Dependent Fuel Bed Model and CFD-based Freeboard Model

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Biofuels and Biomass

Numerical Study of Biomass Grate Boiler with Coupled TimeDependent Fuel Bed Model and CFD-based Freeboard Model Anqi Zhou, Hongpeng Xu, Wenming Yang, Yaojie Tu, Mingchen Xu, and Wenbin Yu Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b01823 • Publication Date (Web): 06 Aug 2018 Downloaded from http://pubs.acs.org on August 11, 2018

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Numerical Study of Biomass Grate Boiler with Coupled TimeDependent Fuel Bed Model and CFD-based Freeboard Model Anqi Zhou a,b *, Hongpeng Xu a,b *, Wenming Yang a,b †, Yaojie Tua,b, Mingchen Xua,b, Wenbin Yua,b a

Sembcorp-NUS Corporate Laboratory, 1 Engineering Drive 2, 117576, Singapore Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, 117575, Singapore b

Abstract In this paper, a coupled numerical model is developed to provide an advanced simulation for industrial scale grate boilers. The detailed process of biomass conversion in the grate bed is captured using the developed one-dimensional fuel bed model, taken into consideration of the separately controlled residence time and primary air supply in different zones. The distributions of gas concentrations, temperature and bed height along the grate bed are described by the transient simulation results, which are then coupled to the three dimensional freeboard simulation model as boundary conditions. The results from the coupled model are compared with the on-site measurement data from a woodchip grate boiler for steam generation, and good agreements are achieved. The in-furnace combustion processes for different quality fuels are evaluated using the model. The results show that higher quality fuel has a larger high temperature combustion zone together with lower bottom ash temperature, which suggests a higher efficiency. Moreover, a comparison between the standard grate speed control scheme and the modified one shows that increasing fuel residence time in the first zone has the potential for improving the boiler’s combustion efficiency. Key words: Biomass combustion, dynamic model, CFD, grate boiler, grate speed control 1. Introduction Nowadays, conventional energy resources are facing great challenges including limited global reserves, growing extraction difficulty and greenhouse gas emissions. As the most reliable and renewable energy resource, the utilization of biomass is playing an increasingly important role in global energy supply. In addition, biomass firing provides a good solution for waste treatment which create economic value from waste such as forestry, industrial wastes and municipal solid wastes (MSW) 1. Grate-firing is one of the most common technologies for heat and power production from biomass 2 . Grate boiler can fire a broad range of fuels with varying compositions and requires less preprocessing of the fuel. However, after years of practice, biomass grate-firing still faces technical challenges such as on-grate fuel conversion, mixing of fuel and air, fouling and corrosion, as well as emission formation. Therefore, a better understanding is the key to the optimization of the biomass grate boilers for efficiency improvement and emission control. *

These authors contributed equally to this work

†

Corresponding author. Email: [email protected]

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Many experimental and numerical studies have been carried out to investigate the combustion dynamics of biomass fuel in both lab and industrial scales. Experimental studies provide fundamental knowledge on the characteristics of biomass conversion. Researchers from Linnaeus University conducted several measuring campaigns for the burning of various biomass fuels in different grate boilers, and results showed that higher primary air flow may increase CO release and decrease NO release from the fuel bed 3–5. Research focuses for industrial scale boilers have also been extended to boiler modifications 6, heat exchange behaviors 7, and emission formation characterization for composite feedstock 8. Other researchers approximated the travelling fuel block by lab-scale fixed bed reactors and evaluated the effects of air supply, particle size, varying moisture contents through controlled experiments 9,10. From these studies, it can be noticed that the scale of experiments can be limited by the cost and accessibility. On the other hand, numerical simulation is both economically and temporally efficient compared to experimental investigations. For grate boilers, because of the difficulty in integrated modelling, a common approach is to divide the boiler into two different parts: the travelling bed and the freeboard 11 . The evaluation of the homogeneous combustion in the freeboard is done with the assistance of commercially available computational fluid dynamics (CFD) software such as Fluent. The freeboard model has been well-explored and progressively improved by many researchers. Researchers have examined the effects of various simulation parameters including turbulence models, gas mixture absorption coefficient and mixing rate on the simulation accuracy for freeboard gas reactions 12–14. Using similar modelling approach, Yu compared the boiler combustion behavior under conventional air and oxygen-enriched air, and concluded that oxy-enriched air provided better environment for pollutant reduction and fuel burn-out 15. The fuel conversion on grate can be modelled in three ways, namely the empirical model, porous media model and discrete element model (DEM). Among the three, the empirical model is more common in providing boundary conditions for industrial scale simulation, but the model is not flexible in studying the grate related parameters. The difference between porous media model and DEM is the way of treating the biomass particles, where the former one treats packed solids as continuous phase while the latter one considers dynamics of individual particles. The empirical model divides the grate into different zones, by assuming conversion rate in each zone empirically, the gas and temperature profile in each zone is calculated based on equilibrium and heat mass exchange between each zones and the freeboard. Rajh et al. used such a model to provide grate input condition for freeboard simulation to evaluate how radiation and buoyancy affect the freeboard modelling results 13. Scharler et al. employed similar model based on previous experimental results on fuel-N release to investigate the NOx emission of both pilot and industrial scale boilers with detailed chemical reaction mechanisms 16. The prediction resolution and accuracy of empirical model improve with increasing number of divisions. However, the limitation of such model is its reliance on experimental prediction of the conversion rate, and therefore the model is not sensitive to changes of boundary conditions. The other method is modelling the moving bed through transient two phase packed bed model. The two approaches for this model can be classified into Euler-Euler method and Euler-Lagrange method. In Euler-Euler method, also called porous media model, the gas and solids are both described as continuous phases 17. Thurmann et al. used porous media model to compare the difference between counter-current and co-current biomass combustion and the influence of devolatilization and mixing rate in several studies 18,19. Kær employed “walking column” approach to 2 ACS Paragon Plus Environment

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simulate moving grate with one-dimensional model 20. Furthermore, to account for the fuel mixing and shrinkage along the grate, researchers from Sheffield University incorporated a semi-empirical solid shrinkage velocity in their FLIC codes based on continuum approach, and the model has been verified by different experiments 21–23. Other researches using transient bed models for optimizing fuel bed conversion include those by Porteiro et al. 24, Garcia and Nussbaumer 25 and Meng et al.26. The Euler-Lagrange model treats the solids as discontinuous particles. Transport equations are formulated and solved for individual particle and all particle conversions are summed to interact with the gases in DEM model 27. Mahmoudi et al. developed XDEM to perform analysis on conversion process initiation and propagation, and analyzed spontaneous heating and ignition in packed bed 28,29. Wissing et al. was able to reproduce the effect of particle size distribution and grate transport mechanism in a boiler with similar approach 30. However, in the same study where the total biomass residence time is 90min, the time step for calculation goes down to 0.0002 s 30. As a result, the computational cost of DEM model is too high to be economical for performing comprehensive parametric studies related to industrial scale boilers. In this study, an industrial-scale woodchip grate boiler with a steam generation rate of 40 tons/hour is investigated. Therefore, the transient porous media modelling approach is chosen in order to balance the computational time and model flexibility. Furthermore, in grate boilers, the solid biomass fuel travelling down the grate is subjected to changing boundary conditions in each zone, and to the best of our knowledge, there is currently no fuel bed model addressing these details. Hence, this study focuses on developing a dynamic one-dimensional fuel bed model, and improving the model to take account of the real operating conditions on boiler grate, including primary air distribution, changing grate speeds and different radiation temperatures over the bed. The extended fuel bed model is then coupled with 3D CFD-based freeboard simulation to provide a robust and advanced modelling for the industrial scale boiler. The combustion dynamics of three quality fuels are simulated and evaluated using the developed model. Besides, numerical study is performed to investigate the effect of varied grate speed control compared with standard operating conditions. More details of the boiler can be found in the second section. The third section of the article includes an introduction of the methodologies used. Section four presents the results and discussions, followed by conclusions in the last section. 2. Grate Boiler furnace under investigation and in-situ measurements The grate boiler under investigation is a woodchip boiler, designed to produce 40 tons of steam per hour. The biomass used for heat generation consists of construction and demolition waste wood collected locally in Singapore. The fuel transmitting grate is divided into five zones of equal length, but the grate speed and the primary air (PA) flowrate are different in each zone to achieve adaptation for different fuels. The PA is supplied into air distribution compartments beneath the grate, and the varying of air flow in each zone is achieved by adjusting individual valves. The PA distribution in the five zones follows a ratio of 1:3:3:2:1. There are also two rows of high speed secondary air (SA) supply nozzles on the two side walls of the furnace respectively. The secondary air located directly above the gas mixing chamber assists the complete burnout of combustible gases. For the monitoring of the in-boiler combustion, there are three temperature measurement positions in the combustion chamber, as shown in Figure 1. In addition, the O2, CO2 concentration in the flue gas after complete combustion is measured at the flue gas exhaust. The volume fractions of these species are analyzed


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and obtained by Nondispersive infrared sensors (NDIR). The primary pollutants emission from the flue gas, including CO, NOx, SO2 and PM, are also monitored at the exhaust.

Figure 1. Grate boiler furnace scheme and temperature sampling locations

3. Modelling Methodology As mentioned in the previous part, the modelling of the boiler is divided into two sections modelled with different methods separately, as in Figure 2. Freeboard is numerically simulated with Fluent. The dynamic one-dimension fuel bed model is developed based on the open-source codes MFIX 31.

Figure 2. Coupled Freeboard and Fuel bed model


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The coupling of the two models is done through the iterative exchange of information as shown in Figure 3. The calculated species distribution, velocity and temperature profile from transient bed model are used as inlet condition for freeboard simulation. In return, the calculated over-bed radiation temperature from the freeboard is then used as boundary condition for the next iteration of fuel bed simulation. The coupling is complete when the change of the radiation temperature from the freeboard simulation is less than 1.5% between two consecutive iterations 32.

Figure 3. Iterative Model Coupling Procedure and Convergence

3.1. Porous media model for combustion in packed bed The fuel bed is modelled as porous media, where the reactions accompanying by the heat and mass exchange between the gas and solids occur. The model is composed of a set of transient onedimensional governing partial differential equations described as follows. 3.1.1. Governing equations for gas Gas mixture continuity equation, where Ssg is the source term describing the mass transfer between solid and gas phases.

∂ ( ρgε ) ∂ ( ρgε ug ) (1) + = Ssg ∂t ∂y Gas flowing through a porous media experiences resistance, resulting in a pressure drop. In addition, the viscous and frictional effects at the gas-solid interface cause momentum losses, F, in the flow 33, so the momentum equation is written as ∂ ( ρgε ug ) ∂ ( ρgε ugug ) ∂p + =− +F ∂t ∂y ∂y

(2)

µ  3.5 1 − ε F = −  g ug + ug ug  3   ψ dp ε α 

(3)

Where α is the permeability coefficient and is related to the gas volume fraction ε. ε3 (4) ψ 2150 (1 − ε )2 The concentration of each gas species with mass fraction Xi is determined by conservation equation with the following form α=

dp2

∂ ( ρgε X i ) ∂ ( ρgε ug X i ) ∂  ∂ ( ρgε X i )   + Si,g + =  ( Di,g + Dg,t )  ∂t ∂y ∂y  ∂y  

(5)


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Where Dg,t is the turbulent diffusion coefficient 34 Re 5 0.5d p ug , The energy conservation equation of gas phase in porous media is

(6)

∂ ( ρgε hg ) ∂ ( ρgε ug hg ) ∂  ∂T  + =  ( kg + kg,t ) g  + S p hsg (Ts − Tg ) + Qg ∂t ∂y ∂y  ∂y 

(7)

where kg,t represents the effect of turbulence on the heat conductivity of gas phase and has similar expression as the turbulent diffusion coefficient 34 Re 5 0.5d p ug ρ g cp,g , The convective coefficient is determined by the following equations34. k 1 hsg = g (2 + 1.1Pr 3 Re0.6 ) (9) dp 3.1.2. Governing equations for Solid phase The continuity equation for solid phase is written as ∂ ( ρs (1 − ε ) ) ∂ ( ρs (1 − ε )vs ) + = −Ssg ∂t ∂y For each solid species Xi,s, namely moisture, volatile matter, char and ash.

∂ ( ρs (1− ε ) Xi,s ) ∂ ( ρs (1− ε )vs Xi,s ) + = S j,s ∂t ∂y

The governing equation of energy is ∂ ( ρ s (1 − ε ) hs ) ∂ ( ρ s (1 − ε )vs hs ) ∂  ∂t

+

∂y

=

∂Ts  λs ∂y  ∂y

(10 )

(11)

  + Qrad ( x ) + S p hsg (Tg − Ts ) + Qs 

(12)

In a grate boiler, the ignition of biomass on the grate is prompted by the radiation from the freeboard combustion. Saastamoinen has experimentally proved that the radiative heat flux from over-bed flame decayed exponentially from the surface down the height of the bed, and the bed local heat absorption is proportional to this flux 35. Therefore, the radiative source term Qrad (y) is represented in the equation below, where decay coefficient β is proportional to the specific surface area of the particle and Trad is the equivalent flame radiation temperature. This source term modelling has been demonstrated to be effective in predicting the biomass ignition by radiative heating 36. 4 Qrad ( y) = βσ ( ε radTrad − ε sTs4 ) e− β ( H0 − y )

(13)

The effective heat transfer coefficient, λs is expressed by the equation below, where the first term is the composite heat conductivity and the last term represents the radiative contribution 37. At high temperature, inter-particle radiation plays an important role in promoting ignition front propagation in packed bed. ε λs = (1 − ε )( X cλc + X mλm + X vmλvm ) + 4σε s dpTs3 (14) 1− ε


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Energy & Fuels

The last two source terms in equation (11) represent the convective heat transfer between gas and solids with a coefficient hsg, and total reaction heat source Qs from heterogeneous reactions. In addition, the term vs in the solid phase transport equations is introduced to account for the volume change of the fuel due to heterogeneous reactions 22. For each heterogeneous reaction, a coefficient ai is introduced to capture the contribution to shrinkage. For example, if a1 for evaporation is 1, the consumption of fuel bounded moisture results in the shrinkage of the packed bed. Based on this assumption, the downward velocity is expressed as: R 'evp R 'pyr R 'char vs = a1 1 − VB,A ) + a2 1 − VC,B ) + a3 ( ( (1 − VD,C ) (15) ρ2ω2A (1 − ε A ) ρ3ω3B (1 − ε B ) ρ4ω4C (1 − ε C ) R’ represents the reaction rate of each process per cross sectional area. More detailed information of these variables can be found in Table 1 of the reference 22. 3.1.3. Heterogeneous reactions Since biomass contains a significant amount of moisture that affects the ignition time and energy exhaustion, a precise drying model is important 25. The over-bed radiation, together with the dry primary air entering from the bottom grate, prompts the evaporation process. Hence, the drying rate is 38: Sp hm ( X m,s − X m,g ) , Ts < 373K  Revp =  (16) 4 4 Sp hsg (Tg − Ts ) + ε sσ (Tenv − Ts ) , Ts > 373K Moreover, volatile matter is lumped into one artificial solid species CHxOy based on the proximate and ultimate analysis of the fuel, and one-step reaction model is employed to describe the pyrolysis reaction 39. The products from pyrolysis are determined by the stoichiometric balance and endothermic nature of the reaction. The reaction rate is represented by Arrhenius equation shown below, and the kinematic parameters are selected from literature according to the properties of the woodchip 38. CH x O y  → β1 ⋅ CH 4 + β 2 ⋅ CO + β 3 ⋅ CO 2 + β 4 ⋅ C l H m O n + β 5 ⋅ H 2 + β 6 ⋅ H 2 O (17)

(

)

R pyr = Apyr exp( − E / ( RT )) m vm

(18)

Char oxidation succeeds evaporation and pyrolysis. The char oxidizes fully or partially to produce a mixture of CO and CO2. The formation ratio of CO over CO2 is dependent on local solid temperature 40 C + ϕ O 2 → 2 (1 − ϕ ) CO 2 + (2ϕ − 1)CO

(19)

ϕ=

0.5rc + 1 rc + 1

(20)

rc =

CO = 33exp(−4700 / Ts ) CO2

(21)

The oxidation rate is given by the following equation: Rchar = Ap CO 2 (1 k r + 1 kd )

(22)

Where kr represents the kinetic reaction coefficient and kd controls the oxygen diffusion to the surface of the char. (23) k r = 497 exp ( −8540 / Ts ) 7 ACS Paragon Plus Environment

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3.1.4. Homogeneous reactions The combustible products from devolatilization and CO from char partial oxidation mix with the air supply and go through oxidation reactions. The rates of the homogeneous reactions are limited by the mixing efficiency and the chemical reaction kinetics at the same time. The mixing rate is expressed in the form of Ergun equation 33, taking into account the fluid viscosity and drag resistance. Rmix

1/3  Dg (1 − ε )2/3 ug (1 − ε )  Cfuel CO2  1.75 min , = Cmix ρg 150 + ×    dp2ε dp ε  Sfuel SO2   

(24)

The homogeneous reactions included are listed in Table 1 35. The gas reaction rates are obtained by comparing the mixing rate with the rate of chemical reaction, and taking the minimum between the two rates. Ri = Min ( Rkinetic , Rmix ) No. 1

(25) Table 1. Homogeneous reaction rates Reaction Rates

Chemical Formula

l m n m Cl H m On + ( + − )O2 → lCO + H 2 O 2 4 2 2

RCl H m O n = AT b e ( − E / RT ) CCl H m O n −0.1CO 2 1.85

2

CH4 +1.5O2 → CO + 2H2O

RCH4 = ATb e(−E/RT) CCH4 0.7CO2 0.8

3

CO + 0.5O2 → CO2

4

H 2 + 0.5O 2 → H 2 O

A

b

E

0

1.26 10

5 10

0

2 10

RCO = ATb e(−E/RT) CCOCO2 0.25

2.24 10

0

1.7 10

RH2 = AT b e(−E/ RT ) CH2 CO2

9.87 10

0

3.1 10

1.35 10

3.2. Fixed bed representation of fuel blocks on moving grate This transient one-dimensional model assumption is based on the fact that when the crosssectional area of the fuel column is sufficiently small, the horizontal gradient of species and temperature is small compared to the vertical gradient. In this study, a mass corresponding to the fuel column transported per second is taken as the initial condition for performing 1D bed model simulation. Various experiments and simulations have been done on fixed-bed reactor based on this approximation to study the fuel conversion characteristics for steady state industrial facilities in literatures 41–43. As demonstrated in Figure 4, to implement this model for industrial scale boiler and simulate the real operating conditions, the following steps are performed: According to the collected data of grate operations, different fuel transport speed ui is used to approximate the fuel residence time in each zone of length Li along the grate, as shown in equation (25). Accordingly, every ti calculated corresponds to a location of division between two zones.


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Figure 4. Travelling columns for changing boundary conditions on grate

At each given time position ti, the boundary conditions for fuel bed model including the radiation temperature and air flow rate are varied accordingly. L ∆ti = i (26) ui In fixed bed combustions 42,44, the gas samples are extracted and analysed from a fixed location on the reactor, which means the change of the bed height is overlooked. However, in the representation of boiler grate operation, such approximation would induce errors in the gas species distribution such as the early oxidation of combustible gases. Therefore, in this study, the property of the released gas is always taken at the vicinity of the bed top instead of fixed-height sampling to be used as freeboard simulation input. 3.3. Model for freeboard combustion The gas mixture produced from the fuel bed enters the freeboard and reacts with the oxygen from the SA for a complete combustion. Reynolds-averaging Navier-stokes form of the gas conservation equations are solved in ANSYS Fluent for freeboard simulation. Fluent employs a finite volume discretization method, and the second-order upwind method is selected for the discretization of all the mixture conservation equations and species transport equations. The three-dimensional model of the furnace zone is firstly created in CAD (computer aided design) software in actual scale, and then meshed to 934,197 cells as shown in Figure 5. The mesh of the 3D freeboard is fixed, ignoring the change in the bed packing height.

(a)

(b)

Figure 5. Mesh of the 3D model of gas combustion zone: (a) XY-plane; (b) YZ-plane front wall

The standard two-equation k-epsilon model with standard wall functions is employed to account for the in-furnace turbulence flow dynamics. The P-1 radiation model is selected to predict the infurnace radiative heat exchange. The absorption coefficient of the gaseous mixture is approximated by cell-based WSGGM (weighted-sum-of grey-gases). The turbulence-chemistry interaction is modelled by finite-rate/eddy-dissipation model (FR/ED), which evaluate the turbulent mixing and kinetic reaction dynamics at the same time 45. The gas mixture includes eight-species with four reactions listed in Table 1.


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4. Results and Discussion 4.1. Fuel bed model for fixed bed combustion The model implemented in MFIX is firstly validated with the experiment done in a lab-scale fixed bed reactor 38. The biomass sample is 2kg, piled to an initial height of 480 mm with a bulk density of 133 kg/m3. The air supply from the bottom is 0.13 kg/m2s. The analysis of the fuel can be found in the reference 38, and the average particle size is 12mm. The comparison of the height change of the fuel bed is exhibited in Figure 6(b). Figure 6(a) shows the simulation results of CO, CO2 and O2 volume fraction compared with experimental measurements. As can be seen from the plot, the trend of all three species and the bed shrinkage agree well with the experiment. According to the two graphs, a complete fuel conversion process can be divided into three stages: bed ignition, steady-state combustion and final char burning. The first stage is the ignition of the fuel, marked by a slow decrease in the bed height. Heat from radiation heats up the solid fuel, and moisture evaporates from the solids at the same time. When the downward speed of the fuel increases gradually, the O2 level begins to fall from 21% to 0% as the flame front passes through the gas sampling location. Meanwhile, when CO2 rises from 0% to 19% with a steep increase in CO concentration, the fuel is considered to be ignited and enters a steady combustion stage.

(a) (b) Figure 6. Validation against fixed bed reactor experiment: (a) profile of simulated gas compositions against the experimental measurements; (b) profile of simulated bed height change against the experimental measurements.

It can also be noticed that the bed height decreases steadily with a nearly constant gradient during the steady state combustion. The local gas concentration remains at a stable level for a relatively long period of time. When the steady burning period reaches its end at around t =1000s, a peak of CO and a fall in CO2 level are observed. Consistent with the observations made in earlier research 24, this corresponds to the sudden increase of the devolatilization rate at the end of steady state conversion. Moreover, the peak of CO towards the end is from the final burnout of the char. 4.2. Modelling of industrial woodchip boiler combustion for different quality fuels The validated fuel bed model is then extended to represent the changing boundary conditions based on the assumption presented in section 3.2. To validate the coupled model, 5 samples of fuel are selected. The properties of the fuel samples are listed in Table 2. The bulk densities of the fuels are around 300 kg/m3. 10 ACS Paragon Plus Environment

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The radiation temperature change during iterative coupling calculation for Sample 1 is presented in Figure 7. A constant radiation temperature of 950K was assumed to start the first iteration of fuel bed simulation, and it can be seen that convergence criterion given in Figure 3 is met within 4 iterations. After successful model coupling for each fuel, the simulated results are then compared with the on-site measurement data. Table 2. Properties of the fuel samples No.

1 2 3 4 5

HHV (db*) MJ/kg 18.47 17.43 15.51 13.58 7.79

Moisture wt% 21.89 25.30 25.91 26.10 20.40

Proximate Analysis (as received) Volatile Char wt% wt% 55.00 17.97 48.27 18.00 44.68 15.41 47.44 11.99 33.00 7.90

Quality Ash wt% 5.14 8.43 14 14.47 38.70

High High Medium Medium Low

C wt% 44.49 46.75 37.78 33.32 23.12

Ultimate Analysis (dry-ash-free basis) H O wt% wt% 5.25 42.4 6.08 34.2 4.76 36.84 4.09 41.3 3.18 23.6

N wt% 1.28 1.64 1.72 1.39 1.46

S wt% 0.29 0.24 0.74 0.72 1.69

4.2.1. Biomass conversion process on grate The fuel bed model is able to predict the species distribution, gas mass flux and temperature along the grate, and the results are presented in Figure 8. The varied air supply and residence time in each zone facilitate an advanced gas distribution for this specific furnace configuration. In the first zone, where fuel goes through heating and evaporation primarily, a low air flow rate limits the air cooling effect which is favorable to achieve a successful fuel ignition. The increases in H2O, CO and CO2 indicate that the volatile also starts to decompose in the first zone. However, the release of H2O does not complete within zone 1 as a result of the relative short residence time in this zone. After the gradual increase, the release of gases enters a steady stage. This stage is dominated by pyrolysis and char oxidation, requiring a generous supply of air for combustion, as well as heat and mass removal from the packed bed. Referring to the constant bed height decrease shown in Figure 9(a), this period is the steady and consistent combustion. The steady combustion takes place in zone 2 and zone 3, and corresponds to a maximum on-grate degassing flux. Moreover, towards the end of zone 3, the release of CO2 and other combustible gases reaches a peak, followed by a sudden drop. The peak is from the intensive pyrolysis of the left volatile matter. In addition, the outgoing gas temperature also increases sharply right after the final devolatilization. The heat generated from char oxidation is no longer actively removed after the completion of the two endothermic reactions, and consequently contributes to a temperature escalation. Another noticeable phenomenon is the small peak of CO and CO2 in the last two zones. This peak is due to final char oxidation. The air-rich condition in the last two zones prompts the final conversion of the left combustibles and unburnt carbon. In addition, the final zone is dominated by air cooling of the ash before quenching to minimize the unaccountable ash loss. Although the air supply amount is low in this zone, the cooling is achieved by a prolonged residence time of the ash.


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Figure 7. Radiation temperature change during iterative calculations (Sample 1)

(a) (b) Figure 8. Results from fuel bed model for Sample 1: (a) bed top gas species distribution; (b) mass flux and temperature profile

Furthermore, comparing the temperature distribution with height change for different quality fuels shown in Figure 9, high quality fuel is witnessed to have a longer steady state conversion reaction that only complete towards the very end of the third zone. The over bed radiation temperature distribution for each fuel is included in Figure 9(d). Moreover, for higher quality fuel with lower ash content, the cooling result is better in the last zone. The final ash temperature of high quality fuel with 5% ash content can be cooled to below 500K according to Figure 9(a). For low quality fuel, the ash is inhibiting the cooling of the biomass residue on the grate.

(a)

(b)


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Energy & Fuels

(c) (d) Figure 9. In bed temperature distribution, fuel bed shrinkage during on-grate conversion: (a) high quality fuel (Sample 1); (b) medium quality fuel (Sample 3); (c) low quality fuel (Sample 5); and (d) over-bed radiation temperature for three quality fuels

4.2.2. Validation with on-line monitoring results The results shown in previous section are used as inputs for the freeboard simulation and model validation. The validation results are presented in Figure 10. The average of the measured data on the day of fuel sampling is used as the reference, and the error bar indicates the upper and lower bound of daily fluctuations. The simulated temperature results at the three locations shown in Figure 1 match quite well with the measurements for different quality fuels. Therefore, the coupled model is capable of predicting the combined on-grate and in-furnace combustion for a wide range of fuel qualities.

Figure 10. Validation against industrial boiler measurements

4.2.3. Combustion and flow dynamics in the furnace The freeboard simulation gives more comprehensive results that explain the in-furnace combustion and flow behaviors. The middle plane temperature distribution for three quality fuels are presented in Figure 11. The combustion characteristics for different quality fuels are similar. The combustion behavior can be demonstrated by the O2 and CO mass fraction distribution on the middle plane for high quality fuel shown in Figure 12, and Figure 13 shows the flow path-lines. Large amount of CO and other combustible gases released from the bed in the second and third grate zone create an O2 deficit zone. Furthermore, the CO in the fourth zone from final char burnout is combusted when it mixes with the O2 from the last two grate sections. There is also a concentrated combustion zone around the planes of SA injection, which is a result of the mixing of the impinging air with the combustible gases from zone 2 and zone 3.


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However, like on-grate conversion, high quality fuel has an extended combustion zone to the second radiation path, passing through the third temperature measuring location with a higher maximum temperature on the middle plane. Moreover, the detached flame after the concentrated combustion is due to the impinging cold SA, and it is more obvious for lower quality fuels. In addition, from the flow path shown in Figure 13, it can be seen that there is a zone of recirculation going downwards from SA plane. This recirculation contributes to the larger high temperature zone near the front wall because of enhanced reactions. On the one hand, this high temperature is beneficial to radiative heating and drying of the incoming woodchips. On the other hand, such phenomenon may also intensify the slagging problem in this region. The second zone of recirculation is close to the rear wall. The recirculation in the post-secondary air zone increases the residence time of the combustibles and ensures a complete combustion. Moreover, the maximum velocity magnitude is higher near the rear wall, which is from the large mass flux in grate zone 2 and 3. 4.2.4. Combustion with different residence time in travelling bed From the standard design, the fuel residence time in the first zone is very short, and not sufficient for fuel drying. Therefore, the newly proposed scheme aims to investigate the effect of increased residence time in the first zone with unchanged total residence time. The simulation results for medium quality fuel sample 3 are shown below. Figure 14 (a) and (b) includes the property of the released gas from the fuel bed, the dashed line and the solid line represent the results from the modified case and the standard case respectively. It can be seen that the modified grate control scheme causes earlier gas release with early completion. For the same total residence time, the fuel residence in zone 1 is increased by 2.7 times, and the peak of H2O is shifted from the third zone to the first. This change means that increasing the residence time of biomass in zone 1 prompts the moisture evaporation. Additionally, the bottom ash temperature is reduced by 20K as a result of early char burnout conclusion after modification. These gas properties affect freeboard combustion consequently. Comparing Figure 14(d) and Figure 11(b), the modified case has a larger combustion zone. By evaluating the temperature distribution above zone 1 and comparing with species distribution, it is obvious that the early gaseous fuel release results in late combustion in the radiation shaft instead of consumption in the bottom mixing zone. In addition, both the average furnace temperature and the furnace outlet temperature increase by 10K. Increased furnace temperature suggests enhanced radiative heat exchange while increased furnace outlet temperature indicates better heat exchange efficiency in the convective path. Therefore, without alteration of the furnace design, a modified grate speed control scheme would improve the current boiler behaviour in several aspects.

(a)

(b)

(c)


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Energy & Fuels

Figure 11. Temperature distribution on the middle plane: (a) high quality; (b) medium quality; (c) low quality

(a) (b) Figure 12. Middle Plane Species Mass Fraction Distribution (Sample 1): (a) O2; (b) CO

(a) (b) Figure 13. Flow pathline: (a) XY-plane; (b) YZ-plane

(b)

(a)


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(d)

(c)

Figure 14. Combustion behavior of Sample 3 (Solid line represents standard case, dashed line represents modified case): (a) bed top gas species distribution along the grate; (b) mass flux and temperature along the grate; (c) comparison of radiation temperature for standard and modified grate speed; (d) Freeboard combustion for modified case

5. Conclusions In this research, the transient one-dimensional model describing the heat and mass transfer during biomass combustion was developed, corrected with bed shrinkage velocity. This validated fuel bed model was then successfully extended for modelling of biomass combustion subject to changing primary air supply, over-bed radiation and grate transport speed, travelling along the grate. According to the simulation results, in the industrial-scale counter flow grate boiler with 5 zones, biomass devolatilization process dominated in zone 2 and zone 3. Moreover, the final burnout in the fourth zone caused a steep temperature increase. After complete degassing, the final ash cooling was achieved through long residence time in the last zone. Furthermore, the completion of steady state combustion process and ash cooling efficiency were affected by the ash content of the fuel. Particularly, high quality fuel had a prolonged biomass incineration on grate with a lower final ash temperature. The coupled fuel bed and freeboard model was successfully established to facilitate advanced modelling of industrial scale grate boilers. While freeboard model provided radiation information for fuel bed simulation, the fuel bed model results acted as boundary conditions for freeboard simulation in return. The predicted temperature and outlet species fraction from the model were compared with the real time measurements in the furnace. The model was successful in predicting the combustion dynamic of different quality fuels. The in-furnace dynamics were evaluated based on the simulation results, and provided a foundation for boiler improvement in future studies. In addition, the practical significance of separately controlled primary air supply and grate speed was demonstrated by the model through comparing combustions of the same fuel with different grate control schemes. The comparison showed that longer residence time in the first zone prompted early combustion completion with lower bottom ash temperature, and further increased freeboard combustion temperature for better heat exchange behavior. All of the above mentioned phenomenon suggested better overall efficiency. Therefore, this developed model is capable of investigating the optimum grate operation conditions for a specific boiler design in application.


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Energy & Fuels

Acknowledgments The National Research Foundation Singapore, Sembcorp Industries Ltd and National University of Singapore under the Sembcorp-NUS Corporate Laboratory support the research. Dr. Zheng Zhimin, Mr. Cai Yongtie and the staff from Sembcorp are gratefully acknowledged for their kind assistance in collecting the experimental data.

Nomenclature A Ap C Cmix cp D dp E h H hm hsg k mvm p Q R rc S Si Sp T ug X

pre-exponential factor internal surface area, m2/m3 mass concentration, kg/m3 mixing-rate constant specific heat capacity at constant pressure, J/(kg·K) diffusion coefficient, m2/s particle diameter, m activation energy, J/kmol enthalpy, J/kg height, m mass transfer coefficient between the solid surface and gas, m/s convective heat transfer coefficient, W/(m2·K) heat conductivity, W/(m·K) remaining mass of volatile matter gas pressure, Pa heat of reaction, J/kg ideal gas constant, J/(mol·K); reaction rate, unit dependent on reaction CO to CO2 ratio stoichiometric coefficients in reactions mass source term, kg/(m3·s) surface area of particle, m2 Temperature, K superficial gas velocity, m/s mass fraction

Greek Letters εs, εrad ε µ ρ σ ϕ

emissivity volume fraction dynamic viscosity, kg/(m·s) density, kg/m3 Stefan–Boltzmann constant, W/(m2·K4) shape factor

Subscripts 0 c char db evp env

initial char char oxidation dry basis evaporation environment


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g i j m mix p pyr rad s vm wt

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gas The ith species The jth reaction moisture gas mixing particle pyrolysis radiation solid volatile matter weight

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Dependent Fuel Bed Model and CFD-based Freeboard Model

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