multi-scale simulation approach for the fluidised … · multi-scale simulation approach for the...

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MULTI-SCALE SIMULATION APPROACH FOR THE

FLUIDISED BED SPRAY GRANULATION PROCESS

STEFAN HEINRICH

HAMBURG UNIVERSITY OF TECHNOLOGY, Germany

NIELS DEEN & HANS KUIPERS

EINDHOVEN UNIVERSITY OF TECHNOLOGY, The Netherlands

dust-freeredispersible

compactfree flowing

soluble coffeeculinary powdersinstant milk

product formulation

tailor-made properties

FLUIDIZED BED SPRAY GRANULATIONmotivation

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Most industrial processes consists of complex interconnection of different apparatuses and production steps

Flowsheet: Network of process units, which are connected by energy and material streams

Flowsheet simulation: Numerical calculation of mass and energy balances for

different process structures

MULTISCALE SIMULATION APPROACHindustrial production process

On the macroscale empirical or semi-empirical models are used, material properties are poorly considered

Description of the process on lower scales leads to exponential increase of computational volume

MULTISCALE SIMULATION APPROACHprocess treatment on different scales

J. Werther, S. Heinrich, M. Dosta, E.-U. Hartge, Particuology, Vol. 9, 320-329, 20114

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MULTI-SCALE MODELLING

Van der Hoef et al., Annu. Rev. Fluid M., 20085

MULTI-SCALE MODELLING

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RESULTS OF LATTICE BOLTZMANN MODELstatic array of bi-disperse particles at low Rep

example of initial particle configuration for

a bi-disperse systemgenerated with a

Monte Carlo procedure

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RESULTS OF LATTICE BOLTZMANN MODELstatic array of bi-disperse particles at low Rep

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RESULTS OF LATTICE BOLTZMANN MODELstatic array of particles with log-normal PSD

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RESULTS OF LATTICE BOLTZMANN MODELstatic array of particles with log-normal PSD

Re=0.1 Re=500

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RESULTS OF LATTICE BOLTZMANN MODELfinal drag closure for mono-disperse and poly-disperse particles

good fit (deviation less then 8%) of basic LB simulation data

generated over wide range of g and Re

strictly valid for homogeneous arrays of particles

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MECHANICAL PARTICLE PROPERTIESmeasurement of restitution coefficient

vn

vR

R

Vacuum nozzle

R

n

t

High speedcamera

vR,t

vR,n

v

vt

R n

nn

,=v

ev

nv

normal restitution coefficientnormal impact velocitynormal rebound velocity

en

vn

vR,n

Target plate

van Buijtenen, M.S., Deen, N.G., Heinrich, S., Antonyuk, S. and J.A.M. Kuipers: A discrete element study of wet particle-particle interaction during granulation in a spout fluidized bed, Canadian Journal of Chemical Engineering (2009), Vol. 9999, 1-10.12

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MECHANICAL PARTICLE PROPERTIESmeasurement of restitution coefficient

vn

vR

R

vacuum nozzle

R

n

t

high-speedvideo camera

vR,t

vR,n

v

vt

R n

nn

,=v

ev

R t

tt

,=v

ev

Restitution coefficient:

kin,R diss

kin kin

RE Ee = = 1-

E E v

v

vR/v relative rebound/impact velocity

n/t normal and tangential component

normal

tangential

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0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5

Res

titu

tio

n c

oef

fici

ent

e [-

]

Impact velocity v [m/s]

-Al2O3

Sodium benzoate

Zeolite

Maltodextrin agglomerates

Glass beads

5 mm

5 mm

5 mm

1 mm

1 mm dominantly elastic

elastic-plastic

dominantly plastic

Normal impact on a dry glass plate

MECHANICAL PARTICLE PROPERTIESmeasurement of “dry” restitution coefficient

Antonyuk, S., Heinrich, S., Tomas, J., Deen, N.G., van Buijtenen, M.S. and J.A.M. Kuipers: Energy absorptionduring compression and impact of dry elastic-plastic spherical granules,Granular Matter (2010) 1, 12.

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Objectives of the study: e = f (impact velocity, liquid film thickness and viscosity)

vR/v relative rebound/impact velocityEkin,R elastic rebound energy Ekin impact energyEdiss irreversible absorbed energy

kin,

in

RR

k

Ee = =

E

v

v

hs

vacuum nozzle

high-speedcamera

steeltarget

precisionstable

polymerfilm

confocal sensor

MECHANICAL PARTICLE PROPERTIESmeasurement of “wet” restitution coefficient

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PARTICLE COLLISION IN WET SYSTEMS

(above) Formation, thinning & eventual breakup of the liquid bridge (simulation results)

(below) Images from high-speed recording of α-Al2O3 granule impacting with velocity of 2.36

m/s on wall with a water layer (thickness 0.4 mm) (Antonyuk et al. (2009)).

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0 200 400 600 800 10000.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Re

stitu

tion

co

effi

cie

nt e

Layer Thickness (µm)

CFD model vel=1.0 vel=2.36

Antonyuk model vel=1.0 vel=2.36

Experimental vel=1.0 vel=2.36

Influence of impact velocity on restitution coefficient of the Al2O3

granules impacting on water layers with varying thickness.

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During fluidized bed granulation the particles are covered by a liquid film

Different types of collisions can occur:

How can the influence of the liquid film be predicted?

PARTICLE COLLISION IN WET SYSTEMScollision types for different moisture contents

kin,

in

RR

k

Ee = =

E

v

v

Awet wetted surfaceAtot total particle surface

For the description of the liquid film, the wetted surface fraction φ is used

φ obtained from the calculations of the model on the macroscale

wet

tot

A=

A

PARTICLE COLLISION IN WET SYSTEMScollision types for different moisture contents

232

21 e)1(2e1ee

kin,

in

RR

k

Ee = =

E

v

v

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11

vn,rel normal relative velocityvt,rel tangential relative velocityh minimum separation distanceha roughnessη viscosityR* average curvature radius in contactFcap capillary force

*2,

,

6 n relv n

R vF

h

**

, ,2 ln 12v t t relR

F R vh

Normal impact1

h Tangential impact2

h 1Adams, M.J., Edmondson, B. (1987). Tribology in particulate technology.2Popov, V. (2009) Kontaktmechanik und Reibung, Springer.

h ≥ 2 ha

ha

Viscous forces

PARTICLE COLLISION IN WET SYSTEMScontact model for wet collisions

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Particles: R = 0.4 mm, = 190 kg/m3, edry = 0.6, G = 6.3 MPa

Liquid layer: = 1 mPas, h = 60 µm, ha = 2.5 µm

model: FHertz-Tsuji

edry = 0.6

model: FHertz-Tsuji + Fv

ewet = 0.05

steel wall

Liquid bridge

PARTICLE COLLISION IN WET SYSTEMSapplication of viscous contact model

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dp = 0.8 mmvimp = 1.2 m/sliquid layer:

= 8 mPas, hm ≈ 60 µm

dp = 0.8 mm vimp = 1.2 m/sliquid layer:

= 4 mPas, hm ≈ 60 µm

PARTICLE COLLISION IN WET SYSTEMSapplication of viscous contact model

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For the description of particle motion in the fluid field the Computational Fluid Dynamics (CFD) model is coupled

COUPLING BETWEEN DEM AND CFD

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RESULTS DISCRETE PARTICLE MODEL spouted bed

“CAPABILITIES” OF DPM(COLLISION PARAMETERS!!!)

(Link et al., CES, 2005)

REGIME PREDICTION

GAS BUBBLES BEHAVIOUR

PRESSURE FLUCTUATIONS

SOLIDS MOTION

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RESULTS DISCRETE PARTICLE MODEL spouted bed (dry system)

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nozzle

ilev

jcspisuspjsusp A

AMM

,

,,

j,cspA

i,levA

cross-cut surface of particle j [m2]

cross-cut surface of level i [m2]

ilev

jcspisusp

isusp A

AMM

,

,1 1

The nozzle zone is described by the suspension mass stream, spraying angle and the height of cone area

In apparatus the different nozzles can be defined simultaneously

For the description of particle wetting the nozzle zone model has been developed

The suspension mass flow which comes to the particle j in the level i is defined as:

Mass stream on the level i+1:

DISCRETE PARTICLE MODEL nozzle region

L. Fries, M. Dosta, S. Antonyuk, S. Heinrich, S. Palzer, Chemical Engineering Technology, 34, 201127

For the calculation of liquid film drying the equations of energy and mass balance are used

The heat and mass transfer coefficients α and β are calculated for each particle

filmairfilmafaf AQ

filmpfilmpfpf AQ

pairfilmpapap )AA(Q

filmA

pASurface of liquid film [m2]

Particle surface [m2]

p

filmParticle temperature [°C]

Temperature of liquid film [°C]

afvappfsuspfilm QHQH

dt

dH

DISCRETE PARTICLE MODEL heat and mass transfer

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• growth rate for each particle:

sw2p

wvapp

xR4

)x1(M

dt

dR

pR Particle radius [m]

vapM Vapor mass stream [kg/s]

s Solid density [kg/m3]

DISCRETE PARTICLE MODEL heat and mass transfer and particle growth

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Exterior view Cross cut

Simplified geometry for DPM

high gas velocity

Segmented distributor plate

low gas velocity

Mesh forCFD-simulation

12.000 tetrahedralmesh cells

Design and process parameters

Gap distance below Wurster

Height and diameter of Wurster

Injection velocity

Fluidization gas velocity

Air split ratio Wurster/annulus

nozzle

RESULTS DISCRETE PARTICLE MODEL / CFD real granulators – Wurster coater

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vnozzle = 20 m/s vnozzle = 100 m/s vnozzle = 160 m/sHigh spout velocity provides stable circulation regimeBackmixing is prevented

150.000 particles, dp = 2 mm, p = 1500 kg/m³, total mass = 0.94 kg, e = 0.8, simulation time t = 2.5 s, fluidization velocity: Wurster: uW = 8 m/s = 10.1·umf , annulus: uann = 3 m/s = 3.8·umf

RESULTS DISCRETE PARTICLE MODEL / CFD real granulators – Wurster coater

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bag filter

liquid binder

bottom plate

fluidizing air

Wurster tube

nozzle

bag filter

liquid binder

fluidized powder

bottom plate

fluidizing air

Top spray Wurster-coater

Application:tablet coating,encapsulation of flavors.

Application:Granulation and agglomeration of food and fertilizers.

RESULTS DISCRETE PARTICLE MODEL / CFD comparison of granulator configurations

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Top-spray-configuration produces wide size distribution of the productCirculating regime allows more homogeneous wetting at equal process conditions

Process conditions

Glatt GCPG 3.1Dextrose syrup (DE21)Fluidization air: 250 m³/h

Injection: water,20% of bed massat constant spray rate


Top-spray vs. Wurster-coater

L. Fries, S. Antonyuk, S. Heinrich, S. Palzer, Chemical Engineering Science, 201133

Top spray granulatorBroad residence time distribution

0.00

0.02

0.04

0.06

0.08

0.10

0.000 0.025 0.050 0.075 0.100

Pa

rtic

le f

ract

ion

[-]

Residence time [s]

1 s

2 s

3 s

5 s

7 s

0.1

0.08

0.06

0.04

0.02

0.0

resi

denc

etim

e [s

]

Simulation time t ↑


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Wurster-coater Cyclic wettingDefined residence time per cycle

0.1

0.08

0.06

0.04

0.02

0

resi

denc

e tim

e [s

]

0.00

0.02

0.04

0.06

0.08

0.10

0.000 0.025 0.050 0.075 0.100

Par

ticl

e fr

acti

on

[-]

Residence time [s]

1 s

2 s

3 s

5 s

7 s


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Wurster-coater:

Lower collision frequency

Higher kinetic energy

→ Reduced agglomeration probability

Collisions between surface-wet particles

Nozzle

30 m

m15

mm

d = 50 mm

Inje

ctio

n de

pth

L

Parameter Wurster-coater Top Spray

Particle fraction inside spray zone 0.53 % 0.36 %

Average angular velocity inside spray zone 43 rad/s 54rad/s

Average collision frequency (inside spray zone) 88 s-1 864 s-1

Average relative collision velocity inside spray zone 0.39 m/s 0.15 m/s

RESULTS DISCRETE PARTICLE MODEL / CFD particle collision dynamics

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Abluft-filter

liquid binder

bottom plate

fluidizing air

Wurster-tube

Wurster-Coater

liquid binder

adjustable gas inlet

fluidizing air

Spouted bed

Application:very fine, cohesive and non-spherical particles

bag filter

Application:tablet coating,encapsulation of flavors.


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Par

ticle

vel

ocity

[m

/s]

2,0

1,5

1,0

0,5

0,0

Spouted bed

150.000 Particle, dp = 2 mm, Density p = 1500 kg/m³, Bed mass = 0.94 kg, e = 0.8, Simulation time t = 5 s150.000 Particle, dp = 2 mm, Density p = 1500 kg/m³, Bed mass = 0.94 kg, e = 0.8, Simulation time t = 5 s

Joint STW/DFG project


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0.0

0.2

0.4

0.6

0.8

1.0

1.0 1.5 2.0 2.5 3.0

Par

ticl

e ve

loci

ty v

p/

m/s

Simulation time t / s

Spouted bed

Wurster-coater

0.62 m/s

0.44 m/s

0.0

0.2

0.4

0.6

0.8

1.0

1.0 1.5 2.0 2.5 3.0

Co

llisi

on

vel

oci

ty v

coll

/ m

/sSimulation time t / s

Spouted bed particle-wall

Spouted bed particle-particle

Wurster-coater particle-wall

Wurster-coater particle-particle

0.13 m/s

0.39 m/s

0.27 m/s

Translation: mean particle velocity Collision dynamics

Spouted bed: Higher impact energy for particle-wall collisions → Increased agglomerate strength


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t = 0.3 s t = 0.5 s t = 0.7 s t = 0.9 s t = 1.0 s

pdf

of v

p,ve

rt[s

/m]

2

1

0

-2 -1 0 1 2 3vp,vert [m/s]

start-up process from fixed bed

(transient), simulation time t = 01 s t = 1.1 s t = 1.2 s t = 1.3 s t = 1.4 s t = 1.5 s t = 1.6 s t = 1.7 s t = 1.8 s

pdf

of v

p,ve

rt[s

/m]

-2 -1 0 1 2 3

1.2

0.6

0.0

vp,vert [m/s]

steady-state spouting, t > 1 s

Typical distribution of vertical particle velocities for spouted bed was found

This kind of parameters can’t be measured up-to-day by available methods. They can be obtained only from a simulation.

RESULTS DISCRETE PARTICLE MODEL / CFD spouted bed behaviour

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21

2

1.5

1

0.5

0

-0.5verti

cal p

artic

le v

eloc

ity [m

/s]

High friction influences the particle mobility β

The spread of the function strongly decreases

The shape of velocity distribution changes from Gumbel Max to Gumbel Min (skewed to

the left)

0

5

10

-0.8 -0.4 0.0 0.4 0.8

PD

F o

f v p

,ve

rt[s

/m]

vp,vert [m/s]

high friction

low friction

0.035

0.175

HF

LF

RESULTS DISCRETE PARTICLE MODEL / CFD stable spouted bed behaviour

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RESULTS DPM MODELsegregation in bidisperse systems

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44

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RESULTS MFM MODELsegregation in bidisperse systems

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RESULTS MFM MODELsegregation in bidisperse systems

New MFMOld MFM

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1. Calculation of the process on the macroscale up to steady-state

2. Generation of initial conditions for DEM+CFD simulation

3. Microscale calculation for short time interval (in order of seconds)

5. Mesoscale calculation of wetting, drying, growth, …

4. Transmission of particle trajectories and fluid profile to the mesoscale

6. Recreation of microscale simulation according to new parameters

7. Microscale simulation -> calculation of coalescence kernel

8. Repeating of the calculation on the macroscale with new kernel

PSDBed massFluidization air mass flow

PSD, particle trajectoriesflow profile

suspension mass flowstreams temperaturessuspension water partfluidization air mass flowbed mass

wetted surface fractionwetted surface fraction

coalescence kernel

INTERSCALE COUPLINGgranulation agglomeration in fluidized beds

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spraydroplets

nuclei / powder solidified shell „onion-like“structure

Granulation (granum (lat.) = grain)

Transfer of solids from liquids (suspensions, solutions, melts) or dusty solids to a well defined granule structure

e.g. detergents, food technology, preservatives

spraying spreading solidification granule

MACROSCALEgranulation

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Input and output streamsGrowthrate

out,0outin,0inp

b,0toteb,0tot qnqnd

qnG

t

qn

tot

ee A

M2G

)x1(MM wsuspe

For the description of the change of particle size distribution, a one-dimensional Population Balance Model (PBM) has been used

Empirical model:

MACROSCALEpopulation balance model for granulation

Ge

Atot

ρ

xw

Growth rate [m/s]Total particle surface [m2]Effective mass stream [kg/s]Particle density [kg/m3]Suspension mass stream [kg/s]Water part in suspension [%]

eM

suspM

SolidSim FlowsheetProduction process

Short-cut models

Multiscale model

MACROSCALEflowsheet of granulation process

M. Dosta, S. Heinrich, J. Werther, Powder Technology, 204, 71-82, 201050

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MICROSCALEDEM simulation of the nozzle zone

ECCE_Heine_2.avi

ECCE_Heine.avi

Stream Median [mm] Mass stream [g/s]

1 1.03 / 0.954 2.25 / 2.32

2 0.9 / 0.9 0.305 / 0.219

3 0.941 / 0.868 1.6 / 2.45

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2

Empirical model / Growth rate from multiscale model

Process flowsheet

Size dependent growth rate

MACROSCALEsimulation results

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binder primary particle

liquid bridge solid bridge

e.g. pharmaceutical technology, combination of active agentsand excipients, instant coffee

of primary particles (powder)

Agglomeration (agglomerare (lat.) = to agglomerate)

spraying agglomeratesolidificationwetting

„blackberry-like“ structure

MACROSCALEagglomeration

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Air

As an simulation example the continuous agglomeration process has been used

Short-cut models

Multiscale model

MACROSCALEflowsheet of agglomeration process

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MACROSCALEpopulation balance model for agglomeration

The agglomeration process on the macroscale has been described by population balance model

PBM of pure agglomeration (no growth, breakage or attrition)

where

One of the main process parameters – coalescence kernel

where - time dependent part, - size dependent part

)v,t(n)v,t(n)v,t(D)v,t(Bv

)v,t(noutinaggagg

du)uv,t(n)u,t(n)u,uv,t()t(N2

1)v,t(B

v

0totaggl

0tot

aggl du)v,t(n)u,t(n)u,v,t()t(N

1)v,t(D

birth rate

death rate

)u,v,t(

)u,v()t()u,v,t( *0

)t(0 )u,v(*

obtaining of physical based coalescence kernel

Collision rate Collision velocities

Sticking rate

Based on the calculation of viscous force, the sticking rate of particles can be obtained.

MICROSCALEsimulation results for agglomeration

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The contact forces which arise from subsequent collisions with neighbour particles can cause the rupture of the formed liquid bridge

A force balance including viscous adhesion forces in the liquid bridge and separating forces due to collisions is solved on the microscale

The received results are used to calculate the probability for the destruction of a liquid bridges connecting two particles due to further collisions with other particles

Time progression of PSD of macro process

MACROSCALEsimulation results for agglomeration

The multiscale simulation leads to more detailed process description, where material microproperties can be considered

Accurate closures are required for fluid-particle and particle-particle interactions

Complex solids processes with multiple process units can be described by flowsheet simulation

In this contribution the general view of simulation framework has been proposed and applied for granulation/agglomeration processes

CONCLUSIONS

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multi-scale simulation approach for the fluidised … · multi-scale simulation approach for the...

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