modeling and optimizatiin of coagulation.pdf

8/22/2019 modeling and optimizatiin of coagulation.pdf

1/7

Chemical Engineering Journal 167 (2011) 7783

Contents lists available at ScienceDirect

Chemical Engineering Journal

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c e j

Modelling and optimization of coagulation of highly concentrated industrial

grade leather dye by response surface methodology

M. Khayet a, A.Y. Zahrim b, N. Hilal b,

a Department of Applied Physics I, Faculty of Physics, University Complutense of Madrid, Spainb The Centre for Water Advanced Technologies and Environmental Research (CWATER), College of Engineering, Swansea University, Swansea SA2 8PP, UK

a r t i c l e i n f o

Article history:

Received 15 October 2010Received in revised form

30 November 2010

Accepted 30 November 2010

Keywords:

Dye removal

Coagulation/flocculation

Tanning industry

Design of experiment

Response surface methodology

a b s t r a c t

High consumption of process water and water scarcity has motivated industry to reuse their wastewa-

ter. Membrane processes are vital to produce water for reuse from dyeing baths in the tanning industry.

In this regard, synthetic dye was recognised as the major foulant. To minimise the membrane fouling,

coagulation/flocculation process is an importantpre-treatment. Due to the complex nature of the process

involving dyes-coagulant, the modelling is challenging. In this study, statistical experimental design and

response surface methodology, RSM, have been applied to optimize removal of C.I. Acid Black 210 dye

from highly concentrated solutions by means of a coagulation/floculation process. Aluminium sulphate

was used as the coagulant. Central composite design (CCD) using as input variables the experimental

temperature, the concentration of aluminium sulphate and the initial pH of the solution have been con-

sidered. Based on the design of experiment the quadratic response surface models have been developed

to link the output response, which is the dye removal factor, with the input variables via mathematical

relationships. The constructed response model has been tested using the analysis of variance (ANOVA).

A Monte Carlo simulation method has been conducted to determine the optimum operating conditions.

Theobtained optimal pointcorresponds to a temperature of40 C, a concentration of aluminiumsulphate

of 0.82 g/L and an initial pH value of 5.61. The maximal value of the dye removal obtained under optimal

process conditions has been confirmed experimentally.

2010 Elsevier B.V. All rights reserved.

1. Introduction

The tanning industry employs huge amounts of water and this

leads to the generation of enormous amounts of wastewater [1].

Due to human activities, water crisis has become a problem in

many countries. This phenomenon has caused water reuse, espe-

cially for the water consuming industry such as tanning industry,

to gain in importance. To cater for the needs of the tanning indus-

try, water reuse via membrane technology has been proposed [2].

However, a major problem for membrane technology is fouling.

High concentrations of dye are reported to cause severe fouling on

the membrane [3] and caused failure at the end-pipe treatment oftanning wastewater [4]. To minimise the fouling, coagulation has

been reported as an efficient and cost-effective pre-treatment [3].

Abbreviations: ANOVA,analysisof variances; CCD, central compositedesign; DF,

degree of freedom;MLR, multi-linear regression method; MS,mean square; SS,sum

of squares. Corresponding author. Tel.: +44 01792 606644; fax: +44 01792 295676.

E-mail addresses: [email protected] (A.Y. Zahrim), [email protected]

(N. Hilal).

Leather making is comprised of several processes and the dye-

ing process is one of the important steps. At the present time, most

tanneries use synthetic dyestuffs for their leather [2,5]. The his-

tory of the modern synthetic dyestuffs industry was begun with

the development of mauveine by Perkin in 1856. During that time,

the primarymarket was textiles,but the leather industryeventually

took advantage half a century later, especially when it was realised

that bright deep shades could be achieved with the new tannage

with chromium (III) [5]. Nevertheless, the presence of residual dyes

in surface water is aesthetically undesirable and causes problems

for the aquatic biosphere due to the reduction of sunlight penetra-

tion anddepletion of thedissolved oxygen.Some dyes aretoxicandmutagenic and have the potential to release carcinogenic amines

[1]. Furthermore, dyeing wastewater from the leather industry

is high in organics and has high temperature. Moreover, dyeing

wastewater contribute about 38% of the total volume of discharge

wastewater [2]. Typical characteristics of dyeing wastewater from

the leather industry are shown in Table 1 [2]. Moreover, industrial

dyestuffs are typically not pure compounds and the purity differs

from one batch to the other. Generally, dyes are marketed with

diluent (a solid or liquid to dilute another) such as sodium chlo-

ride, sodium carbonate, dextrin, sulphite cellulose, naphthalene

sulfonate, sodium sulphite, etc. [5].

1385-8947/$ see front matter 2010 Elsevier B.V. All rights reserved.

doi:10.1016/j.cej.2010.11.108
http://localhost/var/www/apps/conversion/tmp/scratch_9/dx.doi.org/10.1016/j.cej.2010.11.108http://localhost/var/www/apps/conversion/tmp/scratch_9/dx.doi.org/10.1016/j.cej.2010.11.108http://www.sciencedirect.com/science/journal/13858947http://www.elsevier.com/locate/cejmailto:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_9/dx.doi.org/10.1016/j.cej.2010.11.108http://localhost/var/www/apps/conversion/tmp/scratch_9/dx.doi.org/10.1016/j.cej.2010.11.108mailto:[email protected]:[email protected]://www.elsevier.com/locate/cejhttp://www.sciencedirect.com/science/journal/13858947http://localhost/var/www/apps/conversion/tmp/scratch_9/dx.doi.org/10.1016/j.cej.2010.11.108


2/7

78 M. Khayet et al. / Chemical Engineering Journal 167 (2011) 7783

Nomenclature

b0, bi, bii, bij regression coefficients

b (u 1) vector of regression coefficientsb0 intercept regression coefficient

C aluminium sulphate concentration (g/L)

C0 initial dye concentration (g/L)

Cf final dye concentration (g/L)

C* optimal value ofaluminiumsulphate concentration(g/L)

F ratio of variances

N the number of experimental runs

n number of factors (independent variables)

pH* optimal pH value

R2 coefficient of multiple determination

Radj2 adjusted statistic coefficient

SSRegression sum of squares of the regression model

SSResidual sum of squares of the residual

T temperature (C)

T* optimal temperature value (C)

u the number of significant regression coefficients in

the response surface model

X (N u) matrix of the independent variables levelsx1, x2, x3, xI the coded levels of the factors (independent or

control variables)

Y (N 1) vector of the experimental responseY response (experimental value)

Greek letters

statistical error in response surface model

Coagulation of dye-containing wastewater has been used for

many years either as a main or pretreatment due to its low capital

cost [1]. Coagulation shows higher efficiency for azo dye [3] and

this is an advantageous since about 70% of dyes currently in use are

of azo type [1]. Nevertheless, Beltran-Heredia et al. [6] stated thatthe mathematical modelling of coagulation/flocculation of dyes is

very difficult due to:

(1) The complex nature of the phenomenon, which implies

various physicochemical interaction between coagulant-dye

molecules.

(2) The intrinsic composition of the dye molecules and the coagu-

lant is not completely known.

Coagulation in acidic conditions has been reported to have sev-

eral advantages. Klimiuk et al. [7] reported that the flocs have a

better structure and are more stable. Moreover, at an alkali initial

solution, the dosage of alum to achieve the highest colour removal

is relatively higher than at an acidic solution. Consequently, the

Table 1

Typical characteristics of dyeing wastewater from leather industry.

Parameters Min Max

pH 4 10

Temperature,C 20 60

Sedimentable materials, mg/l 100 500

Total suspended solids, mg/l 10,000 20,000

BOD5, mg/l 6000 15,000

COD, mg/l 15,000 75,000

Chromium (III), mg/l 0 3000

Chlorides, mg/l 5000 10,000

Oil and fats, mg/l 20,000 50,000

Chlorinated solvents, mg/l 0 250

Surfactants, mg/l 500 2000

production of sludge will also increase and the higher hydroxide

content andhigherTOC content of the sludges generallycontribute

to the poorer dewatering characteristics [8]. Dosage of metal coag-

ulant, pH and temperature is an important factor for coagulation.

Coagulant reactions and metal coagulant chemistry are strongly

affected by temperature. For example, with decreasing water tem-

perature, the minimum solubility of aluminium hydroxide species

shift to higher pH, and the optimum operating pH value also shifts

to higher pH [8]. However, there is lack of studies on the effect

of high temperature (greater than 30 C) during coagulation of dye

wastewater [3]. In this regard, several studies have been carried out

with reactive dyes. Koprivanac et al. [9] investigated the removal

of Reactive Blue 204 in the range between 20 and 80 C using ferric

chloride.In this study, the authors concludedthat the effective tem-

perature for decolourization should be less than 30 C. However,

Joo et al. [10] found that at 40 C alum/ferric salt plus an organic

polymer can achieve 100% decolourization of several reactive dyes

(Black 5, Blue 2, Red 2 and Yellow 2).

Acid dyes are the most commonly used in the leather indus-

try, particularly for chromed tanned leather. The properties can be

summarized as follows [5]: (i) relativelysmall, typicallyhydrophilic

molecules, (ii) used for penetrating dyeing, producing level shades,

(iii) anionically charged, therefore high affinity for cationic leather,

(iv) fixed with acidification, due to the presence of sulfonategroup,(v) reactpredominatlythroughelectrostaticreaction between their

sulfonate groups and the protonated amino group of lysine, (vi)

secondary reaction is via hydrogen bonding through auxochrome

groups, (vii) some dyes may react with the bound chrome; using

it as a mordant, (viii) good fastness properties: less complex

molecules offer fewer opportunities for structural changes by free

radical mechanisms, and (ix) Wide range of colours, offering bright

deep shades.

It is to be noted that the major part of the reported studies on

dye removal deal with the conventional method of experimenta-

tion, which involves changing one of the independent parameters,

maintaining the others fixed. This classical or conventional method

of experimentation requires many experimental runs, which are

time-consuming, ignores interaction effects between the operat-ing parameters and leads to a low efficiency in optimization. These

limitations of the classical method can be avoided by applying

the response surface methodology (RSM) that involves statisti-

cal design of experiments (DoE) in which all factors are varied

together over a set of experimental runs. In fact, RSM is a collection

of mathematical and statistical techniques useful for developing,

improving and optimizing processes, and can be used to evalu-

ate the relative significance of several affecting factors even in the

presence of complex interactions [11]. The statistical method of

experimental design offers several advantages over the frequently

used conventional experimental method being rapid and reliable,

helps understanding the interaction effects between factors and

reduces the total number of experiments tremendously result-

ing in saving time and costs of experimentation. RSM has beenapplied successfully in various scientific and technical fields such

as applied chemistry andphysics, biochemistryand biology, chem-

ical engineering, environmental protection, membrane science and

technology [1121]. Some recent publications have shown the

effectiveness of RSM modelling for dye removal [2226]. The per-

formance of a lowcostcoagulant,i.e. waterworks sludge(FCS:ferric

chloride sludge) for the removal of acid red 119 (AR119) dye from

aqueous solutionswas carried out [22]. RSMwasappliedinthiscase

to optimize three operating variables of coagulation/flocculation

process including initial pH, coagulant dosage and initial dye con-

centration. The optimum initial pH, ferric chloride sludge dosage

andinitial dyeconcentration were found to be 3.5, 236.68mg dried

FCS/L and 65.91 mg/L, respectively. Dye removal of 96.53% was

obtained and found to be close to the predicted RSM results [22].


3/7

M. Khayet et al. / Chemical Engineering Journal 167 (2011) 7783 79

Other low cost coagulant for treatment of dye wastewater was

also studied in [23]. The investigation was focussed on the steel

industrial wastewater (SIWW) FeCl3 rich as an original coagulant

to remove the synthetic textile wastewater. RSM was used to study

the effects of the coagulant dosage, initial pH of dye solution and

dye concentration, and to optimize the process conditions for the

decolourization and Chemical Oxygen Demand (COD) reduction of

the disperse blue 79 solution. For obtaining the mutual interac-

tion between the variables and optimizing these variables, a 23 full

factorial central composite rotatable design of experiments was

employed. The efficiencies of decolourization and COD reduction

for disperse blue 79 solution were accomplished at the optimum

conditions 99% and 94%, respectively [23].

Thedecolourizationof C.I. Acid Red14 (AR14)azodye byelectro-

coagulation (EC) process was studied in a batch reactor [24]. RSM

was applied to evaluate the simple and combined effects of the

three main independent parameters, current density, time of elec-

trolysis and initial pH of the dye solution on the colour removal

efficiency and optimizingthe operating conditionsof the treatment

process. A 23 full factorial central composite face centered (CCF)

experimental design was employed. Analysis of variance (ANOVA)

showed a high coefficient of determination value (R2 = 0.928) and a

satisfactory predictionsecond-orderregression model was derived.

Maximum colour removal efficiency was predictedand experimen-tally validated. Under optimal value of process parameters, high

removal (> 91%) was obtained for Acid Red 14. The study clearly

showed that RSM was a suitable methods to optimize the operat-

ing conditions and maximize the dye removal. Graphical response

surface and contour plots were used to determine the optimum

point [24].

The effects of pH and initial dye concentration on dye removal

by coagulation/flocculation process with natural coagulant, i.e.

Moringa oleifera seed extract have been investigated [25]. The

study was carried out by using RSM in an orthogonal and rotat-

able design of experiments. Three types of dyes were considered:

anthraquinonic (Alizarin Violet 3R); indigoid (indigo Carmine) and

azoic (Palatine Fast Black WAN). It was observed that the interac-

tion of the two variables studied is higher in the case of azo dye,while it is negligible in thecase of anthraquinonic dye. Indigoid dye

presents an intermediate situation.

The mineralization of C.I. Acid Orange 7 (AO7) azo dye by

UV/H2O2 advanced oxidation was studied in [26]. An experimen-

tal design based on RSM was applied to evaluate the simple and

combined effects of parameters on mineralization efficiency and to

optimize the operating conditions of the treatment process. A 23

full factorial central composite face centered (CCF) experimental

design was employed. ANOVA analysis showed a high coefficient

of determination value and a satisfactory second-order regression

model was derived. Graphical response surface and contour plots

were used to localize the region of the optimum point. Maximal

dye mineralization performance was predicted and experimen-

tally validated. Under optimal value of process parameters, highmineralization of AO7 dye (87.07%) was achieved [26].

In the present study, aluminium sulphate has been used for

coagulation/flocculation to remove C.I. Acid Black 210 dye from

highly concentrated solutions at different temperatures and initial

pH values. The central composite experimental design (CCD) and

RSM has been applied. The main objective is to maximize the dye

removal applying the adequate optimum operating conditions.

2. Materials and methods

2.1. Materials

Durapel Black NT which consists of C.I. Acid Black 210 dye

(purity > 30%) was purchased from Town End (Leeds) plc, (United

Table 2

Actual and coded values of independent variables used for experimental design.

Variable Symbol Real values of coded levels

a 1 0 +1 +a

T(C) x1 20 21.8 30 38.2 40

pH x2 5 5.09 5.5 5.91 6

C(g/L) x3 0.8 0.86 1.15 1.44 1.50

a = 1.215 (star or axial point for orthogonal CCD in the case of 3 independent

variables).

Kingdom) andused without further purification. A dyemass of 20 g

in powder form was dissolved in Milli-Q Plus, 18.2 Mcm (Milli-

pore) water to make 5 l solution at a concentration of 4 g/L. The

initial absorbance at 460 nm for this solution is about 0.7 (100

dilution).

The laboratory reagent aluminium sulphate hexadecahydrate

(Al2(SO4)316H2O) (molecular mass of 630.39 g/mol, purity >96%)

was purchased from Fisher Scientific UK Ltd. (United Kingdom).

Aluminium sulphate solutions were prepared fresh everyday by

dissolving appropriate amounts of powder aluminium sulphate in

Milli-Q Plus (Millipore) water.

2.2. Jar tests

An appropriate volume of dye solution was transferred into the

round jar and pH was adjusted accordingly. The pH adjustment

was made under vigorous stirring with a magnetic stir bar using

solutions of 2 M HCl and 2 M NaOH. The initial temperature was

increased by heating the solution using magnetic hotplate (Fisher

Scientific UK Ltd., United Kingdom). Subsequently, aluminium sul-

phate was added to the dye solution in the jar, making the total

volume of 500 ml. In every experiment, aluminium sulphate solu-

tion was less than 5% v/v in aqueous solution. This was done to

prevent unnecessary dilution effects. A standard jar-test appara-

tus (Bibby-Stuart Flocculator SW6) equipped with stainless steel

paddles and stirrer was used for the coagulation/flocculation tests.

The aqueous solution was then rapidly mixed at a paddle speed of

250 rpm for 3min followed by slow mixing for 20min at 30 rpm.

After allowing settling to occur (120min), about 25 ml of the liq-

uid was withdrawn using a pipette from a height of about 3 cm

below the liquid surface in each jar. This height comprises the first

40% of the total height. Experiments were all triplicate to test their

reproducibility.

2.3. Experimental design

The statistical design of experiments (DoE) is a structured and

systematized method of experimentation in which all factors are

varied simultaneously over a set of experimental runs in order to

determine the relationship between factors affecting the outputresponse of theprocess.As statedearlier,the statistical experimen-

tal design employed has been carried out considering three factors

(controllable variables), namely, the solution temperature (T), the

concentration of the used aluminium sulphate (C) and the initial

pH value (pH). Table 2 shows the controllable variables (factors)

and their levels in coded and actual values. The output response

is the dye removal (%) determined by means of Eq. (1). In order to

describe the factors effects on the responses an orthogonal central

composite design (CCD) with star points was employed with 3 fac-

tors and 5 levels. The central composite experimentaldesign (CCD)

consists of 16 experiments with 8 orthogonal design points (facto-

rial points), 6 star points to form a central composite design with

= 1.215 and 2 center points for replication. The experimentaldesign matrix is summarized in Table 3.


4/7


Table 3

CCD experimental design (DoE) for dye removal by coagulation/flocculation.

Run number

(N) and typeaT(C) pH C( g/ L) Dye r emoval ( %)

x1b x2

b x3b

1 O1 +1 +1 +1 71.96

2 O2 1 +1 +1 88.06

3 O3 +1 1 +1 77.71

4 O4 1 1 +1 97.23

5 O5 +1 +1 1 96.326 O6 1 +1 1 94.85

7 O7 +1 1 1 95.59

8 O8 1 1 1 92.83

9 S1 + 0 0 90.38

10 S2 0 0 93.03

11 S3 0 + 0 59.05

12 S4 0 0 63.72

13 S5 0 0 + 81.11

14 S6 0 0 87.49

15 C1 0 0 0 61.24

16 C2 0 0 0 60.36

a O = orthogonal design points, C= center points, S = star or axial points.b 1 = low value, 0= center value, +1= high value, +/= star point value.

2.4. Residual concentration of dye analysis

Residual concentration of dye (without filtering or centrifuging)

was analysed with a UV/vis-spectrophotometer, (UVmini-1240,

Shimadzu) by measuring the absorbance at the max (460 nm)and final pH of the solution. The absorbance was measured using

water Milli-Q as background and the concentration of dye was

computed from calibration curves preliminary determined at dif-

ferent pH values. If the reading of absorbance was greater than

1.0, then necessary dilution was made (normally 100 times of

dilution). After every experiment, precision cell (10.00 mm, quartz

SUPRASIL (Hellma GmbH & Co, Germany)) was cleaned by soak-

ing it with methanol HPLC Grade (Fischer Scientific UK Ltd, United

Kingdom) overnight. Glassware was cleaned by rinsing with 0.1 M

NaOH. The values of the initial and final concentrations of the dye

measured as outlined above were used to calculate the removalpercentage of the dye (i.e. dye removal factor) using Eq. (1).

dye removal (%) =C0 CfC0

100 (1)

where C0 is the initial dye concentration and Cf is the final dye

concentration.

Measurement of solution pH was done using a Jenaway 3540

pH meter. Baseline flatness and wavelength accuracy for the UV-

spectrophotometer and pH calibration were carried out daily. The

obtained dye removal factors (output responses) for the experi-

mental design matrix are presented in Table 3.

3. Results and discussion

Based on the CCD experimentaldesignresults shown in Table 3,

the RSM has been applied to develop the polynomial regression

equations and find out the relation between the output response,

dye removal, and the input factors. The response surface models of

second-order were developed as follows [11,14,1821,2739] :

Y= b0 +

n

i=1

bixi +

n

i=1

biix2i +

n

i


5/7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

10

20

30

40

50

60

70

80

90

100

N

DyeRem

oval(%)

Experiment

RS-Model

Fig. 1. Comparison of experimental and predicted dye removal values by response

surface model (RS-Model).

are in agreement with the adjusted statistics Radj2. This means thatsignificant terms have been included in the empirical model.

The response values determinedby means of the empiricalmod-

els were compared to the experimental data designed in Table 3.

The results are shown in Fig. 1. As can be seen the response model

shows good fits to the experimental data. Therefore, the model can

be considered adequate for the predictions and optimization. The

graphical representations of the response surfaces were plotted

based on this model, i.e. Eq. (6). Some relevant response surface

plots andthe corresponding contour plots are reported in Figs. 24.

The plots shown in Figs. 2 and 4 indicate the influence of theini-

tial pH on the dye removal. Similar figures can be plotted for other

aluminium sulphate concentrations and temperatures. The results

given by the response model indicate that the increase of the initial

pH leads to an increase of the dye removal up to a maximum. Forhigher pH values the trends are declined suggesting the existence

of an optimal dye removal factor.

It is to be noted that the effect of the temperature upon the

output response is the greatest one. From Figs. 2 and 3, it can be

observed gradual reductions of thedye removal factorto minimum

valueswiththe increase ofthe feed temperaturefrom20 Ctoabout

30 C and then an increase with further temperature enhance-

ments. Similarly, from Figs. 3 and 4, minimum dye removal factors

are obtained for different pH values and temperatures and higher

dye removal factors are observed at low aluminium sulphate con-

centration (i.e. 0.8 g/L).

Zahrim et al. [3] have previously shown that aluminium based

coagulant is superior to metal based coagulant during coagulation

of anionic dyes (e.g. acid and reactive dyes) in acidic condition.Previous work on coagulation of several reactive dyes have shown

that at the same dosage, aluminium based coagulant works better

at acidic pH while ferric, magnesium and calcium based coagulant

works betterat basic solution [40]. Akbariet al.[41] stated that acid

dyes might produce wastewater with acidic condition, therefore it

is expected that the acid dye effluent, in the paper, is in acidic con-

dition. Thus we canconcludethataluminiumbasedcoagulant is the

best coagulant for leather dyeing bath. In a later study, Gaydard-

zhiev et al. [42] found that aluminium sulphate is better than ferric

chloride or aluminiumchloridefor theremoval of C.I. Acid Blue 113

dye due to the corresponding less consumed dosage. In the present

study, high dosage of aluminium sulphate is expected due to very

high concentration of initial dye, i.e. 4 g/l. It has also been shown

in previous work [43] that greater amount of aluminium sulphate

Fig.2. Response surfaceplot (a)and contourplot (b)of predicteddye removalfactor

as function of the operating temperature and initial pH at C=1.15 g/L.

Fig.3. Response surfaceplot (a)and contourplot (b)of predicteddye removalfactor

as function of the operating temperature and concentration of aluminium sulphate

at a pH value of 5.5.


6/7


Fig.4. Responsesurface plot(a) andcontourplot (b)of predicteddye removalfactor

as function of the operating aluminium sulphate concentration and initial pH at

T= 30 C.

was required to achieve a maximum removal in basic condition.

Finally, the aim of this work is to reuse this treated water in the

leather dyeing bath. In addition to this, the process water for the

dyeing stage should be free from Fe [44]. Therefore, the presentwork is designed to avoid the application of Fe metal coagulant.

It is interesting to mention that in allstudiedranges of operating

parameters, strong interaction effects exist between the temper-

ature and the concentration of aluminium sulphate, while the

interaction effects between the pH and the temperature from one

side andthe pH andthe aluminiumsulphatefromthe other side are

negligible. This finding shows that at the equilibrium, the aggre-

gation of dye and the solubility of aluminium suphate might be

similar. Therefore the effects of pH interactions towards the two

parameters temperature and concentration were negligible. In a

previous study [43], at the same concentration range of aluminium

sulphate, the obtained dye removal shows also similar values for

pH values in the range 56.

The main objective of this study is to determine the optimumoperating conditions in order to maximize the dye removal fac-

tor. This has been carried out by means of Monte Carlo simulation

(MCS) method. The MCSis a stochastic optimization technique that

generates the random values of the inputvariables and correspond-

ingly generatesa response insidethe valid region(i.e. experimental

region) that comprises the values of the objective function accord-

ing to the random values of variables [17]. Table 5 presents the

coordinates of the obtained optimal point in terms of the actual

operatingvariablesas well asthe predictedvalue ofthe dyeremoval

factor. As can be observed the optimum predicted dye removal fac-

tor is found to be higher than 100%, which is not logical from the

experimental pointof view.This is because in the optimization step,

a restriction conditionfor the response, 100%,was not considered.

In anyway,the restriction wasnot taken into consideration in order

Table 5

Optimal point in terms of the actual operating variables and the output response,

dye removal (%).

T* (C) pH* C* (g/L ) Dye removal (%)

P re dicte d Expe rimen tal

40 5.61 0.802 115.71 97.78 0.63

* Optimal predicted value by Monte Carlo simulation.

to obtain the optimum point and not the optimum region of exper-

imentation that can only guarantee a dye removal factor of 100%.

The confirmation run was carried out in order to check experimen-

tally the optimal point. Table 5 shows the obtained experimental

dye removal factor. The experimental value of the output response

determinedconsidering the obtained optimal conditions represent

the best (maximal) values throughout all the conducted experi-

mental testsinside the regionof experimentation (Table 3). In other

words, the obtained combination corresponding to the optimum

point is the best one compared to the data presented in Table 3.

4. Conclusions

Coagulation/flocculation using aluminium sulphate is a suitableprocess for the removal of highly concentrated leather dye espe-

cially when the operating parameters are optimized. In fact, failure

in optimizing the process parameters may lead to inefficiencies of

the coagulation process.

The design of experiment (DoE) and response surface method-

ology (RSM) proved to be an effective method for optimization of

coagulation/flocculation process witha viewof maximization of the

C.I. Acid Black 210dye removal from highlyconcentratedsolutions.

The developed response model has been tested using the analy-

sis of variance (ANOVA). The temperature exhibits the strongest

effects and high interaction effects exist between the temperature

and the concentration of aluminium sulphate. On the contrary,

the interaction effects between the pH and the temperature and

between the pH and the aluminium sulphate are negligible. Theoptimal operational conditions are as follows: a feed temperature

of 40 C, an initial pH value of 5.61 and a concentration of alu-

minium sulphate of 0.82 g/L. By applying these parameter values,

maximal dye removal has been predicted and confirmed experi-

mentally.

References

[1] Y. Anjaneyulu, N.S. Chary, D.S.S. Raj, Decolourization of industrial effluents available methods and emerging technologies a review, Rev. Environ. Sci.Biotechnol. 4 (4) (2005) 245273.

[2] A. Cassano, R. Molinari, M. Romano, E. Drioli, Treatment of aqueous effluentsof the leather industry by membrane processes a review, J. Membr. Sci. 181(1) (2001) 111126.

[3] A.Y. Zahrim, C. Tizaoui, N. Hilal, Coagulation with polymers for nanofiltration

pre-treatment of highly concentrated dyes: a review, Desalination 266 (13)(2010) 116.

[4] A.R. Rakmi, M.N. NorZaini, Y. Abu-Zahrim, H.A. Zalina, Chemibiological treat-ment of difficult wastewaters: case study on tanning wastewater, in: Z. Ujang,M. Henze (Eds.), Environmental Technology: Advancement in Water andwastewater Applications in the Tropics, IWA Publishing, London, 2004, pp.315322.

[5] A.D. Covington, Tanning Chemistry: The Science of Leather, Royal Society ofChemistry, Cambridge, 2009.

[6] J. Beltran-Heredia,J. Sanchez-Martin, A.Delgado-Regalado,Removal of carmineindigo dye with Moringa oleifera seed extract, Ind. Eng. Chem. Res. 48 (14)(2009) 65126520.

[7] E. Klimiuk,A. Filipkowska, A. Korzeniowska,Effectsof pH and coagulantdosageon effectiveness of coagulation of reactive dyes from model wastewater bypolyaluminium chloride (PAC), Polish J. Environ. Studies 8 (2) (1999) 7379.

[8] J. Bratby, Coagulation, Flocculation in Water and Wastewater Treatment, 2nded., IWA Publishing, London, 2006.

[9] N. Koprivanac, G. Bosanac,Z. Grabaric, S. Papic, Treatmentof wastewatersfrom

dye industry, Environ. Technol. 14 (4) (1993) 385390.


7/7


[10] D.J. Joo, W.S. Shin, J.H. Choi, S.J. Choi, M.C. Kim, M.H. Han, T.W. Ha, Y.H. Kim,Decolorization of reactive dyes using inorganic coagulants and synthetic poly-mer, Dyes Pigm. 73 (1) (2007) 5964.

[11] D.C. Montgomery, Design and Analysis of Experiments, 5th ed., John Wiley &Sons, New York, 2001.

[12] R.H. Myers, D.C. Montgomery, Response Surface Methodology: Process andProduct OptimizationUsing DesignedExperiments, 2nd ed.,John Wiley& Sons,New York, 2002.

[13] S. Akhnazarova, V. Kafarov, Experiment Optimization in Chemistry and Chem-ical Engineering, Mir Publishers, Moscow, 1982.

[14] M. Khayet,C. Cojocaru,C. Garcia-Payo,Applicationof responsesurface method-

ology and experimental design in direct contact membrane distillation, Ind.Eng. Chem. Res. 46 (17) (2007) 56735685.

[15] M. Khayet, C. Cojocaru, M.C. Garcia-Payo, Experimental design and opti-mization of asymmetric flat-sheet membranes prepared for direct contactmembrane distillation, J. Membr. Sci. 351 (12) (2010) 234245.

[16] M. Tomescu, A. Isaicmaniu, S. Cretu, G. Rachiteanu, Application of statistical-mathematical methods to the operation of chemical processes, Rev. Chim. 35(11) (1984) 10121017.

[17] M. Redhe, M. Giger, L. Nilsson, An investigation of structural optimizationin crashworthiness design using a stochastic approach a comparison ofstochastic optimization and the response surface methodology, Struct. Mul-tidisciplinary Opt. 27 (6) (2004) 446459.

[18] C. Cojocaru, G. Zakrzewska-Trznadel, Response surface modeling and opti-mization of copper removal from aqua solutions using polymer assistedultrafiltration, J. Membr. Sci. 298 (12) (2007) 5670.

[19] K. Ravikumar, K. Pakshirajan, T. Swaminathan, K. Balu, Optimization of batchprocess parameters using response surface methodology for dye removal by anovel adsorbent, Chem. Eng. J. 105 (3) (2005) 131138.

[20] O. Lacin, B. Bayrak, O. Korkut, E. Sayan, Modeling of adsorption and ultrasonic

desorption of cadmium(II) and zinc(II) on local bentonite, J. Colloid InterfaceSci. 292 (2) (2005) 330335.

[21] D.R. Hamsaveni, S.G. Prapulla, S. Divakar, Response surface methodologicalapproach for the synthesis of isobutyl isobutyrate, Process Biochem. 36 (11)(2001) 11031109.

[22] S.S. Moghaddam, M.R.A. Moghaddam, M. Arami, Coagulation/flocculation pro-cess for dye removal using sludge from water treatment plant: optimizationthrough response surface methodology, J. Hazard. Mater. 175 (13) (2010)651657.

[23] A. Anouzla, Y. Abrouki, S. Souabi, M. Safi, H. Rhbal, Colour and COD removal ofdisperse dye solution by a novel coagulant: application of statistical design forthe optimization and regression analysis, J. Hazard. Mater. 166 (23) (2009)13021306.

[24] A. Aleboyeh, N.Daneshvar, M.B. Kasiri, Optimization of C.I. Acid Red14 azodyeremoval by electrocoagulationbatch process withresponse surface methodol-ogy, Chem. Eng. Process. 47 (5) (2008) 827832.

[25] J. Beltran-Heredia, J. Sanchez-Martin, A. Delgado-Regalado, Removal of dyesbyMoringa oleifera seed extract. Study through response surface methodology, J.

Chem. Technol. Biotechnol. 84 (11) (2009) 16531659.[26] M.E.Olya,M.B. Kasiri, H. Aleboyeh,A. Aleboyeh, Application of responsesurfacemethodology to optimise CI Acid Orange 7 azodye mineralization by UV/H2O2process, J. Adv. Oxidation Technol. 11 (3) (2008) 561567.

[27] A. Palamakula, M.T.H. Nutan, M.A. Khan, Response surface methodology foroptimization and characterization of limonene-based coenzyme Q10 self-nanoemulsified capsule dosage form, AAPS PharmSciTech 5 (4) (2004) e66.

[28] H.B. Zhao, R.G. Tonkyn, S.E. Barlow, C.H.F. Peden, B.E. Koel, Fractional factorialstudy of HCN removal over a 0.5% Pt/Al2O3 catalyst: effects of temperature,gas flow rate, and reactant partial pressure, Ind. Eng. Chem. Res. 45 (3) (2006)934939.

[29] R. Lemoine, A. Behkish, B.I.Morsi, Hydrodynamic and mass-transfer character-istics in organic liquid mixtures in a large-scale bubble column reactor for thetoluene oxidation process, Ind. Eng. Chem. Res. 43 (19) (2004) 61956212.

[30] M.Ahmadi,F. Vahabzadeh, B. Bonakdarpour,E. Mofarrah,M. Mehranian, Appli-

cation of the central composite design and response surface methodology tothe advanced treatment of oliveoil processing wastewaterusing Fentonsper-oxidation, J. Hazard. Mater. 123 (13) (2005) 187195.

[31] J.H. Ramirez, C.A. Costa, L.M. Madeira, Experimental design to optimize thedegradation of the syntheticdye Orange II usingFentons reagent, Catal. Today107108 (2005) 6876.

[32] P. Rana-Madaria, M. Nagarajan, C. Rajagopal, B.S. Garg, Removal of chromiumfrom aqueous solutions by treatment with carbon aerogel electrodes usingresponsesurfacemethodology, Ind.Eng. Chem. Res.44 (17)(2005) 65496559.

[33] X.K.Gao,T.S. Low,Z.J.Liu, S.X.Chen,Robust designfor torqueoptimizationusingresponse surface methodology, IEEE Trans. Magn. 38 (2) (2002) 11411144.

[34] I. Xiarchos, A. Jaworska, G. Zakrzewska-Trznadel, Response surface method-ology for the modelling of copper removal from aqueous solutions usingmicellar-enhanced ultrafiltration, J. Membr. Sci. 321 (2) (2008) 222231.

[35] A.Santafe-Moros,J.M. Gozalvez-Zafrilla,J. Lora-Garcia, J.C.Garcia-Diaz,Mixturedesign applied to describe the influence of ionic composition on the removalof nitrate ions using nanofiltration, Desalination 185 (13) (2005) 289296.

[36] A.F. Ismail, P.Y.Lai, Development of defect-free asymmetricpolysulfone mem-branes for gas separation using response surface methodology, Sep. Purif.

Technol. 40 (2) (2004) 191207.[37] M. Khayet, C. Cojocaru, G. Zakrzewska-Trznadel, Response surface modelling

and optimization in pervaporation, J. Membr. Sci. 321 (2) (2008) 272283.[38] C. Cojocaru, M. Khayet, G. Zakrzewska-Trznadel, A. Jaworska, Modeling and

multi-response optimization of pervaporation of organic aqueous solutionsusing desirability function approach,J. Hazard. Mater. 167 (13) (2009) 5263.

[39] M. Khayet, M.N. AbuSeman, N. Hilal,Response surface modelingand optimiza-tionof compositenanofiltrationmodifiedmembranes, J. Membr. Sci. 349 (12)(2010) 113122.

[40] S. Petrov, P.A. Stoichev, Reagent ultrafiltration purification of water contami-nated with reactive dyes, Filtration Separation 39 (8) (2002) 3538.

[41] A. Akbari, J.C. Remigy, P. Aptel, Treatment of textile dye effluent using apolyamide-based nanofiltration membrane, Chem. Eng. Process. 41 (7) (2002)601609.

[42] S. Gaydardzhiev, J. Karthikeyan, P. Ay, Colour removal from model solutionsby coagulation surface charge and floc characterisation aspects, Environ.Technol. 27 (2) (2006) 193199.

[43] A.Y. Zahrim, N. Hilal, C. Tizaoui, Evaluation of several commercial synthetic

polymers as flocculant aids for removal of highly concentrated C.I. Acid Black210 dye, J. Hazard. Mater. 182 (13) (2010) 624630.[44] C.H. Spiers, Chemical engineering in the leather industry, Chem. Eng. Res. Des.

14 (1936) 129142.

Documents

modeling and optimizatiin of coagulation.pdf