沙漠研究 22-1, 323 -327 (2012 )"ICAL 1 / DT X"

Journal of Arid Land Studies－Refereed Paper－Optimization of a Physicochemical Pretreatment Combined with BiologicalTreatment for a Highly loaded Wastewater with Anionic Surfactants,
Using Response Surface Methodology (RSM)
Mohamed BRADAI*1), Sami SAYADI2) and Hiroko ISODA1) 3)
Abstract: In the arid land, water resources are facing two major problems, the first is a quantitative one, which is their scarcity, andthe second is qualitative, related to water pollution and contamination. Trying to solve these problems, suppose that water reuse canbe a potential and strategic solution for these areas. This solution requires developing new methods and technologies for thetreatment of polluted water. In Tunisia, anionic surfactants are very common pollutants found in water, coming mainly fromdetergent and cosmetic industries as well as daily uses such as washing and cleaning. Biological treatment using membranebioreactor (MBR) has shown very good performance for the treatment of this kind of pollutants, but it becomes insufficient at highconcentrations, which cause harmful effects on the reactor biomass. In this work, we tried to optimize a new method for thetreatment of an industrial wastewater, highly loaded with anionic surfactants (about 4 g/l), especially Linear Alkyl Sulfonate (LAS),
coming from a cosmetic industry in Sfax city in Tunisia. This method combines a physicochemical pretreatment bycoagulation-flocculation using lime and alumina sulfate, with a biological treatment using MBR. The optimization was carried out
using Response Surface Methodology (RSM) through a two factors central composite plan, to adopt the physicochemical pretreatment
conditions for the smooth functioning of the whole process, in order to obtain treated wastewater that fit the Tunisian standard NT
106.02 to be released in aquatic environment. As results, the integration and optimization of the physicochemical pretreatment haveallowed to decrease the anionic surfactants concentration by 43 %, the Chemical Oxygen Demand (COD) by 48% and the suspended
matter (clogging factor) by 87.5%, making the MBR operating properly and releasing treated wastewater that respect the standards
specified by law.
Key Words: Anionic surfactants, Coagulation-flocculation, RSM, Wastewater

1. Introduction
In the arid and the semi-arid region, countries like Tunisia
are facing increasingly more serious water shortage problems.
Problems of water scarcity will intensify because of population
growth, rise in living standards, and accelerated urbanization
which threaten the water supply in general and lead to both anincrease in water consumption and pollution of water resources(Maelet and Ruelle, 2002). Continuing increase in demandby the urban sector has led to increased utilization of fresh
water for domestic purposes, on the one hand, and productionof greater volumes of wastewater on the other. One way tocope with these problems is to reuse wastewater. Thissolution requires developing new methods and technologies forthe treatment of polluted water.
In Tunisia, anionic surfactants are very common pollutants
found in water, with more than 100000 m3/year of surfactant
containing wastewater released, coming mainly from detergent
and cosmetic industries as well as daily uses such as washingand cleaning. MBR technology has shown very good
performance for the treatment of this kind of pollutants(Dhouib et al., 2005). But this method is confronted to somelimits in high concentration of AS, due to their toxicity toward
microorganisms and foaming in aerated bioreactors, on thehand, and high COD and Total Suspended Solids (TSS) which
accelerate the clogging of the membrane of the bioreactor, inthe other hand; which made the idea of integrating a physicochemical pretreatment, interesting to overcome these limits.
In this work, we tried to optimize a new method for thetreatment of an industrial wastewater, highly loaded withanionic surfactants (about 4 g/l), especially Linear AlkylSulfonate (LAS), coming from a cosmetic industry in Sfax city
in Tunisia. This method combines a physicochemical
pretreatment by coagulation-flocculation using lime andalumina sulfate, with a biological treatment using MBR. The
optimization was performed on the operational parameters of
the new physicochemical pretreatment to reduce the COD andthe anionic surfactant amount (AS) by, at least, 30% and 25%* Corresponding Author: mohamed.bradai@gmail.com
305-0821 Ibaraki-ken, Tsukuba-shi, kasuga 1-4-2, Gakuen Kasuga Manshon 2011) Alliance of Research on North Africa (ARENA), University of Tsukuba, Japan
2) Laboratory of Environmental Bioprocesses, Center of Biotechnology of Sfax (CBS), Tunisia

3) Graduate School of Life and Environmental Sciences, University of Tsukuba, Japan

Table 1. Wastewater main characteristics.
Characteristic

Mean value

Table 2.Correspondence between coded levels and real values.

Standard deviation

Coded levels (xi)-1.4142-1011.4142

pH

5.56

0.32

pH (X1)

6.79

7.00

7.50

8.00

8.21

COD (g/l)

18.2503.112

Coagulant d. (X2)

0.944

1.400

2.500

3.600

4.056

BOD (g/l)

1.211

0.125TSS (g/l)

2.230

0.950

AS (g/l)

3.900

0.520

Nt (g/l)

0.155

0.012

respectively, in order to allow the good operating of the MBR,and thus, the release of an outlet that fit the standard NT106.02.

2. Materials and Methods
2.1. Industrial wastewater
The wastewater was collected from the equalization tank ofwastewater treatment plant of a cosmetic company (HENKEL,Sfax, Tunisia) during the optimization, characterized and storedat 4˚C. The characteristics are shown on Table 1.
2.4. Analytical techniques• pH was analyzed using a Metrohm pH-meter.
• Methylene Blue Active Substance (MBAS) assay used tothe estimation of anionic surfactants. This assay was
carried out according to Tunisian Norms NT 01-28 (1983).
As well, Hyamine colorimetric method was also used for
estimating the anionic surfactants in wastewater whenconcentrations exceed 40 mgL−1.
• COD was estimated as described by Knechtel (1978).
• Total suspended solids (TSS) were measured as mentioned
in the standard methods for examination of water andwastewater (American Public Health Association /American Water Works Association / Water Environment
Federation, 1992).
2.2. Coagulation-flocculation
Flocculation and coagulation are mainly used, when theapplication of sedimentation is not feasible, due to the presenceof extremely fine particles or globules, which do not possess asignificant settling rate, because the phases do not appreciably
differ in density from the parent liquid (Zouboulis and Avranas,2000).
The experiments were carried out at laboratory bench scaleusing a jar test apparatus “Numerical Flocculator 10408, Fisher
Bioblock Scientific” with alumina sulfate and lime as
described by Aloui et al. (2009).
2.3. Experimental design
The central composite design (CCD), which is the standard

Table 3. Central composite design and responses results.
Run No.

Coded X1

Coded X2

Y1

Y2-1

-1

8.87

24.49

2

1-1

12.51

4.08

3-1

1

33.49

44.90

4

1

1

16.75

16.33

5-1.4142

0

24.63

18.37

6

1.4142

0

25.12

6.21

7

0-1.4142

19.92

2.04

80

1.4142

54.76

42.86

1

900

23.56

8.16

1000

24.08

6.12

1100

34.48

8.16

1200

28.10

6.12

1300

24.68

4.08

1400

16.47

3.04

1500

23.43

3.04

160026.61

4.16

17-0.6124-0.3536

27.67

6.12

180.6124-0.3536

18.95

2.04

1900.7071

33.36

18.37

RSM, was selected to optimize two most effective operating
variables in the coagulation–flocculation process, namely thecoagulant dosage and pH. For statistical calculations, thevariables Xi were coded as xi according to the following
equation:
xi =Xi − X0
δX(1)

Where Xi is the uncoded value of the ith independent variable,
X0 the value of Xi at the centre point of the investigated areaand δX is the step change.
The range and levels of pH (X1) and Coagulant dosage (X2)
are given in Table 2.
The two responses measured, COD removal (Y1) and AS
removal (Y2), were calculated in %, and the response variables
were fitted by a second-order model in the form of quadratic
polynomial equation:kki< ji =1i =1i

Ym = b0 + ∑ bi X i + ∑ bii X i2 + ∑∑ bij X i X j(2)jWhere Ym is the response variable to be modeled; Xi and Xj
the independent variables which influence Ym; b0 , bi , bii and bij
are the offset terms, the ith linear coefficient, the quadratic

Table 4. ANOVA table for COD removal.
Source ofDegrees ofSumsof

variance

freedomsquares

Mean squares

F-statistic

p-value

Regression

1.11111E+003

52.22221E+002

3.82400.0238Residues

7.55453E+002

13

5.81118E+001

Validity

1.76267E+002

6

7.93778E+001

5.14880.0246Error

1.07918E+002

71.54169E+001

Total

1.86656E+003

18

coefficient and the ijth interaction coefficient, respectively.
Model terms were selected or rejected based on the P value(probability) with 95% confidence level. Three additional
experiments were conducted to verify the validity of thestatistical experimental models (test points method) (Run No.
17, 18 and 19 in Table 3).

(a)

3. Results and Discussion
3.1. Modeling of COD removal (Y1)The COD removal values of the coagulation–flocculation
experiments are listed in Table 3. The following equation is aregression model with the experimental results:
Y1 = 24.547 − 0.974 X 1 + 9.557 X 2 − 2.966 X 12+ 3.119 X 22 − 5.503 X 1 X 2(3)Statistical testing of the model was performed with the Fisher’sstatistical test for analysis of variance (ANOVA) (Table 4).
The quadratic regression shows that the model was significant
and the p-value (0.0238) implies that the second-orderpolynomial model fitted the experimental results well. The
contour plots of Y1 (Fig. 1.a), obtained from the model, showthat the latter has an hyperbolic shape describing the double
effects of the factors interaction. The COD removal surface,presented in Figure 1.b, give an idea about the coordinate ofthe desired COD removal percentage, serving for the multiple
responses optimization.
3.2. Modeling of AS removal (Y2)
The following model was obtained from the analysis of AS
removal results shown in Table 3:Y2 = 5.533 − 7.373 X 1 + 11.330 X 2 + 4.608 X+ 9.732 X 22 − 2.687 X 1 X 221(4)The ANOVA analysis (Table 5) shows that the model was
significantly valid (p-value<0.001).
The contour plots (Fig. 2.a) show that the AS removal
model has an elliptic shape proving existing interactions
between factors effects, these plots have allowed to draw thecorresponding response surface (Fig. 2.b), showing different(b)Fig. 1. Contour plots (a) and surface graph (b) for COD removal.
Table 5. ANOVA table for AS removal.
Source ofDegrees of

Sumsofvariance

freedomsquares

Mean squares

F-statistic

p-value

76.3867<0.0018.19050.0069Regression

2.61125E+003

55.22249E+002

Residues

3.83844E+002

13

5.95265E+001

Validity

3.35986E+002

6

5.59976E+001

Error

4.78584E+001

76.83691E+000

Total

2.99509E+00318response levels ranging from non significant removal, about
0.5 to more than 48%.
3.3. Multiple responses optimization
As shown in the contour plots, the two responses have twodifferent behavior in the experimental range, hyperbolic for the
COD removal and elliptic for the AS removal, and the optimal
conditions for the two responses cannot match. Therefore, toobtain a common optimum for the two responses at the sametime, we chose to use the desirability functions.

Table 6. Industrial trials results by treatment step.
Treatment Steps

COD (g/l)

AS (g/l)

pHTSS (g/l)

Raw wastewater

19.752

4.120

5.96

3.200

Standard deviation

0.614

0.280

0.37

0.720

Pretreated ww.

11.630

2.730

7.10

0.410

Standard deviation

0.440

0.04

0.072

Inside bioreactor

3.257

0.184

6.98

8.6

Standard deviation

0.216

0.022

0.16

0.56

MBR outlet

0.824

Not detect.

7.03

Not detect.

Standard deviation

0.073

----

0.14

----

Standard NT106.02

1.000

0.005

6.50-9.00

0.400

D = (D1 * D2 )1/ 2(a)(5)

The global desirability function (Eq. (5))was maximized andwe obtained an optimal global desirability of 85.57% with
83.7% for D1 and 87.48% for D2. This optimum correspondto coded pH level of -0.7328, and coded coagulant dosage of
1.2819, which match with the real values: pH of 7.02 andcoagulant dosage of 4.011g/l.

(b)Fig. 2. Contour plots (a) and surface graph (b) for AS removal.

3.4. Industrial trials of the whole optimized process
The optimized conditions were applied on the industrial
process, and the results in Table 6 were obtained from several
trials and compared to the Tunisian standards.We note that theoutlet of the whole process fit the standard in the AS
concentration allowed, as well as in the other parameters.

4. Conclusion
The integration and the optimization of thephysicochemical pretreatment has allowed the good operatingof the treatment process and the release of a treated surfactant
containing wastewater that fit the standards specified by
Tunisian law.

Fig. 3. Partial desirability functions : D2 for AS removal(left) and D1
for COD removal (right).

The partial desirability functions D1 (for COD removal)
and D2 (for AS removal) were set as follow (Fig. 3.): For D1
satisfaction start from at least 30% of removal which is
required for the MBR smooth functioning, and achieves 100%for 50% of removal, which allow a good operating even withan unexpected increase of pollution loads.
The same for D2, except the satisfaction start from 25% for
the same reason.

Acknowledgement
The authors acknowledged that this research was partially
supported by Japan Society of Promotion of Science (JSPS)
under the Asia Africa Science Platform Program entitled:“Establishment of Integrative Research Base by Humanities
and Sciences on Valorization of Useful Plants for Regional
Development in North Africa”.
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