat transfer within the particles where a significant temperature gradient may build up. However, for particles that have a mean size of about 0.33 mm, the temperature gradient within the particle should not have a significant effect on the heating up of the particle [27]. Numerical modelling of the release of potassium during combustion presents a substantial challenge because the detailed mechanism of the potassium release is not well known. In this paper, we assume that a portion of the potassium is released together with the devolatilisation products. From Fig. 4 it can be seen that the majority of the volatiles is released at about 700 C and this matches with the typical temperature regions where potassium is released from the biomass [18–21]. The potassium that has been released into the gas flow will undergo complex transformations and in different temperature regimes will form KOH, K2H2O2, KCl and K2SO4, and which is expected to nucleate at about 800 C as well as forming aerosols in the flue gas. These will condense and deposit on the surfaces of the fly ash/aerosols and on the superheater system. Modelling of potassium release makes it possible to track ash deposition in the system [28]. The high moisture content of the biomass has an impact on the heating-up and ignition of the fuel. For the relatively low moisture content in the wood used (that is 7%
by mass), the drying process is completed within about 0.15 s. In addition, a much lower temperature in the furnace has been observed compared with the coal combustion in the same furnace due to the lower calorific value of the fuel. A relatively higher percentage of the unburned carbon is observed in the ash and this is because of very low ash content which makes any retained carbon in the ash appear to be large. NOx control in the combustor is accomplished by the combination of the three-staged low NOx burner and the use of over-fire air. The reduced flame temperature also discourages the formation of NOx. The fuel-N produces both NH3 and HCN although the process can be influenced by the presence of the potassium and sodium in the gas stream [5]. This is due to the reactions with species such as O and OH, leading to a net formation of water through the following reactions: KOH þ H ) K þ H2 O H þ O2 ) OH þ O
ð6Þ ð7Þ
KO2 þ OH ) KOH þ O2 K þ O2 þ M ) KO2 þ M
ð8Þ ð9Þ
and this results in a decrease in the radical pool. However preliminary calculations show that the effect is small in this system. 6. Conclusions This paper presents a CFD model that predicts the combustion of typical wood fuel in an industrial pf furnace. The key combustion processes have been modelled in detail with an Eulerian–Lagrangian approach. Models for NOx formation and the potassium species release have been developed. The potassium together with the overall volatile release is modelled using a single rate kinetic model which gives off most of the volatile at 700–1000 K and this matches well with the temperature regimes where potassium species are released. A potassium concentration of about 7 ppmv has been predicted in the exit plane of the furnace which also falls into the typical regions of measured potassium release in the experimental investigations. Good agreement between the predicted and the measured furnace temperature and concentrations of CO2 and NOx has been achieved. The modelling of potassium release makes it possible to calculate both the potassium concentrations in the gas flow and the remaining potassium in the biomass ash thus paving the way to the development of the advanced ash deposition models for the combustion system. Acknowledgements The authors thank B. Goh at E.ON UK plc, and L. Darvell at the University of Leeds for their support in providing key boundary conditions and measurement validations for the study. The authors thank EPSRC for
L. Ma et al. / Fuel 86 (2007) 1959–1965
funding through the Supergen Consortium in Biomass, Biofuels and Energy Crops, GR/S28204. References [1] Sami M, Annamalai K, Wooldridge M. Prog Energ Combust Sci 2001;27:171. [2] Williams A, Pourkashanian M, Jones JM. Prog Energ Combust Sci 2001;27:587. [3] Demirbas A. Prog Energ Combust Sci 2004;30:219. [4] Jenkins BM, Baxter LL, Miles Jr TR, Miles TR. Fuel Process Technol 1998;54:17. [5] Herna´ndez JR, Kilpinen P. The effect of CO addition on extinction limits and NOx formation in lean counterflow CH4/Air premixed flames. In: Proceedings of the fourth mediterranean combustion symposium, 6–10 October 2005, Lisbon, Portugal. [6] Gera D, Mathur MP, Freeman MC, Robinson A. Energ Fuels 2002;16:1523. [7] Yin C, Rosendahl L, Kær SK, Condra TJ. Chem Eng Sci 2004;59: 3281. [8] Backreedy RI, Fletcher LM, Jones JM, Ma L, Pourkashanian M, Williams A. Proc Combust Inst 2005;30:2955. [9] Morsi SA, Alexander AJ. J Fluid Mech 1972;55:193. [10] Haide A, Levenspiel O. Powder Technol 1989;58:63. [11] Chhabra RP, Agarwal L, Sinha NK. Power Technol 1999;101:288. [12] FLUENT Inc. FLUENT 6.0 User’s Guide. Lebanon, NH: Fluent Inc; 2002. [13] Sheng CD, Azevedo JLT. Proc Combust Inst 2002;29:407. [14] Moghtaderi B, Meesri C, Wall TF. Fuel 2004;83:745.
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[15] Chen Y, Charpenay S, Jensen A, Wojtowicz MA, Serio MA. Proc Combust Inst 1998;27:1327. [16] Knudsen JN, Jensen PA, Dam-Johansen K. Energ Fuels 2004;18: 1385. [17] Christensen KA, Stenholm M, Livbjerg H. J Aerosol Sci 1998;29:421. [18] Yu C, Zhang W. In: Bridgwater AV, editor. Progress in thermochemical biomass conversion. Oxford: Blackwell Science Ltd.; 2001. p. 1107. [19] Olsson JG, Ja¨glid U, Pettersson J, Hald P. Energ Fuels 1997;11:779. [20] Jensen PA, Frandsen FJ, Dam-Johansen K, Sander B. Energ Fuels 2000;14:1280. [21] Westberg HM, Bystro¨m M, Leckner B. Energ Fuels 2003;17:18. [22] Backreedy RI, Jones JM, Ma L, Pourkashanian M, Williams A, Arenillas A, et al. Fuel 2005;84:2196. [23] Hansson KM, Amand LE, Habermann A, Winter F. Fuel 2003;82: 653. [24] Wornat MJ, Hurt RH, Yang NYC, Headley TJ. Combust Flame 1995;100:131. [25] Jones JM, Williams A, Darvell LI, Nawaz M, Baxter X. In: Science in thermal and chemical conversion. Vic., Canada; 30 August–2 September 2004. [26] Jones JM, Darvell LI, Bridgeman TG, Pourkashanian M, Williams A. An investigation of the thermal and catalytic behaviour of potassium in biomass combustion. Proc Combust Inst 2007;31:1955. [27] Wei X, Schell U, Hein KRG. Fuel 2005;84:841. [28] Ma L, Pourkashanian M, Williams A, Jones JM. A numerical model for predicting biomass particle depositions in a PF furnace. In: Proceedings of the ASME Turbo Expo. Barcelona, Spain; 2006, GT2006-90356.
Modelling the combustion of pulverized biomass in an industrial combustion test furnace L. Ma *, J.M. Jones, M. Pourkashanian, A. Williams Energy and Resources Research Institute/Centre for Computational Fluid Dynamics, School of Process, Environmental and Materials Engineering, University of Leeds, LS2 9JT, UK Received 27 April 2006; received in revised form 11 December 2006; accepted 13 December 2006 Available online 19 January 2007
Abstract A CFD model that simulates the combustion of biomass in existing pf coal fired furnaces has been developed and model results for the combustion of a typical wood in a 1 MW industrial test facility have been presented. The model is primarily based on coal combustion submodels using an Eulerian–Lagrangian frame of reference. Biomass specific constants that define the submodels have been investigated and employed in the simulation. In particular, potassium release during biomass combustion and the formation of NOx have been simulated. Numerical predictions have been compared with some experimental measurements that have been taken and reasonably good agreement has been achieved. 2006 Elsevier Ltd. All rights reserved. Keywords: Biomass combustion; CFD model; Potassium release; NOx formation
1. Introduction The use of biomass as a fuel in existing coal fired power systems has been considered as an important step in reducing environmental emissions. Currently, co-firing coal with a limited amount of biomass, typically 2–20%, has been widely implemented in large-scale plants [1–3]. With smaller amount of biomass, the coal and biomass are milled together and both enter the same burner. With larger amounts, the biomass is milled separately and enters the furnace in a dedicated burner with other burners operating on coal. Industrial tests on firing pure biomass have been carried out in order to predict any problems that may rise when this is happening in large-scale plants. CFD modelling techniques for biomass combustion however still face significant challenges. One of the primary reasons is the diversity of the biomass fuels and the lack of knowledge of their combustion properties [3]. The presence *
Corresponding author. Tel.: +44 113 343 2481; fax: +44 113 246 7310. E-mail address: [email protected] (L. Ma).
0016-2361/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2006.12.019
of potassium in the biomass has become a significant issue since it is implicated in slagging, fouling and corrosion to the combustion system [4]. However, the detailed mechanisms of the potassium release during combustion and reaction in the combustion gases are still not well known. Modelling NOx formation is different from that of coal and the presence of alkali metals in the fuel can add to the complexity [5]. There are numerous other issues that prove to be problematic, such as the large particle sizes and the significant irregularity in shapes [6]. At the moment, there are a very limited number of numerical simulations of pulverized biomass combustion using detailed combustion models and they are focused on biomass co-firing [7,8]. In this paper, we present a CFD model and some of the model predictions for the firing of pure pulverized biomass in an industrial combustion test facility. The release of potassium and the formation of NOx have been simulated. Model predictions have been compared with the measurement data that were collected during the combustion test performed on the furnace.
1960
L. Ma et al. / Fuel 86 (2007) 1959–1965
2. The fuel and the combustion test facility The fuel that employed is a wood with thermal and chemical properties listed in Table 1. The fuel as received is in a wide range of particle shapes, typically with a fraction of flat chip and cylindrical shapes also with some small spherical particles. The aspect ratio of the particles received is usually very high, typically in the magnitude of 10. The sizes of the particles are also larger than that of typical pf coal with a particle size range of 90 lm–1500 lm. If represented by the Rosin-Rammler particle size distribution then the spread parameter is about 1.56 with a mean size of about 332 lm. Experimental investigations have been carried out in the 1 MW pulverized fuel combustion test facility (CTF) situated at the Power Technology Centre, UK. The CTF consists of a single burner and a combustion chamber with an over-fire air facility which is shown in Fig. 1. The combustor was a three-staged, wall-fired, low-NOx swirl burner of generic design widely used in large-scale power utilities. The furnace is fully refractory-lined and cooled with recirculating water. The CTF is originally designed for performing coal combustion test that replicates the time– temperature history of a coal particle entering a full-scale wall-fired furnace. In this investigation, wood has been used as the fuel and the measurements performed provide necessary boundary conditions and the validation data Table 1 Chemical and thermo physical properties of the fuel (%wt ar) Ultimate C H O Cl K N S
Proximate 47.8 5.9 37.6 0.02 0.26 0.17 0.026
Moisture VM FC Ash Physical property Density, kg/m3 Specific heat, J/kg K
10.1 69.4 18.0 2.5 550 1670
for the CFD model. In the combustion trial pre-ground wood was fed to the burner with the primary air at the ambient temperature. The main combustion air was indirectly heated prior to be split between the secondary, tertiary registers and the over-fire air has been introduced at the upper vertical section of the furnace. Flue gas temperature and composition have been recorded including NOx and CO2 contents via furnace ports. This wood combustion model has been previously validated [8] and was used with little change here. 3. The CFD model When biomass particles are injected into the furnace they quickly heat up at a rate in the order of 103–104 K/s. The particle will then experience four distinctive processes, namely drying, volatile release, volatile combustion and char combustion. It is noted that for those particles with a large aspect ratio and a significant irregular shape will experience significant differences in heating rate at different parts of the particle. In an extreme case, the different combustion stages may coexist within a single particle. 3.1. Particle transport In the framework of the Eulerian–Lagrangian modelling approach, the movements of the biomass particles in the furnace are numerically tracked according to the particle momentum equation. In most cases, aerodynamic drag is the dominant force, particularly for those small fuel particles that possess a high surface-volume ratio. For a spherical particle the drag is often obtained experimentally and it is usually expressed as a function of the particle Reynolds number [9]. In order to take into considerations the aerodynamic effect of the irregularity of the biomass particle shape, a shape factor was introduced defined as the ratio of the surface area of an equivalent spherical particle and the real particle surface area [8,10], so that empirical formulae can be employed to calculated the drag on the non-spherical particles. There are numerous other ways of calculating drag coefficient for irregular particles and inevitably, there are also debates on the accuracy of any of the formulations developed to cover the vast variety of particle shapes [11]. 3.2. Particle heating and drying The heating of a particle is governed by the following equation of heat balance: cp;p
Fig. 1. Computational mesh employed.
dT p ¼ Qc þ Qr Qv dt
ð1Þ
where Tp refers to the temperature of the particle, cp,p is the specific heat of particle, Qc, Qr and Qv are the heat transfer due to convection, radiation and vapourization of the particle. The convection heat transfer to the particle is calculated based on the Reynolds number of the particle and
L. Ma et al. / Fuel 86 (2007) 1959–1965
the Prandtl number of the continuous phase [12]. For radiation heat transfer we used the P1 radiation model. The drying process is controlled by the temperature of the particle and the concentration of the water vapour in the gas around the particle. It is assumed that the rate of vapourization is governed by the diffusion process that is driven by the gradient of the water vapour concentration between the surface of the particle and the bulk gas. Further, it is assumed that the partial pressure of the vapour at the surface of the particle is equal to the saturated water vapour pressure at the temperature of the particle and is not influenced by the dissolved salts. Therefore, the molar flux of water vapour, Nw, is [12]: p p N w ¼ k w sat X w ð2Þ RT RT p where p is the pressure of the bulk gas and psat is the saturated water vapour pressure. Xw is the molar fraction of the water vapour in the bulk gas and R is the gas constant. The mass transfer coefficient, kw, is calculated from a Nusselt correlation as follows: kwd p 1=2 ¼ 2:0 þ 0:6Red Sc1=3 Dw
ð3Þ
where dp is the diameter of the particle, Dw is the diffusion coefficient of water vapour in the bulk gas, Red is the particle Reynolds number and Sc is the Schmidt number. It is assumed that the wood particle starts to give off moisture at a relatively low temperature of about 25 C with a very low vapourization rate controlled by its saturation pressure. The rate of vapourization will increase with increase in the particle temperature until the particle reaches water boiling point where all the heat received would be used for vapourizing the moisture. 3.3. Devolatilisation and potassium release Pyrolysis of biomass typically starts to give off volatiles at a temperature of 160–300 C [13,14]. Therefore, we assume that the devolatilisation temperature of the wood commences at about 200 C and a single rate temperature dependent devolatilisation model expressed in an Arrhenius form has been employed. The data for the kinetic rate of the volatile release is calculated using the network program, FG-BioMass [15], which involves the use of a model that accounts for the decomposition of the fuel matrix during particle heat-up. The values obtained for the pre-exponential factor and the kinetic energy for wood are A = 6 · 1013 1/s and E = 2.5 · 108 J/kg mol. The values used in this model may be subject to debate since there is a considerable diversity in the values of kinetic data in the literature. The exact mechanism of potassium release during biomass combustion is still uncertain. Experiments suggest that the release of potassium in a combustion environment leads to KOH and KCl in the vapour phase. Some loss of potassium occurs during devolatilisation and the chloride in the
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fuel has been cited as a shuttle for potassium between the biomass particle and deposit in the combustor [16] or emitted as an aerosol [17]. The hydroxides, formed in the drying stage can increase the volatility of the potassium due to the high vapour pressure [16,17]. Fuels with a high chlorine content, such as straw, tend to form KCl while fuels with a low chlorine content, such as wood, mostly form of KOH. In contrast, silica can efficiently capture the potassium in the ash and so prevent potassium evaporation [16]. Experiments suggested that the release of K during devolatilisation may occur over two temperature stages [18–21]. At a temperature about 300–500 C, a small amount of potassium is released whilst a significant amount is released at a higher temperature of 700–1000 C. However, most experiments of potassium release have been performed under low heating rate and this raises some uncertainty about how well the findings extrapolate to the high temperature and high heating rates that exist in the situation of pf combustion. Since the chlorine content of the wood investigated here is low, we assume that the release of potassium in the fuel during devolatilisation rapidly forms KOH (or K2H2O2 in the gas phase). In the calculation, only potassium evolution during devolatilisation has been considered and the rate is assumed to be the same as that of the overall volatile release. Potassium is the most readily vapourized metal present and has much greater influence than the other metal components and therefore they were not taken into consideration. Any potassium chloride present is converted to potassium hydroxide by the following overall reaction involving dissociation and reaction with water decomposition intermediates: KCl þ H2 O ) KOH þ HCl
ð4Þ
It is recognized that potassium is also released during the biomass char combustion and it may be in the form of potassium hydroxide, potassium chloride or other potassium compounds [22] and this is still a subject of current investigation. 3.4. Volatile combustion and NOx formation Biomass volatiles consist mostly of CO, CO2, H2O, with CH4, H2 and other inorganic products [1,2]. Although the details of the compositions of the biomass volatile may be obtained through chemical analysis techniques, uncertainty remains for high temperature volatiles. The composition of the volatiles that has been used in the simulation was estimated based on the data in Table 1 from the proximate, ultimate, and the ash analyses of the fuel. During volatile combustion, the organic part of the volatiles is oxidised to form CO2 and water in the gas stream whilst the inorganic vapour may experience other transformations, such as sulphations, etc. For the modelling of oxidation of the hydrocarbon species, a global one step reaction mechanism has been employed: Volatiles ðHCÞ þ 1:1O2 ¼ CO2 þ 0:9H2 O
ð5Þ
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The effect of the turbulence on the reaction has been modeled with the Eddy Dissipation turbulence-chemistry interaction model and the RNG-k-e turbulence model. NO can be formed by the thermal mechanism or from fuel NO in an analogous way to coal [22]. However, in biomass fuel-N can exist both as heterocyclic compounds and as amino acid groups in proteins. Hence, NO can be formed from the former via the HCN intermediate by the De Soete fuel-N mechanism [12]. The protein nitrogen can be converted [23] into both HCN and NH3 which react to N-intermediates and ultimately into NO and other stable combustion products. Again this can be described by the De Soete mechanism, and the only difference between these routes is the choice of kinetic constants. The protein nitrogen content of wood has been estimated to be in the range of 70–90 wt% of the total nitrogen [23] and therefore a combination of HCN and NH3 routes has been considered. 3.5. Char combustion In the case of biomass char, the burnout rate is more complicated as it is affected not only by the composition of the biomass fuel but also by the shape and size of the particles. The biomass char is much more reactive than coal char for a number of reasons. Unlike coal particles that soften and tend to become spherical, biomass particles mainly keep their original irregular shape during devolatilisation. Often the process leads to a partially activated char with a high surface area and this results in a larger oxygen flux into the biomass char particles and contributes to a much higher char combustion rate than spherical coal particles. The structural disorder may also lead to a higher reactivity of biomass in the later stages of combustion [24]. The catalytic effect of metals in the biomass char may also increase the reactivity of the char [25]. However, it is generally believed that at high temperature char combustion in pf furnaces, where a very fast chemistry exists, the catalytic effect whilst still present is not as significant as it is in low temperature combustion [8]. Experiments have shown that the lingnin content maintains the particle shape until the majority of combustion has taken place [26] and it was assumed that fragmentation did not take place. These analyses are basically qualitative and an advanced char combustions model for biomass is yet to be developed. Therefore, here we model char combustion using Smith’s intrinsic model as before [8] and increased the reaction rate by a factor of 4 in order to represent the high burning rate of the biomass char particles [24].
tion of the particle tracks in the furnace which in turn influences the overall combustion of the fuel. The fuel particles are fed in at the entrance plane of the burner with the flow of primary air and this allows the evolution of the particle concentration profile to be simulated. As a result, model predictions show a clear stratified particle concentration at the exit plane of the burner into the combustion chamber generated by the swirling flow inside the burner. 4. Results A number of simulations have been performed and Fig. 2 shows the typical trajectories of the biomass particles predicted in the furnace. The plot shows the situation at about 3 s after the particles are released into the furnace, when the majority of them have not yet fully penetrated the furnace. The typical particle residence time calculated is about 5 s while some of the particles may stay over 10 s. The trajectories in the plot are colored by the mass of the particle and thus we can observe that particles lose mass rapidly just after the front of the burner and this is where the main drying and devolatilisation proceeds. When they have reached the vertical chamber of the furnace most of the particles have become much lighter but a significant number of particles are observed to fall to the bottom of the furnace due to gravity. These particles consist of both large sized particles and the particles that have not completely devolatilised. The prediction is consistent with the experimental observations which showed that the larger
3.6. Computational mesh and boundary conditions The computational domain employed 300,000 computational cells as shown in Fig. 1. Special attention has been paid on the meshing of the burner and the nearby regions since the accurate modelling of the fluid and particle flows inside the burner is considered to be crucial to the predic-
Fig. 2. Predicted tracks of particle colored by particle mass (kg).
L. Ma et al. / Fuel 86 (2007) 1959–1965
biomass particles tend to fall to the bottom, many near the burner, while others followed the gas flow field out through the furnace. Fig. 3 shows the heating up and the mass losses of three typical wood particles entering the furnace, namely, 150 lm, 320 lm and 640 lm. Particles heat up very quickly to about 1500 K in less than 0.25 s after being injected at ambient temperature. During this time the particles dry, then lose volatiles followed by char combustion. Char particle combustion is relatively slow, and as combustion proceeds the reactivity of the char particles decrease resulting in a drop in particle temperature and a slow decrease in the particle mass. Small particles are heated much quicker and release volatiles earlier than large particles. At present, the model accounts for potassium release during devolatilisation although some is retained in the char from which it is slowly lost by evaporation. Further validation of the model was obtained and it is found that experimental measurements of CO were less than 20 ppmv which justified the use of the single step reaction. Fig. 4 shows the simulated
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contours of KOH concentration in the vertical symmetric plane of the furnace. A KOH concentration of 7 ppmv is predicted in the exit plane of the furnace. One of the characteristics of biomass is its relatively low heating value compared with that of the coal and consequently the overall temperature of the furnace is lower than that of the coal combustion. Fig. 5 shows numerically predicted contours of the gas temperature. A temperature of about 1000 C was obtained at the exit of the furnace which is in good agreement with the measured data, and the errors are within 10% of experiment data. The predicted carbon dioxide is 13 mol% at the furnace exit, which
Fig. 3. Typical temperatures and mass losses of wood particles as a function of residence time. Fig. 5. Predicted contours of gas temperature (C).
Fig. 4. Predicted contours of potassium concentration (mol/mol).
Fig. 6. Predicted NO formation in the furnace through the NH3 route (mol/mol).
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L. Ma et al. / Fuel 86 (2007) 1959–1965
is also in good agreement with the measurements with an error of about 15%. Both the HCN and the NH3 route have been considered for the NOx formation and Fig. 6 shows the predicted NO concentrations through NH3 route. At the exit of the furnace a NO concentration of 280 ppmv has been predicted using the NH3 mechanism only. When using the NCH mechanism alone, the calculated NO was 90 ppmv. However, using a combination of the two, on a 1:3 basis (this means 1 NO from NH3 and 3 NO from HCN), yields a predicted concentration of 137 ppmv NO which is similar to the 140 ppmv found experimentally. This is in consistent with the protein content of 70–90% of the total nitrogen content. 5. Discussion Irregularity in the shape of the biomass particle causes substantial complexity for numerical simulation, particularly for those of large aspect ratios. A particle shape factor of 0.25 has been used based on measurements of the fuel samples. The particle shape has a significant influence on the particle trajectories and the residence time, as well as the ash deposition characteristics. Calculations show that if the aspect ratio of the particle is ignored then much more particle material ends up in the bottom ash. The introduction of the shape factor to some extent represents the effect of the aspect ratio on the motion of the particle, but the details, such as the possible tumbling of the particle during their penetrating the furnace are not calculated exactly. In addition, when the particle size becomes large, then the heating up of the particles will be influenced by the heat transfer within the particles where a significant temperature gradient may build up. However, for particles that have a mean size of about 0.33 mm, the temperature gradient within the particle should not have a significant effect on the heating up of the particle [27]. Numerical modelling of the release of potassium during combustion presents a substantial challenge because the detailed mechanism of the potassium release is not well known. In this paper, we assume that a portion of the potassium is released together with the devolatilisation products. From Fig. 4 it can be seen that the majority of the volatiles is released at about 700 C and this matches with the typical temperature regions where potassium is released from the biomass [18–21]. The potassium that has been released into the gas flow will undergo complex transformations and in different temperature regimes will form KOH, K2H2O2, KCl and K2SO4, and which is expected to nucleate at about 800 C as well as forming aerosols in the flue gas. These will condense and deposit on the surfaces of the fly ash/aerosols and on the superheater system. Modelling of potassium release makes it possible to track ash deposition in the system [28]. The high moisture content of the biomass has an impact on the heating-up and ignition of the fuel. For the relatively low moisture content in the wood used (that is 7%
by mass), the drying process is completed within about 0.15 s. In addition, a much lower temperature in the furnace has been observed compared with the coal combustion in the same furnace due to the lower calorific value of the fuel. A relatively higher percentage of the unburned carbon is observed in the ash and this is because of very low ash content which makes any retained carbon in the ash appear to be large. NOx control in the combustor is accomplished by the combination of the three-staged low NOx burner and the use of over-fire air. The reduced flame temperature also discourages the formation of NOx. The fuel-N produces both NH3 and HCN although the process can be influenced by the presence of the potassium and sodium in the gas stream [5]. This is due to the reactions with species such as O and OH, leading to a net formation of water through the following reactions: KOH þ H ) K þ H2 O H þ O2 ) OH þ O
ð6Þ ð7Þ
KO2 þ OH ) KOH þ O2 K þ O2 þ M ) KO2 þ M
ð8Þ ð9Þ
and this results in a decrease in the radical pool. However preliminary calculations show that the effect is small in this system. 6. Conclusions This paper presents a CFD model that predicts the combustion of typical wood fuel in an industrial pf furnace. The key combustion processes have been modelled in detail with an Eulerian–Lagrangian approach. Models for NOx formation and the potassium species release have been developed. The potassium together with the overall volatile release is modelled using a single rate kinetic model which gives off most of the volatile at 700–1000 K and this matches well with the temperature regimes where potassium species are released. A potassium concentration of about 7 ppmv has been predicted in the exit plane of the furnace which also falls into the typical regions of measured potassium release in the experimental investigations. Good agreement between the predicted and the measured furnace temperature and concentrations of CO2 and NOx has been achieved. The modelling of potassium release makes it possible to calculate both the potassium concentrations in the gas flow and the remaining potassium in the biomass ash thus paving the way to the development of the advanced ash deposition models for the combustion system. Acknowledgements The authors thank B. Goh at E.ON UK plc, and L. Darvell at the University of Leeds for their support in providing key boundary conditions and measurement validations for the study. The authors thank EPSRC for
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funding through the Supergen Consortium in Biomass, Biofuels and Energy Crops, GR/S28204. References [1] Sami M, Annamalai K, Wooldridge M. Prog Energ Combust Sci 2001;27:171. [2] Williams A, Pourkashanian M, Jones JM. Prog Energ Combust Sci 2001;27:587. [3] Demirbas A. Prog Energ Combust Sci 2004;30:219. [4] Jenkins BM, Baxter LL, Miles Jr TR, Miles TR. Fuel Process Technol 1998;54:17. [5] Herna´ndez JR, Kilpinen P. The effect of CO addition on extinction limits and NOx formation in lean counterflow CH4/Air premixed flames. In: Proceedings of the fourth mediterranean combustion symposium, 6–10 October 2005, Lisbon, Portugal. [6] Gera D, Mathur MP, Freeman MC, Robinson A. Energ Fuels 2002;16:1523. [7] Yin C, Rosendahl L, Kær SK, Condra TJ. Chem Eng Sci 2004;59: 3281. [8] Backreedy RI, Fletcher LM, Jones JM, Ma L, Pourkashanian M, Williams A. Proc Combust Inst 2005;30:2955. [9] Morsi SA, Alexander AJ. J Fluid Mech 1972;55:193. [10] Haide A, Levenspiel O. Powder Technol 1989;58:63. [11] Chhabra RP, Agarwal L, Sinha NK. Power Technol 1999;101:288. [12] FLUENT Inc. FLUENT 6.0 User’s Guide. Lebanon, NH: Fluent Inc; 2002. [13] Sheng CD, Azevedo JLT. Proc Combust Inst 2002;29:407. [14] Moghtaderi B, Meesri C, Wall TF. Fuel 2004;83:745.
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[15] Chen Y, Charpenay S, Jensen A, Wojtowicz MA, Serio MA. Proc Combust Inst 1998;27:1327. [16] Knudsen JN, Jensen PA, Dam-Johansen K. Energ Fuels 2004;18: 1385. [17] Christensen KA, Stenholm M, Livbjerg H. J Aerosol Sci 1998;29:421. [18] Yu C, Zhang W. In: Bridgwater AV, editor. Progress in thermochemical biomass conversion. Oxford: Blackwell Science Ltd.; 2001. p. 1107. [19] Olsson JG, Ja¨glid U, Pettersson J, Hald P. Energ Fuels 1997;11:779. [20] Jensen PA, Frandsen FJ, Dam-Johansen K, Sander B. Energ Fuels 2000;14:1280. [21] Westberg HM, Bystro¨m M, Leckner B. Energ Fuels 2003;17:18. [22] Backreedy RI, Jones JM, Ma L, Pourkashanian M, Williams A, Arenillas A, et al. Fuel 2005;84:2196. [23] Hansson KM, Amand LE, Habermann A, Winter F. Fuel 2003;82: 653. [24] Wornat MJ, Hurt RH, Yang NYC, Headley TJ. Combust Flame 1995;100:131. [25] Jones JM, Williams A, Darvell LI, Nawaz M, Baxter X. In: Science in thermal and chemical conversion. Vic., Canada; 30 August–2 September 2004. [26] Jones JM, Darvell LI, Bridgeman TG, Pourkashanian M, Williams A. An investigation of the thermal and catalytic behaviour of potassium in biomass combustion. Proc Combust Inst 2007;31:1955. [27] Wei X, Schell U, Hein KRG. Fuel 2005;84:841. [28] Ma L, Pourkashanian M, Williams A, Jones JM. A numerical model for predicting biomass particle depositions in a PF furnace. In: Proceedings of the ASME Turbo Expo. Barcelona, Spain; 2006, GT2006-90356.