application of advanced

APPLICATION OF ADVANCEDMEASUREMENT TECHNIQUES TO CONICALLAB-SCALE FLUIDIZED BED DRYERSCONTAINING PHARMACEUTICAL GRANULE

T. Pugsley, G. Chaplin and P. Khanna

Department of Chemical Engineering, The University of Saskatchewan, Saskatoon, Canada.

Abstract: The advanced measurement techniques of electrical capacitance tomography (ECT),high-frequency pressure fluctuations and radioactive particle tracking (RPT) have been appliedto conical lab-scale fluidized bed dryers containing pharmaceutical granule. Our objective hasbeen to improve the fundamental understanding of the gas and solid flow structure insidethese units and to develop monitoring and control tools that could be implemented in commercialmanufacturing of solid dosage form pharmaceuticals. The results of our research have beenreported in a series of articles since 2000, but until now we have not published a manuscriptthat unifies our results to provide an overall summary of what we have learned. The presentpaper summarizes our key findings.

Keywords: fluidization; fluidized bed drying; pharmaceutical granule; tomography; particle track-ing; pressure fluctuations.

INTRODUCTION

A fluidized bed is a type of fluidsolid contac-tor. The fluid may be either a gas or a liquid,but in the present paper the focus is ongassolid fluidized beds. When a bed ofsolid particles is contacted with an upward-flowing gas stream, the resulting suspensiontakes on certain fluid-like properties that thesolids alone do not exhibit. This leads to theterminology fluidized bed or fluidization.Owing to the intimate contact between gasand solids and the excellent mixing of thesolid phase, the fluidized bed is characterizedby very high rates of heat and mass transfer.For this reason, fluidized bed technology hasreceived widespread application in variousindustrial sectors, including as a chemicalreactor for the effective utilization of veryfine, highly active catalyst powders and asa dryer for processing petrochemical, pharma-ceutical and food products in granular andpowder form. The focus of the present paperis fluidized bed dryers used in the pharma-ceutical industry.Approximately 80% of pharmaceutical pro-

ducts are in the solid dosage form (i.e., tabletsand capsules). Solid dosage form pharma-ceuticals consist of a mixture of the activepharmaceutical ingredient (API) as well asbinders, excipients and fillers, all of whichare in powder form. There are two basic

production pathways: (1) indirect tablet com-paction whereby the dry powder ingredientsare blended and then subjected to a wetgranulation step followed by drying and finallytablet compaction or (2) direct compaction inwhich the dry powder blend is fed directly tothe tablet press from a bin or hopper withouta wet granulation step occurring. All of theseproduction steps are carried out batch-wise.Evidently it is in the indirect compactionapproach where fluidized bed dryers areutilized.Batch fluidized bed drying of pharma-

ceutical granule is made complex by severalfactors. Wet granules (3040 wt% moisture)can range in size from 50 to 3000 mm. Thewet granule is initially very cohesive and ahigh flow of air is needed to fluidize the bed,however as drying proceeds, interparticlecapillary forces are reduced due to loss of sur-face moisture and a gas velocity that is toohigh can lead to granule attrition and entrain-ment. As with any fluidized bed process,scale-up of fluidized bed dryers is challengingand must be done carefully. Gas and solidsmixing patterns will change between lab, clini-cal and production scale dryers and this canlead to variations in product moisture contentof 5% or more in large units. This is a signifi-cant issue for the highly-regulated pharma-ceutical industry where a demonstrablecontrol over product quality is paramount.

273 Vol 85 (C3) 273283

Correspondence to:Dr T. Pugsley, Department ofChemical Engineering, TheUniversity of Saskatchewan,57 Campus Dr., Saskatoon,SK, Canada, S7N 5A9E-mail:[email protected]

DOI: 10.1205/fbp07022

09603085/07/$30.00 0.00

Food and BioproductsProcessing

Trans IChemE,Part C, September 2007

# 2007 Institutionof Chemical Engineers

Finally, most dryers used in the industry have a conical pro-duct bowl. While there is a large body of fundamentalresearch on the gas-solid flow structure inside fluidizedbeds dating back nearly six decades, surprisingly little ofthis previous research has considered beds with a conicalgeometry.The high rates of solidgas interphase heat and mass

transfer that are associated with fluidized bed technologyrely on intimate mixing and contacting of these phases.Achieving this mixing and contacting can be hindered bythe complexities discussed above. It is therefore importantto fully understand the nature of the gas and solids flow pat-terns inside a fluidized bed dryer and how these patterns areinfluenced by operating conditions and bed dimensions. Thisknowledge allows for the better design and operation of flui-dized bed dryers to maximize rates of heat and mass transferwhile minimizing drying time and optimizing product quality.Recognizing the need to improve the fundamental under-standing of the gas and solids flow patterns inside fluidizedbed dryers containing pharmaceutical granule and the influ-ence of these flow patterns on the drying process, nearlyten years ago our group embarked on a research programwith the aim of applying advanced, non-invasive measure-ment techniques to lab-scale conical fluidized bed dryers.These techniques included high-frequency pressure transdu-cers, electrical capacitance tomography, X-ray tomographyand radioactive particle tracking. This work has been disse-minated in a series of publications, but to date we have notreviewed and summarized our findings in a single paper.With the announcement of this special issue of Food andBioproducts Processing being coincident with ten years ofactivity by our group, we felt that the timing was right for asummary review of our findings. The present paper thereforebriefly describes the experimental equipment and advancedinstrumentation that we have used and then reviews key find-ings from pressure fluctuation and tomography measure-ments. We then proceed to present heretofore unpublishedresults from particle tracking experiments and conclude withsome thoughts on future research needs and our plans toaddress some of these needs.

MATERIALS AND METHODS

Fluidized Beds

The two lab-scale fluidized bed dryers that have been usedin our research are illustrated in Figure 1. The first[Figure 1(a)] is a Plexiglas cone taken from a Strea-1 unit(Aeromatic-Fielder, Bubendorf, Switzerland). In our earlywork (Tanfara et al., 2002) the cone was placed in the alu-minium housing that was part of the Strea-1 system. In sub-sequent studies (e.g., Chaplin et al., 2005a), the cone wasremoved from the housing, and windbox and freeboard sec-tions were added. The fluidized bed was also hooked up to alarger blower so that the influence of higher gas velocitiescould be studied. This Plexiglas fluidized bed is located atthe University of Saskatchewan.The second fluidized bed that we have used [Figure 1(b)] is

a Glatt GPCG-1 (Glatt Air Techniques, Ramsey, NJ). This unitis located in the labs of Merck Frosst Canada Ltd (Kirkland,Quebec) and is fabricated of stainless steel.Both systems use electrical heating to provide warm drying

air to the fluidized bed. Air temperature is monitored in the

windbox by means of a thermocouple. Air flow to thePlexiglas unit at Saskatchewan is metered with an orificeplate while the Glatt unit makes use of a pitot tube locatedin the square duct upstream of the windbox. In a typicaldrying experiment, it was necessary to operate at a

Figure 1. Geometries of the two conical fluidized beds: (a) the GlattGPCG-1 at Merck Frosst Canada Limited; (b) the Plexiglas bed atthe University of Saskatchewan. All dimension in m.

Trans IChemE, Part C, Food and Bioproducts Processing, 2007, 85(C3): 273283

274 PUGSLEY et al.

superficial gas velocity of 22.5 m s21 for the first 10 min ofdrying, after which the velocity was reduced to1.4 m s21and held constant for the remainder of the exper-iment. The higher velocity was required to turn the bed overwhen it was very wet initially. The humidity of the drying airis not controlled in either lab-scale unit, but in both casesthe air intake is from rooms with climate control.The Glatt unit is outfitted with filter bags in the freeboard

section of the bed to capture any entrained material. Thebags are periodically shaken to return the entrained materialto the bed. The Plexiglas bed at Saskatchewan does nothave filter bags, but instead has a cyclone above the free-board section to capture entrained material and send it to asmall external collection pot that is connected to a load cell.This configuration was adopted for the purpose of makingentrainment measurements, the results of which aredescribed later.Both the Glatt and Strea-1 fluidized beds are equipped with

sample thieves on the product bowls that are able to removesamples during drying. The samples are analyzed for moist-ure content using a moisture balance (Mettler-Toledo,Columbus, OH), allowing drying curves to be constructed.Also, both beds have thermocouples immersed in the bedand in the exhaust gas stream.

Bed Material

All drying experiments were carried out with wet placebogranule as the test material. The formulation for the placebogranule is provided in Table 1. For the experiments at MerckFrosst Canada Ltd., a high-shear granulator (T.K. FielderPMA25, GEA Process Engineering Ltd, Aeromatic-Fielder,Eastleigh, UK) was available for preparation of the wet gran-ule. The dry powder ingredients of Table 1 were pre-mixed for5 min, followed by 5 min of water addition from a high-pressure nozzle positioned 7.5 cm above the powder bed,and finally two minutes of post-spray mixing.Wet granule for experiments at the University of

Saskatchewan was prepared in a low-shear mixer (KitchenAid Classic Mixer) using the same ingredients and thesame pre-mixing, spray addition, and post-mixing procedureas was used with the high-shear granulator. The importantdifference was that water was added drop wise using a peri-staltic pump at a controlled flowrate. This resulted in the for-mation of some large granules that were manually removedafter post-spray mixing was complete.Both granulation procedures led to bimodal particle size

distributions. Table 2 presents the size distribution data forthe granule prepared at Saskatchewan. It is acknowledged

that the distributions obtained by the high and low sheargranulation techniques are not exactly the same and thatthe high-shear granulation technique is what is actuallyused in industry. However, the particle size distributionswere sufficiently similar to allow us to perform meaningfulexperiments in both locations, especially the application oftomography at Saskatchewan that was not possible in theMerck Frosst labs.

Electrical Capacitance Tomography

The word tomography is derived from the Greek wordtomos, meaning slice. Hence, when tomographic imagingtechniques are applied to a fluidized bed, they generateimages of a slice or cross section of the bed at the axiallocation where the tomography sensors are positioned. Oneof the most attractive features of tomographic imaging is itsnon-intrusive nature since the measurement electrodes areexternal to the bed itself. This is in contrast to devices suchas fibre optic probes that must be inserted through a port inthe vessel wall and into the bed, hence disturbing the flow.In wet granule environments, these probes may also befouled by the sticky bed material.Tomographic imaging techniques may be classified as

nuclear or non-nuclear. The former class includes X-rayand gamma-ray imaging. The main non-nuclear tomographicimaging technique is electrical capacitance tomography orECT (Chaouki et al., 1997). The focus of the present paperis ECT.An ECTsystem is made up of measurement sensors and a

computerized data acquisition module (DAM). ECT sensorsconsist of a series of electrodes wrapped around the periph-ery of the vessel to be imaged. These must be custom-man-ufactured for the vessel on which they are applied. In ourcase, the sensors have been applied to the conical productbowl depicted in Figure 1(a). The sensor system consists oftwo sets of eight measurement electrodes, one immediatelyabove the other, wrapped around the outside of the fluidizedbed vessel. This constitutes a so-called twin-plane ECT. Theelectrodes are 4 cm in length with the centreline of the lowerplane of electrode located 7.5 cm above the base of the cone.Driven axial guard electrodes above and below the measure-ment electrodes mitigate stray capacitance losses in the axialdirection, thus ensuring that capacitance discharge takesplace over the bed cross section. Figure 2 is a cross-

Table 1. Wet granulation ingredients.

ComponentPercentage bymass (dry basis)

Amount (g) for a1 kg (wet) batch

Lactose monohydrate(filler)

50% 375

Microcrystalline cellulose(filler)

44% 330

Croscarmellose sodium(disintegrant)

2% 15

Hydroxypropylmethylcellulose (binder)

4% 30

Water (reverse-osmosis) 43% 315

Table 2. Particle size distribution of dry pharmaceuticalgranule.

Particle size (mm)Mass percentage

in range

,75 3.4175105 6.04105149 11.32149210 12.95210297 10.99297420 8.23420590 6.89590850 8.328501190 9.611901680 10.4116802380 7.9623803360 3.71.3360 0.17


ADVANCED MEASUREMENT TECHNIQUES CONTAINING PHARMACEUTICAL GRANULE 275

sectional view of a fluidized bed vessel showing the 8 electro-des wrapped around the periphery.The measurement principle of ECT has been described in

several of our papers (Chaplin et al., 2005b; Tanfara et al.,2002) as well by other research groups (e.g., Huang et al.,1992) and so will be kept purposely brief here. In themeasurement sequence, each electrode is sequentially sup-plied with an electrical potential while the others remaingrounded. Thus, an electrical field is applied across themeasurement cross-section. The distribution of the field isrelated to the distribution of the gas and solids phasesbetween the electrodes, since these phases have differentelectrical relative permittivities. This difference is exploitedto reconstruct an image of the gas and solid distributionover the bed cross section where the ECT sensors aremounted. An important feature of the measurement is that ithappens very rapidly: images of the vessel cross sectionare generated at a frequency of 100 Hz. This allows the for-mation and passage of bubbles and voids to be capturedwith this imaging technique.Image reconstruction from the basic capacitance measure-

ments has been the subject of extensive research and isbeyond the scope of the present document. The reader isreferred to the review paper of Isaksen (1996) as well asthe study of McKeen and Pugsley (2002) for details onimage reconstruction. In our work with fluidized bed dryingof pharmaceutical granule, we have used the iterative linearback projection method (see Isaksen, 1996). An importantstep in image reconstruction is the choice of permittivitymodel, which relates the measured electrical permittivity tothe voidage in the bed (McKeen and Pugsley, 2002). Earlyin the drying process when the granule is very wet, theBottcher model is used, while later in the process, it is necess-ary to switch to the parallel model (Chaplin et al., 2005b).The need for the aforementioned switching between per-

mittivity models when applying ECT to fluidized bed dryersarises as a result of a significant technical issue that ourgroup was the first to overcome. The basis of the issueresides in the high relative permittivity of water. While therelative permittivity of dry powders and particles typicallyranges between 2 and 4, for water it is 81. Hence the pre-sence of water in the wet granule tends to dominate theECT image and any influence of bed dynamics on the ECT

measurement, which is what we are interested in discerning,is lost. To overcome this issue, we developed a novel cali-bration technique (Chaplin et al., 2005b) that separates theinfluence of moisture in the granule and allowed us to captureimages of the bed dynamics.

High-Frequency Pressure Sensor AndSignal Analysis

Dynamic pressure fluctuations are an inherent feature offluidized beds (van der Schaaf et al., 1998). These arecaused by the formation of bubbles as gas enters thebottom of the bed through the distributor grid, the passageof bubbles and voids upward through the bed, and the erup-tion of bubbles and voids at the bed surface. Since the for-mation and movement of the bubbles and voids directlyaffects gas and solid mixing patterns in the bed, the measure-ment of pressure fluctuations represents a means of quantify-ing the fluidized state. These pressure fluctuations originateat different locations and propagate throughout the bed;therefore pressure fluctuations constitute a global measure-ment, as opposed to a local measurement as is the casewith ECT. This has the advantage that all dynamic effectsthat contribute to the state of fluidization in the bed can bemeasured with a single process variable. With suitable analy-sis, it becomes possible to monitor the fluidized state of thebed and to potentially develop a control system to operatethe bed around a desired set point. This would be advan-tageous in a batch drying scenario where granule initially isvery wet and requires a higher fluidization velocity to achieveadequate mixing, but later becomes dry and requires areduced gas velocity to minimize attrition and entrainmentof granule. Presently in the pharmaceutical industry thislevel of control is carried out manually by an operator whowatches the process through a sight-glass on the side ofthe vessel and adjusts the gas velocity as he or she deemsnecessary. It is unlikely that this method is able to maintainthe bed in an optimal state that maximizes product qualitywhile minimizing batch drying time.We have applied pressure fluctuation measurements in

both the Glatt GPCG-1 and the Strea-1. These units weremodified to allow a pressure sensor with a stainless steeldiaphragm (the PCB106B from Piezotronics, Depew, NY) tobe flush mounted at various axial locations (see Figure 1).The flush mounted sensor is made of stainless steel and isnon-intrusive and easily cleaned, which meets CGMP criteriafor the manufacture of finished pharmaceuticals.Measurement of pressure fluctuations with a high-

frequency transducer yields a pressure time series thatmay be analysed in various ways. Our group has focusedon the S-statistic approach pioneered by van Ommen et al.(2000). This approach tests whether or not a statistically sig-nificant change has taken place between a given evaluationstate and a reference state corresponding to a known ordesired fluidized behaviour. Hence the S-statistic approachprovides a way of quantifying the relative fluidized state ofthe bed. Furthermore, it exploits the fact that a fluidized bedis a chaotic system, which means that it is not completelyrandom but instead exhibits limited short-term predictability.By testing if the measured pressure time series comes fromthe same underlying probability distribution as the referencestate, the S-statistic approach can provide an early warningof pending fluidization issues that may compromise product

Figure 2. Top view of the conical product bowl at the University ofSaskatchewan showing the eight ECT electrodes wrapped aroundthe external perimeter of the vessel.


276 PUGSLEY et al.

quality. This feature will be discussed further in the Resultsand Discussion section of the present paper.The reader is referred to the work of Chaplin et al. (2004)

for a description of how the S-statistic is calculated from apressure time series. If this calculation yields a value of theS-statistic greater than 3, the null hypothesis that the refer-ence and evaluation states arise from the same underlyingfluidized bed dynamic behavior can be rejected at the 95%confidence level. One can then conclude that a statisticallysignificant change has occurred with respect to the fluidizedstate of the bed.

Radioactive Particle Tracking

Radioactive particle tracking (RPT) is a non-invasiveadvanced measurement technique for fluidized beds inwhich a radioactive tracer particle emitting high energyg-rays and dynamically similar to the bed particulate phase,is introduced into the system to be studied. Scintillationdetectors installed around the system track the motion ofthe particle. The amount of radiation falling on the detectormainly depends on the following parameters: the solidangle (V) between tracer and detector; radiation attenuationinside the equipment, which depends on the depth at whichthe tracer lies inside the vessel; and the straight pathlength the g-ray will travel before being scattered inside thedetector crystal (Chaouki et al., 1997).The intensity of g-rays falling on the detector is proportional

to the location of the tracer particle with respect to the scintil-lation detector. Therefore, by recording the amount of radiationfalling on each detector, the position of the tracer particle canbe determined with respect to time. By tracking its motion overtime, the velocity profile of the tracer particle and subsequentlythe entire particulate phase can be generated.Scintillation materials are those which produce light when

ionizing radiation falls on them; this property can be usedfor the detection of ionizing photons or radiations. Scintillationdetectors containing NaI crystals are the most successful andcommonly used scintillation detectors in the RPT systems ofother researchers (e.g., Godfroy et al., 1997; Larachi et al.,1997). Twelve scintillation detectors having NaI crystals of5 cm 5 cm size were used in the present study. Eachdetector was attached to an Ortec DigiBASE (AdvancedMeasurement Technology Inc., Oak Ridge, USA), a 14-pinphotomultiplier tube base for gamma-ray spectroscopy appli-cations. The DigiBASE combines a miniaturized preamplifier,detector high voltage, multi-channel analyser (MCA), lowerlevel discriminator (LLD) and upper level discriminator(ULD) all incorporated in into a light weight (287 g) compact(63 mm diameter 87 mm length) system with a USB con-nection. High voltage, LLD and ULD were adjusted torecord only the photopeak counts of the 198Au gold isotopethat was formed when the tracer particle was irradiated(see tracer particle description below). When operated inthe LIST mode, DigiBase can time stamp each photoncount, which can be used to determine the number ofcounts recorded in a specified time interval. A softwarecode was written in Visual Basic programming language tooperate the DigiBASE in LIST mode. An IBM IntellistationM Pro computer was used for data acquisition and analysis.Tracer particles were successfully prepared by mixing gold

powder with epoxy resin following the approach of Godfroyet al. (1997). This tracer particle was subjected to irradiation

for approximately 30 min in the Slowpoke II nuclear reactorat the Saskatchewan Research Council (SRC, Saskatoon,Canada). The density of the tracer particle matched the dryplacebo pharmaceutical granule to within 5%.The Plexiglas unit at the University of Saskatchewan was

used for conducting RPT experiments. A special frame wasfabricated so as to place the 12 scintillation detectors strate-gically around the conical fluidized bed dryer. The 12 detec-tors were situated in four planes with three detectors ineach axial plane. Figure 3(a) depicts the axial arrangementof detectors and Figure 3(b) depicts the radial arrangementof detectors at each axial plane.The application of this RPT technique requires determi-

nation of total efficiency and photon counts of the detectorwith respect to particle position. The first step in designingthe RPT system was to calculate the efficiency and photoncounts of each scintillation detector with respect to thelarge possible number of tracer positions inside the bed.This was done by using a Monte-Carlo simulation technique(Chaouki et al., 1997). Once the map of photon counts withrespect to each detector was constructed and calibrated,the RPT equipment was used to trace the movement of thetracer particle, wherein, the radioactive tracer particle emit-ting high energy g-rays and dynamically similar to the

Figure 3. Schematic of the arrangement of NaI scintillation countersin the radioactive particle tracking system: (a) the four axial measure-ment planes; (b) radial positions of the detectors in the four axialplanes.



phase under study was introduced into the bed and its motionwas tracked by the 12 strategically located NaI scintillationdetectors. The photon counts were recorded by each detec-tor with respect to time and by comparing the countsrecorded by the detector with the simulated counts from theMonte-Carlo method, the position of the tracer was calculatedoffline. This information was further used to calculate thelocal velocities and trajectory of the tracer particle. A least-squares search approach was adopted for the search ofthe best position of the tracer particle by comparing themeasured photon counts to the counts obtained by Monte-Carlo simulation.

RESULTS AND DISCUSSION

Pressure Fluctuation Measurement andS-Statistic Analysis

Figure 4 presents a typical drying curve for a batch dryingexperiment in the Glatt GPCG-1, corresponding to an initialwet bed mass of 3 kg and a drying air temperature of 658C.During the constant rate period of drying, the rate of moistureremoval from the granule is constant, as are the bed andexhaust air temperatures. Once the surface moisture isdriven from the granule, the drying process is controlled bythe rate of moisture diffusion though the pores of the granuleto the surface. This is known as the falling rate period andcorresponds to a rise in both the bed and exhaust air temp-eratures, as seen in Figure 4. All drying experiments exhib-ited similar trends, but with lower wet bed masses andhigher drying air temperatures leading to the expectedreduction in drying times.Pressure fluctuations were measured during the batch

drying of the placebo pharmaceutical granule. A sample seg-ment of one of these pressure time series is presented inFigure 5. The fluctuations in the signal are an inherent featureof any fluidized bed and not simply noise. However, meaning-ful interpretation of these fluctuations requires further analy-sis as little in the way of useful information can be obtainedfrom simply comparing plots such as Figure 5.As noted in the Materials and Methods section, we have

focused on the application of the S-statistic for analysing

the pressure time series. Figure 6 illustrates the change inthe S-statistic relative to two different reference states forthe same batch drying experiment depicted in Figure 4.One reference state corresponds to 26 wt% moisture, whichfrom Figure 4 is the moisture content at about 12 min intothe batch drying process. The other reference state is8 wt% or about 42 min into the drying process.Focusing first on the 26 wt% reference state and recalling

that a value of S . 3 means a statistically significantchange in the fluidized state of the bed, it is seen inFigure 6 that, during the first 1520 min of drying, the flui-dized state of the bed does not change significantly eventhough the moisture content of the granule drops more than10% over the same period. At about 20 min, the bed divergesfrom the behaviour of the reference state and changes con-tinuously over the next 1520 min before reaching a new flui-dized state late in the drying process. When the referencestate corresponds to 8 wt% moisture, the S-statistic continu-ously decreases to reach a value of 3 at approximately35 min. This is about the same time that the S-statisticlevels off when the reference state is 26 wt% moisture. Fur-thermore, Figure 4 shows that 35 min is the point in timeduring the experiment when the bed and exhaust tempera-tures begin to increase, signifying the onset of the falling

Figure 4. Drying curve and bed/exhaust temperature-time profiles inthe Glatt GPCG-1 for a drying air temperature of 658C and an initialwet bed mass of 3 kg.

Figure 5. Sample pressure-time series from the Glatt GPCG-1.

Figure 6. Progression of the S-statistic for two different referencestates corresponding to the drying experiment of Figure 4.


278 PUGSLEY et al.

rate period and the loss of granule surface moisture. Hencewe conclude that the trends in the S-statistic seen inFigure 6 signify the transition between two fluidized statesthat are established due to the presence of surface moistureearly in the drying process and the absence of surface moist-ure later in the process. The S-statistic varies continuouslybetween the two states, which suggests it can be used topredict pending behaviour of the fluidized bed that may beconsidered undesirable. This will be discussed furtherbelow with respect to entrainment measurements made inthe Plexiglas fluidized bed at Saskatchewan.Experiments similar to those performed in the Glatt unit

were also carried out in the Plexiglas fluidized bed atSaskatchewan. During drying experiments in the Plexiglasfluidized bed, the rate of entrainment of fine particles elu-triated from the bed was measured with a load cell connectedto a collection pot situated below the cyclone outlet. Bed andexhaust gas temperatures and pressure fluctuations weremeasured simultaneously and granule samples were col-lected periodically with the sample thief and analysed formoisture content. Figure 7 presents drying curves and temp-erature-time profiles for three different wet bed loadings at adrying air temperature of 658C, while Figure 8 presents thecorresponding plots of entrainment as a function of time.Comparison of these two plots shows that entrainment isnegligible during the constant rate drying period, butincreases rapidly once the surface moisture is removedfrom the granule. The onset of entrainment corresponds toapproximately 40, 46 and 53 min for the 3, 3.5 and 4 kg load-ings, respectively. Referring to Figure 7, the onset of entrain-ment coincides with a granule moisture content of roughly10 wt%, irrespective of bed loading.Simultaneous measurement of pressure fluctuations

allowed the S-statistic to be calculated and plotted as a func-tion of time for the three bed loadings. These results areplotted in Figure 9 for a reference state corresponding to10 wt% moisture. This reference state was purposely chosenbecause it corresponds to the onset of entrainment (i.e., thefluidized state is such that entrainment of granule becomessignificant). While there is more scatter in the S-statistic plotsfor the Plexiglas unit compared to the Glatt (see Figure 5),

the basic trend is still the same; the S-statistic continuouslyconverges to a value of 3. What is important to note fromFigure 9, however, is that the bed reaches a fluidized statesimilar to the reference state nearly 10 min before entrainmentactually begins. Hence the S-statistic gives an early warning ofthe transition of the fluidized bed into a state that will eventuallycause entrainment. This early warning would allow an operatorto take remedial action, such as reducing gas velocity, to miti-gate entrainment. The bed temperature-time profile, which isthe traditional measurement made for monitoring fluidizedbed drying processes, gives no such early warning since itbegins to increase at essentially the same time as the onsetof entrainment.

Tomographic Imaging During Fluidized BedDrying of Granule

Analysis of the bed pressure fluctuations using theS-statistic approach reveals that statistically significantchanges occur in the fluidized state as drying proceeds.

Figure 9. Progression of the S-statistic for the drying experiments ofFigure 7 and a reference state of 10 wt% moisture.

Figure 8. Measured entrainment rates for the three experiments ofFigure 7.

Figure 7. Drying curves and temperature-time profiles for three differ-ent drying experiments (3, 3.5 and 4 kg initial wet bed masses) in thePlexiglas fluidized bed at the University of Saskatchewan.



Since pressure fluctuations are a global indication of bedbehaviour, they provide no information on the nature ofthese changes or where they occur. A local measurementtechnique is needed for this and hence we have implementedtomographic imaging. This section of the paper presents indi-vidual images corresponding to different times in the dryingexperiment with a 3.5 kg initial wet bed loading. Images col-lected during experiments with other bed loadings were simi-lar and hence provide the same information. The imagespresented in Figures 1012 are from data sets collected atgranule moisture contents of 28, 18 and 10 wt%, respectively.There is an interval of 50 ms between images such that eachsequence of 20 frames spans 1 s. The scale in the lower rightcorner of each figure spans a relative permittivity between 0and 1, corresponding to the respective relative permittivitiesof air and a packed bed of granule. Time increases from

left to right in the sequence. The first frame of the sequenceis in the top left hand corner of the composite while the finalframe is in the bottom right hand corner. A frame index isgiven at the top right hand corner of each image.Figure 10 presents tomographic images corresponding to

a granule moisture content of 28 wt%. At this moisture con-tent, which was less than 10 min into the batch drying exper-iment, the superficial gas velocity was 2.5 m/s21. It is evidentfrom these images that the drying air preferentially flowsupward through a centralized dilute core. The core is sur-rounded by a highly dense bed of solids. Visual observationsat the wall of the product bowl indicate that this dense bed cir-culates downward. Visual observations of the upper surfaceof the bed through the Plexiglas walls of the vessel revealedthat the air tended to erupt from the centre of the bed, consist-ent with the tomographic images.

Figure 10. Electrical capacitance tomography images corresponding to a moisture content of 28 wt%.



280 PUGSLEY et al.

In the tomographic images of Figure 11, which corre-spond to a moisture content of 18 wt%, it is evident thatthe centralized core has broken down and more of thebed is involved in fluidization. Evidence of the coreappears in the first three images of this sequence, butthis rapidly breaks down into several voids that are seento flow in an annular ring. Referring to Figure 5, this moisturecontent is in the middle of the transition of the S-statisticbetween two fluidized states. Figure 11 suggests that thefluidized bed behaviour in this transition period fluctuatesbetween the centralized core flow and the annular gasflow.In Figure 12, an annular flow of bubbles and voids is pre-

dominant at a moisture content of 10 wt%. However, com-paring Figures 11 and 12, it appears that less of the bedcross-section is involved in the fluidization in the latterfigure. This is borne out by the darker region near thewall, which suggests that there is less gas there at10 wt% moisture than there is at 18 wt%. Hence, althoughthe gas flowrate to the bed is the same, the gas tends toflow upward through a more restricted cross-sectional areain the form of bubbles and voids that reach the bed surfaceat a higher velocity (since they flow through a smaller frac-tion of the bed cross section) and then collapse. This sendsparticles into the freeboard at a relatively higher velocity andin addition, these particles are dryer and less dense thanthey were at the start of the batch drying process. Thusbed dynamics and the dry condition of the granule combineto increase the entrainment rate, as was observed inFigure 8.The tomographic images of Figures 1012 illustrate how a

high-frequency localized measurement technique can eluci-date the hydrodynamic behaviour behind the statistically sig-nificant changes captured by the global pressure fluctuationmeasurements. Furthermore, since tomography confirmsthat changes are occurring, it supports the findings of theS-statistic and, while it is unlikely that tomography wouldever be implemented as a monitoring and control tool for flui-dized bed processing of pharmaceutical granule, one or more

flush mounted pressure sensors certainly could be. Hence incombination, the pressure fluctuation and tomography dataprovide justification for further development of the pressuresensor as an on-line monitoring and control tool for batchfluidized bed dryers.

Radioactive Particle Tracking During FluidizedBed Drying of Wet Granule

Two tracer particles of 2.6 mm and 1.6 mm were trackedduring two separate drying experiments to quantify theeffect of particle size on particle mixing and velocity duringdrying. Location of the tracer particle was determined afterevery 30 ms, each drying experiment lasted for about25 min and the data obtained from the RPT was dividedinto three groups of 7 min each. It is important to note thatonly one tracer particle was tracked in each experiment.Particle mixing was quantified by calculating the percen-

tage of time the tracer particle spent in different axial sectionsof the bed during drying. For the purpose of this analysis, thebed was divided into 4 axial sections of 4 cm each. Perfectmixing of the granule would mean that the tracer spentalmost an equal amount of time in each section. However,it is evident from Figures 13(a) and 14(a) that, during thefirst 7 min, of drying, the 2.6 mm tracer spent almost 80%of its time and 1.6 mm tracer spent almost 40% of its timein the bottom 4 cm of the bed. This indicates that the bedwas poorly mixed at the start of the drying process. Thiscan be due to high moisture content in the beginning andthe presence of dilute core region, with most of the gas pas-sing through a narrow cross-section at the centre of the bedresulting in poor circulation of the larger granules. The differ-ence in the time spent by the two tracers can be explained bythe size difference of the tracers, with the larger tracer particleshowing a greater tendency to segregate to the bottom of thebed. This is consistent with the findings of an earlier segre-gation study carried out by our group (Wormsbecker et al.,2005). During the next 7 min, particle mixing improved sig-nificantly with both the tracers spending almost equal time




in 4 cm and 8 cm sections of the bed. This change in the par-ticle mixing suggests that a change in the hydrodynamics ofthe bed with the gas being evenly distributed throughout thebed (as shown by our tomography measurements), resultingin better particle mixing. The final 7 min of the drying processdid not show any appreciable change in the particle mixing,suggesting that, with respect to particle mixing, the bed hadreached an equilibrium state state. However, the effect of par-ticle size can be clearly seen on the mixing of tracers with thebigger tracer particle showing higher segregation tendencythan the smaller one throughout the drying process.Radioactive particle tracking also allowed us to study the

speed of the granule as it was mixed during the drying pro-cess. Starting from the centre, the entire bed was dividedinto radial sections of 1 cm radius and the speed of thetracer particles was calculated in each radial section. Particlespeed was calculated by determining the distance travelledby the tracer between two successive tracer locations in a30 ms time interval. It is important to note that the tracer

particle location was determined after every 30 ms. There-fore, 7 min of particle tracking produced 14 000 successivetracer locations, which made the interpretation and represen-tation of data very challenging. Therefore, all the tracerspeeds in a given radial cross-section was averaged to facili-tate the representation of the experimental data.Figures 13(b) and 14(b) show that average speed of the

1.6 mm tracer particle was always higher than the averagespeed of the 2.6 mm tracer in a given radial cross-section,which is consistent with the greater segregation tendencyof the larger tracer. Furthermore, it is evident that in general,the average particle speeds are higher at the centre of thebed and decrease with increasing distance from the centre.However, the rate of decrease of the average speed withradial distance from the centre in the first 7 min of thedrying process is greater than the remaining 14 min andthis observation is consistent with both the tracers. Thismay be explained by considering the tomograms presentedin Figures 1012. During the first 7 min of drying there is a

Figure 13. Results of radioactive particle tracking with the 1.6 mmgold tracer during drying in the Plexiglas fluidized bed: (a) percentageof time spent in the four axial divisions; (b) radial profile of averagegranule speed.

Figure 14. Results of radioactive particle tracking with the 2.6 mmgold tracer during drying in the Plexiglas fluidized bed: (a) percentageof time spent in the four axial divisions; (b) radial profile of averagegranule speed.


282 PUGSLEY et al.

highly restrictive dilute core region at the centre of the bedthrough which most of the air passes. This resulted inhigher particle velocities at the centre but poor particlemixing in the bed. In the latter stages of drying, the tomo-graphic images indicated that the gas tended to spread outover the vessel cross section. According to Figures 13(b)and 14(b), there is still a radial gradient of particle speed,but the gradient is noticeably less steep. Hence the spread-ing of the gas and the passage of bubbles and voidsseems to favour better granule mixing by involving more ofthe bed in the drying process and increasing the velocity ofthe particles closer to the vessel wall.

CONCLUSIONS

Our group has been actively involved for the past ten yearsin the application of advanced measurement techniques toconical fluidized bed dryers containing pharmaceutical gran-ule. This paper has described this advanced instrumentationand summarized some of the key findings. Our analysis ofpressure fluctuations measured during drying shows thatthe S-statistic is sensitive to changes in bed hydrodynamicsassociated with the loss of granule surface moisture andthe corresponding reduction in interparticle capillary forces.Most importantly, the S-statistic provides an early warningof a change in the fluidized state that could be detrimentalto final product quality. While pressure fluctuations provideonly a global measurement of bed behaviour, our work withelectrical capacitance tomography (ECT) and radioactive par-ticle tracking (RPT) has provided insight into local fluidizationphenomena inside the bed. Based on ECT, it appears thatwhen the granule is initially very wet, the flow of fluidizinggas is centralized. Over this initial period, RPT data showsthat mixing of the larger granule fractions is poor. As the gran-ule dries, the centralized gas flow breaks apart and more ofthe bed becomes involved in the fluidization process. At thesame time, particle mixing improves as the larger granulesare able to reach higher axial positions in the bed and circu-late more freely. This is likely associated with improved gas-solid contacting and more efficient and higher rates of drying.

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The manuscript was received 5 March 2007 and accepted for pub-lication after revision 29 June 2007



APPLICATION OF ADVANCEDMEASUREMENT TECHNIQUES TO CONICALLAB-SCALE FLUIDIZED BED DRYERSCONTAINING PHARMACEUTICAL GRANULEINTRODUCTIONMATERIALS AND METHODSFluidized BedsBed MaterialElectrical Capacitance TomographyHigh-Frequency Pressure Sensor AndSignal AnalysisRadioactive Particle TrackingRESULTS AND DISCUSSIONPressure Fluctuation Measurement andS-Statistic AnalysisTomographic Imaging During Fluidized BedDrying of GranuleRadioactive Particle Tracking During FluidizedBed Drying of Wet GranuleCONCLUSIONSREFERENCES

application of advanced

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