measurement techniques to monitor and controlfluidization quality in fluidized bed dryers: areview

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Drying TechnologyAn International Journal

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Measurement Techniques to Monitor and ControlFluidization Quality in Fluidized Bed Dryers: AReview

Mortaza Aghbashlo , Rahmat Sotudeh-Gharebagh , Reza Zarghami , Arun S.Mujumdar & Navid Mostoufi

To cite this article: Mortaza Aghbashlo , Rahmat Sotudeh-Gharebagh , Reza Zarghami , ArunS. Mujumdar & Navid Mostoufi (2014) Measurement Techniques to Monitor and ControlFluidization Quality in Fluidized Bed Dryers: A Review, Drying Technology, 32:9, 1005-1051, DOI:10.1080/07373937.2014.899250

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Review Article

Measurement Techniques to Monitor and ControlFluidization Quality in Fluidized Bed Dryers: A Review

Mortaza Aghbashlo,1 Rahmat Sotudeh-Gharebagh,2 Reza Zarghami,2

Arun S. Mujumdar,3 and Navid Mostou21Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering andTechnology, University of Tehran, Karaj, Iran2Multiphase Systems Research Lab., Oil and Gas Processing Centre of Excellence, School ofChemical Engineering, College of Engineering, University of Tehran, Tehran, Iran3Department of Food Engineering, King Mongkuts University of Technology Thonburi,Bangkok, Thailand

Fluidized bed dryers (FBD) are commonly employed in manyindustries to dry particulate solids. FBDs provide good solids mixing,high rates of heat and mass transfer, and relative ease of materialhandling. For efcient operation, it is important to be able to monitorand control the uidization regime, particle size distribution (PSD),moisture content, and bulk density as well as product chemicalproperties. This review provides an overview of the trends in theapplication of different experimental techniques to monitor andcontrol the hydrodynamic conditions of FBDs which inuence theparticle physiochemical properties. This review covers a wide rangeof measurement techniques, including infrared moisture sensor(IR), near infrared (NIR) spectroscopy, analysis of pressure uctua-tions, optical imaging techniques, acoustic emission (AE), electricalcapacitance tomography (ECT), spatial lter velocimetry (SFV),Raman spectroscopy, focused beam reectance measurement(FBRM), microwave resonance technology (MRT), triboelectricprobes, positron emission particle tracking (PEPT), and some noveltechniques for monitoring and control of FBDs. The present reviewsummarizes the use of the diverse techniques and outlines their meritsand limitations. Prospects for future research in this area are alsoidentied. The measurement techniques can be used for research,development, and operation of uidized bed equipment used innon-drying applications as well.

Keywords Bed hydrodynamics; Control; ECT; IR; Moisturecontent; NIR; Particle size distribution (PSD); PEPT;Raman spectroscopy

INTRODUCTION

As widely used drying systems, FBDs have foundmany applications in almost all agricultural, biochemical,chemical, pharmaceutical, food, ceramics, polymer,dyestuff, and other process industries. FBDs can be utilizedas an efcient dehydration method, not only for moistparticulate and granular products with susceptibility to ui-dization, but also for removing moisture from suspensions,solutions, dilute pastes, or sludges in a bed of inert parti-cles.[1,2] Particulate materials are commonly dried by hotair or superheated steam to a desired level of moisture con-tent. Fluidization provides high rates of heat and masstransfer, improves uniformity of temperature prole acrossthe bed, facilitates material handling and solids mixing, per-mits processing of temperature-sensitive solids, and offershigh thermal efciency of drying process. Generally, FBDscan be used for drying of particulate materials, agglomer-ates, granules, coatings, and layers. In uidized bed granu-lation, coating, and agglomeration, the complexity becomeseven more serious because of a series of transient intercon-nected phenomena; i.e., binding liquid spraying, particleagglomeration, and wall deposition. Thus, these processesare very strict and even impossible to estimate, monitor,and control during the operation. Moreover, scale-up ofill-dened processes, such as drying, is well-known to be aproblematic practice of industry and can be expensivewhen it goes wrong. Traditionally, FBDs are inspected bysimple methods with easily measurable variables, such astemperature and humidity of the exhaust air, bed axialand average temperatures, and variation of pressure dropand temperature throughout the bed. However, thesemeasurements provide little insight into the complex hydro-dynamic phenomena such as incipient deuidization and

Correspondence: Rahmat Sotudeh-Gharebagh, MultiphaseSystems Research Lab., Oil and Gas Processing Centre of Excel-lence, School of Chemical Engineering, College of Engineering,University of Tehran, P.O. Box 11155-4563, Tehran, Iran; E-mail:[email protected]

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Drying Technology, 32: 10051051, 2014

Copyright # 2014 Taylor & Francis Group, LLCISSN: 0737-3937 print=1532-2300 online

DOI: 10.1080/07373937.2014.899250

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physicochemical properties of particles and are, therefore,ineffective and not useful for real-time monitoring andgood closed-loop process control.[3] Stability of uidizationin FBDs plays an important role in the quality of thenished product by uniform distribution of the ow ofthe drying media, enhancing heat and mass transfer ratesand preventing collapse of the bed. Moisture content, bulkdensity, and PSD are other critical parameters, signicantlyaffecting the stability of uidization and the quality ofend-products. In some cases, knowledge of the chemicalproperties of materials being dried is also very critical. Onthe other hand, to develop an automated FBD system forspecied end-product quality and monitoring, real-timemeasurement techniques are often integrated with mechan-ical and instrumental facilities to avoid in-process manualmanipulation.

According to the Food and Drug Administration(FDA), Process Analytical Technology (PAT) can bedivided into three categories, including at-line, on-line,and in-line analyzers,[4] as shown in Fig. 1. PAT has beenused to dene a systematic approach for real-time measure-ments to design, analyze, scale up, and control manufactur-ing processes through the monitoring of critical quality andperformance properties for primary and in-process materi-als. At-line analyzers measure the required properties bytaking a sample, isolating it from the environment, and ana-lyzing it in close proximity to the process stream. On-lineanalyzers determine the desired characteristics of materialsby directing the sample from the process to measurementdevice and returning it to the process stream in most situa-tions. It should be mentioned that in on-line mode the dry-ing uid is commonly circulated in the measuring loop tokeep constant the temperature of the sample. In-line analy-zers (intrusive or non-intrusive) are quick measuring devicesor probes to record the sensing data without removing asample by direct placing of them into the process stream.[5]

In the complex and dynamic environment associatedwith FBDs, it becomes necessary to explore technologicaland scientic solutions for preventing deuidization andensuring mean quality parameters of the particles duringthe process. Furthermore, application of monitoring andcontrol techniques plays an important role in several majorareas in research and industry to optimize and automate theprocess, to assure high reproducibility in the end-productquality, to enhance security aspects of the process, tominimize the number of failures in batches, and to lowerexpenditures of energy and human resources.[3] The aimof this article is to review and update the usage of varioustechniques and instruments available for monitoring andcontrolling of uidization and physiochemical characteris-tics of particles. In particular, this review covers applica-tions of an infrared moisture sensor (IR), near infraredspectroscopy (NIR), pressure uctuations, optical imagingtechniques, acoustic emission (AE), electrical capacitancetomography (ECT), spatial lter velocimetry (SFV),Raman spectroscopy, focused beam reectance measure-ment (FBRM), microwave resonance technology (MRT),triboelectric probes, positron emission particle tracking(PEPT), and some other miscellaneous and innovative stra-tegies for control.

van Ommen and Mudde[6] have reported the use ofdifferent techniques for measuring the voidage distributionin uidized beds. Burggraeve et al.[4] have reviewed the analy-tical techniques for monitoring and control of uidized bedgranulators for pharmaceutical application. da Silva et al.[3]

discussed tools for monitoring and control of coating andgranulation processes in uidized beds. Published reviewshave also looked at the application of measurement techni-ques in uidized beds,[7] process control methods and scale-upof pharmaceutical wet granulation processes containinguidized bed granulating,[8] control engineering in drying tech-nology consisting of FBDs,[9] and near infrared spectroscopyand chemometrics in pharmaceutical technologies includingFBDs.[10] However, this review differs from previous onesby including all aspects of FBDs utilized for drying itself inuidized beds as well as drying during uidized bed coating,granulation, and agglomeration processes and presenting themost employed techniques for process monitoring, control,and automation. In addition, advantages and disadvantagesof each technique are mentioned and recommendationsand perspectives are provided for future works.

The diagnostic techniques discussed in this review arealso applicable to other gas-solids contacting devices suchas modied uidized beds, spouted beds, circulating uidbeds, vibrating uid beds, pneumatic conveyors, and vari-ous designs of uidized bed reactors as well. An interestingfeature of FBDs is that particle wetness, particularly atthe surface, can affect uidization quality signicantly.For very high wetness it is possible to cause deuidization,which affects the performance adversely.FIG. 1. Schematic of in-line, online, and at-line product measurements.

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MONITORING TECHNIQUES

Conventional Techniques

Traditionally, monitoring particle moisture and processend-points, the point at which a drying is completed,in FBDs has been carried out based on measurement ofprocess variables such as outlet air temperature, outlet airhumidity, inlet and outlet temperature difference, and othersimple measurements of miscellaneous parameters accord-ing to the dryer characteristics. Particle size measurementand tests of the chemical properties of the product areperformed by acquiring samples during the processingand subsequent analysis using off-line techniques. Fluidiza-tion quality was usually identied by visual observation andevaluation of global hydrodynamic parameters by applyingempirical models. The most important studies regarding theapplication of conventional techniques for monitoring ofFBDs are listed in Table 1.

Alden et al.[11] applied the temperature difference techni-ques using simple psychometric calculation to control theprocess end-point in FBD. The end-point of the granulationprocess was successfully identied and controlled by a com-puter program. Watano et al.[12] successfully examined theuctuations of power consumption in an agitated uidizedbed granulator to monitor granule growth and to determinethe process end-point by computing the coefcient ofvariation of the utilized power. In another investigation,Watano et al.[13] developed a fuzzy controller for control-ling the bed height, which was successively measured byan ultrasonic sensor during uidized bed granulation. Thecontroller effectively prevented deuidization and channel-ing by controlling the bed height. Sivashanmugam andSundaram[14] developed two different empirical models forpredicting the pressure drop for both dilute and dense phaseow regimes in FBD for ragi drying with an acceptabledeviation, and presented a correction coefcient for thedense slugging ow regime. In a similar study, they dis-tinguished the mixed ow behavior up to a certain particleReynolds number from the residence time distributionstudies.[15] El-Nans et al.[16] measured minimum spoutingvelocity as a function of moisture content for drying ofsludge and found a decrease in the minimum spoutingvelocity by progressing of the drying process. Temple andvan Boxtel[17,18] used simple measurements for continuousand batch tea FBD based on wet material feed rate anddirect feedback of the moisture content and intermediateexhaust temperature to design a full automated controlsystem. In addition, this research group has published sev-eral papers about design and application of a full automatecontrol system based on different methods of controllertuning,[19] a custom-built electronic data logger applied tocombination of experimental ndings with modeling andsimulation results,[20] and an algorithm-employed transientexhaust air temperature.[21] These strategies appropriately

controlled the drying process better in most cases comparedto the manual control.

Larsen et al.[22] proposed a control strategy based onin-process thermodynamics calculation using an alternativethermodynamic factor according to enthalpies of actual andadiabatic vented drying air during uidized bed coating.Two separate control loops were suggested in order tomaximize the coating spray rate and keep the process inmass and energy balance. An inner control loop controlledthe product temperature by the inlet air temperature and anouter control loop controlled the relative outlet airhumidity and the degree of consumption of the potentialvaporization energy by the spray rate. Devahastin et al.[23]

correlated empirically the size of shrimp, bed height, andnozzle diameter to the hydrodynamic characteristics of ajet spouted bed of shrimp using the Buckingham p theorem.The temperature difference method has shown an appropri-ate accuracy level in approximating the drying end-point,in which the uidization activity signicantly affecteddetection of the process end-point.[24] Yuzgec et al.[25] satis-factorily employed a model-predictive controller based onthe dynamic recurrent neural networks to predict moisturecontent and product activity during bakers yeast dryingusing data calculated from heat and mass equationsthrough dried granules. This methodology gave simulationresults in the short time required for real-time control appli-cations. In a similar work, Koni et al.[26] optimized dryingconditions to maximize product quality while minimizingthe energy consumption by proposing a recurrent neuralnetwork-based algorithm for developing quality andprocess models which were solved by a genetic algorithm.Matero et al.[27] satisfactorily used multi-way methods witha few process variables to recognize successful batchgranulations from unsuccessful runs and achieved usefulinformation that can improve comprehension of the FBD.

Traditionally, easy-to-use simple techniques were used tomonitor FBDs. More recently, due to the dynamic andcomplex nature of the FBD process, these simple techniquesare found to be unsuitable for some industrial applicationsbecause of their great operator-dependency and often poorrepeatability. In some cases, these techniques can modifythe internal ow of the uidized bed, leading to interferencewith the actual process measurements. On the other hand,heat and mass balance equations commonly used to deter-mine the end-point and estimate the moisture content ofparticles in FBD with respect to inlet and outlet drying airtemperatures. However, drying of particles stronglydepends on the humidity of the inlet-air, a small changeof which may result in a considerable error in the predictedvalue. Moreover, mechanistic models may fail due to sim-plifying assumptions and non-consistency between dryingbatches.[28] Monitoring of FBDs by traditional techniquesis time-consuming and sometimes causes rejection of the

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TABLE1

MostimportantstudiesregardingtheapplicationconventionalforFBDsmonitoring

Author(s)

Measurement

technique(s)

Application(s)

Dryer

type(s)

Process

target(s)

Rem

arks

Alden

etal.[11]

Tem

perature

difference

technique

Tocontroland

instrumenttheFBD

Laboratory-scaleFBD

DryingofAllopurinoland

lactose

mixture

granules

obtained

with

ProvidoneK30in

water

Thedeveloped

program

basedon

thermodynamicsconcept

successfullydetected

end-pointofgranulation

process.

Watanoet

al.[12]

Power

consumption

forgranule

agitationalongwith

coefcientof

variation

Toanalyze

the

granulationprocess

andprogress

of

particles

Top-sprayagitated

uidized

bed

granulator

Granulationofmixture

oflactose

andcorn

starchwith

hydroxypropylcellulos

Apracticalmethodforthe

determinationofan

optimum

operational

end-pointin

thetumbling

uidized

bed

granulation

wasdeveloped.

Watanoet

al.[13]

Intelligentcontrol

basedonultrasonic

heightmeasurement

Developingafuzzylogic

controller

forbed

heightcontrolling

duringuidized

bed

granulating

Lab-scaletop-spray

agitateduidized

bed

granulator

Lactose

andcornstarch

granulatingwith

hydroxypropylcellulose

Fuzzylogiccontroller

favorably

maintained

the

bed

heightatthe

predetermined

valuefrom

initialto

nalstageof

uidized

bed

granulation.

Sivashanmugam

and

Sundaram

[14]

Pressure

drop

measurement

Topropose

theem

pirical

modelsforcalculating

pressure

dropat

differentowregimes

Laboratory-scale

annularcirculating

FBD

DryingofRagi

Twoseparate

modelswere

suggestedforcomputing

pressure

dropfordense

sluggingowanddilute

phase

owregimes

with

theaveragedeviationof

10%.

Sivashanmugam

and

Sundaram

[15]

Residence

time

distributionusing

pulseinputoftracer

Todeterminetheow

pattern

Experimentalannular

circulatingFBD

DryingofRagiparticle

Thetheoreticalmean

residence

timeusing

one-dimensionaltanksin

series

anddispersion

modelagreed

wellwith

theexperimentaldata.

El-Nanset

al.[16]

Measuringminimum

spoutingvelocity

Tocharacterizethebed

hydrodynamicsand

mass

transfer

coefcient

Lab-scalespoutedFBD

Dryingofsludge

Increasingthemoisture

contentofsludgeparticles

ledto

anincrem

entin

minimum

spouting

velocity.

Tem

pleandvan

Boxtel[1

7]

Conventionalcontrol

Toidentify

someofthe

limitationsona

controller

inan

industrialFBD

based

onmodelingresults

Industrial-scaleFBD

Tea

drying

Wet

product

feed

rate

should

beprecisely

controlled

tomaintain

moisture

dischargefrom

dryer

atconstantvalue.

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Tem

pleandvan

Boxtel[1

8]

Conventionalcontrol

Tocontrolandmonitor

thedried

product

moisturecontentbased

ontwofeedback

controllers

ContinuousFBD

Tea

drying

Inferentialcontrolbasedon

outlet

airtemperature

successfullycontrolled

moisture

ofoutlet

product.

Tem

pleet

al.[19]

Conventionalcontrol

Tuningofcontrollers

usingdifferentstrategy

toprecise

controlling

ofnalproduct

moisture

ContinuousFBD

Tea

drying

Theintegratedqualitative

measure

andintegral

squarederrorwasthebest

choicein

controller

tuningofFBD

under

differentoperating

condition.

Tem

pleet

al.[20]

Custom-m

ade

electronicdata

logger

and

controller

Tomonitorandcontrol

teadryer

ExperimentalFBD

Tea

drying

Thedeveloped

system

was

satisfactorily

installed

inthemajority

ofrelated

factories

anddrying

process

wascontrolled

betterthanmanual

controlling.

Tem

pleet

al.[21]

Conventionalcontrol

Todetectthedrying

end-pointusinginlet

andexhaust

temperature

measurement

Lab-scaleFBD

Developinganalgorithm

forautomaticend-point

determination

Theend-pointwas

successfullydetermined

bydeveloped

algorithm.

Larsen

etal.[22]

Conventionalcontrol

Tocontrolthedrying

process

basedon

thermodynamics

concept

Lab-scaleuidized

bed

coaterwithboth

top

andbottom

spraying

techniques

Sugarspheres

and

microcrystalline

cellulose

pelletscoating

withEudragit1NE

30D,Eudragit1RS

30D,andAquacoat

ECD1lm

polymers

Thethermodynamicsmodel

wassuccessfully

incorporatedinto

anew

process

controlstrategy

bycalculatingthedegree

ofutilizationofthe

potentialevaporation

energyoftheventedair

andtherelativehumidity

ofexhausted

dryingair.

Devahastin

etal.[23]

Recodingthe

minimum

spouting

velocity

and

maximum

and

steadyspouting

pressure

drops

Todeterminethebed

hydrodynamics

Experimentaljet

spoutedFBD

Shrimpdrying

Threeem

piricalcorrelations

weredeveloped

for

Reynoldsnumber,

maximum

pressure

drop

andspoutingpressure

dropbasedonthe

Buckingham

pmethodas

functionshrimpand

dryer

characteristics.

(Continued

)

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TABLE1

Continued

Author(s)

Measurement

technique(s)

Application(s)

Dryer

type(s)

Process

target(s)

Rem

arks

Lipsanen

etal.[24]

Tem

perature

difference

method

Todetectthedrying

end-point

Bench-scaleuidized

bed

granulator


of

ibuprofenanda-lactose

monohydrate

by

aqueoussolutionof

polyvinylpyrrolidone

Thetemperature

difference

techniquesatisfactorily

identied

drying

end-pointatdifferent

humidityofdryingair.

Yuzgec

etal.[25]

Articialintelligent

controlbasedon

simpleprocess

measurement

Topredictthemoisture

contentandproduct

activityusinga

dynamic

neural-network-based

model-predictive

controlstructure

solved

bygenetic

algorithm

Industrial-scaleFBD

Bakersyeastdrying

Thepresentedmethodology

successfully

approximatedthe

moisture

contentand

product

activityand

accordingly

anintelligent

controlsystem

suggested

basedonsimulation

results.

Koniet

al.[26]

Intelligentcontrol

basedon

neural-network-

basedmodelsand

modied

genetic

algorithm

using

process

variables

Todetermineand

controltheoptimal

conditionsto

maximize

product

quality

while

minimizingenergy

consumption

Large-scalebatchFBD

Dryingofbakersyeast

Theoptimalcontrol

algorithm

byapplyingthe

recurrentneuralnetwork

modelsforestablishing

thequality

andprocess

models,solved

througha

modied

genetic

algorithm,wassuggested

topromote

the

perform

ance

ofthedrying

processofbakersyeastin

batchuidized

bed.

Matero

etal.[27]

Multi-waymodels

withprocess

variablesincluding

mass

temperature,

inletairtemperature

andoutlet

air

temperature

Todistinguishthe

successfulbatch

granulationsfrom

unsuccessfulruns

Bench-scaletop-spray

uidized

bed

granulator

Granulationof

hydrophobic

pharm

aceutical

ingredient,hydrophilic

excipientin

amonohydrate

form

and

polymericexcipientwith

polyvinylpyrrolidoneas

binder

liquid

Theparallelfactoranalysis

PARAFAC2method

provided

agood

separationbetweenthe

successfuland

unsuccessfulbatches

comparedwiththe

PARAFACmethod.

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product if the target condition is not met. Conventionaltechniques frequently provide time-averaged informationabout the bed hydrodynamics and particle properties.

Infrared (IR) and Near Infrared (NIR) Spectroscopy

IR spectroscopy applies the infrared zone ofelectromagnetic radiation with longer wavelengths thanthose of the visible light, varying from 700nm to 1mm,and a frequency span of approximately 300GHz up to430THz. The IR technique has been commonly utilizedfor measuring the moisture in FBDs and is based on certainIR wavelengths which are absorbed due to the hydrogen ofwater molecules as they pass through the particulatematerial (in the wave number range 1600 and 1700 cm1).NIR spectroscopy is a technique covering the transitionfrom the visible spectral range to the mid-infrared regionof the electromagnetic spectrum in the wavelength rangeof 8002500 nm (wave number range 12,5004000 cm1),mainly indicating the vibrations of CH, OH, SH andNH bands. Absorbance in the NIR region results frommolecular overtone and combination vibrations of the fun-damental mid-infrared bands. Thus, the particle size andmoisture content can be tracked effectively using NIR spec-troscopy due to the sensitivity of absorbance in this region tovariations in the moisture content, particle size, and chemi-cal state.[3] Pasikatan et al.[29] have illustrated the basicprinciples of NIR spectroscopy relevant to particle sizemeasurement and its dependency on sample preparations,technique of presentation, reference methods, calibrationdevelopment, and validation. Roggo et al.[30] reviewed phar-maceutical applications of NIR spectroscopy chemometricsin three different subsections, including qualitative analysesand classications, regression methods and quantitativeapplications, and online applications. The chemometricmethods used for analysis of NIR spectra were categorizedinto three main approaches: mathematical pretreatments,classication methods, and regression methods. Their studyalso contained an overview of NIR spectroscopy applicationto uidized bed granulation and coating to monitor onlinethe moisture content, chemical compound content,end-point detection, and coating thickness. De Beer et al.[5]

comprehensively reviewed the use of NIR spectroscopy forthe in-process monitoring of pharmaceutical productionprocesses with special emphasis on pharmaceutics and dos-age forms. A review of the literature, a summary of whichis given in Table 2, shows that a considerable number ofresearchers have used NIR for monitoring of physico-chemical properties of the product within the FBDs.

Watano et al.[31,32] employed an IR moisture sensor tomonitor moisture content during a uidized bed granu-lation process and established a fully automated systemby means of a moisture feedback controller and an adaptivefuzzy controller for controlling the moisture, respectively. Again scheduling based on drying capacity was introduced

into a fuzzy control system, which effectively controlled themoisture content at various inlet air temperatures. Further,Watano et al.[33] investigated IR absorption with variouspowder characteristics, such as water-absorbing potentialand granule size, to create a relationship between granulewater content and the IR absorbance spectra. Generally,the relationship between moisture content and the IR absor-bance should be identied before granulation and drying foraccurate monitoring of the moisture using an IR probe. Inanother study, the same research group reported that therelationship between granule moisture content and the absor-bance of IR spectra is not inuenced by the air ow rate ofthe purge air for preventing particle adhesion, uidizing airvelocity, agitator rotational speed and spray mist size, whileit is affected signicantly by the uidizing air temperatureand the liquid ow rate at extremely low agitating speed.[34]

Kirsch and Drennen[35] successfully employed NIR spec-troscopy in at-line mode to estimate the amount of polymercoat to tablet core with a maximum standard error of 1.07%.Inline NIR spectroscopy was also successfully applied forpredicting water uptake and size of particles during the ui-dized bed drying stage of granulating.[36] A good correlationwas also presented between NIR wavelengths and granulesize.[37] Rantanen et al.[38] proved that the moisture contentof granules during spraying and drying phases can beapproximated using the NIR spectrum with a standard errorof 0.2%. Morris et al.[39] employed a fast drying method byusing a higher inlet air temperature to accelerate the dryingprogress without increasing the bed temperature beyondsafe limits towards the end of uidized bed drying of Ibupro-fen granules along with real-time monitoring of moistureusing NIR spectroscopy. The NIR absorbance showed asimilar trend with the moisture ratio of the product duringthe drying process. In a similar study, Wildfong et al.[44]

showed that NIR spectroscopy can successfully detect theend-point of the granulation process and determine themoisture content of the product quite well.

Rantanen et al.[40] employed a four-wavelength NIR sen-sor for monitoring uidized bed granulation=drying andutilized three of the recorded spectra for moisture contentdetermination. In similar works, the same research groupproved that the measurement of water content duringgranulation can be accurately carried out by NIR spec-troscopy at a wavelength of around 1940 nm.[41,42] Theyalso studied the effects of different parameters on a four-wavelength NIR probe and compared ANN and PLSregression for the prediction of the particles moisturecontent. It was reported that ANN is more suitable forapproximation of the water content over PLS regression.[43]

Rasanen et al.[45] applied NIR spectroscopy to measurethe moisture content of several materials during drying inmultichamber microscale FBD equipment. Moisture con-tent and drying phase were effectively tracked through threewavelengths of the NIR region. However, inlet air humidity


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TABLE2

ApplicationofNIR

spectroscopyformonitoringofFBDs

Author(s)

Technique(s)

Application(s)

Dryer

type(s)

Process

target(s)

Rem

arks

Watanoet

al.[31]IR

moisture

sensor

withfeedback

moisture

controller

Tocontrolthegranules

moisture

Experimentaltop-spray

uidized

bed

granulator

Lactose

andcornstarch

granulatingwith

Hydroxypropylcellulose

Thegranulesmoisture

content

wassuccessfullymeasured

via

IRmoisture

sensorand

thegranulegrowth

wasfully

controlled

usingamoisture

feedback

controller.

Watanoet

al.[32]Smartcontrol

Real-timemonitoring

andcontrollingof

moisture

contentusing

IRmoisture

sensor

alongwithadaptive

fuzzycontroller

Experimentalagitated

uidized

bed

granulator

Mixture

oflactose

and

cornstarchgranulation

with


Granulemoisture

contentwas

effectivelycontrolled

at

variousdryingair

temperaturesbyintroducing

again

schedulingto

adaptivefuzzysystem

based

ondryingcapacity.

Watanoet

al.[33]IR

moisture

sensor

Toinvestigate

the

relationship

between

granulemoisture

contentandIR

absorption

Lab-scaletop-spray

agitateduidized

bed

granulator

Granulationoflactose

and

cornstarchwith


Therelationship

between

granulemoisture

content

andabsorbance

ofIR

spectrawasprofoundly

affectedbythe

water-absorbingpotentialof

powder.However,granule

size

could

affectthis

relationshipduringdryingof

wet

granules.

Watanoet

al.[34]IR

moisture

sensor

Toinvestigate

theeffect

ofoperatingcondition

ontheaccuracy

of

moisture

measurement

byIR

moisture

sensor

Lab-scaletop-spray

agitateduidized

bed

granulator

Mixture

oflactose

and

cornstarchgranulating

with


Theoperationalconditiondid

notaffecttherelationship,

exceptatextrem

elylow

dampeningspeed,whilethe

relationship

wasinuenced

bytheuidizationair

temperature

andliquid

ow

rate.

Kirschand

Drennen

[35]

NIR

spectroscopy

Todeterminethe

amountofpolymer

coatapplied

totablet

cores

Experimentaluidized

bed

coater

Coatingofplacebo

containinglactose,

microcrystallinecalluses,

andmagnesium

stearate

withethylcellulose

and


asbindingsolution

TheNIR

spectroscopy

satisfactorily

predictedthe

amountoflm

applied

for

coating.

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Frakeet

al.[36]

NIR

spectroscopy

Totrack

thegranule

moisture

contentand

particlesize

variations

Bench-scaletopspray

uidized

bed

granulator

Granulationofmagnesium

carbonate

with

Polyvinylpyrrolidoneand

Hydroxypropyl

methylcellulose

NIR

spectroscopyem

ployed

formonitoringparticle

growth

andmoisture

contentwithacceptable

accuracy

duringuidized

bed

granulating.

Rantanen

and

Yliruusi[37]

NIR

spectroscopy

Tomeasure

thePSD

Bench-scaleuidized

bed

granulator

Granulationof

microcrystallinecellulose

NIR

spectroscopywavelengths

wereaccurately

correlated

withthegranuleparticle

size.

Rantanen

etal.[38]

MultichannelNIR

techniquewiththree

statistical

parameters

Tomonitorthemoisture

content

Bench-scaleuidized

bed

granulator

Granulationofthree

differentform

ulations

containing

microcrystallinecellulose,

lactose

monohydrate,

maizestarch,mannitol,

verapamilhydrochloride

withpolyvinlpyrrolidone

asbinder

Theem

ployed

multichannel

NIR

techniquesatisfactorily

estimatedthemoisture

contentofgranulesduring

sprayinganddryingphases.

Morriset

al.[39]

NIR

spectroscopy


content

ExperimentalFBD

Ibuprofen-starch

granulationusing

Polyvinylpyrrolidoneas

thebinder

solution

NIR

spectroscopysuccessfully

tracked

themoisture

variationduringuidized

bed

granulation.

Rantanen

etal.[40]

Four-wavelength

NIR

sensor

Tomeasure

themoisture

content

Laboratory

uidized

bed

granulator

Granulationofglass

ballotiniand


withpoly[1-(2-oxo-

1-pyrrolidinyl)ethylene]

withgelatinasbending

solution

Thegranulewatercontent

could

betrustworthyand

quicklymeasuredusingonly

afewNIR

wavelengths

aroundthewaterband.

Rantanen

etal.[41]

MultichannelNIR

sensorin

conjunctionwith

PCA

Toselect

theNIR

wavelengthsfor

measuringmoisture

content

Bench-scaleuidized

bed

granulator

Granulationoftheophylline

anhydrate

andsilicied


with


solutionasbinder

NIR

spectraanalyzedwith

PCA

accurately

detectedthe

granulesmoisture

content

duringprocessing.

Rantanen

etal.[42]

NIR

spectroscopy


content

Bench-scaletop-spray

uidized

bed

granulator

Productionofgranule

containingmannitol,

pregelatinized

starch,and


solution

Theapplied

NIR

spectroscopy

set-upwithamultichannel

detectorwasapowerful

techniqueformoisture

measurementduring

granulation.

(Continued

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TABLE2

Continued

Author(s)

Technique(s)

Application(s)

Dryer

type(s)

Process

target(s)

Rem

arks

Rantanen

etal.[43]

Four-wavelength

NIR

sensorin

combinationwith

partialleastsquare

(PLS)andarticial

neuralnetwork

(ANN)

Topredictthegranule

moisture

content

Bench-scaleuidized

bed

granulator

Granulationofanhydrous

theophyllinewith

Polyvinylpyrrolidoneas

bendingsolution

TheANN

wasfoundto

have

more

estimativepower

with

theindependenttestdata

thanthePLS.

Wildfong

etal.[44]

NIR

spectroscopy


contentvariationand

todetectend-pointin

onlinemode

ExperimentalFBD

Granulationof

Ibuprofen-starchwith

Polyvinylpyrrolidone

solutionasbinder

Moisture

contentofgranules

andprocess

end-pointwas

precisely

estimatedbyNIR

spectroscopy.

Rasanen

etal.[45]NIR

spectroscopy

Tomeasure

moisture

contentandto

detect

dryingphase

in-line

mode

Multichamber

microscaleFBD

Dryingofdisodium

hydrogen

phosphates

withthreedifferentlevels

ofhydrate

waterandwet

theophyllinegranules

NIR

spectroscopywas

successfultechniquein

in-linedeterminationof

productmoisture

anddrying

phase.

Daviset

al.[46]

NIR

spectroscopy

withstandard

norm

alvariate

Tomonitorthe

polymorphic

transform

ationsof

glycineduringdrying

phase

Lab-scaleFBD


of

c-glycineand


withwaterasthe

granulatingliquid

NIR

spectroscopywas

successfullyapplied

toidentify

theaandcform

sof

glycineduringgranulation

anddrying.

Green

etal.[47]

NIR

spectroscopyin

conjunctionwith

PLS


contentandto

investigate

theeffects

ofsamplingonmethod

precision

Pilot-scaleFBD

Dryingofgranules

containingdifferentratios

ofthesamemajor

excipients(lactose

monohydrate

and

microcrystallinecellulose)

anddifferentbulk

drug

andother

minor

excipients

Usefulinform

ationwas

obtained

regardingthe

impact

ofsamplingon

inaccuracy

ofin-lineNIR

spectroscopymethod.

PaulFindlay

etal.[48]

NIR

spectroscopy


contentandparticle

size

fordetectingthe

end-pointof

granulationprocess


uidized

bed

granulator


of

acetaminophen

or

ibuprofenwithlactose

monohydrate

and


bypovidoneasbending

solution

Thegranulationend-pointwas

effectivelydetectedusing

calibratedNIR

spectroscopy

bymoisture

contentand

particlesize

measurement.

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Nieuwmeyer

etal.[49]

NIR

spectroscopyin

conjunctionwith

principle

componentanalysis

(PCA)andPLS

Toidentify

themoisture

contentandparticle

size

ofgranules

Laboratory-scaleFBD


usingde-mineralized

water

TheNIR

spectrum

was

precisely

correlatedwiththe

granulemoisture

content

andparticlesize.

Romer

etal.[50]

NIR

spectroscopy

withPCA

Tomonitorthephase

transform

ations

MicroscaleFBD

Dryingofpelletscontaining

erythromycindihydrate

andmicrocrystalline

cellulose

TheNIR

spectroscopy

detectederythromycin

dihydrate

transform

ations

toitsisomorphicdehydrate

form

inthepelletsat

temperaturesabove45 C

.Lee

etal.[51]

NIR

spectroscopyin

conjunctionwith

averagingand

clusteringofspectra

Tocontrolthecoating

thickness

Custom-fabricated

uidized

bed

coater

Coatingofmixture

ofve

pharm

aceutical

ingredients,including


lactose

monohydrate,

d-m

annitol,magnesium

stearate,andcorn

starch,

usingbinder

solutionsof

polyethyleneglycoland

hydroxypropyl

methylcellulose

Thecoatingthicknesswas

controlled

aswellas3%

deviatedfrom

theactual

thickness.

Alcala`

etal.[52]

NIR

spectroscopy

withPLSandPCA

Topredictthemoisture

content,particlesize

distributionandbulk

density

Industrial-scaleuidized

bed

granulator

Granulatingof


withmaizestarch

solutionasbinder

Thepharm

aceuticalgranule

properties

such

asmoisture

content,bulk

density,and

particlesize

wereexcellently

monitoredusingtheNIR

spectroscopy.

Mark

etal.[53]

NIR

spectroscopy

withmultivariate

calibrations

Todeterminetheideal

end-pointbytaking

into

accountproduct

quality,watercontent

andresidualsolvent

ExperimentalFBD

Dryingofantibiotic

Anautomatedmonitoring

system

basedonthe

continuousassessm

entof

NIR

spectroscopydata

was

developed

forauidized-bed

dryingprocess

ofa

pharm

aceutical

interm

ediate.

Peinadoet

al.[28]NIR

spectroscopyand

experimental

moisture

determinationalong

withPLS

Tospecifythedrying

end-point

Fullcommercial-scale

FBD

Dryingofhydrochloride

saltcontaining

micro-crystalline

cellulose,sodium

starch

glycolate

andpovidone

Thepresentedmethodology

waseffectivelyapplied

for

in-linedeterminationof

productmoisture

anddrying

end-point.

(Continued

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TABLE2

Continued

Author(s)

Technique(s)

Application(s)

Dryer

type(s)

Process

target(s)

Rem

arks

Lee

etal.[54]

NIR

spectroscopyin

conjunctionwith

PLS

Toquantify

thecoating

thickness

Custom-m

ade

experimentaluidized

bed

coater

Coatingofmixture

of


lactose

monohydrate

and


withpolyethyleneglycol

andhydroxypropyl

methylcellulose

ascoating

materials

Thecoatingthicknessasahigh

quality

end-point

designationcould

be

precisely

estimatedvia

in-lineNIR

spectroscopy

measurement.

Hartungetal.[55]NIR

spectroscopy


content

Experimentaluidized

bed

granulator

GranulatingofEnalapril

maleate

withlactose

monohydrate,maize

starch,andsodium

hydrogen

carbonate

NIR

spectroscopywas

successfulin

monitoringof

granulesmoisture

content.

Konaet

al.[56]

IntegratingNIR

spectroscopywith

humidityand

temperature

data

loggersalongwith

PLSandPCA

Process

understanding

andfaultdiagnosing


uidized

bed

granulator

Fexofenadine

hydrochloride,


andlactose

monohydrate

blendsgranulationwith

polyvinylpyrolidoneas

bendingsolution

Process

understandingand

faultdiagnosingsuccessfully

carriedoutbycouplingNIR

spectroscopy,humidity,and

temperature

data

inconjunctionwith

multivariatebatchmodeling.

Heiglet

al.[57]

On-andoff-lineNIR

spectroscopyalong

withPLS

Topredicttheresidual

moisture

content

Small-scalecold-m

odel

FBD

Dryingofdibasiccalcium

phosphate

anhydrous

On-andoff-lineNIR

spectroscopywassuccessful

inpredictionofmoisture

contentwhen

comparedwith

actualmoisture

content

data.However,onlineNIR

spectroscopyledto

more

precise

resultsthanoff-line

NIR

spectroscopy.

Hayashiet

al.[58]NIR

spectroscopy

withPLS

Toevaluate

thewater

contentofgranules

andto

estimate

the

constantdryingrate.

Lab-scaleFBD

Dryingofextruded

Riboavin

granules

obtained

bycombination

oflactose

astheller,

potato

starchasthe

disintegratingagent,and


asthebindingagent

Moisture

contentofgranules

waspredictedaccurately

usingthePLSmodeland

accordingly

constantdrying

rate

wasestimated.

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had a profound effect on the apparent absorbance of waterand subsequent NIR measurement results. Davis et al.[46]

quantied the polymorphic transformations of c-glycine toa-glycine during the drying phase of a wet granulation usingNIR spectroscopy. The results were qualitatively conrmedusing X-ray diffraction of the powder. Green et al.[47] carriedout different experiments in FBDs at scales of 65, 300, and600L using different sampling schemes (dynamics, owing,and stationary solids) to better understand and improvethe accuracy of theNIR spectroscopy technique. The processheterogeneity had an undesirable role in the measurementaccuracy. In another investigation, the NIR spectroscopygauge was calibrated for simultaneous real-time monitoringof particle size and moisture content as well as detecting thegranulation end-point.[48] Their results were in good agree-ment with ofine analytical measurements for determiningthe end-point. Nieuwmeyer et al.[49] discriminated variousstages of the drying process using NIR spectroscopy with asmall relative error compared to Karl-Fischer analysis. Theparticle size was also determined with a small predictionerror between nes and granules. NIR spectroscopy andX-ray diffractometry detected the modication of erythro-mycin dehydrate solid state to its isomorphic dehydrate formthrough FBD based on the moisture content of the pellets attemperatures greater than 45C.[50]

Lee et al.[51] introduced NIR spectra averaging and clus-tering procedures to establish a proper dynamic calibrationmodel for the measurement of coating thickness. The spec-tra averaging method for a small number of spectra provedto be a reasonably good dynamic calibration model with ahigh correlation coefcient. However, the PCA-based clus-tering technique was proposed for a large number of NIRspectra. Alcala` et al.[52] utilized PCA and PLS as qualitativeand quantitative methods for analyzing NIR spectra tomonitor the uidized bed granulation process of pharma-ceutical materials. A good correlation was found betweenthe absorbed NIR spectra and granule properties. Inanother investigation, the absorbed NIR spectra were ana-lyzed by the multivariate statistical method for evaluatingthe quality, water content, and residual solvent of antibio-tics on FBD. The results were conrmed by ofine measure-ments such as high performance liquid chromatography(HPLC), Karl Fischer back-titration method, and gas chro-matography (GC).[53]

Peinado et al.[28] developed a PLS model using NIR spec-tra and experimental moisture measurement for real-timedetermination of the end-point in a FBD. Conrmationtests revealed that the employed strategy was useful in inlineend-point specication. Lee et al.[54] developed excellentcorrelations between the coating thickness of pellets pre-dicted by inline NIR monitoring in conjunction with PLSand two ofine methods, including confocal laser scanningmicroscopy and laser diffraction particle size analysis,during uidized bed coating. Hartung et al.[55] found a good

relationship between water content determined by NIRspectroscopy and Karl-Fischer titration for Enalaprilmaleate formulation during uidized bed granulating.Kona et al.[56] integrated real-time product moisturecontent detection obtained using NIR spectroscopy withhumidity and temperature of the drying bed and establishedstatistical process monitoring charts (SPMC) by simul-taneous application of PLS and PCA techniques foruidized bed granulation and drying. NIR spectroscopy,along with humidity and temperature data loggers, appearsto be promising for effective process control and fault deter-mination. Heigl et al.[57] compared the PLS modeling ofonline NIR spectra with PLS modeling of ofine NIR spec-tra according to a reference method (loss-on-drying) formonitoring the moisture content of dibasic calcium phos-phate anhydrous on FBD. The results of ofine NIRspectroscopy indicated that the amount of withdrawn sam-ple and sampling time interval can cause bias in the moist-ure content predictions. Hayashi et al.[58] continuouslypredicted the water content of granules with negligible errorby employing a PLS model based on the NIR spectra andloss-on-drying measurements and accordingly dis-tinguished the constant drying regime.

NIR spectroscopy is a real-time, fast, safe, reliable, andnon-intrusive technique which requires no sample prep-aration, little or no modication to the existing facility,and minimal or no analyst intervention.[28,39] However, thistechnique is formulation-specic and requires calibrationbased on reference methods. In this technique, the measur-ing window sometimes becomes coated with the wet pow-der, making a moisture measurement impossible or false.However, the fouling problem of the window of the NIRmeasurement can be generally solved by the use of a suitableair supply system and specialized interfacing in order togain reliable data during the drying process.[4] The equip-ment cost is relatively high and multiple sensors must beinstalled to monitor local moisture content or particle sizein a uidized bed.[59] This technique is insensitive to impu-rities and only the surface moisture of the material can bedetermined due to the short wavelength. Absorption spectrastrongly depend on the particles distribution within thebed, the powder density of the solid material,[60] and sampletemperature. NIR spectroscopy needs black-box multi-variate calibration techniques and sophisticated softwarefor interpretation of very diffuse and non-specic spectraldata.[61] The accuracy of the measurement is confoundedby the alteration in the chemical composition of the solidduring processing and the O-H band interferes with otherbands of interest.[62] On the other hand, scattering andabsorptive attributes of solid product differed because ofmodications in the color and surface structure of the par-ticle. Location of the NIR within the bed sensor is a criticalparameter in the measurements and it is useless for monitor-ing of bed hydrodynamic behavior or assuring of spatial


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resolution. It is worth mentioning that the NIR imaging sys-tem (hyperspectral imaging spectrometer) has been developedwithin the last few years with the ability to simultaneouslyrecord spectral and spatial information of particles. Thisnew system can be a useful tool in the future for real-timemonitoring of particle physiochemical attributes and bedhydrodynamics. The moisture content affects the measure-ment in monitoring the particle size,[63] and it gives only stat-istical characteristics without giving actual information aboutparticle size.[64] This technique, especially the IR moisturesensor, is not actually non-intrusive due to its direct effecton the heat and mass transfer coefcient.

Pressure Fluctuations

Pressure uctuations in a uidized bed are mainly relatedto the formation, rise, and collapse or eruption of bubbles,clusters, and agglomerates.[65] However, transient pressureuctuations are quite complex and dynamic phenomenawhose exact origin is not yet entirely understood.[66] Inthe case of FBDs, variation of the water content of particlesinuences bubble characteristics. Therefore, monitoringand proper analysis of pressure uctuations can lead to amore detailed and deeper insight into the process andpossibly generate new ideas for improvement. Real-timecondition monitoring of uidization, particle size, andmoisture content can be obtained by simple measurementof pressure uctuations. Nevertheless, interpretation andunderstanding of pressure signals is complicated due totheir intrinsically non-local nature.[66] It should be men-tioned that pressure uctuations measurement is not a noveltechnique for monitoring of FBDs. However, various inno-vative methods, including statistical, frequency domain(fast Fourier and wavelet), nonlinear (fractal, chaos), andrecurrent plot analyses, have been developed and utilizedin recent years for interpretation of pressure uctuationsof uidized beds.[6771] Recently, several researchers haveattempted to review papers on measurement and analysisof pressure uctuations in uidized beds. Bi[72] criticallyreviewed the complex pressure uctuations phenomenonin gassolid uidized beds. Sasic et al.[73] reviewed bothmodeling and experimental techniques for investigatingthe uid-dynamic behavior of gassolid uidized beds usingpressure signals. Van Ommen and Mudde[6] focused onmeasurement techniques employed for elucidating the voi-dage distribution in gas-solid uidized beds. Van Ommenet al.[66] provided a critical review of time-series analysistechniques applied for interpreting the pressure signals inuidized beds. Table 3 summarizes some of the recentresearch and the most important results obtained usingpressure uctuations monitoring and analysis of FBDs.

Li et al.[74] found that the addition of smaller particlesincreases the frequency and amplitude of pressure uctua-tions and improves the gassolids contact of soybean inFBD. Chaplin et al.[75] studied the inuence of inlet air

temperature, initial mass of the wet bed, and pressure sensorposition on bed pressure uctuations as well as bedmass andPSD by the S-statistic. Chaos theory was applied to interpretand monitor the bed behavior for measuring the productstate and moisture content.[75] Hydrodynamic changes ofthe FBD specied by the S-statistic were profoundly inu-enced by the product moisture content. The S-statistic wassensitive to the PSD only in the dry bed and superior overthe frequency and amplitude analyses in identifying thehydrodynamic changes of the FBD. Chaplin et al.[76] contin-ued their studies by implementing S-statistic analysis ofpressure uctuations to a lab-scale FBD for online compari-son of the S-statistic with entrainment, bed temperature, andoutlet air temperature. The employed methodology was ableto give an early warning of the undesirable hydrodynamicstate by selecting an appropriate reference state. In a similarwork, the S-statistic of the pressure uctuations was notsensitive to the changes in particle size during uidizedbed granulating of Mannitol.[77] However, two differentregions in the initial stages of granulation and the nalstages of drying and granules moisture content weredetected by this method. Lopes et al.[78] compared visualobservations along with the statistical and spectral analysesof data obtained from online pressure uctuations measure-ments during spouted bed coating. It was shown that stat-istical analysis is an adequate technique for identicationof the spout instability while the dominant frequency wasnot suitable for distinguishing between the uidizationregimes perceived during coating.

Wormsbecker et al.[79,80] investigated the effect of vesselgeometry and uidization regime on hydrodynamics duringdrying of placebo pharmaceutical granules. They foundtransitions from high to low frequency in pressure uctua-tions of the conical bed and an increment in the bubblingfrequency of the cylindrical bed resulting from differentparticle circulations patterns, both prior to and after uidi-zation of the granule. They also reported a multiple bub-bling regime and a coalescence-dominated regime in theconstant rate and falling rate periods, respectively. The sim-ple bed pressure drop and temperature measurements wereable to detect the end-point of the granulation process.[81]

Karimi et al.[65] decomposed the raw pressure uctuationsinto 10 sub-signals by wavelet transform and successfullydeveloped a linear relationship based on the seventh sub-signal, corresponding to the macrostructure (large bubbles)and the supercial air velocity to predict the moisture con-tent of wet rice through FBD. Three various approachesof signal processing, used to pressure uctuation measure-ments, including dominant frequency analysis, narrow-band standard deviation analysis, and attractor compari-son, were compared to extract quantitative informationabout the granule size in a uidized bed granulator.[82]

The standard deviation of the narrow band ltered signalwas satisfactorily applied to monitor particle size and


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TABLE3

Someoftherecentresearchandthemostimportantresultsobtained

usingpressure

uctuationsmonitoringandanalysisofFBDs

Author(s)

Technique(s)

Application(s)

Dryer

type(s)

Process

target(s)

Rem

arks

Liet

al.[74]

Pressure

uctuations

withstandard

deviationand

power

spectra

Tostudytheuidization

velocities,mixing

mechanisms,and

uidizationquality

Lab-scaleFBD

Dryingofsoybeanseeds

Profoundimprovem

entwas

obtained

ingassolids

contactingbyintroducing

smallparticlesinto

thebed

oflargeparticles.

Chaplinet

al.[75]Pressure

uctuationin

conjunctionwith

chaosanalysis

(S-statistic)

Totrack

thebed

hydrodynamic

Experimentalconical

lab-scaleFBDs

Dryingofwet

placebo

granulecontaining

lactose

monohydrate

and


asller,croscarm

ellose

sodium

asdisintegrant,

hydroxypropyl

methylcellulose

asbinder

andUSPwaterassolvent

S-statisticwassuperiorin

identifyingtheuidized

bed

state

over

thestandard

deviationordominant

frequency

techniques.

Chaplinet

al.[76]Pressure

uctuation

withS-statistic

Tostudythebed

hydrodynamic

Lab-scaleFBD

Dryingofgranule

consistinglactose

monohydrate

(ller),


(ller),croscarm

ellose

sodium

(disintegrant),

hydroxypropyl

methylcellulose

(binder),

andwater(solvent)

Main

hydrodynamic

variationswerespecied

by

theS-statisticanalysisof

high-frequency

pressure

uctuationdata.

Chaplinet

al.[77]Pressure

uctuation

analyzedby

S-statistic


content


uidized

bed

granulator

Mannitolgranulatingwith


asbindingsolutionand

waterassolvent

Granulemoisture

changes

within

thegranulator

monitoredbyusingthe

applied

techniquewithout

theneedforthedirect

measurementofmoisture.

Lopes

etal.[78]

Pressure

uctuations

alongwiththe

statisticandspectral

analysis

Tomonitorthechanges

thatoccurred

during

particlecoatingin

aspoutedbed

byusing

real-timepressure

uctuation

measurement

Lab-scalecone-

cylindricalspoutFBD

ABSandpolystyrene

particlescoatingby

EudragitL30-D

551

basedpolymeric

suspension

Statisticalmethod

satisfactorily

differentiated

thespoutuidized

bed

instabilitybasedonpressure

uctuationdata.

(Continued

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TABLE3

Continued

Author(s)

Technique(s)

Application(s)

Dryer

type(s)

Process

target(s)

Rem

arks

Worm

sbecker

etal.[79]

Pressure

uctuation

analysisin

conjunctionwith

averagecycle

frequency

and

standard

deviation

analyses

Tounderstandthebed

hydrodynamic

behavior

ExperimentalFBD

Dryingofwet

placebo

granulecontaining

lactose

monohydrate

and


asller,croscarm

ellose

sodium

asdisintegrant,

hydroxypropyl

methylcellulose

asbinder,

andUSPwaterassolvent

Thepotentialofusingpressure

uctuationmeasurementsto

monitorandcontrol

uidizationstate

was

dem

onstrated.

Worm

sbecker

etal.[80]

Pressure

uctuation

analysiswithboth

timedomain

(standard

deviation

andaveragecycle

frequency)and

frequency

domain

(dominant

frequency

and

power

spectra)

analyses

Tostudytheinuence

of

vesselgeometry

onthe

hydrodynamic

behavior

Cylindricalandconical

laboratory-scalebatch

FBDs

Dryingofgranule

containingcroscarm

ellose

sodium

(disintegrant),

lactose

monohydrate

(ller),microcrystalline

cellulose

(ller),

hydroxypropyl

methylcellulose

(binder).

andwater(solvent)

Thedominantfrequency

decreasedduringdryingin

theconicalbed,whileit

increasedduringdryingin

thecylindricalbed.

Royet

al.[81]

Pressure

dropand

temperature

measurement

Todetectthegranulation

end-point


uidized

bed

granulator

Granulationofurea

Thebed

pressure

dropand

temperature

were

successfullyauthenticated

foridentifyingtheend-point

ofuidized

bed

granulation.

Karimiet

al.[65]

Pressure

uctuations

withtime-domain

andfrequency

domain

analyses


contentandbed

hydrodynamic

ExperimentalFBD

Dryingofwettedrice

particles

Theoriginalpressure

uctuationsignalwas

decomposedinto

10

subsignalswhichweremore

sensitiveto

moisture

variationsthanother

investigatedparameters.

DeMartin

etal.[82]

Pressure

uctuation

withthreedifferent

techniques

ofsignal

processing,

includingdominant

frequency

analysis,

narrow-band


contentandparticle

size

duringuidized

bed

granulation

Lab-scaleuidized

bed

granulator


of

lactose

monohydrate

and

starchwith


asbindingsolutionand

waterassolvent

Theparticlesize

andwater

contentwerecorrelatedto

thedata

derived

withthe

pressure

uctuationsensor

bynarrow-bandstandard

deviationsignalprocessing

technique.

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standard

deviation

analysis,and

attractor

comparisontool

Silvaet

al.[83]

Pressure

uctuationin

conjunctionwith

Gaussianspectral

pressure

distributionand

controlofthe

airowrate

andthe

coatingsuspension

owrate

usingPI

controllers

Tomonitorandcontrol

thedeuidization

phenomenonin

auidized

bed

coating

process

basedon

pressure

uctuation

data

Experimentaluidized

bed

coater

Microcrystallinecellulose

coating

Gaussianspectralanalysisof

pressure

uctuationdata

indicatedhighpotentialfor

applicationsin

uidized

bed

coatingprocesses,whilethe

PIcontroller

successfully

maintained

uidization

dynamicin

stablestate

basedonpresentedanalysis.

Prata

etal.[84]

Pressure

uctuation

measurementfor

process

control

Topreventthe

agglomerationby

pausingtheliquid

injection

Lab-scaleuidized

bed

coaterequipped

with

two-uid

nozzle

Microcellulose

beads

coatingwithgum

Arabic

Thedeveloped

controller

effectivelycontrolled

uidized

bed

coating,while

granulesagglomerationwas

avoided

bystoppingthe

liquid

sprayingbasedonbed

pressure

measurements.

Donget

al.[85]

Pressure

uctuations

withstatistical

methods

Toquantify

thebed

hydrodynamics

ExperimentalFBD

Dryingofspentliquor

mixed

withcorn

branfor

theproductionofyeast

Thebed

hydrodynamic

behaviorwasreasonably

qualied

bymeasuringthe

localbed

pressure

uctuations,exhaustair

temperature,andrelative

humidity.

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moisture content during uidized bed drying and granulationprocesses. Silva et al.[83] developed PI and PID controllers forcontrolling drying air ow rate and spraying liquid to avoidthe deuidization phenomenon during the uidized bed coat-ing, according to the Gaussian spectral analysis of pressureuctuations. The PI controller showed a good performanceover the PID controller and subsequently the stable uidiza-tion conditions were obtained using the PI control system.Prata et al.[84] designed a single-input=single-output controlstrategy based on the bed pressure measurement to preventagglomeration of granules by manipulating liquid sprayingduring uidized bed coating. Agglomeration was effectivelyavoided and homogeneous coating and separated particleswere obtained by the developed control system. Recently,Dong et al.[85] found that a small variation in the averagepressure drop and the standard deviation can be used forearly warning of the transition of the bed state from channel-ing to uniform uidization.

Pressure uctuation measurement is sensitive, accurate,fast, robust, relatively cheap, virtually non-intrusive, andrelatively easy to implement in lab-, pilot-, and industrial-scale units, even under harsh conditions. This techniquecan be considered to be a truly non-intrusive technique ifthe pressure transducer is ush-mounted at the vessel wallor if differential pressure measurements are applied[75];thus, distortion of the ow around the point of measure-ment is avoided.[66] Otherwise, this technique can modifythe local hydrodynamics of the uidized bed and mightnot be a reliable means of measurement.[86] This techniqueprovides information only about global or time-averagedhydrodynamic behavior of the bed. Therefore, it is uselessfor monitoring the local uidization phenomena inside thebed[87] and ascertaining the location in the bed in whichvariations in the dynamic behavior are taking place duringdrying.[88] On the other hand, signal processing is an essen-tial tool for extracting information from the recoded press-ure uctuations related with the particle physical propertiesand the bed hydrodynamics. Unfortunately, few techniquesare available for successful and satisfactory processing ofpressure signals to monitor physical properties of particlesbeing processed, such as signal energy, average cycle time,dominant frequency, and attractor comparison tools.Pressure measurement needs intrusive pressure taps andthe pressure transducer needs to be placed inside the pro-cess itself for industrial and experimental applications. Thistechnique does not provide detailed information about bedheight of uidization media and clear knowledge duringprocessing of very ne particles. Furthermore, to preventfouling of the pressure transducers with ne wet powder,continuous back-ushing with costly pressurized air or amechanical scraper is necessary, which in turn diminishesthe sensitivity of the probe.[89] Pressure uctuations analy-sis provides statistical information without resolvingspecic particle sizes.[64] In addition, identication of the

source of uctuations amongst many simultaneouslyoccurring phenomena is very difcult due to the extremelycomplex local ow structure through the uidized bed.[86]

Optical Imaging Technique

Optical imaging is one of the earliest techniquesemployed in the uidized bed drying process for real-timedetermination of physical properties of the granule,including granule size, PSD, and shape,[4] as well as theuidization regime. This transforms an image captured bya charge coupled device (CCD) camera into the digital formand performs some pretreatments on it, such as ltration,noise reduction, and pattern recognition, in order to obtaina rened image or to extract useful insights from it.[3] Imageacquisition, preprocessing, segmentation, extraction, andrepresentation of the characteristic parameters are the mainsteps in image processing analysis.[90] Recently, particleimage velocimetry (PIV) has been introduced for determin-ing velocity elds by capturing two images shortly aftereach other and computing the distance individual particlestraveled within this time. From the specied time intervaland the recorded movement, the instantaneous velocityvector eld can be identied in a cross-section of a ow.As illustrated in Fig. 2, the PIV apparatus contains aCCD camera, a strobe or laser with an optical arrangementto conne the physical region illuminated, a synchronizer toperform as an external trigger for adjusting the CCDcamera and laser and the seeding particles. There areremarkable applications of the optical imaging techniquein the literature to characterize uidized bed hydrodyn-amics as well as physical properties of particles, which aresummarized in Table 4.

The rst application of image processing in FBD goesback to 1995, when Watano and Miyanami[91] developeda system based on the image processing technique for onlinemonitoring of PSD and shape of granules in uidized bedgranulation. The particle imaging probe was able to predictthe granule shape precisely, being equivalent to the accu-racy of the ofine sieve method. They extended their inves-tigations to more precise controlling of FBD using an imageprocessing system supplemented with fuzzy rules,[92] a fuzzylogic controller based on image processing technique,[93]

and an adaptive feedback fuzzy controller based on aparticle image probe and an image processing system.[94]

Saadevandi and Turton[95] found that particle velocityand bed voidage are functions of both axial and radialpositions using the data obtained by the video imagingtechnique. However, the spray rates did not inuence theparticle velocity and voidage measurements. Particle trajec-tories at different particle loading, jet air velocity, andposition of the Wurster tube were investigated using theuorescent technique by Karlsson et al.[96] Trajectories ofparticles were successfully tracked, even with unexpectedparticleparticle collision and particlewall collision.


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Narvanen et al.[97] employed a 3D image analysis techniquefor determination of particle size within a uidized bedgranulator. Three basic colors, red-green-blue (RGB), wereused to lighten a at granule bed surface from threedifferent orientations. The PSD obtained by the image pro-cessing method satisfactorily corresponded to those of thesieve analysis. Good agreement was observed between theresults of confocal laser scanning microscopy (CLSM)and the chemical analysis in determining the coating thick-ness of microparticles through the uidized bed coater.[98]

It is worth mentioning that the CLSM is a technique forcapturing high-resolution optical images with depth selec-tivity. The key property of the confocal microscopy is itscapability to capture blur-free images of thick specimensat different depths, a process known as optical sectioning.Images are acquired locally and reconstructed with anappropriate computer program, permitting 3D reconstruc-tions of topologically intricate objects. Mozina et al.[99]

employed digital visual imaging to assess the sphericaldiameter, coating thickness, and undesirable agglomerationof pellets as well as classication and analysis of pelletswithin the uidized bed coater. Accuracy, precision, stab-ility, and speed of the developed technique were conrmedby experimental results. Feasibility of image analysis withdifferent feature selection approaches for excludingirrelevant and redundant information was investigated tomonitor the coating thickness of pharmaceutical pelletsduring uidized coating.[100] A strong correlation wasfound between image features and process parameters.Wang et al.[101] applied PIV to visualize and classify annu-lar bed ow patterns in the bottom spray uidized bedcoater and accordingly detected three types of ow patternswithin the drying bed. The ow patterns were considerablyinuenced by the coating uniformity, which was determined

by color coating and subsequent tristimulus colorimetry ofinline samples. Liew et al.[102] quantied particle recircula-tion through the partition column using a high-speed-imaging-based visiometric process analyzer and ensemblecorrelation PIV. Their results were in good agreement withthe data obtained using an image tracking method.

The optical imagining technique is a real-time, non-intrusive, rapid, low-cost, efcient, repeatable, accurate,high-resolution, consistent, and objective inspection toolbased on image analysis. Image processing provides reliableinformation not only on the PSD, but also direct infor-mation on particle sizes. The PIV offers several advantages,such as simplicity of the experimental set-up and ease ofscale-up procedures, with applicability for non-intrusivelyobtaining a complete velocity vector eld.[102] However,the PIV can only detect close-wall velocity vector eldsand its measurement may be negatively inuenced by walleffects.[103] The CLSM has also several advantages, includ-ing high-resolution blur-free images, easy visualization of3D structures by stereo pairs, capability of controllingdepth of eld, elimination of background knowledge awayfrom the focal surface, and the capability of collectingsequential optical proles from coarse samples.[104] How-ever, image processing necessitates large computationalefforts for data processing due to the very large amountsof data generated when compared with other monitoringtechniques. Such a disadvantage would be diminished withfurther progress in computer technology. On the otherhand, the wavelength of light, the desired eld of inspection,and the pixel density of the digital camera conne the resol-ution of the optical imaging. Non-uniform illuminating,variable solid density in the bed, imperfections or dirt onthe bed wall, background light, and photo-bleaching cannegatively inuence the light intensity and following image

FIG. 2. Schematic diagram of typical PIV apparatus and its measurement principle.


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TABLE4

ApplicationofimageprocessingformonitoringofFBDs

Author(s)

Technique(s)

Application(s)

Dryer

type(s)

Process

target(s)

Rem

arks

Watanoand

Miyanami[9

1]

Imageprocessing

Tomonitorthegranule

size

distributionand

shape

Laboratory-scaletop-

sprayagitateduidized

bed

granulator


and

cornstarchwith


Thegranulesize

obtained

usingimageprocessing

techniquewasagreed

well

withdata

determined

by

sieveanalysis.

Watanoet

al.[92]Im

ageprocessingin

combinationwith

fuzzylogic

Tocontrolthegranule

growth

Lab-scaletop-spray

agitateduidized

bed

granulator


of

lactose

andcornstarch

granulatingwith


solution

Thedeveloped

system

accurately

controlled

the

granulegrowth

atvarious

operatingconditionsand

samplesattributes.

Watanoand

Miyanami[9

3]

Intelligentcontrol

Tocontrolthegranule

size

byem

ploying

imageprocessing

techniquein

conjunctionwith

fuzzylogic

Dryer

type:Lab-scale

agitateduidized

bed

granulator

Mixture

oflactose

and

cornstarchgranulation

Particlesize

obtained

using

imageprocessingtechnique

precisely

agreed

withsieve

analysisandsubsequently

theuidized

bed

granulator

accurately

controlled

by

fuzzylogiccontroller.

Watano[94]

Imageprocessing

techniquewith

fuzzylogic

controller

Tomeasure

and

controlthe

granulegrowth

Lab-scaleagitated

top-sprayuidized

bed

granulator

Granulationof

pharm

aceuticalpowders

composedoflactose

and

cornstarchwithasolid

binder

byspraying

puried

water

Granulegrowth

inuidized

bed

granulationwasdirectly

andcontinuouslymonitored

usinganimageprocessing

system

andalsoan

automatedcontrolsystem

of

granulegrowth

basedona

fuzzycontrolsystem

was

presented.

Saadevandiand

Turton[95]

Video

imaging

technique

Tomeasure

the

velocity

ofparticle

andvoidageofbed

Experimental

bottom-sprayuidized

bed

coater

Coatingofglass

particleas

modelmaterial

Hydrodynamicsofuidized

particlespassingthroughthe

liquidsprayinasemicircular

uidized

bed

coatingdevice

andtheeffectsoftheliquid

sprayonparticlevelocity

andvoidageprolesin

the

sprayingzonewasexactly

surveyed.

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Karlssonetal.[96]High-speedvideo

cameraequipped

withanopticallter

withaUVlampto

excite

the

uorescence-m

arked

particles

Tofollowthetrajectory

ofsingleparticlesin

thefountain

region

Laboratory-scale

spoutedbed

coater

withdrafttube

Dryingofsandparticles

coatedwith

ethyl-cellulose

Auorescenttechniquewas

satisfactorily

applied

for

detailed

studiesofthe

trajectoriesofparticlesinthe

fountain

region.

Narvanen

etal.[97]

3Dtopographicimage

processing

Onlinemonitoringof

particlesize

distributionduring

uidized

bed

granulation

Bench-scaleuidized

bed

granulator

Theophyllin

anhydrate

and

a-lactose

monohydrate

granulatingwith7.5%

aqueoussolutionof


Particlesize

measurementwas

satisfactorily

perform

edusing3D

imageprocessing

techniqueandtheresults

corresponded

quitewellto

those

ofoff-linesieve

analysis.

Depypere

etal.[98]

Confocallaser

scanning

microscopy

Todeterminethelm

coatingthicknessand

subsequentthickness

heterogeneity

Laboratory-scale

uidized

bed

withboth

thetop-sprayand

bottom-spray

conguration

Coatingofglass

beads,microbeadswith

sodium

caseinate,and

gelatinASF=A

aqueous

solutions

Theapplied

methodologywas

ableto

predictmicrocapsule

coatingthicknessdownto

11.5mm

.

Mozinaet

al.[99]

Digitalvisual

imaging

Todeterminethesize

andshapeofpellets,to

detecttheadverse

agglomerationof

pellets,andto

classify

thepellets

Full-scaleuidized

bed

coater

Pelletcoatingwithsolution

consistingthe

hypromellose

phthalate

anddibutylsebacate

inmixture

ofacetoneand

ethanol

Pelletsize

andcoating

thicknesswereeffectively

determined.

Kucheryavski

etal.[100]

Imageprocessing

withtwodifferent

feature

selection

approaches

Tomonitorthecoating

thicknessin

at-line

mode

Pilot-scaleuidized

bed

coater

Coatingofnonpareil

sugar=starchpellets

withacetaminophen

(Paracetamol),

Acryl-EZE1and

Opadry

Red

Theanglemeasure

approach

resultswerefoundto

be

more

subtleandconsistent

thanwavelet

decomposition

inmonitoringcoating

thickness.

Wanget

al.[101]

PIV

Tostudytheannularbed

owpatternsandits

effect

oncoating

uniform

ity

Lab-scalebottom

spray

uidized

bed

coater

Nonpareilscoatingwith

Hydroxypropyl

methylcellulose

Coatuniform

itywas

profoundly

affectedby

annularbed

owpatterns

identied

byPIV

.Liewet

al.[102]

High-speedimaging

coupledwith

ensemblePIV

Tomonitortheparticle

recirculationwithinthe

partitioncolumnand

toobtain

theparticle

displacement

probabilitydensity

function

Experimentalbottom

sprayuidized-bed

coater

Coatingofsugarpellets

with

hydroxypropylmethylcel-

lulose

Theparticledisplacement

probabilitydensity

function

wasconsistentwithresults

ofanimagetracking

method.

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attributes. Most imaging techniques are limited totranslucent media and two-dimensional images, apart fromconfocal laser scanning imaging, which is suitable forthree-dimensional imaging.[97] Thus, the optical imagingtechnique is not usable for large-scale FBDs in which opa-que metal chambers are used. Solids moisture content canaffect the reective attributes of the particles, particularlyfree surface water, and the captured image quality andcharacteristics.

Acoustic Emission (AE)

Many years earlier, human hearing was utilized tomonitor a drying process and the process end-point wasdetermined by listening to the acoustic signals emitted fromthe dryer.[63] An acoustic monitoring technique can beapplied in both active and passive modes. In the activeacoustics mode, an acoustic wave is transmitted into th

measurement techniques to monitor and controlfluidization quality in fluidized bed dryers: areview

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