Liquid and solid bridges during agglomeration in spray fluidized beds

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1 Liquid and solid bridges during agglomeration in spray fluidized beds Evangelos Tsotsas Thermal Process Engineering Otto von Guericke University Magdeburg PIKO-Workshop, July 2014, Paderborn 1

2 SFB agglomeration, scales Picture von Glatt Co. SFB: spray fluidized bed Spray droplets (here: binder in water) Deposited droplets (drying) Particle collisions on (still) wet spots Aggregation by liquid bridge Solidified binder bridge by drying 2

3 SFB equipment 3

4 Modeling of SFB agglomeration PhD at OVGU Method Allocation Breakage R. Hampel, 2010 conv. PBE - - Terrazas, 2010 CVMC COP St* Dernedde, 2013 CNMC Ballistic Model Hussain, 2014 CNMC new PBE COP PBE: Population balance equation, MC: Monte Carlo CV: Constant volume COP: Concept of positions, CN: Constant number, St*: Breakage Stokes number More literature: 4

5 Conventional PBE, Hampel Population balance equation (PBE) n: number density, t: time, u, v: volumes of colliding particles Aggregation rate v n(v,t) 1 (t,v u,u)n(t,v u)n(t,u)du (t,v,u)n(t,v)n(t,u)du t β(t, v, u) = β 0 (t) β*(v, u), some models for β*(v, u) Problem β 0 (t) strongly time-dependent and impossible to correlate with operating conditions (incl. wetting/drying) at the macroscale 5

6 MC, Terrazas, Dernedde Key events/processes: Droplet capture Particle wetting Droplet deposition Sessile droplet drying Wet particle collision Aggregation, or not Deposited droplet drying Particle collisions Rebound Coalescence Liquid bridge rupture Liquid bridge drying Solid bridge rupture 6

liquid bridge No: Rebound, no aggregation Drying kinetics influences

7 MC, Terrazas, Dernedde Stokes criterion Sessile droplet drying h a d h 2Muc 1 h St = < 1+ ln = St 2 3πµ d e h l a crit Yes: Aggregation by liquid bridge No: Rebound, no aggregation Drying kinetics influences sessile droplet height, h Dried sessile droplets don't build bridges 7

8 SFB agglomeration, influence of drying Variation of gas inlet temperature (Terrazas) higher temperature faster drying less droplet height less viscous dissipation upon collision slower agglomeration 8

9 Breakage of bridges Terrazas Dernedde If St > St*, then breakage to two particles of equal total volume to the original agglomerate and randomly chosen individual volumes/sizes Breakage of one or more specific bridges, depending on their individual state and history (liquid, partly solidified, fresh solid, old solid) 9

10 Breakage of bridges, Dernedde 1) Collision Breakage 2) Contact 3) Rebound Collision 4) Breakage Agglomerates that don t stick upon collision may break, depending on: E break : Collision energy, W bridge : Binding energy of bridge 10

11 Binding energy of bridges, Dernedde Liquid bridge: Usual models, from F cap + F vis, F cap = F σ + F p Fresh solid bridge: From solid volume in one droplet, failure stress Partly solidified bridge: Combination of the above by transition function, f (x b -x b,crit ), x b,crit from solubility of binder in solvent, binder mass fraction x b from bridge drying model, depending on thermal conditions in the fluidized bed Old solid bridge: W red from dissipated collision energy (depending on restitution coefficient, e), equally distributed among all bridges; Reduction by: 100% W red for < 100 coll., 50% W red for < 500 coll., 10% W red for > 500 coll., i.e. bridge fatigue 11

12 Breakage of bridges, Dernedde Experiment: Hampel, glass, 70 C, 6 w% HPMC, 200 ml/h, 30 kg air/h Simulation: Dernedde 12

13 Breakage of bridges, Dernedde Experiment: Hampel, γ-al 2 O 3, otherwise as before Simulation: Dernedde, incl. liquid penetration in porous substrate 13

14 New PBE, Hussain By mimicking the microscale (MC) New macroscale PBE N 2N p,wet p,tot Np,wet Np,wet 1 fc 2 wd ww N p,tot Np,tot 1 Np,tot 1 dn 2 p,tot fc N N p,wet p,wet Np,wet Np,wet 1 wd ww dt 2 Np,tot 1 Np,tot 1 dn 1 1 = in N N N N N N drop 1 f ψη 1 + c wd N p,wet dt 1 Np,tot N τ p,tot Ndrop Np,tot p,wet p,wet p,wet p,wet p,wet p,wet dn = N N N f η N dt N τ drop in drop p,wet drop c wd drop p,wet Only fitting parameter: Prefactor of correlation for collision frequency, f c 14

15 New PBE, Hussain MC-PBE comparison,variation in liquid mass flow rate MC: M l = 100 g/h PBM: M l = 100 g/h MC: M l = 300 g/h PBM: M l = 300 g/h MC: M l = 500 g/h PBM: M l = 500 g/h 1 17 x 105 number of particles number of wet particles time [s] time [s] 15

16 New PBE, Hussain MC-PBE comparison, Variation in liquid mass flow rate MC: M l = 100 g/h PBM: M l = 100 g/h MC: M l = 300 g/h PBM: M l = 300 g/h MC: M l = 500 g/h PBM: M l = 500 g/h number of droplets 2.4 x number of agglomerates 5.91 x M l = 300 g/h MC at t = 200 sec PBM at t = 200 sec time [s] number of primary particles 16

17 New PBE, Hussain MC-PBE comparison, Variation in liquid mass flow rate aggregation efficiency 16 x MC: M l = 100 g/h PBM: M l = 100 g/h MC: M l = 300 g/h PBM: M l = 300 g/h MC: M l = 500 g/h PBM: M l = 500 g/h time [s] New macroscale model (PBE) predicts very accurately microscale model (MC) results Computational time: MC: hours, PBE: minutes Full account of drying Intrinsic variables concerning wetting become predictable 17

18 Real agglomerates and bridges Structure of agglomerates (placement of primary particles)? Terrazas: Agglomerate porosity assumed, nothing else in COP Dernedde: Structure tracked by ballistic algorithm, but no rearrangement, very (too) fluffy structures predicted Structure of solid bridges? Terrazas: Nothing Dernedde: Compact bridges (solid volume in one droplet), fitting parameters (failure stress) in breakage model Real structures? PhD Dadkhah, 2014, X-ray µ-ct 18

19 X-ray µ-ct X-ray-CT provides volume data with 3D resolution Why µ-ct? Non-destructive and contact-free Excellent tool for studying disordered systems Spatial resolution < 1 µm Detector resolution is 2400x2400 pixels 19

X-ray µ-ct Axis of rotation Detector plane

Image reconstruction 3D image represented by

20 X-ray µ-ct Axis of rotation Detector plane Tomographic reconstruction X-ray source Rotating sample Series of projection images I. Scanning II. Data acquisition III. Image reconstruction 3D image represented by series of 2D slices VOLEX software (Fraunhofer Institute for Integrated Systems) 20

21 Structure of agglomerates Morphological descriptors Number of primary particles Radial distribution of primary particles Radius of gyration Fractal dimension and pre-factor Average porosity Radial distribution of porosity Coordination number (average, distribution) Coordination angle (average, distribution) 21

22 Fractal properties Np kg R g /ri D f 1000 N p : number of primary particles N p vs. R g / r i Regression R g : radius of gyration 100 r i : radius of primary particle k g : fractal pre-factor (lacunarity) D f : fractal dimension N p 10 1 kk 1.77 g = D f = 2.44 r 2 = R / r g i D f indicates how completely a fractal object can fill the space: From D f = 1 (string) to D f = 3 (regular 3D object) 22

23 Average porosity Three different methods for determination of average porosity, shown for an agglomerate consisting of 71 primary particles Gyration radius Convex hull Dilation Porosity

24 Structure of agglomerates X-ray µ-ct, influence of gas inlet temperature T g,in (drying rate) on morphological descriptors for placement of primary particles: D f : fractal dimension, MCN: mean coordination number, ε: porosity 289, 90 C 215, 30 C 24

25 Solidified binder Penetrated binder Taxonomy of solidified binder elements Bridges Hollow Full Clustered Single Hollow bridges can be: Doughnut-like Arc-like Fragmented Unsuccessful sessile droplets or collapsed bridges 25

Structure of solid bridges Length: 84 µm, Resolution: 3.11 µm, Each step: 3 slices, 9.33 µm Apparently full bridges are extremely rare Bridges are typically hollow Gray Intensity 1.0 0.8 0.6 0.

26 Structure of solid bridges Length: 84 µm, Resolution: 3.11 µm, Each step: 3 slices, 9.33 µm Apparently full bridges are extremely rare Bridges are typically hollow Gray Intensity A B C D Doughnut-like bridges are rare, for marginal particles with CN = 1 Bridges in agglomerates are arc-like or fragmented (at high temperature) Distance [Voxel] 26

27 X-ray µ-ct at the limit A B C A B C To preserve small structures (A) during thresholding/binarization, larger structures (B, C) are further enlarged Systematic error in volumes, but still reliable trends 27

28 Structure of bridges Macroscopic Bridge Porosity T = 30 C T = 60 C Bridge Number Macroscopic porosity (hollowness) of bridges increases with increasing drying rate (i.e. increasing gas inlet temperature) 28

29 Structure of bridges Filling ratio: Binder-filled part of voids volume Filling Ratio T = 30 C T = 90 C N p Apparently full parts of hollow bridges are microporous Microscopic porosity decreases with increasing drying rate 29

30 Structure of bridges Drying has a very strong influence on the morphology of solidified bridges Faster drying (high gas temperature) increases the hollowness of bridges, but makes their arcs more compact, up to their fragmentation to a number of connecting legs/strings 30

31 Structure of bridges SEM picture Fragmented bridge at high temperature Binder patches from dried sessile droplets or collapsed bridges 31

32 Bridge clusters Inner bridges merge to clusters in large agglomerates Single Double Trimer Tetramer Heptamer Octamer Nonamer 32

33 Bridge clusters Trimers most frequent among clusters Less trimers than single bridges at N p < More trimers than single bridges at N p > q 3 *10-6 [%/µm 3 ] N p = 36 N p = 53 1st 2nd Volume [µm 3 ]*10 4 q 3 Q Q 3 q 3 * 10-6 [% / µm 3 ] rd 3 rd st 2 nd Volume [µm 3 ]* 10 4 q 3 Q Q 3 33

34 Spray SFB layering St < St crit Agglomeration Fast growth process St > St crit Layering Slow growth process In layering: Particles grow by solidified binder layers to onion-like structures Binder = Seed Granulation Binder Seed Coating Layer porosity depends on drying (gas inlet temperature)! Structure formation during drying of multiple, sequentially deposited droplets of solution, suspension, nanosuspension 34

35 Structure of binder layers 500 g/h 1100 g/h 50 C 95 C Spraying rate (ml/h) T ( C) % 51% 95 11% 25% 35

36 Structure of binder layers T=95 C, 500 g/h ε shell =0.46 T=50 C, 1100 g/h ε shell =0.64 X-ray µ-ct NaB on glass 36

37 Drying of particle packings PhD Wang, 2014 (in preparation) Packed bed of glass particles or glass particle aggregates Initially full with water (plus salt indicator), no binder In-situ measurement of saturation fields in X-ray µ-ct vs. predictions of pore network models Important results: Complex structures of liquid bridges ( rings ) and bridge clusters formed at low saturation Rings behind the main drying front increase significantly the drying rate upgraded pore network model 37

38 Evolution of liquid clusters S=16% S=13% S=11% S=9% The largest clusters are shown in blue 38

39 Classification of liquid rings clusters 113 clusters 37 clusters clusters 21 clusters 39

40 Conclusions Liquid, solidifying or solid bridges are important in Drying processes of granular/porous materials (technical or natural, e.g. soil) Wet particle formulation processes, e.g. spray fluidized bed agglomeration Microscale characterization/modelling and scale transition (micro-macro) methods are increasing our potential of understanding/improving respective processes/products 40

Available online at ScienceDirect. Procedia Engineering 102 (2015 )

Available online at ScienceDirect. Procedia Engineering 102 (2015 ) Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 102 (2015 ) 996 1005 The 7th World Congress on Particle Technology (WCPT7) Stochastic modelling of particle coating in fluidized