Simulation of municipal solid waste incineration in grate

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VGB PowerTech 1/2 l 2014 Discrete element method to simulate waste combustion

Authors

Kurzfassung

Diskrete Elemente Methode zur Simulation einer Müllverbrennungsanlage mit Rostfeuerung

Für die Auslegung und Optimierung von Feuerungssystemen für granulare Brennstoffe stellen numerische Methoden eine sinnvolle Alternative zu experimentellen Untersuchungen dar. Unter den verschiedenen Simulationsmethoden, bei denen es sich oft um stark vereinfachende Kontinuumsmodelle handelt, hat sich die Diskrete Elemente Methode als vielseitigster, aber auch komplexester Ansatz herausgestellt.Die Diskrete Elemente Methode ist ein dreidimensionaler, partikelbasierter Ansatz, der für die Simulation der Bewegung von Systemen mit großer Partikelanzahl verwendet werden kann. Die Methode wurde um die Wärmeübertragung (Leitung, Strahlung, Konvektion) und die thermochemischen Prozesse (Trocknung, Pyrolyse, Koksabbrand) für die Einzelpartikel der Schüttung erweitert, die simultan zur mechanischen Bewegung berechnet werden. Außerdem wird der erweiterte „Diskrete Elemente Ansatz“ mit einem dreidimensionalen CFDModell des darüber liegenden Feuerraums gekoppelt. l

Simulation of municipal solid waste incineration in grate firing systems with a particle based novel Discrete Element MethodBjörn Brosch*, Viktor Scherer and Siegmar Wirtz

Dr.-Ing. Björn BroschProduct/Technology Development Doosan Lentjes GmbH Ratingen/GermanyProfessor Dr.-Ing. Viktor SchererHead of Department Department of Energy Plant Technology (LEAT)Dr.-Ing. Siegmar WirtzDepartment of Energy Plant Technology (LEAT) Ruhr-University Bochum Bochum/Germany

Introduction

Thermal treatment of municipal waste (combustion) is an efficient and ecologi-cally favourable way to utilise waste for the production of electrical power and heat. The majority of the waste incinera-tion plants uses grate firing systems, which have been established during the last dec-ades.Experimental investigations for the opti-misation of these plants are time consum-ing and expensive compared to numerical simulations. Measurements, as they are required for reliable development of the plants, are technically ambitious or partial-ly impossible because of the harsh condi-tions in these plants, especially within the waste bed itself. Thus, numerical methods remain for a detailed spatial and time re-solved investigation of waste incineration plants. These numerical methods have to be carefully examined.The main components to be considered for simulating grate firing systems are the waste feeding system, the grate and the movement of the grate bars, the waste bed, the primary air injected from underneath the grate, the combustion chamber and the secondary air injection. Nowadays, several different grate types (forward acting, back-ward acting, roller grate) and combustion chamber types (counter-current, co-cur-rent, centre-flow firing) in combination with different concepts for secondary air injection are in operation.Since the combustion chamber and the fol-lowing gas path equipped with radiative or convective heat transfer surfaces can be modelled as a single-phase flow, commer-cial CFD (computational fluid dynamic) codes can be used for their simulation. However, the accuracy of the CFD predic-tion strongly depends on an appropriate sub-model for the description of the pro-cesses inside the waste layer. Unfortunate-ly, the current CFD codes do not contain appropriate sub-models for the complex processes of the waste bed movement, the heat transfer and the thermochemical pro-cesses of drying, pyrolysis and char com-bustion. For obtaining the required spatial-ly distributed boundary conditions from

the grate several approaches are common. A detailed summary of the existing mod-els can be found in a work in [1]. These approaches start from specifying unidi-mensional boundary conditions based on energy and mass balances in combination with estimations and experiences [2]. The methods reach up to continuum approach-es, which are state of the art nowadays [3 to 5]. However, even continuum ap-proaches have serious disadvantages due to the basic principles of the approach. The intrinsic heterogeneity of the waste bed is modelled by homogeneous and transport-able parameters, like porosity or particle size distribution. Operational parameters of the grate (e.g. alignment and movement pattern of the grate bars) are only insuffi-ciently considered by the approach.Municipal solid waste consists of a large variety of objects with different sizes, geo-metrical forms as well as mechanical, phys-ical and thermochemical properties. Even for a particle based approach, as it is used in this work, many assumptions are neces-sary. For an efficient numerical description and for the purpose of a simplified illus-tration the waste objects are abstracted as “representative” spheres. The diameter of these spheres represents the spatial range for the mechanical interaction of the waste objects, which are in the following referred to as particles.

Mechanical movement and cohesion force modelThe description of the movement of such particle collectives is based on the Discrete Element Method (DEM) introduced by [6]. By this approach the motion of every single particle is calculated by consider-ing all forces acting on each particle and integrating Newton’s and Euler’s equation of motion. n

m r = m g +∑F

i (1) (i=1)

n

J– · φ = ∑R

l × F

i (2) (i=i)

The force vector F

l is decomposed into the normal and the tangential direction. The particles in a discrete element model are

* VGB-FORSCHUNGSSTIFTUNG awarded the Heinrich-Mandel-Price in 2013 to Dr Björn Brosch for his scientific work.


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Discrete element method to simulate waste combustion VGB PowerTech 1/2 l 2014

generally assumed rigid and for modelling the deformation a virtual overlapping be-tween particles is allowed. For computing the forces and the corresponding dissipa-tion, semi-empirical force models are used for both directions [7, 8].For municipal solid waste the accurate ge-ometries are generally unknown. For this purpose, a procedure for considering the waste-specific mechanical peculiarities (cohesion due to waste moisture or parti-cle interlinkage due to particle shape) has to be found. The method introduced here for modelling the waste-specific mechani-cal movement is based on a cohesion force model initially proposed by [9] for sinter-ing processes. Further details on the use and verification of the cohesion force mod-el within the discrete element method can be found in [10, 11, 12].

Heat transfer processesWithin the waste bed several heat and mass transfer processes occur in parallel. These processes are generally all combina-tions of conduction, convection and radia-tion between the particles, the grate bars and the passing fluid. Since most of the mechanisms have a neglectable influence and therefore needlessly increase the com-putation time, only the relevant processes are considered. For heat transfer these are convection between particles and fluid (3), conduction among particles (4) as well as radiation between particles and between the gas flame in the furnace and the top layer of the particles (5). For a detailed description of the heat transfer processes see [13].

Ql = α A (Tsurf,i – T∞) (3)

Qij = Hc (Tsurf,i – Tsurf,j) (4)

Qrad,i = σ ε A (T4rad,i – T4

surf,i) (5)

In these equations A is the particle surface, α the heat transfer coefficient, which is cal-

culated based on standard Nusselt correla-tions, Hc the contact conductance derived from Hertz therory, ε the emissivity and Trad the background temperature of the combined radiation from other particles and the furnace. The calculated heat fluxes Qi acting on each particle are summarised and form the boundary condition for the heat conduction within the particles. Be-cause the fuel particles can be large (large Biot numbers) inner temperature gradients have to be taken into account and the most common approach of assuming homoge-nous particle temperatures is not adequate. Furthermore, for reacting particles an ad-ditional temperature gradient is imposed on the solid due to chemical surface reac-tions (e.g. char combustion). Hence, in this case a temperature profile develops even for smaller Biot numbers. For the computa-tion of the temperature distribution within the particles the unidimensional heat con-duction equation is solved for each particle numerically [13].

∂T ∂2T η ∂T ∂λ ∂Tρ cp –– = λ ∙––– + – –– ∙ + –– –– + q (6) ∂t ∂r2 r ∂r ∂r ∂r

Thermochemical processes (drying, pyrolysis, char combustion)For large particles (large Biot numbers), as in the case of grate firing systems, the thermochemical processes of drying and pyrolysis are dominated by the heat con-duction within the particle, because the time scale of the chemical decomposition is much smaller than the time scale of the heat transfer. Hence, it is sufficient to de-scribe the evolution of a temperature front moving through the particle and assume the chemically reaction to be infinitely fast.For drying, this temperature is equivalent to the boiling point (about 100 °C at am-bient pressure). The characteristic tem-perature of the pyrolysis front depends on the material and can be determined by

experiments. In contrast to the drying and the pyrolysis the chemical rate of the char combustion is very slow. Furthermore, the presence of an oxidant at the particle sur-face is essential for char combustion. For this reason, char combustion cannot be de-scribed solely by a temperature front. The chemical reaction rate as well as the mass transfer of the oxidant to the surface has to be considered. Based on investigations by [14, 15] a model for char combustion is included within the discrete element code that assumes the following reactions oc-curring at the particle surface:

1C + – O2 CO (7) 2

C + CO2 2 CO (8)

C + H2O H2 + CO (9)

It has also to be taken into account that the carbon monoxide released form the surface flows outward to the surrounding environment. Within the gas phase bound-ary layer around the particle the CO reacts with the counter current flow of oxygen to-wards the particle.

1CO + – O2 CO2 (10) 2

Hence, the global reaction rate for the char combustion is defined by this gas phase reaction and the heterogeneous reactions [13].

Coupling between DEM and CFDThe flow and reaction of the gas phase above the grate is calculated with the com-mercial program ANSYS FLUENT using the common standard sub-models. For exam-ple, the reaction is modelled by a combined “Eddy Dissipation and Finite Rate Chemis-try” model, for turbulence modelling the SST k-ω model is used and the radiation is described by the P1 model. Since the time

Boundary conditions from DEM to CFD (gas)

Boundary conditions from CFD to DEM (radiation)

Fig. 1. Coupling surface between grate and furnace model.

Tem

pera

ture

in °C

Time in s

300

250

200

150

100

50

00 1,000 2,000 3,000 4,000 5,000 6,000

Inlet Exp. 20 mmSim. 20 mm

Exp. 100 mmSim. 100 mm

Exp. 180 mmSim. 180 mm

Fig. 2. Comparison of the measured and simulated gas temperature at different heights.


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scales related to the transport and reaction of the solid phase are much larger than the time scales related to the processes in the gas phase, the gas phase is modelled as be-ing stationary (in contrast to the transient simulation of the Discrete Element Method for the waste bed). Since the ratio of waste bed height to grate width is large it might be feasible in a first approach (in principle, if sufficient computing time is invested DEM is able to resolve the influence of the side walls) to neglect the mechanical in-fluence of the walls. In the current study a representative “strip” of the grate width is modelled. The width of this strip has to be large enough (width of the strip must be large compared to typical particles size) to reflect 3 dimensional segregation effects of the particles. For a reasonable coupling of both models an appropriate time aver-age of the quantities passed from the waste bed to the furnace model has to be used. In this context the average over the time period of one feeding stroke turned out to be a reasonable choice. The values passed from the grate to the furnace are spatially extrapolated to the furnace width. The coupling between both models is realized by a surface that represents the waste bed surface (F i g u r e 1).At this surface the flow data (chemical spe-cies, enthalpy, velocity) of the grate model is passed to the furnace model and the ra-diation data of the furnace model is passed to the grate model. Since the coupling runs in both directions it is called bidirectional coupling. Within the fuel bed, the direction of the primary air passing through the porous material is assumed to be vertical only. Thus, water vapour from drying, the vola-tiles released as well as CO and CO2 from the char reactions are successively added to the flow at the position of the individual fuel objects. All chemical conversions, ex-cept for the oxidation of CO, which is de-scribed kinetically, are considered to occur instantaneously (thermochemical equilib-rium). The mixture obtained at the top of the bed is then averaged spatially (to fit to the extent of the corresponding CFD-cells) and temporally, as described above.

Comparison with measured data

A computational procedure of the com-plexity described requires a very detailed and thorough verification of the validity of all sub-models involved and the correct im-plementation of their interaction. In order to demonstrate this validity, increasingly complex experimental investigations are compared with corresponding simulations.

Heating up of a static bedSince the heat transfer to the particles and within the particles significantly influenc-es the thermochemical process of drying, pyrolysis and char combustion, the analy-

sis of the heating of an inert and static packed bed is mandatory. The simulation results are compared to measurement data obtained from the Karlsruher Institut für Technologie at the test plant PANTHA [16, 17]. The reactor of the plant consists of a basket for bulk material. Pre-heated air passes the material consisting of mono-size spheri-cal particles. Thermocouples are attached at different heights of the packed bed for recording the temperature over time. The details of the reactor, the fluid and the bed material are summarised in Ta b l e 1.For the simulation, the fluid distribution entering the packed bed is assumed to be spatially constant. F i g u r e 2 shows the temperature development over time at three different heights of the packed bed. The first position is close to the fluid inflow at 20 mm (blue), one is adjusted in the middle of the bed at 100 mm (red) and one is close to the exit at 180 mm (green). Fur-thermore, the temperature directly at the inlet is illustrated.It can be seen that the largest difference (approximately 30 K) between measure-ment and simulation is close to the fluid inlet at 20 mm height. Hence, this occurs at the position of the largest tempera-ture gradients. Since the simulation as-sumes an equally distributed flow at the inlet, which cannot be expected for the experiment, the largest differences are anticipated to occur at this position. By the homogenisation of the flow through the packed bed, the simulation results converge towards the measured results with increasing height. At medium height (100 mm) and close to the exit (180 mm) the difference is below 10 K throughout the heating process. Altogether, the 3 curves reflect the measurement very well. Hence, the heat transfer models implemented in the discrete element code are suitable for the simulation of the heat transfer within packed beds passed by a fluid.

Combustion of a static bedThe combustion experiments used for veri-fication of the combustion model in DEM were carried out by [18] at the test plant

KLEAA which consist of 3 main compo-nents: combustion chamber, post-combus-tion chamber and flue gas treatment.

For the investigation performed here only the combustion chamber is relevant, which is subdivided into a movable bottom part and a fixed chamber. Prior to the experi-ments, the bottom part is removed and the pass to the upper chamber is closed. The combustion chamber is heated to a pre-defined temperature by an electrical heater. The bottom part is filled at ambi-ent temperature with the investigated solid fuel. The experiment begins by put-ting the bottom part under the combustion chamber. The solid material is immediately heated by radiation of the pre-heated up-per part. Additionally, the packed bed is passed by primary air from underneath. For obtaining an equally distributed flow, the primary air passes a perforated plate before entering the packed bed. Hence, for the simulation an equally distributed inflow is assumed. Due to the radiation of the combustion chamber, the top layer of the packed bed is heated up and ignited. Characteristic for the KLEAA experiments is a reaction front that propagates with constant velocity against the primary air flow through the packed bed. Although the combustion chamber is isolated a thermal loss is inevitable. Bleckwehl [18] specifies this loss with an average value of about 20 % of the introduced waste and primary air enthalpy. In order to measure the time-dependent gas temperature, thermocou-ples are placed at different heights of the bed. Furthermore, the movable bottom part of the combustion chamber is ar-ranged on a weighing system that records the time-dependent weight of the fuel bed. With the obtained data [18] derived some characteristic numbers. From these num-bers the so-called rate of converted mass per area (MCR) is used here for a com-parison to the simulation results. A more detailed analysis and comparison can be found in [13].

The calculation of the rate of the convert-ed mass is based on the measured weight loss. By differentiating the mass loss with respect to the time, a time-depended fuel conversion rate mfuel is computed. Accord-ing to the following equation (11) the fuel conversion rate is scaled by the cross-sec-tion of the fuel bed and the fuel content re-duced by the initial mass fraction of ash ya.

mfuel (t)MCR(t) = –––––––– (11) A (1 – Ya)

For comparison with the simulation the combustion experiments with mono-sized spherical particles are selected. The fuel material is beechwood and the di-ameters of the spherical particles are 10, 30 and 50 mm (Ta b l e 2).

As can be seen from Table 2, the porosity in-creases with increasing particle size, while

Tab. 1. Details of the heating experiment in the test plant PANTHA.

Material Slate

Mass [kg] 6

Bed height [mm] 190

Bed diameter [mm] 250

Particle diameter [mm] 12,6

Density [kg/m3] 1440

Heat conductivity [W/(m K)] 0,16

Heat capacity [J/(kg K)] 104,429(0.34877·T)

Air mass flow [kg/h] 16

Air temperature [°C] 200 to 300


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the corresponding bulk density decreases. The composition of the beechwood parti-cles and the operational parameters of the facility are summarised in Ta b l e 3. The simulation is carried out according to these values.Since the work of [18] gives no further details on the heat loss, the value of 20 % mentioned is assumed spatial and temporal constant for the simulation. For a better understanding of the simulations performed F i g u r e 3 shows temperature snapshots of the bed for the three different cases during the combustion.The figure shows the temperatures on the particle surfaces. For all three cases the point in time shown coincides with the time when first burn-out is attained in the top layer. It can be seen that the particles change their volume during the combus-tion process, which is due to char conver-sion. This results in the effect that the ra-diation of the combustion chamber can act on the particle layer underneath the initial top layer. F i g u r e 4 shows the mass loss over the simulation time for the case of the 10 mm beechwood particles. The negative gradi-ent of this curve corresponds to the fuel

conversion rate mfuel. The rate of converted mass (MCR) is then calculated according to (11).Finally, F i g u r e 5 shows the comparison between the measured (blue) and the simulated (red) rate of mass conversion per area. It can be seen that the largest difference can be found for the 10 mm beechwood particles. The simulated value is 0.0363 kg/(m2s) and is therefore 5.1 % higher than the measured value of 0.0345 kg/(m2s).The differences for the cases with 30 and 50 mm beechwood particles are 1.3 and 1.9 %, respectively. Altogether, the com-parison between measurement and simu-lation shown here and in [13] confirms that the simulation method of the discrete element method has the ability to describe reacting, static packed beds.The combustion of a static bed can be well transferred to the combustion on a grate. The thermochemical processes that oc-cur in time in a static bed correspond to a spatial sequence on a grate. However, due to the slow rates of the thermochemical processes, the position on a grate is more dependent on the movement of the waste bed particles than on the rate of the com-

bustion processes.

Application to a waste incineration plant and comparison with measured data

The grate firing system MHKW Frank-furt (57 MWth) with a grate and a boiler designed by Doosan Lentjes GmbH [19] is used as a test case for the discrete ele-ment method. The simulation results are compared to measurement data that was obtained by the Karlsruher Institut für Technologie on behalf of the Hitachi Zosen Inova AG [20, 21].The grate is built out of two sections. The first one is inclined by an angle of 10°, has a length of 7.3 m and consist of 11 movable and 12 static grate bars. The grate bars have an inclination angle of 15° measured from the horizontal plane. The second grate section is attached 0.54 m below the first section. The length of this horizontally arranged section is 3.06 m. The 5 movable and 6 static grate bars have an inclina-tion angle of 25°. The width of the grate is 7.36 m. Furthermore, the two grate sec-tions differ because of their movement pat-tern. The first section is a counter recipro-cating grate (while one bar moves forward, the next one moves backward) and the sec-ond one is a classical forward acting grate. The waste bed is supplied by primary air

Tab. 3. Details of the combustion experiments in the KLEAA.

Fuel Beechwood

Lower heating value [MJ/kg] 11.5

Water [mass-%] 30

Volatiles [mass-%] 53.8

Fixed carbon [mass-%] 15.2

Ash [mass-%] 1

Primary air [°C] 25

Combustion chamber temperature (electrical heated) [°C]

900

Primary air flow [kg/(m2 h)] 310

Container diameter [mm] 230

Bed height [mm] 250

Thermal loss [%] 20

Tab. 2. Particle diameters and packed bed properties.

Particle diameter [mm] 10 30 50

Bulk density [kg/m3] 489 452 413

Porosity [-] 0.40 0.44 0.48

Tsurface in °C1,2001,1001,00090080070060050040030020010025

Fig. 3. Distribution of the particle surface temperatures during the combustion of the 10, 30 and 50 mm bechwood particles.

Tab. 4. Distribution of the waste fractions.

Waste fraction Wood Plastic Organics Inert Residual Mean value

Mass fraction [mass-%] 15.00 15.00 30.00 20.00 20.00 100.00

LHV [MJ/kg] 15.63 34.50 1.96 0.00 2.66 8.64

Water [mass-%] 8.21 2.32 86.91 0.00 9.71 29.59

Inert [mass-%] 2.64 7.78 0.65 100.00 14.52 24.66

C [mass-%] 42.75 75.12 5.62 0.00 20.53 23.47

H [mass-%] 5.66 11.50 0.79 0.00 2.76 3.36

O [mass-%] 37.75 3.00 5.95 0.00 50.36 17.97

N [mass-%] 2.83 0.20 0.06 0.00 1.39 0.75

S [mass %] 0.16 0.08 0.01 0.00 0.73 0.19


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through six primary air zones and the grate bar movement can be adjusted for 3 different grate zones (1 grate zone corre-sponds to 2 primary air zones). During the measurement the first grate zone was op-erated with 13.24 double strokes per hour. The second and third zone was operated at 15.93 and 16.51 double strokes per hour, respectively. The mean value of the waste feed rate was 22,920 kg/h, while the waste feeder cycle was 586.5 s (580 s forward and 6.5 s backward). For the simulation the waste consists of the fractions wood, plastics, organics, inert waste and a “re-sidual fraction” (everything not considered by the other fractions). The composition and the lower heating value LHV of these fractions are summarized in Ta b l e 4. Ta b l e 5 shows the material properties.

Based on the radial distributed moisture within the particle, the radially distributed material properties are used for the calcu-lation of every particle of the waste bed. The properties are calculated as mass aver-aged value from the dry properties (Table 5) and the moisture properties (density: 1,000 kg/m3, heat capacity: 4,200 J/(kg K), heat conductivity: 0.64 W/(m K)).For the determination of the particle size distribution illustrated in Ta b l e 6, data obtained at the MVA Flingern (Düsseldorf) is used [3]. For further details on the pa-rameters and models for the mechanical movement of the particles see [13].The data of the waste which was used dur-ing the measurement was not determined by a detailed waste analysis, but calculated as average values from boiler balances.

Due to that circumstance, a more precise characterisation of the different waste frac-tions is not possible. The above-mentioned distribution of the waste fractions is based on estimations of the waste observed in the waste bunker. The geometry of the furnace is discretised by approximately 800,000 finite volume elements for the CFD simulation with AN-SYS FLUENT. Since the boiler is symmetric with respect to the secondary air injection and all other relevant components, only a symmetric half-model is used for the CFD. The mass flows and the corresponding temperatures used in the simulation are summarised in Ta b l e 7.Furthermore, the heat transfer from the furnace wall to the water-steam cycle must be considered in the CFD simulation. For

1

0.8

0.6

0.4

0.2

00 10 20 30 40 50 60

Time in min

Nor

mal

ised

mas

s

Simulation Best-fit line

Fig. 4. Simulated mass loss of the 10 mm beechwood particles.

0.05

0.04

0.03

0.02

0.01

00 10 20 30 40 50

Particle diameter in mm

MCR

in k

g/(m

²s)

KLEAA DEM

Fig. 5. Comparison of the mass conversion rate (MCR) between measurement and simulation.

T in °C

Tmean in °C

v in m/s1,500

1,353

1,205

1,058

910

763

615

468

320

173

25

30

27

24

21

18

15

12

9

6

3

0

1,4001,3001,2001,1001,00090080070060050040030020010025

Fig. 6. Temperature (top left) and velocity (top right) distribution in the furnace; surface temperaure of waste particles (bottom).

Tab. 5. Material properties of the waste fractions.

Waste fraction Wood Plastic Organics Inert Residual

Density (dry) [kg/m3] 640 1,500 800 7,000 1,730

Heat capacity (dry) [J/(kg K)] 2,170 1,500 2,000 630 2,020

Heat conductivity (dry) [W/(m K)] 0.2 0.2 0.3 20 1.7

Pyrolysis temperature [K] 600 450 500 - 500


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the lower furnace, a part that is lined with refractory concrete, a heat transfer coeffi-cient of 8.3 W/(m2 K) is used. The lower and the upper part of the first pass are sim-ulated with a heat transfer coefficient of 81 and 95 W/(m2 K), respectively.

The top part of F i g u r e 6 illustrates the temperature and velocity distribution in the symmetry plane of the CFD model. The lower part shows the combustion cham-ber with the waste bed surface, which is used for the coupling of both models (see above). As in the illustration of the grate, the waste is transported from left to the right. The temperature distribution of the waste bed particle surface resulting from the grate simulation is shown at the bottom of Figure 6. Approximately after 50 % of the distance along the declined grate the maximum temperature of about 1,400 °C is reached. With proceeding transport and burn-out of the waste bed par ticles, the temperature decreases due to the cooling effect of the passing primary air. The temperature is between 200 and 300 °C when the particles leave the grate into the ash discharger.

The gas temperature distribution (Fig. 6, top left) shows the maximum value in the middle of the declined grate. Thus, the maximum of the gas temperature has the same position as the maximum waste temperature. However, the value of about 1,500 °C is approximately 100 °C higher than the solid waste temperature. This is due to the gas phase reaction of the CO above the waste bed. Moreover, a tempera-ture increase can be observed at the posi-tion of the secondary air injection, because of the post-combustion of the unburned fuel gases. Locally, the temperature again increases to 1,500 °C. Furthermore, it can be seen that the temperature close to the boiler front wall (left) is higher that the temperature at the back wall (right). This temperature asymmetry decreases along the first pass. After the deflection into the second pass the temperature is homogene-

ously distributed over the cross-section. Along the flue gas pass, the temperature continuously decreases, due to the consid-eration of the heat transfer to the water-steam cycle. The top right part of Figure 6 shows the absolute velocity and the velocity vectors. This view shows the secondary air injec-tion very clearly. After the injection into the boiler, the velocities are above 30 m/s. The injected air of the front and the back wall overlaps and induces a flow towards the grate. As consequence of this flow, the gas leaving the grate is forced to the left and the right side. Above the secondary air injection, a streak can be observed that moves from the front to the back wall and again to the front wall. Besides this main flow, large circulating eddies can be seen. Like the temperature, the velocity distribu-tion homogenises along the flue gas pass. After the deflection, the flow shows an equal velocity distribution.In the following, the simulation results presented are compared to measurement data. The measurement positions that are used for the comparison are illustrated in F i g u r e 7.At these positions the gas composition (O2, CO2, H2O) is measured by gas extraction and a FTIR spectrometer. Additionally, the gas temperature is measured by a thermo-couple located at the head of the extraction system. All measurements are obtained in the centre of the grate width.

The next diagram (F i g u r e 8) shows a comparison of the measured data (blue) and the simulation results (red). The gas composition is referred to on a wet basis. All values are plotted versus the relative grate length. The top left part shows the comparison of the temperatures. In gen-eral, the simulation reflects the measure-ment very well. In both cases, the tempera-ture in the first half of the grate length is high, due to the release of volatiles and the heterogeneous combustion. After char combustion, the passing primary air cools the waste particles. Hence, the waste tem-perature decreases with increasing grate length. The maximum difference of about 150 °C can be found at the second measure-ment position (approximately 25 % grate length). At the beginning of the grate the simulated temperatures are higher while they are lower compared with the meas-urements in the middle section of the grate. This suggests that the pyrolysis and the heterogeneous combustion are predict-ed too early or too fast by the model. The top right part of the figure compares simulated and measured O2 content. Both curves increase along the grate length from zero (oxygen completely consumed by gas phase reactions) to about 20 vol.-% (almost pure air). An exception is the last measure-ment point, which slightly decreases in the measurement curve and increases in the simulated curve. However, as a good ap-proximation both cases show a constant value for the last two positions. In the first and middle section of the grate the simu-lated curve has an offset to the left, but shows the same qualitative characteristic. Again, this could be explained by an ear-lier or faster pyrolysis and char combus-tion. Nevertheless, the difference is only 10 % of the grate length and thus, meas-urement and simulation show a reasonable agreement.The comparison between the simulated and the measured CO2 content is illustrat-ed by the bottom left part of Figure 8. It can be observed an identical qualitative char-acteristic for both curves. From the first to the second position the value increases before it continuously decreases until the end of the grate. Only in the first part of the grate a small difference can be found. From the middle to the end of the grate both curves are almost identical.The last comparison (bottom right) shows the H2O content. First it has to be men-tioned that the measurement of the water

Tab. 6. Particle size distribution of the waste fractions.

Mean particle size [m] 0.270 0.150 0.100 0.075

Mass fraction wood [mass-%] 7.5 42.5 50.0 0.0

Mass fraction plastic [mass-%] 5.0 10.0 85.0 0.0

Mass fraction organics [mass-%] 0.0 10.0 80.0 10.0

Mass fraction inert [mass-%] 9.1 24.3 58.3 8.3

Mass fraction rest [mass-%] 5.4 21.7 68.3 4.6

Tab. 7. Mass flows and temperatures for the simulation.

Primary air mass flow [kg/h] 80,825

Distribution of the six primary air zones [%] 5.3 – 16.4 – 36.8 – 29.6 – 7.7 – 4.2

Primary air temperature [K] 366

Secondary air mass flow [kg/h] 44,339

Secondary air temperature [K] 373

Global air ratio (λ) 1.85

2

1

3 4 5

Fig 7. Measurement positions.


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At Doosan Lentjes we help our customers convert millions of tonnes of residual waste into valuable energy every year. Combining our proven grate-fired ‘chute to stack’ technology with industry-leading turbines from Doosan Škoda Power, we provide solutions that make us the perfect partner for all your waste-to-energy needs.

www.doosanlentjes.comTo learn how Doosan Lentjes’ technologies can help you, contact:

Doosan Lentjes GmbHDaniel-Goldbach-Strasse 19 40880 Ratingen, Germany

Tel: +49 (0) 2102 166 0 or email: [email protected]

Doosan LentjesHelping you recover energy from waste

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vapour was only carried out at the first three positions. For completeness, the simulated values are shown for the missing two positions, anyway. Both curves have their maximum value at the beginning of the grate, due to drying and vapour from the combustion processes. With increasing grate length both curves decrease continu-ously. The qualitative characteristics of the curves as well as the quantitative values show only little differences.In summary it can be concluded that the simulation reflects all measured values very well. The model describes the main characteristics of the plant. The largest differences can be observed for the tem-perature, because of the strong sensitivity to the thermochemical processes. It seems that the simulation predicts the pyrolysis and the char combustion too early or too fast. However, regarding the uncertainties of the measurement and the high com-plexity of the system the existing accuracy is convincing. It has been shown that the discrete element method developed is well suited for the simulation of industrial grate firing systems.

Summary

This work presents a novel discrete element method that is closely linked to a CFD code and therefore can be used for simulating grate firing in municipal waste incineration

plants. The simulation method is verified by experimental data for heat transfer and combustion progress within a static packed bed. Furthermore, the simulation of an in-dustrial grate firing system is compared to measurement data obtained during plant operation.Altogether, the simulation shows a good agreement with all measurements ob-tained. Hence, the novel discrete element method is suitable for the simulation of static and agitated reacting packed beds.

Acknowledgement

The authors would like to thank Doosan Lentjes GmbH for providing the grate and boiler geometry data and Hitachi Zosen Inova AG as well as Karlsuhe Institut für Technolgie for providing the measurement data.

References[ 1] Yin, C., Rosendahl, L. and Kær, S.K.: Grate-

firing of biomass for heat and power pro-duction. Progress in Energy and Combus-tion Science 34 (6), 725–754, 2008.

[ 2] Klasen, T.: Erstellung und Validierung ei-nes mathematischen Modells für die hete-rogene Verbrennung auf dem Müllrost und dessen Anwendung bei CFD-Simulationen hinsichtlich einer optimierten Feuerungs-technik. Dissertation, Universität-Gesamt-hochschule Essen, 2003.

[ 3] Krüll, F.: Verfahren zur numerischen Si-mulation von Müllrostfeuerungen. Disser-tation, Ruhr-Universität Bochum, 2001.

[ 4] Yang, Y.B., Goh, Y.R., Zakaria, R., Nasserzadeh, V., and Swithenbank, J.: Mathemat-ical modelling of MSW incineration on a travelling bed. Waste management 22 (4), 369–80, 2002.

[ 5] Bruch, C.: Beitrag zur Modeliierung der Festbettverbrennung in automatischen Holzfeuerungen. Dissertation, Eidgenössi-sche Technische Hochschule Zürich, 2001.

[ 6] Cundall, P.A., and Strack, O.D.L.: A discrete numerical model for granular assemblies. Géotechnique 29 (1), 47–65, 1979.

[ 7] KruggelEmden, H., Simsek, E., Rickelt, S., Wirtz, S., and Scherer, V.: Review and extension of normal force models for the Discrete Element Method. Powder Tech-nology 171 (3), 157–173, 2007.

[ 8] KruggelEmden, H., Simsek, E., Wirtz, S., and Scherer, V.: A comparison and vali-dation of tangential force models for use within Discrete Element Simulations, DEM07 Conference, Brisbane, Australia, 2007.

[ 9] Luding, S., Manetsberger, K., and Müllers, J.: A discrete model for long time sinter-ing, Journal of the Mechanics and Physics of Solids 53 (2), 455–491, 2005.

[10] Brosch, B., Wirtz, S., and Scherer, V.: A par-ticle based model for the combustion of municipal waste in grate firing systems, 9th European Conference on Industrial Furnaces and Boilers, Estoril, Portugal, 2011.

300

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Fig. 8. Comparison between measurement and simulation; temperature (top left), O2 content (top right), CO2 content (bottom left), H2O content (bottom right).


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[11] Simsek, E.: Mischung und Segregation auf Rostsystemen: Experimentelle Unter-suchung und numerische Simulation mit Hilfe der Diskreten Elemente Methode, Dissertation, Ruhr-Universität Bochum, 2011.

[12] Wirtz, S., Sudbrock, F., Brosch, B., and Scherer, V.: Simulation des reagierenden Brennbetts auf Rosten von Müllverbren-nungsanlagen. VGB PowerTech 6, 85–91, 2013.

[13] Brosch. B.: Erweiterung der Diskreten Ele-mente Methode zur Simulation bewegter und reagierender Feststoffschüttungen mit der Anwendung auf Rostfeuerungs-systeme. Dissertation, Ruhr-Universität Bochum, 2012.

[14] Specht, E.: Kinetik der Abbaureaktion. Habilitation, Clausthaler Umwelttechnik-Institut GmbH, Clausthal-Zellerfeld, 1993.

[15] Specht, E. and Jeschar, R.: Ermittlung der geschwindigkeitsbestimmenden Mecha-nismen bei der Verbrennung von dichten Kohleteilchen. VDI-Berichte Nr. 645, 45–56, 1987.

[16] Peters, B., Schröder, E., Bruch, C., and Nussbaumer, T.: Measurements and particle re-solved modelling of heat-up and drying of a packed bed. Biomass and Bioenergy 23 (4), 291–306, 2002.

[17] Peters, B., Schröder, E., and Bruch, C.: Measurements and particle resolved mod-elling of the thermo- and fluid dynamics of a packed bed. Journal of Analytical and Applied Pyrolysis 70 (2), 211–231, 2003.

[18] Bleckwehl, S.: Charakterisierung der ver-brennungstechnischen Eigenschaften fes-ter Brennstoffe im Festbett. Dissertation, Karlsruher Institut für Technologie, Uni-versität Stuttgart, 2010.

[19] Lohe, G.: Current Examples for the Exten-sive Modernisation of European Energy-from-Waste-Plants during Operation. VGB Power Tech 86 (9), 109–117, 2006.

[20] Halter, R., Waldner, M., Brosch, B., Gehrmann, H.J., and Sigg, A.: Energy from waste – Clean, efficient, renewable. Thir-teenth International Waste Management and Landfill Symposium, Sardinia, 2011.

[21] Waldner, M.H., Halter, R., Sigg, A., Brosch, B., Gehrmann, H.J., and Keunecke, M.: En-ergy from Waste – Clean, efficient, renew-able: Transitions in combustion efficiency and NOx control. Waste management 33 (2), 317–326, 2013. l

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Simulation of municipal solid waste incineration in grate