ed

e a

rga

Keywords:Articial neural network (ANN)Exergetic performanceSpray drying processMultilayer perceptron (MLP)

uras vit d

(MLP) ANN was utilized to correlate the output parameters (inlet exergy, outlet exergy, lost exergy, des-tructed exergy, entropy generation, exergy efciency, and improvement potential rate) to the four inputs

sed meor slu

inefciency of using hot air as the most common drying medium(Mujumdar, 2006). It also releases signicant amounts of carbonoxides to the environment. Thus, one of the most important chal-lenges for drying industry is to reduce the energy consumption inorder to reach a cost-efcient, reliable, sustainable, and environ-mentally friendly drying process.

able operation, designing the exergy efcient equipments, controlof drying process, managing of fuel consumption, recycling of ex-haust air, and etc. (Nazghelichi et al., 2011b). Drying is a quite com-plex and uncertain phenomenon, whose mechanisms are not yetentirely discovered. In the case of spray drying, the complexity be-comes even more pronounced due to a series of transient intercon-nected stages i.e. liquid atomization, curst formation at dropletsurface, extensive heat and mass transfer, rapid water evaporation,droplets agglomeration, and wall deposition. Quantication ofrelationships between inputs and outputs of an ill-structured pro-cess such as spray drying operation using mathematical, statistical

Corresponding author. Tel.: +98 2612801011; fax: +98 261 2808138.

Computers and Electronics in Agriculture 88 (2012) 3243

Contents lists available at

Computers and Electr

elsE-mail address: mortazaaghbashlo@yahoo.com (M. Aghbashlo).liquid into a stream of hot gas and subsequently rapid vaporiza-tion of moisture from generated droplets. Spray drying providesimportant advantages such as handling the heat sensitive andheat-resistant uids; production of dry materials with controlla-ble particle size, shape, form, moisture content, and other specicproperties; possibility for a continuous operation adaptable toboth conventional and PLC controls; application of a wide rangeof production rates; and extensive exibility in dryer apparatusdesign. However, drying is the most energy-intensive unit opera-tion due to the high latent heat of vaporization and the inherent

cess sustainability and new unforeseen ideas for improvements,and therefore it is applicable for the processes evaluation, optimi-zation, and modeling purposes. Recently, several studies have beenundertaken on exergy analysis of different drying methods (Ozgen-er and Ozgener, 2006, 2009; Colak and Hepbasli, 2007; Aghbashloet al., 2008, 2009; Liapis and Bruttini, 2008; Yongzhong et al.,2008; Hancioglu et al., 2010; Nazghelichi et al., 2010; Icier et al.,2010; Hepbasli et al., 2010; Gungor et al., 2011).

Exergetic modeling of a drying operation provides variousadvantages in many facets of process such as selection of sustain-1. Introduction

Spray drying is an extensively udry powders from pumpable liquids0168-1699/$ - see front matter 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.compag.2012.06.007parameters. Various error minimization algorithms, transfer functions, number of hidden neurons, andtraining epochs were investigated to nd the optimum ANN model. The MLP ANN with Levenberg-Mar-quardt error minimization algorithm, logarithmic sigmoid transfer function, 20 hidden neurons, and 100training iterations was selected as the best topology to map the exergetic performance of microencapsu-lation process according to statistical parameters and model simplicity. The model predicted exergeticparameters of spray drying process with R2 values greater than 0.98 indicating the delity of the selectednetwork. Accordingly, the selected ANN model can be applied to determine the exergy efcient dryingconditions to achieve a sustainable spray drying process.

2012 Elsevier B.V. All rights reserved.

thod for production ofrries by atomizing the

In recent years, exergy based performance evaluation and sub-sequent optimization/modeling of drying facilities and processeshas been a growing interest amongst the researchers. Exergy anal-ysis can provide a comprehensive and deeper insight into the pro-Accepted 30 June 2012 pump rates (mass ow rates), and spraying air ow rates as inputs for ANN. A multilayer perceptronThe use of articial neural network to prdrying process: A preliminary study

Mortaza Aghbashlo a,, Hossien Mobli a, Shahin Raea Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, IranbDepartment of Food Technology, Institute of Chemical Technologies, Iranian Research O

a r t i c l e i n f o

Article history:Received 19 November 2011Received in revised form 12 June 2012

a b s t r a c t

A feedforward articial nemicroencapsulation procesconducted at different inle

journal homepage: www.ll rights reserved.ict exergetic performance of spray

, Ashkan Madadlou b

nization for Science & Technology (IROST), Tehran, Iran

l network (ANN) was applied to predict the exergetic performance of aa spray drying. The exergetic data was obtained from drying experimentsrying air temperatures, aspirator rates (drying air ow rates), peristaltic

SciVerse ScienceDirect

onics in Agriculture

evier .com/locate /compag

A surface area (m )C specic heat (kJ/kg K)

n nozzle

lectrp

e specic energy (kJ/kg)ex specic exergy (kJ/kg)_Ex rate of exergy (kJ/s)h specic enthalpy (kJ/kg)hfg latent heat of water (kJ/kg)

I _P improvement potential rate (kJ/s)_m mass ow rate (kg/s)MAE mean absolute errorMSE mean square errorP pressure (kPa)Q volume ow rate (m3/s)_Q rate of heat transfer (kJ/s)R specic gas constant (kJ/kg K)R2 coefcient of determinations specic entropy (kJ/kg K)_S rate of entropy (kJ/s K)T temperature (K or C)v velocity (m/s)x mole fractionX fraction of component (%)Y valuey average valueZ exponentNomenclature

2

M. Aghbashlo et al. / Computers and Eand analytical methods is much more difcult. Articial neuralnetworks (ANNs) are nonlinear mapping structures based on thefunction of human brain have been extensively used for modelingand prediction, especially when underlying relationships are un-known. It is worth noting that ANNs provide the potential to iden-tify and classify network activity based on limited, incomplete,noisy, dynamic and nonlinear data sources.

Several studies have been conducted to identify nonlinear andcomplex drying systems and process behaviors using ANNs (Eren-turk et al., 2004; Wu and Avramidis, 2006; Erenturk and Erenturk,2007; Movagharnejad and Nikzad, 2007; Chegini et al., 2008; Tri-pathy and Kumar, 2009; Omid et al., 2009; Khoshhal et al., 2010;Balbay et al., 2011; Aghbashlo et al., 2011; Nazghelichi et al.,2011a,b,c; Gorjian et al., 2011; Cakmak and Yildiz, 2011). Resultsof previous studies indicated that ANN models, developed basedon the experiential data, accurately predicted the drying character-istics of different biological and industrial products, and can be ap-plied to other complex and nonlinear processes. A literature surveyshowed that there is no information about the exergetic modelingof spray drying process using ANN. Therefore, the aim of this re-search is to develop and evaluate the feedforward ANN topologyas an approximating tool for prediction of exergetic performanceof spray drying process.

2. Materials and methods

Selection of the optimal ANN topology involved four steps: (i)preparation of data required for training, cross-validation and test-ing, (ii) training of networks by designing the ANN topology usingdifferent learning algorithms, transfer functions, processing

AbbreviationsAR aspirator rate (%)PPR peristaltic pump rate (%)SAFR spraying air ow rate (l/h)out outletp productv water vaporw water0 dead stateGreek symbolsq density (kg/m3)x humidity ratio of air ()w exergy efciency (%)

Subscriptsa airat atmospherecalc calculatedd/p droplet/particledc drying chamberdes destructione emulsionexp experimentalf saturated liquid stateg saturated vapor stategen generationi numeratorin inletl loss

onics in Agriculture 88 (2012) 3243 33elements (neuron numbers), and training iterations, (iii) testingthe obtained networks with a unseen data set and nally (iv) selec-tion of optimal network according to statistical parameters andsimplicity of ANN topology (Madadlou et al., 2009a).

2.1. Data preparation

Database for development of ANNs were compiled from ourprevious report on exergetic performance of spray drying process(Aghbashlo et al., 2012b). The detailed information on the feedpreparation, spray drying system and exergetic calculation wasalso presented in the previous study (Aghbashlo et al., 2012b).However, a brief explanation of emulsion preparation method,schematic diagram of the experimental spray dryer system and re-lated theoretical consideration for exergetic calculation is repre-sented in the following subsections. The inlet drying airtemperature, aspirator rate (drying air mass ow rate), peristalticpump rate (liquid mass ow rate), and spraying air ow rate wereused as input parameters to ANN model. The total inlet exergy todrying chamber, total outlet exergy from drying chamber, lostexergy, destructed exergy, entropy generation, exergy efciency,and improvement potential rate were used as desired parametersin ANN model. The total inlet exergy to drying chamber includingthe exergy of drying air, exergy of entering water and product, andexergy of spraying air indicated the maximum useful work possibleduring atomization and drying process when the system broughtto the equilibrium state by means reversible processes. The totaloutlet exergy from drying chamber including exergy of outlet dry-ing air, exergy of outlet product, and water inside product was ameasure of the potential of outlet streams to cause change, as a

1 inlet drying air10 spraying air2 inlet wet product3 outlet moist air4 dry product

consequence of not being completely stable relative to the refer-ence environment. The exergy loss from drying chamber to ambi-ent including the exergy losses by means of conductive, convective,and irradiative heat transfer was the maximum useful work thatcould be obtained from lost heat at a kwon state in a given environ-ment. The destructed exergy including destroyed exergy in atom-ization, heat and mass transfer, moisture evaporation, and etc.was the possible useful work wasted in process due to irreversibil-ity. The entropy generation could be used to determine the energynot available for work in atomization and drying process. The exer-gy efciency could be dened as maximum useful work properlyapplied for droplets generation and moisture evaporation to max-imum possible useful work supplied to drying chamber by dryingair and spraying air. The improvement potential rate indicatedthe potential of effective exergy usage in atomization and dryingprocess.

The 81 patterns were divided into 49 (60%), 12 (15%), and 20(25%) data sets for the training, validation, and testing the neuralnetworks, respectively. Before developing the different ANN topol-ogy, some preprocessing operations were carried out: rstly, allsample data were randomized. Then, data normalization (0.9,0.9) was achieved through the min-max function. Then, data nor-malization (0.9, 0.9) was achieved through the min-max function.The normalized value (Ynormalized) for each raw input/output dataset(Yi) was calculated as:

Ynormalized 0:9 1:8 Yi YminYmax Ymin 1

2.1.1. Materials and feed preparationFish oil and skim milk powder (SMP) were purchased from

Iran), respectively. Phosphate buffer (pH7.0) and sodiumazidewerepurchased from Merck (Darmstadt, Germany).

Twenty gram of skim milk powder was dissolved in seventygram of phosphate buffer (5 mM, pH 7.0) and was stirred at500 rpm for 30 min at room temperature. The wall material solu-tion was stored at 4 C for 10 h to allow complete hydration (Mad-adlou et al., 2009b) and sodium azide (100 mg/L) was added toprevent microbial growth. The emulsion was prepared by progres-sively blending the ten gram sh oil and the wall solution, using arotor-stator blender (Ultra-turrax IKA T18Basic, Wilmington, USA),at 3500 rpm for 2 min. Then, the emulsion was homogenized at24,000 rpm for 5 min. The prepared emulsion was used as feed toproduce sh oil microcapsule.

2.1.2. Spray drying system and exergetic calculationThe drying of liquid was accomplished using a BCHI Mini

Spray dryer B-191 (Flawil, Switzerland). Fig. 1 shows a schematicview of used spray dryer at working state with more details.

The compressed air up to 800 l/h was used to disperse the feed-ing liquid to small droplets through the two-uid internal mixingnozzle with diameter of 0.7 mm. The dryer was equipped with un-der pressure creating aspirator motor to provide and regulate theamount of hot air up to 35 m3/h required for drying of atomizeddroplets. The inlet drying air temperature up to 220 C was con-trolled and monitored on control panel using the PT 100 thermo-couple with precision of 1 C. Temperature of outlet air carryingthe particles was measured before entering to the cyclone andmonitored on control panel using PT 100 thermocouple. The peri-staltic pump rate was also used to change the liquid feeding rateinto the atomizing nozzle.

During the experiments, the data required for exergetic calcula-

34 M. Aghbashlo et al. / Computers and Electronics in Agriculture 88 (2012) 3243QeshmFishOil Co. (QFOC,Qeshm, Iran) andPegahDairy Co. (Tehran,Fig. 1. Schematic view of spray dryer at working condition: (1) Two-uid nozzle, operatethe drying medium. (3) Spray cylinder for drying the droplets to solid particles. (4) SepAspirator for generating the ow.tion such as temperature and relative humidity of ambient, inletd by compressed air to disperse the solution into ne droplets. (2) Electric heating ofaration of the particles in the cyclone. (5) Outlet lter to remove ne particles. (6)

1 An

g p

lectrand outlet air, temperature of dryer chamber, initial and nalweight of liquid container, and weight of generated particles wererecorded. Also, 2 g of nished particles was dried in an oven(Memmert, Germany) at 70 C for 24 h to determine the moisturecontent. Weight measurement were performed using AAA 250Lbalance (Adam Co., UK) with precision of 0.0001 g. Drying exper-iments were carried out at inlet drying air temperatures of 140,160 and 180 C, nominal aspirator rates of 55%, 65%, and 75%, nom-inal peristaltic pump rates of 5%, 10%, and 15% and spraying airow rate of 600, 700, and 800 l/h.

Fig. 2 indicates the spray drying system with input and outputterms, schematically (Dincer and Sahin, 2004).

The mass balance equation for product, air and water were asfollows:

Product _mp2 _mp4 _mp40 _mp 2Air _ma1 _ma10 _ma3 3Water x1 _ma1x10 _ma10 _mw2x3 _ma3 _mw4 _mw40 4

The energy balance equation for drying system was written asfollows:

_ma1ea1 _ma10 ea10 _mp2ep2 _mw2ew2 _ma3ea3 _mp4ep4 _mp40 ep40 _mw4ew4 _m 0 e 0 _Q 5

Fig. 2. Schematic illustration of dryin

M. Aghbashlo et al. / Computers and Ew 4 w 4 l

where

_ma1 qa1Qa1 6

ea1 Cpa1T1 T0 x1hfg1 va21

2 1000 7

va1 Qa1Aa1

8

Qa is the volume ow rate of drying air (m3/s) and was determinedfrom dryer operation manual.

The specic heat capacity of air was determined as follows(Moran and Shapiro, 1995).

Cpa 1:04841 0:000383719T 9:45378T2

107 5:49031T

3

1010

7:92981T4

10149

where the unit of temperature is Kelvin (K).The latent heat of vaporization was computed at the saturation

condition by Brooker (1967) equation: _ma10 qa10 Qa10 13qa10

P10RaT10

14

Qe2 _me2qe2

15

Qa10 is the volume ow rate of spraying (l/h).The specic energy of sprayed wet product was calculated using

Eq. (16).

ep2 Cpp2T2 T0 va2d=phfg 2:5031062:386103T273:16 273:166 TK6338:72hfg 7:3310121:60107T20:5 338:726 TK6533:16

10The specic energy of the spraying air at the given temperatureswas obtained as follows:

ea10 Cpa10 T10 T0 x10 hfg10 va210

2 1000 11

The spraying air velocity was approximately equal to the velocity ofsprayed droplets and was approximated as follows:

va 0 vd=p Qa10 Qe2 12

rocess with input and output terms.

onics in Agriculture 88 (2012) 3243 352 1000 Cpp2T10 T0

va2102 1000 16

The specic heat of the fresh and dried products was determined asfollows (Choi and Okos, 1986):

Cpp Xi

XiCpi 17

Equations used in calculation of specic heat of product are pre-sented in Appendix (Table A1). The chemical composition of prod-uct used in specic heat calculation was 23.33% protein, 34.66%fat, 34.66% carbohydrate, 5% ber, and 6% ash, according to skimmilk powder and sh oil manufacturers reports.

The specic energy of sprayed water inside the droplets wascalculated as follows:

ew2 Cpw2T10 T0 va2d=p2 1000

Cpw2T10 T0 va210

2 1000 18

_mw40 exw40 _Exl _Exdes 26

lectrThe specic ow exergy of spraying air was determined from thefollowing equation:

exa10 Cpa10 x10 Cpv 10 T10 T0T0 Cpa10 x10 Cpv 10 ln

T10T0

Rax10Rv ln

P10P0

T0 Rax10Rv ln11:6078x011:6078x10

1:6078x10Ra ln

x10x0

Specic exergy of inlet drying air was obtained as follows:

exa1 Cpa1x1Cpv 1T1T0

T0 Cpa1x1Cpv 1 lnT1T0

Rax1Rv ln P1P0

T0 Rax1Rv ln 11:6078x011:6078x1

1:6078x1Ra ln x1x0

va21

2100027

The pressure of inlet drying air was determined using Bernoulli law.

P1qa1

Patqaat va

21

228

The specic heat of water vapor was obtained using following equa-tion with a correlation coefcient of 0.9949 (R2 = 0.9949).

Cpv 1:6083 8 104T 1 107T2 7 1012T3175 6 TK 6 6000 29The specic energy of outlet air was identied as follows:

ea3 Cpa3T3 T0 x3hfg3 va23

2 1000 19

va3 _ma3

qa3Aa320

The specic energy of outlet dried product and water was calcu-lated using Eqs. (21) and (22).

ep4Cpp4T4T0va24

21000Cpp4T3T0va23

21000 21

ew4Cpw4T4T0va24

21000Cpw4T3T0va23

21000 22

The specic energy of stuck products (wall deposited product) wascalculated using following equation. It was assumed that productheated up to the temperature of inlet drying air and capsules losttheir moisture completely.

ep40 Cpp40 T40 T0 Cpp40 T1 T0 23 _mw40 0 24 _mw40 ew40 0 25

The exergy balance equation was written for the dryer systems asfollows:

_ma1exa1 _ma10 exa10 _mp2exp2 _mw2exw2 _ma3exa3 _mp4exp4 _mp40 exp40 _mw4exw4

36 M. Aghbashlo et al. / Computers and E va210

21000 30The specic exergy of sprayed wet product was determined usingEq. (31).

exp2 hpT2;P10 hpT0;P0T0spT2;P10 spT0;P0v2d=p

21000Cpp2 T2T0T0 ln

T2T0

va

210

21000 31

The specic exergy of water inside the sprayed droplets was deter-mined as follows:

exw2 hf T2 hgT0 v f P10 PgT2 T0sf T2

sgT0 T0Rv ln PgT0x0vP0

va

210

2 1000 32

The specic exergy of outlet air was obtained as

exa3 Cpa3x3Cpv 3T3T0

T0 Cpa3x3Cpv 3 lnT3T0

Rax3Rv ln P3P0

T0 Rax3Rv ln 11:6078x011:6078x3

1:6078x3Ra ln x3x0

va23

21000 33

P3qa3

Patqaat va

23

234

Specic exergy of nished particle and water inside the capsuleswere obtained as follows:

exp4 Cpp4 T4 T0 T0 lnT4T0

va

24

2 1000

Cpp4 T3 T0 T0 lnT3T0

va

23

2 1000 35

and,

exw4 hf T3 hgT0 v f P3 PgT3 T0sf T3

sgT0 T0Rv ln PgT0x0vP0

va

23

2 1000 36

The specic exergy of stuck products was computed as follows:

exp40 Cpp40 T40 T0T0 lnT40T0

Cpp40 T1T0T0 ln

T1T0

37

_mw40 exw40 0 38Moreover, the exergy associated with heat loss to ambient wasidentied as follows:

_Exl 1 T0Tdc

_Ql 39

Exergy efciency of the spray drying process is the ratio of exergyuse (investment) in the drying of the product to exergy of the dryingair supplied (including the exergy of spraying air) to the system:

wExergy investment in the evaporation of moisture in the productExergy of drying air suppliedExergy of spraying air 100 40

w _mwev exw3exw2 _ma1exa1 _ma10 exa10100 41

where

_mwev _mw2 _mw4 42

exw3 hf T3;Pv3hgT0T0sT3;Pv3 sgT0T0Rv lnPgT0x0vP0

43

onics in Agriculture 88 (2012) 3243and

Pv3 xv3P3 44

uctu

lectrThe mole fraction of vapor was approximated as follows:

Fig. 3. Schematic strM. Aghbashlo et al. / Computers and Exv x1 0:622x0:622 45

The exergy destroyed or the irreversibility may be expressed asfollows:

_Exdes T0 _Sgen 46Van Gool (1997) has proposed that maximum improvement in theexergy efciency for a process or system was obviously achievedwhen the exergy loss or irreversibility was minimized. Conse-quently, he suggested that it was useful to use the concept of anexergetic improvement potential when analyzing different pro-cesses or sectors of the economy and this improvement potentialin the rate form given by Hammond and Stapleton (2001):

_IP 1 w100

_Exin _Exout 47

The experiments were performed in a room with T0 25 C; P0 101:325 kPa; x0v 0:003211, and x0 = 0.002 kg water/kg dry air.

The total inlet and outlet exergy was computed as follows:

_Exin _ma1exa1 _ma10 exa10 _mp2exp2 _mw2exw2 48_Exout _ma3exa3 _mp4exp4 _mw4exw4 49

2.2. Development of ANN

A multilayer perceptron (MLP) neural network with variousnumbers of hidden layers (one, two, and three) was trained andtested. MLP is a layered feedforward network typically trained withstatic backpropagation. Its main advantage is that it is easy to use,and that it can approximate any input/output map. Amongst thedeveloped networks with different hidden layers, one hidden layerMLP neural network has presented the best results. Therefore, itwas determined that an one hidden layer ANN with appropriate er-ror minimization algorithms and transfer function and with a suf-

re of the MLP ANN.

onics in Agriculture 88 (2012) 3243 37cient number of hidden neurons and training epochs was capableof approximating exegetic parameters of spray drying process.Structure of the MLP ANN used in this study for predicting the inletexergy, outlet exergy, lost exergy, destructed exergy, entropy gen-eration, exergy efciency, and improvement potential is shown inFig. 3.

As shown in Fig. 3, this type of neural network is a super-vised network because it requires a desired output in order tolearn. The goal of MLP ANN is to establish a model that accu-rately maps the input/output relationships using experimentaldata so that the model can then be used to produce the out-put when the desired output is unseen. The MLP ANN learnsusing a backpropagation algorithm i.e., the input data isrepeatedly presented to the ANN and the error is calculatedfor each presentation by comparing the output of the neuralnetwork with the desired output. Then, the computed erroris fed back or backpropagated to the ANN to adjust theweights (Jha, 2007).

The effect of various error minimization algorithms includinggradient descent momentum (GDM), Levenberg-Marquardt (LM),conjugate gradient (CG), and quick propagation (QP) on ANN per-formance were investigated. Different transfer functions includinghyperbolic tangent sigmoid (tansig), logarithmic sigmoid (logsig),linear hyperbolic tangent sigmoid, and linear logarithmic sigmoidtransfer functions were utilized for determination of neuron out-put. After using appropriate learning algorithm and transfer func-tion, the effect of neurons number and training epochs wereinvestigated on ANN performance. To develop a statisticallysound model, networks were trained three times and the bestvalues were recorded for each parameter (Omid et al., 2009).NeuroSolutions software version 6.0 (NeuroDimension Inc.,Gainesville, FL) was used for the design and testing of ANNmodels.

2.3. Selection of optimal ANN

The nal MLP ANN was selected on the basis of the lowest erroron cross-validation data set. Goodness of t of the selected ANN tothe experimental data was based on the coefcient of determina-tion (R2), mean square error (MSE), and mean absolute error(MAE) for tested models (Madadlou et al., 2009a). These statisticalparameters are as follows (Aghbashlo et al., 2011):

R2 1PN

i1 yicalc yiexp

2PN

i1 yicalc y

2 47

MSE 1N

XNi1

yicalc yiexp

2

48

MAE 1N

XNi1

yicalc yiexp 49

loss because of utilizing more exergy for drying process. The exergydestruction increased by increasing drying air temperature due tointensive heat and mass transfer. The effect of feed mass ow rateand spraying air ow rate on exergy destruction previously clari-ed. The increasing aspirator rate did not lead to a known relationwith exergy destruction. The inuence of dryer operational param-eters on entropy generation was similar to the exergy destructiondue to linear relation between exergy destruction and entropy gen-eration (Eq. (46)). The exergy efciency decreased with increasingdrying air temperature, aspirator rate, and spraying air ow ratebecause of reverse correlation between these parameters and exer-getic efciency (Eq. (41)). However, increasing feed mass ow rateenhanced exergy efciency since major part of supplied exergy todrying chamber utilized for evaporation of moisture from sprayeddroplets. The improvement potential increased with increasingdrying air temperature, aspirator rate, and spraying air ow ratedue to an increase in total inlet exergy and a decrease in exergyefciency. Whereas, the feed mass ow rate had inverse inuenceon improvement potential due to an increase in exergy efciency,as previously elucidated.

mi

ns/1

4443

3543

4443

3

38 M. Aghbashlo et al. / Computers and Electronics in Agriculture 88 (2012) 3243Table 1Variation of training and cross-validation MSE for different congurations of the error

Transferfunction

Learningalgorithm

5 Neurons/500 epochs 10 Neuro

Training Cross-validation

Training

tan sigm GD 1.237E03 1.314E03 7.402E0LM 6.313E04 7.189E04 1.037E0CG 8.410E04 8.191E04 3.954E0QP 2.289E03 1.815E03 1.512E0

log sigm GD 2.669E03 2.114E03 2.209E0LM 6.270E04 7.036E04 9.621E0CG 1.250E03 1.091E03 8.769E0QP 1.456E02 9.859E03 2.653E0

Linear tan sigm GD 1.811E03 1.446E03 7.746E0LM 1.223E03 1.111E03 3.356E0CG 1.096E03 1.237E03 5.180E0QP 1.973E03 1.657E03 1.495E0

Linear log sigm GD 2.010E03 1.943E03 1.800E03. Results and discussion

The exergetic performance of spray drying process of sh oilmicroencapsulation has been extensively discussed in our previ-ously published works (Aghbashlo et al., 2012a; Aghbashlo et al.,2012b). The amount of total inlet exergy entering to drying cham-ber increased by increasing drying air temperature and aspiratorrate due to higher amount of energy and mass ow rate suppliedto drying chamber. Higher amount of kinetics exergy of sprayingair was responsible for higher inlet exergy as spraying air ow rateincreased. However, the changing mass ow rate of feed did notlead to signicant difference in total inlet exergy due to smalleramount of feeding volume and lower specic heat of feed. Increas-ing drying air temperature and aspirator rate was associated withthe higher outlet exergy due to higher amount of inlet exergy, aspreviously explained. Destructing of exergy in atomization process,heating up of cold spraying air, and ne sprayed droplets were themain reasons for lower outlet exergy as spraying air ow rate in-creased. The lower outlet exergy obtained for lower feed rate dueto higher exergy loss from drying chamber to ambient and higherexergy destruction for drying of ne droplets. Increasing drying airtemperature, aspirator rate, and spraying air ow rate heightenedthe exergy loss to ambient possibly due to an increase in side heatloss coefcient. Increasing feed mass ow rate reduced the exergyLM 2.532E03 1.803E03 1.776E03CG 2.160E03 1.725E03 1.487E03QP 1.239E02 6.622E03 2.288E03The variation of training and cross-validation MSE for differentconguration of the error minimization algorithms and transferfunctions is shown in Table 1. It is interesting to note that thetransfer functions are mathematical equations that specify the out-put of a neuron. The transfer functions of articial neurons hinderoutputs from reaching very large magnitude which can disableANN and thus inhibit the training (Jha, 2007). Activation functionexhibits great variety, and has the meaningful inuence on learn-ing and performance of ANNs. Also, a function optimization prob-lem or error minimization algorithm is dened as a procedure todetermine the best network parameters (weights and biases) in or-der to minimize the network error.

In ANN designing procedure, the linear transfer function wasused in the output layer due to regression nature of this work. Also,in the GDM and QP error minimization algorithms, the step sizewas set to 0.1 and the momentum term to 0.7 (Omid et al.,2009). The step size is a measure of steps taken in the weight spaceto escape from local minima in the error surface. This means that ifthe step size to be too small, the learning is slow and the network isless capable. If the step size to be too high, the learning is fast andrapid uctuations for the mean squared error and resulting slowconvergence to the lower error state (Abraham and Nath, 1999;Abraham, 2004). Too small momentum term leads to an extremely

nimization algorithms and transfer functions using MLP ANN.

000 epochs 15 Neurons/1500 epochs 20 Neurons/2000 epochs

Cross-validation

Training Cross-validation

Training Cross-validation

8.660E04 4.946E04 5.457E04 3.731E04 5.604E043.709E04 6.961E06 2.718E04 9.745E08 4.517E045.612E04 3.137E04 3.985E04 2.353E04 3.644E041.305E03 8.792E04 1.112E03 7.146E04 9.858E041.756E03 2.247E03 1.840E03 2.118E03 1.882E032.862E04 4.326E06 3.088E04 8.092E08 3.993E041.018E03 6.543E04 7.816E04 5.914E04 7.400E042.131E03 2.437E03 1.979E03 2.417E03 1.921E031.092E03 6.551E04 8.162E04 4.350E04 7.191E048.269E04 2.074E04 7.432E04 1.481E04 7.294E046.363E04 3.331E04 5.474E04 4.058E04 8.519E041.432E03 1.212E03 1.206E03 8.650E04 1.290E031.479E03 1.007E03 1.505E03 7.663E04 1.019E03

1.803E03 7.370E04 1.185E03 9.262E04 1.358E031.709E03 1.350E03 1.434E03 7.870E04 1.157E031.835E03 1.987E03 1.901E03 1.535E03 1.209E03

s.

ergy

R2 0.9984 0.9958 0.9841 0.9842

lectrTable 2Performance of the selected MLP ANN for exergetic prediction of spray drying proces

Parameter Inlet exergy Outlet exergy Lost exergy Destructed ex

MSE 0.7052 2.4379 2.6189 7.2712MAE 0.68528 1.1829 1.3159 2.2341

M. Aghbashlo et al. / Computers and Eslow convergence of algorithm in error minimization procedure,and too high momentum termmay probably lead to the divergenceof algorithm (Aghbashlo et al., 2011, Abraham and Nath, 1999). It isclear from Table 1 (bolded line) that LM algorithm with logarith-mic sigmoid transfer function was the best selection based onthe results of training and cross-validation errors for the seven out-puts. This algorithm provides a numerical solution to the problemof minimizing a (generally nonlinear) function, over a space ofparameters for the function. The sigmoidal functions such as loga-rithmic sigmoid function are prevalent in approximating ANN dueto nonlinear and continuously differentiable nature of them whichare favorable for network learning (Jha, 2007). The mathematicaldenition of logarithmic sigmoid transfer function is (Erenturkand Erenturk, 2007)

log sigm 11 expZ 50

Therefore, this study was continued by changing the number ofhidden neurons and training epochs from 230 and 1002000,respectively, using selected transfer function and error minimiza-tion algorithms to create nonlinear mapping between inputs andoutputs. It is mentioned that number of input and output neurons

Fig. 4. MSE of various MLP ANN vs. the number of hidden layers for differentnumber of neurons and training epochs.are determined by dimensions of input and output data, so thatonly the number of hidden neurons is to be decided by users (Omidet al., 2009). To select the number of neurons and training epochs,45 trial congurations of MLP ANN with LM error minimizationalgorithm and logarithmic sigmoid transfer function were designedand tested.

The optimal network was selected to give the minimumMSE ontraining and cross-validation set during the training process. The

Entropy generation Exergy efciency Improvement potential rate

0.0092 7.7968E05 2.46930.0759 0.0073 1.33690.9859 0.9994 0.9983

Fig. 5. The average values of training and validating errors vs. the number oftraining epochs for the selected network.

onics in Agriculture 88 (2012) 3243 39variation of training MSE for different congurations of developedANNs is shown in Fig. 4. It is obvious from this gure that numberof neurons had a signicant effect on ANNs performance, while thetraining epoch showed a negligible effect on training and cross-val-idation MSE. Also, it is clear that the increasing neurons numberfrom 10 to 30 did not lead to a large reduction in MSE. Therefore,the ANN with 10 neurons, 100 training epochs, LM error minimiza-tion algorithm and logarithmic sigmoid transfer function was se-lected according to simplicity of ANN topology and little timerequired for training process.

Too few hidden neurons reduce the capability of ANN to mapthe input/output relation. As well, in ANNs with too many hiddenneurons, networks learn insignicant details and therefore overt-ting occurs (Ebrahimpour et al., 2008). It is worth noting that theincrease in number of neurons enhance the training time and com-plicate the ANN topology, considerably. Too few epochs diminishthe capability of the ANN to model the process i.e., ANN cannotlearn the details and therefore undertrain occurs. On the otherhand, too many epochs lead to an overtraining in ANN trainingand its failure to learn the input/output data relationship resultingin an increased mean square error values (Nazghelichi et al.,2011a). Also, it is mentioned that the increase in the number ofneurons decrease the number of epochs required and seems toaid in convergence (reduced uctuations).

Fig. 5 shows the average values of training and validating errorsvs. the number of training epochs for the selected network. It isclear from this gure, that the error for training and cross-valida-tion had a falling form since by increasing the learning iteration,the errors were fed back to the neurons and used to adjust theweights such that the error reduces by iteration and the neural

Fig. 6. Comparison of predicted and desired output values for the inlet exergy, outlet exergy, lost exergy, destructed exergy, entropy generation, exergy efciency, andimprovement potential rate using selected MLP ANN.

40 M. Aghbashlo et al. / Computers and Electronics in Agriculture 88 (2012) 3243

y dry

P PPPR91ARPPAR

nctio

ion,

lectrTable 3Seven regression models to predict 7 exergetic parameters of spra

The empirical models

Inlet exergy (J/s) = 296.749 + 1.515 Tin + 2.204 AR + 2.065Outlet exergy (J/s) = 83.967 + 0.459 Tin + 1.283 AR + 3.92Lost exergy (J/s) = 85.704 + 0.506 Tin + 0.73 AR0.964 PDestructed exergy (J/s) = 127.07 + 0.548 Tin + 0.19 AR0.8Entropy generation (J/s K) = 0.426 + 0.0018 Tin + 0.00063 Exergy efciency (%) = 14.570.052 Tin0.076 AR + 0.778 Improvement potential (J/s) = 207.92 + 1.047 Tin + 0.9466

Table A1Specic heat of different product components and water as the fu

Component Equat

M. Aghbashlo et al. / Computers and Emodel gets closer and closer to produce the desired output. As well,it is clear from this gure that ANN model successfully trained,indicating that the selected ANN topology was able to properlyestablish the relationship between the input and output parame-ters. It has been mentioned that a well-trained ANN model is vitalto map and create input/output relations (Chegini et al., 2008).

Performance of the selected ANN with a LM error minimizationalgorithm, logarithmic sigmoid transfer function, 20 neurons, and100 training epochs for exergetic predation of spray drying processis shown in Table 2. Values of R2, MSE, and MAE obtained using theselected ANNmodel in testing step was within the acceptable level.According to Table 2, the best approximation belonged to exergyefciency, with respective MSE, MAE, R2 values of 7.7968 105,0.0073, and 0.9994, and the least acceptable approximation be-longed to lost exergy, with respective MSE, MAE, R2 values of2.6189, 1.3159, and 0.9841.

In selected MLP ANN, the network returned 10 5 weights and10 bias values connecting input layer and hidden layer, and 5 10

Protein (Cp)Protein =Fat (Cp)Fat = 1.Carbohydrate (Cp)CarbohydFiber (Cp)Fiber = 1Ash (Cp)Ash = 1Water (Cp)Water =

Table A2Weights and biases of the MLP ANN model for exergetic prediction of sh oil microencap

Weights connecting input layer and hidden layer (W104)1.838 1.112 0.089 1.010

0.551 2.233 0.208 0.3700.768 0.568 2.030 2.1020.204 0.064 0.916 3.0381.206 0.680 1.118 1.0241.139 1.165 0.588 0.004

0.128 0.519 1.179 0.4632.166 0.666 1.612 0.9900.808 0.356 0.725 0.113

0.815 1.075 1.379 0.565

Biases in hidden layer (B101)0.472 0.214 0.911 2.160 0.830

Weights connecting hidden layer and output layer (W710)0.486 0.789 0.546 0.591 0.0340.189 0.728 0.250 0.849 0.0470.772 1.405 0.178 0.696 1.3140.345 0.308 1.653 2.800 0.6730.270 0.903 0.316 0.008 0.3780.313 0.297 1.631 2.785 0.7040.512 0.493 0.826 1.413 0.006

Bias in output layer (B71)0.661 1.063 0.411 0.580 1.115ing process.

R2

PR + 0.125 SAFR 0.9906R0.043 SAFR 0.99060.007 SAFR 0.9697 PPR + 0.16 SAFR 0.96650.0029 PPR + 0.00053 SAFR 0.9665R0.0039 SAFR 0.9681 2.607 PPR + 0.162 SAFR 0.9879

n of temperature (C) (Singh and Heldman, 2001).

T (C)3 6 2

onics in Agriculture 88 (2012) 3243 41weights and 5 bias value connecting hidden layer and output layer(Tables A2).

The exact and predicted values for each unseen data by the se-lected ANN are shown in Fig. 6. It is observed that the predictedvalues are in good agreement with exact values and prediction er-ror is negligible. Therefore, the selected MLP ANN model with thedeveloped structure for exergetic performance of spray drying pro-cess is capable to predict the responses with the lowest error andhas the potential to be generalized to a new input which has neverbeen used during its training.

In this research the multiple linear regression models weredeveloped for seven output parameters as the function of four in-put parameter and are reported in Table. 3. A comparison betweencorrelation coefcients, R2, of the ANN model (Table 2) with thoseof 7 regression models (Table 3) indicates that ANN model is supe-rior to regression models in predicting the exergetic performanceof spray drying process for all parameters. This is an additional evi-dence of the applicability of MLP ANN for simulating the complex

2.0082 + 1.2089 10 T 1.3129 10 T9842 + 1.4733 103T 4.8008 106T2rate = 1.5488 + 1.9625 103T 5.9399 106T2.8459 + 1.8306 103T 4.6509 106T2.0926 + 1.8896 103T 3.6817 106T24.0817 5.3062 103T + 9.9516 104T2 0 6 T (C) 6 150

sulation process by spray drying.

2.844 1.178 1.400 1.775 0.326

2.082 0.360 0.095 1.170 0.1562.477 1.010 0.246 0.990 1.1271.073 0.162 0.664 2.162 0.9450.524 0.664 0.864 1.669 0.3850.435 0.725 1.606 2.256 2.8460.522 0.578 0.889 1.837 0.4070.655 0.571 0.235 2.585 0.859

0.492 0.132

matically improves itself through learning. Moreover, ANN models

peristaltic pump rate, and spraying air ow rate, the exergetic per-

MLP ANN use only one set of weights for all drying conditions and

lectrwas concluded that the MLP ANN approach for exergetic predictionof spray drying process is capable of yielding good results and canbe considered as an attractive alternative to traditional regressionmodels and other related statistical approaches. This approach wasable to determine the nonlinear relationship between input andoutput data supplied to the system during the training phase andon that basis, makes a prediction of what the exergetic perfor-mance would be in any dryer operational condition. The developedmodels could be utilized to determine the appropriate drying con-ditions of spray drying process to reach the sustainable and exergyefcient process in industrial drying.

Acknowledgement

The authors would like to extend their appreciation for nancialsupport provided by University of Tehran.

Appendix A

See Tables A1 and A2.

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4. Conclusion

In this study, supervised ANN and mathematical models weredeveloped for determining the exergetic performance of spray dry-ing process. For this purpose, experimental results of spray dryingprocess at different inlet drying air temperature, aspirator rate,peristaltic pump rate, and spraying air ow rate were used. Itformance of drying process can be predicted, controlled, and opti-mized in real-time by one set of MLP ANN weights.

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M. Aghbashlo et al. / Computers and Electronics in Agriculture 88 (2012) 3243 43

The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study1 Introduction2 Materials and methods2.1 Data preparation2.1.1 Materials and feed preparation2.1.2 Spray drying system and exergetic calculation

2.2 Development of ANN2.3 Selection of optimal ANN

3 Results and discussion4 ConclusionAcknowledgementAppendix A References