numerical modelling of biomass grate furnaces...10th european conference on industrial furnaces and...

10th

European Conference on Industrial Furnaces and Boilers – Porto, Portugal, April 2015

1

Numerical modelling of biomass grate furnaces

with a particle based model

Ramin Mehrabian1,*

, Ali Shiehnejadhesar1,2

, Robert Scharler1,2,3

,Ingwald Obernberger2,3

1BIOENERGY 2020+ GmbH, Inffeldgasse 21b, 8010 Graz, Austria

2Institute for Process and Particle Engineering, Graz University of Technology, Inffeldgasse 21b, A-8010 Graz, Austria

3BIOS BIOENERGIESYSTEME GmbH, Inffeldgasse 21b, A-8010 Graz, Austria

*[email protected], Tel.: +43 (0) 316 873-9232; Fax: +43 (0) 316 873-9202

Abstract

A particle based 3D packed bed combustion model for the simulation of biomass moving grate furnaces

which couples the modelling of packed bed and the freeboard and considers their interactions was applied

in this paper. The particle trajectories on the grate are calculated based on the velocity of the grate bars

and the shrinkage of the packed bed. The thermal conversion of particles in the bed is modelled by the

validated particle model for the thermal degradation and combustion of thermally thick biomass particles.

Two methods, i.e. a global reaction mechanism with the Eddy Dissipation Model (EDM) and a skeletal

reaction mechanism together with the advanced Eddy Dissipation Concept (EDC), are used to model the

turbulent reactive flow. The results of these two methods are compared to investigate the effect of different

gas phase models on the simulation results. Additionally, the results of an empirical packed bed model

which excludes the fuel bed from the simulation domain are compared to the results of the model

presented in this paper, to highlight the relevance of an incorporation of the packed bed in grate furnace

simulations. The results show that the new packed bed model is able to give detailed insight on the

processes occurring in the packed bed and, therefore, constitutes an efficient tool for the computer aided

design of grates as well as the evaluation of influencing parameters on the behaviour of packed fuel beds.

Furthermore, due to the comparably high computing times with the new models at present, simulations

which are only focussing on the optimisation of the combustion chamber should be rather performed with

the simpler empirical packed bed model.

Keywords: biomass, grate furnace, combustion, CFD, 3D packed bed.

1. Introduction and objectives

CFD simulations are an efficient tool for the design and optimisation of biomass grate furnaces to

complement or even to substitute experimental investigations. The existing bed models resolve packed

bed combustion separately from the gas phase above it and produce heat and mass release profiles

which serve as boundary conditions for gas phase simulation by a CFD code. Therefore, developing an

appropriate packed bed model which can be coupled with available CFD tools is of relevance in order to

enable a direct link of the bed model with the gas phase combustion models and thus to make

simultaneous simulations of the entire biomass grate furnace possible.

The current paper demonstrates the validation of a particle model for the thermal degradation and

combustion of thermally thick biomass particles. The particle model is incorporated into ANSYS FLUENT

to simultaneously resolve the thermal conversion of the particles and the gas phase around. The model

has been validated at two levels. Firstly, the model predictions have been compared with the measured

particle surface and centre temperatures as well as the particle mass loss of a single particle reactor. In

the next step, a lab-scale fixed bed reactor has been used to validate the model performance under

packed bed conditions. The fixed bed is modelled as an ensemble of thermally thick biomass particles

which interact with each other and the gas phase. Since the lab-scale fixed bed experiments were

conducted in a batch mode, the simulation of the fixed bed reactor had to be transient. The results of the

model were compared with a measured mass loss profile and temperatures at different heights in the bed.

mailto:[email protected]

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In the second part of the paper, the application of the model for the 3D simulation of a complete biomass

grate furnace has been performed. Using the particle model enables to avoid the separation between the

packed bed and the freeboard simulation. The particle trajectories on the grate are postulated based on

the velocity of the grate bars and the shrinkage of the packed bed. A global 4-step mechanism together

with the Eddy Dissipation Model, which is the state-of-the-art for modelling of gas phase combustion in

biomass grate furnaces, is used. In order to investigate the effect of different combustion models and

reaction mechanisms, a simulation with a detailed mechanism together with the more advanced Eddy

Dissipation Concept has also been performed. It allows evaluating the accuracy and calculation time of

the combined 3D packed bed model with the EDC model for real-scale simulations. The simulation results

have been compared with measured temperatures at different positions in the combustion chamber.

Additionally, the results of the particle based 3D packed bed combustion model are compared with the

results of an empirical packed bed model. Since in the empirical model the fuel bed is not included in the

simulation domain, this comparison allows evaluating the advantages of the coupled simulation of packed

bed and freeboard.

2. Methodology

The paper consists of two main parts. In the first part, the model equations and assumptions as well as the

validation of the particle model for the thermal degradation and combustion of thermally thick biomass

particles are presented. Then, in the next part of the paper the application of the model for 3D simulations

of biomass grate furnaces has been tested for a 180 kW grate furnace.

2. 1 Particle model

The particle model accounts for intra-particle transport processes and the simultaneous sub-processes of

the thermal conversion of thermally thick solid biomass particles. To reduce the model complexities and

calculation time, as a usual simplification, only the radial temperature gradient in the particle is considered.

To apply the one-dimensional model for a finite cylindrical geometry (as an approximation for the biomass

particle shape), Thunman’s discretisation [1] approach has been applied. This approach assumes that the

particle boundary conditions are homogeneous and every point in the particle at a certain distance from

the particle surface has the same temperature and conversion state. The particle is divided into four

layers: drying layer, pyrolysis layer, char and ash layer. The boundaries between the layers are related to

the conversion sub-processes drying, pyrolysis and char burnout fronts. At the beginning of the thermal

conversion process only drying is of relevance. Due to heating up, moisture starts to get released from the

particle. The pyrolysis layer consists of dry biomass and is located around the drying layer as the drying

front moves towards the particle centre. When pyrolysis commences, the dry biomass converts to char

and volatiles. Volatiles leave the particle and char builds a layer around the pyrolysis layer. Finally, char

burnout also creates another layer which contains only ash and surrounds the char layer. As the

conversion of the fuel particle proceeds, drying, pyrolysis and char burnout fronts move from the surface

to the centre of the particle.

The conversion of each layer is simulated by separate sub-models. It is assumed that drying occurs at a

fixed boiling temperature in an infinitely thin zone that separates the wet and the dry part of the particle.

The drying process acts as a heat sink, it means that any amount of heat flow above the boiling

temperature is consumed by the drying process. It is assumed that there is no resistance to mass transfer,

and therefore the water vapour instantaneously leaves the particle. However, the cooling effect of the

water vapour transfer through the particle is considered.

Biomass pyrolysis is described by the decomposition of its three pseudo-components hemicellulose,

cellulose, and lignin. This model implicitly assumes the hypothesis of an independent decomposion of

these three constituents. An Arrhenius equation is used to describe the pyrolysis of each pseudo-

component. It represents the dependence of the kinetic rate constant k on the absolute temperature T:

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expE

k ART

(1)

A is the pre-exponential factor, E is the activation energy and R is the universal gas constant. The

empirical constants needed in the pyrolysis model are obtained from the fast heating rate experiments

reported by Branca et al. [2].

The overall mass loss rate of a particle during pyrolysis is given as:

3

1

ii

i

ddmc

dt dt

(2)

where i is related to each pseudo-component, ci = m0,i - mf,i is a measure of the contribution of the partial

decomposition processes to the overall mass loss m0,i - mf,i (m0,i: initial mass; mf,i: final mass). The

conversion of each pseudo-component αi can be expressed by:

0,

0, ,

i i

i

i f i

m m

m m

(3)

The pseudo-components are all assumed to decompose individually according to a first-order reaction

rate, therefore the conversion rate of each pseudo-component is given by:

exp 1i ii i

d EA

dt RT

(4)

The volatiles yielded from pyrolysis include a complex mixture and several hydrocarbons have been found

in it. This complex mixture mainly consists of CO, CO2, H2O, H2, light hydrocarbons and heavy

hydrocarbons (tar). To simplify the combustion behaviour of the volatiles, the formed tars in the primary

pyrolysis reaction are assumed to be fully converted into its main secondary products, e.g. CO, CH4. This

assumption is in line with dedicated experiments [3] [4], in which it was observed that the carbon

monoxide is quantitatively the most important product from the homogeneous tar conversion. Over two-

thirds of the tar at high temperatures converts to carbon monoxide. Methane accounts for about 10 wt% of

the tar converted. This assumption is plausible for packed bed combustion, because it is known that tar is

very reactive when it is in contact with a hot char layer and it easily breaks down.

However, the three-component mechanism describes the pyrolysis rate, but the product yields cannot be

predicted. Therefore, in this study the composition of volatiles is determined by mass and energy

balances, explained in [5]. Most probably a mechanistic pyrolysis scheme, where the composition of

volatiles is temperature dependent, could further improve the results.

Char conversion models are based on heterogeneous reactions for which both intrinsic kinetics and

transport phenomena are important. Due to the structure of the particle model, the char conversion

reactions are assumed to occur at the interface between the char and the ash layer. Char oxidation with

O2 and gasification with CO2, H2O and H2 are considered as char conversion reactions. The rate of char

conversion reactions is a function of both kinetic rate at the reaction surface and mass transfer rate

to/from the reaction surface. Assuming a global reaction rate of first order with respect to the

oxidising/gasifying agent concentration at the reaction surface, leads to a char conversion rate as:

4

,1

,1 1 1 ( )

ch i cii

c i m e

dm MX

dt k S h S D S r

(5)

where i = 1 to 4 corresponds to the heterogeneous reactions in Table 1 and Ωi is the stoichiometric ratio of

moles of carbon per mole of oxidising/gasifying agent in the corresponding reaction. Mc; S; ki and X∞;i are

the molecular weight of carbon, the surface area of the char burnout front, the kinetic rate constant of

heterogeneous reaction i and the molar concentration of oxidising/gasifying agent of reaction i in the bulk

flow, respectively.

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The mass transfer coefficient of reactants in the boundary layer around the particle hm, is obtained by the

Sherwood number:

1/ 2 1/32 0.69

m p

ab

h dSh Re Sc

D (6)

where dp is the particle diameter, Dab is the binary diffusivity, Re is the Reynolds number and Sc is the

Schmidt number.

The effective diffusivity of the ash layer De, depends on the ash porosity and the molecular diffusivity of

the penetrating gaseous component. Since particle shrinkage during drying is much lower compared to

that occurring during pyrolysis and charcoal combustion [7], it is postulated that during drying, the size of

the particle remains constant and its density decreases. However, both shrinkage and density change

during pyrolysis and char burnout are considered in the particle model. A detailed description of the

particle model is given in [8].

Table 1: Heterogeneous reaction kinetic rate constants [6]

ΩC + O2 →2(Ω-1)CO + 2Ω CO2 kc,1 = 1.715 T exp(-9000/T)

Ω = 2(1 + 4.3exp(-3390/T)/(2 + 4.3exp(-3390/T)

C + CO2 → 2CO kc,2 =3.42 T exp(-15600/T)

C + H2O → CO + H2 kc,3 =3.42 T exp(-15600/T)

C + 2H2 → CH4 kc,4 =3.42e-3

T exp(-15600/T)

2.1.1 Validation

Application of the particle model has already been validated in two levels: particle-scale and laboratory-

scale fixed bed reactor.

The particle model has some limitations such as the assumption that the drying pyrolysis and char

burnout, are happening at the boundaries between the layers. This assumption can be justified for the

combustion front but, an infinitely thin drying front may not be acceptable for small particles, where the

width of the drying zone is not negligible compared to the particle size. Also this assumption can

undesirably affect the pyrolysis results, since pyrolysis is not happening at such a narrow region.

Additionally, the pyrolysis model is not able to predict the yields of volatiles depending on temperature

variations. Therefore, to evaluate the results of the simulations with the particle model, these limitations

have to be considered.

Single particle reactor

To validate the particle model, experimental data of a single-particle reactor reported by Lu et al. [9] were

utilised. Measured particle surface temperatures, centre temperatures and mass loss during thermal

conversion of cylindrical particles under oxidising and non-oxidising conditions were compared with the

predictions of the particle model. A selected result of validation simulations is presented in Figure 1. The

particle mass predicted by the model decreases faster than the measured one. This discrepancy is

believed to be due to coarse spatial discretisation and the empirical constants used in the pyrolysis model.

The coarse spatial discretisation might lead to an overprediction of the temperature of the boundary

between dry fuel and char layers, where the pyrolysis is assumed to occur. Consequently, the pyrolysis

rate model which is an exponential function of temperature overvalues the particle mass loss rate.

Furthermore, the empirical constants of the pyrolysis model have been obtained under fast heating and

non-oxidising conditions. Therefore, any changes in these conditions, might impact the validity of the

pyrolysis empirical constants.

However, the model predictions for particle centre and surface temperature as well as the particle mass

loss profile are in good agreement with experimental data. In a previous paper [8] more results of model

validation have been presented.

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Figure 1: Comparison between simulated and measured temperature and normalised mass profiles during combustion of a cylindrical poplar wood particle; D = 9.5 mm, L = 9.5 mm, MC = 40% w.b.

Laboratory-scale fixed bed reactor

A test run of a laboratory-scale fixed bed reactor of the Graz University of Technology [10] was simulated

in order to validate the application of the particle model for simulation of the combustion of thermally thick

biomass particles under packed bed conditions. The laboratory-scale reactor is a discontinuously operated

pot furnace. The biomass (410g spruce pellets) is put into a cylindrical sample holder (0.1 m height and

0.095 m inner diameter) which is located inside the cylindrical retort. The retort is heated electrically by

two separated PID controlled heating circuits. Air is introduced through a porous plate at the bottom of the

fuel bed. Five thermocouples are measuring the temperatures inside the fuel bed at different positions.

The pot rests on a weight balance to measure the mass loss during conversion.

In addition to the particle model several models were applied to simulate the most relevant processes

during the fixed bed combustion, such as the shrinkage of the packed bed, particle-particle radiation heat

transfer and the variations of bed porosity due to the uneven consumption of the fuel as well as the gas

phase combustion in the freeboard. The models are explained in [11]. Some results of the comparison

between the model prediction and measurement data are presented in Figure 2. Both experimental and

simulation results depict the same combustion behaviour in the packed bed. The model predicts lower

temperatures at P2b after the first temperature peak in comparison to the measured values. In addition,

the steep increase in temperature at P3 is slightly delayed in comparison to the measured values. It

implies that in the simulation the heat diffusion in the packed bed and the conversion rate is slower than in

the experiments. This might be attributed to the heat transfer model inside the packed bed and also

uncertainties of the physical properties, i.e. thermal conductivity.

The time and the height of the temperature peaks are reasonably well predicted. The predicted mass loss

profile complies well with the experimental data. The model performance was also validated with gas

phase measurements (CO, CO2, CH4, H2, H2O and O2) above the fuel bed and the temperature in the

freeboard which are presented in [11]. There are deviations from the experiments regarding H2O

concentrations right after the ignition of volatiles and CO and CO2 concentrations at the end of pyrolysis

and the beginning of char burnout.

It should be noted that the accurate prediction of the time and the height of the temperature peaks as well

as the mass loss profile by the transient simulation of the lab-scale reactor is more important for the

intended steady state simulation of a biomass grate furnace. The temperature peaks determine the

maximum particle temperature in the packed bed. On the other hand the reasonable predictions of the

mass loss profile and occurrence time of the temperature peaks assure the correct predictions of the

positions of drying, pyrolysis and combustion zones in the packed bed. However, a plausible modelling of

the particle movement on the grate as well as the gas phase combustion in the freeboard above the

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packed bed have significant effects on correctly predicting the positions of the thermal conversion sub-

processes in the packed bed.

Figure 2: Comparison between simulated and measured mass loss profiles and temperatures at different positions inside the packed bed. P1 is 1cm below the initial surface of the packed bed, P2b is at the middle of the packed bed (5cm above the grate) and P3 is 1cm above the grate.

2. 2 Application of the model for a biomass grate furnace

The validated particle model is applied to simulate the combustion of biomass particles in a pilot-scale

grate furnace (180 kW). Using the particle model enables to avoid the separation between packed bed

and freeboard modelling. In addition to the particle model some other models are used for the simulation

of the grate furnace which is explained in this section.

2.2.1 Furnace geometry

The pilot-scale moving grate furnace used for the test of the particle based 3D packed bed combustion

model is shown in Figure 3. The simulation domain comprises the packed bed and the combustion

chamber above the fuel bed up to the boiler entrance. Primary air is introduced from below the grate. Flue

gas recirculation is injected through the six nozzles above the packed bed. In this case study the tertiary

air nozzles are closed and secondary air is injected through eight nozzles at the entrance of the secondary

combustion chamber. There is false air leaking into the combustion chamber, due to not perfect sealing of

the fuel feeding system. A certain amount of leakage air, obtained from the measurements during the test

run, was taken into account in the simulation as false air. It enters the simulation domain from the fuel

inlet. Due to the symmetry of the furnace only half of the furnace was simulated. Therefore, the symmetry

boundary condition was used in the mid plane of the furnace. Table 2 provides the most relevant operating

conditions of the furnace and the fuel composition.

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2.2.2 Fuel bed

Movement of grate bars is not considered in this study for simplicity and allowing steady state simulations.

Therefore, a position of grate bars had to be chosen. Measurement data show that even in steady state

operation of the furnace, the fuel conversion rate fluctuates around its average value. This time dependent

variation of fuel conversion is due to the grate movement. The position of the grate corresponding to the

average value of the fuel conversion has been selected in this study as the fixed position of the grate bars.

Afterwards the grate geometry was simplified by projecting it to a plane, as it is shown in Figure 3.

Figure 3: Left: geometry of the pilot-scale biomass grate furnace; right top: scheme of the simplified grate used for the simulation; right down: two examples of particle trajectories

Primary air is introduced below the grate, where it is distributed and passes through the gaps. The

pressure drop through the bed and the grate bars was also modelled by treating the fuel bed and the gaps

in the grate as porous media. The coefficients have been adjusted to reach the same pressure loss in the

simulation as in the test run; around 100 Pa from below the grate to above the fuel bed on average (in

reality, the pressure loss varies over the grate length due to the decrease of the fuel bed height).

Table 2: Operating conditions of the pilot-scale grate furnace

Operating conditions

Fuel heat load related to NCV kW 194

Gross calorific value MJ/kg d.b. 19.69

Net calorific value MJ/kg w.b. 13.83

Fuel feeding rate (wet) kg/h w.b. 50.45

Moisture content wt.% w.b. 21.76

Adiabatic furnace temperature °C 1069

λprim - 0.69

λPCZ,eff - 0.92 λtotal - 1.67

λprim: primary air ratio related to primary air supplied below the grate λPCZ,eff: effective air ratio in the primary combustion zone; related to primary air, false air and total amount of re-circulated flue gas in the primary combustion zone λtotal: total air ratio related to total amount of air supplied

Due to experience with operating the furnace, it was assumed that the major pressure loss is caused by

the grate. Due to the simplification of grate bars, the particle velocities are pre-defined. Above the first

Primary

air inlet

Flue gas

recirculation

nozzles

Secondary

air nozzles

entrance

to boiler

grate bars

Fuel inlet

Tertiary air nozzles

(deactivated)

projected grate bars

gap gap

grate bar

air holes

primary

air path

lines

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grate bar the particle shrinkage due to combustion and the influence of the gravity is low, hence a

constant bed height is assumed. The particle velocity is computed by the fuel feeding rate and there is no

velocity component in the gravity direction. Afterwards, particle shrinkage and gravity become important

and the particles‘ path changes into a downward movement, where the horizontal component of the

velocity is the velocity of the grate bars and the vertical component is computed by the angle α for each

particle trajectories, see Figure 3. Experimental observation during the test run showed that the biomass

fuel is entirely burned out before it reaches the last grate bar. Therefore α is defined in a way that the

height of the fuel bed becomes zero at the beginning of the last grate bar. The velocity components are

included in a UDF and coupled to ANSYS Fluent.

2.2.3 Gas phase model

The accurate description of the reaction kinetics is of high relevance, since they have a considerable

impact on the simulation results. As a first approach, the gas phase combustion was modelled by a global

4-step mechanism considering volatiles, CH4, CO, CO2, H2, H2O, and O2, together with the Eddy

Dissipation Model (EDM) with modified Magnussen constants [12] for turbulence-chemistry interaction. So

far it was the usual approach applied for the qualitative simulations of gas phase combustion in

engineering applications, where in the most regions the reaction rates are limited by the mixing rate.

Additionally, the low calculation time of the EDM is of interest for engineering applications, primarily when

the 3D packed bed model with its large calculation time is included in the simulation.

In this work, for the first time the Eddy Dissipation Concept (EDC), was applied together with the 3D

packed bed model for a real-scale application. The EDC is an extension of the EDM and both of them are

based on the turbulent energy cascade, which means that larger eddies break up into smaller eddies and

the mixing of the fluid plays an important role in the reaction progress. The EDC assumes that the mixing

happen in micro scales (Kolmogorov scale), however, the EDM considers that the mixing is happening in

the large scales. In addition to the length scale, the time scale is also important for the calculation of the

mixing rate in the EDC. The time scale is responsible for the mass transfer from the fine structures to the

bulk flow and also for the residence time in the fine structures. Moreover, the EDC has a higher potential

concerning the simulation of kinetically influenced processes by enabling the incorporation of the detailed

reaction mechanisms into turbulent reacting flows. It is essential for NOx modelling and it also provides

advantages for CO emission predictions in colder regions of the boiler.

However, in the region above the fuel bed and in small-scale biomass combustion applications

(< 500 kWth), the gas phase mixing and reaction progress is highly influenced by laminar and low

turbulence zones. Therefore, the EDC model, which is originally developed for highly turbulent flows, is

not valid and leads to wrong predictions of the reaction progress [13].

Additionally, in this study within the EDC simulations a detailed mechanism is also used for the

homogeneous reactions, to investigate the effect of different reaction mechanisms. In the previous study

for the simulation of the laboratory-scale fixed bed reactor [11], the results of the GRI mechanism (49

species and 277 reactions), the DRM22 reduced mechanism (22 species and 104 reactions) and the C–

H–O subset of the skeletal Kilpinen97 mechanism (12 species and 25 reactions) have been compared to

the measured species concentrations in the freeboard. The comparison depicts that the skeletal Kilpinen

mechanism with a considerably lower number of reactions and species can predict the species and

temperature profiles with acceptable accuracy, particularly for engineering applications. Therefore, in this

paper the skeletal Kilpinen mechanism is used as a detailed mechanism to compare the results with the

global 4-step mechanism.

2.2.4 Empirical packed bed model

To evaluate the new 3D packed bed model, its results are compared with the results of an empirical

packed bed model which has been developed by TU Graz in cooperation with BIOS

BIOENERGIESYSTEME GmbH for thermal decomposition of the solid fuel [14] [15] [16] [17]. The core of

the empirical model is the definition of one-dimensional profiles for the most important flue gas

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components, i.e. CH4, CO, CO2, H2, H2O and O2, along the grate concerning the degradation of the fuel

components as well as fuel drying which is based on assumptions and experimental data as well as

stepwise balancing of mass and energy fluxes released from the fuel bed. The calculated profiles are used

as boundary conditions for subsequent CFD simulations of the turbulent reactive flow above the packed

bed. Therefore, the empirical model separates the simulation of the combustion chamber from the packed

bed. The gas phase combustion above the packed bed is modelled by the global 3-step mechanism

together with the Eddy Dissipation Model (EDM) with modified Magnussen constants [12] .This

comparison with the 3D packed bed model helps to evaluate possible advantages of the integration of the

packed bed model into the overall model for the biomass combustion plant. Since the EDM was applied

together with the empirical bed model, only the results of the 3D packed bed model with the EDM can be

used for a meaningful comparison.

3. Results and discussion

Positions of the three thermal sub-processes (drying, pyrolysis and char burnout) are shown in Figure 4.

As it can be seen they occur sequentially with an overlap between each other, especially where pyrolysis

and char burnout take place. Since it is not possible to simulate the fuel bed with the empirical model, the

results of the particle based 3D packed bed model with two different models for the turbulent reactive flow

are shown in Figure 4. Due to the fact that the EDC model predicts higher temperatures in the primary

combustion chamber (shown later); the drying process predicted by the EDC is completed closer to the

fuel inlet. Consequently, pyrolysis starts earlier and it happens in a narrower region than the results of the

EDM.

The CO/CO2 product ratio of the char oxidation reaction depends on the particle temperature, see Table 1.

Simulation results of both models show that the char oxidation reaction produces more CO2 than CO. This

is in line with the fact that at high temperatures the produced CO rapidly oxidises to CO2 in the pores of

the particles and in a layer adjacent to the particle surface. However, the CO/CO2 ratio predicted by the

EDC model is higher than the one of the EDM. It is again due to the higher temperatures in the EDC

simulation.

Figure 4: Contours of release rates of different species [mg/s] from biomass particles at a vertical cross section in the packed bed, predicted by the global mechanism with the EDM (left) and the skeletal mechanism with the EDC (right).

H2O

Volatiles

CO

CO2

EDM EDC

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As it can be seen, particularly in the contours of CO2 release, char burnout occurs in two zones. They are

related to the areas where primary air is introduced into the fuel bed through the gaps between the grate

bars. Since the oxygen is depleted between two inlets of primary air, there is no CO2 production and only

CO is produced which can also be attributed to the Boudouard reaction.

The temperature of the grate bars and the particle tracks coloured by the simulated particle temperatures

are shown in Figure 5. Since the EDC predicts faster reaction rates, the temperatures in the primary

combustion chamber are higher than with the EDM simulation (shown later). It leads to higher

temperatures in the fuel bed and for the grate bars. Additionally, the char burnout occurs earlier in the

EDC simulation. As a consequence, the temperature of the first grate bar is higher in the EDC simulation.

Figure 5: Left: particle tracks coloured by particle temperatures [°C] at the symmetry plan; right: grate temperature [°C] along the grate length

The contours of gas phase temperature predicted by the empirical packed bed model and the 3D packed

bed model are shown in Figure 6. As it can be seen, including the packed bed in the simulation domain

results in a different temperature field, particularly in the primary combustion zone. In the empirical model,

the primary air distribution and the released species from the fuel bed are defined based on the empirical

profiles obtained by experimental data. Therefore, there are no sharp gradients in and above the packed

bed, contrary to the results of the 3D packed bed model, which is closer to the reality and lab-scale

packed bed reactor tests (see e.g. Figure 2 and [10]).

The 3D packed bed model, both EDM and EDC, predicts lower gas temperatures above the first one-third

part of the fuel bed in comparison to the empirical model. It is attributed to the heat-up and drying of the

biomass particles in the fuel bed. As it is mentioned, the EDC model predicts higher local temperatures

than the EDM. Since the Magnussen constant of the EDM systematically adapted for biomass grate

furnaces by a comparison with CO emission and temperature measurements [12], a reduction of the

mixing and the reaction rate compared to the original model is achieved. Therefore, the EDC, where no

constant tuning has been done, predicts higher reaction rates, which rises the temperature.

The gas phase temperature in the 3D packed bed model shows two hot regions in the fuel bed and also

above the bed, which is more pronounced in the EDC model. They are corresponding to the combustion

of volatiles after mixing with the primary air and to the two separate zones of the char burnout, shown in

Figure 4. In Table 3, the simulated gas phase temperatures are compared with measurement data. The

temperatures predicted in the primary air combustion zone with the empirical packed bed model and the

3D packed bed model with the EDM are closer to the experimental data than the 3D packed bed model

with the EDC, which predicts higher local temperatures due to the faster reaction rates. Since the

measurements have been done by non-shielded thermocouples, the experimental data can be only used

for a qualitative comparison.

EDM

EDC

EDM

EDC

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Empirical bed model (EDM) 3D bed model (EDM) 3D bed model (EDC)

Figure 6: Gas phase temperatures [°C] at a vertical cross section of the furnace. PZ’s and SZ’s are the position of the thermocouple measurement points in the primary and secondary combustion zones. The arrows show the position of the gaps between the grate bars where primary air enters the packed bed.

Table 3: Comparison between the predicted and thermocouple measured temperatures [°C] at measurement points shown in Figure 6.

PZ1 PZ2 PZ3 SZ1 SZ2

Measurement 875 1001 1052 1037 987

Empirical model 891 1052 1108 1018 1043

3D packed bed model (EDM) 972 942 1051 1007 997

3D packed bed model (EDC) 1081 1010 1237 985 988


Figure 7: Oxygen concentrations [vol.% dry] at a vertical cross section of the furnace.

As it can be seen in Figure 7, the predicted oxygen concentrations in the primary combustion zone are

different. In the 3D packed bed model, at the first two-thirds of the fuel bed, oxygen is totally consumed by

PZ1 PZ2 PZ3

SZ1

SZ2

PZ1PZ2 PZ3

SZ1

SZ2

PZ1PZ2 PZ3

SZ1

SZ2

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volatile combustion and char burnout, while the empirical model predicts some oxygen released from the

fuel bed at this region. This difference can be due to the different primary air distribution. In the 3D packed

bed model, the primary air can enter the fuel bed only through the gaps between the grate bars; while in

the empirical model the primary air inlet is the entire grate length (continuous profiles are assumed). In

both models, the excessive primary air leaves the packed bed at the last part of the grate. It is in line with

the experimental findings of several test runs with this plant. The result of the 3D packed bed model

shows again sharper gradients in the packed bed in comparison with the empirical model. This gradual

increase of the oxygen concentrations at the last part of the grate in the empirical model is due to the

assumed profile for the release of oxygen.


Figure 8: CO concentrations [ppmv dry] at a vertical cross section of the furnace. Reported values are related to the inlet of the boiler.

The comparison between the predicted CO concentrations by these three simulation methods is shown in

Figure 8. The measured value for the CO concentration at boiler outlet was 2 [ppmv dry]. The flue gas

temperature in the boiler is low enough that it is plausible to assume that no CO burnout happens in the

boiler. Therefore, it is fairly acceptable to assume that CO is almost fully combusted at the inlet of the

boiler. The simulation data also show almost complete CO burnout at that position.

The CO concentrations at entrance of the secondary combustion zone are similar for all three simulations

(around 3.3 vol.%). After the secondary air inlet due to the air staging and the efficient nozzle design, the

flow is almost fully mixed, thus the kinetics play the crucial role. Therefore, in this fully turbulent region, the

results of the EDC simulation might be closer to the reality, since in the EDM model only global reactions

are considered.

The calculation time of each simulation method is of importance, especially for engineering applications.

The empirical packed bed model has the lowest calculation time and it can deliver the results within a day.

The calculation time with the 3D packed bed model and the EDM is about 10 days. Finally, the

combination of the 3D packed bed model with the EDC has the calculation time of 15-20 days. These

calculation times are related to the simulations with an Intel Xeon E5-2600 v2 CPU with 8 cores. A high

calculation time of the 3D packed bed model is one of its weaknesses. Although increasing the number of

computing cores might help to some extent, further measures to reduce the calculation time of the 3D

packed bed model need to be addressed in future.

4. Summary and Conclusions

A 3D packed bed combustion model, which is applicable for the simulation of biomass moving grate

furnaces, is presented. The particle trajectories on the grate are calculated based on the velocity of the

8 [ppm dry] 14 [ppm dry] 1 [ppm dry]

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grate bars and the shrinkage of the packed bed. The thermal conversion of particles in the bed is

modelled by the validated particle model. Two gas phase reaction models, i.e. a skeletal reaction

mechanism together with the Eddy Dissipation Concept (EDC) and a global reaction mechanism with the

eddy dissipation model (EDM), have been used to model the turbulent reactive flow. The results of these

two methods have been used to investigate the effect of different gas phase models on the simulation

results. Additionally, the results of an empirical packed bed model combined with a global reaction

mechanism and the eddy dissipation model (EDM) are compared to the results of the models presented in

this paper, to evaluate the relevance of the incorporation of the packed bed in the in grate furnace

simulations.

The calculation time of each simulation approach has been compared. The 3D packed bed model in

comparison to the empirical packed bed model requires much longer simulation times. Therefore, when

only gas phase combustion is of relevance, the application of the empirical model combined with the EDM

is meaningful due to its short calculation time.

The 3D packed bed model integrates the fuel bed model into the simulation domain and it considers the

interaction between the fuel bed and the freeboard. Moreover, it resolves the thermal conversion of

biomass particles with a thermally thick particle model. Therefore, the 3D packed bed model enables to

evaluate the influence of particle related parameters, e.g. size, physical properties, moisture content and

chemical composition as well as primary air distribution and pre-heating on the combustion behaviour in

the packed bed. Therefore, when information concerning the fuel bed and the grate is needed, the

application of the 3D packed bed model with the EDM is of interest.

The real-scale application of the EDC together with the 3D packed bed model has been tested for the first

time. The application of the EDC is recommended, where the kinetics are important The application of the

EDC for NOx modelling is inevitable. Therefore, some simplifications to reduce the calculation time of the

EDC model combined with the 3D packed bed model are necessary for the future applications of the

model. . It should also be mentioned, that the EDC model overpredicts the reaction rates in low turbulence

regions such as above the fuel bed and in small-scale biomass combustion systems, because it is

developed for highly turbulent flows. To overcome this weakness a hybrid gas phase model presented in

[13] should be applied instead of the EDC model.

The 3D packed bed model itself also has some limitations, e.g. the movement of the particles which is

modelled with a priori particle trajectories. The shape of the grate bars is simplified and the effect of their

movements on fuel mixing is not considered. Moreover, the effect of streaks forming above the fuel bed is

not considered in this study. These issues should be addressed in future.

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numerical modelling of biomass grate furnaces...10th european conference on industrial furnaces and...

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