- Email: [email protected]
CFD modeling of ash deposition for co-combustion of MBM with coal in a tangentially fired utility boiler
Fuel Processing Technology 114 (2013) 126–134
Contents lists available at SciVerse ScienceDirect
Fuel Processing Technology journal homepage: www.elsevier.com/locate/fuproc
CFD modeling of ash deposition for co-combustion of MBM with coal in a tangentially fired utility boiler Taha J. Taha a,⁎, Arthur F. Stam a, b, Kurt Stam c, Gerrit Brem a a b c
University of Twente, Faculty of Engineering, Laboratory of Thermal Engineering, P.O. Box 217, 7500 AE Enschede, The Netherlands KEMA, P.O. Box 9035, 6800 ET Arnhem, The Netherlands E.ON Benelux, P.O. Box 8642, 009 AP Rotterdam, The Netherlands
a r t i c l e
i n f o
Article history: Received 5 November 2012 Received in revised form 18 March 2013 Accepted 18 March 2013 Available online 20 April 2013 Keywords: Slagging Biomass Co-firing MBM CFD Coal
a b s t r a c t Ash deposition is one of the main challenges that needs to be tackled in response to increased percentage of biomass co-firing in pulverized fuel boilers. In this study, a model has been developed to investigate the slagging behavior of meat and bone meal (MBM) at higher co-firing rates in the Maasvlakte boiler operated by E.ON Benelux. The model includes the combustion of solid fuels in a tangentially fired boiler and post-processing of ash deposition on the heat exchange surfaces. The deposition tendency of the impacting ash particles is predicted on the basis of ash viscosity, which is calculated with the Urbain viscosity model. Thermodynamic equilibrium is used to calculate the various fuel ash properties for both oxidizing and reducing conditions. On the basis of the thermal heat input, solid fuel combustion is modeled and evaluated for various co-firing rates which consists of 0%, 12.5%, 25% and 40% of MBM. The calculation results show that the deposition propensity is the highest for a co-firing ratio of 25% MBM. The preferred deposition locations in the boiler calculated by the CFD model are in line with observations in operational practice. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Co-firing biomass in a pulverized coal plant is a promising renewable energy strategy to help reduce greenhouse gas emissions. It enjoys many operational, efficiency, and cost-effective advantages not available to dedicated biomass plants. Relatively high efficiencies are possible with only a minor addition of secondary fuels. Steam temperatures that can be applied are higher than those for stand-alone biomass plants that would suffer from severe corrosion if such high temperatures were applied. Although ash deposition has been investigated for many years, it remains a significant operational constraint in coal-fired power plants. Ash deposition increases with higher co-firing ratios of biomass. The presence of inorganic elements in biomass ash is significantly higher than in coal ash, thereby often contributing to operational problems such as slagging, fouling and corrosion. For instance, alkalis can be more volatile and therefore lead to an increase in the deposition related problems caused by biomass fuels [1]. Increasing the biomass co-firing rate in coal-fired boilers has until now been mainly a process of empiricism. Since ash-related problems occur when specific biomass–coal blends are applied, especially with co-firing rates well in excess of 10% by mass, there is a need to predict ash deposition rates. Empirical slagging and fouling prediction methods exist, such as indices and ash fusion tests [2]. Stam et al. [3] gave an ⁎ Corresponding author. Tel.: +31 53 489 2559; fax: +31 53 489 3663. E-mail address: [email protected] (T.J. Taha). 0378-3820/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.fuproc.2013.03.042
overview of available models and tools for predicting ash deposition during co-firing. He pointed out that empirical methods do not provide reliable prediction, especially not for biomass co-firing. A mechanistic route, and the application of numerical modeling, is therefore expected to provide more reliable results. However, little quantitative experimental data yet exists for the validation of numerical simulations. Previous work has concentrated on the CFD-based prediction of ash deposition from coal-firing [4,5] and on straw-firing [6]. In this study, a model for predicting slagging resulting from the co-firing of meat and bone meal (MBM) with coal in a full-scale boiler is presented and the obtained calculation results are discussed. The boiler under consideration is an E.ON Benelux owned tangentially fired 518 MWel pulverized fuel boiler at the Maasvlakte in The Netherlands. Simulation of combustion and slagging is performed using CFD with an integrated ash deposition model based on thermodynamic equilibrium. Slagging propensities for coal-firing and for various co-firing rates (12.5%, 25% and 40% on basis of thermal heat input) are studied. 2. Modeling 2.1. Combustion model Comprehensive combustion models have been developed and successfully applied for industrial coal-fired boiler processes. Complex phenomena in tangentially fired boilers including multiphase flow (gas–solid), multiphase combustion, and heat transfer have been widely studied using simulations [7–9].
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
Nomenclatures R T P D K C0 V Y x A μ fstick
radius [m] temperature [K] pressure [Pa] dynamic diffusivity [kg/(m −1·s −1)] reaction kinetics [s −1] fuel mass concentration [kg/m 3] volatile [kg/m 3] volatile mass fraction [−] mole fraction [−] pre-exponential factor [s −1] viscosity [Pa·s] sticking ratio [−]
Subscript p g A ref. crit. d c
particle gas atmospheric reference critical diffusion char
2.1.1. Devolatilization When a solid fuel particle enters the boiler, it is heated and devolatilization occurs as thermal decomposition of the solid fuel. Devolatilization is a complicated thermo-chemical process which is dependent on both physical and chemical properties of the solid fuel. This process involves heating of solid fuel materials in the absence of an oxidizer, thermal decomposition of the components, mass transport of the devolatilization products in the particle by means of advection and diffusion, and finally convection of the gas products leaving the particle. Different fuels have different rates of volatilization. Single rate kinetics is used to model the release of the volatile matters from the raw fuels (shown graphically featured in Fig. 1). It is assumed that the diameter of the particle remains constant, while the density of the particle reduces due to the escaping of the volatile gases from the fuel particles. The rate of the volatile releases is dependent on the fuel's composition, size, shape and moisture content. MBM particles contain a significantly greater amount of volatile matter than coal particles (see Table 1). As a result, the contribution of heat energy to the boiler by MBM particles is highly dependent on the gas phase combustion than char combustion whereas for coal particles the heat energy contribution is dominated by the heterogeneous combustion kinetics [12]. The rate of fuel volatile release is estimated by a first order reaction (see Eq. (1)). dV dt
Multiphase combustion modeling was achieved using a commercial code ANSYS-CFX (CFX) which allows for the necessary user-defined sub-model routines. Reynolds Averaged Navier-Stokes (RANS) equations were used to model the fluid phase in Eulerian frame of reference. The turbulent fluid flow inside the boiler was modeled using standard k-epsilon model. Homogenous gas phase chemical reactions were modeled using the eddy dissipation concept assuming that the reaction rate is controlled by the mixing rate of the reactants. Solid fuel modeling of a discrete phase in co-firing applications is done through numerical particle tracking in a Lagrangian reference frame. This model approximates the ensemble of all particle tracks in a turbulent flow with a user-defined number of particles (1600) that travels throughout the boiler domain. The discrete phase and the continuous phase were fully coupled to each other. To reconcile the Eulerian and Lagrangian behavior of the fluid and particle flows and the interactions between them, two simulations were performed with the Eulerian flow simulation affecting the Lagrangian particles through drag force, diffusion, and thermal transport and the particles affecting the fluid phase through source terms in transport equations at each time-step. In the present study, mean fluid flow velocity is considered for particle tracking calculations and the effect of turbulent velocity fluctuation is neglected. Heat transfer to the particles was modeled as the sum of contributions from convective, latent heat transfer associated with latent heat transfer, and radiative heat transfer. Ranz–Marshal correlation was used to model the convective heat transfer process. Discrete phase radiation model was used to simulate the radiation process. As for pulverized coal, the size of the MBM particles entering the furnace strongly affects the combustion process. The dominating mechanism for devolatilization of particles in microlevel (less than 200 μm) is chemical kinetics [10] while for large particles (exceeding 150–200 μm) the controlling process is heat and mass transfer [11]. This requires different approaches in the modeling strategy of different sizes of fuel particles. After the devolatilization stage, char (fixed carbon) remains in the particles. Char combustion is also considerably influenced by the size of the particles. As a result, characterization of the particle size distribution is implemented. The modeling of devolatilization, homogeneous and heterogeneous reactions are subsequently discussed hereafter.
127
¼ −Y ⋅K ⋅C 0 :
ð1Þ
During the simulation, the CFX model assumes that the composition of the fuel gas (volatiles) consists of CH4, CO, H2 and H2O without specifying the individual mass fraction of each component. However, it computes the average molecular mass of the volatile from volatiles elementary composition assuming it to be composed of CxHyOz. The molecular weights attained from the hydrocarbon fuel analysis are 18.191 kg/kmol, and 20.583 kg/kmol for Coal and MBM respectively. CFX calculates the stoichiometric coefficients for the complete combustion process in which it gives a way to identify the carbon, hydrogen and oxygen contents of the volatiles produced from both coal and MBM. The molecular formula attained for coal volatiles is C0.8804H1.95918O0.35419 and for MBM volatiles is C0.9451H2.046O0.4498. 2.1.2. Homogeneous reaction The combustible gases that are released during devolatilization may further combust, which will generate thermal energy. The generated heat contributes to both the volatile release and char ignition. As described in the preceding section, the volatiles are assumed to be CxHyOz molecules, rather than individual gas components. Instead of various components, reacting in different ways, the homogenous gas combustion has been simplified into two overall reactions [13]. Cx Hy Oz þ
i 1h y y x þ − z O2 → xCO þ H O 2 2 2 2
Fig. 1. Single step devolatilization reaction scheme.
ð2Þ
128
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134 Table 1 Solid fuel properties.
Table 2 Fuel feed rates for the different co-firing ratios.
Fuel composition C (%) H (%) N (%) S (%) O (%) Total water (%) Volatile (%) Ash (%) LHV (MJ/kg) Al (%) Ca (%) Cl (%) Fe (%) K (%) Mg (%) Na (%) P (%) Si (%) Ti (%)
Daf Daf Daf Daf Daf Ar Ar Ar Dry Dry Dry Dry Dry Dry Dry Dry Dry Dry
1 CO þ O2 → CO2 : 2
Coal
MBM
Co-firing rate
Coal mass flow rate [kg/s]
MBM mass flow rate [kg/s]
84 5.3 1.8 0.1 8.8 9 32.3 12.8 29.2 1.89 0.34 0.03 0.51 0.14 0.09 0.04 0.04 3.22 0.12
54.1 7.67 11.09 1.67 24.33 4.1 70.3 18.7 19.7 0.02 5.67 0.58 0.17 0.71 0.11 0.46 2.69 0.11 0.002
0% 12.5% 25% 40%
49.6 45.7 41.14 34.8
0 6.53 13.72 23.2
is used to obtain the particle displacement. The particle velocity calculated at the beginning of the time step is assumed to be the same over the entire time-step. The new particle velocity is calculated using the solution of the particle momentum equation at the end of the each time-step. Both MBM and coal particles are modeled as spherical particles where individually particles are tracked from their feed point until they leave the boiler domain (Fig. 2). These particles generate source terms to the fluid mass, momentum and energy equations which are calculated by a set of ordinary differential equations. The particle temperature is determined by solving each particle's energy balance. For each particle, this includes heat generation by char combustion, and heat transfer to the flue gas by radiation and convection. ð3Þ
2.1.3. Heterogeneous reaction Char oxidation is modeled as a global reaction and first order in oxygen (field reaction model) [14]. The char particle is considered to be spherical and surrounded by a stagnant boundary layer through which oxygen must diffuse before it can react with the char. The oxidation rate of the char is modeled on the assumption that the process is controlled by the diffusion of oxygen to the external surface of the char particle [14] and by the effective char reactivity. The char oxidation reaction is described as: CðsÞ þ O2 ðgÞ → CO2 ðgÞ:
ð4Þ
The rate of external diffusion of oxygen is given by kd(Pg − PS), where Pg is the partial pressure of oxygen in the furnace gases far from the particle boundary layer and PS is the oxygen pressure at the particle surface. The value of kd is given by: kd ¼
Dref Rp
T P þ T g 0:75 P A : 2T ref P
ð5Þ
The char oxidation rate per unit area of particle surface is given by kcPS. The chemical rate coefficient kc is given by: kc
T a;C ¼ Ac exp − TP
ð6Þ
where the parameters Ac and Ta,C depend on the type of coal (see Table 4). The diffusion and the kinetic reactions are assumed to occur simultaneously. The overall char reaction rate of a particle is given in Eq. (7). The reaction is limited by the lower of the rates kd and kc. dmc dt
2
¼ −4πRP xO2
kd kc P : kd þ kc P A
ð7Þ
2.1.4. Fuel particle tracking During the co-firing simulation, both MBM and coal particles are injected as a mixture based on the thermal input ratio into individual burners (see Table 2). In total 1600 representative particles are tracked to model the total flow of the particle phase through the continuum phase. Forward Euler integration of the particle velocity over time-step
2.2. Slagging model Ash deposits resulting from slagging and fouling can be formed by at least five mechanisms: thermophoresis, condensation, chemical reaction, eddy impaction, and inertial impaction [15]. Each of these mechanisms has a specific driving force, namely temperature gradients, vapor pressure of alkali salt vapors, species concentration gradients, turbulence intensity, and momentum, respectively. This leads to a large range of parameters controlling each of these mechanisms. However, Zhou et al. [16] experimentally compared the deposition fluxes of the various mechanisms and found that inertial impaction contributes almost 100 times more than any other. As a result, in this work only particle inertial impaction is considered. Impaction is evaluated by means of particle tracking, but whether a particle will stick or deflect depends on the particle properties (in this study clean boiler surfaces are assumed). The sticking probability is related by Walsh et al. [17] to the viscous flow of liquid slag droplets. Upon impaction, the surface area increases as a result of deformation, thereby increasing the chance of sticking. Deformation of a liquid particle is determined by its viscosity μ. A critical viscosity μcrit is set, below which a particle is assumed to always stick such that the sticking probability is unity. Above the critical viscosity, deflection can occur and the probability that a particle will stick to a surface is inversely proportional to its viscosity (see Eq. (8)).
f stick
8 < μ crit ; μ > μ crit ¼ : μ : 1; μbμ crit
ð8Þ
Clearly, the model depends critically on the value chosen for μcrit. This value is estimated from experimental outputs and is here chosen to be 1 · 105 Pa·s [18]. Particle viscosity can be calculated as function of the particle temperature and composition using Urbain's model. The composition of an ash particle was calculated on basis of thermodynamic equilibrium using the FactSage computer program. The approach taken for the calculations has been previously described by Stam and Brem [19]. The calculation outcome is the slag composition and the amounts of slag and solid phase. However, it was assumed that the solid mass will not deposit. This differs from the approach followed in Walsh et al. [17] who used the composition from laboratory fuel ash, thereby neglecting the influence of the combustion process and the local oxygen content, and assuming that all ashes are at least partly molten. The slagging model has been implemented in the CFD calculations as a post-processing step.
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
129
Fig. 2. Schematic representation of the boiler domain (left) and representative fuel and air inlet ports (right).
2.3. Full-scale model 2.3.1. Boiler, fuel analysis and boundary conditions The 518 MWel Maasvlakte pulverized fuel unit (40% efficiency on LHV basis) is tangentially fired, its base boiler dimensions are 14.6 by 17.2 m and the approximate height is 53 m. The boiler has 5 burners at each corner, with each burner having inlets for fuel, primary, secondary, and tertiary air. Over fire air inlets are situated at the inlet of the free board. The calculations were performed with an air excess ratio of 1.15. The amount of fuel introduced in to the boiler relates to a thermal heat input of 1295 MW for various co-firing rates (see Table 2). The particle size of coal varies over a wide range, due to variation in milling. A sieve analysis of milled coal (provided by E.ON Benelux) is curve-fitted using a Rosin–Rammler particle distribution. Due to the fibrous nature and the high volatile content, milling of MBM is more difficult. As a result, MBM particles occur over a narrow range of sizes compared to coal particles (see Table 3). The MBM particle size distribution is obtained from Heikkinen et al. [20] and was provided by E.ON Benelux. The homogenous combustion behavior of the volatiles depends on the cumulative volatile release of the mixture. However, the devolatilization and char combustion behavior cannot be averaged for the fuels. As a result, separate coal and MBM particle trajectories are involved. The residence time and conversion process of both coal and MBM are monitored throughout the boiler. Reaction kinetics is listed in Table 4. The boiler wall is assumed to have a constant temperature. For the evaporator tubes, the wall temperature is assumed to be 50 K above the steam temperature, while for the high-temperature superheater tubes it is assumed to be 60 K above the steam temperature (the superheater tubes are made of austenitic steel which has a lower heat conductivity than the ferritic steel used in the evaporator tubes in the membrane wall). The boiler is operated under atmospheric pressure condition and the model output is assumed to be at zero relative pressure.
2.3.2. Mesh A mesh dependency test for the CFD model is conducted for the case in which pure coal is fired. An initial mesh of about 789,772 evenly distributed elements has been created in the computational domain. The mesh is then refined progressively, resulting in finer meshes of 940,141 and 1,868,592 elements. Mesh independence is checked by comparing the area average gas temperature along the boiler height. The fine mesh (1,868,592 elements) and medium density mesh (940,141 elements) yield almost identical results. Therefore, the mesh density of 940,141 elements is accurate enough to reliably simulate the combustion process. Further refinement is made around the high temperature superheater area to compensate for numerical diffusion and to achieve higher accuracy of measuring physical properties. As a result, a total of 1,710,145 elements are used. 3. Results and discussion 3.1. Combustion Due to the high volatile content of MBM compared to coal, the cumulative release of volatiles increases with an increasing co-firing ratio. As a consequence, the flame height increases which results in
Table 3 Fuel particle size distribution. MBM particles
Coal particles
Average size (mm)
Weight share (%)
Rosin–Rammler distribution
b0.2 0.2–0.7 0.7–1.25 1.25–1.75
36 40 18 6
Minimum value Maximum value Mean value Standard deviation n spread parameter
0.63 μm 363.08 μm 59.77 μm 90.66 4.4238
130
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
Table 4 Reaction kinetics for coal and MBM. Devolatilization MBM coal
E [kcal·mol−1] 47.3 17.6
Heterogeneous combustion
Ta,C = E/R [K]
MBM char coal char
9000 8540
A [s−1] 4.78 · 1014 1.3 · 105 A [kg/m2s] 254 497
Reference [21] [22] Reference [23] [24]
Fig. 3. Cumulative volatile mass fraction during (a) coal combustion and (b) co-firing 40% MBM with coal.
higher temperatures in the free-board section. The calculations show that the amount of volatile release in dedicated coal combustion is small compared to that achieved in co-firing (see Fig. 3). Moreover, increasing the co-firing ratio to 12.5% and 25% leads to a smoothly increasing trend of the amount of volatiles released (not seen in Fig. 3). A co-firing ratio above 40% is expected to result in volatiles combusting beyond the superheater section as well as higher CO emissions. The oxygen concentration in the freeboard with 40% co-firing MBM is lower than with coal combustion (see Fig. 4). Similar decreasing trend in oxygen concentration is achieved for 12.5% and 25% co-firing case. This is because high volatile content of MBM particles reduces the local oxygen content in the vicinity of the particles during the homogenous reaction
Fig. 4. Oxygen mass fraction during (a) coal combustion and (b) co-firing 40% MBM with coal.
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
process. In addition to the larger size MBM particles, which influences the diffusion of oxygen to the particle surface (see Eq. (5)), there is less oxygen available for complete combustion of the MBM char particles. As a result, fuel to energy conversion efficiency reduces with an increase of the MBM co-firing ratio. Combustion of volatiles further up in the boiler leads to higher temperatures in the superheaters' region, as can be observed in Fig. 5. Peak temperatures occur near the burners. The addition of biomass
Fig. 5. Flue gas temperature profile along the boiler height (14, 16, 18, 20, 22, 26 and 35 m above the lower edge of the boiler) during (a) coal combustion and (b) co-firing of 40% MBM.
131
causes an increase of volatiles and moisture concentration in the zone nearest to the burner as compared to coal firing. With co-firing 12.5% and 25% of MBM, this early volatile combustion results in higher peak temperatures (not shown in Fig. 5). With addition of 40% of MBM, heat absorption by the moisture (and MBM) offsets the effect of early volatile combustion, in turn resulting in a lower peak temperature compared to coal firing (see Fig. 5). The coal particles have an instantaneous devolatilization phase and therefore char particles start to combust around the burner area. Conversely, due to the highly volatile content of MBM, combustion of the char is hindered, i.e. the volatile release hinders the oxygen from reaching the char. Calculations show that most particles have a residence time of about 3 s, while particles entering the lowest burners recirculate and have a longer residence time. The particle velocity is primarily dependent on the gas velocity (see Fig. 6). As the co-firing rate increases, the volumetric source from the fuel particles is added to the gas phase. Hence, it increases the cumulative volatile volumetric flow rate. As a result, both gas flow velocities and recirculation strength increase. Fig. 6 compares the vertical gas velocity between 40% MBM co-firing and dedicated coal combustion which shows higher gas flow magnitude during the 40% co-firing case than coal firing case. Gas velocity works adversely on residence time and burnout while recirculation promotes residence
Fig. 6. Vertical gas velocity profile along the boiler height for both (a) 40% MBM co-firing and (b) dedicated coal firing.
132
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
time. In addition, MBM particles have a lower density and are therefore more easily blown out of the boiler, also adversely influencing burnout. The average boiler residence times of coal and MBM particles are 5.02 and 3.01 s, respectively. MBM particles are larger than coal particles. Due to the large particle size of MBM (on average >200 μm), char combustion is mainly dominated by diffusion and less by kinetics. As a result, it takes more time to achieve complete combustion for MBM particles than for coal particles. Due to the smaller average residence time of the MBM particles, the rate of burn-out or complete conversion of the MBM char particles is not achieved. In addition, the higher volatile yield consumes the local oxygen during the early stage of combustion lowering the combustion rate
Fig. 8. Slag viscosity as function of temperature and MBM co-firing rate for an air excess level (air to fuel ratio = 1.16).
Fig. 7. Ash formation (i.e. burnout) and trajectories for 25% co-firing. (a) Coal particles and (b) MBM particles.
Fig. 9. Ash deposition propensities on boiler walls using μcrit = 105 Pa·s. (a) Coal firing and (b) co-firing 25% MBM.
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
of the large char particles. The semi-combusted char particles continue combusting in the region beyond the superheaters' zone. Fig. 7 shows ash mass fraction of the individual particles which is calculated as gram of ash in the fuel particle per gram of fuel particle as received. 3.2. Slagging The thermodynamic equilibrium calculations, as reported in Stam and Brem et al. [19], indicate that with an increasing co-firing ratio, the effect on the slag is that more slag is formed and that it is less viscous, i.e. more prone to sticking. The latter is shown in Fig. 8 for an air excess factor of 1.16 for various co-firing rates. The main reason is that the Ca
133
concentration in the slag increases, where Ca is a network modifier. Phosphorus like silicon is a network former and (as is calcium) abundantly present in MBM. However, the increase of the P content in the slag is limited and completely offset by the effect of the increased Ca content. At a critical viscosity of 105 Pa·s, results show that the wall slagging propensity increases throughout the boiler at a 25% co-firing rate compared to coal-firing (see Fig. 9). The recirculating flow causes an increase especially at and above the highest burner rows, and on the right-side wall and the right side of the boiler roof. These modeling results are in line with operator observations. It is predicted that, although viscosity is lower, the slagging propensity for 40% co-firing is lower than for 25%. The reason for this
Fig. 10. Slagging propensity calculated using μcrit = 8 Pa·s for (a) 12.5%, (b) 25% and (c) 40% MBM co-firing on the super-heaters' surfaces.
134
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
modeling outcome is the higher unburned fraction in the boiler: it was assumed that only particles consisting of 100% ash can stick, and as a result the semi-combusted particles leave the domain without contributing sticking behavior. Results for coal and co-firing 12.5%, 25% and 40% show little difference for slagging at the superheaters. The results were obtained for an assumed μcrit of 10 5 Pa·s. However, when a lower critical viscosity of 8 Pa·s is used, a higher slagging propensity can be seen clearly with an increased co-firing rate (see Fig. 10). While co-firing has been relatively modest in practice (typically below 10%), no operational data or operational experience are yet available for validation of the model results. The present results may be used to prevent slagging problems. Modeling results are clearly highly dependent on the chosen value of the critical viscosity. However, this value is not well established, and various values are proposed in literature; e.g., Walsh et al. [17] propose a value of 8 Pa·s, whereas Richards et al. [25] suggest 104 Pa·s. Srinivasachar et al. [18] found that the critical viscosity for deposition below which particle adhesion will occur is 105 Pa·s. A well-established value for the critical viscosity should be based on independent experimental work.
4. Conclusions A sophisticated CFD model has been developed and demonstrated to simulate the multiphase flow and combustion with an integrated ash deposition model based on thermodynamic equilibrium. Slagging propensities for coal-firing and for various MBM co-firing rates (12.5%, 25% and 40% on basis of thermal heat input) have been studied. Using the geometry and conditions of a Dutch pulverized fuel boiler at Maasvlakte in The Netherlands as test case for co-firing MBM, the ash deposition model results were found to be in line with operational trends observed. Inertial impaction was modeled using Lagrangian particle tracking. The sticking propensity of the ash particles on boiler surface was modeled using an ash viscosity model. Wall slagging increases with increasing co-firing ratio. However, at 40% MBM co-firing ratio, wall slagging seemed to reduce. This is due to incomplete combustion of the MBM particles which restricts the deposition due to the assumption that only 100% ash particles can deposits. In addition, the high volatile content of MBM, which increases the cumulative volatile combustion, results in increasing the flame height. This leads to an increase of particle temperature which help them to retain their low viscosity and hence stickiness. Moreover, slagging problems are also expected to increase with co-firing and to move further away from superheaters as well.
Acknowledgments The authors wish to acknowledge Jan Withag and Artur Pozarlik for their valuable technical support and advice throughout this work.
References [1] L.L. Baxter, T.R. Miles Sr., T.R. Miles Jr., B.M. Jenkins, T. Milne, D. Dayton, R. Bryers, L.L. Oden, The behavior of inorganic material in biomass-fired power boilers: field and laboratory experiences, Fuel Processing Technology 54 (1998) 47–78. [2] M. Pronobis, Evaluation of the influence of biomass co-combustion on boiler furnace slagging by means of fusibility correlations, Biomass and Bioenergy 28 (2005) 375–383. [3] A.F. Stam, W.R. Livingston, M.F.G. Cremers, G. Brem, Review of models and tools for slagging and fouling prediction for biomass co-combustion, IEA Task 32 Biomass Combustion and Co-firing, 2010. [4] H. Wang, J.N. Harb, Modelling of ash deposition in large-scale combustion facilities burning pulverized coal, Progress in Energy and Combustion Science 23 (1997) 267–282. [5] F.C.C. Lee, F.C. Lockwood, Modeling ash deposition in pulverized coal-fired applications, Progress in Energy and Combustion Science 25 (1999) 117–132. [6] S.K. Kaer, L.A. Rosendahl, L.L. Baxter, Towards a CFD-based mechanistic deposit formation model for straw-fired boilers, Fuel 85 (2006) 833–848. [7] P.G. Costen, D. Dajnak, M. Messina, F.C. Lockwood, T. Abbas, C. Bertrand, On the Prediction and Control of Industrial Combustors by Mathematical Modeling; Australian Symposium on Combustion and the Seventh Australian Flame Days, Adelaide, Australia, 2002. [8] M.A. Habib, R. Ben-Mansour, M.A. Antar, Flow field and thermal characteristics in a model of a tangentially fired furnace under different conditions of burner tripping, Heat and Mass Transfer 41 (2005) 909–920. [9] C. Yin, L. Rosendahl, T.J. Condra, Further study of the gas temperature deviation in large-scale tangentially coal-fired boilers, Fuel 82 (2003) 1127–1137. [10] Hong Lu, Warren Robert, Gregory Peirce, Bryan Ripa, Larry L. Baxter, Comprehensive study of biomass particle combustion, Energy & Fuels 22 (2008) 2826–2839. [11] Yao B. Yang, Vida N. Sharifi, Jim Swithenbank, Lin Ma, Leilani I. Darvell, Jenny M. Jones, Mohamed Pourkashanian, Alan Williams, Combustion of a single particle of biomass, Energy & Fuels 22 (2008) 306–316. [12] J. Dai, S. Sokhansanj, J.R. Grace, X. Bi, C.J. Lim, S. Melin, Overview and some issues related to co-firing biomass and coal, Canadian Journal of Chemical Engineering 86 (2008) 367–386. [13] N. Schaffel, M. Mancini, k A. Szle, R. Weber, Mathematical modeling of MILD combustion of pulverized coal, Combustion and Flame 156 (2009) 1771–1784. [14] M.A. Field, Rate of combustion of size-graded fractions of char from a low rank coal between 1200 K–2000 K, Combustion and Flame 13 (1969) 237–252. [15] L.L. Baxter, Ash deposition during biomass and coal combustion: a mechanistic approach, Biomass and Bioenergy 4 (1993) 85–102. [16] H. Zhou, P.A. Jensen, F.J. Frandsen, Dynamic mechanistic model of superheater deposit growth and shedding in a biomass fired grate boiler, Fuel 86 (2007) 1519–1533. [17] P.M. Walsh, A.N. Sayre, D.O. Loehden, L.S. Monroe, J.M. Beer, A.F. Sarofim, Deposition of bituminous coal ash on an isolated heat exchanger tube: effects of coal properties on deposit growth, Progress in Energy and Combustion Science 16 (1990) 327–346. [18] S. Srinivasachar, C.L. Senior, J.J. Helbe, J.W. Moore, A fundamental approach to the prediction of coal ash deposit formation in combustion systems, Twenty-Fourth Symposium (International) on Combustion, The Combustion Institute, 1992, pp. 1179–1187. [19] A. Stam, G. Brem, Slagging Prediction of Slagging With Enhanced Biomass Co-Firing in Dutch Utility Boilers Using an Engineering Method; 34th International Conference on Coal Utilization & Fuel Systems, Clearwater, FL, 2009. [20] J. Heikinen, Characterization of supplementary fuels for co-combustion with pulverized coal. PhD thesis, TU Delft, 2005. [21] X. Zhang, W. De Jong, Estimating kinetic parameters in TGA using B-spline smoothing and the Friedman method, Biomass and Bioenergy 33 (2009) 1435–1441. [22] T.H. Duong, Modelling of brown coal combustion in one-dimension, NERDDP Project 931, Report No ND/87/040the State Electricity Commission of Victoria, Australia, 1987. [23] D.D. Evans, H.W. Emmons, Combustion of woods charcoal, Fire Safety Journal 1 (1977) 57–66. [24] T.F. Wall, W.J. Phelan, S. Bartz, The prediction of scaling of burnout in swirled pulverized coal flames, International Flame Research Foundation Report F388/a/3, IJmuiden, The Netherlands, 1976. [25] G.H. Richards, J.N. Harb, C.J. Zygarlicke, The Effect of variations in particle-to-particle composition of the formation of ash deposits, in: S.A. Benson (Ed.), Inorganic Transformations and Ash Deposition During Combustion, ASME Press, NY, 1992, p. 713.
Contents lists available at SciVerse ScienceDirect
Fuel Processing Technology journal homepage: www.elsevier.com/locate/fuproc
CFD modeling of ash deposition for co-combustion of MBM with coal in a tangentially fired utility boiler Taha J. Taha a,⁎, Arthur F. Stam a, b, Kurt Stam c, Gerrit Brem a a b c
University of Twente, Faculty of Engineering, Laboratory of Thermal Engineering, P.O. Box 217, 7500 AE Enschede, The Netherlands KEMA, P.O. Box 9035, 6800 ET Arnhem, The Netherlands E.ON Benelux, P.O. Box 8642, 009 AP Rotterdam, The Netherlands
a r t i c l e
i n f o
Article history: Received 5 November 2012 Received in revised form 18 March 2013 Accepted 18 March 2013 Available online 20 April 2013 Keywords: Slagging Biomass Co-firing MBM CFD Coal
a b s t r a c t Ash deposition is one of the main challenges that needs to be tackled in response to increased percentage of biomass co-firing in pulverized fuel boilers. In this study, a model has been developed to investigate the slagging behavior of meat and bone meal (MBM) at higher co-firing rates in the Maasvlakte boiler operated by E.ON Benelux. The model includes the combustion of solid fuels in a tangentially fired boiler and post-processing of ash deposition on the heat exchange surfaces. The deposition tendency of the impacting ash particles is predicted on the basis of ash viscosity, which is calculated with the Urbain viscosity model. Thermodynamic equilibrium is used to calculate the various fuel ash properties for both oxidizing and reducing conditions. On the basis of the thermal heat input, solid fuel combustion is modeled and evaluated for various co-firing rates which consists of 0%, 12.5%, 25% and 40% of MBM. The calculation results show that the deposition propensity is the highest for a co-firing ratio of 25% MBM. The preferred deposition locations in the boiler calculated by the CFD model are in line with observations in operational practice. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Co-firing biomass in a pulverized coal plant is a promising renewable energy strategy to help reduce greenhouse gas emissions. It enjoys many operational, efficiency, and cost-effective advantages not available to dedicated biomass plants. Relatively high efficiencies are possible with only a minor addition of secondary fuels. Steam temperatures that can be applied are higher than those for stand-alone biomass plants that would suffer from severe corrosion if such high temperatures were applied. Although ash deposition has been investigated for many years, it remains a significant operational constraint in coal-fired power plants. Ash deposition increases with higher co-firing ratios of biomass. The presence of inorganic elements in biomass ash is significantly higher than in coal ash, thereby often contributing to operational problems such as slagging, fouling and corrosion. For instance, alkalis can be more volatile and therefore lead to an increase in the deposition related problems caused by biomass fuels [1]. Increasing the biomass co-firing rate in coal-fired boilers has until now been mainly a process of empiricism. Since ash-related problems occur when specific biomass–coal blends are applied, especially with co-firing rates well in excess of 10% by mass, there is a need to predict ash deposition rates. Empirical slagging and fouling prediction methods exist, such as indices and ash fusion tests [2]. Stam et al. [3] gave an ⁎ Corresponding author. Tel.: +31 53 489 2559; fax: +31 53 489 3663. E-mail address: [email protected] (T.J. Taha). 0378-3820/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.fuproc.2013.03.042
overview of available models and tools for predicting ash deposition during co-firing. He pointed out that empirical methods do not provide reliable prediction, especially not for biomass co-firing. A mechanistic route, and the application of numerical modeling, is therefore expected to provide more reliable results. However, little quantitative experimental data yet exists for the validation of numerical simulations. Previous work has concentrated on the CFD-based prediction of ash deposition from coal-firing [4,5] and on straw-firing [6]. In this study, a model for predicting slagging resulting from the co-firing of meat and bone meal (MBM) with coal in a full-scale boiler is presented and the obtained calculation results are discussed. The boiler under consideration is an E.ON Benelux owned tangentially fired 518 MWel pulverized fuel boiler at the Maasvlakte in The Netherlands. Simulation of combustion and slagging is performed using CFD with an integrated ash deposition model based on thermodynamic equilibrium. Slagging propensities for coal-firing and for various co-firing rates (12.5%, 25% and 40% on basis of thermal heat input) are studied. 2. Modeling 2.1. Combustion model Comprehensive combustion models have been developed and successfully applied for industrial coal-fired boiler processes. Complex phenomena in tangentially fired boilers including multiphase flow (gas–solid), multiphase combustion, and heat transfer have been widely studied using simulations [7–9].
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
Nomenclatures R T P D K C0 V Y x A μ fstick
radius [m] temperature [K] pressure [Pa] dynamic diffusivity [kg/(m −1·s −1)] reaction kinetics [s −1] fuel mass concentration [kg/m 3] volatile [kg/m 3] volatile mass fraction [−] mole fraction [−] pre-exponential factor [s −1] viscosity [Pa·s] sticking ratio [−]
Subscript p g A ref. crit. d c
particle gas atmospheric reference critical diffusion char
2.1.1. Devolatilization When a solid fuel particle enters the boiler, it is heated and devolatilization occurs as thermal decomposition of the solid fuel. Devolatilization is a complicated thermo-chemical process which is dependent on both physical and chemical properties of the solid fuel. This process involves heating of solid fuel materials in the absence of an oxidizer, thermal decomposition of the components, mass transport of the devolatilization products in the particle by means of advection and diffusion, and finally convection of the gas products leaving the particle. Different fuels have different rates of volatilization. Single rate kinetics is used to model the release of the volatile matters from the raw fuels (shown graphically featured in Fig. 1). It is assumed that the diameter of the particle remains constant, while the density of the particle reduces due to the escaping of the volatile gases from the fuel particles. The rate of the volatile releases is dependent on the fuel's composition, size, shape and moisture content. MBM particles contain a significantly greater amount of volatile matter than coal particles (see Table 1). As a result, the contribution of heat energy to the boiler by MBM particles is highly dependent on the gas phase combustion than char combustion whereas for coal particles the heat energy contribution is dominated by the heterogeneous combustion kinetics [12]. The rate of fuel volatile release is estimated by a first order reaction (see Eq. (1)). dV dt
Multiphase combustion modeling was achieved using a commercial code ANSYS-CFX (CFX) which allows for the necessary user-defined sub-model routines. Reynolds Averaged Navier-Stokes (RANS) equations were used to model the fluid phase in Eulerian frame of reference. The turbulent fluid flow inside the boiler was modeled using standard k-epsilon model. Homogenous gas phase chemical reactions were modeled using the eddy dissipation concept assuming that the reaction rate is controlled by the mixing rate of the reactants. Solid fuel modeling of a discrete phase in co-firing applications is done through numerical particle tracking in a Lagrangian reference frame. This model approximates the ensemble of all particle tracks in a turbulent flow with a user-defined number of particles (1600) that travels throughout the boiler domain. The discrete phase and the continuous phase were fully coupled to each other. To reconcile the Eulerian and Lagrangian behavior of the fluid and particle flows and the interactions between them, two simulations were performed with the Eulerian flow simulation affecting the Lagrangian particles through drag force, diffusion, and thermal transport and the particles affecting the fluid phase through source terms in transport equations at each time-step. In the present study, mean fluid flow velocity is considered for particle tracking calculations and the effect of turbulent velocity fluctuation is neglected. Heat transfer to the particles was modeled as the sum of contributions from convective, latent heat transfer associated with latent heat transfer, and radiative heat transfer. Ranz–Marshal correlation was used to model the convective heat transfer process. Discrete phase radiation model was used to simulate the radiation process. As for pulverized coal, the size of the MBM particles entering the furnace strongly affects the combustion process. The dominating mechanism for devolatilization of particles in microlevel (less than 200 μm) is chemical kinetics [10] while for large particles (exceeding 150–200 μm) the controlling process is heat and mass transfer [11]. This requires different approaches in the modeling strategy of different sizes of fuel particles. After the devolatilization stage, char (fixed carbon) remains in the particles. Char combustion is also considerably influenced by the size of the particles. As a result, characterization of the particle size distribution is implemented. The modeling of devolatilization, homogeneous and heterogeneous reactions are subsequently discussed hereafter.
127
¼ −Y ⋅K ⋅C 0 :
ð1Þ
During the simulation, the CFX model assumes that the composition of the fuel gas (volatiles) consists of CH4, CO, H2 and H2O without specifying the individual mass fraction of each component. However, it computes the average molecular mass of the volatile from volatiles elementary composition assuming it to be composed of CxHyOz. The molecular weights attained from the hydrocarbon fuel analysis are 18.191 kg/kmol, and 20.583 kg/kmol for Coal and MBM respectively. CFX calculates the stoichiometric coefficients for the complete combustion process in which it gives a way to identify the carbon, hydrogen and oxygen contents of the volatiles produced from both coal and MBM. The molecular formula attained for coal volatiles is C0.8804H1.95918O0.35419 and for MBM volatiles is C0.9451H2.046O0.4498. 2.1.2. Homogeneous reaction The combustible gases that are released during devolatilization may further combust, which will generate thermal energy. The generated heat contributes to both the volatile release and char ignition. As described in the preceding section, the volatiles are assumed to be CxHyOz molecules, rather than individual gas components. Instead of various components, reacting in different ways, the homogenous gas combustion has been simplified into two overall reactions [13]. Cx Hy Oz þ
i 1h y y x þ − z O2 → xCO þ H O 2 2 2 2
Fig. 1. Single step devolatilization reaction scheme.
ð2Þ
128
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134 Table 1 Solid fuel properties.
Table 2 Fuel feed rates for the different co-firing ratios.
Fuel composition C (%) H (%) N (%) S (%) O (%) Total water (%) Volatile (%) Ash (%) LHV (MJ/kg) Al (%) Ca (%) Cl (%) Fe (%) K (%) Mg (%) Na (%) P (%) Si (%) Ti (%)
Daf Daf Daf Daf Daf Ar Ar Ar Dry Dry Dry Dry Dry Dry Dry Dry Dry Dry
1 CO þ O2 → CO2 : 2
Coal
MBM
Co-firing rate
Coal mass flow rate [kg/s]
MBM mass flow rate [kg/s]
84 5.3 1.8 0.1 8.8 9 32.3 12.8 29.2 1.89 0.34 0.03 0.51 0.14 0.09 0.04 0.04 3.22 0.12
54.1 7.67 11.09 1.67 24.33 4.1 70.3 18.7 19.7 0.02 5.67 0.58 0.17 0.71 0.11 0.46 2.69 0.11 0.002
0% 12.5% 25% 40%
49.6 45.7 41.14 34.8
0 6.53 13.72 23.2
is used to obtain the particle displacement. The particle velocity calculated at the beginning of the time step is assumed to be the same over the entire time-step. The new particle velocity is calculated using the solution of the particle momentum equation at the end of the each time-step. Both MBM and coal particles are modeled as spherical particles where individually particles are tracked from their feed point until they leave the boiler domain (Fig. 2). These particles generate source terms to the fluid mass, momentum and energy equations which are calculated by a set of ordinary differential equations. The particle temperature is determined by solving each particle's energy balance. For each particle, this includes heat generation by char combustion, and heat transfer to the flue gas by radiation and convection. ð3Þ
2.1.3. Heterogeneous reaction Char oxidation is modeled as a global reaction and first order in oxygen (field reaction model) [14]. The char particle is considered to be spherical and surrounded by a stagnant boundary layer through which oxygen must diffuse before it can react with the char. The oxidation rate of the char is modeled on the assumption that the process is controlled by the diffusion of oxygen to the external surface of the char particle [14] and by the effective char reactivity. The char oxidation reaction is described as: CðsÞ þ O2 ðgÞ → CO2 ðgÞ:
ð4Þ
The rate of external diffusion of oxygen is given by kd(Pg − PS), where Pg is the partial pressure of oxygen in the furnace gases far from the particle boundary layer and PS is the oxygen pressure at the particle surface. The value of kd is given by: kd ¼
Dref Rp
T P þ T g 0:75 P A : 2T ref P
ð5Þ
The char oxidation rate per unit area of particle surface is given by kcPS. The chemical rate coefficient kc is given by: kc
T a;C ¼ Ac exp − TP
ð6Þ
where the parameters Ac and Ta,C depend on the type of coal (see Table 4). The diffusion and the kinetic reactions are assumed to occur simultaneously. The overall char reaction rate of a particle is given in Eq. (7). The reaction is limited by the lower of the rates kd and kc. dmc dt
2
¼ −4πRP xO2
kd kc P : kd þ kc P A
ð7Þ
2.1.4. Fuel particle tracking During the co-firing simulation, both MBM and coal particles are injected as a mixture based on the thermal input ratio into individual burners (see Table 2). In total 1600 representative particles are tracked to model the total flow of the particle phase through the continuum phase. Forward Euler integration of the particle velocity over time-step
2.2. Slagging model Ash deposits resulting from slagging and fouling can be formed by at least five mechanisms: thermophoresis, condensation, chemical reaction, eddy impaction, and inertial impaction [15]. Each of these mechanisms has a specific driving force, namely temperature gradients, vapor pressure of alkali salt vapors, species concentration gradients, turbulence intensity, and momentum, respectively. This leads to a large range of parameters controlling each of these mechanisms. However, Zhou et al. [16] experimentally compared the deposition fluxes of the various mechanisms and found that inertial impaction contributes almost 100 times more than any other. As a result, in this work only particle inertial impaction is considered. Impaction is evaluated by means of particle tracking, but whether a particle will stick or deflect depends on the particle properties (in this study clean boiler surfaces are assumed). The sticking probability is related by Walsh et al. [17] to the viscous flow of liquid slag droplets. Upon impaction, the surface area increases as a result of deformation, thereby increasing the chance of sticking. Deformation of a liquid particle is determined by its viscosity μ. A critical viscosity μcrit is set, below which a particle is assumed to always stick such that the sticking probability is unity. Above the critical viscosity, deflection can occur and the probability that a particle will stick to a surface is inversely proportional to its viscosity (see Eq. (8)).
f stick
8 < μ crit ; μ > μ crit ¼ : μ : 1; μbμ crit
ð8Þ
Clearly, the model depends critically on the value chosen for μcrit. This value is estimated from experimental outputs and is here chosen to be 1 · 105 Pa·s [18]. Particle viscosity can be calculated as function of the particle temperature and composition using Urbain's model. The composition of an ash particle was calculated on basis of thermodynamic equilibrium using the FactSage computer program. The approach taken for the calculations has been previously described by Stam and Brem [19]. The calculation outcome is the slag composition and the amounts of slag and solid phase. However, it was assumed that the solid mass will not deposit. This differs from the approach followed in Walsh et al. [17] who used the composition from laboratory fuel ash, thereby neglecting the influence of the combustion process and the local oxygen content, and assuming that all ashes are at least partly molten. The slagging model has been implemented in the CFD calculations as a post-processing step.
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
129
Fig. 2. Schematic representation of the boiler domain (left) and representative fuel and air inlet ports (right).
2.3. Full-scale model 2.3.1. Boiler, fuel analysis and boundary conditions The 518 MWel Maasvlakte pulverized fuel unit (40% efficiency on LHV basis) is tangentially fired, its base boiler dimensions are 14.6 by 17.2 m and the approximate height is 53 m. The boiler has 5 burners at each corner, with each burner having inlets for fuel, primary, secondary, and tertiary air. Over fire air inlets are situated at the inlet of the free board. The calculations were performed with an air excess ratio of 1.15. The amount of fuel introduced in to the boiler relates to a thermal heat input of 1295 MW for various co-firing rates (see Table 2). The particle size of coal varies over a wide range, due to variation in milling. A sieve analysis of milled coal (provided by E.ON Benelux) is curve-fitted using a Rosin–Rammler particle distribution. Due to the fibrous nature and the high volatile content, milling of MBM is more difficult. As a result, MBM particles occur over a narrow range of sizes compared to coal particles (see Table 3). The MBM particle size distribution is obtained from Heikkinen et al. [20] and was provided by E.ON Benelux. The homogenous combustion behavior of the volatiles depends on the cumulative volatile release of the mixture. However, the devolatilization and char combustion behavior cannot be averaged for the fuels. As a result, separate coal and MBM particle trajectories are involved. The residence time and conversion process of both coal and MBM are monitored throughout the boiler. Reaction kinetics is listed in Table 4. The boiler wall is assumed to have a constant temperature. For the evaporator tubes, the wall temperature is assumed to be 50 K above the steam temperature, while for the high-temperature superheater tubes it is assumed to be 60 K above the steam temperature (the superheater tubes are made of austenitic steel which has a lower heat conductivity than the ferritic steel used in the evaporator tubes in the membrane wall). The boiler is operated under atmospheric pressure condition and the model output is assumed to be at zero relative pressure.
2.3.2. Mesh A mesh dependency test for the CFD model is conducted for the case in which pure coal is fired. An initial mesh of about 789,772 evenly distributed elements has been created in the computational domain. The mesh is then refined progressively, resulting in finer meshes of 940,141 and 1,868,592 elements. Mesh independence is checked by comparing the area average gas temperature along the boiler height. The fine mesh (1,868,592 elements) and medium density mesh (940,141 elements) yield almost identical results. Therefore, the mesh density of 940,141 elements is accurate enough to reliably simulate the combustion process. Further refinement is made around the high temperature superheater area to compensate for numerical diffusion and to achieve higher accuracy of measuring physical properties. As a result, a total of 1,710,145 elements are used. 3. Results and discussion 3.1. Combustion Due to the high volatile content of MBM compared to coal, the cumulative release of volatiles increases with an increasing co-firing ratio. As a consequence, the flame height increases which results in
Table 3 Fuel particle size distribution. MBM particles
Coal particles
Average size (mm)
Weight share (%)
Rosin–Rammler distribution
b0.2 0.2–0.7 0.7–1.25 1.25–1.75
36 40 18 6
Minimum value Maximum value Mean value Standard deviation n spread parameter
0.63 μm 363.08 μm 59.77 μm 90.66 4.4238
130
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
Table 4 Reaction kinetics for coal and MBM. Devolatilization MBM coal
E [kcal·mol−1] 47.3 17.6
Heterogeneous combustion
Ta,C = E/R [K]
MBM char coal char
9000 8540
A [s−1] 4.78 · 1014 1.3 · 105 A [kg/m2s] 254 497
Reference [21] [22] Reference [23] [24]
Fig. 3. Cumulative volatile mass fraction during (a) coal combustion and (b) co-firing 40% MBM with coal.
higher temperatures in the free-board section. The calculations show that the amount of volatile release in dedicated coal combustion is small compared to that achieved in co-firing (see Fig. 3). Moreover, increasing the co-firing ratio to 12.5% and 25% leads to a smoothly increasing trend of the amount of volatiles released (not seen in Fig. 3). A co-firing ratio above 40% is expected to result in volatiles combusting beyond the superheater section as well as higher CO emissions. The oxygen concentration in the freeboard with 40% co-firing MBM is lower than with coal combustion (see Fig. 4). Similar decreasing trend in oxygen concentration is achieved for 12.5% and 25% co-firing case. This is because high volatile content of MBM particles reduces the local oxygen content in the vicinity of the particles during the homogenous reaction
Fig. 4. Oxygen mass fraction during (a) coal combustion and (b) co-firing 40% MBM with coal.
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
process. In addition to the larger size MBM particles, which influences the diffusion of oxygen to the particle surface (see Eq. (5)), there is less oxygen available for complete combustion of the MBM char particles. As a result, fuel to energy conversion efficiency reduces with an increase of the MBM co-firing ratio. Combustion of volatiles further up in the boiler leads to higher temperatures in the superheaters' region, as can be observed in Fig. 5. Peak temperatures occur near the burners. The addition of biomass
Fig. 5. Flue gas temperature profile along the boiler height (14, 16, 18, 20, 22, 26 and 35 m above the lower edge of the boiler) during (a) coal combustion and (b) co-firing of 40% MBM.
131
causes an increase of volatiles and moisture concentration in the zone nearest to the burner as compared to coal firing. With co-firing 12.5% and 25% of MBM, this early volatile combustion results in higher peak temperatures (not shown in Fig. 5). With addition of 40% of MBM, heat absorption by the moisture (and MBM) offsets the effect of early volatile combustion, in turn resulting in a lower peak temperature compared to coal firing (see Fig. 5). The coal particles have an instantaneous devolatilization phase and therefore char particles start to combust around the burner area. Conversely, due to the highly volatile content of MBM, combustion of the char is hindered, i.e. the volatile release hinders the oxygen from reaching the char. Calculations show that most particles have a residence time of about 3 s, while particles entering the lowest burners recirculate and have a longer residence time. The particle velocity is primarily dependent on the gas velocity (see Fig. 6). As the co-firing rate increases, the volumetric source from the fuel particles is added to the gas phase. Hence, it increases the cumulative volatile volumetric flow rate. As a result, both gas flow velocities and recirculation strength increase. Fig. 6 compares the vertical gas velocity between 40% MBM co-firing and dedicated coal combustion which shows higher gas flow magnitude during the 40% co-firing case than coal firing case. Gas velocity works adversely on residence time and burnout while recirculation promotes residence
Fig. 6. Vertical gas velocity profile along the boiler height for both (a) 40% MBM co-firing and (b) dedicated coal firing.
132
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
time. In addition, MBM particles have a lower density and are therefore more easily blown out of the boiler, also adversely influencing burnout. The average boiler residence times of coal and MBM particles are 5.02 and 3.01 s, respectively. MBM particles are larger than coal particles. Due to the large particle size of MBM (on average >200 μm), char combustion is mainly dominated by diffusion and less by kinetics. As a result, it takes more time to achieve complete combustion for MBM particles than for coal particles. Due to the smaller average residence time of the MBM particles, the rate of burn-out or complete conversion of the MBM char particles is not achieved. In addition, the higher volatile yield consumes the local oxygen during the early stage of combustion lowering the combustion rate
Fig. 8. Slag viscosity as function of temperature and MBM co-firing rate for an air excess level (air to fuel ratio = 1.16).
Fig. 7. Ash formation (i.e. burnout) and trajectories for 25% co-firing. (a) Coal particles and (b) MBM particles.
Fig. 9. Ash deposition propensities on boiler walls using μcrit = 105 Pa·s. (a) Coal firing and (b) co-firing 25% MBM.
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
of the large char particles. The semi-combusted char particles continue combusting in the region beyond the superheaters' zone. Fig. 7 shows ash mass fraction of the individual particles which is calculated as gram of ash in the fuel particle per gram of fuel particle as received. 3.2. Slagging The thermodynamic equilibrium calculations, as reported in Stam and Brem et al. [19], indicate that with an increasing co-firing ratio, the effect on the slag is that more slag is formed and that it is less viscous, i.e. more prone to sticking. The latter is shown in Fig. 8 for an air excess factor of 1.16 for various co-firing rates. The main reason is that the Ca
133
concentration in the slag increases, where Ca is a network modifier. Phosphorus like silicon is a network former and (as is calcium) abundantly present in MBM. However, the increase of the P content in the slag is limited and completely offset by the effect of the increased Ca content. At a critical viscosity of 105 Pa·s, results show that the wall slagging propensity increases throughout the boiler at a 25% co-firing rate compared to coal-firing (see Fig. 9). The recirculating flow causes an increase especially at and above the highest burner rows, and on the right-side wall and the right side of the boiler roof. These modeling results are in line with operator observations. It is predicted that, although viscosity is lower, the slagging propensity for 40% co-firing is lower than for 25%. The reason for this
Fig. 10. Slagging propensity calculated using μcrit = 8 Pa·s for (a) 12.5%, (b) 25% and (c) 40% MBM co-firing on the super-heaters' surfaces.
134
T.J. Taha et al. / Fuel Processing Technology 114 (2013) 126–134
modeling outcome is the higher unburned fraction in the boiler: it was assumed that only particles consisting of 100% ash can stick, and as a result the semi-combusted particles leave the domain without contributing sticking behavior. Results for coal and co-firing 12.5%, 25% and 40% show little difference for slagging at the superheaters. The results were obtained for an assumed μcrit of 10 5 Pa·s. However, when a lower critical viscosity of 8 Pa·s is used, a higher slagging propensity can be seen clearly with an increased co-firing rate (see Fig. 10). While co-firing has been relatively modest in practice (typically below 10%), no operational data or operational experience are yet available for validation of the model results. The present results may be used to prevent slagging problems. Modeling results are clearly highly dependent on the chosen value of the critical viscosity. However, this value is not well established, and various values are proposed in literature; e.g., Walsh et al. [17] propose a value of 8 Pa·s, whereas Richards et al. [25] suggest 104 Pa·s. Srinivasachar et al. [18] found that the critical viscosity for deposition below which particle adhesion will occur is 105 Pa·s. A well-established value for the critical viscosity should be based on independent experimental work.
4. Conclusions A sophisticated CFD model has been developed and demonstrated to simulate the multiphase flow and combustion with an integrated ash deposition model based on thermodynamic equilibrium. Slagging propensities for coal-firing and for various MBM co-firing rates (12.5%, 25% and 40% on basis of thermal heat input) have been studied. Using the geometry and conditions of a Dutch pulverized fuel boiler at Maasvlakte in The Netherlands as test case for co-firing MBM, the ash deposition model results were found to be in line with operational trends observed. Inertial impaction was modeled using Lagrangian particle tracking. The sticking propensity of the ash particles on boiler surface was modeled using an ash viscosity model. Wall slagging increases with increasing co-firing ratio. However, at 40% MBM co-firing ratio, wall slagging seemed to reduce. This is due to incomplete combustion of the MBM particles which restricts the deposition due to the assumption that only 100% ash particles can deposits. In addition, the high volatile content of MBM, which increases the cumulative volatile combustion, results in increasing the flame height. This leads to an increase of particle temperature which help them to retain their low viscosity and hence stickiness. Moreover, slagging problems are also expected to increase with co-firing and to move further away from superheaters as well.
Acknowledgments The authors wish to acknowledge Jan Withag and Artur Pozarlik for their valuable technical support and advice throughout this work.
References [1] L.L. Baxter, T.R. Miles Sr., T.R. Miles Jr., B.M. Jenkins, T. Milne, D. Dayton, R. Bryers, L.L. Oden, The behavior of inorganic material in biomass-fired power boilers: field and laboratory experiences, Fuel Processing Technology 54 (1998) 47–78. [2] M. Pronobis, Evaluation of the influence of biomass co-combustion on boiler furnace slagging by means of fusibility correlations, Biomass and Bioenergy 28 (2005) 375–383. [3] A.F. Stam, W.R. Livingston, M.F.G. Cremers, G. Brem, Review of models and tools for slagging and fouling prediction for biomass co-combustion, IEA Task 32 Biomass Combustion and Co-firing, 2010. [4] H. Wang, J.N. Harb, Modelling of ash deposition in large-scale combustion facilities burning pulverized coal, Progress in Energy and Combustion Science 23 (1997) 267–282. [5] F.C.C. Lee, F.C. Lockwood, Modeling ash deposition in pulverized coal-fired applications, Progress in Energy and Combustion Science 25 (1999) 117–132. [6] S.K. Kaer, L.A. Rosendahl, L.L. Baxter, Towards a CFD-based mechanistic deposit formation model for straw-fired boilers, Fuel 85 (2006) 833–848. [7] P.G. Costen, D. Dajnak, M. Messina, F.C. Lockwood, T. Abbas, C. Bertrand, On the Prediction and Control of Industrial Combustors by Mathematical Modeling; Australian Symposium on Combustion and the Seventh Australian Flame Days, Adelaide, Australia, 2002. [8] M.A. Habib, R. Ben-Mansour, M.A. Antar, Flow field and thermal characteristics in a model of a tangentially fired furnace under different conditions of burner tripping, Heat and Mass Transfer 41 (2005) 909–920. [9] C. Yin, L. Rosendahl, T.J. Condra, Further study of the gas temperature deviation in large-scale tangentially coal-fired boilers, Fuel 82 (2003) 1127–1137. [10] Hong Lu, Warren Robert, Gregory Peirce, Bryan Ripa, Larry L. Baxter, Comprehensive study of biomass particle combustion, Energy & Fuels 22 (2008) 2826–2839. [11] Yao B. Yang, Vida N. Sharifi, Jim Swithenbank, Lin Ma, Leilani I. Darvell, Jenny M. Jones, Mohamed Pourkashanian, Alan Williams, Combustion of a single particle of biomass, Energy & Fuels 22 (2008) 306–316. [12] J. Dai, S. Sokhansanj, J.R. Grace, X. Bi, C.J. Lim, S. Melin, Overview and some issues related to co-firing biomass and coal, Canadian Journal of Chemical Engineering 86 (2008) 367–386. [13] N. Schaffel, M. Mancini, k A. Szle, R. Weber, Mathematical modeling of MILD combustion of pulverized coal, Combustion and Flame 156 (2009) 1771–1784. [14] M.A. Field, Rate of combustion of size-graded fractions of char from a low rank coal between 1200 K–2000 K, Combustion and Flame 13 (1969) 237–252. [15] L.L. Baxter, Ash deposition during biomass and coal combustion: a mechanistic approach, Biomass and Bioenergy 4 (1993) 85–102. [16] H. Zhou, P.A. Jensen, F.J. Frandsen, Dynamic mechanistic model of superheater deposit growth and shedding in a biomass fired grate boiler, Fuel 86 (2007) 1519–1533. [17] P.M. Walsh, A.N. Sayre, D.O. Loehden, L.S. Monroe, J.M. Beer, A.F. Sarofim, Deposition of bituminous coal ash on an isolated heat exchanger tube: effects of coal properties on deposit growth, Progress in Energy and Combustion Science 16 (1990) 327–346. [18] S. Srinivasachar, C.L. Senior, J.J. Helbe, J.W. Moore, A fundamental approach to the prediction of coal ash deposit formation in combustion systems, Twenty-Fourth Symposium (International) on Combustion, The Combustion Institute, 1992, pp. 1179–1187. [19] A. Stam, G. Brem, Slagging Prediction of Slagging With Enhanced Biomass Co-Firing in Dutch Utility Boilers Using an Engineering Method; 34th International Conference on Coal Utilization & Fuel Systems, Clearwater, FL, 2009. [20] J. Heikinen, Characterization of supplementary fuels for co-combustion with pulverized coal. PhD thesis, TU Delft, 2005. [21] X. Zhang, W. De Jong, Estimating kinetic parameters in TGA using B-spline smoothing and the Friedman method, Biomass and Bioenergy 33 (2009) 1435–1441. [22] T.H. Duong, Modelling of brown coal combustion in one-dimension, NERDDP Project 931, Report No ND/87/040the State Electricity Commission of Victoria, Australia, 1987. [23] D.D. Evans, H.W. Emmons, Combustion of woods charcoal, Fire Safety Journal 1 (1977) 57–66. [24] T.F. Wall, W.J. Phelan, S. Bartz, The prediction of scaling of burnout in swirled pulverized coal flames, International Flame Research Foundation Report F388/a/3, IJmuiden, The Netherlands, 1976. [25] G.H. Richards, J.N. Harb, C.J. Zygarlicke, The Effect of variations in particle-to-particle composition of the formation of ash deposits, in: S.A. Benson (Ed.), Inorganic Transformations and Ash Deposition During Combustion, ASME Press, NY, 1992, p. 713.