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c h e m i c a l e n g i n e e r i n g r e s e a r c h a n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472

avai lab le at www.sc iencedi rec t .com

journa l homepage: www.e lsev ier .com/ locate /cherd

evelopment of an artificial neural network modelor prediction of cell voltage and current efficiencyn a chlor-alkali membrane cell

. Shojai Kaveha, S.N. Ashrafizadeha,∗, F. Mohammadib

Research Lab for Advanced Separation Processes, Department of Chemical Engineering, Iran University of Science and Technology,armak, Tehran 16846, IranIran Polymer and Petrochemical Institute, P.O. Box 14965/115, Tehran, Iran

r t i c l e i n f o

rticle history:

eceived 5 June 2007

ccepted 18 December 2007

eywords:

hlor-alkali

embrane cell

rine

lectrolysis

rtificial neural network

a b s t r a c t

This paper presents the development of an artificial neural network (ANN) model for the

prediction of cell voltage and caustic current efficiency (CCE) versus various operating

parameters in a lab scale chlor-alkali membrane cell. In order to validate the model predic-

tions, the effects of various operating parameters on the cell voltage and current efficiency

of the membrane cell were experimentally studied. The membrane cell incorporated a stan-

dard DSA/Cl2 electrode as the anode, a nickel electrode as the cathode and a Flemion 892

polymer film as the membrane. Each of the six process parameters including anolyte pH

(2–5), operating temperature (25–90 ◦C), electrolyte velocity (2.2–5.9 cm/s), brine concentra-

tion (200–300 g/L), current density (1–4 kA/m2), and run time were thoroughly studied at four

levels and low caustic concentrations (5–22 g/L). The predictions of ANN model as well as

those from other statistical methods were evaluated versus the measured values of cell

voltages.

The developed ANN model is not only capable to predict the cell voltage and caustic cur-

rent efficiency but also to reflect the impacts of process parameters on the same functions.

The predicted cell voltages and current efficiencies using ANN modeling were found to be

close to the measured values, particularly at higher current densities.

© 2008 Published by Elsevier B.V. on behalf of The Institution of Chemical Engineers.

quantify the process parameters including statistical methods

. Introduction

hlor-alkali (CA) production is the largest industrial scale elec-ro synthesis with a rate of 4.81 × 107 Cl2 t/y world-wide in004 (Linak et al., 2005). The progress in chlor-alkali technol-gy has been reviewed extensively (e.g., Caldwell, 1981; Corry,995; Coulter, 1980; Jackson, 1983; Kinoshita, 1992; Pletchernd Walsh, 1993; Prout and Moorhouse, 1990; Trasatti and’Grady, 1981; Varjian, 1981; Wall, 1986; Wellington, 1990). At

he current densities used (i.e. 1–5 kA/m2) one of the majorssues confronting the chlor-alkali industry is the high poweronsumption, i.e. about 1.5 × 108 MW h/y (Linak et al., 2005).

t was estimated that the power consumption accounts forver 50% of the operating costs (Cameron et al., 1990; Silver,

∗ Corresponding author. Tel.: +98 21 73912747; fax: +98 21 77240495.E-mail address: ashrafi@iust.ac.ir (S.N. Ashrafizadeh).

263-8762/$ – see front matter © 2008 Published by Elsevier B.V. on behoi:10.1016/j.cherd.2007.12.009

1981). Improvement of the electrolytic process in this respect,i.e. reduction in cell voltage, would be beneficial from botheconomical and environmental point of views.

Cell voltage and current efficiency are the most importantprocess parameters proportional to the power consumptionof a CA plant. Therefore, process evaluation becomes impor-tant in order to quantify the impact of process variables onthese two parameters. At the same time, prediction of the cellvoltage and current efficiency can facilitate achieving the opti-mum conditions and therefore reducing the intercalary costsof trial and error experiments.

Several approaches have been employed to predict and

(Jalali et al., 2008), analytical formulations (Chikhi et al., 2002)and non-parameter regression methods like artificial neural

alf of The Institution of Chemical Engineers.

462 c h e m i c a l e n g i n e e r i n g r e s e a r c h a

Nomenclature

ANN artificial neural networksCbrine brine concentration (g/L)F Faraday’s constant (96,458 C/mol)F electrolyte velocity (cm/s)ip applied current density (kA/m2)I current (kA)m(t = 0) mass of initial caustic soda (5 g)m(t) mass of produced caustic at time t (g)MwNaOH caustic molecular weight (40 g/mol)n number of exchange mol electront run time (s)y(x) artificial neural network output

Greek letters˛ artificial neural network parameterω artificial neural network parameter�NaOH caustic current efficiency

In turn, these layers have a certain number of nodes orneurons, so the nodes are also called input nodes, hiddennodes, and output nodes. Fig. 1 shows a network with one

network (ANN). However, literatures do not show any pub-lished work on the application of ANN for such predictions inchlor-alkali industry, though the ANN methods have alreadybeen applied to other fields (Bezerra et al., 2007; Cheng Yeh,2007; Huang et al., 2003; Razmi-Rad et al., 2007; Shahsavandand Ahmadpour, 2005; Shahsavand and Pourafshari Chenar,2007; Yilmaz and Atik, 2007).

Statistical methods are used to analyze the results of theexperiments and models on response as well as to determinethe contribution of each influencing factor. However, the mainconcern with statistical methods is the difficulties encoun-tered in fulfilling a large number of rigid assumptions that areessential for justifying their applications such as those of sam-ple size, linearity, and continuity. One alternative approach forthe system prediction is the technique of ANN based on thetheory of artificial intelligence. The enormous interconnec-tions in the ANN framework create a great number of degreesof freedom, i.e. fitting parameters, which permits to reflectthe system’s complexity more effectively than conventionalstatistical techniques.

On the other hand, the CA model achieved by ANN canbe employed not only to examine the effects of variousoperating parameters but also to compare the model pre-dictions with the experimental values. Using the developedANN model, one can further study the variations of dependentparameters versus independent ones. ANN’s main differencefrom other statistical methods is attributed to its relinquish-ment in terms of strict conditions for data samples andassociated assumptions. This is applicable to the existingsituation of data availability for the cell voltage and CCEfactors, which are not good enough for either statistical ornumerical modeling. At the same time, analytical modelsare more appropriate than ANN in terms of their touch-ing the detailed mechanisms of interactions among variousimpact factors. Nevertheless, such methods’ limitations arealso from their attempts to specify the complicated processesby detailed mathematical formulations, since many doubtful,interactive, and dynamic system components can barely beexpressed as precise analytical formulations. Under such con-ditions, ANN becomes the only usable means for analyzing

the related effects and interactions; it can be used withoutdisturbing either a number of prerequisites associated with

n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472

statistical models or being forced to assume unrealistic orover-simplified system conditions that are needed for analyt-ical simulations.

The effects of operating parameters (five factors) on theperformance of a CA membrane cell using Taguchi and ANOVAtechniques was recently studied by Jalali et al. (2008). How-ever, the effect of electrolysis time was neglected in thatstudy. Besides, to the knowledge of the current authors, therehas been no published literature on the ANN modeling of aCA membrane cell. Though, there have been reports on theapplication of this technique for the modeling of other pro-cesses (Shahsavand and Ahmadpour, 2005; Shahsavand andPourafshari Chenar, 2007).

The main aim of this study was thus to investigate theimpacts of operating parameters on the cell performance indi-cators such as cell voltage and CCE and to predict the sameby means of ANN technique. Process parameters that havebeen studied at four levels include anolyte pH (2–5), cell tem-perature (25–90 ◦C), electrolyte velocity (2.2–5.9 cm/s), brineconcentration (200–300 g/L), current density (1–4 kA/m2) andrun time (up to 150 min). A low caustic concentration (5 g/L)was however used since the membrane conductivity is highunder such a condition.

2. Theoretical background of the ANNs

Artificial neural networks have several attractive propertiesfor the modeling of complex production systems, i.e. capabil-ity of universal function approximation, resistance to noisy ormissing data, accommodation of multiple nonlinear variableswith unknown interactions, and good generalization ability(Coit et al., 1998). For manufacturing processes where eitherno satisfactory analytical model exists or a low-order empiri-cal polynomial model is inappropriate, neural networks are agood alternative approach.

Neural networks were beginning to be used for the model-ing of complex manufacturing processes, usually for processand quality control. Often these models are used to identifyoptimal process settings (Chegini et al., 2008).

Artificial neural networks are massively parallel intercon-nected networks of simple elements and their hierarchicalorganizations which are intended to interact with the objectsof the real world in the same way as biological nerves do, orsimply a system of interconnected computational units, ornodes (Schalkoff, 1997). A simple neural network is shown inFig. 1. As shown, it consists of an input layer, a hidden layer,and an output layer.

Fig. 1 – A simple ANN with one input node, two hiddennodes, and one output node.

h a n

nnisnfaogbimtntmfaAvfa

y

w

i(a(I(ii

y

Fp

c h e m i c a l e n g i n e e r i n g r e s e a r c

ode in the input layer, two nodes in the hidden layer, and oneode in the output layer. The output y(x) is a function of the

nput x and a set of parameters. Input nodes receive data fromources external to the network and send them to the hiddenodes, in turn the hidden nodes send and receive data only

rom other nodes in the network, and output nodes receivend produce data generated by the network which goes outf the system. In general, the number of input nodes may bereater than one. Likewise, the number of hidden layers cane greater than one, but a network with a single hidden layer

s simpler and thus useful for various applications. Further-ore, the output layer can have several nodes. In general, a

ypical network has n input nodes, one hidden layer with hodes, and m output nodes. A typical problem is to estimate

he output as a function of input. This function is unknown butay be approximated by a superposition of certain activation

unctions such as hyperbolic tangents, sigmoids, polynomi-ls, and sinusoids in a neural network fashion. For a singleNN, such as that in Fig. 1, and a hyperbolic tangent acti-ation function in the hidden layer and a linear activationunction in the output layer, the output y(x) may be expresseds:

(x) = ˛1 tanh(ω1x) + ˛2 tanh(ω2x) (1)

here ˛i, ωi, i = 1, 2 are coefficients (parameters).The ANN architecture most commonly used for forecast-

ng has been the multi-layer feed-forward neural networkMFNN). This popularity stems mainly from the theoreticalbility of the MFNN to approximate complicated nonlineardifferentiable and bounded) functions to arbitrary accuracy.n practice, only a three-layer feed-forward neural networkdenoted by TFNN (nI, nH, nO)) is usually necessary, as shownn Fig. 2. The mathematical form of a TFNN (nI, nH, nO) networks given by (Schalkoff, 1997):

(p) = f (sh(p)) = f

(nI∑

whx (p)

)for j = 1, . . . n (2)

j j

i=0

ji i H

ig. 2 – A three-layer feed-forward neural network and itsrocessing element.

d d e s i g n 8 6 ( 2 0 0 8 ) 461–472 463

zk(p) = f (sok (p)) = f

⎛⎝ nH∑

j=0

wokjyj(p)

⎞⎠ for k = 1, . . . nO (3)

where f(s) = 1/(1 + e−s), xi (p) is the input to the node i of theinput layer, yj (p) is the quantity computed by node j of the hid-den layer, and zk (p) is the output computed by the node k of theoutput layer. Note that an input layer bias x0 = 1.0 and a hiddenlayer bias y0 = 1.0 are included to permit adjustments of themean level at each stage. The model has two sets of adjustable“weights” or parameters; one wh

jicontrols the strength of the

connection between input node i and hidden node j, and theother one wo

kjcontrols the strength of the connection between

hidden node j and output node k. The activation function fusually selected to be a continuous and bounded nonlineartransfer function; the sigmoid (logistic) and hyperbolic tan-gent functions are most commonly used (Schalkoff, 1997).

When ANNs are used for prediction and forecasting, theunderlying philosophy is similar to that used in traditionalstatistical approaches. In both cases, the unknown modelparameters, i.e. the connection weights in the case of ANNs,are adjusted in order to obtain the best match between a his-torical set of model inputs and the corresponding outputs.Therefore, ANNs and statistical models are closely related.Consequently, the principles that are considered good prac-tice in the development of statistical models need to begiven careful consideration. The major areas that should beaddressed include data pre-processing, the choice of ade-quate model inputs, choice of appropriate network geometry,parameter estimation and the model validation. However, inmost applications of ANNs, the model building process hasbeen described poorly or carried out incorrectly. The mainsteps in the ANN modeling process are outlined in Fig. 3. Ateach stage, a number of alternatives are available to model-ers. Although this offers great flexibility, but can also createdifficulties as there are no clear guidelines to indicate underwhat circumstances particular approaches should be adopted.For example, the back-propagation algorithm requires selec-tion of a number of parameters, which can have a significantimpact on the results obtained. Again, there are no clear guide-lines for choosing appropriate values of these parameters, andthey are generally optimized by trial and error (Govindarajuand Ramachandra Rao, 2000).

3. Experimental

3.1. Materials

The electrolyte was prepared from analytical grade NaCl andNaOH from Merck Inc. (Germany) using double distilled water.All other chemicals used for analysis were also of analyticalgrade.

3.2. Apparatus

The cell performance test was carried out in a CA set-up simi-lar to a scaled-down industrial brine electrolysis unit. Fig. 4shows a simplified flow diagram of the set-up used in thisstudy.

The cell was a divided filter-press type (Electrocell AB,

Sweden) with Flemion® 892 membrane as the separator, astandard DSA Cl2 and a Ni plate as the anode and the cath-ode, respectively (see Fig. 5). The electrode membrane gap

464 c h e m i c a l e n g i n e e r i n g r e s e a r c h a

Fig. 3 – Main steps in the development of ANN models

(Schalkoff, 1997).

was 2 mm. The feed tanks were heated by jacketed heatersand their temperature was monitored by digital thermome-ters. Galvanostatic operation was employed using a dc powersupply. Anolyte pH was measured by a pH-meter inserted inthe anolyte feed tank. The membrane was immersed in NaOHsolution for a day to reach equilibrium prior to each experi-ment.

3.3. Experimental procedure

The anolyte and catholyte were circulated in separatehydraulic circuits during the experiments by means of twomagnetic pumps according to Fig. 4. The overflows from theanolyte and catholyte compartments of the cell were fed todistinct separators. The bubble-free electrolytes were returnedto the appropriate feed tanks for further recirculation. Duringelectrolysis, Cl2 gas produced was absorbed by 2 M NaOH solu-

tion in D-03 and then D-04. Constant currents were appliedto the cell and the established cell voltages were measured.After each test, the set-up was washed thoroughly with dis-

n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472

tilled water, drained and dried. The electrolysis run times wereeither of 30, 60, 90, 120 and 150 min.

3.4. Chemical analysis

In order to measure the caustic produced, catholyte sampleswere collected from D-02 and titrated against 0.1N HCl. Thesame data were then used for the calculation of the causticcurrent efficiency (CCE).

3.5. ANN modeling

Various neural network architectures were investigated toobtain desired model for the prediction of cell performanceas a function of selected input variables. To predict cell volt-age and CCE of the cell, six parameters including pH, operatingtemperature, electrolyte velocity, brine concentration, currentdensity and run time were selected as input variables whilecell voltage and CCE were selected as output variables.

Different scenarios on the number of hidden layers, thenumber of neurons in each hidden layer, and the type of trans-fer function for each neuron were analyzed. Architecture ofone or two hidden layers is initially used, followed by theselections for the number of neurons and the types of trans-fer functions (logic, sigmoid, or hyperbolic tangent), with thetarget of obtaining the best fit to the given data.

3.5.1. ANN model trainingThe problem at this stage was to estimate the model param-eters (weights). The method for estimating the parameters iscalled training in ANN jargon. Out of the total data sets, 65%and 15% data pairs were used for the training and validationof the model, respectively. To validate the model, 20% datapairs were used for the testing purpose. Usually, in the train-ing phase, a larger part of the data is used to train the model.In this study, back-propagation (BP) learning method is usedto adjust the model parameters. The BP learns from its errorand adjusts parameters to reduce error less than a certaintolerance. The parameters modified iteratively based on thegradient descent method until convergence is reached.

The patterns were randomly divided into 65%, 15%, and20% data sets for the training, validation, and testing the neu-ral networks, respectively. The first (learning) partition wasused to perform the training of the network. The second par-tition was used to evaluate the quality of the network duringtraining. In order to estimate the performance of the trainednetwork on new data not used in training, a third test par-tition was used. The training process was carried on until aminimum of the error was reached in the second (validation)partition. The estimation of the performance of the trainednetwork was based on the accuracy of the network on thetest partition. A feed-forward multi-layered perception (MLP)ANN trained by back-propagation (BP) algorithm was selectedto develop the prediction of the model.

The performances of the ANN models were comparedusing the root mean square error (RMSE), correlation coef-ficient, R2, and T statistics Eq. (4). T value measures thescattering around the line (1:1). Where T is close to 1.0, a goodfitting is prevailed (Weiss et al., 2000):

√√ n ∑

RMSE =

√√1n

∑i=1

(Xim − Xip)2, T = 1 −n

i=1(Xim − Xip)2∑n

i=1(Xim − X)2

(4)

c h e m i c a l e n g i n e e r i n g r e s e a r c h a n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472 465

Fig. 4 – Process flow diagram of the chlor-alkali set-up utilized: 1, membrane flow cell (M.F. cell); 2, electrolyte tank (D-01,D-02); 3, magnetic pumps (p-01, p-02); 4, gas separator (S-01, S-02); 5, rotameter (RM-01, RM-02); 6, dc power supply; 7, twofeed tank consist of NaOH for neutralization produced chlorine (D-03, D-04).

wtvfiotef2

M

tt

vAdr

here n is the number of data points, X the average of X overhe n samples, and Xim and Xip are the measured and predictedalues of process and product parameters, respectively. Thenal network was selected on the basis of the lowest errorn train and test sets of data. Meanwhile, the accuracy ofhe ANN models was evaluated by calculating mean absoluterror (MAE) or average deviation (AD) for each of the outputsor testing. MAE and AD were calculated as (Razmi-Rad et al.,007):

AE = 1n

n∑i=1

|Xim − Xip|, AD = 1n

n∑i=1

|Xim − Xip|Xim

× 100 (5)

Preliminary trials indicated that the learning and predic-ion ability of two hidden layer networks were better thanhose of one hidden layer.

Different neural networks were made and the optimumalues of network parameters obtained by trial and error.

djustment of ANN parameters included the number of hid-en layers and neurons, the type of transfer function, learningate, momentum, and the number of patterns.

4. Results and discussions

4.1. Data collection

According to the evaluation of previous researchers(Chatterjee, 1984; Mohammadi et al., 2004; Ogata et al.,1990; Pierre and Wragg, 1993; Xiong et al., 1992; Yeager andMalinsky, 1984) the important parameters affecting the CAcell performance along with the levels of these parametersare as follows: (1) anolyte pH: 2, 3, 4 and 5; (2) cell tem-perature (◦C): 25, 50, 70 and 90; (3) flow velocity (cm/s): 1.3,2.2, 3.7 and 5.9; (4) brine concentration (g/L): 200, 235, 270and 300; (5) current density (kA/m2): 1, 2, 3 and 4; and (6)run time: from 30 to 150 min. The latter are summarized inTable 1.

By conducting experiments under these conditions usingthe previously mentioned procedure, cell voltage and CCE datawere obtained.

4.2. Calibration and development of ANN model

The cell voltage and CCE data were divided into three data sets,i.e. training, validation and testing data. The main aim of this

466 c h e m i c a l e n g i n e e r i n g r e s e a r c h a n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472

Fig. 5 – Side view of the membrane cell used in this study.1, cell body (Teflon); 2, EPDM gasket; 3, standard DSA/Cl2anode; 4, Flemion 892 membrane; 5, nickel cathode; 6, flowframe (Teflon); 7, electrolyte inlet; 8, electrolyte outlet.

Fig. 6 – The ANN-simulated vs. measured for cell voltage:(a) train and (b) test values.

sitivity is most likely important to the modeled process. Thesensitivity was calculated for each of the input parameters. A

activity was to obtain an ANN model with a minimal dimen-sion and minimum errors in training and testing calculations.Accordingly the most suitable back-propagation neural net-work with a minimum number of neurons in hidden layersto correlate the input and output parameters was selected as6–7–5–1 and 6–9–4–1 for cell voltage and CCE, respectively. Forthis structure, the best combination of the ANN parametersthat were used to predict the cell voltage and CCE is shown inTable 2.

Scatter data of Figs. 6 and 7(a and b) provide comparisons ofthe measured cell voltage and CCE levels with those of ANN-derived test and train calculations. Tables 3 and 4 also givethe overall outputs of experiments and the results obtainedbased on the ANN model predictions for cell voltage and CCE.According to these results, both data sets provide a low aver-age deviation (AD) among experimental data and the modelpredictions. However, the results of developed ANN modelfor cell voltage have a lower average error (AD = 1.27%) com-pared with that of CCE (AD = 3.14%); the latter is likely to bein part due to the titration errors, i.e. experimental measure-ments. This implies that the developed ANN model in thisstudy can reasonably forecast the cell voltage and CCE val-

ues.

Table 1 – Levels of process parameters

pH Cell temperature (◦C) Flow velocity (cm/s) Brine

Level 1 2 25 1.3Level 2 3 50 2.2Level 3 4 70 3.7Level 4 5 90 5.9

4.3. Sensitivity analysis

The analysis of variance (ANOVA) approach was used todemonstrate the sensitivity analysis of the new model andthe dependence of the dependent variables on each of theindependent variables.

On the other hand, model sensitivity to each of the inputsis given by:

Sensitivity = % change in output% change in input

× 100 (6)

This relates the change in output for a change in a giveninput. Eq. (6) shows the relative importance of an individualparameter for a given input set. A variable having a high sen-

graphical and quantitative presentation of the impacts of the

concentration (g/L) Current density (kA/m2) Run time (min)

200 1 30235 2 60270 3 120300 4 150

c h e m i c a l e n g i n e e r i n g r e s e a r c h a n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472 467

Table 2 – The structure and performance of the final selected ANN model with optimum values of model parametersused to predict (a) cell voltage and (b) CCE

Optimum Transfer function Mean value

MLP structure Learning rate Momentum Training RMSE Testing RMSE T

(a) Cell voltage6–7–5–1 0.65 0.35 Tanh for hidden layers and linear for output 0.036 0.043 0.94

(b) CCE6–9–4–1 0.72 0.28 Tanh for hidden layers and linear for output 0.043 0.054 0.91

Table 3 – Experimental and ANN-simulated cell voltage values

Time (min) pH Temperature(◦C)

Flow rate(cm/s)

Brine concentration(g/L)

Current density(kA/m2)

Experimental cellvoltage (V)

ANN-pred. cellvoltage (V)

Error(%)

30 4 50 5.9 270 1 3.6 3.56 0.9930 5 70 2.2 300 1 3.7 3.70 0.0930 3 90 3.7 235 1 3.8 3.79 0.1430 2 50 2.2 235 2 4.7 4.82 2.6330 3 70 5.9 200 2 4.8 4.83 0.6530 5 90 1.3 270 2 4.8 4.85 1.1030 2 25 1.3 200 1 5.2 5.29 1.7730 4 25 3.7 300 2 5.6 5.65 0.9230 2 70 3.7 270 3 5.9 5.95 0.8730 4 90 2.2 200 3 6 5.96 0.6530 3 50 1.3 300 3 6.8 6.93 1.8930 2 90 5.9 300 4 7.5 7.07 5.7630 4 70 1.3 235 4 7.8 7.80 0.0430 5 25 5.9 235 3 7.9 7.88 0.2830 5 50 3.7 200 4 8.4 8.41 0.1530 3 25 2.2 270 4 10 9.54 4.5760 4 50 5.9 270 1 3.5 3.51 0.1860 5 70 2.2 300 1 3.5 3.53 0.9060 3 90 3.7 235 1 3.6 3.56 1.2260 3 70 5.9 200 2 4.4 4.48 1.7660 2 50 2.2 235 2 4.6 4.54 1.2060 5 90 1.3 270 2 4.7 4.56 2.8960 2 25 1.3 200 1 5.1 5.14 0.7260 2 70 3.7 270 3 5.3 5.28 0.4660 4 25 3.7 300 2 5.4 5.32 1.4060 4 90 2.2 200 3 5.4 5.41 0.1660 2 90 5.9 300 4 6.2 6.21 0.0960 3 50 1.3 300 3 6.2 6.21 0.1060 4 70 1.3 235 4 6.9 6.91 0.0860 5 25 5.9 235 3 6.9 6.95 0.6660 5 50 3.7 200 4 7.5 7.55 0.7160 3 25 2.2 270 4 8.7 8.94 2.76

120 3 90 3.7 235 1 3.3 3.36 1.83120 4 50 5.9 270 1 3.3 3.45 4.62120 5 70 2.2 300 1 3.4 3.46 1.67120 3 70 5.9 200 2 4 4.06 1.57120 2 50 2.2 235 2 4.1 4.21 2.56120 5 90 1.3 270 2 4.3 4.29 0.34120 2 25 1.3 200 1 4.6 4.58 0.46120 2 70 3.7 270 3 4.7 4.60 2.22120 4 90 2.2 200 3 4.8 4.83 0.66120 4 25 3.7 300 2 4.9 4.91 0.12120 2 90 5.9 300 4 5 5.00 0.10120 3 50 1.3 300 3 5.6 5.59 0.12120 4 70 1.3 235 4 6 5.84 2.65120 5 25 5.9 235 3 5.9 5.88 0.29120 5 50 3.7 200 4 6.5 6.48 0.36120 3 25 2.2 270 4 7.3 7.29 0.15150 3 90 3.7 235 1 3.2 3.33 4.07150 4 50 5.9 270 1 3.3 3.37 2.16150 5 70 2.2 300 1 3.4 3.39 0.31150 3 70 5.9 200 2 3.6 3.73 3.63150 2 50 2.2 235 2 4 3.81 4.70150 5 90 1.3 270 2 4.2 4.17 0.79150 2 70 3.7 270 3 4.5 4.38 2.60150 2 25 1.3 200 1 4.5 4.43 1.50150 4 25 3.7 300 2 4.7 4.67 0.67150 4 90 2.2 200 3 4.8 4.77 0.72150 2 90 5.9 300 4 4.9 4.89 0.19150 3 50 1.3 300 3 5.5 5.49 0.21150 4 70 1.3 235 4 5.8 5.71 1.51150 5 25 5.9 235 3 5.8 5.78 0.31150 5 50 3.7 200 4 6.3 6.31 0.22150 3 25 2.2 270 4 7.5 7.47 0.45

AD = 1.27%

468 c h e m i c a l e n g i n e e r i n g r e s e a r c h a n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472

Table 4 – Experimental and ANN-simulated CCE values

Time (min) pH Temperature(◦C)

Flow(cm/s)

Brineconcentration (g/L)

Current density(kA/m2)

ExperimentalCCE

ANN-pred.CCE

Error(%)

29 4 25 3.7 300 2 0.86 0.89 3.3030 5 70 2.2 300 1 0.87 0.86 0.4530 5 50 3.7 200 4 0.89 0.86 3.6330 2 25 1.3 200 1 0.90 0.86 4.5530 3 70 5.9 200 2 0.91 0.91 0.1730 2 90 5.9 300 4 0.90 0.92 2.9430 3 25 2.2 270 4 0.84 0.87 2.8431 4 90 2.2 200 3 0.86 0.86 0.7631 2 70 3.7 270 3 0.82 0.90 9.9131 3 90 3.7 235 1 0.96 0.90 6.7031 4 70 1.3 235 4 0.76 0.84 11.4131 4 50 5.9 270 1 0.92 0.92 0.6250 3 50 1.3 300 3 0.80 0.85 6.0660 3 25 2.2 270 4 0.82 0.85 3.6060 3 90 3.7 235 1 0.93 0.89 4.6760 4 90 2.2 200 3 0.82 0.84 2.4460 4 25 3.7 300 2 0.86 0.87 1.9660 2 90 5.9 300 4 0.95 0.92 2.8860 3 70 5.9 200 2 0.89 0.91 1.6161 5 90 1.3 270 2 0.81 0.83 2.8861 5 70 2.2 300 1 0.83 0.85 2.0361 4 50 5.9 270 1 0.89 0.91 1.5061 2 70 3.7 270 3 0.84 0.89 6.9061 5 25 5.9 235 3 0.79 0.89 12.7261 2 25 1.3 200 1 0.85 0.85 0.1661 4 70 1.3 235 4 0.76 0.83 9.5590 5 25 5.9 235 3 0.78 0.87 12.4290 3 90 3.7 235 1 0.92 0.87 5.5890 4 25 3.7 300 2 0.85 0.86 1.2990 3 70 5.9 200 2 0.87 0.90 2.8690 2 25 1.3 200 1 0.82 0.83 1.4990 4 50 5.9 270 1 0.89 0.89 0.0890 2 90 5.9 300 4 0.96 0.91 4.5391 5 70 2.2 300 1 0.84 0.84 0.5791 3 25 2.2 270 4 0.85 0.84 0.9291 5 90 1.3 270 2 0.83 0.82 0.3291 4 90 2.2 200 3 0.82 0.83 1.4391 2 70 3.7 270 3 0.86 0.88 2.8891 5 50 3.7 200 4 0.86 0.83 2.4292 4 70 1.3 235 4 0.79 0.82 3.87

100 3 50 1.3 300 3 0.81 0.83 2.31120 2 70 3.7 270 3 0.83 0.86 3.78120 2 90 5.9 300 4 0.92 0.90 2.22120 3 70 5.9 200 2 0.87 0.88 1.29120 5 25 5.9 235 3 0.78 0.86 9.29120 4 25 3.7 300 2 0.88 0.85 3.26120 3 25 2.2 270 4 0.87 0.83 4.72120 4 50 5.9 270 1 0.87 0.88 1.76121 5 90 1.3 270 2 0.83 0.82 0.85121 4 70 1.3 235 4 0.80 0.82 1.38121 3 90 3.7 235 1 0.88 0.86 2.69121 5 70 2.2 300 1 0.83 0.83 0.32121 4 90 2.2 200 3 0.81 0.82 1.49122 2 25 1.3 200 1 0.85 0.82 3.06150 3 25 2.2 270 4 0.84 0.82 1.99150 4 70 1.3 235 4 0.81 0.81 0.79150 4 90 2.2 200 3 0.82 0.81 0.81150 5 25 5.9 235 3 0.79 0.85 6.97150 5 50 3.7 200 4 0.87 0.82 5.54150 3 70 5.9 200 2 0.88 0.87 1.51150 5 90 1.3 270 2 0.82 0.81 0.79150 2 50 2.2 235 2 0.79 0.83 4.24150 2 70 3.7 270 3 0.85 0.85 0.16150 5 70 2.2 300 1 0.84 0.82 1.84150 4 25 3.7 300 2 0.85 0.84 1.04150 3 50 1.3 300 3 0.82 0.82 0.55150 4 50 5.9 270 1 0.87 0.87 0.03150 2 90 5.9 300 4 0.93 0.89 4.18150 2 25 1.3 200 1 0.82 0.82 0.71

operating parameters with contribution of each factor on the

mean response for experimental and ANN-simulated cell volt-age and current efficiency are compared in Figs. 8 and 9. First ofall, it is obvious that the ANN model predictions are very close

AD = 3.14%

to the experimental data. Moreover, as it is obvious from Fig. 8,

current density has the most prominent effect on the cell volt-age with an effect value of 58.2% while the temperature andrun time are the second and third influencing parameters with

c h e m i c a l e n g i n e e r i n g r e s e a r c h a n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472 469

Fig. 7 – The ANN-simulated vs. measured for CCE: (a) trainand (b) test values.

Fig. 8 – Sensitivity analysis results based on this work,Jalali’s experimental (Jalali et al., 2008) and ANN-simulatedcell voltage results.

ectas

cts

Fig. 9 – Sensitivity analysis results based on this work,Jalali’s experimental (Jalali et al., 2008) and ANN-simulatedCCE results.

Flow (5.9 cm/s), Cbrine (300 g/L), ip(1 kA/m2) and the run time(150 min).

ffect values of about 22.8% and 17.1%, respectively. Brine con-entration has the lowest effect with an impact value of lesshan 1%. The impacts of pH and flow rate on the cell voltagere comparable and quite low within the experimental rangetudied.

Brine pH and concentration are important parameters inontrolling CCE according to Fig. 9. However, in respect to CCE,

he effects of operating parameters are more or less of theame order. Here, pH has the highest impact with an effect

value of 22.75% while the lowest impact corresponds to runtime with an effect value of 12.53%.

For comparison purposes, the experimental and ANNresults from this study were also compared with thoseobtained by Jalali et al. (2008) in the same figures. Thesegraphs compare sensitivity analysis for five parameters with-out run time input from Jalali’s data. Although Jalali et al.(2008) neglected the influence of run time, but their resultsare in good agreement with our experimental data and thuswith ANN model outputs. Finally, it can be realized that cur-rent density, brine concentration and pH variables have higherimpacts on the CA cell voltage and CCE, respectively.

4.4. Effect of operating parameters on cell performanceindicators

Based on the ANN outputs, the effect of controllable operatingparameters on the mean response for the CA cell voltage andCCE are displayed in Figs. 10 and 11. According to these results,cell voltage enhances dramatically with current density whileit is changed only slightly with anolyte pH. On the other hand,the cell voltage significantly decreases with increasing celltemperature while slightly with flow rate and brine concen-tration. The cell voltage also decreases with run time.

The conditions to obtain a minimum value for cell volt-age simulated by ANN model are as follows: pH (2), T (90 ◦C),

Fig. 10 – Effect of various operating parameters on the CAcell voltage resulted from the new ANN-based model.

470 c h e m i c a l e n g i n e e r i n g r e s e a r c h a n d d e s i g n 8 6 ( 2 0 0 8 ) 461–472

Fig. 11 – Effect of various operating parameters on the CA

Fig. 12 – Effect of various operating parameters on the CAcell voltage (Jalali et al., 2008).

CCE resulted from the new ANN-based model.

Another valuable response which is directly proportionalto total energy consumed by an electrolysis cell is the cur-rent efficiency. The CCE was thus measured according to theprocedure described in Section 3 and calculated based on thefollowing equation (Chandrand and Chin, 1986):

�NaOH = m(t) − m(t = 0)(It/nF) × MWNaOH

= m(t) − m(t = 0)(It/nF) × 40

(7)

The impacts of various operating parameters on the CCEaccording to ANN results are shown in Fig. 11.

Fig. 13 – Cell voltage and current efficiency at different levels of ovelocity, (D) brine concentration, (E) current density (Jalali et al., 2

Regarding the trends of variables shown in Figs. 10 and 11, ithas been postulated that the current efficiency decreases withpH due to the production of by products such as hypochloriteand chlorate in the anolyte at higher pHs while pH does nothave a sensible impact on the CA cell voltage.

Furthermore, as it is seen, CCE is improved with the celltemperature. That is attributed to the suppression of side reac-tions at higher temperatures. On the other hand, the overallcell voltage decreases with temperature. The latter is presum-ably due to the decrease in the voltage components of the cell

such as decomposition potential, IR drops, and the overpoten-tials.

perating parameters. (A) pH, (B) temperature, (C) electrolyte008).

h a n

caCr1ba

tcaoc

aefi

thbct

(sFtcm(at

5

CeTuptcat

(

(

(

(

r

c h e m i c a l e n g i n e e r i n g r e s e a r c

By increasing electrolyte velocity, a slight decrease in theell voltage can be observed, as shown in Fig. 10. This might bettributed to the reduction of the amount of attached H2 andl2 bubbles on both sides of the membrane and the bubblesemained within the catholyte and anolyte (Pierre and Wragg,993; Xiong et al., 1992). It is notable that the presence of theubbles decreases the actual conductivity of the electrolytend thus increases the cell voltage.

The cell voltage is slightly decreased with brine concen-ration within the range studied, but the effect of brineoncentration on the current efficiency is more pronounced,s seen in Fig. 11. This is likely to be due to the suppressionf the oxygen evolution as a major side reaction at low brineoncentration.

The current density was discovered to be the most remark-ble parameter influencing the cell voltage and currentfficiency. This fact was expected considering the ohms lawor the cell voltage (�VCell = IRCell); and Eq. (7) describing thenverse relationship between current density and CCE.

Both functions have a downtrend with increasing runime. The lower CCE achieved at higher run times, i.e. atigher caustic concentration, might be attributed to the OH−

ack migration toward anolyte at higher run times. Theatholyte conductivity is also increased with NaOH concen-ration resulting in a decrease in the cell voltage.

For comparison purposes, the results from Jalali et al.2008), for an experimental system similar to that used in thistudy but at a different conditions, are also represented inigs. 12 and 13. As it is obvious, the general trends of predic-ions developed in this study by the new ANN model are quiteonsistent with experimental results and trends obtained byeans of Taguchi and ANOVA techniques used by Jalali et al.

2008). Nevertheless, a slight difference in values is observ-ble due to the differences in the operating conditions andhe negligence of the run time parameter in that study.

. Conclusions

ell voltage and CCE are widely employed for improving ornhancing the performance of chlor-alkali membrane cells.herefore, accurate prediction of CCE and cell voltage is oftmost importance. In this study a new ANN model for therediction of cell voltage and CCE was developed. The effec-iveness of the developed ANN model was also validated byomparing the predicted results with measured cell voltagend CCE values. Based on the results obtained in this study,he following conclusions are drawn:

1) ANN model can be used to predict the impacts of operat-ing parameters including pH, temperature, flow rate, brineconcentration, current density and run time on the cellvoltage and current efficiency of a chlor-alkali cell.

2) The ANN technology has been shown to be a useful tool tosimultaneously approximate and predict cell voltage andCCE versus process parameters in membrane chlor-alkalicell.

3) The final selected ANN models were able to predict simul-taneously the cell voltage and CCE values with RMSE of0.043 and 0.054, R2 of 0.988 and 0.802, and T values of 0.94and 0.91, respectively.

4) The current density and the cell temperature have thehighest effect values on the cell voltage, i.e. 59% and 20%,respectively.

d d e s i g n 8 6 ( 2 0 0 8 ) 461–472 471

(5) Based on the ANN-simulated outputs, the followingoperating conditions are proposed to maximize the cur-rent efficiency and to minimize the cell voltage: pH 2;temperature = 90 ◦C; flow rate = 5.9 cm/s; brine concentra-tion = 300 g/L; and current density = 1 kA/m2.

(6) Evaluation of the relative effectiveness of operatingparameters by the ANN model revealed that while currentdensity and temperature have the largest contributionto the cell voltage, pH and brine concentration have thelargest impact on the current efficiency. Other factors havealmost similar effects on the response.

(7) The values of predicted cell voltages and CCE were quitecomparable with the measured ones with an average devi-ation of only 1.27% and 3.14%, respectively.

Acknowledgement

The authors would like to thank National PetrochemicalResearch and Technology Company of Iran (NPC-RT) for itsfinancial support.

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