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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
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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].
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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.
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80 M. Khayet et al. / Chemical Engineering Journal 167 (2011) 7783
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
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M. Khayet et al. / Chemical Engineering Journal 167 (2011) 7783 81
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.
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82 M. Khayet et al. / Chemical Engineering Journal 167 (2011) 7783
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.
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