estimation of agglomerate properties€¦ · product formulation tailor -made properties fertilizer...

PiKo workshop in Siegen, 1st and 2nd of October 2012 1

Estimation of agglomerate properties

from experiments for microscale simulations

Institute of Solids Process Engineering and Particle Technology

Hamburg University of Technology

Sergiy Antonyuk


Content

Introduction: granulation and agglomeration processes

Use of micro scale simulation for the description of an industrial agglomeration macro process

Discrete Element Method

Influence of agglomerate microstructure

Important parameters of the DEM models: their experimental estimation and calibration


IntroductionAgglomeration of powders to improve the properties

dust-freeredispersible

compactfree flowing

soluble coffee instant milk

product formulation

tailor-made properties

fertilizer detergentdryer, catysators


❑ high heat and mass transfer❑ intensive mixing of particles❑ compact design...

❑ high heat and mass transfer❑ intensive mixing of particles❑ compact design...

IntroductionParticle formulation in fluidized beds

nozzle

fluidizedparticles

fluidizationair

exaustair

binderliquid

1. Agglomeration

Fluidized bed spray agglomeration

2. Coating, granulation

Particle formulation processes:

„onion-like“ structure

hardenedshell

Timesprayed droplets

sprayeddroplets

Time

liquid bridge solid bridge

porous structure

„blackberry-like“ structure

primaryparticle

spraying wetting hardening granulatdencestructure

growth due tolayeringnucleus


Most industrial processes consist of complexinterconnection of different apparatuses andproduction steps.

Flowsheet simulation: Numerical calculation of mass and energy balances for different process structures

Flowsheet simulation: Numerical calculation of mass and energy balances for different process structures

Time of the granulation: some hours. Simulation of plant performance is the ultimate goal of modeling!

IntroductionIndustrial production processes

Agglomeration process flowsheet


The same process can be described on different scales:

On the macroscale the flowsheet simulation is performed: the empirical or semi-empiricalmodels are used, material properties are poorly considered. Description of the process on lower scales leads to exponential increase of computational volume.Dosta M., Antonyuk, S., Heinrich, S.: Multiscale simulation of the fluidized bed granulation process, Chem. Eng. Technol. 35 (2012)

Multiscale simulationProcess treatment on different scales


Microscale simulation

interactions between particles in the fluid field are described on microscale level

local fluid-mechanical effects are considered

for simulation of particle dynamics the coupled Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) are used


Droplet impact

Wetting

Agglomeration, Sintering

Breakage

Dropletrebound

Rebound

Rupture of theliquid bridge

Spray dropletsOverspray

Particle collision

Binder drying

MicroscaleMicromechanisms of agglomeration processes


air

particle-particle

particle-wall

particle-droplet

Interactions Stress conditions

field (gravitation Fg, electrostatic…)

impact Fc

adhesion FA (capillary, viscous…)

pgas-particle drag Fd, flow pressure Fp

Example ofDEM-CFDsimulation ofa fluidized bed

MicroscaleInteractions – stress conditions


Description via equations of motion:z

yx

Fg

Fcvp

p

Fp,i Force acting on a particlevp Translational and angular velocity

Solid

FA

, ..n

p p i g c a d pp

ni

dm F F +F F +F F F

dvt

vy,py

vz,pz

vx,pz

Fluid

g g g g g g g g p g g( u) ( uu) p ( ) S gt

g g g g( ) ( u) 0t

g and g Cell porosity and gas densityū Volume-averaged gas velocityS

g→p Sink term for coupling with DEM

CFD: Description via volume-averaged Navier-Stokes-equations

Continuity equation

Momentum equation

Fd

MicroscaleDEM-CFD


MicroscaleMUSEN DEM: General description

Novel MUltisacle Simulation ENvironment system was

developed to investigate the behavior of granular material

and to predict the properties of agglomerates

DEM is used as a basic computational approach on the

microscale

Visualization: OpenGL library and GLSL language

The system allows to:

1. Replicate the microstructure: calculate the highest package density of particles for a given particle size distribution

2. Calculate the sticking of particles: specify solid/liquid bonds between particles and their properties, such as: diameter, length, strength, stiffness, viscosity, etc.

Examples of visualizationin MUSEN DEM

Dosta M., Antonyuk, S., Heinrich, S.: Multiscale simulation of the fluidized bed granulation process, Chem. Eng. Technol. 35 (2012)

3. Perform the calibration of the material parameters: using experimental data from compression and impact tests

4. Investigate the behavior of particles and agglomerates during their loading


The primary particles can be randomly generated in different volume types

Developed algorithm allows to obtain the highest package density

The heterogeneous bonded particle structures can be specified

Box filled with particles

Internalagglomerate structure

Cylindrical agglomeratewith solid bridge bonds

1. Agglomerate microstructure


• biological model: nacre, enamel, dentin

• high fracture strain, strength, toughness

• highly-filled structure

• Hard phase with very small amount of the soft material

• Hierarchically structured materials

nacre nacre

10 µm

1. Agglomerate microstructureMaterial design

hard & stiff + elastic + strong + customized

hard phase soft phase hierarchical structure

control of interfaces

hierarchically structured composite materialSFB98 6


size ratio optimum mixing packing density (max.)dL/dS < 100 bimodal ~ 83 %dL/dS > 100 trimodal ~ 94 %

C. C. Furnas, „Grading AggregatesI –Mathematical Relations for Beds ofBroken Solids of Maximum Density“, Industrial and Engineering Chemistry, 1931, vol. 23. pp 1052-1058.

For size ratios < 100,binary mixtures yieldbetter packingthan ternary mixtures

1. Agglomerate microstructurePacking density: multimodal composition


Liquid bridge model(dry agglomerates)

Liquid bridge Rebound

2. Modeling of the particle contact with the adhesion


2. Modeling of ahesion forcesImpact behavior of wet and dry agglomerates

High-speed videos: impact of agglomerates produced from: - Al2O3 particles dp = 0.8 mm, - solution of methylcellulose (Pharmacoat)

Variation of the binder viscosityImpact velocity vimp = 1.2 m/s

High-speed videos: impact of agglomerates produced from: - Al2O3 particles dp = 0.8 mm, - solution of methylcellulose (Pharmacoat)

Variation of the binder viscosityImpact velocity vimp = 1.2 m/s

Viscosity of the liquid binder:wet wet dryh = 4 mPas h = 30 mPas


Contact model according to Hertz-Tsuji

nn n nc,vis c,el,Hertz ijF = F + η v

* 1/4*n n n= 2 m k s

Damping coefficient n:

2 2

ln , 0(ln )

1, 0

nn

n

n

e if ee

if e

e = 1 elastic0 < e < 1 elastic-plastice = 0 plastic

Energetic restitution coefficient:

kin,R diss

kin kin

RE Ee = = 1-E E v

v

m* equivalent massvR/v relative rebound/impact velocityEkin,R elastic rebound energy Ekin impact energyEdiss irreversible absorbed energy

Reviews of other contact models which can be used in DEM: Tomas, J.: Adhesion of ultrafine particles - A micromechanical approach, Chem.Eng.Scie. 62(2007).Antonyuk, S., Heinrich, S., Tomas, J., Deen, N.G., van Buijtenen, M.S. and J.A.M. Kuipers: Energy absorption during compression and impact of dry elastic-plastic spherical granules, Granular Matter 1 (2010), 12, 15-47.

2. Modeling of ahesion forcesVisco-elastic behavior without the adhesion


vrel normal relative velocity (n - normal, t - tangential)h minimum separation distanceha roughnessη viscosityR* average curvature radius in contactVb liquid bridge volume

*2,

,6 rel

vR v

Fh

nn

Simulation of wet agglomerateLiquid bridge bond model

Normal impact1

1Adams, M., Edmondson, B. (1987). Tribology in particulate technology.2Popov, V. (2010) Contact mechanics and friction, Springer.3Butt, H.-J, Kappl, M., (2009) Adv. Colloid Interface Sci., 146, 48.

h ≥ 2 ha

ha

Viscous forces

h

h

**

, ,2 ln 12v relRF R v

h

t tTangential impact2

Capillary force

normalimpact

normalrebound

obliqimpact


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

2. Modeling of ahesion forcesLiquid bridge model - Application examples


3. Parameter estimationCalibration of the models


Piezo driveParticle

allows to carry out tests of particles at compressive and tensile loading with adjustable relative humidity and temperature in a climate box

Device Minimumvalue

Maximum value

Resolution

Piezo drive (displacement) 0 µm 250 µm 0.2 nmLaser vibrometer (displacement) -

∞

∞ 0.2 nm

Force sensor - 200 mN + 200 mN 40 µNBox (Temperature) 15 °C 35 °C 1 °CBox (Relative humidity) 10 % 90 % 2 %

20 mm

Microscope Force sensor

Current set-up

3. Parameter estimation Experimental set-up (in Birkenfeld)


Agglomeration process: 1: feed material → 2: heating

→ 3: wetting

→ 4: drying

→ 5: cooling

→ 6: product

Due to increasing humidity of the air inside the fluid bed and softening of the particles a bed collapse can take place. The forces acting on the particles in the fluid bed are no longer sufficient to destroy the generated sinter bridges.

Problem:

0

20

40

60

80

100

120

0 5 10 15

tem

pera

ture

[°C

]

water content [%wb]

glassy

rubbery

Tg,dry Gordon & Taylor modelexperimental data

2

3

4

5

1 6

liquid

heatedair

Diagram: glass transition temperature of maltodextrin DE21 -model material for an amorphous food powder

3. Parameter estimation Collapse of the fluidized bed


Dextrose syrup (DE 21) lumps obtained by high relative humidity of the air in spray agglomeration

plasticized agglomerate surface

overwetting of the particle surface

3. Parameter estimation: Influence of glass transition temperature on the mechanical behavior

The amorphous particles show a phase transition from the brittle glassy state to the viscous liquid state.


particle-particleparticle-particle pendulum testspendulum testsfree fallfree fall

Goldsmith, 1960Walton and Braun (1986)Kharaz et al. (2001)Fu et al. (2004)Dong & Moys (2006)Mangwandi et al. (2007)

Foerster et al., 1994Labous et al., 1997

Weir & Tallon, 2005 Stevens & Hrenya, 2005

Iveson & Litster, 1998Coaplen et al., 2004

3. Parameter estimationMethods for measuring of restitution coefficient


vn

vR

R

vacuum nozzle

Q

QR

n

t

high-speedvideo camera

vR,t

vR,n

v

vt

Q

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 velocityn/t normal and tangential component

normal

tangential

3. Parameter estimation Free-fall apparatus


0

0.2

0.4

0.6

0.8

1

0 1 2 3 4impact velocity in m/s

"dry

" re

stitu

tion

coef

ficie

nt

en,d

ry GlassAl2O3Maltodextrin

predominantly elastic

elastic-plastic

predominantly plastic

d = 2.5-2.8 mm

d = 1.7-1.9 mm

d = 2.0-3.0 mm

Antonyuk, S., Heinrich, S., Tomas, J., Deen, N.G., van Buijtenen, M.S. and J.A.M. Kuipers: Energy absorption during compressionand impact of dry elastic-plastic spherical granules, Granular Matter (2010) 1, 12.Dopfer, D., Heinrich, S., Fries, L., Antonyuk, S., Haider, C., Salman, A.D., Palzer, S.: Adhesion mechanisms between water soluble particles, Powder Technology (2012), DOI: 10.1016/j.powtec.2012.06.029.

Normal impactNormal impact

3. Parameter estimation Experimental results: “dry” restitution coefficient


0

0.2

0.4

0.6

0.8

1

0 30 60 90impact angle Q in °

en

en

et

rolling sliding

et

en

QR

RQ

reboundOblique impactOblique impact

g-Al2O3 granules

t ne =1- 1+e c o t Q

3. Parameter estimation Experimental results: “dry” restitution coefficient

Müller, P., Antonyuk, S., Tomas, J., Heinrich, S.: Ermittlung der normalen und tangentialen Stoßzahl von Granulaten, Chemie Ingenieur Technik 83 (2011) 5, 638-642.


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 = =

Evv

hs

vacuum nozzle

high-speedcamera

steeltarget

precisionstable

polymerfilm

confocal sensor

= 21 mPas, dp = 1.75 mm vimp = 0.95 m/s

3. Parameter estimation Set-up for measurement “wet” restitution coeff.


Parametersof experiments: Sticking takes place at a minimum layer thickness hst = f (h, v)

viscosity in mPa s:

0

0.2

0.4

0.6

0.8

1

0 200 400 600 800 1000

layer thickness hs in mm

rest

itutio

n co

effic

ient

en

1.0

4.5

15.0

50.0

en(h s = 0)

stickingen(h s,st ) = 0

.

g-Al2O3 granules d50 = 1.75 mm impacted on the flat steel wallvimp = 2.4 ± 0.2 m/s

3. Parameter estimation Influence of viscosity h and thickness hS

Antonyuk, S., Heinrich, S., Deen, N.G. andJ.A.M. Kuipers: Influence of liquid layers on energyabsorption during particle impact, Particuology 7 (2009), 245-259.


Calibrations are automatically performed on a specified parameters domain

As variation parameters the following values can be specified:all material properties (Poisson ratio, restitution coefficient, Young modulus, etc.)strength and stiffness of solid bonds, viscosity and size of liquid bridgespositions, velocity, rotation angles of each agglomerate

For the calibration the experimental obtained deformation and breakage behavior of agglomerates can be used

0.2 m/s 1 m/s 2 m/sPrimary particles Bonds structure

3. Parameter calibrationMUSEN


Bed stressing of the granules Single granule stressing

impact / attritionimpact

impact / attritionfree fall double impact granule-granule

impact

compression tension bending

3. Parameter calibrationImpact tests of single particle: recent papers


4

1

2

3

6

5

5

78

9

air flow from the compressor

to the filter

particle feeding

1 – vibrational feeder

2 – injector

3 – acceleration tube

4 – rotameter

5 – photodiodes

6 – steel target

7 – impact chamber

8 – high-speed camera

9 – laser diffraction

spectrometer

1 – vibrational feeder

2 – injector

3 – acceleration tube

4 – rotameter

5 – photodiodes

6 – steel target

7 – impact chamber

8 – high-speed camera

9 – laser diffraction

spectrometer

Particle velocity can be varied from 3 to 40 m/s.Impact angle can be varied from 90° to 0°.

3. Parameter calibrationPneumatic gun


Breakage functionBreakage function

0

200

400

0 10 20 30 40Impact velocity v [m/s]

Раrt

icle

siz

e d i

,3 [µ

m] d10,3

d50,3d90,3

0

2

4

6

8

0 200 400 600Particle size d in [mm]

q 3(d

) [1/

µм]

initial distribution1020253035

impactvelocityin m/s

d10,3

d50,3

d90,3

q 3(d

) [1/

µm]

Particle size in µm

0

0.25

0.5

0.75

1

0 0.3 0.6 0.9

Mass-related impact energy in J/g

Bre

akag

e pr

obab

ility

P

400-1000200-600100-300

agglomerates

initial powder

size

Wm

impact angle = 90°

Breakage probabilityBreakage probability

3. Parameter calibrationPneumatic gun: breakage function and probability


Simulation of agglomerate breakageBreakage of agglomerates in a spouted bed

Diameter [mm]

Par

ticle

num

ber

breakage fraction

initial distribution

Change of PSD in the apparatusdue to impact on the target

EimpBreakageprobabilityBreakagefunction

steeltarget

agglomerates

uspoutub


The breakage of agglomerates in a fluidized bed apparatus during the impact

To obtain the breakage characteristics the impact test are carried out

DEM Simulation: a) velocity of particles b) bonds destruction

high-speed recording of agglomerate breakage at the impact

3. Parameter calibrationDouble impact of granules


DEM-Simulation: Particle velocities Network of the liquid bridges

Experiment: wet cylindrical agglomerate from glass particles witha diameter d = 1 mm bonded with a binder: 4 Mass %, = 0.3 Pa∙s

3. Parameter calibrationFree-fall test: the liquid bridge model


DEM simulation DEM simulation = 0.3 Pa∙s = 0.8 mPa∙s

3. Parameter calibrationFree-fall test: the liquid bridge model

estimation of agglomerate properties€¦ · product formulation tailor -made properties fertilizer...

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