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ptimization of coagulation–flocculation process for wastewater derived fromauce manufacturing using factorial design of experiments
.A. Martín ∗, I. González, M. Berrios, J.A. Siles, A. Martínepartamento de Química Inorgánica e Ingeniería Química, Facultad de Ciencias, Universidad de Córdoba, Campus Universitario de Rabanales, Ctra. Madrid-Cádiz, km 396, Edificio-3, planta baja, CP 14071 Córdoba, Spain
r t i c l e i n f o
rticle history:eceived 4 April 2011eceived in revised form 20 June 2011ccepted 21 June 2011
A coagulation–flocculation process was used to pre-treat wastewater derived from sauce manufacturingprior to a subsequent biological treatment. A 52 full factorial experimental design and response surfacemethodology were employed to evaluate and optimize the coagulant and flocculant dosages and toachieve a compromise between efficiency, operational costs and the effects of a possible subsequentbiological treatment. The influence of pH was also evaluated to determine the most suitable pH condition(alkaline, neutral or acidic). Although the results were quite similar under all pH conditions, alkaline pHwas selected as it permitted ease of operation and lower operational costs due to the elimination ofpH adjustment stages. The best regression coefficients (R2) were obtained for chemical oxygen demand
urbidityrganic matter removal
(COD), turbidity and total soluble organic carbon (TOCsoluble) at alkaline pH, reaching values of 0.9136,0.8397 and 0.8512, respectively. At alkaline pH, the most significant factor in the analysis of variance(ANOVA) study was coagulant dosage for COD and turbidity removals. However, coagulant and flocculantdosages were both significant factors. Multiple response optimization fits the optimum values of thefactors and the responses as 0.4 mL/L of coagulant, 7.0 mL/L of flocculant and 82, 72 and 13% of COD,turbidity and TOC removal at alkaline pH, respectively.
soluble
. Introduction
Petroleum refining, metal manufacturing and machiningnvolve large amounts of lipids in the food industry. These lipids,
hich are mainly grease, fats or oils, are passed on to the processedater and consequently to wastewater [1,2], causing adverse
nvironmental effects. Given the importance of the sauce man-facturing industry and that this market is expected to growignificantly by the year 2015 [3], the oily wastewaters generatedy the industry must be treated before spillage in order to meeturrent quality standards related to environmental protection.
According to Zheng et al. [4], these wastewaters contain aarge amount of chemical oxygen demand (COD), suspended solidsresidues of broken colloid and indissoluble particles), solublerganic compounds, microorganisms and inorganic salts. Due toheir oily composition, these wastewaters can hinder the diffusionf oxygen required for many forms of aquatic life or block waterrainage lines [5].
Several physical, chemical and biological treatment meth-ds have been proposed to deal with the oily wastewaterroblem. These methods include flotation, membrane pro-
cesses (microfiltration and ultrafiltration), chemical destabilization(conventional coagulation), electrochemical destabilization (elec-trocoagulation) and activated sludge [6,7]. It is common to combinephysical–chemical processes for pre-treatment and biologicaltreatment.
The coagulation–flocculation process is used worldwide inwastewater treatment before spillage of the treated water as it isefficient and simple to operate. Many coagulants are widely usedin conventional wastewater treatment processes [8,9]. These coag-ulants can be inorganic (e.g. aluminum sulfate and polyaluminumchloride), synthetic organic polymers (e.g. polyacrylamide deriva-tives) or naturally occurring flocculants (e.g. microbial flocculants).These coagulants and flocculants are used for different purposesdepending on their chemical characteristics [9,10]. Specifically, inthe coagulation–flocculation of an emulsion, dispersed oil dropletsare destabilized by the neutralization of charges followed by theremoval of the separated oil as flocs. Ferric and aluminum salts arethe most widely used coagulant–flocculant agents for demulsifi-cation. However, other reactants have proven to be very efficientin wastewater treatment processes [9,11–15]. The addition of thereactants is carried out sequentially for neutralization of charges by
coagulation and subsequent agglomeration of particles by floccu-lation. Flotation and settling are the most widely used methods forthe removal of the flocs. Many factors can influence the efficiencyof the process such as the type and dosage of coagulant and floccu-
ant, pH, mixing speed and time, temperature or retention time [16].he optimization of these factors may significantly improve processfficiency and ensure the efficiency of the subsequent biologicalreatment.
A factorial experimental design and response surface method-logy can be summarized as a collection of statistical tools andechniques for exploring an approximate functional relationshipetween a response variable and a set of factors [13,17]. Basically,he main variations in the optimization process involve performinghe statistically designed experiments, estimating the coefficientf the mathematical model, predicting the response and checkinghe adequacy of the model [14]. The classical method to opti-
ize significant variables in the coagulation–flocculation processone factor at a time) is an extremely time-consuming, expensive,nd complicated process for a multivariable system. To overcomehis difficulty, statistical experimental design techniques using theesponse surface methodology are often applied [18].
Several authors have used response surface methodology andptimization to improve the coagulation–flocculation processes ofastewater of different origins [9,11,13,14,16,18]. These authors
gree that the type and dosage of coagulant and flocculant reac-ants are decisive to the success of the coagulation–flocculationrocess.
The aim of this study is to evaluate and optimize variables of theoagulation–flocculation process in wastewater from sauce manu-acturing from the standpoint of a compromise between efficiencynd operational costs. This work is novel as it statistically analyzesxperimental data in order to improve a real industrial process,hich is usually carried out under optimal conditions.
. Materials and methods
.1. Materials
The wastewater used in this study was derived from the manu-acturing process carried out by the Musa S.A. Company (Cordoba,pain), which chiefly produces mayonnaise in addition to gar-ic mayonnaise, ketchup, mustard and other special sauces suchs roquefort dressing or tartar sauce. The company manufacturesround 35,000 metric tons of sauce per year, accounting for approx-mately 15% of total Spanish sauce production (Annual Companyeport), and generates around 15,000–30,000 m3 wastewater/yeart a flow rate of 3–4 m3/h. Due to the variability in wastewater com-osition, three 100-L samples were taken from the homogenizationank in order to optimize the experimental representativity ofhe physical–chemical treatment carried out at laboratory scale.he mean value and standard deviation of the most relevant
ariables are shown in Table 1. As can be seen, organic mat-er content is considerably high (around 18 g COD total/L) due tohe presence of fat, sugars and acetic acid in the manufactured
products. Moreover, given that alkaline solutions are used forwashing the production lines, the wastewater pH is nearly alka-line (7.9). Aluminum polychloride 18 wt% was used as a coagulantagent (pH: 3.9 ± 0.2, boiling point: 110 ± 10 ◦C, density (20 ◦C):1.36 ± 0.02 g/mL. Brenntag Quimica, S.A.) and Actipol A-401 (1 g/L)(anionic polyacrylamide-based flocculant, active at pH 4–9. Bren-ntag Química S.A.) as a flocculant reactant.
2.2. Equipment
The experimental set-up used for the coagulation–flocculationexperiments at laboratory scale consisted of a Jar-test device(Magna Equipments S.L., F6 Model) in which six stirring blades wereconnected to a motor that operated under adjustable conditions.The system permitted the experiments to be performed with easeand the different variables affecting the removal of suspended fatand organic matter to be interpreted such as pH, stirring time andspeed, retention time or reactant concentrations.
2.3. Experimental procedure
As pH is one of the most restrictive parameters in the coagu-lation step and affects the hydrolysis equilibrium produced by thepresence of the coagulant agent [19] (Company, 2000), the experi-ments were carried out at pH 7.9 as this was the natural pH valuedetermined in the original wastewater, pH 7.0 following the spec-ifications of the coagulant manufacturer, and pH 5.3 in order toevaluate the effectiveness of the pre-treatment under acidic con-ditions. A wider pH range was not evaluated in order to preventproblems in the subsequent biological treatment and to reducethe costs of pre-treatment. The pH conditions were modified byadding diluted sulfuric acid. Coagulant dosages (aluminum poly-chloride 18 wt%) varied in the range of 0.2–1.0 mL/L (equivalentto 0.01–0.05 mg Al3+/L), while flocculant dosages (Actipol A-4011 g/L) ranged from 2 to 10 mL/L. The technical limitations for thereactants dosage at full-scale have conditioned the selection of itslowest level. Moreover, the highest level coincides with the con-centration that was being used by the company, which preventedachieving satisfactory efficiency. During the initial design of thereactants dosage, lower dosages were chosen in order to optimizethe treatment, while minimizing its cost. As these concentrationsimproved treatment efficiency, it was not necessary to evaluatehigher dosages. Twenty-five experiments were carried out undereach pH condition. After the addition of coagulant, the wastewa-ter was stirred at 160–180 rpm for 2 min. The flocculant was thenadded and the medium stirred at 40–50 rpm for 1 min. Sampleswere taken from the supernatant and analyzed after leaving themedium to stand for 20 min.
Total chemical oxygen demand (COD), turbidity and solubletotal organic carbon (TOCsoluble) were determined in the samplestaken across the coagulation–flocculation treatment. All the anal-yses were performed in accordance with the Standard Methods ofthe APHA [20].
2.4. Statistical analysis
A factorial experimental design was used to determine theinfluence of the reactants dosages on the efficiency of thecoagulation–flocculation process. A full 52 factorial experimentaldesign (two factors each at five levels) was used in the study. Threesets of experiments were carried out under alkaline, neutral oracidic pH conditions. Twenty-five experiments were carried out
under each pH condition studied. COD, turbidity and TOCsolubleremoval responses were selected to evaluate wastewater treat-ment efficiency. The factors chosen were coagulant (aluminumpolychloride) and flocculant (Actipol A-401) dosages. These fac-
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. M
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Engineering Journal
172 (2011) 771– 782773
Table 2Experimental matrix (random) and selected responses.
a Significant at the 95% confidence level.b Sum of squares: the sum of squares is a mathematical approach to determining the dispersion of data points. The sum of squares is used as a mathematical way to find
the function which best fits (varies least) from the data.c Degrees of freedom: an estimate of the number of independent categories in a particular statistical test or experiment.d Mean square: the mean square of a set of values is the arithmetic mean of the squares of their differences from some given value, namely their second moment about
that value.e F-value: value calculated by the ratio of two sample variances. The F statistic can test the null hypothesis: (1) that the two sample variances are from normal populations
w ction
t statist
tclC0fr1
oTpdadirTmrt
2
as
ith a common variance; (2) that two population means are equal; (3) that no connef p-Value: p value is associated with a test statistic. It is the probability, if the tes
est statistic [as extreme as, or more extreme than] the one actually observed.
ors were selected due to our interest in reducing the operationalosts of the wastewater treatment. In the same way, the factorevels were marked by operational limits and preliminary tests.oagulant dosages ranged from 0.2 to 1.0 mL/L (equivalent to.01–0.05 mg Al3+/L) at 0.2 mL/L intervals (0.01 mg Al3+/L) (codedactors: −2, −1, 0, 1, 2), while flocculant dosages varied in theange of 2–10 mL/L at 2 mL/L intervals (coded factors: −2, −1, 0,, 2).
The experimental matrix for the factorial design and the resultsf COD, turbidity and TOCsoluble removal are shown in Table 2.he experiments were run at random to minimize errors due toossible systematic trends in the variables. The use of factorialesign and analysis allowed the selected factors to be evalu-ted in terms of their significance and the optimum values to beetermined. This was done to obtain the best COD and turbid-
ty removals from the polynomial models and to study TOCsolubleemoval relative to the COD and turbidity removals obtained.he coagulation–flocculation process is not the most suitableethod for removing soluble organic matter. However, TOCsoluble
emoval was evaluated in order to study the subsequent biologicalreatment.
.5. Software
STATGRAPHICS Plus 5.1® was used to perform the statisticalnalysis, optimize the responses, and fit the experimental data pre-ented in this work.
exists between the dependent variable and all or some of the independent variables.tic really were distributed as it would be under the null hypothesis, of observing a
3. Results and discussion
The mechanism by which the coagulation–flocculation processtakes place may vary depending on the experimental conditions.In general, the typical steps that occur are charge neutralization,agglutination of the neutralized particles and separation by settle-ment or flotation [19].
If ion adsorption is due to chemical interactions, hydrogen, cova-lent or ionic bonds are formed between the adsorpted moleculesand the colloidal surface. The molecules adhere to fixed adsorp-tion points and their number might increase until modifying thecolloidal charge (from negative to positive) with the consequentstabilization process. Moreover, should the adsorption points beabundant, a higher coagulant concentration will be required, thusfavoring the adsorption of the largest polymers. This fact couldexplain why the coagulation step is not always carried out at zeropotential and may not even take place if the coagulant dosage istoo high, as chemical adsorption takes place instead of electrostaticinteraction [21].
On the other hand, heavy polymeric molecules (containinglong ionic chains) may be chemically adsorpted on the col-loidal particles. In this case, coagulation is not mainly influencedby electrostatic interactions, but by the colloid phenomenonat one or more fixed adsorption points. This leaves the rest
of the chain free, which may float or adhere to another col-loidal particle. In this case, molecular bonds are formed amongparticles, leading to their agglutination and the generation offlocs. However, coagulation by incorporation does not exclude
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M.A. Martín et al. / Chemical Eng
he possibility of simultaneous coagulation by chemical bondingr even coagulation by adsorption–neutralization. Consequently,oagulation–flocculation may occur through different superim-osed and complementary mechanisms [21].
Following the literature [9,11,13,14,16,18] and based on the pre-iminary results, coagulant and flocculant dosages were selected ashe variable factors for optimization. Although pH was also ana-yzed, it was not included in the factorial design.
The experiments and selected responses are shown in Table 2.he results were analyzed using STATGRAPHICS Plus 5.1® to deter-ine the estimated effects and interactions between the factors,
est the statistical significance of the effects, find the regressionquation and optimize the responses.
.1. COD removal
The analysis of variance test (ANOVA) for the second-orderesponse surface model is provided in Table 3 for the three pHonditions tested. Since the p-value for the model was lower than.05, there was a statistical relationship between COD removal and
he selected variables at a 95% confidence level under all pH con-itions. As can be observed in the ANOVA table (Table 3) and theain effects plotted in Fig. 1, coagulant dosage was the most signif-
cant factor under alkaline and acidic pH conditions. However, COD
Basic pH
CO
D re
mov
al (%
)
Coagulant dosage2,0-2,0
Flocculant dosage 2,0-2,0
20
30
40
50
60
70
80
Neutral pH
CO
D re
mov
al (%
)
Coagulant dosage2,0-2,0
Flocculant dosage 2,0-2,0
67
70
73
76
79
82
Acidic pH
CO
D re
mov
al (%
)
Coagulant dosage2,0-2,0
Flocculant dosage 2,0-2,0
69
72
75
78
81
84
Fig. 1. Main effects plot for COD removal under all pH conditions.
ng Journal 172 (2011) 771– 782 775
removal response was found to vary since a high level of coagulantdosage led to a high COD removal percentage at alkaline pH and alow COD removal percentage at acidic pH. At neutral pH, the floccu-lant dosage was more significant than the coagulant dosage. Fig. 1shows the individual effect of coagulant and flocculant dosages forCOD removal under all pH conditions tested. The extreme codedlevels (−2 or 2) cause the most significant variation in COD removalas can be observed, for example, at acidic pH. The coded level 2for coagulant dosage prompts the best COD removal (82%). Theseresults show that the coagulation–flocculation mechanism differsdepending on the pH value.
The following regression equations (in coded factors) wereobtained under each pH condition, where CODr (%) is the CODremoval percentage:
Alkaline pH:
CODr (%) = 63.292 − 11.980A + 2.321B − 4.721A2
+ 0.381AB − 0.322B2 (1)
r2 = 0.9136
Neutral pH:
CODr (%) = 77.114 + 0.462A + 2.556B + 0.928A2
− 0.358AB − 1.043B2 (2)
r2 = 0.4215
Acidic pH:
CODr (%) = 70.562 + 2.281A + 0.631B + 1.702A2
+ 0.184AB + 0.566B2 (3)
r2 = 0.7795
The goodness of fit of the models was tested by checking the deter-mination coefficient (R2). The determination coefficient indicatesthe sample variation explained by the model. As the pH decreased,the determination coefficient decreased from 0.914 to 0.422 and0.780 for alkaline, neutral and acidic pH, respectively, thus show-ing that the model at neutral pH does not sufficiently explain thevariability of the experimental data.
The plots of the experimental value versus the predicted valuefor COD removal are shown in Fig. 2. The experimental values aredistributed relatively near to the straight line except at neutral pHwhere data dispersion was detected.
The 3D response surface graph is the most common graphicalrepresentation of the regression equation and can be observed inFig. 3 for COD removal. COD removal behaved differently undereach pH condition. At alkaline pH, COD removal increased at lowcoagulant dosage levels and high flocculant dosage levels. At neu-tral pH, however, the highest COD removal was observed at veryhigh coagulant dosage levels (−2 or 2) and at intermediate floc-culant dosage levels. On the other hand, the best result for CODremoval at acidic pH was found at high coagulant dosage levels for
all the flocculant dosages tested.
When the main goal of a study is to obtain the highestCOD removal, COD removal must be optimized to maximize theresponse. Optimization was achieved using STATGRAPHICS Plus
776 M.A. Martín et al. / Chemical Engineering Journal 172 (2011) 771– 782
Basic pH
predicted values
expe
rimen
tal v
alue
s
8060402000
20
40
60
80
Neutral pH
predicted values
expe
rimen
tal v
alue
s
8580757065605555
60
65
70
75
80
85
Acidic pH
predicted values
expe
rimen
tal v
alue
s
9086827874706666
70
74
78
82
86
90
F
5t
3
f
Basic pH
Coagulant dosageFlocculant dosageC
OD
rem
oval
(%)
-2 -1 0 1 2 -2 -1 0 1 20
20
40
60
80
Neutral pH
Coagulant dosageFlocculant dosageC
OD
rem
oval
(%)
-2 -1 0 1 2 -2 -1 0 1 267
71
75
79
83
Acidic pH
Coagulant dosageFlocculant dosageC
OD
rem
oval
(%)
-2 -1 0 1 2 -2 -1 0 1 269727578818487
TS
ig. 2. Experimental values versus predicted values for the COD removal models.
.1® software. Optimum COD removal and the optimum values ofhe coagulant and flocculant dosages are shown in Table 4.
.2. Turbidity removal
The results of the second-order response surface model in theorm of analysis of variance for turbidity under each pH condition
able 4ingle response optimizations under all pH conditions.
Fig. 3. Response surface plots for COD removal as a function of coagulant and floc-culant dosages.
are shown in Table 5. The analysis shows that the model is highlysignificant as the p-value was lower than 0.05 for all pH conditionstested. Hence, there is a statistical relationship between turbidityremoval and the selected variables at a 95% confidence level. Ascan be observed in Table 5, the significant terms in the model werethe main effect (A) and the second-order effect (A2) of coagulantdosage for alkaline pH; the main effect of flocculant dosage (B) andthe second-order effect (A2) of coagulant dosage for neutral pH;and the main effect of flocculant dosage (B) for acidic pH. Othermodel terms were not significant. Fig. 4 shows the main effects ofthe two factors under each pH condition. The plots at neutral andacidic pH behaved in a similar manner to the factors in the response.
This behavior is a consequence of the chemical adsorption mech-anism, which is thought to be the predominant procedure takingplace at neutral and acidic pH. However, the coagulant dosage wasmore significant at alkaline pH than under other pH conditions. This
778 M.A. Martín et al. / Chemical Engineering Journal 172 (2011) 771– 782
Basic pHTu
rbid
ity re
mov
al (%
)
Coagulant dosage2,0-2,0
Flocculant dosage2,0-2,0
-13
7
27
47
67
87
Neutral pH
Turb
idity
rem
oval
(%)
Coagulant dosage2,0-2,0
Flocculant dosage2,0-2,0
80
84
88
92
96
Acidic pH
Turb
idity
rem
oval
(%)
Coagulant dosage2,0-2,0
Flocculant dosage 2,0-2,0
82
84
86
88
90
caec
or
Basic pH
predicted values
expe
rimen
tal v
alue
s
1209060300-30-30
0
30
60
90
120
Neutral pH
predicted valuesex
perim
enta
l val
ues
9994898479746969
74
79
84
89
94
99
Acidic pH
predicted values
expe
rimen
tal v
alue
s
939087848178
78
81
84
87
90
93
Fig. 4. Main effects plot for turbidity removal under all pH conditions.
ould be due to the hydrolysis and precipitation of the coagulants hydroxide is favored at alkaline pH [22]. Therefore, higher lev-ls of coagulant dosage at alkaline pH can increase turbidity due tohemical interactions.
The following regression equations (coded factors) werebtained under each pH condition where Turbr (%) is the turbidityemoval percentage:
Alkaline pH:
Turbr (%) = 71.704 − 21.795A − 1.709B − 10.151A2
+ 0.503AB − 3.179B2 (4)
r2 = 0.8397
Neutral pH:
Turbr (%) = 89.797 + 0.408A + 2.602B + 1.317A2
− 0.331AB − 1.095B2 (5)
r2 = 0.5501
Fig. 5. Experimental values versus predicted values for the turbidity removal mod-els.
Acidic pH:
Turbr (%) = 86.909 + 0.540A + 1.273B + 0.320A2
− 0.170AB − 0.511B2 (6)
r2 = 0.4631
The goodness of fit of the models was again evaluated by the deter-mination coefficients (R2). The determination coefficient decreasedwhen pH varied from 7.9 to 5.3. In the case of alkaline pH, the 84.0%sample variation observed for turbidity removal was attributedto the independent variables selected (coagulant and flocculantdosages), while the model did not explain 16.0% of the total varia-tions.
Another way to assess the goodness of fit of the model is byplotting the experimental values versus the predicted values forturbidity removal. Fig. 5 shows these plots for the three pH con-ditions. As can be seen, the models approximately represent the
M.A. Martín et al. / Chemical Engineering Journal 172 (2011) 771– 782 779
Basic pH
Coagulant dosage Flocculant dosage
Turb
idity
rem
oval
(%)
-2 -1 0 1 2 -2 -1 0 1 2-30-101030507090
Neutral pH
Coagulant dosage Flocculant dosage
Turb
idity
rem
oval
(%)
-2 -1 0 1 2 -2 -1 0 1 279828588919497
Acidic pH
Coagulant dosageFlocculant dosageTu
rbid
ity re
mov
al (%
)
-2 -1 0 1 2 -2 -1 0 1 2818385878991
Ffl
esc
Tcathf
wr
3
oswmsrraccc
Basic pH
TOC
rem
oval
(%)
Coagulant dosage2,0-2,0
Flocculant dosage2,0-2,0
6,5
8,5
10,5
12,5
14,5
Neutral pH
TOC
rem
oval
(%)
Coagulant dosage2,0-2,0
Flocculant dosage2,0-2,0
11
13
15
17
19
21
Acidic pHTO
C re
mov
al (%
)
2,0-2,0 2,0-2,015
15,4
15,8
16,2
16,6
17
17,4
ig. 6. Response surface plots for turbidity removal as a function of coagulant andocculant dosages.
xperimental data over the range studied. The plot at alkaline pHhows the best fit as it may also be observed by the regressionoefficient in ANOVA (Table 5).
Fig. 6 shows 3D response surface plots for turbidity removal.he best results for turbidity removal were obtained at very highoagulant dosage levels and intermediate flocculant dosage levelss can be observed by the saddle shape at neutral and acidic pH. Onhe other hand, the mound shape at alkaline pH indicates that theighest percentages can be obtained at intermediate values of the
actors.To maximize turbidity removal, a single response optimization
as carried out as explained in the section on COD removal. Theesults are summarized in Table 4.
.3. TOCsoluble removal
The models were tested statistically using ANOVA. The resultsf the ANOVA for TOCsoluble removal under each pH condition arehown in Table 6. The quadratic regression shows that the modelas significant for alkaline and acidic pH since the p-value of theodels was lower than 0.05 at the 95% confidence level. Fig. 7
hows the main effects plots for TOCsoluble removal. The TOCsolubleemoval responses varied under the three pH conditions. TOCsolubleefers mainly to dissolved organic matter that is removed from the
queous solution by sweeping the coagulation–flocculation pro-ess. Hence, no clear trend was observed regarding the influence ofoagulant and flocculant dosages. For example, under neutral pHonditions, TOCsoluble increases linearly with enhanced coagulant
Coagulant dosage Flocculant dosage
Fig. 7. Main effects plot for TOCsoluble removal under all pH conditions.
dosages. At alkaline and acidic pH, however, the opposite behav-ior is observed. At pH values in which the medium is charged, theadsorption of dissolved organic matter is hindered as the chargemay ionize or reduce polarity and thus prevent adsorption on theflocs.
The following equations refer to the regression models (codedfactors) with the experimental results under each pH conditiontested, where TOCr (%) is the TOCsoluble removal percentage:
Alkaline pH:
TOCr (%) = 11.778 − 0.989A + 1.407B − 0.412A2
+0.431AB − 0.607B2 (7)
r2 = 0.8512
Neutral pH:
TOCr (%) = 15.704 + 2.127A + 0.806B − 0.086A2
−0.346AB − 0.476B2 (8)
r2 = 0.7536
7 ineering Journal 172 (2011) 771– 782
camitpTpr
afsftcpnldr
c
3
daiacscffreqtcba
pt
Basic pH
predicted values
expe
rimen
tal v
alue
s
1411852-1-1
2
5
8
11
14
Neutral pH
predicted values
expe
rimen
tal v
alue
s
21181512966
9
12
15
18
21
Acidic pH
predicted values
expe
rimen
tal v
alue
s
232119171513
13
15
17
19
21
23
TV
80 M.A. Martín et al. / Chemical Eng
Acidic pH:
TOCr (%) = 16.269 − 0.314A − 0.098B − 0.095A2
+0.157AB + 0.169B2 (9)
r2 = 0.1698
Table 6 also shows the determination coefficients (R2) for all pHonditions. The models at alkaline and neutral pH explain 85.1%nd 75.4% of the total variation, respectively, while the acidic pHodel shows a worse fit (R2 = 0.170). The prediction of the exper-
mental data was quite satisfactory at alkaline and neutral pH ashe lines have a slope close to 1 as shown in Fig. 8. Hence, thelots of the experimental values versus the predicted values forOCsoluble removal provide a visual representation of the model’serformance. The plot therefore highlights the deficiency of theesponse surface model at acidic pH (R2 = 0.1698).
Contour plots of the response surface methodology are drawns a function of two factors at a time, while maintaining all otheractors at fixed levels (normally at intermediate levels) [18]. Fig. 9hows the contour plots for TOCsoluble removal. The contour curveor alkaline pH has a considerable curvature, thus indicating thathe interaction between the factors was significant [16]. The moundontour at alkaline pH indicated that the best TOCsoluble removalercentages were obtained at intermediate levels of the factors. Foreutral pH, the best results were observed at high coagulant dosage
evels and intermediate flocculant dosage levels. Finally, the sad-le contour obtained at acidic pH demonstrated that the TOCsolubleemoval percentages were quite low at zero levels of both factors.
The single maximization results for TOCsoluble removal in theoagulation–flocculation process can be observed in Table 4.
.4. Model validation and multiple response optimization
To further validate the models under even higher reactantosages, an additional experiment (0.2 mL/L of coagulant, equiv-lent to 0.01 mg Al3+/L, and 12.0 mL/L of flocculant) was performedn each set of experiments. The conditions selected for coagulantnd flocculant dosages were constant under all pH conditions. Theonditions are listed in Table 7 along with the predicted and mea-ured results. As can be seen in the table, the three responses werelose to the responses that were estimated using response sur-ace methodology. The variation coefficients were lower than 5%or COD and turbidity removals and lower than 20% for TOCsolubleemoval. Although the regression coefficients were low in somexperiments at neutral or acidic pH, the experimental results wereuite similar to the predicted results when the models were appliedo higher factor levels as can be observed in Table 7. Therefore, itan be concluded that the models accurately represent COD, tur-idity and TOCsoluble removals over the experimental range studied
nd at even higher factor levels.
The multiple response optimization method is an algorithmrovided by STATGRAPHICS Plus 5.1® software and combines thehree selected responses to obtain the best operational conditions.
able 7alidation of the models at 0.2 mL/L of coagulant and 12.0 mL/L of flocculant.
pH COD removal (%) Turbidity r
Experimental Predicted Experimen
Alkaline 68.22 70.15 57.82
Neutral 81.72 80.33 95.09
Acidic 77.89 78.69 91.40
Fig. 8. Experimental values versus predicted values for the TOCsoluble removalmodels.
Although COD, turbidity and TOCsoluble removals were optimizedas individual responses, a compromise between the conditions forthe responses is desirable. STATGRAPHICS Plus 5.1® software uses adesirability function approach to achieve the highest removal per-
centages. Table 8 shows the optimum values for the responses andthe factors at each pH. The values were calculated by means ofthe desirability function and the models obtained using responsesurface methodology. A comparison of Tables 4 and 8 shows that
e COT removal (%)0,01,53,04,56,07,59,010,512,013,5
210-1-2-2
-1
0
1
2
Neutral pH
Coagulant dosage
Floc
cula
nt d
osag
e TOC removal (%)6,07,59,010,512,013,515,016,518,019,521,0210-1-2
-2
-1
0
1
2
Acidic pH
Coagulant dosage
Floc
cula
nt d
osag
e TOC removal (%)15,015,415,816,216,617,017,417,818,2
210-1-2-2
-1
0
1
2
Fd
mspa
cliahwCtmdspm
TM
ig. 9. Contour plots for TOCsoluble removal as a function of coagulant and flocculantosages.
ultiple response optimization achieves better overall results thaningle response optimizations. Thus, the coagulation–flocculationrocess can reduce operational costs as lower dosages of reactantsre required.
The coagulation–flocculation mechanism occurs once enoughoagulant has been dispersed to sufficiently destabilize the col-oidal particles and the flocculant allows particles to agglomeratento larger flocs [18,23,24]. For destabilization to occur, theluminum polychloride requires sufficient alkalinity for properydrolysis. This leads to the formation of insoluble hydroxidehose precipitation reduces COD, turbidity and TOCsoluble [18].onsequently, an initial alkaline pH could be suitable in ordero favor the formation of Al(OH)3. On the other hand, the high
olecular weight flocculant used destabilizes the particles byeveloping bridges with the functional groups, thus forming larger
tructural units that are readily separated from the aqueous dis-ersing medium [25]. Other authors have used response surfaceethodology to select the best operational conditions for the
able 8ultiple responses optimization under each pH condition.
coagulation–flocculation process [9,11–13,16,18]. Ahmad et al. [18]and Bathia et al. [9] treated palm oil mill effluent, obtaining the bestresults at acidic pH (6.0 and 5.0, respectively).
Although similar results were found in our study, the differencesin removal percentages across the tested pH conditions were notsignificant. Although these removal percentages are higher than80%, the treated wastewater cannot be spilled and a subsequentbiological treatment is needed. Alkaline pH was therefore selectedas the most suitable pH condition since it reduces the operationalcosts of the initial pH adjustment stage and allows the biologicaltreatment to be applied directly without correcting the pH.
4. Conclusions
A 52 full factorial experimental design and response surfacemethodology were used to optimize the coagulation–flocculationprocess of wastewater from sauce manufacturing with a view toreducing the number and cost of experiments and improving theprocess at industrial scale. The goodness of fit of the model at eachpH was effectively verified by validating the experimental data.The best regression coefficients (R2) were obtained for COD, tur-bidity and TOCsoluble at alkaline pH: 0.9136, 0.8397 and 0.8512,respectively. Coagulant dosage seems to be the most significantfactor in the removal of COD and turbidity under all the pH con-ditions tested. For TOCsoluble, however, the trend is not clear giventhat this parameter is an indicator of the dissolved organic matterthat is removed from the aqueous solution by sweeping during thecoagulation–flocculation process.
Multiple response optimization allowed the coagulant andflocculant dosages to be minimized, while maximizing the COD,turbidity and TOCsoluble removal percentages.
pH was also evaluated to determine the most suitable pH con-dition for the coagulation–flocculation process of wastewater fromsauce manufacturing from the standpoint of operational, economicand post-treatment factors. Although the results were quite simi-lar under all pH conditions, alkaline pH was selected due to easeof operation and lower costs resulting from the omission of pHadjustment stages.
Nevertheless, although organic matter removal was high, itsconcentration was still not adequate for spillage, making a sub-sequent biological treatment mandatory.
With regard to TOCsoluble removal, the coagulant and floc-culant dosages did not show a clear influence due to the factthat dissolved organic matter is removed for sweeping thecoagulation–flocculation process.
Once pH was fixed at the alkaline value, the optimum values ofthe factors and the responses were 0.4 mL/L of coagulant (equiva-lent to 0.02 mg Al3+/L), 7.0 mL/L of flocculant and 82, 72 and 13% ofCOD, turbidity and TOCsoluble removal, respectively.
Acknowledgements
The authors are very grateful to the MUSA S.A. firm, specifi-cally the Baldomero Moreno Factory (Cordoba, Spain) for fundingthis research. We also wish to express our gratitude to laboratorytechnician Inmaculada Bellido Padillo for her help.
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