modelling side-effect spray drying in top-spray fluidised bed coating processes

drg

ete

y, C

vers

rm

A model was developed for heat and mass transfer processes in top-spray uidised bed coating processes. The proposed modelcombines the use of population balances, describing key particle characteristics during coating, with a 3-phase (namely gas, solids

niques, the uidised bed has been successfully applied in

Dziezak, 1988; Guignon et al., 2002; Jackson and Lee,1991; Reineccius, 1995).

bed of uidised core particles, usually by means of a pneu-

face (Werner et al., 2007a).The addition of liquid binders or dissolved coating poly-

mers to the uidised bed results in two distinguishablegrowth modes, depending on the physicochemical proper-ties of the raw materials, the evaporative capacity of thebed and the rate at which these binders are sprayed into

* Corresponding author. Tel.: +32 9 2646200; fax: +32 9 2646235.E-mail address: [email protected] (F. Ronsse).

Available online at www.sciencedirect.com

Journal of Food Engineering x

ARTICLE IN PRESSthe coating of particulate solids. This process thereby con-fers new or modied physicochemical capabilities com-pared to the original uncoated particle. Specically, inthe food industry, the benets of encapsulating or coatingsolid particles include controlling the bioavailability offunctional food constituents; protection against reactiveenvironments (i.e. moisture, oxygen, pH or other, incom-patible food ingredients); taste masking and improvingprocessability of particulate food ingredients (such asincreasing owability or decreasing dustiness) (Abe et al.,1998; Arshady, 1993; Dewettinck and Huyghebaert, 1999;

matic nozzle which may be submerged in or positionedabove the bed (Jozwiaskowski et al., 1990; Link andSchlunder, 1997; Werner et al., 2007a; Zank et al., 2001).Each particle receives a small amount of coating materialeach time it passes through the spraying region which isthe region of the bed in which sprayed droplets and ui-dised particles coexist. The heated uidisation air also sup-plies the energy required to evaporate the solvent or incase of hot melt coating, cold uidisation air is used to pro-mote the congealing of the molten liquid coating material leaving a layer of hardened coating lm on the particle sur-and sprayed liquid) heat and mass transfer model. The model is capable of calculating the dynamic coating mass distributions, thedynamic one-dimensional temperature and humidity distributions for both particles and gas (air) along with the extent to whichpremature droplet evaporation (spray drying loss) takes place. The model has been experimentally validated using a pilot scale GlattGPCG-1 uidised bed unit in which dierent size fractions of salt (NaCl) crystals were coated with Na-caseinate under varying processconditions. 2007 Elsevier Ltd. All rights reserved.

Keywords: Fluidisation; Coating; Heat transfer; Mass transfer; Modelling; Population balance; Spray drying loss

1. Introduction

Among the wide range of microencapsulation tech-

The basic operating principle of a uidised bed coaterconsists of the atomisation of a dissolved or molten,hence the term hot melt coating coating polymer into aModelling side-eect spraybed coatin

F. Ronsse a,*, J.G. PiaBiosystems Engineering, Ghent Universit

bFood Technology and Engineering, Ghent Uni

Received 20 August 2007; received in revised fo

Abstract0260-8774/$ - see front matter 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.jfoodeng.2007.11.003

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003ying in top-spray uidisedprocesses

rs a, K. Dewettinck b

oupure Links 653, B-9000 Gent, Belgium

ity, Coupure Links 653, B-9000 Gent, Belgium

5 November 2007; accepted 7 November 2007

www.elsevier.com/locate/jfoodeng

xx (2007) xxxxxxect spray drying in top-spray uidised ..., Journal of Food En-

En

ARTICLE IN PRESSNomenclature

a droplet impingement eciency calculation con-stant

a0 slopeB birth process (population balance), s1

b droplet impingement eciency calculation con-stant

b0 intercept

2 F. Ronsse et al. / Journal of Foodthe uidised bed (Hemati et al., 2003). Agglomerationoccurs due to the formation of liquid bridges during thecollision of wetted particles. Evaporation of the solventcontained within these liquid bridges results in the transfor-mation of liquid into solid inter-particle bridges, giving riseto larger agglomerates. In layering, the coating solutioncollected on the particle surface has been dried suciently,preventing liquid bridge formation during particle colli-sion. Consequently, a lm of solid coating material buildsup gradually at the particle surface (Becher and Schlunder,1998; Jimenez et al., 2006; Link and Schlunder, 1997; Salehet al., 1999; Smith and Nienow, 1983).

Bi Biot number, dimensionless [=ad/k]Cp specic heat, J kg

1 K1

D death process (population balance), s1

d diameter, mDM dry matter content, kg kg solution1

F force, NG air mass ow rate, kg dry air s1

g gravitational constant, 9.81 m s2

h height, mJ mass ow rate, kg s1

M mass, kgMW molecular weight, kg mol1

N number of particlesn number of control volumesP pressure, barp population density functionQ heat, WR rate term (population balance), s1

R0 universal gas constant, 8.314 J K1 mol1

r particle exchange rate, s1

rC droplet collection rate, kg sprayed liquidkg core1 s1

rD drying rate (core particles), kg waterkg core1 s1

rD drying rate (droplets), kg water kg droplet1 s1

Re Reynolds number, dimensionless [=vdq/l]S control volumeSt Stokes number, dimensionless qdvad2d=18ladpT temperature, Kt time, sv velocity, m s1

W particle moisture content at surface, kg waterkg core1

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003X absolute air humidity, kg water vapour kgdry air1

x internal coordinate (population balance)Y particle coating mass content at surface, kg

coating kg core1

Greek symbols2 1

gineering xxx (2007) xxxxxxThe preferred growth mechanism in coating applicationsis layered growth. To promote layered growth and conse-quently to suppress agglomeration, dry process conditionscombined with intense uidisation are required (Link andSchlunder, 1997). However, the combination of dry processconditions and the countercurrent spraying of the coatingsolution in a typical top-spray reactor congurationfavours complete droplet evaporation before droplet/parti-cle adhesion could be established. This eect is also termedpremature droplet evaporation or side-eect spray drying(Dewettinck and Huyghebaert, 1998; Jones, 1985). Thespray-dried coating material could either be elutriated from

a convective heat transfer coecient, W m Ka0 convective mass transfer coecient, m s1

b loss, dimensionlessg eciency, dimensionlessq density, kg m3

h contact angle, k thermal conductivity, W m2 K1

l viscosity, Pa sv impingement eciency, dimensionlessu relative humidityU heat transfer rate, J s1

Subscripts

a airb bottombed uidised bedbouy bouyancyc coatingcrit criticald droplet (individual)dp droplet phasedrag dragf lmim impactlat latentp particle (individual)pp particulate phaser reactorsd spray-driedsim simulatedsol coating solutiont topv vapour

ect spray drying in top-spray uidised ..., Journal of Food En-

the bed or, in case of heavier dry nes, the spray-dried nesare entrapped within the coating lm, resulting in coatingimperfections (Smith and Nienow, 1983). Besides thereduced coating quality, spray drying losses increase pro-duction costs due to the losses in coating material andthe increased processing times required to reach the samedegree of coating compared to a process where spray dry-ing losses are absent (Gouin, 2005).

Premature droplet evaporation is the result of a complexinteraction between several factors including the evapora-tive capacity of the bed, the droplet travel distance andvelocity, the droplet impingement eciency and the dropletadhesion probability (Dewettinck and Huyghebaert, 1998;Heinrich et al., 2003; Jones, 1994). Spray drying losses andagglomeration are two side eects occurring at both ends ofthe beds drying capacity range which implies that uidisedbed coating is often characterised by a narrow operationalregion as illustrated in Fig. 1.

Fluidised bed coating is a complex process consisting ofmultiple and interacting microprocesses including uidi-sation, atomisation, drying, droplet impingement anddroplet adherence (Werner et al., 2007b). As a result, asmany as 20 key process variables could be identied (Knez-evic et al., 1998). Therefore, establishing optimum coatingconditions largely relies on elaborate trial-and-error proce-dures, combined with appropriate statistical analysis andregression. Besides being time consuming and expensive,

behind the uidised bed coating process, limiting its appli-cability in process optimisation to a single specic product/process-combination. However, several (semi-)phenomeno-logical modelling techniques that are capable of quantify-ing key process variables have been described (Turton,2007). The majority of these models are based on the con-cepts of surface renewal, uidised bed compartmentalisa-tion and population balances, as described by Sherony(1981), Wnukowski and Setterwall (1989) and Marongaand Wnukowski (1997).

In a compartment model, particles are assumed to owbetween sections or compartments of the uidised bed anddeposition of coating material onto the particles surfacetakes place within one or more specic compartments.Consequently, the amount of coating material collectedby a single particle is a function of the number of passesand the residence time of each pass in these spraying orcoating compartments. Ronsse et al., 2007a,b proposed acompartment model which combined population balancesto describe the solid phase (particles) with a heat and masstransfer model. This model enabled the prediction of boththe dynamic coating mass distribution and the one-dimen-sional thermodynamic behaviour of the uidised bed dur-ing batch coating operation. This studys aim is tocontinue the development of the mass and heat transfermodel, as described in Ronsse et al. (2007a,b) by addingthe capability of predicting the extent to which spray dry-

F. Ronsse et al. / Journal of Food Engineering xxx (2007) xxxxxx 3

ARTICLE IN PRESSthe aforementioned approach ignores the actual physicsFig. 1. Relationship between particle growth kinetics in uidised bed coating anfrom Gouin, 2005).

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003ing losses during uidised bed processing occur.d the spraying rate and uidisation air ow rate process variables (adapted


2. Mathematical model

2.1. Model overview

Whereas the original model proposed by Ronsse et al.(2007a,b) only included two phases the gas and the solidphases the model in its current state is a fully developedthree-phase model, including the droplet (spray) phase.Both models are based on the horizontal discretisationof the uidised bed in n control volumes (or layers, Si),each having a constant volume and containing a constantnumber of particles as shown in Fig. 2. All threephases within each control volume are assumed to beperfectly mixed. Consequently, the air (and particle) tem-perature and humidity proles along the cross-sectionalarea of the uidised bed are assumed to be uniform. Adetailed overview of a single control volume is given inFig. 3.

Furthermore, in modelling the three dierent phaseswithin the uidised bed, the following assumptions werealso made:

The uidisation air is in ideal plug ow, i.e., the massow of dry air, Ga, is constant and is equal for all con-trol volumes.

surface moisture content or coating layer thickness.The continuous particle transport between the dierentcontrol volumes (layers) is expressed by the variable ri,the fraction of the particle population exchanged pertime unit from Si towards Si+1.

Particles are non-porous and mechanically inert; there isneither agglomeration nor attrition.

The droplet phase migrates downward through the par-ticle bed. If no successful adhesion occurs before com-plete droplet evaporation, dry nes are produced. Drynes are assumed to elutriate completely from the bedby the uidising air.

2.2. Particles population balance

In each control volume Si, the population balancemethod was applied to model the coating of the particles.The population balance is a statement of continuity thatdescribes how the distribution of one or more particle-related variables change with time and space. It describeshow the rate of variation of the number of particles in agiven interval of one or more particle-related propertyvariables can be related to the rate at which particlesenter and leave that interval due to the dierent phenom-ena occurring such as particle removal from or

4 F. Ronsse et al. / Journal of Food Engineering xxx (2007) xxxxxx

ARTICLE IN PRESS Given the random surface renewal concept (Marongaand Wnukowski, 1997), it was further assumed that,for a given interface between two control volumes, theparticle exchange probability was equal for any givenparticle, irrespective of its properties such as particleFig. 2. Scheme of the batch top-spray uidised bed coater and the overall modethe sprayed coating solution.

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003introduction to the system, coating, agglomeration, break-age, etc. (Saleh et al., 2003). The general form of thepopulation balance for a continuous particulate systemcan be written as (Hounslow et al., 1988; Verkoeijenet al., 2002):l, including mass ows of the gas phase (air), the solid phase (particles) and


e m

En

ARTICLE IN PRESSopot rRp B D 1

In Eq. (1) is p(x1,x2, . . . ,xn,t), the population density func-tion which gives the distribution of the population as afunction of the dierent population property variables xi

Fig. 3. Detail of a singl

F. Ronsse et al. / Journal of Foodand time. The population property variables (xi) are typi-cally classied into internal and external coordinates. Theexternal coordinates are the variables used to specify thelocation of the particles within the system, while internalcoordinates refer to intrinsic properties of the particles,such as diameter, coating layer thickness, moisture contentor even residence time (Burgschweiger and Tsotsas, 2002;Cameron et al., 2005). The term R in Eq. (1) representsthe change over time of each variable xi, such as the growthrate of the coating layer. Finally, the terms B and D repre-sent the so-called birth and death rates. These are thesource (for instance particle solidication, particle input)and sink (i.e. particle removal) terms in the population bal-ance. With respect to the coating model presented in thisstudy, a normalised population density function was de-ned in each control volume Si, based on three internalcoordinates, namely particle temperature (Tp), particlesurface moisture content (Wp, in kg water kg core

1) andparticle surface coating content (Yp, in kg coatingkg core1):

pp;iT p;W p; Y p; t 2

Assuming that neither agglomeration nor attrition takesplaces, and by taking particle exchange with neighbouringcontrol volumes into account, the population balance ap-plied to the coating process becomes,

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003opp;iot

ooT p;i

pp;idT p;idt

ooW p;i

pp;idW p;idt

ooY p;i

pp;idY p;idt

rinpp;i1

ri1n

pp;i1

ri ri1

odelled control volume.

gineering xxx (2007) xxxxxx 5n

pp;i 3

In Eq. (3), the terms dTp,i/dt, dWp,i/dt and dYp,i/dt repre-sent the rate at which the particle temperature, moistureand coating content change over time for a specic controlvolume Si. These rate terms have to be derived from theheat and mass balances of the gas, solid and liquid (drop-let) phase which are discussed in the next paragraph.

2.3. Rate terms

2.3.1. Coating mass deposition rate

In the original model by Ronsse et al. (2007a,b), thecoating material was assumed to be homogeneously dis-tributed over a certain part of the bed, the so-called spray-ing region, or

dY p;idt

DMsol J solMpp;bedV bedV spray

4

In Eq. (4) is Vspray the volume of the spraying region, whileMpp,bed is the total mass of core particles present in the ui-dised bed. In reality however, droplet collection will not beuniformly distributed within the spraying region as receiv-ing particles that are further away from the nozzle will beshielded or sheltered (Cheng and Turton, 2000) by parti-cles close to the spraying nozzle. Furthermore, as the drop-let decelerates, its impingement eciency is altered as will


residing in control volume Si. This heat balance containsthe terms describing the convective heat exchange withthe uidising air, the sensible heat attributed by the impact-ing droplets, the substraction of latent heat due to vapori-sation of the water contained in the adhered droplets andthe direct particle-to-wall heat losses, or

dT p;idt

rC;iCp;dp;iCp;p

T dp;i T p;i ap;iApp;iCp;pMpp;i T a;i T p;i

rD;iQlat;iCp;p

Uloss;p;iCp;pMpp;i

9

Engineering xxx (2007) xxxxxx

ARTICLE IN PRESS(water) in the liquid phase being the sprayed droplets have to be solved. Fig. 3 gives an overview of the dierentheat and mass transfers occurring between the dierentphases in a single control volume. The following mass bal-ances for the liquid phase were derived:

d

dtMdp;i1DMdp;i Jdp;i11DMdp;i1 Jdp;i1DMdp;i rD;iMdp;i rC;iMpp;i1DMdp;i 6

d

dtMdp;iDMdp;i Jdp;i1DMdp;i1 Jdp;iDMdp;i rC;iMpp;iDMdp;i J sd;i

7In Eqs. (6) and (7) areMdp,i andMpp,i, the total mass of thedroplets and particles in control volume Si, respectively.The variable rD;i represents the drying rate of droplets, ex-pressed as mass unit of water evaporated per mass unit ofdroplets per time unit. The drying rate was calculatedthrough the dimensionless Sherwood number, which wasapproximated using the Whitaker equation for forcedexternal ows around spherical bodies (Sparrow et al.,2004). Finally, in Eq. (7) is Jsd,i, the mass ow rate of theproduced spray-dried nes as a result of premature dropletevaporation in Si. Both the droplet collection rate (rD,i) andspray-dried nes mass ow rate (Jsd,i) are the remaining un-known variables to calculate the coating deposition rate inEq. (5); in Section 2.4, a method will be discussed to quan-tify these variables.

2.3.2. Particle humidication and/or drying rate

The second rate term, dWp,i/dt, describes the change inparticle moisture content at its surface due to droplet depo-sition and evaporation taking place simultaneously. Conse-quently, this rate term equals to

dW p;idt

rC;i1DMdp;i rD;i 8

With rD,i being the drying rate at the particles surface (kgwater kg core1 s1).

2.3.3. Particle heating and/or cooling ratebe described in Section 2.4 (Zank et al., 2001). Conse-quently, a control volume-specic droplet collection rate,rC,i (kg spraying liquid kg core

1 s1), was dened in themodel. The control volume-specic coating deposition rate,dYp,i/dt is then calculated as

dY p;idt

rC;iDMdp;i 5

In Eq. (5) is DMdp,i, the dry matter content of the dropletswithin the control volume Si. To calculate the dry mattercontent of the droplet fraction residing in control volumeSi, both the mass balances of the dry matter and the solvent

6 F. Ronsse et al. / Journal of FoodThe rate at which the particle temperature changes,dTp,i/dt, is derived from the heat balance of the particles

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003In Eq. (9) are Cp,p and Cp,dp,i (J kg1 K1), the specic heat

values of the particles and the droplets, respectively; App,i isthe cumulative particle surface in Si, while Qlat,i is the latentheat of vaporisation (water: 2.5 106 J kg1). The convec-tive heat transfer coecient, ap,i (W m

2 K1), was calcu-lated using the Whitaker equation for forced externalows around spherical bodies (Sparrow et al., 2004). Final-ly, Uloss,p,i is the heat loss rate (in J s

1) originating fromthe contact between the uidised particles and the innerreactor wall. Its calculation has been adopted from Kuniiand Levenspiel (1991).

2.4. Droplet submodel

2.4.1. Introduction

Two major variables in the model described in the pre-vious section, remain unknown up to this point: the dropletcollection rate per control volume, rC,i, and the amount ofdry nes produced per control volume, Jsd,i. These vari-ables not only depend on the characteristics of the sprayedliquid (droplet size distribution, viscosity, surface tension,etc.), but are also inuenced by the thermodynamic condi-tions within each control volume (air temperature, air rel-ative humidity).

To quantify these unknown variables, a separate dropletsubmodel in which the trajectories of the droplets through-out the computational domain were modelled, was con-structed. The submodels aim was to calculate the spatialdistribution of the sprayed liquid inside the uidised bed,the rate at which droplets adhere onto the uidised parti-cles and the solvent evaporation occurring between dropletproduction at the nozzle and droplet collection onto theFig. 4. Schematic overview of the interaction between the main uidisedbed coating model and the droplet submodel.


using the Whitaker equation for forced external owsaround spherical bodies (Sparrow et al., 2004).

A second possibility of ending its trajectory besidesdroplet evaporation is adhesion between the dropletand a uidised core particle. The ability of a droplet tocome into contact with a particle is expressed by theimpingement eciency vd, which is determined as the ratioof the eective and the geometrical cross-sectional area, asshown in Fig. 5:

vd d imdp

214

The eective cross-sectional area of impact, or pd2im=4, isunknown and therefore, Eq. (14) cannot be used to calcu-late the impingement eciency. However, the impingementeciency can also be estimated through the followingempirical equation (Heinrich et al., 2003; Loer, 1988;Zank et al., 2001):

vd Std

Std 2a b

15

Engineering xxx (2007) xxxxxx 7

ARTICLE IN PRESSparticle surface as a function of the uidised beds air andparticle thermodynamic properties, as shown in Fig. 4.

In modelling the dispersion of the atomised droplets, theindividual trajectories of the sprayed droplets exiting fromthe nozzle until successful adhesion onto the surface of auidised core particle, or until the droplet has been fullyevaporated, were calculated the method being similar toKushaari et al. (2006). It was assumed that droplets wereperfectly spherical, without droplet coalescence or dropletbreak-up. The initial droplet diameter was calculated usingthe droplet diameter model for pneumatic nozzles, as pro-posed by Lefebvre (1988). Based on Newtons second lawof motion, the trajectory of the droplets could be derived.The droplets force balance includes gravity, buoyancyand drag, or

~F d Md d~vddt

Md~g ~F bouy ~F drag 10

In Eq. (10), the drag force (Fdrag) was calculated based onthe eective drag force coecients for spherical bodies sub-merged in uidised beds, as adopted from Mostou andChaouki (1999).

Along its trajectory, the droplet exchanges heat andmass with the surrounding gas phase (air), whose the tem-perature and humidity are derived from the main model.Considering that the diameter of the droplets producedwith a pneumatic nozzle in typical coating operations isgenerally lower than 40 lm (Guignon et al., 2002), the tem-perature inside the droplets was considered to be uniform(Bid 0.1). By including the thermal energy required forthe evaporation and the convective heating of a singledroplet, the resulting heat and mass balances can then bewritten as

MdCp;ddT ddt

adpd2dT a;i T d rD;iQlat;iMd 11dMddt

rD;iMd 12

In Eq. (11), the index i refers to the control volume thedroplet resides in at time t. Eq. (12) is only valid as longas the droplet dry matter content, DMd < 1. When thedroplet dry matter content is equal to 1, the droplet hasbeen fully evaporated. The weight of the subsequently gen-erated dry nes per unit time is cumulated to calculate Jsd,i being the rate at which dry nes are produced in eachcontrol volume. In modelling the drying process of thedroplets in Eq. (12), the diusion of water inside the drop-let was assumed not to be limitative (constant rate period).Hence, the droplet drying rate is assumed to be only limitedby the vapour pressure dierence between the bulk of theuidising air and the surface of the droplet, or

rD;i a0dpd

2dP vT d P vT a;iMdR0=MW vT f 13

F. Ronsse et al. / Journal of FoodSimilar to the particles, the droplet/air convective masstransfer coecient, a0d (m s

1) in Eq. (13), was calculated

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003In Eq. (15) is St the particles dimensionless Stokes num-ber and describes the tendency of the droplets trajectoryto follow the uid streamline, a and b are coecientsdepending on the ow regime around the receiving particle,which is expressed by means of the particle Reynolds num-ber, Rep, as shown in Table 1.

Depending on the kinetic energy of the droplet and thewettability of the core particle substrate, droplets remainadhered or bounce o the particle surface once initial par-ticle-droplet contact has been made (Link and Schlunder,1997). Various authors use the concept of critical impinge-ment velocity, above which the droplets are reected on the

Fig. 5. Droplet collision mechanism and the concept of impingementeciency.

Table 1The parameters a and b to calculate the droplet impingement eciency(Heinrich et al., 2003; Loer, 1988; Zank et al., 2001)

Rep a b

< 1 0.65 3.710 1.24 1.9540 1.03 2.07

60 0.506 1.84100 0.25 2


tings for the particle diameter, atomisation air pressure,and the inlet air temperature) was repeated ve times toget an estimate of the pure experimental error. As shownin Table 2, the particle exchange rate (ri) is a function ofthe particle diameter and was calculated by means ofRowes equation (1973) for the particle circulation timein bubbling uidised beds, as demonstrated in Ronsseet al., 2007c. Each coating experiment was terminatedwhen the total amount of coating solution introduced intothe bed was equal to 0.5 kg. After nishing the coating pro-cess, coating eciency was determined by means of theLowry-method.

Also, during each experiment, the steady-state bed tem-perature was recorded by means of a T-type thermocouplesuspended in the uidised bed approximately 0.06 m abovethe air distributor (see Fig. 6). The bed temperature wasused as an additional validation of the model. It wasretrieved from the control volume corresponding at aheight of 0.06 m above the air distributor. However, themeasured bed temperature did not correspond to the actual

pump; (20) coating solution ow control.

Table 2Studied process variables

Variable Symbol Unit Values studied

Particle diameter dp lm 250350450Corresponding bed height hbed m 0.0920.0860.081Corresponding particleexchange rate

ri s1 2.482.342.14

Inlet air temperature Ta,in C 707886Atomisation air pressure Pat bar 1.52.53.5Corresponding mass ow rate Gat kg s

1 1.45 1032.04 1032.38 103

En

ARTICLE IN PRESSparticle surface (Heinrich et al., 2003; Link and Schlunder,1997; Zank et al., 2001). The critical impingement velocityfor at, non-porous and dry surfaces is described in Zanket al. (2001). Considering the diameter of the droplet beingat least one order of a magnitude smaller than the diameterof the uidised particles, this equation for at surfaces isstill applicable:

vcrit 4ld3 tanh=2 tan3h=22=3

ddqd tan2h=2 16

By combining the impingement eciency with the criticalimpingement velocity, the overall droplet collection e-ciency is calculated. In the model, the droplet collectioneciency was not calculated in se. Instead, a representa-tively large number of individual droplet trajectories wassimulated. Depending on the critical impingement velocityat the moment of droplet/particle adhesion, part of thedroplets rebound and trajectory tracking continues untilthe next particle/droplet impingement. If the dropletimpingement velocity is below critical, then the droplet suc-cessfully adheres onto the uidised particle and thus con-tributes to the growth of the coating layer. Hence, thespatial distribution in the bed of the droplet/particle collec-tion rate was derived and used to solve the heat and massbalances of the main coating model.

3. Materials and methods

3.1. Experimental set-up

Experimental data provided by Dewettinck (1997) andDewettinck and Huyghebaert (1998) were used for thedetermination of the coating eciency and the side-eectspray drying losses. In his research work, a batch of 1 kgof NaCl crystals was uidised and coated with a 5 wt%sodium caseinate solution. The adhered sodium caseinate,which is a protein, was then quantitatively determinedusing the chemical Lowry assay. The experimental coatingeciency, gc, was subsequently calculated as the ratio ofthe amount of protein recovered from the particles (Mc,pp)to the total amount of coating material injected into thebed throughout the batch coating process with tc, beingthe total coating time:

gc M c;pp

J solDMsoltc17

All coating experiments were performed in the GlattGPCG-1 uidised bed unit (Fig. 6) using the top-spray in-sert with the nozzle in the upper position (h = 0.225 m). Ineach coating experiment, the spraying rate and the inlet airow rate were kept constant (Jsol = 7 g min

1, Ga,in =1.34 102 kg s1), while the particle diameter, atomisa-tion air pressure, and the inlet air temperature were variedbetween dierent experiments as shown in Table 2. Asshown in Table 2, each variable was studied at a low, inter-

8 F. Ronsse et al. / Journal of Foodmediary and high setting resulting in a total of 27 (=33)coating experiments. The centre point (intermediary set-

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003Fig. 6. Schematic overview of the Glatt GPCG-1 uidised bed coatingunit: (1) uidisation air; (2) heating coils; (3) air ow control; (4) inlet airplenum; (5) inlet air temperature sensor; (6) gas distributor; (7) bedtemperature sensor; (8) uidised bed; (9) expansion chamber; (10)pneumatic nozzle; (11) air lter; (12) outlet air temperature sensor; (13)turbine; (14) exhaust air; (15) compressed air; (16) oil/water separator; (17)atomisation air pressure control; (18) coating solution; (19) peristaltic

gineering xxx (2007) xxxxxxlocal solids temperature, but rather to a mixture of the gasand solids temperature. Due to the impossibility to dier-


Comparing the model-predicted spray drying loss in Eq.(18) with the experimental spray drying loss in Eq. (19) di-rectly implies that the fraction of coating material that isnot (experimentally) recovered on the core particles, is as-sumed to correspond with the amount of coating materialthat is spray-dried and consequently collected in the ltersystem.

4. Results and discussion

4.1. Overall comparison between model and experiment

The experimental spray drying losses were comparedwith the model-predicted spray drying losses for all coatingexperiments with varying process conditions. The experi-mental losses in dry matter, expressed in weight percentof dry coating matter, ranged from 23.8 2.3 to51.0 2.3. These spray drying losses are relatively highfor uidised bed coating processes, but this is due to the rel-atively dry conditions in which the coater operated(Ta,inP 70 C; Jsol = 7 g min1). Fig. 7 shows the model-predicted values for spray drying losses, plotted againstthe experimental values. As can be seen from the verticalerror bars in Fig. 7, a certain error is present in themodel-predicted spray drying losses. These are due to thestochastic and discrete nature of the used simulation proce-

Atomisation air temperature Tat C 20Atomisation air relative humidity uat 0.30

F. Ronsse et al. / Journal of Food En

ARTICLE IN PRESSentiate between the gas and solids temperatures, the exper-imental bed temperatures were treated as such, and com-pared to a model-predicted bed temperature. The model-predicted bed temperature was calculated by including anadditional heat balance for the thermocouple into themodel. This heat balance included terms such as the heattransfer from both the gas and solid phase to the immersedthermocouple, based on the heat transfer theory for ui-dised beds by Kunii and Levenspiel (1991), and the even-tual wetting of the thermocouple and subsequent latentheat removal.

3.2. Simulation procedures

The simulation method, described by Ronsse et al.(2007a), was used for the simulations in this study. To sum-marise, the proposed method solves the population bal-ances by simulating a nite number of particles,distributed over n control volumes. Dierent phenomena,including particle transfer to adjacent control volumesand droplet collection are imposed as random events uponthe nite particle population in each control volume. How-ever, the overall frequency at which these events (particlemigration, droplet collection) are imposed, correspondsto the actual particle exchange rate (ri) and droplet collec-tion rate (rD,i). Due to the stochastic nature of the modeland its simulation procedure, the minimum number of par-ticles and droplets needs to be assessed to reduce the statis-tical error on the modelled output variables. The minimumnumber of control volumes (nP 24) and particles(NsimP 10

4) has already been assessed in Ronsse et al.(2007a), while the representative number of simulateddroplets (nd,simP 5000) has been discussed in Ronsseet al. (2007c). An overview of all the model and processvariables, which were kept constant in each process simula-tion, is given in Table 3.

As can be seen in Table 3, a total time period of 900 swas simulated for each case which is equivalent to the timerequired to reach thermodynamic steady-state in the simu-lation in all modelled cases. On an Intel Pentium IV(3.6 GHz) equipped computer, this corresponded to anaverage simulation time of 6.9 2.3 hours. By comparison,in the absence of the spraying submodel, the original modelof Ronsse et al. (2007a,b) completed similar simulations inan average of 3.0 0.1 h.

To compare the experimental spray drying loss with themodel-predicted one, the model-predicted spray drying losswas calculated as

bsd;mod Pni1

J sd;i

J solDMsol18

While the experimental spray drying loss was calculatedbased on the coating eciency,

bsd; exp 1 gc 19Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003Table 3Constant model and operational variables of the simulation

Variable Symbol Unit Value

Main model parameters

Control volumes n 24Coating control volumes c 3Number of simulated particles Nsim 10008Simulation time step Dtsim s 0.001Simulation time tsim s 900

Fluidisation air properties

Inlet air volumetric ow rate Ga,in kg s1 1.34 102

Inlet air relative humidity ua,in 0.5Reactor and bed dimensions

Reactor height hr m 0.56Reactor bottom diameter db m 0.15Reactor top diameter dt m 0.30

Bed material (NaCl salt crystals)

Overall bed mass Mbed kg 1.0Specic density qp kg m

3 2170Specic heat Cp,p J kg

1 K1 854Thermal conductivity kp W m

1 K1 1.15

Coating solution properties

Spraying rate Jsol kg s1 1.17 104

Dry matter content DMsol kg kg1 0.05

Coating solution temperature Tsol C 20Coating solution viscosity lsol Pa s 4.2 103Particle/coating solution contact angle h 14

Atomisation air properties

gineering xxx (2007) xxxxxx 9dure (Ronsse et al., 2007a,b).


l sp

t fra

g l

al o

En

ARTICLE IN PRESS0.16

0.21

0.26

0.31

0.36

0.41

0.46

0.51

0.56

0.16 0.21 0.26 0.31

Mod

elle

d sp

ray

dryi

ng lo

ss (-

)

Experimenta

Fig. 7. The measured spray drying loss (expressed as the spray-dried weighduring steady-state coating versus the model-predicted spray drying loss.

Table 4Regression analysis between model-predicted and experimental spray dryin

bsd,exp = a0bsd,mod + b0

R2 SSR Slope, a0 95% condence interv

0.701 0.058 0.769 [0.528;1.010]

10 F. Ronsse et al. / Journal of FoodApplying regression analysis on the predicted versusexperimental spray losses (as demonstrated in Table 4), amodest correlation was achieved, given the complex ther-modynamic nature of the spray drying eect. As can beseen from the regression analysis, the model tended tounderestimate the spray drying loss (slope = 0.769), butthis could be due to the fact that any (experimental) lossin coating material throughout the process was assumedto be solely the result of the spray drying of the coatingsolution. In reality, however, eects such as attrition ofdeposited coating material and subsequent entrainment ofcoating material in the lter will contribute in decreasingthe overall coating eciency. Consequently the experimen-tal spray drying losses may be signicantly smaller than theoverall loss in coating material during the process. In otherwords, the proposed method for measuring the spray dry-ing loss is prone to overestimation.

On the other hand, some of the model assumptionscould also contribute to an underestimation of the spraydrying losses. First of all, the trajectories of the atomiseddroplets were modelled in the absence of turbulence, result-ing in shorter path lengths and hence, lower spray dryinglosses. Secondly, the height of the uidised bed was usedas a boundary condition in the model; incorrect estimationof the bed height could also result in incorrect droplet pathlength calculation. Droplet path length calculation errorsresult from the diculties arising from the bed height esti-mation using the two-uid model as described by Kuniiand Levenspiel (1991) and from the uctuations in bed

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.0030.36 0.41 0.46 0.51 0.56ray drying loss (-)

ction of the total amount of dry matter introduced in the coating process)

oss

f slope Intercept, b0 95% condence interval of intercept

0.096 [0.017;0.175]

gineering xxx (2007) xxxxxxheight during bubbling uidisation regime. Finally, asalready stated in the model assumptions, drying of thedroplets was not limited by internal diusion (constant rateperiod). Consequently, the drying rate is likely to be over-estimated at elevated coating matter concentrations in thedroplet. Although the non-inclusion of the rst and secondfalling rate periods in the model should result in an overes-timation of the spray drying losses, this eect was notnoticeable in the model-predicted results. This could beexplained by the fact that once the droplets have reacheda suciently high concentration in dry matter for the rstfalling rate to commence, their size has been reduced to acritical value for entrainment with the uidising air. Irre-spective of their moisture content and evaporation rate,the entrained droplets are then subsequently collected inthe lter system and considered a loss in coating material.

Next to the spray drying loss, the measured bed temper-ature during steady-state coating regime was comparedwith the model-predicted bed temperature for each of the27 process variable combinations. The results of this com-parison are shown in Fig. 8. From the results of the regres-sion analysis, it could be deduced that a good correlation(R2 = 0.97) was attained between the experiment and themodel. Fig. 8 also shows that the model tended to overes-timate the bed temperature at higher inlet air temperatures(seen also in Fig. 9 in Section 4.2). A possible explanationfor this deviation is the one-dimensional nature of theapplied discretisation in the model. By dividing the bedinto horizontal control volumes (Fig. 2), it is assumed that


ed

te c

En

ARTICLE IN PRESSthe temperature of both the uidising air and the particles,and the air humidity are uniform at each height in the ui-dised bed. However, due to the conical shape of the sprayproduced by the pneumatic nozzle and the release ofcompressed air at ambient temperature in the uidisedbed to assist in atomising the coating solution, one canexpect pronounced radial temperature and humidity gradi-ents in the vicinity of the spraying nozzle especially athigher inlet air temperatures. As the bed temperature probeis suspended in the centre of the uidised bed, 0.165 mbelow the spraying nozzle, it consequently records a bedtemperature lower than the average temperature value forthe entire height at which the probe is suspended; the latterbeing the value which is also predicted by the one-dimen-sional model.

37

39

41

43

45

47

49

51

53

55

37 39 41 43

Sim

ulat

ed b

ed te

mpe

ratu

re (

C)

Measured b

Fig. 8. Measured bed temperature during steady-sta

F. Ronsse et al. / Journal of Food4.2. Eect of process conditions on spray drying losses

To further analyse the similarities and discrepanciesbetween the model-predicted and the experimental results as demonstrated in Section 4.1. both results were ana-lysed on a single process variable basis. Fig. 9 gives a com-parison between the average experimental and the averagemodel-predicted values for spray drying losses and steady-state bed temperatures, as a function of the studied processvariables: particle diameter, inlet air temperature andatomisation air pressure.

When increasing the particle diameter, both the experi-ment and the model clearly show an increase in spray dry-ing loss (Fig. 9a). In fact, increasing the particle diameterwithout modifying the uidisation air ow rate will yielda lower bed height for larger particles (see also Table 2),eectively increasing the droplet travel distance betweenthe nozzle and the uidised bed resulting in a larger frac-tion of the droplets that evaporate completely beforeimpact upon a uidised core particle. Fig. 9a also showshigher values for experimental spray drying losses as

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.003opposed to the model-predicted values. As already dis-cussed in Section 4.1, a possible explanation is the occur-rence of attrition of the coating layer on the particlesduring uidisation, which is also measured as loss in drymatter and results in overestimation of the spray dryingloss (using Eq. (19)). Considering the bed temperature(Fig. 9b), no pronounced eect of the particle size on thebed temperature could be found.

Similar to the particle diameter, higher values for theinlet air temperature contributed to increased spray dryinglosses as demonstrated in Fig. 9c. This mechanism isexplained by the higher droplet evaporation rates resultingfrom higher inlet air temperatures. From Fig. 9c, it appearsthat the discrepancy between the experimental and model-predicted spray drying losses reaches zero at an inlet air

45 47 49 51 53temperature (C)

oating versus the model-predicted bed temperature.

gineering xxx (2007) xxxxxx 11temperature of 86 C. However, this is largely due to themodel which overestimates the temperatures of both parti-cles and uidising air at higher inlet air temperature asshown in Fig. 9d. As already mentioned in Section 4.1,the models mechanism of this overestimation lies withinthe one-dimensional nature of the model and the fact thatradial temperature and humidity gradients were not takeninto account.

Finally, the eect of atomisation air pressure was ana-lysed, as shown in Fig. 9e and f. Increasing the atomisa-tion air pressure results in two eects: on the one hand,smaller droplets are produced which in turn are more sus-ceptible to complete evaporation. On the other hand, theatomisation air is introduced at ambient temperature.Increasing the air pressure, increases the volumetric owrate of the compressed air, which in turn, results in a cool-ing eect in the uidised bed (Fig. 9f), thereby eectivelyreducing the evaporation rate. From Fig. 9e and f it isclear that the reduction in spray drying losses due to bedcooling is signicantly larger than the increase in spraydrying loss owing to the reduced droplet size at higheratomisation air pressures. The same remark has to be


bEn

ARTICLE IN PRESS0.25

0.30

0.35

0.40

0.45

Spra

y dr

ying

loss

es

a

12 F. Ronsse et al. / Journal of Foodmade as in the case of inlet air temperature variation: thediscrepancy in spray drying loss between model and exper-iment is almost zero at atomisation air pressures equal to3.5 bar. Again, this is the result of overestimating the bedtemperature (due to the absence of radial temperature andhumidity gradients in the model) at higher atomisation airpressures.

5. Conclusions

A model has been presented to calculate the dynamicbehaviour of a top-spray uidised bed coater. The modelcombines the one-dimensional discretised representation

0.20200 250 300 350 400 450 500

Particle diameter (m)

0.24

0.27

0.30

0.33

0.36

0.39

0.42

68 73 78 83 88

Spra

y dr

ying

loss

esSp

ray

dryi

ng lo

sses

Inlet air temperature, C

0.24

0.28

0.32

0.36

0.4

1.0 1.5 2.0 2.5 3.0 3.5 4.0

Atomisation air pressure, bar

c d

e f

Fig. 9. Comparison between experimental (N) and model-predicted (M) values fas a function of core particle diameter (a and b), as a function of inlet air tem

Please cite this article in press as: Ronsse, F. et al., Modelling side-egineering (2007), doi:10.1016/j.jfoodeng.2007.11.00344.0

44.5

45.0

45.5

Bed

tem

pera

ture

, C

gineering xxx (2007) xxxxxxof the uidised bed with a spraying submodel which servesto calculate the droplet collection rates through individualdroplet trajectories. Although the model has shown to bequite reliable in predicting the overall bed thermodynam-ics, it also has proven to be capable of roughly estimatingthe spray drying losses. An additional diculty in this kindof validation experiments is that not only the dry matterlosses due to spray drying are measured, but also lossesin coating material due to attrition. As a consequence,there is an overestimation of the experimentally determinedspray drying losses. Furthermore, it was also observed thatthe model tended to overestimate the bed temperature incases of high inlet air temperatures or high atomisation

43.5200 250 300 350 400 450 500

Bed

tem

pera

ture

, C

Bed

tem

pera

ture

, C

Particle diameter (m)

37.0

40.0

43.0

46.0

49.0

52.0

68 73 78 83 88Inlet air temperature, C

42.0

43.0

44.0

45.0

46.0

47.0

1.0 1.5 2.0 2.5 3.0 3.5 4.0

Atomisation air pressure, bar

or spray drying losses and bed temperature, for varying process conditions:perature (c and d) and as a function of atomisation air pressure (e and f).


En

ARTICLE IN PRESSair pressures. Both these cases result in pronounced radialtemperature and humidity gradients in the uidised bed gradients which were neglected due to models one-dimen-sional discretisation.

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gineering xxx (2007) xxxxxx 13ect spray drying in top-spray uidised ..., Journal of Food En-

Modelling side-effect spray drying in top-spray fluidised bed coating processesIntroductionMathematical modelModel overviewParticles ' population balanceRate termsCoating mass deposition rateParticle humidification and/or drying rateParticle heating and/or cooling rate

Droplet submodelIntroduction

Materials and methodsExperimental set-upSimulation procedures

Results and discussionOverall comparison between model and experimentEffect of process conditions on spray drying losses

ConclusionsReferences

modelling side-effect spray drying in top-spray fluidised bed coating processes

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