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Cite this article: Anouzla A, Abrouki Y, Souabi S, Hicham Rhbal MS (2017) COD Reduction of Food Wastewater using SIWW Coagulant Optimization by Re-sponse Surface Methodology. Chem Eng Process Tech 3(1): 1036.

*Corresponding author

Abdelkader Anouzla Imm A13 N°19 ELmanzeh CYM Rabat, Morocco, Tel: 212 537792679; Tel: 212-661835108; Email:

Submitted: 18 April 2017

Accepted: 19 June 2017

Published: 21 June 2017

ISSN: 2333-6633

Copyright© 2017 Anouzla et al.

OPEN ACCESS

Keywords•Coagulation•SIWW•Food wastewater•Statistical design method

Review Article

COD Reduction of Food Wastewater using SIWW Coagulant Optimization by Response Surface MethodologyAbdelkader Anouzla*, Younes Abrouki, Salah Souabi, and Mohamed Safi Hicham RhbalLaboratoire de Génie de l’Eau et de l’Environnement, Université Hassan II, Morocco

Abstract

Response surface methodology involving central composite design was employed to optimize the removal of food wastewater by coagulation-flocculation treatment using SIWW as coagulant. The interaction between process variables i.e. coagulant dosage and pH of wastewater was studied and modelled. The optimum conditions were found to be at 0.5 mL of coagulant dosage with 7 of initial pH to give 37 mg/L of chemical oxygen demand (COD) reduction for food wastewater, in other words 91 % of COD removal.

NOMENCLATURESIWW: Steel Industrial Wastewater (new coagulant); COD:

Chemical Oxygen Demand (mg/L); BOD: Biochemical Oxygen Demand (mg/L); TSS: Total Suspended Solids (mg/L); YCOD: COD reduction of food wastewater (mg/L); Ŷ: Predicted Response; X1: pH of food wastewater; X2: Dosage of SIWW; ANOVA: Analysis of variance; F-value: Fischer’s Value; Prob: Probability; R2: Square of the correlation coefficient; βo: Constant coefficient; βi: Linear coefficients; βij: Cross-product coefficients; βii: Quadratic coefficients

INTRODUCTIONThe food manufacturing used a large volume of water to

process food products and clean plant equipment, yielding large amounts of wastewater that must be treated [1]. Excessive water use and wastewater production adds financial and ecological burdens to the industry and to the environment. These effluents aren’t easily biodegradable and can cause pollution if not properly treated before discharge to the environment.

Several physico-chemical methods [2] have been used, including adsorption technique, photo-degradation, electrochemical technique, ion exchange and coagulation-flocculation, for treatment of industrial wastewater.

Coagulation-flocculation is a relatively simple physical-chemical technique commonly used for water and wastewater treatment [3]. The removal mechanism of this process mainly consists of charge neutralization of negatively charged colloids

by cationic hydrolysis products, followed by incorporation of impurities in an amorphous hydroxide precipitate through flocculation. The appropriate implementation of this method depends upon how precisely coagulant dosage and pH are chosen. Therefore, trial and error has been conventionally practiced to optimize these variables.

These studies were conducted using “changing one factor at a time” method, i.e. a single factor is varied while all other factors are kept unchanged for a particular set of experiments. Likewise, other variables would be individually optimized through the single dimensional searches which are time consuming and incapable of reaching the true optimum as interaction among variables is not taken into consideration. As a solution, the statistical method of response surface methodology has been proposed to include the influences of individual factors as well as their interactive influences.

The response surface methodology [4] which is a technique for designing experiment helps researchers to build models, evaluate the effects of several factors and achieve the optimum conditions for desirable responses in addition to reducing the number of experiments. Analysis of variance provides the statistical results and diagnostic checking tests which enables researchers to evaluate adequacy of the models.

The objective of the present research is to find the optimum process parameters using design of experiments for coagulation-flocculation treatment of food wastewater using SIWW [5,6] as coagulant. The coagulation-flocculation process variables

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evaluated are coagulant dosage and pH of wastewater. A quadratic model is proposed based on the central composite design incorporating the effect of process variables over the reduction of chemical oxygen demand (COD). Interactions between the process variables are also investigated and elucidated.

MATERIALS AND METHODSAnalytical procedure

The food wastewater used in this work was obtained from KOUTOUBIA specialized in the cooked meats, (Morocco society).

Raw wastewater samples were collected for testing and shipped to our laboratory in 20L plastic carboys and then were stored at 4°C before use in experimental runs. The characteristics of food wastewater are summarized in Table 1.

The initial pH of food wastewater was adjusted by addition of NaOH or H2SO4 to a desired value in the range of 5-11. The COD removal of food wastewater was detected by using standard methods at the beginning and at the end of the experiment [7,8].

The SIWW was taken from Magreb Steel (Morocco society) and was used as a novel coagulant in this study [9].

Experimental design

A central composite rotatable design for k independent variables was employed to design the experiments [10,11] in which the variance of the predicted response, Ŷ, at some points of independent variables, X, is only a function of the distance from the point to the design centre. The design of experiment is intended to reduce the number of experiments and to arrange the experiments with various combinations of independent variables. In the rotatable design, the standard error, which depends on the coordinates of the point on the response surface at which Ŷ is evaluated and on the coefficients β, is the same for all points that are the same distance from the central point. These designs consist of a 2k factorial (coded to the usual ±1 notation) augmented by 2×k axial points (± α, …, 0), …, (0, …, ± α), and 2 centre points (0, …, 0). The value of α for rotatability [9] depends on the number of points in the factorial portion of the design, which is given in Eq. (1):

α = (NF)1/4 (1)

where NF is the number of points in the cube portion of the design (NF = 2k, k is the number of factors). Since there are two factors, the NF number is equal to 22 (=4) points, while α is equal to (4)1/4 (=1.4142) according to Eq. (1).

In this study, the response was COD reduction (YCOD) of food manufacturing wastewater. This response was used to develop an empirical model [12] that correlated the removal of food wastewater to the coagulation processes variables using a second-degree polynomial equation as given by Eq. (2):

Ŷ = βo + β1X1 + β2X2 + β12X1X2 + β11X12 + β22X2

2 (2)

where βo the constant coefficient, βi the linear coefficients, βij the interaction coefficient and βii the quadratic coefficients.

The software STATGRAPHICS-Plus (Version 4) was used for the experimental design, data analysis, model building, and graph plotting.

RESULTS AND DISCUSSIONExperimental results

Preliminary experiments were carried out to screen the appropriate parameters and to determine the experimental domain. From these experiments, the effects of initial pH (X1) and coagulant dosage (X2) are investigated on COD reduction. The parameter levels and coded values were given in Table 2. The experimental design matrix and the corresponding experimental parameters and response value were shown in Table 3.

Development of regression model equationSTATGRAPHICS-Plus computer software was used to model

and optimize the experimental results. At the end of coagulation processes with SIWW, the final empirical models in terms of coded factors after excluding the insignificant terms for COD reduction (ŶCOD) of food wastewater is showed in Eq. (3).

ŶCOD = 37.9999 + 13.1015X1 + 13.2908X2 + 19.7502X12 + 12X1X2

+ 29.7504X22 (3)

Positive sign in front of the terms indicates synergistic effect, whereas negative sign indicates antagonistic effect. The quality of the model developed was evaluated based on the correlation coefficient value. The R value for Eq. (3) was 0.9843.

Table 1: The characteristics of food wastewater.

Parameter Value

pH 7.2

Turbidity (NTU) 180

COD (mg/L) 400

BOD (mg/L) 130

TSS (mg/L) 560

Table 2: Study field and coded factors.

Natural variables (xj) UnitCoded variables X1, X2 *

- α -1 0 1 + αx1 = Initial pHx2 = Coagulant dosage

-mL

5.170.36

60.4

80.5

100.6

10.830.64

* The coded values Xj = ± 1 are obtained by the equation: Xj = (xj - j)/∆

Table 3: Experimental design and results for food wastewater removal.OrderLogical actual

Coded units of variableX1 X2

Responses*YCOD exp.

1 9 1 1 127

2 8 1 -1 76

3 1 -1 1 81

4 6 -1 -1 78

5 2 1.4142 0 96

6 5 -1.4142 0 53

7 10 0 1.4142 113

8 4 0 -1.4142 76

9 7 0 0 38

10 3 0 0 38*COD reduction (YCOD) of food wastewater.

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Table 4: Analysis of variance for COD reduction of food wastewater.Source

of variation Sum

of SquaresDegree

of freedomMean

square Fexp Significance

test Regression 7682.744 5 1536.5488 57.09 ***

β1 1373.17 1 1373.17 51.02 ***

β 2 1413.15 1 1413.15 52.51 ***

β 11 1783.14 1 1783.14 66.25 ***

β 12 576 1 576 21.40 ***

β 22 4046.01 1 4046.01 150.33 ***

Residue 107.656 4 26.9141 - -

***: Prob. ≤ 0.01, **: Prob. ≤ 0.025, *: Prob. ≤ 0.05, NS: No significant.

Figure 1 Response surface plot showing the effect of coagulant dosage and initial pH on food wastewater COD reduction.

The R values obtained was relatively high, indicating that there was a good agreement between the experimental and the predicted values from the model. The R2 value for Eq. (3) was 0.9689. This indicated that 96.89 % of the total variation in the COD reduction of food wastewater was attributed to the experimental variables studied. From the statistical results obtained, it was shown that the above model was adequate to predict the COD reduction of food wastewater by coagulation processes with SIWW within the range of variables studied.

Analysis of variance

The adequacy of the models was further justified through analysis of variance (ANOVA). The ANOVA for the model for COD reduction of food wastewater by coagulation processes with SIWW is listed in Table 4.

From this ANOVA, the Model F-value of 57.09 implied that the model was significant. Values of Prob. > F less than 0.05 indicated that the model terms were significant. In this case, the linear terms (X1

and X2) and the interaction terms (X12, X2

2 and X1X2) were significant model terms.

A geometrical representation for the COD reduction of food wastewater by coagulation processes with SIWW, which is dependent on the coagulant dosage and initial pH, is indicated on (Figure 1).

As can be seen from (Figure 1), when the coagulant dosage (X2 = -α to 0) increase and the initial pH remaining unchanged, then the COD reduction of food wastewater decrease up to 36 mg/L,

in other words the percent of COD removal of food wastewater increase up to 91 %.

Process optimization

The main objective of this research is to determine the experimental conditions required to remove food wastewater organic pollution. Then, using above mentioned methodology for experimental design, the ranges of the parameters required to obtain optimum conditions were determined. In this optimization study, COD reduction of the food wastewater was chosen as the objective function. Furthermore, optimum conditions are often calculated in the presence of some constraints which ensure them to be more realistic. If the model used in the optimization study is an empirical one, high and low levels of the process parameters in the experimental design are considered, inevitably, as explicit constraints, in order to avoid extrapolation. Thus, the optimization problem [13] is defined as;

- Optimum;

Max/Min ŶCOD (4)

- Constraints on the parameters Xi;

-αi < Xi < +αi i = 1, 2 (5)

The investigation of equations 3, 4 and 5 showed that, if X1 = -0.5 and X2 = 0; the value predict of response is 36.4 mg/L. The experimental checking in this point, i.e. under the conditions such as: initial pH = 7 and coagulant dosage = 0.5 mL with 37 mg/L of COD reduction (91 % of COD removal), confirms this results.

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CONCLUSIONThis work has demonstrated the use of a full factorial

central composite design by determining the optimum process conditions leading to the maximum percentage removal of food wastewater by coagulation with SIWW.

Using this experimental design and multiple regression, the parameters namely, coagulant dosage and initial pH were studied effectively and optimized with a lesser number of experiments. This methodology could therefore be successfully employed to study the importance of individual, cumulative and interactive effects of the test variables in coagulation and other processes.

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Anouzla A, Abrouki Y, Souabi S, Hicham Rhbal MS (2017) COD Reduction of Food Wastewater using SIWW Coagulant Optimization by Response Surface Meth-odology. Chem Eng Process Tech 3(1): 1036.

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