Tool to Improve Spray Drying Control

7/28/2019 Tool to Improve Spray Drying Control

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Dairy Sci. Technol. 88 (2008) 3143 Available online at:c INRA, EDP Sciences, 2008 www.dairy-journal.orgDOI: 10.1051 / dst:2007006

Original article

Residence time distribution: a tool to improvespray-drying control

Romain Jeantet 1*, Fabrice Ducept 1,2, Anne Dolivet 1, Serge Mejean 1,3,Pierre Schuck 1

1 Agrocampus Rennes, INRA, UMR1253, Science et Technologie du Lait et de luf,35000 Rennes, France

2 AgroParisTech, CEMAGREF, INRA, UMR1145 Gnie Industriel Alimentaire,1 avenue des Olympiades, 91744 Massy Cedex, France

3 BIONOV, 85 rue de Saint-Brieuc, 35042 Rennes cedex, France

Abstract Dairy powders are mainly obtained by spray drying, which is an eff ective process asit makes possible long-term storage at an ambient temperature. However, the control and designof this operation is still based on empirical knowledge. Improvement in product quality, whichis governed by time / temperature history, thus involves greater understanding of the process viaphysico-chemical, thermodynamic and kinetic approaches. With regard to the latter, the residencetime distribution (RTD) of the product provides valuable information about the product ow patternin the dryer according to the operating conditions. The aim of this study was to determine the RTDof skim milk in a drying plant with diff erent congurations, according to ne particle recycling(top of the chamber or internal uid bed) and internal uid bed thickness (4 to 16 cm). The RTDsignal of the atomisation device was established rst; then the RTD signals of the diff erent spray-dryer congurations were obtained by deconvolution of the experimental curves obtained and theRTD signal of the atomisation device, and modelled according to a combination of four reactorsets. The mean residence time of the product was only slightly modied by the dryer conguration(range 9.5 to 12 min). However, the results showed that a thicker internal uid bed tends to increasemean residence time due to higher product retention, whereas top recycling of ne particles tendsto decrease the mean residence time because of better agglomeration. RTD modelling providesadditional information concerning the product ow rate fraction and the residence time distribu-tion of each part of the dryer (chamber, cyclones and uid bed), indicating that 60 to 80% of thepowder passes through the cyclones, depending on the conguration. This study provides greaterunderstanding of dryer operation, and allows further correlation between process parameters andbiochemical changes (protein denaturation, Maillard reaction, etc.).

residence time distribution / spray drying / dairy powders

, ,

; / ,,

( ) (416 cm),,

,, , ,

* Corresponding author ( ): [email protected]


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32 R. Jeantet et al.

( 9.512 min); ,, ; ,

() , , 60%80%

, ()

/ /

Rsum Distribution des temps de sjour en schage par pulvrisation de lait crm. Latechnique la plus employe pour la dshydratation des produits laitiers est le schage par atomisa-tion, qui permet le report des produits sur de longues dures temprature ambiante. Cependant,le contrle de cette opration est encore fond sur des savoir-faire empiriques, et lamliorationde la qualit des poudres, qui dpend notamment des couples temps / temprature subis, ncessiteaujourdhui une dmarche plus rigoureuse base sur des approches physico-chimiques, thermody-namiques et cintiques. ce titre, la dtermination de la distribution des temps de sjour (DTS)permet dobtenir une modlisation des ux produits au sein du schoir, en fonction des conditionsopratoires. Lobjectif de cette tude tait de dterminer la DTS du lait crm au sein dune tourde schage pilote sous diff rentes congurations, en fonction de la zone de recyclage des nes (hautde tour ou lit uidis interne) et de lpaisseur du lit uidis interne (4 16 cm). Les fonctions DTScorrespondant aux diff rentes congurations ont t dtermines par dconvolution des courbes ex-primentales obtenues et de la fonction DTS du systme datomisation, tablie dans un premiertemps ; ces fonctions ont ensuite t modlises partir dune combinaison de quatre ensembles deracteurs agits. Le temps de sjour moyen du produit est faiblement modi par la congurationmise en uvre, et varie de 9,5 12 min. Cependant, nos rsultats montrent quun lit uidis interneplus pais tend accrotre le temps de sjour moyen du fait dune rtention accrue du produit, tan-dis que le recyclage des nes en haut de tour le diminue. La modlisation des signaux DTS apportedes informations supplmentaires concernant la fraction de dbit massique et le temps de sjour duproduit circulant dans les diff rentes parties du schoir (chambre, cyclones, lit uidis) : selon laconguration, 60 80 % de la poudre transiterait au travers des cyclones. Ces rsultats contribuent une meilleure comprhension du fonctionnement du schoir, et permettent denvisager des cor-rlations ultrieures entre paramtres du procd et modications biochimiques (dnaturation desprotines, raction de Maillard, etc.).

distribution des temps de sjour / schage par atomisation / poudres laitires

1. INTRODUCTION

Most dairy powders are currently ob-tained by spray drying, which consistsof spraying the concentrated dairy liq-uid (skim and whole milk, whey, dairyfractions resulting from membrane ltra-tion and chromatographic separation) indroplets of about 50m diameter into alarge drying chamber where it is mixedwith air heated to 200 C. As the dropletdries, its temperature gradually rises fromthe wet bulb temperature until it reachesthe temperature of the surrounding outletair, i.e. it remains below 100 C. Classicalspray-dryersare combined with a uid bed,

which usually agglomerates the ne pow-der coming from the drying chamber inthe wet zone, completes the drying processand cools the powder. In recent 3-stage in-stallations, another uid bed (internal)withagglomeration and additional drying func-tions is included at the bottom of the dryingchamber [4,9].

The nal powder quality includesgeneral properties (moisture content andbiochemical composition, water activity,sensorial quality, etc.) and propertiesdepending on the process parameters(rehydration, constituent denaturation,granulometry, owability, oodability,hygroscopicity, stickiness, cakiness,


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Residence time distribution in a spray-dryer 33

density and colour). These latter prop-erties are mainly determined rst by the

agglomeration process and then by thetime / temperature history of the productin the system, including the preliminarysteps (heat treatment, membrane sepa-ration, evaporation, etc.). They are alsodependent on the water content, becausedroplet temperature and water content areconnected. Moreover, the rate of enzy-matic and chemical changes depends onfactors such as water content, temperatureand time. It is therefore necessary toconsider these three parameters in orderto control thermal damage of constituentsand process-dependent properties. Sincedrying occurs within a few seconds, thethermal damage is often considered aslimited. However, the ow pattern of 2-and 3-stage installations (including recy-cling of ne particles) results in a longerprocessing time, and makes it difficult topredict the residence time distribution of

the product in the dryer, because of varyingparticle sizes and unknown recirculation.Moreover, the control and design of this

operation is still based on empirical knowl-edge and relies heavily on user and de-signer experience [5]. There have been fewscientic studies to date making it pos-sible to optimise drying operating condi-tions and equipment design in terms of en-ergy costs and powder quality [6]. Some

recent publications have proposed toolsbased on physico-chemical and thermody-namic ndings in order to prevent stickingin the drying chamber and to control pow-der quality [7]. Diff erent methods can beconsidered in order to determine the res-idence time of the product in the system,which is a necessary step for drying con-trol. Computational uid dynamics (CFD)allow study of the consequences of modi-cations in operating conditions (temper-ature, ow rate) and process congura-tion with regard to particle characteristics(temperature, water content) and trajecto-ries in the dryer [3, 10]. On the basis of

process knowledge, and by using appropri-ate physical laws, this numerical approach

can provide very precise local information.However, the model construction needssubsequent simplications involving eitherthe evolution of the product characteristics(e.g., density or porosity) or the process(heat or mass transfer laws). Moreover,several experiments are required to deter-mine unknown model parameters and toverify hypotheses (boundary conditions),and model validation therefore remainsdifficult.

However, determination of the productresidence time distribution (RTD) is anoverall and experimental approach whichprovides valuable information about theproduct ow pattern in the dryer, accord-ing to the operating conditions [11,12]. Itconsists of following a tracer introducedbefore the dryer inlet until the dryer out-let; the signal obtained can be numericallydeconvoluted in order to provide specicRTD signals of the diff erent sections of the equipment. It does not provide a physi-cal understanding of the operation as CFDdoes, but on the other hand, it integratesthe product and process complexity fully.In this sense, this method provides overallunderstanding and management of the owpattern in the dryer, as well as additionalinformation on CFD in order to optimisedrying operating conditions and equipmentdesign. Moreover, measurement of RTD ina dryer plant has never been previouslypublished to our knowledge.

The aim of this study was to determinethe RTD of the skim milk in a drying plantwith diff erent drying congurations, withregard to the recycling of ne particles(top of the chamber or internal uid bed)and the internal uid bed thickness. TheRTD signals obtained corresponded to theoverall response of the dryer. Their mod-elling was then undertaken according to acombination of plug ow reactors in orderto obtain a physical interpretation of the


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Internal fluid bed

Chamber

Air inlet

External fluid bed

Cyclones

Air outlet

High pressure pump

Concentrate

Fluid bed air

Fluid bed air Fine recycling

3.9 m

0.3 m

Powder

2.1 m

2.1 m

3.4 m

1 m

Figure 1. 3-Stage spray dryer pilot.

operation of each dryer section (chamber,internal uid bed, cyclones, etc.).

2. MATERIALS AND METHODS

2.1. Spray-drying experiments

Skim milk powder was recombined into40 ( 1)% (w / w) dry matter prior to dry-ing. Spray drying was performed at Bionov(Rennes, France) in a 3-stage spray-dryerplant with an evaporation capacity of 70 to100 kgh 1 (Fig. 1; GEA, Niro Atomiser,St Quentin-en-Yvelines, France), accord-ing to Schuck et al. [8]. Five experimentswere performed, corresponding to diff erentspray-drying congurations related to neparticle recycling (top of the chamber orinternaluid bed) and the internaluid bedthickness (thin or thick; Tab. I and Fig. 2).Conguration was tested twice, in order

to assess the experimental reproducibility.The atomiser was equipped with a pressurenozzle (0.73 mm diameter orice; n 69)and a 4-slot core (0.51 mm nominal width;n 421), providing a 60 spray angle. Thetemperature of the concentrate before dry-ing was 50 C, and the concentrate owrate was 94.5 5.6 Lh 1. The inlet airow rate and outlet air temperature were3715 101 kgh 1 and 88.0 0.7 C, re-spectively. The internal uid bed temper-ature and external uid bed temperature(rst and second part) were 74.3 0.3 C,34.4 0.3 C and 19.9 0.1 C, respec-tively.

2.2. Chemical and physical analysis

Solid concentration and free mois-ture content were calculated according toweight loss after drying 1.5 g of the


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T a

b l e I . E x p e r i m e n t a l s p r a y d r y i n g c o n d i t i o n s .

C o n g u r a t i o n

F i n e p a r t i c l e

F l u i d

F l u i d b e d

I n l e t a i r

I n l e t a i r A H

O u t l e t a i r A H

A t o m i z a t i o n

P o w d e r t e m p e r a t u r e

r e c y c l i n g

b e d

t h i c k n e s s

t e m p e r a t u r e

( g w a t e r k g

1 d r y a i r ) ( g w a t e r k g

1 d r y

a i r )

p r e s s u r e

o n u i d b e d

p o s i t i o n

( c m )

( C )

( M P a )

( C )

I n t e r n a l u i d b e d

T h i n

4 ( 1 )

1 8 5 . 2 ( 0 . 1 )

4 . 5

( 0 . 1 )

2 1 . 2

( 0 . 7 )

1 0 . 5

( 0 . 5 )

6 8 . 4

( 1 . 1 )

T o p o f t h e c h a m b e r T h i c k

1 6 ( 3 )

1 9 1 . 6 ( 0 . 6 )

5 . 1

( 0 . 1 )

2 1 . 2

( 0 . 6 )

1 0 . 5

( 0 . 5 )

7 7 . 0

( 2 . 0 )

T o p o f t h e c h a m b e r

T h i n

4 ( 1 )

1 9 1 . 6 ( 0 . 6 )

5 . 2

( 0 . 1 )

2 1 . 5

( 0 . 6 )

1 0 . 5

( 0 . 5 )

7 3 . 4

( 2 . 0 )

4 ( 1 )

1 9 1 . 6 ( 1 . 7 )

8 . 2

( 0 . 2 )

2 4 . 7

( 0 . 7 )

1 0 . 5

( 0 . 5 )

7 7 . 7

( 0 . 6 )

I n t e r n a l u i d b e d

T h i c k

1 4 ( 2 )

1 8 7 . 1 ( 1 . 0 )

7 . 2

( 0 . 2 )

2 6 . 3

( 0 . 6 )

1 1 . 8

( 0 . 4 )

6 9 . 8

( 0 . 3 )


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Configuration Configuration

Configuration Configuration

Figure 2. Spray drying congurations according to ne particle recycling (top of chamber or inter-nal uid bed) and internal uid bed thickness (thin or thick).

sample mixed with sand in a forced airoven at 105 C for 5 h (powder) or7 h (concentrate). Sodium chloride con-tent (NaCl) was based on chloride mea-surement, and determined by conductime-try using a silver electrode (Corning 926,

Humeau, La Chapelle, France). Powderparticle D(0.5) diameter (median diame-ter) measurements were assessed by lasergranulometry (Mastersizer 2000, MalvernInstruments, Malvern, UK) and the uni-formity index was calculated according toCarr [2].

2.3. Determination of RTDand modelling

RTD characterisation was based onmeasurementof NaCl concentration; 20 kgof tracer, corresponding to a skim milk

concentrate of 40% (w / w) dry matter towhich 1.2% of NaCl were added, wereused for each experiment. All the powderwas collected at the dryer outlet for 80 minafter tracer injection, each sample corre-sponding to the quantity of powder exiting

every 2 min. Sodium chloride concentra-tion at the inlet and the outlet was obtainedby chloride measurement, and determinedby conductimetry using a silver electrode.It was then possible to plot sodium chlo-ride concentrations according to time. Thetracer was placed in a tank connected to thefeed line with a three-way valve, so that theshape of the injection signal could be con-sidered as square.

In order to determine the specicRTD function of the tower, we used thefollowing experimental strategy. First, theRTD signal ( E ad (t )) of the atomisation


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device (corresponding to injection pump,lters and nozzle) was established. After

2 min of tracer injection, the NaCl contentwas measured over time by the continuousand full sampling of skim milk at the noz-zle outlet, on a 30-s period basis: this pro-vided the experimental output signal (y(t )). E ad (t ) was obtained by numerical decon-volution of y(t ) by the injection square sig-nal ( x (t )).

It was then possible to calculate the re-sponse of the atomisation device to a tracerinjection of any sort. For a given tracersquare signal ((t )), the corresponding injectionx (t ) signal at the atomisation deviceoutlet (i.e., at the tower entrance) was ob-tained by convolution of E ad (t ) and(t ).

Finally, theE (t ) RTD signals of thespray-dryer in the diff erent congurationswere determined by numerical deconvolu-tion of the experimentaly(t ) output signal(corresponding to an experimental particleRTD of the complete system [atomisationdevice+ tower]) and thex (t ) injection sig-nal. Having determinedE (t ), it was thenpossible to calculate the residence timeof each uid element, and conversely, thetime, t , at which a certain fraction of thematerial entering att = 0 is no longerpresent in the equipment. The mean res-idence time,, corresponds to the timewhen 50% of the material entering att = 0has passed through the equipment.

The modelling of the RTD functions bya combination of plug ow reactor setswas constructed on physical bases in or-der to take into account the ne particlerecycling at the top of the chamber or in-ternal uid bed (model A or B; Fig. 3).Model A was used for uid bed recycling:the product passes through the rst reac-tor set, and then continues on its way withparallel recirculation in three other reactorsets. On the other hand, top recycling of ne particles corresponds to model B: theproduct passes through the rst reactor setcombined with parallel recirculation in thesecond reactor set, and then continues on

its way with parallel recirculation in twoother reactor sets.

Whatever the model, Q is the productow rate through the equipment, anda irefers to the Q fraction going into theith re-actor set. Each reactor set includesJ i plugow reactors in series, and can be charac-terised by a mean residence time (i). ItsownE i(t ) RTD function is dened by:

E i (t ) = J ii

J i t J i 1i exp( J i t i/ i)( J i 1)!

(1)

The number of plug ow reactor sets andthe three parameters of each reactor set (a i, J i and ) were adjusted step by step inorder to correspond to the experimental E (t ) RTD functions obtained for the diff er-ent congurations, according to model A(Eq. (2)) or B (Eq. (3)):

E (t )= (1 a 4) E b(t )+ a 4 E b(t ) E 4(t ) E b(t )= (1 a 3) E a (t )+ a 3 E a (t ) E 3(t ) E a (t )= (1 a 2) E 1(t )+ a 2 E 1(t ) E 2(t )(2) E (t )= (1 a 4) E b(t )+ a 4 E b(t ) E 4(t ) E b(t )= (1 a 3) E a (t )+ a 3 E a (t ) E 3(t ) E a (t )= E 1(t )+ (a 2 E 1(t ) E 2(t )) E 1(t )

(3)where is the convolution product.

It should be noted that 4 plug ow reac-tor sets are required and are sufficient forthe modelling of E (t ), whatever the cong-

uration. Model accuracy was evaluated bythe standard deviation between the modeland experimental RTD curves.

3. RESULTS AND DISCUSSION

3.1. Powders

The diff erent powders obtained showedthe same biochemical results, with no sig-nicant diff erences between powder awand dry matter (96.3 0.8 %). D(0.5) diam-eter values were greater with top ne parti-cle recirculation (congurationsand :


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A BFigure 3. Plug ow reactor scheme used for RTD modelling. A: model corresponding to uid bedrecycling of ne particles; B: model corresponding to top recycling of ne particles.

D(0.5) = 196 3 m; uniformity in-dex = 1.9) than with uid bed recircula-tion (conguration : D(0.5) = 158 m;uniformity index= 2.3 / conguration :D(0.5) = 109 m; uniformity index= 2.0).This point is important because the smallerthe powder diameter, the higher the pow-der owability in the retention zones suchas rotary discharge valves; it can thus af-fect the powder residence time distributionin the equipment. The diff erence observedcan be explained by the fact that top neparticle recirculation increases agglomera-tion, a cloud of ne particles being formedin the direct environment of the liquid

droplets. On the other hand, agglomera-tion is limited with uid bed recircula-tion, in which case the ne particles aremixed with almost dried particles. Thesmaller D(0.5) value obtained for congura-tion can be attributed to the higher atom-isation pressure in this case (Tab. I), result-ing in smaller droplets during atomisation.

3.2. Atomisation device RTD

First, the RTD of the atomisation devicewas assessed. Figure 4a represents the 2-min tracer injection ( x (t )) and the output


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x (t )

b

y (t )

E ad (t )

aFigure 4. RTD determination of atomisation device. a: Tracer injectionx (t ) and output signaly(t );b: E ad (t ).

ba

x (t )

y (t )

E (t )

(t )

Figure 5. Determination of tower RTD for conguration. a: Tracer square signal(t ), injectionsignalx (t ) and output signaly(t ); b:E (t ).

signal (y(t )). The areas of y(t ) and x (t )are normalised by the overall tracer mass.The deconvolution of y(t ) by x (t ) gives E ad (t ) (Fig. 4b). As previously stated, thiscurve is a probability distribution: for ex-ample, the probability of a uid elementremaining in the atomisation device for3 min is 0.2. The RTD signal increasesrapidly att 0 + 2.5 min, the maximum isreached att 0 + 4 min and the trail ends ataround 18 min. The mean residence time is4.5 min.

3.3. Tower RTD

It is thus possible to simulate the x (t ) injection signal at the injection de-vice outlet, i.e. the tower entrance. Forconguration (thin uid bed / ne par-ticle recirculation on uid bed),x (t ) wascalculated by convolution of E ad (t ) andthe tracer(t ) 10 min square signal: Fig-ure 5a represents(t ), x (t ) and they(t )output signal obtained for this congura-tion. Deconvolutingy(t ) by x (t ) gives the


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RTD functionE (t ) (Fig. 5b) for the tower.This gure shows that some powder par-

ticles leave the tower almost immediately,whilst others remain in the installation formore than 70 min (end of the trail). Themean residence time is 12 min for this con-guration. This is in close agreement withthe mean time of the output signaly(t ),which is here 22 min, and corresponds tothe sum of the mean residence time of thesquare signal (5 min), the atomisation de-vice RTD (4.5 min) and the tower RTD(12 min).

The E (t ) functions corresponding tocongurations (Fig. 6a), (Fig. 6b)and (Fig. 6c) were obtained in the sameway. Figure 6b showsE (t ) functions ob-tained for conguration in replicate,compared with that obtained for congu-ration (black dotted line). The repro-ducibility of the results is satisfactory, asthe two curves are almost merged. From E (t ) functions, it is possible to determinethe mean residence time () for each con-guration. is 12 min for congurationsand , 9.510 min for congurationand11 min for conguration.

It clearly shows that the mean residencetime is in the same range whatever theconguration, as these diff erences are verysmall when compared with the time rangeof E (t ) functions (up to 70 min). However,the values can be discussed in relation to

internal uid bed thickness and to locationof ne particle recycling.Congurations and diff er by in-

ternal uid bed thickness. It can thereforebe concluded that increasing the uid bedthickness tends to increase the residencetime. This can be explained by the fact thatthe internal uid bed is a place where thepowder remains for a certain time, and thethicker the uid bed, the greater the massof powder retained.

On the other hand, congurationsand diff ered in the place where the neparticles were recycled. As compared withne particle recycling on the uid bed,

the top recycling of ne particles greatlyreduced the mean residence time. This

can be attributed to better agglomeration,which reduces the trail of the RTD signal(Fig. 6b). These results were not expected,in the sense that adding a recirculation tothe system conguration should result inlonger residence time. This was probablydue to the specic nozzle conguration of the dryer, where the ne particles are di-rectly recycled on the spraying cone. In thecase of an industrial drying plant, wherethe cloud of ne particles is recirculatedin the environment of several nozzles, thene particle agglomeration, and hence thebenet of top recycling on mean residencetime, should be lower.

Finally, the medium value of con-guration , despite the thick uid bedand ne particle recycling on the uidbed, which corresponded to the mostunfavourable conguration, could be at-tributed to the low D(0.5) value of the pow-der obtained in this case. Indeed, the uidbed thickness was very difficult to maintainin this trial, because of lower retention of the powder by the rotary discharge valve atthe uid bed outlet. This result highlightsthe strong link between processparametersand product quality, a slight modicationof one parameter (e.g., higher atomisationpressure) leading to a diff erent product (re-duced median diameter), and hence modi-ed behaviour in the equipment (mediumvalue).

3.4. RTD modelling

Table II gives the standard deviation be-tween model and experimental RTD mea-surements for the diff erent congurationsconsidered. Table III gives theJ i, a i andireactor set values obtained for the mod-elling of each conguration. The parame-ter values given for conguration(testedtwice) correspond to the mean parametervalue ( standard deviation). The accuracy


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a b

c

Figure 6. E (t ) function obtained for congurations, and , compared to conguration(dotted line). a: Congurations and ; b: conguration in replicate and conguration; c:congurations and .

Table II. Standard deviation of the model com-pared with the experimental curves.

Conguration Standard deviation ( E(t ) unit)

0.002

0.002

0.003

0.001

and reproducibility of the model is satis-factory, considering the very low standarddeviations obtained.

In general, and considering one re-actor set, the model parameter valueswere similar whatever the conguration.Nevertheless, certain diff erences can be

discussed, in order to provide a physi-cal understanding of the operation of eachdryer section.

Thea

i value of the rst reactor set isone, which means that the whole productenters this reactor. It can therefore repre-sent the chamber.

The i values of the second reactor setrange from 6 to 7 min for congurationsand to 1 min for congurationsand .In other words, the ratio of rst to secondreactor seti values is 6 for congura-tions and . This is in close agree-ment with the measured ne particle re-circulation ow rate (253 12 kgh 1) topowder ow rate (close to 37 kgh 1) ra-tio, which is 6.7. The second reactor setcould thus act as the cyclones and the ne


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Table III. Model parameters.

Reactor setParameter Conguration 1 2 3 4

J i

1 2 50 140

2 2 10 140

2 ( 0) 2 ( 0) 70 ( 0) 140 ( 0)

1 2 40 140

a i

1 0.8 0.10 0.09

1 0.6 0.15 0.05

1 ( 0) 0.6 ( 0.0) 0.08 ( 0.03) 0.11 ( 0.00)

1 0.8 0.11 0.09

I (min)

6 6 18 33

6 1 15 30

6 ( 0) 1 ( 0) 16 ( 2) 31 ( 0)

6 7 15 33

particle recirculation pattern. Regardingthea i value of this reactor set, it can be as-sumed that 60% (congurationsand )to 80% (congurations and ) of theproduct would pass through the cyclones.The highera i value obtained for congu-rations and is in agreement with thelower agglomeration, i.e. the higher neparticle content, observed in this case. Fi-nally, the higha i values of the rst andsecond reactor sets clearly indicate thatthe tower and the cyclones account for thegreater part of the overall residence timedistribution of the product.

The third reactor set shows lowerJ i andhighera i values for congurationsandthan for congurations and . In addi-tion, conguration shows lowerJ i andhighera i values than those of congura-tion (10 versus 40 and 0.15 versus 0.11,respectively).RTD theory indicates that in-creasing theJ i number of plug ow reac-tors in series tightens the residence timedistribution and leads to piston ow, andconversely, that a lowerJ i value expands

the residence time distribution [1]. This islogical because with increasing values of J i , the probability that a particle remainsfor a relatively short or relatively long pe-riod in all reactors becomes smaller. Thisreactor set could therefore correspond tothe internal uid bed, the increase in uidbed thickness in congurationleading tothe retention of a larger amount of product(highera i value) and thus to a wide res-idence time distribution (lowerJ i value).Conversely, theJ i anda i values of congu-ration correspond to lower powder reten-tion in the uid bed compared with cong-uration , in agreement with our previousassumptions concerning the inuence of granulometry on powder retention. How-ever, it would be better to consider that thethird reactor set is not limited to the singleinternal uid bed but is fairly strongly in-uenced by it, as the model does not takeinto account the backow of powder fromthe internal uid bed towards the chamber.

Finally, the fourth reactor set, with analmost innite number of reactors, may


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correspond to the overall recirculation andow in the equipment, which is piston type

(highJ i value) with a mean residence timeof 30 to 33 min.

4. CONCLUSIONS

To conclude, the RTD approach pro-vides greater understanding of the dryingoperation, according to the process cong-urations considered in this paper.

Our results show that the mean resi-dence time () is only slightly modied bythe changes in the dryer conguration con-sidered here. Nevertheless, a thicker inter-nal uid bed results in a higher value be-cause of higher product retention, whereastop recycling of ne particles decreases the value. We attribute this to better agglom-eration, which reduces the stay of the neparticles in the dryer. RTD modelling pro-vides additional information throughJ i , a iand coefficients, thus also providing aphysical understanding of certain sectionoperations according to dryer congura-tion. As RTD is a statistical representa-tion of the residence time of the productin the equipment, it statistically describesthe time / temperature the product is sub- jected to during treatment. This approachthus complements CFD, and can be usefulfor CFD validation.

The future prospects are at the level of combining this approach with the producttemperature and water content in the dryer.These parameterscan be accessed either bymodelling or measurement, and will makeit possible to describe the time / temperaturehistory of the product, including the eff ectsof pre-drying treatments by RTD mea-surement in concentration by evaporation,

and to establish further correlations withchanges in constituents and dependent

powder properties.

REFERENCES

[1] Broyart B., Lameloise M.L., Dispersion destemps de sjour, in: Bimbenet J.J., DuquenoyA., Trystram G., Gnie des procds alimen-taires - des bases aux applications, Dunod,Paris, 2002, pp. 288304.

[2] Carr R.L., Evaluating ow properties of solids, Chem. Eng. 72 (1965) 163168.

[3] Ducept F., Sionneau M., Vasseur J.,Superheated steam dryer: simulations andexperiments on product drying, Chem. Eng.J. 86 (2002) 7583.

[4] Masters K., Spray Drying, Ed. LongmanScientic & Technical and John Wiley &Sons Inc., Essex, UK, 1991.

[5] Masters K., Scale-up of spray dryers, DryingTechnol. 12 (1994) 235257.

[6] Schuck P., Spray drying of dairy products:state of the art, Lait 82 (2002) 375382.

[7] Schuck P., Mjean S., Dolivet A., Jeantet R.,Thermo hygrometric sensor: a tool for opti-mizing the spray drying process, Innov. FoodSci. Emerging Technol. 6 (2005) 4550.

[8] Schuck P., Roignant M., BrulG., Mjean S.,Bimbenet J.J., Caractrisation nergtiquedune tour de schage par atomisation multi-ple eff et, Ind. Alim. Agric. 115 (1998) 914.

[9] Sougnez M., Lvolution du schage paratomisation, Chimie Magazine 1 (1983) 14.

[10] Verdurmen R.E.M., Straatsma H.,Verschueren M., van Haren J.J., Smit E.,Bergeman G., De Jong P., Modelling spraydrying processes for dairy products, Lait 82(2002) 453463.

[11] Villermaux J., Gnie de la raction chimique- conception et fonctionnement des rac-teurs, Lavoisier, Tec et Doc, Paris, 1993.

[12] Wen C.Y., Fan L.T., Models for ow systemsand chemical reactors, Marcel Dekker, Inc.,New York, USA, 1975.

Documents

Tool to Improve Spray Drying Control