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Response surface methodological approach to optimize the coagulation–flocculation process in drinking water treatment
chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
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Response surface methodological approach to optimize the coagulation–flocculation process in drinking water treatment Thuy Khanh Trinh, Lim Seok Kang ∗ Department of Environmental Engineering, Pukyong National University, 599-1, Daeyeon 3-Dong, Nam-Gu, Busan, Republic of Korea
a b s t r a c t Performing jar tests often requires carrying out a time consuming iteration procedure to find out the right amount of chemical for coagulation–flocculation process in water treatment plants. Applying the response surface method (RSM) in jar tests as an alternative to the conventional methods was investigated in this study. The purpose is finding out the optimum combination of coagulant dose and pH with respect to the highest removal efficiency of turbidity and dissolved organic carbon (DOC). The results achieved using poly-aluminum chloride (PACl) were compared to those achieved using conventional coagulant such as alum. The quadratic models developed for the two responses (turbidity removal and DOC removal) indicated that the optimum conditions to be PACl concentration of 0.11 mM at pH 7.4 and alum concentration of 0.15 mM at pH 6.6. Compromising to simultaneously optimize the two responses resulted in 91.4% turbidity removal and 31.2% DOC removal using PACl whereas 86.3% turbidity and 34.3% DOC were removed using alum. Confirmation of experimental results was found to be close to the prediction derived from the models. This demonstrates the benefits of the approach based on the RSM in achieving good predictions while minimizing the number of required experiments. © 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords:
Central
composite
design
(CCD);
DOC
removal;
Drinking
water
treatment;
Optimizing;
Coagulation–flocculation; Response surface methodology (RSM)
1.
Introduction
Coagulation–flocculation has played, and will still play an important role, directly or indirectly, in the control of particulates, microorganisms, natural organic matter (NOM), synthetic organic carbon, precursors of disinfection byproducts (DBPs), and some inorganic ions and metals, and ultimately, in the control of drinking water quality (Jiang, 2001). In this process, coagulants, such as alum or polyaluminum chloride (PACl), are added to water, and a metal ion such as Al3+ undergoes hydrolysis reactions to form other dissolved Al species and Al-hydroxide precipitates. These aluminum hydrolysis species help to aggregate various aquatic particles into larger flocs and then these flocs are settled, filtered and removed from bulk water in subsequent processes. The turbidity and NOM of water are the target substances to be removed during coagulation–flocculation treatment. Charge
∗
neutralization and sweep flocculation are two mechanisms for removal of turbidity and the concentration of colloids and coagulant dosages are critical factors that determine the predominant mechanism for removal (Snodgrass et al., 1984; Gregory and Duan, 2001; Shin et al., 2008). Two mechanisms of NOM removal most commonly referred to are charge neutralization of cationic aluminum species and anionic NOM, and adsorption of NOM or Al-NOM complexes on amorphous Al(OH)3 (s) (Semmens and Field, 1980; Hundt and O’Melia, 1988; Randtke, 1988; Edzwald and Van Benschoten, 1990; Edzwald and Tobaison, 1999). The predominance of each mechanism depends not only on the NOM characteristics, but also on the aluminum hydrolysis species, which are quickly formed after coagulant addition. Many factors such as the characteristics of raw water, type of coagulant, coagulation pH, and dose of coagulant have been considered to influence to the coagulation performance. An
Corresponding author. Tel.: +82 51 629 6527; fax: +82 51 629 6523. E-mail addresses: [email protected] (T.K. Trinh), [email protected] (L.S. Kang). Received 27 August 2010; Received in revised form 13 November 2010; Accepted 1 December 2010 0263-8762/$ – see front matter © 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.cherd.2010.12.004
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Table 1 – Characteristics of raw water from Nakdong river. Parameter Range
Turbidity (NTU) 7.5–8.5
UV254 (cm−1 )
DOC (mg/L)
0.047–0.050
3.69–3.76
SUVA (L mg−1 m−1 ) 1.27–1.33
pH
Alkalinity (mg CaCO3 /L)
8.0–9.4
80–83
DOC: dissolved organic carbon, SUVA: specific ultraviolet absorbance.
appropriate combination of these factors is desirable to obtain a high efficiency of treatment. To seek the optimal conditions of these factors, jar tests using the trial and error approach, also known as the one factor at a time (OFAT) method, are employed. This classical way is changing the levels of one factor and at the same time, keeping the others in constant, running the experiment, observing the results, and moving on to the next factor. Therefore, the OFAT method is not only time and energy consuming, but also is usually incapable of revealing the optimal combination of factors due to ignoring the interaction among them (Mason et al., 2003) To solve this problem, the response surface method (RSM) offers a better alternative to the conventional method because it includes the influences of individual factors as well as the influences of their interaction. RSM is a technique for designing experiments, building models, evaluating the effects of several factors, and achieving the optimum conditions for desirable responses with a limited number of planned experiments (Khuri and Cornell, 1996). The RSM has been widely used in various fields such as biochemistry for optimizing fermentation conditions (Murthy et al., 2000; Li et al., 2007), and in pharmaceutics for optimizing self-nanoemulsified capsule dosage form (SNCDF) of a highly lipophilic model compound, Coenzyme Q10 (CoQ) (Palamakula et al., 2004). In the field of water treatment, applying RSM to optimize the coagulation–flocculation process for the treatment of wastewater was reported in many studies (Wang et al., 2007; Ghafari et al., 2009; Pham et al., 2009). However, in the drinking water treatment area, few studies were reported for applying RSM to optimize the conditions of coagulation–flocculation process with respect to the highest removal of turbidity and DOC. Therefore, an attempt has been made to optimize the key factors of coagulation–flocculation process, such as coagulant dose and pH, using an experimental design approach of RSM. In this way, the interactions of parameters and the non-linear dependencies among experimental variables are studied and, therefore, a real optimum is achieved.
2.
Materials and methods
2.1.
Characteristics of raw water
This study used water samples taken from the Nakdong river which is the major water source of the city of Busan, Korea. The characteristics of the water are listed in Table 1.
2.2.
Coagulation–flocculation test
Two different coagulants were used in this study: alum as a metal salt and PACl as a pre-hydrolyzed metal salt. The alum (Al2 (SO4 )3 ·16H2 O) used was purchased from Sigma, USA. The hydrolyzed PACl solution with the formula Al(OH)1.5 Cl1.5 (degree of neutralization r = [OH]/[Al] = 1.5, basicity = 50%) was prepared by adding 0.5 M NaOH into 0.2 M AlCl3 solution
according to a procedure described by Kang et al. (2001). The coagulation pH was adjusted according to pH levels in Table 3. These pH levels achieved by adding 0.1 M HCl or 0.1 M NaOH just before dosing of the coagulant. The coagulation–flocculation process using the jar test was carried out using 2 L square jars, with six paddle stirrers (Phipps and Bird). The time and speed for rapid and slow mixing were set with an automatic controller. Samples were mixed at 250 rpm (G = 550 s−1 ) for 1 min after coagulant addition to provide rapid mixing, and then the speed was reduced to 30 rpm (G = 22 s−1 ) for 30 min to provide flocculation. After 30 min of settling, the supernatant from each jar was withdrawn from the sampling port and analyzed for pH, settled turbidity and DOC. A pH meter (F-54 BW, Horiba, Japan) was used to measure the solution pH. Turbidity was measured by the Turbidimeter (2100N, HACH, USA) following the Nephelometric method (Standard Method 2130B). DOC was analyzed immediately after filtering the sample through 0.45 m membrane by the TOC Analyzer (TOC-VCPH, Shimadzu, Japan) using the HighTemperature Combustion method (Standard method 5310B) (APHA-AWWA/WEF, 1998)
2.3.
Response surface methodology
The principle of RSM was described by Khuri and Cornell (1996). To achieve adequate and reliable measurements of the responses of interest, the design of experiment is necessary. Normally, the relationship between the responses (turbidity removal and DOC removal) and the independent variables (coagulant dose and pH) in coagulation–flocculation process cannot be well modeled by a linear function or a first order model. A model that incorporates curvature is usually required to approximate the response in the region close to optimum, and in most cases, a second order model is adequate (Montgomery, 2001). A central composite design (CCD), which is a very efficient design tool for fitting the second-order models (Montgomery, 2001), was selected for use in the present study. A CCD is made rotatable by the choice of ˛ (Montgomery, 2001). A value of ˛ = 1.414 for 2 factors in the study assured rotation of the CCD. A CCD containing 13 experiments with 4 cube points, 4 axial points, and 5 replicates at the center point, is illustrated in Fig. 1. The relationship between the coded (xi ) and actual (Xi ) values of factors is shown in Table 2, in which the range of factors is expressed by the values of Xmin and Xmax . For each coagulant, the range of coagulant dose was choTable 2 – Relationship between the coded and actual values of a factor. Code (xi )
Actual value of factor (Xi )
−˛
Xmin
−1 0 +1 +˛
(˛−1)Xmax +(˛+1)Xmin 2˛ Xmax +Xmin 2 (˛−1)Xmin +(˛+1)Xmax 2˛
Xmax
Xmin and Xmax : minimum and maximum values of X, respectively.
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chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
(0,+α) (-1,+1)
+ (-1,-1)
(0,+α) (-1,+1) (+1,+1)
(+1,+1) (-α, 0)
(+α, 0)
=
(-α, 0)
(0,0)
(+1,-1)
(-1,-1)
(+1,-1) (0,-α)
4 “cube” points
(+α, 0)
(0,-α)
4 axial points
CCD design
Fig. 1 – CCD for 2 factors: 4 “cube” points, 4 axial points and 5 replicates at center point (0,0).
Table 3 – The factor levels presented in terms of the actual unit of measurement and coded unit. Factor
Symbol
Coded factor level (xi ) Lowest −1.414 (−˛)
PACl Coagulant dosea pH Alum Coagulant dosea pH a
Low −1
Center 0
High +1
Highest +1.414 (+˛)
X1 X2
0.03 5.50
0.05 6.10
0.09 7.50
0.13 8.90
0.15 9.50
X1 X2
0.05 4.50
0.07 5.10
0.12 6.50
0.17 7.90
0.19 8.50
Unit of dose: mM as aluminum.
sen based on the normal range of dose used in treatment of Nakdong river water and the pH range chosen was the most effective pH ranges for coagulation–flocculation in water treatment. The range of PACl dose was from 0.03 to 0.15 mM while the range of alum dose was from 0.05 to 0.19 mM. Coagulation pH using PACl ranged from 5.5 to 9.5 and a lower pH range of 4.5–8.5 was used using alum. Applying the relationship in Table 2, using ˛ = 1.414 as previously mentioned, the levels of the factors are given in Table 3 and the CCD of this work is presented in Table 4. The 13 experiments found above were run in a random manner in order to minimize the effect of uncontrolled variables on the responses. In order to determine if a relationship existed between the factors and the responses investigated, the collected data was analyzed statistically using regression analyses. The responses can be expressed as second-order polynomial equa-
tions, according to Eq. (1):
Y = f (x) = ˇ0 +
k
ˇi Xi +
i=1
k k
ˇij Xi Xj +
k
i=1 j=i+1
ˇii Xi2
(1)
i=1
where Y is the predicted response (turbidity removal and DOC removal); k the number of factors; Xi , and Xj the factors which influence predicted response Y; ˇ0 is the constant coefficient; ˇi , ˇij , and ˇii are the coefficients of linear, interaction, and quadratic term, respectively. The coefficient parameters of the second-order models were estimated using a multiple linear regression analysis employing the Design-Expert Software (version 8.0.1.0, Stat-Ease, Inc., Minneapolis, USA). The Design-Expert was also used to demonstrate the 3D surface and 2D contour plots of the response models.
Table 4 – CCD in coded unit and results obtained for PACl and alum. Run no.
PACl CCD PACl dose (X1 )
1 2 3 4 5 6 7 8 9 10 11 12 13
−1 +1 −1 +1 −1.414 +1.414 0 0 0 0 0 0 0
Alum Results (removal (%))
pH (X2 ) −1 −1 +1 +1 0 0 −1.414 +1.414 0 0 0 0 0
Turbidity (Y1 ) 80.67 80.56 84.40 93.13 83.60 87.33 72.10 91.49 90.47 89.91 91.97 92.19 93.47
CCD: central composite design, DOC: dissolved organic carbon.
DOC (Y2 ) 29.00 34.42 12.20 18.97 20.87 31.17 34.42 15.45 30.89 29.00 30.89 29.03 29.21
CCD Alum dose (X1 ) −1 +1 −1 +1 −1.414 +1.414 0 0 0 0 0 0 0
Results (removal (%)) pH (X2 ) −1 −1 +1 +1 0 0 −1.414 +1.414 0 0 0 0 0
Turbidity (Y1 ) 52.94 64.59 58.59 79.41 65.65 79.65 43.18 56.24 83.18 85.29 83.76 81.76 84.71
DOC (Y2 ) 30.32 32.18 9.57 22.34 19.15 35.64 22.34 11.70 31.94 32.05 32.45 32.18 35.64
1129
Y1 (PACl) = −59.28 + 45.04X1 + 35.34X2
(3)
d.f
DOC removal Y2 (%):
0.0598 5.89
0.0003
0.0551 6.20
23.13
5 881.93 7 53.38 3 43.53 4 9.85 R2 = 94.29%, R2 adjusted = 90.22%
Y1 (alum) = −302.74 + 396.09X1 + 105.73X2 − 1987.99X12 − 8.17X22 + 32.77X1 X2
<0.0001
(2) Sum of square
− 1557.50X12 − 2.32X22 + 36.83X1 X2
176.39 7.63 14.51 2.46
Alum
F-value
The CCD shown in Table 4 allowed the development of mathematical equations where each response Y = f(X) was assessed as a function of dose and pH, and calculated as the sum of a constant, two linear effects, two quadratic effects, and one interaction effect according to Eq. (1). The results of the fitted models for turbidity removal and DOC removal are given in Eqs. (2)–(5). Turbidity removal Y1 (%):
80.59
Fitting the models
492.56 6.11 11.74 1.89
3.1.
5 2462.82 7 42.78 3 35.22 4 7.57 R2 = 98.29%, R2 adjusted = 97.07%
Results and discussion
Mean square
3.
p>F
chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
<0.0001
0.1178 3.73
0.1490 3.14
56.98
122.39 2.15 3.69 0.99 611.93 15.40 11.08 3.96 5 7 3 4 R2 = 97.60%, R2 adjusted = 95.89% DOC removal Regression Total error Lack of fit Pure error
0.0003 23.99
93.30 3.89 6.37 2.03 466.50 27.22 19.11 8.11 5 7 3 4 R2 = 94.49%, R2 adjusted = 90.55%
Mean square d.f
The models are found to be significant at 95% confidence level by the F-test as shown in Table 5, with all p-values of regression ≤ 0.05). In addition, the models do not exhibit lackof-fit (p > 0.05). The lack-of-fit test measures the failure of the model to represent data in the experimental domain at points that are not included in the regression. If a model is significant, meaning that the model contains one or more important terms, and the model does not suffer from lack-of-fit, does not necessarily mean that the model is a good one. If the experimental environment is quite noisy or some important variables are left out of the experiment, then it is possible that the portion of the variability in the data not explained by the model, also called the residual, could be large. Thus, a measure of the model’s overall performance referred to as the coefficient of determination and denoted by R2 must be considered. At the same time, adjusted R2 allowing for the degrees of freedom associated with the sums of the squares is also considered in the lack-of-fit test, which should be an approximate value of R2 . When R2 and adjusted R2 differ dramatically, there is a good chance that non-significant terms have been included in the model (Montgomery, 2001). Here, the two R2 values are not significantly different as shown in Table 5, and the normal probability plots of residuals do not show evidence of strong departures from normality as depicted in Figs. 2 and 3 for PACl and alum, respectively. Therefore, the overall secondorder models, as expressed in Eqs. (2)–(5), for the response measures are significant and adequate.
PACl
(5)
Basic
− 971.34X12 − 3.78X22 + 38.94X1 X2
Table 5 – ANOVA results for the two responses: turbidity removal and DOC removal.
Y2 (alum) = −95.87 + 75.81X1 + 40.47X2
Turbidity removal Regression Total error Lack of fit Pure error
F-value
(4)
Sum of square
− 1305.28X12 − 1.45X22 + 5.63X1 X2
p>F
Y2 (PACl) = −26.24 + 271.59X1 + 15.96X2
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a
b
99
99
95 90 80 70
95 90 80 70
50
50
30 20 10 5
30 20 10 5
1
1
-3.00
-2.00
-1.00
0.00
1.00
2.00
-3.00 -2.00 -1.00 0.00
1.00 2.00
3.00
Fig. 2 – Normal probability plots of residuals using PACl for (a) turbidity removal and (b) DOC removal.
a
b 99
99
95 90 80 70
95 90 80 70
50
50
30 20 10 5
30 20 10 5
1
1
-3.00 -2.00 -1.00 0.00
1.00
2.00
-3.00 -2.00 -1.00 0.00
3.00
1.00
2.00
3.00
Fig. 3 – Normal probability plots of residuals using alum for (a) turbidity removal and (b) DOC removal.
3.2.
Process analysis
3.2.1.
Fig. 4a and b shows the turbidity vs. DOC removal for PACl and alum, respectively. As shown in these figures, no clear correlation between the two responses is noticeable. Therefore, the removal mechanisms of turbidity and TOC were different and the optimum conditions for the removal of each substance would also be different. Previous studies also reported that optimum conditions for turbidity removal are not always the same as those for NOM removal (Semmens and Field, 1980; Gregor et al., 1997).
b
40
DOC removal (%)
DOC removal (%)
a
30
20
10 70
75
80
85
90
Turbidity removal (%)
95
Optimization of the turbidity removal efficiency
Table 6 gives an insight into the linear, quadratic, and interaction effects of the factors for turbidity removal. These analyses were done by means of Fisher’s ‘F’ test. The ‘F’ test was used to determine the significance of the regression coefficients of the parameters. The p-value is used as a tool to check the significance of each factor and interaction between factors. The larger the magnitude of F-value and correspondingly the smaller the “p > F”, the more significant are the corresponding model and the individual coefficient (Montgomery, 2001). A pvalue less than 0.05 indicates that the effect is significant and
40
30
20
10
0
40
50
60
70
80
Turbidity removal (%)
Fig. 4 – Turbidity removal vs. DOC removal using (a) PACl and (b) alum.
90
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chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
Table 6 – Estimation of the second-order response surface parameters for turbidity removal as response. Parameter
PACl d.f
Dose pH Dose × pH Dose × dose pH × pH
1 1 1 1 1
Estimate (coded) 1.74 5.47 2.21 −2.80 −4.64
Std. Err. 0.70 0.70 0.99 0.75 0.75
Alum F-value 6.21 61.44 5.02 14.06 38.49
the smaller p-value expresses the more significant effect. The statistical analysis in Table 6 shows that both the linear (dose, pH) and quadratic effects (dose × dose, pH × pH) of the dose and pH were very significant with all p-values < 0.05 for both PACl and alum. However, the interaction effects of the two factors were less significant. In other words, only the linear and quadratic effects of the factors were the major determining conditions that might cause considerable effects on the turbidity removal. The interaction between them, however, were not much present in the experimental domain. Fig. 5a and b illustrates the 3D response surface plots and 2D contour plots of the quadratic models with respect to turbidity removal for PACl and alum, respectively. As shown in Fig. 5a, increased turbidity removal was observed with increasing PACl dose and pH values, and a removal efficiency higher than 90% was obtained at pHs higher than 7. However, an increase in both PACl dose and pH beyond the optimum region resulted in a decrease in the removal efficiency. This trend can also be observed in Fig. 5b for alum. At doses higher than 0.13 mM PACl and 0.18 mM alum, the removal efficiency began to decrease at all of the coagulation pHs. This implies that overdosing happened in the reaction solution. Overdosing deteriorated supernatant quality, referring to the “restabilization” of the colloidal particles, and therefore the particles could not be coagulated well. Two removal mechanisms of turbidity have been identified: (a) charge neutralization of negatively charged particles by positively charged metal hydrolysis species followed by aggregation of the destabilized particles and (b) formation of flocs composed of metal hydroxide precipitates accompanied or followed by sweep flocculation of colloidal particles (Snodgrass et al., 1984; Gregory and Duan, 2001; Shin et al., 2008). The former needs a high concentration of colloids to provide sufficient contact opportunities for aggregation of destabilized particles. At a certain colloid concentration, such as the order 50–100 mg/L, base on kaolinite, contact opportunity is sufficient for hydrolysis species to become adsorbed and effect destabilization by charge effects or bridging, before precipitation takes place (Kim et al., 1965; Packham, 1965; Bratby, 2006). The later one is predominant mechanism for removal of low colloid concentration in water. It is necessary to overwhelm the widely dispersed particles with a large quantity of coagulant in the “sweep floc” zone. This is typical of most natural surface waters that contain “low” turbidities, less than 50 JTU (Faust and Aly, 1998). The turbidity of the raw water used was low (7.5–8.5 NTU). Therefore, “sweep flocculation” mechanism was probably predominant in the coagulation–flocculation of turbidity in the present study. As a comparison between PACl and alum, it is interesting to note that PACl gave a considerable higher of turbidity removal at low dosages (Fig. 5). While coagulation–flocculation by “sweep flocculation” mechanism requires a high dosage of coagulant to remove turbidity, charge neutralization is the
p>F
d.f
0.0415 0.0001 0.0600 0.0072 0.0004
11 11 11 11 11
Estimate (coded) 6.53 4.87 2.29 −4.87 −16.34
Std. Err. 0.87 0.87 1.24 0.94 0.94
F-value 55.88 31.01 3.44 27.00 303.93
p>F 0.0001 0.0008 0.1085 0.0013 <0.0001
predominant mechanism at low dosages of coagulant. Positive charge species are responsible for removal of particles by charge neutralization and the advantage of PACl over alum could be explained by the predominance of (Al13 O4 (OH)24 7+ , Al13 ) species that is highly stable and positively charged in PACl. The two contour plots in Fig. 5a and b show that they appeared to have a single optimum point, and the optimal conditions were located inside the design boundary. For the combination of the pH range from 7.5 to 9.0 and PACl dose range from 0.06 to 0.15 mM, more than 90% turbidity was removed and a maximum value of 94% appeared at the point of pH 8.5 and 0.11 mM PACl. When using alum, a removal efficiency of 86% turbidity was observed at pH 6.8 and 0.16 mM alum.
3.2.2.
Optimization of the DOC removal efficiency
Table 7 illustrates that except for the quadratic effect of alum dose, all the linear and quadratic effects of dose and pH were significant (p < 0.05) for both PACl and alum. Coagulant dose and pH played an important role in DOC removal, regardless of alum coagulant or PACl coagulant was used. The significance of dose and pH was also expressed in the case of removal of turbidity. The roles of the coagulant dose and pH in coagulation–flocculation process were also underlined in other studies (Faust and Aly, 1998; Pernitsky, 2003). The surface and contour plots of the quadratic models for DOC removal are presented in Fig. 6a and b. Fig. 6a shows an optimum of 35% DOC removal obtained with PACl dose range from 0.10 to 0.14 mM, corresponding to the pH range from 5.5 to 6.5. Likewise, Fig. 6b demonstrates that the optimum removal of 35% DOC occurred in the range of 0.14–0.19 mM alum and pH range of 5.8–6.6. Removal efficiencies are found to reduce when moving away from these ranges, meaning that either increase or decrease in any of the factors resulted in decline of the response. For both coagulants, it is important to note that the optimal pH for DOC removal was lower than the one for turbidity removal. As previously shown in Fig. 5, the removal of turbidity was more efficient at high pH values (>7 and >6.5 for PACl and alum, respectively), while lower pH values (<6.5, Fig. 6) were favorable for DOC removal. The optimum pH range for removal of DOC is usually rather less (typically, pH 5–6) than for the removal of suspended particles (Edwards and Amirtharajah, 1985). Differences in the optimal pH range between coagulation of turbidity and DOC were also reported in previous studies (Hall and Packham, 1965; Edwards and Amirtharajah, 1985). It is well known that humic substances (HS) are the major component of DOC in natural waters, and that HS can be operationally defined and separated into the more soluble fulvic acids (FAs) and the less soluble humic acids (HAs). The low range of Specific UV Absorbance (SUVA)
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chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
Fig. 5 – 3D surface and 2D contour plots for turbidity removal using (a) PACl and (b) alum. from 1.27 to 1.33 L mg−1 m−1 of the water in this study indicates that the major content of DOC was FAs which are low molecular weight, more soluble, and hydrophilic. FAs are therefore, more difficult to coagulate (Edzwald and Van Benschoten, 1990). This explains why DOC removal efficiency by coagulation–flocculation was not so high. An optimum removal efficiency of 35% was obtained for both PACl and alum. This result is consistent with earlier findings suggesting that for the case where SUVA value less than 3 L mg−1 m−1 , DOC removal efficiency is relatively low and likely in a range of 20–50% (Van Benschoten and Edzwald, 1990). The nature of HS and the differences in behaviors between HS and colloidal materials in coagulation–flocculation also give an explanation for the difference in the optimal pH used to remove them. Coagulation of colloids can perform well at high pH values by enmeshment in a precipitate or sweep flocs. HAs, a small fraction of HS, although may not
be true colloids, are large enough that they can be referred to as macromolecules, and also be removed from water in a pathway similarly to the removal of colloidal materials/turbidity (Bratby, 2006). However, the largest fraction of HS in water is generally FAs, and their nature is different with the nature of colloids. Because FAs are low molecular weight, high soluble, and hydrophilic, they are considered as a true solution and therefore, they cannot be coagulated by enmeshment at high pH conditions. Coagulation of HS bases mainly on complexaxion–precipitation and/or adsorption onto metal hydroxide particles. At high pH values, there is competition between OH− and organic anions for metal hydrolysis products and thus, limits the opportunities for functional groups of HS can serve as ligands in metal complex (Bratby, 2006). Adsorption of HS or Al-HS complexes onto Al(OH)3 precipitate forming at high pH is also limited. Randtke (1988) presented that as pH increases, natural organic compounds become
Table 7 – Estimation of the second-order response surface parameters for DOC removal as response. Parameter
PACl d.f
Dose pH Dose × pH Dose × dose pH × pH
1 1 1 1 1
Estimate (coded) 3.34 −7.38 0.34 −2.35 −2.89
Std. Err. 0.52 0.52 0.73 0.56 0.56
Alum F-value 41.66 203.10 0.21 17.88 27.09
p>F
d.f
0.0003 <0.0001 0.6591 0.0039 0.0012
1 1 1 1 1
Estimate (coded) 4.74 −5.70 2.73 −2.38 −7.57
Std. Err.
F-value
p>F
0.98 0.98 1.38 1.05 1.05
23.60 34.13 3.90 5.17 52.22
0.0018 0.0006 0.0889 0.0572 0.0002
chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
1133
Fig. 6 – 3D surface and 2D contour plots for DOC removal using (a) PACl and (b) alum. more negatively charged and the metal hydrolysis species become less positively charged, resulting in less adsorption propensity. For these reasons, coagulation of HS (also NOM in water) is mainly performed under low pH conditions along with the presence of soluble cationic aluminum hydrolysis species. These species react with anionic functional groups on NOM to precipitate as an aluminum-NOM.
3.2.3. Optimization of both turbidity removal and DOC removal efficiency The turbidity removal and DOC removal are the two individual responses and their optimizations were achieved under different optimal conditions. The optimum turbidity removal might impact DOC removal and vice versa. Therefore, a compromise between the optimum conditions for the two responses is desirable. By defining the desirable limits of 90% turbidity removal and 30% DOC removal, the optimum condition can be visualized graphically by superimposing the contours for the two responses in an overlain plot, as shown in Fig. 7. Graphical optimization displays the area of feasible response values in the factor space and the regions that fit the optimization criteria was shaded. Base on the shaded-area of overlain contour in Fig. 7a, a compromise for 91.4% turbidity removal and 31.2% DOC removal can be met at 0.11 mM PACl and pH 7.40. Likewise, the combination of 0.15 mM alum and pH 6.6 resulted in 86.3% turbidity and 34.3% DOC removal as shown in Fig. 7b. To
confirm the agreements of the results achieved from the models and experiments, additional experiments were conducted by applying the coagulant dose and pH that was determined as the optimum region in this study. As shown in Table 8, the turbidity and DOC removals obtained experimentally were very close to those estimated using the models. This implies that the RSM approach used in this study was appropriate to optimize the conditions of the coagulation–flocculation process. The results of optimizing conditions for the simultaneous removal of turbidity and DOC illustrate that with a lower dose (0.11 mM PACl vs. 0.15 mM alum), PACl gave a higher turbidity removal than alum (91.4% vs. 86.3% using PACl vs. alum, respectively). However, the advantage of PACl over alum was not always present when considering simultaneous optimizing both responses, such as in the case of DOC removal. Separate optimizing shows the removal efficiency of DOC obtained was same for PACl and alum (35%, as previously shown in Fig. 6). The compromise in simultaneously optimizing the turbidity removal and DOC removal resulted in lower DOC removal efficiency in comparison with alum (31.2% vs. 34.3% using PACl vs. alum, respectively). The permissible values for the removal efficiencies in this study were set in order to simultaneously obtain the removals as high as possible for both responses. However, depending on the target of treatment, a lower permissible value of turbidity removal can be set to get a higher value of DOC removal, and vice versa. For
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chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
a
b
9.5
8.0
Turbidity removal: 90% 8.5
pH
8.5
7.5 7.0
Turbidity removal: 85%
pH
7.5
6.5 DOC removal: 34%
6.0 DOC removal: 30%
6.5
5.5 5.0
5.5
4.5 0.03
0.06
0.09
0.12
0.15
0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19
Alum dose (mM Al)
PACl dose (mM Al)
Fig. 7 – Overlain contour plots of turbidity removal and DOC removal for (a) PACl and (b) alum.
Table 8 – Confirmation experiments at optimum conditions. Experiment conditions Dose (mM Al) PACl Experimental value Predicted value Error (%) Alum Experimental value Predicted value Error (%)
pH
Turbidity removal (%)
DOC removal (%)
0.11 0.11
7.40 7.40
90.67 91.40 0.79
31.02 31.20 0.58
0.15 0.15
6.6 6.6
84.93 86.30 1.59
34.34 34.68 0.98
example, to maintain 35% DOC removal as obtained from separate optimizing, compromises turbidity removal, reducing it to 80% (data not shown).
4.
Responses
Conclusions
The physicochemical process that is known as coagulation–flocculation is common and necessary in water treatment. This work has demonstrated the application of RSM in seeking optimal conditions for this process. Simultaneous removals of turbidity and DOC were investigated. RSM using CCD was applied to evaluate effects of coagulant dose and pH on the coagulation–flocculation effectiveness, and then determine the optimum conditions. The results showed that the two factors considered in this study played an important role on removal efficiency of turbidity and DOC. As a compromise to simultaneous removal of turbidity and DOC from bulk water in a single unit, the optimum conditions obtained were 0.11 mM PACl at pH 7.4, and 0.15 mM alum at pH 6.6. Under these optimum conditions, 91.4% turbidity removal and 31.2% DOC removal were obtained using PACl whereas removals using alum were 86.3% turbidity and 34.3% DOC. The results of the confirmation experiment agreed with predictions. This demonstrates that RSM can be successfully applied for modeling and optimizing the coagulation–flocculation process and it is the economical way of obtaining the maximum amount of information in a short period of time and with the least number of experiments.
This study reveals that PACl is more efficient than alum for removal of turbidity. PACl is recommended for the coagulation–flocculation of high turbidity water. For water treatment in which DOC is a significant concern, both PACl and alum should be considered, with a compromise between the removal efficiencies of turbidity and DOC, along with the dose required, and other factors, such as the chemical used to adjust pH, the temperature of water, and the cost of coagulants.
References APHA-AWWA/WEF, 1998. Standard Methods for the Examination of Water and Wastewater, 20th ed. American Public Health Association, Washington, DC, USA. Bratby, J., 2006. Coagulation and Flocculation in Water and Wastewater Treatment, 2nd ed. IWA Publishing, London, UK. Edwards, G.A., Amirtharajah, A., 1985. Removing color caused by humic acids. J. Am. Water Works Assoc. 77, 50–57. Edzwald, J.K., Van Benschoten, J.E., 1990. Aluminum coagulation of natural organic matter. In: Hahn, H.H., Klute, R. (Eds.), Chemical Water and Wastewater Treatment. Springer-Verlag, Berlin, Germany, pp. 341–359. Edzwald, J.K., Tobaison, J.E., 1999. Enhanced coagulation: US requirements and a broader view. Water Sci. Technol. 40, 63–70. Faust, S.D., Aly, O.M., 1998. Removal of particulate matter by coagulation. In: Chemistry of Water Treatment, 2nd ed. CRC Press, Florida, USA, pp. 217–270. Ghafari, S., Aziz, H.A., Hasnain, M.I., Zinatizadeh, A.K., 2009. Application of response surface methodology (RSM) to
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optimize coagulation flocculation treatment of leachate using poly aluminum chloride (PAC) and alum. J. Hazard. Mater. 163, 650–656. Gregor, J.E., Nokes, C.J., Fenton, E., 1997. Optimising natural organic matter removal from low turbidity waters by controlled pH adjustment of aluminium coagulation. Water Res. 31, 2949–2958. Gregory, J., Duan, J., 2001. Hydrolyzing metal salts as coagulants. Pure Appl. Chem. 73, 2017–2026. Hall, E.S., Packham, R.F., 1965. Coagulation of organic color with hydrolysing coagulant. J. Am. Water Works Assoc. 57, 1149–1166. Hundt, T.R., O’Melia, C.R., 1988. Aluminum–fulvic acid interactions: mechanisms and applications. J. Am. Water Works Assoc. 80, 176–186. Jiang, J.Q., 2001. Development of coagulation theory and pre-polymerized coagulants for water treatment. Sep. Purif. Rev. 30, 127–141. Kang, L.S., Han, S.W., Jung, C.W., 2001. Synthesis and characterization of polymer inorganic coagulants. Korean J. Chem. Eng. 18, 965–970. Khuri, A.I., Cornell, J.A., 1996. Response Surface, Design and Analyses, 2nd ed. Marcel Dekker Inc., New York, USA. Kim, W., Ludwig, H.F., Bishop, W.D., 1965. Cation exchange capacity and pH in the coagulation process. J. Am. Water Works Assoc. 57, 327–348. Li, Y., Liu, Z.Q., Zhao, H., Xu, Y.Y., Cui, F.J., 2007. Statistical optimization of xylanase production from new isolated Penicillium oxalicum ZH-30 in submerged fermentation. Biochem. Eng. J. 34, 82–86. Mason, R.L., Gunst, R.F., Hess, J.L., 2003. Statistical Design and Analysis of Experiments with Applications to Engineering and Science, 2nd ed. John Wiley and Sons, New York, USA. Montgomery, D.C., 2001. Design and Analysis of Experiments, 5th ed. John Wiley & Sons, New York, USA, pp. 427–510.
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Murthy, M.S.R.C., Rakshit, T.S.S.K., Kosugi, Y., 2000. Statistical optimization of lipase catalyzed hydrolysis of methyloleate by response surface methodology. Bioprocess Eng. 22, 35–39. Packham, R.F., 1965. Some studies of the coagulation of dispersed clays with hydrolyzing salts. J. Colloids Interface Sci. 20, 81–92. Palamakula, A., Nutan, M.T.H., Khan, M.A., 2004. Response surface methodology for optimization and characterization of limonene-based coenzyme Q10 self-nanoemulsified capsule dosage form AAPS. Pharm. Sci. Technol. 5, 1–8. Pernitsky, D.J., 2003, Coagulation 101, Calgary. Available from: http://www.awwoa.ab.ca/Acrob at/Coagulation%20101.pdf. Pham, T.T.H., Brar, S.K., Tyagi, R.D., Surampalli, R.Y., 2009. Ultrasonication of wastewater sludge – consequences on biodegradability and flowability. J. Hazard. Mater. 163, 891–898. Randtke, S.J., 1988. Organic contaminant removal by coagulation and related process combinations. J. Am. Water Works Assoc. 80, 40–56. Semmens, M.J., Field, T.K., 1980. Coagulation: experiences on organics removal. J. Am. Water Works Assoc. 72, 476–483. Shin, J.Y., Spinette, R.F., O’ Melia, C.R., 2008. Stoichiometry of coagulation revisited. Environ. Sci. Technol. 42, 2582–2589. Snodgrass, W.J., Clark, M.M., O’Melia, C.R., 1984. Particle formation and growth in dilute aluminum (III) solutions. Water Res. 18, 479–488. Van Benschoten, J.E., Edzwald, J.K., 1990. Chemical aspects of coagulation using aluminum salts-II coagulation of fulvic acid using alum and polyalurninum chloride. Water Res. 24, 1527–1535. Wang, J.P., Chen, Y.Z., Ge, X.W., Yu, H.Q., 2007. Optimization of coagulation–flocculation process for a paper-recycling wastewater treatment using response surface methodology. Colloids Surf. A: Physicochem. Eng. Aspects 302, 204–210.
Contents lists available at ScienceDirect
Chemical Engineering Research and Design journal homepage: www.elsevier.com/locate/cherd
Response surface methodological approach to optimize the coagulation–flocculation process in drinking water treatment Thuy Khanh Trinh, Lim Seok Kang ∗ Department of Environmental Engineering, Pukyong National University, 599-1, Daeyeon 3-Dong, Nam-Gu, Busan, Republic of Korea
a b s t r a c t Performing jar tests often requires carrying out a time consuming iteration procedure to find out the right amount of chemical for coagulation–flocculation process in water treatment plants. Applying the response surface method (RSM) in jar tests as an alternative to the conventional methods was investigated in this study. The purpose is finding out the optimum combination of coagulant dose and pH with respect to the highest removal efficiency of turbidity and dissolved organic carbon (DOC). The results achieved using poly-aluminum chloride (PACl) were compared to those achieved using conventional coagulant such as alum. The quadratic models developed for the two responses (turbidity removal and DOC removal) indicated that the optimum conditions to be PACl concentration of 0.11 mM at pH 7.4 and alum concentration of 0.15 mM at pH 6.6. Compromising to simultaneously optimize the two responses resulted in 91.4% turbidity removal and 31.2% DOC removal using PACl whereas 86.3% turbidity and 34.3% DOC were removed using alum. Confirmation of experimental results was found to be close to the prediction derived from the models. This demonstrates the benefits of the approach based on the RSM in achieving good predictions while minimizing the number of required experiments. © 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords:
Central
composite
design
(CCD);
DOC
removal;
Drinking
water
treatment;
Optimizing;
Coagulation–flocculation; Response surface methodology (RSM)
1.
Introduction
Coagulation–flocculation has played, and will still play an important role, directly or indirectly, in the control of particulates, microorganisms, natural organic matter (NOM), synthetic organic carbon, precursors of disinfection byproducts (DBPs), and some inorganic ions and metals, and ultimately, in the control of drinking water quality (Jiang, 2001). In this process, coagulants, such as alum or polyaluminum chloride (PACl), are added to water, and a metal ion such as Al3+ undergoes hydrolysis reactions to form other dissolved Al species and Al-hydroxide precipitates. These aluminum hydrolysis species help to aggregate various aquatic particles into larger flocs and then these flocs are settled, filtered and removed from bulk water in subsequent processes. The turbidity and NOM of water are the target substances to be removed during coagulation–flocculation treatment. Charge
∗
neutralization and sweep flocculation are two mechanisms for removal of turbidity and the concentration of colloids and coagulant dosages are critical factors that determine the predominant mechanism for removal (Snodgrass et al., 1984; Gregory and Duan, 2001; Shin et al., 2008). Two mechanisms of NOM removal most commonly referred to are charge neutralization of cationic aluminum species and anionic NOM, and adsorption of NOM or Al-NOM complexes on amorphous Al(OH)3 (s) (Semmens and Field, 1980; Hundt and O’Melia, 1988; Randtke, 1988; Edzwald and Van Benschoten, 1990; Edzwald and Tobaison, 1999). The predominance of each mechanism depends not only on the NOM characteristics, but also on the aluminum hydrolysis species, which are quickly formed after coagulant addition. Many factors such as the characteristics of raw water, type of coagulant, coagulation pH, and dose of coagulant have been considered to influence to the coagulation performance. An
Corresponding author. Tel.: +82 51 629 6527; fax: +82 51 629 6523. E-mail addresses: [email protected] (T.K. Trinh), [email protected] (L.S. Kang). Received 27 August 2010; Received in revised form 13 November 2010; Accepted 1 December 2010 0263-8762/$ – see front matter © 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.cherd.2010.12.004
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chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
Table 1 – Characteristics of raw water from Nakdong river. Parameter Range
Turbidity (NTU) 7.5–8.5
UV254 (cm−1 )
DOC (mg/L)
0.047–0.050
3.69–3.76
SUVA (L mg−1 m−1 ) 1.27–1.33
pH
Alkalinity (mg CaCO3 /L)
8.0–9.4
80–83
DOC: dissolved organic carbon, SUVA: specific ultraviolet absorbance.
appropriate combination of these factors is desirable to obtain a high efficiency of treatment. To seek the optimal conditions of these factors, jar tests using the trial and error approach, also known as the one factor at a time (OFAT) method, are employed. This classical way is changing the levels of one factor and at the same time, keeping the others in constant, running the experiment, observing the results, and moving on to the next factor. Therefore, the OFAT method is not only time and energy consuming, but also is usually incapable of revealing the optimal combination of factors due to ignoring the interaction among them (Mason et al., 2003) To solve this problem, the response surface method (RSM) offers a better alternative to the conventional method because it includes the influences of individual factors as well as the influences of their interaction. RSM is a technique for designing experiments, building models, evaluating the effects of several factors, and achieving the optimum conditions for desirable responses with a limited number of planned experiments (Khuri and Cornell, 1996). The RSM has been widely used in various fields such as biochemistry for optimizing fermentation conditions (Murthy et al., 2000; Li et al., 2007), and in pharmaceutics for optimizing self-nanoemulsified capsule dosage form (SNCDF) of a highly lipophilic model compound, Coenzyme Q10 (CoQ) (Palamakula et al., 2004). In the field of water treatment, applying RSM to optimize the coagulation–flocculation process for the treatment of wastewater was reported in many studies (Wang et al., 2007; Ghafari et al., 2009; Pham et al., 2009). However, in the drinking water treatment area, few studies were reported for applying RSM to optimize the conditions of coagulation–flocculation process with respect to the highest removal of turbidity and DOC. Therefore, an attempt has been made to optimize the key factors of coagulation–flocculation process, such as coagulant dose and pH, using an experimental design approach of RSM. In this way, the interactions of parameters and the non-linear dependencies among experimental variables are studied and, therefore, a real optimum is achieved.
2.
Materials and methods
2.1.
Characteristics of raw water
This study used water samples taken from the Nakdong river which is the major water source of the city of Busan, Korea. The characteristics of the water are listed in Table 1.
2.2.
Coagulation–flocculation test
Two different coagulants were used in this study: alum as a metal salt and PACl as a pre-hydrolyzed metal salt. The alum (Al2 (SO4 )3 ·16H2 O) used was purchased from Sigma, USA. The hydrolyzed PACl solution with the formula Al(OH)1.5 Cl1.5 (degree of neutralization r = [OH]/[Al] = 1.5, basicity = 50%) was prepared by adding 0.5 M NaOH into 0.2 M AlCl3 solution
according to a procedure described by Kang et al. (2001). The coagulation pH was adjusted according to pH levels in Table 3. These pH levels achieved by adding 0.1 M HCl or 0.1 M NaOH just before dosing of the coagulant. The coagulation–flocculation process using the jar test was carried out using 2 L square jars, with six paddle stirrers (Phipps and Bird). The time and speed for rapid and slow mixing were set with an automatic controller. Samples were mixed at 250 rpm (G = 550 s−1 ) for 1 min after coagulant addition to provide rapid mixing, and then the speed was reduced to 30 rpm (G = 22 s−1 ) for 30 min to provide flocculation. After 30 min of settling, the supernatant from each jar was withdrawn from the sampling port and analyzed for pH, settled turbidity and DOC. A pH meter (F-54 BW, Horiba, Japan) was used to measure the solution pH. Turbidity was measured by the Turbidimeter (2100N, HACH, USA) following the Nephelometric method (Standard Method 2130B). DOC was analyzed immediately after filtering the sample through 0.45 m membrane by the TOC Analyzer (TOC-VCPH, Shimadzu, Japan) using the HighTemperature Combustion method (Standard method 5310B) (APHA-AWWA/WEF, 1998)
2.3.
Response surface methodology
The principle of RSM was described by Khuri and Cornell (1996). To achieve adequate and reliable measurements of the responses of interest, the design of experiment is necessary. Normally, the relationship between the responses (turbidity removal and DOC removal) and the independent variables (coagulant dose and pH) in coagulation–flocculation process cannot be well modeled by a linear function or a first order model. A model that incorporates curvature is usually required to approximate the response in the region close to optimum, and in most cases, a second order model is adequate (Montgomery, 2001). A central composite design (CCD), which is a very efficient design tool for fitting the second-order models (Montgomery, 2001), was selected for use in the present study. A CCD is made rotatable by the choice of ˛ (Montgomery, 2001). A value of ˛ = 1.414 for 2 factors in the study assured rotation of the CCD. A CCD containing 13 experiments with 4 cube points, 4 axial points, and 5 replicates at the center point, is illustrated in Fig. 1. The relationship between the coded (xi ) and actual (Xi ) values of factors is shown in Table 2, in which the range of factors is expressed by the values of Xmin and Xmax . For each coagulant, the range of coagulant dose was choTable 2 – Relationship between the coded and actual values of a factor. Code (xi )
Actual value of factor (Xi )
−˛
Xmin
−1 0 +1 +˛
(˛−1)Xmax +(˛+1)Xmin 2˛ Xmax +Xmin 2 (˛−1)Xmin +(˛+1)Xmax 2˛
Xmax
Xmin and Xmax : minimum and maximum values of X, respectively.
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chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
(0,+α) (-1,+1)
+ (-1,-1)
(0,+α) (-1,+1) (+1,+1)
(+1,+1) (-α, 0)
(+α, 0)
=
(-α, 0)
(0,0)
(+1,-1)
(-1,-1)
(+1,-1) (0,-α)
4 “cube” points
(+α, 0)
(0,-α)
4 axial points
CCD design
Fig. 1 – CCD for 2 factors: 4 “cube” points, 4 axial points and 5 replicates at center point (0,0).
Table 3 – The factor levels presented in terms of the actual unit of measurement and coded unit. Factor
Symbol
Coded factor level (xi ) Lowest −1.414 (−˛)
PACl Coagulant dosea pH Alum Coagulant dosea pH a
Low −1
Center 0
High +1
Highest +1.414 (+˛)
X1 X2
0.03 5.50
0.05 6.10
0.09 7.50
0.13 8.90
0.15 9.50
X1 X2
0.05 4.50
0.07 5.10
0.12 6.50
0.17 7.90
0.19 8.50
Unit of dose: mM as aluminum.
sen based on the normal range of dose used in treatment of Nakdong river water and the pH range chosen was the most effective pH ranges for coagulation–flocculation in water treatment. The range of PACl dose was from 0.03 to 0.15 mM while the range of alum dose was from 0.05 to 0.19 mM. Coagulation pH using PACl ranged from 5.5 to 9.5 and a lower pH range of 4.5–8.5 was used using alum. Applying the relationship in Table 2, using ˛ = 1.414 as previously mentioned, the levels of the factors are given in Table 3 and the CCD of this work is presented in Table 4. The 13 experiments found above were run in a random manner in order to minimize the effect of uncontrolled variables on the responses. In order to determine if a relationship existed between the factors and the responses investigated, the collected data was analyzed statistically using regression analyses. The responses can be expressed as second-order polynomial equa-
tions, according to Eq. (1):
Y = f (x) = ˇ0 +
k
ˇi Xi +
i=1
k k
ˇij Xi Xj +
k
i=1 j=i+1
ˇii Xi2
(1)
i=1
where Y is the predicted response (turbidity removal and DOC removal); k the number of factors; Xi , and Xj the factors which influence predicted response Y; ˇ0 is the constant coefficient; ˇi , ˇij , and ˇii are the coefficients of linear, interaction, and quadratic term, respectively. The coefficient parameters of the second-order models were estimated using a multiple linear regression analysis employing the Design-Expert Software (version 8.0.1.0, Stat-Ease, Inc., Minneapolis, USA). The Design-Expert was also used to demonstrate the 3D surface and 2D contour plots of the response models.
Table 4 – CCD in coded unit and results obtained for PACl and alum. Run no.
PACl CCD PACl dose (X1 )
1 2 3 4 5 6 7 8 9 10 11 12 13
−1 +1 −1 +1 −1.414 +1.414 0 0 0 0 0 0 0
Alum Results (removal (%))
pH (X2 ) −1 −1 +1 +1 0 0 −1.414 +1.414 0 0 0 0 0
Turbidity (Y1 ) 80.67 80.56 84.40 93.13 83.60 87.33 72.10 91.49 90.47 89.91 91.97 92.19 93.47
CCD: central composite design, DOC: dissolved organic carbon.
DOC (Y2 ) 29.00 34.42 12.20 18.97 20.87 31.17 34.42 15.45 30.89 29.00 30.89 29.03 29.21
CCD Alum dose (X1 ) −1 +1 −1 +1 −1.414 +1.414 0 0 0 0 0 0 0
Results (removal (%)) pH (X2 ) −1 −1 +1 +1 0 0 −1.414 +1.414 0 0 0 0 0
Turbidity (Y1 ) 52.94 64.59 58.59 79.41 65.65 79.65 43.18 56.24 83.18 85.29 83.76 81.76 84.71
DOC (Y2 ) 30.32 32.18 9.57 22.34 19.15 35.64 22.34 11.70 31.94 32.05 32.45 32.18 35.64
1129
Y1 (PACl) = −59.28 + 45.04X1 + 35.34X2
(3)
d.f
DOC removal Y2 (%):
0.0598 5.89
0.0003
0.0551 6.20
23.13
5 881.93 7 53.38 3 43.53 4 9.85 R2 = 94.29%, R2 adjusted = 90.22%
Y1 (alum) = −302.74 + 396.09X1 + 105.73X2 − 1987.99X12 − 8.17X22 + 32.77X1 X2
<0.0001
(2) Sum of square
− 1557.50X12 − 2.32X22 + 36.83X1 X2
176.39 7.63 14.51 2.46
Alum
F-value
The CCD shown in Table 4 allowed the development of mathematical equations where each response Y = f(X) was assessed as a function of dose and pH, and calculated as the sum of a constant, two linear effects, two quadratic effects, and one interaction effect according to Eq. (1). The results of the fitted models for turbidity removal and DOC removal are given in Eqs. (2)–(5). Turbidity removal Y1 (%):
80.59
Fitting the models
492.56 6.11 11.74 1.89
3.1.
5 2462.82 7 42.78 3 35.22 4 7.57 R2 = 98.29%, R2 adjusted = 97.07%
Results and discussion
Mean square
3.
p>F
chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
<0.0001
0.1178 3.73
0.1490 3.14
56.98
122.39 2.15 3.69 0.99 611.93 15.40 11.08 3.96 5 7 3 4 R2 = 97.60%, R2 adjusted = 95.89% DOC removal Regression Total error Lack of fit Pure error
0.0003 23.99
93.30 3.89 6.37 2.03 466.50 27.22 19.11 8.11 5 7 3 4 R2 = 94.49%, R2 adjusted = 90.55%
Mean square d.f
The models are found to be significant at 95% confidence level by the F-test as shown in Table 5, with all p-values of regression ≤ 0.05). In addition, the models do not exhibit lackof-fit (p > 0.05). The lack-of-fit test measures the failure of the model to represent data in the experimental domain at points that are not included in the regression. If a model is significant, meaning that the model contains one or more important terms, and the model does not suffer from lack-of-fit, does not necessarily mean that the model is a good one. If the experimental environment is quite noisy or some important variables are left out of the experiment, then it is possible that the portion of the variability in the data not explained by the model, also called the residual, could be large. Thus, a measure of the model’s overall performance referred to as the coefficient of determination and denoted by R2 must be considered. At the same time, adjusted R2 allowing for the degrees of freedom associated with the sums of the squares is also considered in the lack-of-fit test, which should be an approximate value of R2 . When R2 and adjusted R2 differ dramatically, there is a good chance that non-significant terms have been included in the model (Montgomery, 2001). Here, the two R2 values are not significantly different as shown in Table 5, and the normal probability plots of residuals do not show evidence of strong departures from normality as depicted in Figs. 2 and 3 for PACl and alum, respectively. Therefore, the overall secondorder models, as expressed in Eqs. (2)–(5), for the response measures are significant and adequate.
PACl
(5)
Basic
− 971.34X12 − 3.78X22 + 38.94X1 X2
Table 5 – ANOVA results for the two responses: turbidity removal and DOC removal.
Y2 (alum) = −95.87 + 75.81X1 + 40.47X2
Turbidity removal Regression Total error Lack of fit Pure error
F-value
(4)
Sum of square
− 1305.28X12 − 1.45X22 + 5.63X1 X2
p>F
Y2 (PACl) = −26.24 + 271.59X1 + 15.96X2
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chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
a
b
99
99
95 90 80 70
95 90 80 70
50
50
30 20 10 5
30 20 10 5
1
1
-3.00
-2.00
-1.00
0.00
1.00
2.00
-3.00 -2.00 -1.00 0.00
1.00 2.00
3.00
Fig. 2 – Normal probability plots of residuals using PACl for (a) turbidity removal and (b) DOC removal.
a
b 99
99
95 90 80 70
95 90 80 70
50
50
30 20 10 5
30 20 10 5
1
1
-3.00 -2.00 -1.00 0.00
1.00
2.00
-3.00 -2.00 -1.00 0.00
3.00
1.00
2.00
3.00
Fig. 3 – Normal probability plots of residuals using alum for (a) turbidity removal and (b) DOC removal.
3.2.
Process analysis
3.2.1.
Fig. 4a and b shows the turbidity vs. DOC removal for PACl and alum, respectively. As shown in these figures, no clear correlation between the two responses is noticeable. Therefore, the removal mechanisms of turbidity and TOC were different and the optimum conditions for the removal of each substance would also be different. Previous studies also reported that optimum conditions for turbidity removal are not always the same as those for NOM removal (Semmens and Field, 1980; Gregor et al., 1997).
b
40
DOC removal (%)
DOC removal (%)
a
30
20
10 70
75
80
85
90
Turbidity removal (%)
95
Optimization of the turbidity removal efficiency
Table 6 gives an insight into the linear, quadratic, and interaction effects of the factors for turbidity removal. These analyses were done by means of Fisher’s ‘F’ test. The ‘F’ test was used to determine the significance of the regression coefficients of the parameters. The p-value is used as a tool to check the significance of each factor and interaction between factors. The larger the magnitude of F-value and correspondingly the smaller the “p > F”, the more significant are the corresponding model and the individual coefficient (Montgomery, 2001). A pvalue less than 0.05 indicates that the effect is significant and
40
30
20
10
0
40
50
60
70
80
Turbidity removal (%)
Fig. 4 – Turbidity removal vs. DOC removal using (a) PACl and (b) alum.
90
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Table 6 – Estimation of the second-order response surface parameters for turbidity removal as response. Parameter
PACl d.f
Dose pH Dose × pH Dose × dose pH × pH
1 1 1 1 1
Estimate (coded) 1.74 5.47 2.21 −2.80 −4.64
Std. Err. 0.70 0.70 0.99 0.75 0.75
Alum F-value 6.21 61.44 5.02 14.06 38.49
the smaller p-value expresses the more significant effect. The statistical analysis in Table 6 shows that both the linear (dose, pH) and quadratic effects (dose × dose, pH × pH) of the dose and pH were very significant with all p-values < 0.05 for both PACl and alum. However, the interaction effects of the two factors were less significant. In other words, only the linear and quadratic effects of the factors were the major determining conditions that might cause considerable effects on the turbidity removal. The interaction between them, however, were not much present in the experimental domain. Fig. 5a and b illustrates the 3D response surface plots and 2D contour plots of the quadratic models with respect to turbidity removal for PACl and alum, respectively. As shown in Fig. 5a, increased turbidity removal was observed with increasing PACl dose and pH values, and a removal efficiency higher than 90% was obtained at pHs higher than 7. However, an increase in both PACl dose and pH beyond the optimum region resulted in a decrease in the removal efficiency. This trend can also be observed in Fig. 5b for alum. At doses higher than 0.13 mM PACl and 0.18 mM alum, the removal efficiency began to decrease at all of the coagulation pHs. This implies that overdosing happened in the reaction solution. Overdosing deteriorated supernatant quality, referring to the “restabilization” of the colloidal particles, and therefore the particles could not be coagulated well. Two removal mechanisms of turbidity have been identified: (a) charge neutralization of negatively charged particles by positively charged metal hydrolysis species followed by aggregation of the destabilized particles and (b) formation of flocs composed of metal hydroxide precipitates accompanied or followed by sweep flocculation of colloidal particles (Snodgrass et al., 1984; Gregory and Duan, 2001; Shin et al., 2008). The former needs a high concentration of colloids to provide sufficient contact opportunities for aggregation of destabilized particles. At a certain colloid concentration, such as the order 50–100 mg/L, base on kaolinite, contact opportunity is sufficient for hydrolysis species to become adsorbed and effect destabilization by charge effects or bridging, before precipitation takes place (Kim et al., 1965; Packham, 1965; Bratby, 2006). The later one is predominant mechanism for removal of low colloid concentration in water. It is necessary to overwhelm the widely dispersed particles with a large quantity of coagulant in the “sweep floc” zone. This is typical of most natural surface waters that contain “low” turbidities, less than 50 JTU (Faust and Aly, 1998). The turbidity of the raw water used was low (7.5–8.5 NTU). Therefore, “sweep flocculation” mechanism was probably predominant in the coagulation–flocculation of turbidity in the present study. As a comparison between PACl and alum, it is interesting to note that PACl gave a considerable higher of turbidity removal at low dosages (Fig. 5). While coagulation–flocculation by “sweep flocculation” mechanism requires a high dosage of coagulant to remove turbidity, charge neutralization is the
p>F
d.f
0.0415 0.0001 0.0600 0.0072 0.0004
11 11 11 11 11
Estimate (coded) 6.53 4.87 2.29 −4.87 −16.34
Std. Err. 0.87 0.87 1.24 0.94 0.94
F-value 55.88 31.01 3.44 27.00 303.93
p>F 0.0001 0.0008 0.1085 0.0013 <0.0001
predominant mechanism at low dosages of coagulant. Positive charge species are responsible for removal of particles by charge neutralization and the advantage of PACl over alum could be explained by the predominance of (Al13 O4 (OH)24 7+ , Al13 ) species that is highly stable and positively charged in PACl. The two contour plots in Fig. 5a and b show that they appeared to have a single optimum point, and the optimal conditions were located inside the design boundary. For the combination of the pH range from 7.5 to 9.0 and PACl dose range from 0.06 to 0.15 mM, more than 90% turbidity was removed and a maximum value of 94% appeared at the point of pH 8.5 and 0.11 mM PACl. When using alum, a removal efficiency of 86% turbidity was observed at pH 6.8 and 0.16 mM alum.
3.2.2.
Optimization of the DOC removal efficiency
Table 7 illustrates that except for the quadratic effect of alum dose, all the linear and quadratic effects of dose and pH were significant (p < 0.05) for both PACl and alum. Coagulant dose and pH played an important role in DOC removal, regardless of alum coagulant or PACl coagulant was used. The significance of dose and pH was also expressed in the case of removal of turbidity. The roles of the coagulant dose and pH in coagulation–flocculation process were also underlined in other studies (Faust and Aly, 1998; Pernitsky, 2003). The surface and contour plots of the quadratic models for DOC removal are presented in Fig. 6a and b. Fig. 6a shows an optimum of 35% DOC removal obtained with PACl dose range from 0.10 to 0.14 mM, corresponding to the pH range from 5.5 to 6.5. Likewise, Fig. 6b demonstrates that the optimum removal of 35% DOC occurred in the range of 0.14–0.19 mM alum and pH range of 5.8–6.6. Removal efficiencies are found to reduce when moving away from these ranges, meaning that either increase or decrease in any of the factors resulted in decline of the response. For both coagulants, it is important to note that the optimal pH for DOC removal was lower than the one for turbidity removal. As previously shown in Fig. 5, the removal of turbidity was more efficient at high pH values (>7 and >6.5 for PACl and alum, respectively), while lower pH values (<6.5, Fig. 6) were favorable for DOC removal. The optimum pH range for removal of DOC is usually rather less (typically, pH 5–6) than for the removal of suspended particles (Edwards and Amirtharajah, 1985). Differences in the optimal pH range between coagulation of turbidity and DOC were also reported in previous studies (Hall and Packham, 1965; Edwards and Amirtharajah, 1985). It is well known that humic substances (HS) are the major component of DOC in natural waters, and that HS can be operationally defined and separated into the more soluble fulvic acids (FAs) and the less soluble humic acids (HAs). The low range of Specific UV Absorbance (SUVA)
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Fig. 5 – 3D surface and 2D contour plots for turbidity removal using (a) PACl and (b) alum. from 1.27 to 1.33 L mg−1 m−1 of the water in this study indicates that the major content of DOC was FAs which are low molecular weight, more soluble, and hydrophilic. FAs are therefore, more difficult to coagulate (Edzwald and Van Benschoten, 1990). This explains why DOC removal efficiency by coagulation–flocculation was not so high. An optimum removal efficiency of 35% was obtained for both PACl and alum. This result is consistent with earlier findings suggesting that for the case where SUVA value less than 3 L mg−1 m−1 , DOC removal efficiency is relatively low and likely in a range of 20–50% (Van Benschoten and Edzwald, 1990). The nature of HS and the differences in behaviors between HS and colloidal materials in coagulation–flocculation also give an explanation for the difference in the optimal pH used to remove them. Coagulation of colloids can perform well at high pH values by enmeshment in a precipitate or sweep flocs. HAs, a small fraction of HS, although may not
be true colloids, are large enough that they can be referred to as macromolecules, and also be removed from water in a pathway similarly to the removal of colloidal materials/turbidity (Bratby, 2006). However, the largest fraction of HS in water is generally FAs, and their nature is different with the nature of colloids. Because FAs are low molecular weight, high soluble, and hydrophilic, they are considered as a true solution and therefore, they cannot be coagulated by enmeshment at high pH conditions. Coagulation of HS bases mainly on complexaxion–precipitation and/or adsorption onto metal hydroxide particles. At high pH values, there is competition between OH− and organic anions for metal hydrolysis products and thus, limits the opportunities for functional groups of HS can serve as ligands in metal complex (Bratby, 2006). Adsorption of HS or Al-HS complexes onto Al(OH)3 precipitate forming at high pH is also limited. Randtke (1988) presented that as pH increases, natural organic compounds become
Table 7 – Estimation of the second-order response surface parameters for DOC removal as response. Parameter
PACl d.f
Dose pH Dose × pH Dose × dose pH × pH
1 1 1 1 1
Estimate (coded) 3.34 −7.38 0.34 −2.35 −2.89
Std. Err. 0.52 0.52 0.73 0.56 0.56
Alum F-value 41.66 203.10 0.21 17.88 27.09
p>F
d.f
0.0003 <0.0001 0.6591 0.0039 0.0012
1 1 1 1 1
Estimate (coded) 4.74 −5.70 2.73 −2.38 −7.57
Std. Err.
F-value
p>F
0.98 0.98 1.38 1.05 1.05
23.60 34.13 3.90 5.17 52.22
0.0018 0.0006 0.0889 0.0572 0.0002
chemical engineering research and design 8 9 ( 2 0 1 1 ) 1126–1135
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Fig. 6 – 3D surface and 2D contour plots for DOC removal using (a) PACl and (b) alum. more negatively charged and the metal hydrolysis species become less positively charged, resulting in less adsorption propensity. For these reasons, coagulation of HS (also NOM in water) is mainly performed under low pH conditions along with the presence of soluble cationic aluminum hydrolysis species. These species react with anionic functional groups on NOM to precipitate as an aluminum-NOM.
3.2.3. Optimization of both turbidity removal and DOC removal efficiency The turbidity removal and DOC removal are the two individual responses and their optimizations were achieved under different optimal conditions. The optimum turbidity removal might impact DOC removal and vice versa. Therefore, a compromise between the optimum conditions for the two responses is desirable. By defining the desirable limits of 90% turbidity removal and 30% DOC removal, the optimum condition can be visualized graphically by superimposing the contours for the two responses in an overlain plot, as shown in Fig. 7. Graphical optimization displays the area of feasible response values in the factor space and the regions that fit the optimization criteria was shaded. Base on the shaded-area of overlain contour in Fig. 7a, a compromise for 91.4% turbidity removal and 31.2% DOC removal can be met at 0.11 mM PACl and pH 7.40. Likewise, the combination of 0.15 mM alum and pH 6.6 resulted in 86.3% turbidity and 34.3% DOC removal as shown in Fig. 7b. To
confirm the agreements of the results achieved from the models and experiments, additional experiments were conducted by applying the coagulant dose and pH that was determined as the optimum region in this study. As shown in Table 8, the turbidity and DOC removals obtained experimentally were very close to those estimated using the models. This implies that the RSM approach used in this study was appropriate to optimize the conditions of the coagulation–flocculation process. The results of optimizing conditions for the simultaneous removal of turbidity and DOC illustrate that with a lower dose (0.11 mM PACl vs. 0.15 mM alum), PACl gave a higher turbidity removal than alum (91.4% vs. 86.3% using PACl vs. alum, respectively). However, the advantage of PACl over alum was not always present when considering simultaneous optimizing both responses, such as in the case of DOC removal. Separate optimizing shows the removal efficiency of DOC obtained was same for PACl and alum (35%, as previously shown in Fig. 6). The compromise in simultaneously optimizing the turbidity removal and DOC removal resulted in lower DOC removal efficiency in comparison with alum (31.2% vs. 34.3% using PACl vs. alum, respectively). The permissible values for the removal efficiencies in this study were set in order to simultaneously obtain the removals as high as possible for both responses. However, depending on the target of treatment, a lower permissible value of turbidity removal can be set to get a higher value of DOC removal, and vice versa. For
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a
b
9.5
8.0
Turbidity removal: 90% 8.5
pH
8.5
7.5 7.0
Turbidity removal: 85%
pH
7.5
6.5 DOC removal: 34%
6.0 DOC removal: 30%
6.5
5.5 5.0
5.5
4.5 0.03
0.06
0.09
0.12
0.15
0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19
Alum dose (mM Al)
PACl dose (mM Al)
Fig. 7 – Overlain contour plots of turbidity removal and DOC removal for (a) PACl and (b) alum.
Table 8 – Confirmation experiments at optimum conditions. Experiment conditions Dose (mM Al) PACl Experimental value Predicted value Error (%) Alum Experimental value Predicted value Error (%)
pH
Turbidity removal (%)
DOC removal (%)
0.11 0.11
7.40 7.40
90.67 91.40 0.79
31.02 31.20 0.58
0.15 0.15
6.6 6.6
84.93 86.30 1.59
34.34 34.68 0.98
example, to maintain 35% DOC removal as obtained from separate optimizing, compromises turbidity removal, reducing it to 80% (data not shown).
4.
Responses
Conclusions
The physicochemical process that is known as coagulation–flocculation is common and necessary in water treatment. This work has demonstrated the application of RSM in seeking optimal conditions for this process. Simultaneous removals of turbidity and DOC were investigated. RSM using CCD was applied to evaluate effects of coagulant dose and pH on the coagulation–flocculation effectiveness, and then determine the optimum conditions. The results showed that the two factors considered in this study played an important role on removal efficiency of turbidity and DOC. As a compromise to simultaneous removal of turbidity and DOC from bulk water in a single unit, the optimum conditions obtained were 0.11 mM PACl at pH 7.4, and 0.15 mM alum at pH 6.6. Under these optimum conditions, 91.4% turbidity removal and 31.2% DOC removal were obtained using PACl whereas removals using alum were 86.3% turbidity and 34.3% DOC. The results of the confirmation experiment agreed with predictions. This demonstrates that RSM can be successfully applied for modeling and optimizing the coagulation–flocculation process and it is the economical way of obtaining the maximum amount of information in a short period of time and with the least number of experiments.
This study reveals that PACl is more efficient than alum for removal of turbidity. PACl is recommended for the coagulation–flocculation of high turbidity water. For water treatment in which DOC is a significant concern, both PACl and alum should be considered, with a compromise between the removal efficiencies of turbidity and DOC, along with the dose required, and other factors, such as the chemical used to adjust pH, the temperature of water, and the cost of coagulants.
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