Estimation of agglomerate properties from experiments for microscale simulations

Transcription

1 Estimation of agglomerate properties from experiments for microscale simulations Sergiy Antonyuk Institute of Solids Process Engineering and Particle Technology Hamburg University of Technology PiKo workshop in Siegen, 1 st and 2 nd of October

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

3 Introduction Agglomeration of powders to improve the properties free flowing redispersible dust-free product formulation tailor-made properties compact fertilizer dryer, catysators detergent soluble coffee instant milk PiKo workshop in Siegen, 1 st and 2 nd of October

4 Introduction Particle formulation in fluidized beds Particle formulation processes: exaust air 1. Agglomeration porous structure binder liquid nozzle sprayed droplets primary high heat and mass transfer particle intensive mixing of particles compact Time design... liquid bridge solid bridge blackberry-like structure fluidized particles fluidization air Fluidized bed spray agglomeration 2. Coating, granulation spraying wetting hardening granulat nucleus Time sprayed droplets hardened shell dence structure growth due to layering onion-like structure PiKo workshop in Siegen, 1 st and 2 nd of October

5 Introduction Industrial production processes Most industrial processes consist of complex interconnection of different apparatuses and production steps. Time of the granulation: some hours. Simulation of plant performance is the ultimate goal of modeling! Flowsheet simulation: Numerical calculation of mass and energy balances for different process structures Agglomeration process flowsheet PiKo workshop in Siegen, 1 st and 2 nd of October

6 Multiscale simulation Process treatment on different scales The same process can be described on different scales: On the macroscale the flowsheet simulation is performed: the 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. Dosta M., Antonyuk, S., Heinrich, S.: Multiscale simulation of the fluidized bed granulation process, Chem. Eng. Technol. 35 (2012) PiKo workshop in Siegen, 1 st and 2 nd of October

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

8 Microscale Micromechanisms of agglomeration processes Wetting Spray droplets Droplet impact Overspray Droplet rebound Particle collision Breakage Binder drying Rupture of the liquid bridge Rebound Agglomeration, Sintering PiKo workshop in Siegen, 1 st and 2 nd of October

9 Microscale Interactions stress conditions Example of DEM-CFD simulation of a fluidized bed air Interactions particle-particle particle-wall particle-droplet gas-particle Stress conditions field (gravitation F g, electrostatic ) impact F c adhesion F A (capillary, viscous ) drag F d, flow pressure F p p PiKo workshop in Siegen, 1 st and 2 nd of October

10 Microscale DEM-CFD v y, p y x z v x, p z F c v p p v z, p z F d F g F A y Solid Fluid F p,i v p Description via equations of motion: dv n p mp F p, i F g +F c F a +F d F p.. F dt i Force acting on a particle Translational and angular velocity CFD: Description via volumeaveraged Navier-Stokes-equations n Continuity equation Momentum equation ( g g) ( g gu) 0 t ( g gu) ( g guu) g p ( g g) Sg p g gg t g and g Cell porosity and gas density ū Volume-averaged gas velocity Sink term for coupling with DEM S g p PiKo workshop in Siegen, 1 st and 2 nd of October

11 Microscale MUSEN 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 Examples of visualization The system allows to: in MUSEN DEM microscale 1. Replicate the microstructure: calculate the highest package density of particles for a given particle size distribution Visualization: OpenGL library and GLSL language 2. Calculate the sticking of particles: specify solid/liquid bonds between particles and their properties, such as: diameter, length, strength, stiffness, viscosity, etc. 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 Dosta M., Antonyuk, S., Heinrich, PiKo workshop S.: Multiscale in Siegen, simulation 1 st and of the 2 nd fluidized of October bed granulation 2012 process, Chem. Eng. Technol (2012)

12 1. Agglomerate microstructure 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 Internal agglomerate structure Cylindrical agglomerate with solid bridge bonds PiKo workshop in Siegen, 1 st and 2 nd of October

13 1. Agglomerate microstructure Material design nacre nacre 10 µm 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 hard & stiff + elastic + strong + customized hard phase soft phase control of interfaces hierarchical structure SFB hierarchically structured composite material PiKo workshop in Siegen, 1 st and 2 nd of October

14 1. Agglomerate microstructure Packing density: multimodal composition C. C. Furnas, Grading AggregatesI Mathematical Relations for Beds of Broken Solids of Maximum Density, Industrial and Engineering Chemistry, 1931, vol. 23. pp For size ratios < 100, binary mixtures yield better packing than ternary mixtures size ratio optimum mixing packing density (max.) d L /d S < 100 bimodal ~ 83 % d L /d S > 100 trimodal ~ 94 % PiKo workshop in Siegen, 1 st and 2 nd of October

15 2. Modeling of the particle contact with the adhesion Liquid bridge Rebound Liquid bridge model (dry agglomerates) PiKo workshop in Siegen, 1 st and 2 nd of October

16 2. Modeling of ahesion forces Impact behavior of wet and dry agglomerates High-speed videos: impact of agglomerates produced from: - Al 2 O 3 particles d p = 0.8 mm, - solution of methylcellulose (Pharmacoat ) Variation of the binder viscosity Impact velocity v imp = 1.2 m/s Viscosity of the liquid binder: wet wet dry h = 4 mpa s h = 30 mpa s PiKo workshop in Siegen, 1 st and 2 nd of October

17 2. Modeling of ahesion forces Visco-elastic behavior without the adhesion Contact model according to Hertz-Tsuji F = F + η v n n n c,vis c,el,hertz n ij Damping coefficient n : = 2 m k s * * 1/4 n n n ln en, if e n (ln en) 1, if en 0 m* equivalent mass v R /v relative rebound/impact velocity E kin,r elastic rebound energy E kin E diss impact energy irreversible absorbed energy e 0 = < 0e 1 < 1 plastic elastic-plastic Energetic restitution coefficient: Ekin,R E v diss R e = = 1- E E v kin kin 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 PiKo workshop spherical granules, in Siegen, Granular 1 st and Matter 2 nd of 1 October (2010), 12,

18 Simulation of wet agglomerate Liquid bridge bond model normal impact Viscous forces Normal impact 1 F v, n *2 6 R vn, rel h h 2 h a h a normal rebound h Tangential impact 2 Capillary force F * R 2 ln 1 2 h * v, t R vt, rel obliq impact h v rel normal relative velocity (n - normal, t - tangential) h minimum separation distance h a roughness η viscosity R* average curvature radius in contact V b liquid bridge volume 1 Adams, M., Edmondson, B. (1987). Tribology in particulate technology. 2 Popov, V. (2010) Contact mechanics and friction, Springer. 3 Butt, H.-J, Kappl, M., (2009) Adv. Colloid Interface Sci., 146, 48. PiKo workshop in Siegen, 1 st and 2 nd of October

19 2. Modeling of ahesion forces Liquid bridge model - Application examples model: F Hertz-Tsuji e dry = 0.6 Liquid bridge steel wall model: F Hertz-Tsuji + F v e wet = 0.05 Particles: R = 0.4 mm, = 190 kg/m 3, e dry = 0.6, G = 6.3 MPa Liquid layer: = 1 mpa s, h = 60 µm, h a = 2.5 µm PiKo workshop in Siegen, 1 st and 2 nd of October

20 3. Parameter estimation Calibration of the models PiKo workshop in Siegen, 1 st and 2 nd of October

21 3. Parameter estimation Experimental set-up (in Birkenfeld) Microscope Force sensor Current set-up 20 mm Particle Piezo drive allows to carry out tests of particles at compressive and tensile loading with adjustable relative humidity and temperature in a climate box Device Minimum value Maximum value Resolution Piezo drive (displacement) 0 µm 250 µm 0.2 nm Laser vibrometer (displacement) nm Force sensor mn mn 40 µn Box (Temperature) 15 C 35 C 1 C Box (Relative humidity) 10 % 90 % 2 % PiKo workshop in Siegen, 1 st and 2 nd of October

22 3. Parameter estimation Collapse of the fluidized bed liquid heated air Diagram: glass transition temperature of maltodextrin DE21 - model material for an amorphous food powder temperature [ C] T g,dry 1 6 glassy Gordon & Taylor model experimental data rubbery Problem: water content [%wb] Due to increasing humidity of the air inside the fluid bed and softening of the particles a Agglomeration bed collapse process: can take place. The forces acting on the particles in the fluid bed are no longer 1: feed sufficient material to destroy 2: heating the generated 3: wetting sinter 4: bridges. drying 5: cooling 6: product 5 3 PiKo workshop in Siegen, 1 st and 2 nd of October

23 3. Parameter estimation: Influence of glass transition temperature on the mechanical behavior plasticized agglomerate surface Dextrose syrup (DE 21) lumps obtained by high relative humidity of the air in spray agglomeration overwetting of the particle surface The amorphous particles show a phase transition from the brittle glassy state to the viscous liquid state. PiKo workshop in Siegen, 1 st and 2 nd of October

24 3. Parameter estimation Methods for measuring of restitution coefficient free fall particle-particle pendulum tests Goldsmith, 1960 Foerster et al., 1994 Weir & Tallon, 2005 Iveson & Litster, 1998 Walton and Braun (1986) Labous et al., 1997 Stevens & Hrenya, 2005 Coaplen et al., 2004 Kharaz et al. (2001) Fu et al. (2004) Dong & Moys (2006) Mangwandi et al. (2007) PiKo workshop in Siegen, 1 st and 2 nd of October

25 3. Parameter estimation Free-fall apparatus Restitution coefficient: E E v diss E E v kin,r e = = 1- kin kin R vacuum nozzle v R /v relative rebound/impact velocity n/t normal and tangential component v R v R,n n v t e e n t = v v = v v R, n n R, t t normal tangential v R,t high-speed video camera t R Q R v Q v n Q PiKo workshop in Siegen, 1 st and 2 nd of October

26 3. Parameter estimation Experimental results: dry restitution coefficient Normal impact "dry" restitution coefficient en,dry Glass Al2O3 Maltodextrin predominantly elastic d = mm elastic-plastic d = mm predominantly plastic d = mm impact velocity in m/s 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 (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: /j.powtec PiKo workshop in Siegen, 1 st and 2 nd of October

27 3. Parameter estimation Experimental results: dry restitution coefficient Oblique impact g-al 2 O 3 granules rebound Q R R Q e t 0.6 e n en rolling sliding e n e =1-1+e c o t Q t n e t impact angle Q in Müller, P., Antonyuk, S., Tomas, J., Heinrich, S.: Ermittlung der normalen und tangentialen Stoßzahl von Granulaten, Chemie Ingenieur Technik 83 (2011) 5, PiKo workshop in Siegen, 1 st and 2 nd of October

28 3. Parameter estimation Set-up for measurement wet restitution coeff. vacuum nozzle E kin, R e = = E kin v v R high-speed camera confocal sensor polymer film v R /v relative rebound/impact velocity E kin,r elastic rebound energy E kin impact energy irreversible absorbed energy E diss steel target precisions table h s = 21 mpas, d p = 1.75 mm v imp = 0.95 m/s Objectives of the study: e = f (impact velocity, liquid film thickness and viscosity) PiKo workshop in Siegen, 1 st and 2 nd of October

29 3. Parameter estimation Influence of viscosity h and thickness h S restitution coefficient en e n (h s = 0) viscosity. in mpa s: sticking e n (h s,st ) = 0 Antonyuk, S., Heinrich, S., Deen, N.G. and J.A.M. Kuipers: Influence of liquid layers on energy absorption during particle impact, Particuology 7 (2009), layer thickness h s in mm Parameters g-al 2 O 3 granules d 50 = 1.75 mm impacted on the flat steel wall of experiments: v imp = 2.4 ± 0.2 m/s Sticking takes place at a minimum layer thickness h st = f (h, v) PiKo workshop in Siegen, 1 st and 2 nd of October

30 3. Parameter calibration MUSEN 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 bridges positions, 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/s Primary particles Bonds structure PiKo workshop in Siegen, 1 st and 2 nd of October

31 3. Parameter calibration Impact tests of single particle: recent papers Bed stressing of the granules Single granule stressing impact / attrition impact compression tension bending impact / attrition free fall double impact granule-granule impact PiKo workshop in Siegen, 1 st and 2 nd of October

32 3. Parameter calibration Pneumatic gun particle feeding vibrational feeder 2 injector 3 acceleration tube 3 4 rotameter 5 photodiodes steel target 7 impact chamber 8 high-speed camera 4 to the filter 9 laser diffraction spectrometer air flow from the compressor Particle velocity can be varied from 3 to 40 m/s. Impact angle can be varied from 90 to 0. PiKo workshop in Siegen, 1 st and 2 nd of October

33 3. Parameter calibration Pneumatic gun: breakage function and probability Breakage function Breakage probability q q3(d) [1/µм] 3 [1/µm] initial distribution impact 25 velocity 30 in m/s Particle size d in [mm] Breakage probability P W m size agglomerates initial powder Particle size in µm impact angle = Mass-related impact energy in J/g PiKo workshop in Siegen, 1 st and 2 nd of October

34 Simulation of agglomerate breakage Breakage of agglomerates in a spouted bed E imp agglomerates Breakage probability Breakage function steel target Particle number breakage fraction initial distribution Diameter [mm] Change of PSD in the apparatus u u b spout due to impact on the target PiKo workshop in Siegen, 1 st and 2 nd of October

35 3. Parameter calibration Double impact of granules The breakage of agglomerates in a fluidized bed apparatus during the impact To obtain the breakage characteristics the impact test are carried out high-speed recording of agglomerate breakage at the impact DEM Simulation: a) velocity of particles b) bonds destruction PiKo workshop in Siegen, 1 st and 2 nd of October

36 3. Parameter calibration Free-fall test: the liquid bridge model Experiment: wet cylindrical agglomerate from glass particles with a diameter d = 1 mm bonded with a binder: 4 Mass %, = 0.3 Pa s DEM-Simulation: Particle velocities Network of the liquid bridges PiKo workshop in Siegen, 1 st and 2 nd of October

37 3. Parameter calibration Free-fall test: the liquid bridge model DEM simulation DEM simulation = 0.3 Pa s = 0.8 mpa s PiKo workshop in Siegen, 1 st and 2 nd of October