combined application of experimental design and artificial neural networks in modeling and...

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Combined Application of Experimental Designand Artificial Neural Networks in Modeling andCharacterization of Spray Drying Drug: CyclodextrinComplexesTijana Miletić a , Svetlana Ibrić a & Zorica Đurić aa Department of Pharmaceutical Technology , Faculty of Pharmacy, University of Belgrade ,Belgrade , SerbiaPublished online: 27 Dec 2013.

To cite this article: Tijana Miletić , Svetlana Ibrić & Zorica Đurić (2014) Combined Application of Experimental Design andArtificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes, Drying Technology:An International Journal, 32:2, 167-179, DOI: 10.1080/07373937.2013.811593

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Combined Application of Experimental Design and ArtificialNeural Networks in Modeling and Characterization of SprayDrying Drug: Cyclodextrin Complexes

Tijana Miletic, Svetlana Ibric, and Zorica ÐuricDepartment of Pharmaceutical Technology, Faculty of Pharmacy, University of Belgrade,Belgrade, Serbia

The aim of this study was to investigate the usefulness ofcombined application of quality by design tools such as centralcomposite design (CCD), response surface methodology (RSM),and artificial neural networks (ANN) in the characterization, mod-eling, and optimizaton of spray drying of a poorly soluble drug :cyclodextrin complex. Models were developed by RSM and ANNfrom different pools of data. The model with best predictabilitywas the ANN multilayer perceptron (MLP)1 model developed fromthe largest group of data (R2 for response yield 0.854, moisture con-tent 0.886). On the other hand, analysis of equations derived fromthe application of RSM contributed in better understanding thecomplex relationships between input and output variables. By appli-cation of a desirability function approach, optimal process para-meters that resulted in the best process yield (86%) and minimalmoisture content in the powder (3.3%) were established (25% feedconcentration, 180�C inlet air temperature, 10% pump speed).

Keywords Aripiprazole; Artificial neural network;Cyclodextrins; Design of experiments; Spray drying

INTRODUCTION

A quality by design (QbD) approach in pharmaceuticalproduct development has the advantage that it facilitatesimplementation of the changes in pharmaceutical pro-duction processes and enables their continuous improve-ment. Through a systematic approach to the developmentand improved product and process understanding, achiev-ing the desired product quality is accomplished throughappropriate control strategies and by maintaining the pro-duction within the limits of the defined design space.[1–5]

A production process that can answer the modern requestsfor quality, efficacy, and safety of the pharmaceutical pro-duct is spray drying, a single-step continuous process bywhich the liquid or semisolid is transferred into the powder.A systematic approach to examining the factors that influ-ence the spray drying process, such as design of experiments

(DOE), helps in recognizing which factors are the most sig-nificant for the application of interest and enables definingthe formulation and process design space.[6–12] A centralcomposite design (CCD) can provide information aboutmain effects of factors, effects as a consequence of factorinteractions, and nonlinear effects of variables.[7–11] It istherefore suitable for application of response surfacemethodology (RSM) as a QbD tool that can be used for sys-tematic and simultaneous evaluation of multiple variables(process or formulation), for modeling based on mathemat-ical expressions formed according to the provided resultsand subsequently for the response prediction and optimiza-tion.[8,12,13] In addition, artificial neural networks (ANNs)are being used as a tool for development of models thatdescribe the design space and can be used for predictionof investigated product quality attributes.[13–19] There arepros and cons regarding each methodology, so when thecharacterization of the formulation and better understand-ing of the process needs to be established, a combination ofthese methods is preferred.[20–23] There are only few exam-ples in literature describing how DOE and ANN can becombined in new ways in pharmaceutical development,which are the object of this study. One is the use of ANN’sgood predictability to increase or complete the data listfrom which the models are processed by DOE-basedmathematical modeling, which provides better insight intoinput–output variables relationships but with a reducednumber of practically executed experiments.[21] The otherbenefit of combined application could be the improved pro-cess understanding through definition of intervariable rela-tionships using DOE-based modeling for an ANN’spredictions, which are usually more accurate (decipheringthe black box nature of ANNs).[21]

One of the advantages of spray drying that wasexploited in this study is the suitability of spray dryingfor efficient preparation of drug: cyclodextrin complexesin the form of a powder with improved solubility of apoorly soluble drug substance.[8,24–27] The formulationused in this study consisted of aripiprazole as a practically

Correspondence: Svetlana Ibric, Department of Pharmaceuti-cal Technology, Faculty of Pharmacy, University of Belgrade,Vojvode Stepe 450, 11221 Belgrade, Serbia; E-mail: [email protected]

Drying Technology, 32: 167–179, 2014

Copyright # 2014 Taylor & Francis Group, LLC

ISSN: 0737-3937 print=1532-2300 online

DOI: 10.1080/07373937.2013.811593

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insoluble drug substance and (2-hydroxy)propyl-beta-cyclodextrin (HPBCD) as a suitable solubilizer, developedaccording to results of a solubilization study performed byMihajlovic et al.[27] Therefore, the aim of the present studywas to investigate the usefulness of combined applicationof QbD tools such as CCD, RSM, and ANN in the char-acterization and modeling of spray drying of aripiprazole :cyclodextrin complexes. For process optimization, the goalwas to find the parameters that would result in the highestpossible production yield and powder with the lowestpossible moisture content.

MATERIALS AND METHODS

Materials

In this study, the following materials were used in theformulation, which was subjected to spray-drying experi-ments: aripiprazole (batch number AP20909003, NeulandLaboratories Limited, Hyderabad, India), (2-hydroxy)propyl-beta-cyclodextrin (batch number E2004, KleptoseHP, Roquette, Lestrem, France), and citrate buffer witha pH value of 3 (Ph.Eur. 4008000), which was preparedfrom ingredients of analytical grade.

Spray Drying Process

The composite powder with an aripiprazole : HPBCDratio of 5:95 was produced by spray drying the solution ofaripiprazole : HPBCD complex. Solubilization of the drugsubstance was performed in HPBCD solution in citratebuffer with a pH value of 3, which was determined as theoptimal medium for solubilization in a previous study byMihajlovic et al.[27] Spray drying was performed on a Buchi290-Mini Spray Dryer (Buchi Laboratoriums-Technik AG,Flawil, Switzerland) fitted with a standard 0.7-mm two-fluid nozzle; a cylindrical drying chamber made of borosili-cate glass, with 15.5 cm diameter and 48 cm length; and acocurrent flow of drying air and feed spray. The amountof solution used in all experiments was 10 g.

Input Variables

Keeping in mind previous experience with the selectedformulation,[27] responses selected for monitoring, andreview of the relevant literature,[28–33] which of the follow-ing process parameters would be studied as the input vari-ables was determined: pump speed (the rate at which thefeed solution is delivered to the atomizer), inlet air tem-perature (the temperature of drying air that enters thedryer), feed concentration (solids concentration in theliquid being spray dried), compressed air flow rate (feedatomization), and aspirator speed (the rate at which thedrying air is drawn through the spray dryer).

Although the atomizing air flow rate could potentiallyaffect the droplet size during spray drying and conse-quently the drying kinetics and the final moisture content,

the main effect that it has is on the particle size, which wasnot the powder property of primary interest in this study.Generally, powders produced in laboratory-scale spray-drying devices have very small particles that are cohesive,and powders are poorly flowable, due to a small chamber sizeand short droplet residence time (about 1 s).[34] This does notallow room for adjustment, without creating unsatisfactoryproduction yield and=or moisture content. For example, yieldmight be compromised for higher values for atomization, dueto droplets that are too small and particles that are moredifficult for the cyclone to separate. In the case of lowervalues for atomization, the moisture might be higher due todroplets that are too large, which are harder to dry, or dueto more cool air to be heated up. Therefore, compressed airoperating at 6 bar pressure was set for the feed atomizationto level of 5 cm on rotameter scale, corresponding to a flowrate of 600L=h.[13,22,33,35]

The aspirator speed was set to 100%, representinga maximum air flow of 40m3=h, which can provide bettercyclone separation and production yield, as suggested bythe spray dryer manufacturer, and may help in removalmoisture at higher rate.[32] Therefore, feed atomizationand aspirator speed were not selected as part of theexperimental design study and were kept constant in allexperiments at a defined level.

Three remaining input variables (factors) were selectedfor investigation: pump speed, inlet air temperature, andfeed concentration. They were set according to the require-ments of the individual runs within the selected experi-mental design (CCD). A CCD is considered to be a veryefficient technique, providing much information on experi-mental variable effects and overall experimental error in aminimum number of runs.[29] The range for inlet airtemperature (corresponding to �1 and þ1 values of theCCD) was chosen after several preliminary experiments toreach an outlet air temperature of about 70–80�C, whichwas reported in previous studies for spray drying of formu-lations with cyclodextrins.[36,37] The feed concentration ofthe liquid feed was chosen as 10 and 30% (correspondingto �1 and þ1 values of the CCD, respectively), because asimilar range was used in previous studies of spraydrying.[22,27,37,38] The pump speed corresponding to �1 andþ1 values of the CCD was selected as 10% and 30% as theoptimal recommended by the spray dryer manufacturer.

Output Variables

Numerous powder properties can be controlled (particlesize and shape, porosity, bulk density, flowability, moisturecontent, stability, etc.) in the spray-drying process.[6]

However, as previously discussed, powders produced fromsolutions in laboratory spray dryers consist of small, verycohesive particles, and the powders are therefore volumin-ous and poorly flowable, which is not favorable for pow-ders for solid oral dosage forms such as tablets, as it

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would be favorable for inhalation drug products. There-fore, it makes more sense to leave the detailed monitoringof outputs such as particle size, porosity, bulk density, andflowability for the scale-up phase. On a laboratory scale,formulation suitability and stability should be primarilychecked, together with processability (the ability to achievesatisfying production yield). Therefore, responses that weremonitored in this study were the production yield, moisturecontent, and outlet air temperature, parameters that areprimary indicators of the process quality and the potentialfor appropriate drug stability.[8,35,38–41]

Production yield, the mass of the powder collected afterevery spray drying run, was expressed as the percentage ofthe initial amount of the solids taken for solution preparation,from which a result for moisture content was subtracted.

Moisture content (loss on drying) was determined usinga Mettler-Toledo HR 83 Halogen moisture analyzer(Mettler-Toledo GmbH, Greifensee, Switzerland) withthe temperature set at 105�C, using a sample size of 0.5 gand a test duration until the mean mass loss drops below1mg per 50 s. The results are average values of a minimumof two measurements.

The design of the dryer was such that the outlet air tem-perature, contrary to inlet air temperature, could not be setwith a temperature regulator but resulted from a combi-nation of the inlet air temperature, pump speed, feed con-centration, and aspirator speed. Outlet air temperature wasmeasured by a temperature sensor that was positioned inthe part of spray dryer right after the spraying cylinder(drying chamber), thus enabling the measurement of theair temperature with solid particles before entering the cyc-lone separator. Therefore, the measured temperature wasdesignated as the outlet air temperature.

The parameter that could potentially influence spraydrying production yield is inadequate glass transition tem-perature (Tg) of the resulting amorphous powder, whichtogether with the potentially higher hygroscopicity andhigher moisture content can be a cause of stickiness andpoor product collection. This is an especially importantissue for products that contain sugar.[33] In the formulationchosen for this study, preliminary experiments did not indi-cate problems with processability or stability, and Tg wasnot monitored for modeling experiments.[27]

Models to Be Developed and Analyzed by DifferentApproaches

Several approaches regarding process modeling byapplication of CCD, RSM, and ANN were consideredand assessed. A scheme that describes which experimentswere used for development and testing of each model ispresented on Fig. 1.

Firstly, models RSM1 andmultilayer perceptron (MLP)1were developed from experimental data that correspond tothe CCD (Fig. 1, Table 1) by applying RSM and an MLP

neural network, respectively. Another set of experimentswas performed with randomly designed input parameters(Fig. 1, Table 2), and these results were used for testingthe predictability of models.

Then, it was considered whether an MLP can be used tocomplete the CCD data pool necessary for application ofRSM. The main idea was to use the smaller experimentaldata pool (of which all or some experiments could fit intothe CCD) for training the MLP and to use the obtainedMLP model for prediction of the results of the remainingexperiments within the entire CCD data pool. Therefore,practical execution of all necessary experiments would beavoided, and the CCD data pool to be used for modelingby RSM would consist of experimental as well as predicteddata. Additionally, the idea was to examine how the qualityof data that would be used for MLP training could affectthe quality of the resulting CCD data pool and thedeveloped RSM models because it was presumed that theresults derived from DOE application would be of betterquality. Therefore, two different types of experimental datawere used for training the MLP neural networks thatwould create the models for completing the CCD datapool. Model MLP2a was created using experimental data

FIG. 1. Scheme of experiments used for development and testing of

models (color figure available online).

MODELING AND CHARACTERIZATION OF SPRAY DRYING 169

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that corresponded to a 23 factorial design within the CCDwere used (Fig. 1, Table 1), representing the data obtainedby systematically designed experiments. Model MLP2bwas created using experimental data obtained from eightexperiments with randomly designed input parameters

(Fig. 1; Table 1, experimental run numbers 1, 10, and 20within the CCD; Table 2, experimental run numbers 21–25), representing the historical type of data that can beobtained by so-called trial-and-error experiments, meaningthat they were not systematically designed and executed.

TABLE 1Independent process variables in the central composite design (real and coded values) and obtained responses

Inputs Responses—Experimental data

A: Feedconcentration (%)

B: Pumpspeed (%)a

C: Inlet airtemperature (�C)

R1:Yield (%)

R2:Moisture

content (%)

R3: Outlet airtemperature

(�C)Run no. Coded Real Coded Real Coded Real

1 0 20 �1.68 3.18 0 170 90.7 3.0 932 �1 10 þ1 30 �1 160 91.5 5.0 653 þ1 30 þ1 30 �1 160 79.9 4.9 654 0 20 0 20 �1.68 153.18 86.7 4.4 685 0 20 0 20 0 170 91.1 3.8 806 þ1 30 þ1 30 þ1 180 79.0 4.5 717 �1.68 3.18 0 20 0 170 93.6 4.4 698 0 20 0 20 0 170 90.8 4.0 799 �1 10 �1 10 �1 160 94.4 3.5 84

10 þ1.68 36.82 0 20 0 170 82.0 3.6 8111 0 20 0 20 0 170 90.1 4.1 7812 0 20 0 20 0 170 88.2 3.9 8013 0 20 0 20 0 170 88.8 3.9 7514 �1 10 þ1 30 þ1 180 90.6 4.4 7215 þ1 30 �1 10 þ1 180 84.9 3.3 8616 þ1 30 �1 10 �1 160 87.1 4.0 8117 0 20 þ1.68 36.82 0 170 76.1 4.9 6018 0 20 0 20 0 170 89.4 3.8 7419 �1 10 �1 10 þ1 180 93.1 3.4 9020 0 20 0 20 þ1.68 186.82 86.2 3.7 83

aPump speed values of 3.18, 10, 20, 30, and 36.82% correspond to approximate feed flows of 1, 3, 6, 9, and 11mL=min, respectively.

TABLE 2Independent process variables for randomly designed experiments and obtained responsesa

Run no.

Inputs Responses—Experimental data

A: Feedconcentration (%)

B: Pumpspeed (%)

C: Inlet airtemperature (�C)

R1: Yield(%)

R2: Moisturecontent (%)

R3: Outlet airtemperature (�C)

21 20 10 160 89.8 3.6 8522 10 20 165 91.5 4.4 7223 20 15 170 89.1 3.6 8324 22 13 180 89.6 3.6 9125 15 15 180 89.5 3.6 8826 22 15 160 90.6 3.6 8327 10 10 170 91.6 3.7 8428 15 10 165 90.2 3.5 83

aPump speed values of 10, 13, 15, and 20% correspond to approximate feed flows of 3, 3.9, 4.5, and 6mL=min, respectively.

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Models developed in this way were then used to predict dataand complete the data pool corresponding to CCD, in thecase of model MLP2a the remaining set of 12 experiments,and in the case of model MLP2b the remaining set of 17experiments (Fig. 1). By mathematical modeling of thesedata pools, consisting of a combination of experimentaland predicted data, models RSM2a and RSM2b weredeveloped. The results obtained from experimental run num-bers 26–28 (Fig. 1, Table 2) were used for testing the models.

Another goal was to examine the potential of usingDOE-based modeling for deciphering the black box natureof the artificial neural network’s predictions by developingequations that best fit the predicted results. Therefore, pre-dictions of the MLP model with the best predictability,obtained for all experimental runs defined according tothe CCD, were processed by RSM in order to define thesignificance and relationships between examined inputand output variables (RSM3 models). The derived math-ematical equations were analyzed in order to determinewhat the similarities and differences between equationsderived from RSM1 and RSM3 models were and to tryto explain why use of MLP models gives better predictions.

Predictability of all developed models was expressedthrough calculation of coefficients of determination (R2)for experimentally obtained and predicted data and formodel comparison.

Specific details of RSM and MLP application are givenin the following two subsections.

Response Surface Modeling

For RSM, the input variables were pump speed, inlet airtemperature, and feed concentration, and the outputs wereyield, moisture content of the obtained powder samples,and outlet air temperature. Process parameters were setaccording to the CCD, which corresponded to a five-levelexperimental plan consisting of 20 experiments. Thisexperimental design is suitable for application of RSM,which allows the estimation of interaction between theinput variables and quadratic effects of input on the outputvariables and provides an idea of the shape of the responsesurface investigated.[42] Real and coded values of evaluatedvariables, monitored responses, and the experimentalmatrix are shown in Table 1. Linear and quadratic modelswere tried to get a better fit of the model, and polynomialequations were generated to establish the relationshipbetween the inputs (factors) and the outputs (responses):

Y ¼ b0 þ b1X1 þ b2X2 þ b3X3 þ b12X1X2

þ b13X1X3 þ b23X2X3 þ b11X21 þ b22X

22

þ b33X23 þ Experimental error; ð1Þ

where Y represents monitored response (output), Xi are themain effects (inputs), XiXj are the interactions, X 2

i are the

quadratic effects, and bi are the coefficients. An equationthat describes the quadratic model has all of these constitu-ents; an equation that describes the linear model does nothave quadratic constituents. Analysis of variance (ANOVA)was performed to determine the significance of the equa-tion parameters for each factor. Linear and quadraticmodels were tried, and effects that were not significant(p> 0.05) were eliminated from the models without damag-ing the model hierarchy to get a better fit of the model. Themodel adequacy was checked on the basis of the coefficientof determination, R2 and the lack of fit (goal was to obtaina nonsignificant lack of fit relative to the pure error).

The equation coefficients were calculated using thecoded values; thus, the various terms can be compareddirectly regardless of their magnitude. The coding was usedthroughout the statistical analysis and it denotes that, forexample, �1 was taken instead of the actual value for thefactor on its lower level and þ1 for the higher level. There-fore, a positive parameter coefficient indicates that theoutput increases with increasing variable level and a nega-tive coefficient indicates that the output increases withdecreasing variable level. Numerical output of ANOVAincludes F-value indicating the magnitude of impact ofeach factor and the statistical significance is shown as a pvalue, with smaller figures indicating greater importance,at a 5% of significance level.

Multilayer Perceptron Neural Network

In this study, a feedforward MLP was selected todevelop the prediction models. For the MLP, the inputvariables were pump speed, inlet air temperature, and feedconcentration, and the outputs were yield, moisturecontent of the obtained powder samples, and outlet airtemperature.

An MLP can model functions of almost arbitrary com-plexity. The units in the network are arranged in a layeredfeedforward topology and the network has a simpleinterpretation as a form of input–output model. For theMLP there are five training algorithms and the verywell-known back-propagation algorithm was used in thisstudy, with the main advantage that it is the easiestalgorithm to understand. This algorithm progressesiteratively through a number of epochs. In each epochthe training cases are submitted in turn to the network,and target and actual outputs are compared and the erroris calculated. Important issues in MLP design includespecification of the number of the hidden layers and thenumber of units in these layers.[43,44] In this case, onlyone hidden layer was considered (Fig. 2). The number ofneurons in the first and third layers is determined by thenumber of inputs and outputs; therefore, the first layerhad three input units and the third layer had three outputunits (Fig. 2). However, the number of neurons in thesecond layer was varied during the training of the network


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from one neuron to five; after testing each network, thenetwork with the best prediction of the test data wasselected.[15] Adjustment of neural network parameters alsoincludes the type of transfer function, learning rate,momentum, and number of patterns.

A hyperbolic tangent sigmoid transfer function wasconsidered in the hidden layer and the output layer. Thetraining process was run until a minimum of the root meansquare error (RMSE) was reached in the training andvalidation process:

RMSE ¼ ½Rðyip � yimÞ2=n�1=2 ð2Þ

where RMSE represents the root mean square error, yprepresents predicted response, ym represents the responseobtained in the experiment, and n represents number ofexperiments, training data for the MLP.

Test data were presented to the network after the train-ing process was completed.

MLP1 was trained using known inputs and outputsobtained from the CCD (Fig. 1). The data set was dividedinto a training set (17 randomly selected experimentswithin CCD), a validation set (an additional 3 experimentswithin the CCD), and a testing set consisting of an addi-tional 9 experiments (8 experiments from Table 2 andone experiment from Table 3; responses expressed as themean of three values, trials 1–3, experiment number 29 inFig. 1).

MLP2a was trained using experimental data that corre-sponded to a 23 factorial design within the CCD (Fig. 1,Table 1), and nine experiments were used for network test-ing (all eight experiments from Table 2 and one experimentfrom Table 3; responses expressed as the mean of threevalues, trials 1–3, experiment number 29 in Fig. 1).

MLP2b was trained by using experimental data thatwere obtained from eight experiments with randomlydesigned input parameters (Fig. 1; Table 1, experimentalrun numbers 1, 10, and 20; Table 2, experimental run num-bers 21–25), and four experiments were used for networktesting (three experiments from Table 2, run numbers26–28; one experiment from Table 3; responses expressed

as the mean of three values, trials 1–3, experiment number29 in Fig. 1).

Optimization of Spray-Drying Process

Finally, after analysis of the spray-drying process, pro-cess optimization was performed using the desirabilityfunction approach, which is one of the most widely usedmethods in industry for dealing with the optimization ofmultiple response processes.[8,29,30,32,45] The method findsoperating conditions Xi that provide the most desirableresponse values. For each response Yi(X), a desirabilityfunction di(Yi) assigns numbers between 0 and 1 to thepossible values of Yi, with di(Yi)¼ 0 representing a com-pletely undesirable value of Yi and di(Yi)¼ 1 representinga completely desirable or ideal response value. The individ-ual desirabilities are then combined using the geometricmean, which gives the overall desirability value.

In this case, the optimal or target product property forspray-dried powder was the minimal value for moisturecontent. The goal was to determine the optimal processparameters in order to obtain the best yield. A numericaloptimization technique was used to generate the optimumsettings for the process parameters pump speed and inletair temperature for a solution with 25% solids concen-tration, corresponding to the concentration that was foundto be the most suitable for complexation in previous workby Mihajlovic et al.[27] The most desirable response valuesneeded to be defined, including maximized yield, minimizedmoisture content, and outlet temperature in the range ofpreviously recorded values.

The optimization capability of the RSM1 models,generated with the experimental results of the CCD, wasevaluated using this approach. After determination of theoptimal process variable setting (the software-generatedoptimal solution with the highest calculated overal desir-ability value), drug : cyclodextrin complex powders wereprepared accordingly and the obtained results were com-pared with the predictions.

TABLE 3Validation of model RSM1 optimization

Responses PredictedTrial1a

Trial2a

Trial3a

Trial4b

Yield (%) 88.2 85.3 86.4 87.0 83.0Moisturecontent (%)

3.2 3.2 3.4 3.3 3.4

Outlet airtemperature(�C)

90 90 90 90 89

aBatch size 10 g.bBatch size 300 g.

FIG. 2. Multilayer perceptron (MLP) topology.

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Computational Tools

All investigations based on experimental design (DOEand RSM application, ANOVA estimation, includingdesirability approach in process optimization) were sup-ported by the use of the software package Design-Expert7.0.0 (Stat-Ease Inc., Minneapolis, MN, USA). MLP wasapplied using commercially available Statistica NeuralNetwork Software.[46]

RESULTS AND DISCUSSION

Experimental Results and Response Surface Modeling

By applying RSM, models RSM1 were developed fromexperimental data within the CCD (Table 1) for eachresponse. Established models were all considered to be sig-nificant and the lack of fit was nonsignificant, and thereforefactors recognized as the most important by observing thecalculated p values were analyzed in detail. According toANOVA, there were no statistically significant interactionsbetween the factors for the composite formulation examinedin this study at a 5% significance level. Other authors con-cluded that in the spray-drying process many interactionsbetween the process parameters can be significant forpowder properties, such as particle size and productionyield.[8,12,47] In the present study, based on analysis of theadequacies of the consideredmodels, a quadratic polynomialequation was generated only for process yield final, wherethe influence of pump speed was the most important. Thecoefficients of the regression equation that link the responsesto the experimental variables are indicated in Table 4.

In this study, relatively good production yields wererealized (76.1–94.4%), considering that on a laboratoryscale the yield is usually lower due to limitations of thesmall equipment. Production yield was mostly influencedby pump speed, which had a quadratic effect. Highervalues for pump speed had a negative influence on yield,because the thermal energy supplied by the inlet air

temperature was not sufficient to allow complete drying,and this was also observed by a decrease in the outlet airtemperature. Sticking occurred in the drying chamberand therefore the yield was lower and the moisture contentwas higher. A trend between the results of the moisturecontent and the yields recovered was seen (Fig. 3a). Thepoints on the graph are scattered but a trend can beobserved. Generally, more product was recovered whenthe powders contained less residual moisture. Similarobservations were made in previous studies.[8,9,12,22] Feedconcentration had a negative effect on production yieldin this study, as reported in previous studies.[22,38,48]

Elversson et al. reported that this was due to the effect ofincreased solids content of the feed on the increased tend-ency for droplets to coalesce, on collision with each otheror the wall, due to higher impact force.[48] Goula andAdamopoulos observed that higher feed solids concen-tration causes a decrease in the solid mass carried awayby the exhausted air due to its effect on higher feed viscos-ities, increase of droplet and therefore particle size.[38] Onthe other hand, many authors found that the feed concen-tration had a positive impact on spray-drying yieldsbecause with the higher temperatures and higher feed con-centration more complete drying should prevent sticking ofthe product and enable better separation. With concen-trated solutions larger particles are produced and bettercyclone separation should occur.[9,47,49] The effect of theinlet air temperature was not significant for productionyield, which was also concluded by Tajber et al.[12] Someauthors observed that a higher yield was obtained withthe reduction in feed flow rate and to a certain extent withan increase in the inlet air temperature,[32,33] with thepotential for reduced yield after a further increase in inletair temperature.[32]

The results for moisture content presented in this studyare expressed as mean values of at least two measurements,with a standard deviation within the range 0.00–0.19%

TABLE 4Coefficients of the polynomial equations linking the spray-drying parameters (in terms of coded factors)

with responses—RSM1 model

R1: Yield R2: Moisture content R3: Outlet air temperature

F-value (p value) F-value (p value) F-value (p value)

Model 88.89 38.54 (<0.0001) 4.03 40.88 (<0.0001) 76.7 51.53 (<0.0001)A: Feed concentration �4.25 66.82 (<0.0001) �0.08 2.24 (0.1535) þ0.89 1.29 (0.2733)B: Pump speed �3.15 36.79 (<0.0001) þ0.57 105.59 (<0.0001) �9.04 132.28 (<0.0001)C: Inlet air temperature — — �0.21 14.79 (0.0014) þ3.60 21.02 (0.0003)B2: Quadratic pump speed �1.74 12.03 (0.0032) — — —Lack of fit 3.79 (0.0764) 3.27 (0.1004) 1.39 (0.3784)R2 0.878 0.884 0.906

Statistically significant values are bolded.


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(average 0.11%). In addition to the negative influence ofpump speed, which was discussed earlier, inlet airtemperature was observed to be a significant parameter(Table 4). These two parameters were also found to be sig-nificant in studies by Mestry et al.[31] and Telang andThorat.[32] Moisture content decreases with increasing inletair temperature, because the hotter the air, the more moist-ure it can hold before becoming saturated, therefore pre-venting vapor release from the particle surface.[29,40,41,50]

We also examined whether the outlet air temperature datacan be associated with the moisture content of thespray-dried samples. It has been found that a relationshipmay be present between these data, implying that higheroutlet air temperature resulted in lower moisture content(Fig. 3b; R2¼ 0.880). This correlation has also been recog-nized by other authors.[9,12,30] The significance of outlet airtemperature was also recognized in study by Ahi et al.,where the effect of parameters such as feed rate and inlet

air temperature were justified considering their effect onoutlet temperature.[28]

Optimization

The software generated an optimal solution with thehighest calculated overall desirability value of 0.838 andthe following parameters: pump speed 10% and inlet airtemperature 180�C. Three experiments were performedunder the optimal parameters and an additional experi-ment was performed with a larger batch. The values foryield, moisture content, and outlet air temperature werecomparable to those predicted by the model and are sum-marized in Table 3. For process yield there was a somewhatgreater difference between predicted and observed values,which was expected considering that the lack of fit for thismodel was larger than for models for other responses. Forthe larger batch (trial 4), a slight decrease in process yieldwas observed. This was influenced by the different designof the cyclone separator used with the collector suitablefor a larger batch, which does not separate the smallestparticles as efficiently as the smaller high-performance cyc-lone that is paired with the smaller collector vessel.[51]

Overall, the results showed good agreement for processand product quality properties with theoretical predictions,confirming good predictability and validity of model usedin applied experimental design.

Multilayer Perceptron Neural Network Training andTesting Using Only Experimental Results

The selected MLP structure had a second layer withthree hidden units (Fig. 2).

FIG. 3. Relationships between (a) moisture content and yield and (b)

moisture content and outlet air temperature.

FIG. 4. Comparison between experimental and predicted data obtained by MLP1 and RSM1 for prediction of outputs: (a) yield (R2MLP1 ¼ 0:854;

R2RSM1 ¼ 0:846), (b) moisture content (R2

MLP1 ¼ 0:886; R2RSM1 ¼ 0:871), and (c) outlet air temperature (R2

MLP1 ¼ 0:893; R2RSM1 ¼ 0:899).

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The structure and optimal values for the MLP1 networktraining were as follows: 1,000 epochs, learning rate 0.6,momentum 0.3, training RMSE 0.058, validation RMSE0.17, and test RMSE 0.12.

A comparison of the predictive capacity of RSM1 andMLP1 for model outputs as well as outputs predicted forthe test samples are presented in correlation plots (exper-imentally observed vs. predicted values; see Fig. 4). It canbe observed that MLP1 showed better prediction of outputyield and moisture content compared to RSM1, which isexpressed through higher coefficient of determination(R2) values between experimentally observed and predictedresults. For outlet air temperature the predictability ofboth models was similar.

Response Surface Modeling Using Combination ofExperimental Results and Results Predicted by MLP2aand MLP2b Models

The selected MLP structure had a second layer withthree hidden units for both models (Fig. 2).

The structure and optimal values for theMLP2a networktraining were as follows: 1,000 epochs, learning rate 0.6,momentum 0.3, training RMSE 0.029, and test RMSE 0.15.

The structure and optimal values for theMLP2b networktraining were as follows: 1,000 epochs, learning rate 0.6,momentum 0.3, training RMSE 0.065, and test RMSE 0.12.

The performances of the developed models were com-pared using the RMSE andR2 between predicted and experi-mental data for model outputs as well as outputs predictedfor the test samples. RMSE was quite similar for both mod-els; R2 was higher for MLP2a: 0.872, 0.918, and 0.873 com-pared to 0.777, 0.851, and 0.852 for MLP2b for the outputsyield, moisture content, and outler air temperature, respect-ively. This is probably due to the fact that MLP2a wasdeveloped using experiments systematically designed toreduce the amount of work necessary to create an adequatemodel without compromising the significance and accuracy.

Models developed in this way were used to complete thedata pool corresponding to the CCD (Fig. 1). In the case ofmodel MLP2a, predictions were made for the remaining setof 12 experiments, and in the case of model MLP2b, pre-dictions were made for the remaining set of 17 experiments.Two CCD data pools were established by combining real(experimental) data and data predicted using MLP2a andMLP2b models. Using these data pools, polynomial mod-els RSM2a and RSM2b were developed. The resultsobtained from experimental run numbers 26–28 (Table 2)were used for testing the RSM2a and RSM2b models. Acomparison of the predictive capacity of RSM2a andRSM2b for model outputs as well as outputs predictedfor the test samples are presented in correlation plots(experimentally observed vs. predicted values; see Fig. 5).The model with better predictability was RSM2a, whichwas apparently the consequence of better performance of

MLP2a used for completion of the CCD data pool. Thesame conclusion was reached in a study by Miguelez-Moran.[21] This confirms the presumption that with betterquality data are obtained using DOE, enabling the devel-opment of more accurate models and improvement of theprocess understanding, in comparison to using trial-and-error experiments that are not systematically designed.On the other hand, the idea of using an ANN to completethe missing data within a specific DOE and developing theRSM model without having to perform the entire set ofexperiment still remains attractive. When using a largernumber of experiments that cover a larger number ofpotential factors of influence, better model can be expected.

FIG. 5. Comparison between experimental and predicted data obtained

by RSM2a and RSM2b for prediction of outputs: (a) yield (R2RSM2a ¼

0:828; R2RSM2b ¼ 0:338), (b) moisture content (R2

RSM2a ¼ 0:783; R2RSM2b ¼

0:656), and (c) outlet air temperature (R2RSM2a ¼ 0:856; R2

RSM2b ¼ 0:737).


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In a study by Miguelez-Moran[21] where a larger data poolwas analyzed, over a third of the runs needed for a selectedDOE could be completed with the so-called historical(trial-and-error) data, so the number of experiments thatneeded to be practically executed was reduced, and modelswith relatively good performance could be developed.

Predictability was also compared with that obtained bymodels RSM1 and MLP1 developed from all experimentaldata (Figs. 4 and 5) and it can be observed that the bestpredictions were obtained by MLP1 and then RSM1 mod-els, which were developed from larger data pools.

For the RSM1, RSM2a, and RSM2b models, a compari-son of the coefficients of the polynomial equations linkingthe spray-drying parameters with responses was used totry to explain the differences in predictability of modelsand to better understand the relationships between the vari-ables (Tables 4, 5, and 6). Predictability for yield was the

least accurate for model RSM2b, because it did not considerthe pump speed sufficiently significant, unlike modelsRSM2a and RSM1. In addition to conclusions made bythe RSM1 model analysis, RSM2a recognized that feedconcentration had a quadratic effect on yield and that theinteraction between feed concentration and pump speedmight also be significant. For moisture content and outletair temperature, both RSM1 and RSM2a did not find anysignificant interactions and the coefficients of polynomialequations were similar. The RSM2b model, which hadpretty good R2 obtained during model establishment formoisture content and outlet air temperature, led to the ideathat relationships between inputs and outputs might bemore complex, having found more factors to be significant,some interactions, and quadratic effects. For example, itwas found that feed concentration might be significant formoisture content because more concentrated solutions


with responses—RSM2a model



Model 89.69 53.69 (<0.0001) 4.06 51.16 (<0.0001) 77.56 70.78 (<0.0001)A: Feed concentration �4.49 170.85 (<0.0001) þ0.03 0.38 (0.544) �0.48 0.63 (0.440)B: Pump speed �3.00 76.58 (<0.0001) þ0.53 110.81 (<0.0001) �7.75 160.00 (<0.0001)C: Inlet air temperature — — �0.33 42.28 (<0.0001) þ4.41 51.72 (<0.0001)AB interaction �0.97 4.64 (0.049) — — — —A2: Quadratic effect �0.98 8.65 (0.011) — — — —B2: Quadratic effect �1.01 9.19 (0.009) — — — —R2 0.950 0.906 0.930



with responses—RSM2b model



Model 88.17 19.60 (<0.0001) 3.90 57.02 (<0.0001) 79.00 50.22 (<0.0001)A: Feed concentration �2.68 52.56 (<0.0001) �0.20 64.45 (<0.0001) þ2.33 28.35 (0.0001)B: Pump speed �0.02 0.002 (0.968) þ0.31 160.25 (<0.0001) �5.48 156.82 (<0.0001)C: Inlet air temperature — — �0.14 33.40 (<0.0001) þ2.75 39.34 (<0.0001)AB interaction — — — — — —BC interaction — — þ0.07 4.74 (0.047) �1.35 5.54 (0.034)A2: Quadratic effect — — — — — —B2: Quadratic effect �0.89 6.23 (0.024) �0.11 22.25 (0.0003) þ1.94 21.07 (0.0004)R2 0.786 0.953 0.947


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might be dried easier due to the small amount of water to beevaporated from each droplet.[9] In addition, the interactionbetween inlet air temperature and pump speed was recog-nized as significant for both moisture content and outletair temperature, which were well correlated (Fig. 3b).

Response Surface Modeling with Results Predicted bythe MLP Model with the Best Predictability

In order to examine the potential of using DOE-basedmodeling to decipher the black box nature of the predictions

of the ANNs, results predicted by the MLP model with themost accurate predictions (bestR2) for all experimental runswithin the CCD were processed by RSM in order to definethe significance and relationships between the factors foreach response (model RSM3). Out of the three MLP mod-els, the model with the best predictability was MLP1because it was developed from the larger data pool and itspredictions were used for RSM3 modeling. The derivedmathematical equations were analyzed in order to deter-mine the similarities and differences between the equations


with responses—RSM3 model



Model 89.31 50.51 (<0.0001) 4.02 61.16 (<0.0001) 76.30 60.09 (<0.0001)A: Feed concentration �4.05 98.68 (<0.0001) �0.09 3.57 (0.077) þ1.77 7.19 (0.017)B: Pump speed �3.58 77.12 (<0.0001) þ0.61 162.83 (<0.0001) �9.51 20.89 (<0.0001)C: Inlet air temperature �1.31 10.28 (0.006) �0.20 17.08 (0.0008) þ3.07 21.56 (0.0003)AB interaction — — — — þ1.88 4.73 (0.046)BC interaction — — — — — —A2: Quadratic effect — — — — — —B2: Quadratic effect �1.57 15.95 (0.0012) — — — —R2 0.931 0.920 0.941


TABLE 8Short resume of applied modeling approaches and the type of information they provided

Model type Usefulness of model Problematic issue for the model

RSM1—all experimental data Gives precise information regarding whetherand how each factor influences outputs,which improves process understanding

Cannot be adapted with new dataoutside predefined DOE

MLP1—all experimental data Best predictability (R2)

Update with new data possible

Black box nature of predictions doesnot enable improvement ofprocess understanding

Overtraining

RSM2a—experimental þMLP2apredicted data (MLP2a—23

experimental data)

Reduced number of practically executedexperiments

DOE provides data of good quality

MLP needs to be robust to enablegood predictions

RSM2b—experimental þMLP2bpredicted data (MLP2b—randomexperimental data)

Reduced number of practically executedexperiments

Use of historical (trial-and-error) data

MLP needs to be robust to enablegood predictions

Experiments that are not part ofDOE can result in models withpoor predictability

RSM3—all data predicted byMLP1

Deciphering black box nature of predictions Cannot be adapted with new data


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for RSM1, the other model with good predictability. It wasnoticed that RSM3 models that were developed fromMLP1 predictions had slightly higher R2 values comparedto the RSM1 model (Tables 4 and 7), which resulted inoverall better predictability (Fig. 4). Although calculatedparameter coefficients were similar for both RSM1 andRSM3, the main difference for RSM3 models was thatthey defined all three examined process parameters assignificant for the observed responses, which might explainwhy they performed better.

Short Resume of Applied Modeling Approaches and theType of Information They Provided

This study demonstrated that there are pros and consregarding each methodology, and when the characteriza-tion of the formulation and better understanding of theprocess needs to be established, the best way is to combinethese methods, as previously reported (Table 8).[21,22]

CONCLUSION

Better predictions can be obtained with ANNs becausethey are better able to model nonlinear, intricate systemsand because the influence of interactions has a significantimpact but it could not be approached by simple use ofRSM. The use of ANNs (models MLP2a and MLP2b) incompleting one portion of data in the experimental designdata pool resulted in satisfying results for some outputs,considering the number of experimental data used for mod-eling. This idea can be useful in spray-drying process char-acterization and modeling by reducing the number ofperformed experiments. Nevertheless, the ANN used forsuch a purpose has to be sufficiently robust. Even if math-ematical models obtained by RSM do not achieve valuesthat provide as good a fit, valuable information can beextracted through the analysis of the mathematical expres-sions, which can help to improve the understanding of thespray-drying process or can be used as an attempt to solvethe black box nature of the ANN predictions by findingmathematical relationships between variables.

FUNDING

This work was supported by Project TR 34007 of theMinistry of Education and Science, Republic of Serbia.

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combined application of experimental design and artificial neural networks in modeling and...

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