Optimization of Coagulation− Flocculation Process for Palm Oil Mill

The coagulation-flocculation process incorporated with membrane separation technology will become a new approach for palm oil mill effluent (POME) ...
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Environ. Sci. Technol. 2005, 39, 2828-2834

Optimization of Coagulation-Flocculation Process for Palm Oil Mill Effluent Using Response Surface Methodology A. L. AHMAD,* S. ISMAIL, AND S. BHATIA School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia

The coagulation-flocculation process incorporated with membrane separation technology will become a new approach for palm oil mill effluent (POME) treatment as well as water reclamation and reuse. In our current research, a membrane pilot plant has been used for POME treatment where the coagulation-flocculation process plays an important role as a pretreatment process for the mitigation of membrane fouling problems. The pretreated POME with low turbidity values and high water recovery are the main objectives to be achieved through the coagulationflocculation process. Therefore, treatment optimization to serve these purposes was performed using jar tests and applying a response surface methodology (RSM) to the results. A 23 full-factorial central composite design (CCD) was chosen to explain the effect and interaction of three factors: coagulant dosage, flocculent dosage, and pH. The CCD is successfully demonstrated to efficiently determine the optimized parameters, where 78% of water recovery with a 20 NTU turbidity value can be obtained at the optimum value of coagulant dosage, flocculent dosage, and pH at 15 000 mg/L, 300 mg/L, and 6, respectively.

Introduction Palm oil mills are traditionally situated near rivers from which water is drawn for their milling operations. Before the enforcement of strict environmental control, some palm oil mills discharged their untreated or partially treated effluents into the river. Excessive quantities of these palm oil mill effluent (POME) discharge cause severe pollution of waterways by oxygen depletion and other related effects. This is due to raw or partially treated POME having an extremely high content of degradable organic matter that is contributed in part by the presence of unrecovered palm oil. The organic content of POME, as measured by biochemical oxygen demand (BOD, 3-day, 30 °C), typically averages about 25 000 mg/L with a chemical oxygen demand of 50 000 mg/L, suspended solids of about 18 000 mg/L, and oil content might ordinarily exceed 6000 mg/L (1). It is estimated that for each ton of crude palm oil (CPO) produced, 2.5-3.5 m3 POME is generated. In the year 2003, Malaysia produced 13 million ton of CPO and thus about 32.5-45.5 million m3 POME was generated (2). POME is a colloidal suspension of 95-96% water, 0.6-0.7% oil, and * Corresponding author telehone: (604)593-7788; fax: (604)5941013; e-mail: [email protected]. 2828

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4-5% total solids including 2-4% suspended solids originating from the mixture of a sterilizer condensate, separator sludge, and hydrocyclone wastewater. The palm oil mills in Malaysia faced the challenge of balancing environmental protection, their economic viability, and sustainable development after the Department of Environment enforced the regulation for the discharge of effluent from the CPO industry, under the Environmental Quality (Prescribed Premises) (Crude Palm Oil) Order and Regulations, 1977. There is an urgent need to find a way to preserve the environment while maintaining the economy in good condition (3). Conventional biological treatments of anaerobic or aerobic digestion are the most commonly applied methods for treatment of different types of wastewater (4-7). However, biological treatment of POME requires proper maintenance and monitoring as the processes rely solely on microorganisms to break down the pollutants. The microorganisms are very sensitive to changes in the environment; thus, great care has to be taken to ensure that a conducive environment is maintained for the microorganisms to thrive. Besides, it also generates a vast amount of biogas. This biogas contains methane, carbon dioxide, and trace amounts of hydrogen sulfide. Some of these gases are corrosive and odorous. The treatment of POME using various methods has shown an increased interest from many researchers. Oswal et al. (8) have treated POME using tropical marine yeast (Yarrowia lipolytica) NCIM 3589, and 95% of COD reduction was achieved with a retention time of 2 d. Fakhru’l-Razi and Noor (9) have reported that between 91.7 and 94.2% of COD removal at an average retention time of 3.03 d was achieved through a membrane anaerobic system (MAS) operated by using the membrane to achieve external solid/liquid-phase separation so that a sufficient quantity of active biomass is retained at the surface of the membrane. Nik Nurulaini et al. (10) have shown that an attached film bioreactor using a trickling filter for the treatment of diluted POME managed to achieve a removal efficiency of BOD and COD of more than 50%. Treatment of POME requires a sound and efficient system in facing the current challenges. There are some mills that still fail to comply with the DOE standard discharge limit even though a biological treatment system has been applied. Therefore, an alternative POME treatment system is required. A technological shift from biological treatment to coagulation-flocculation treatment with membrane separation could lead to improved effluent treatment as well as gaining other value added through the recovery and recycling of water to the plant. The proposed treatment system is more predictable and inherently subject to control by simple techniques. It is also relatively tolerant to temperature changes. Space requirements are usually less as compared to biological treatment. In addition, the byproduct of this treatment can be useful, and the treated water might be recycled to the plant with a little further treatment (11).

Coagulation-Flocculation Process The coagulation-flocculation process supported with membrane separation technology will play the role as a pretreatment process where raw POME will be treated mainly to reduce its suspended solids content to an acceptable level before entering the membrane unit. One of the approaches used to mitigate the membranes from fouling and degradation during operation is through pretreatment of feed (1214). 10.1021/es0498080 CCC: $30.25

 2005 American Chemical Society Published on Web 03/01/2005

In the pretreatment of feed, the coagulation-flocculation process permits removal of organic colloids that play an important role in the fouling phenomena. Coagulation is a term used to describe the process of aggregation of colloidal particles into large aggregates to attain better stability. Aggregation of the particles occurrs through two distinct mechanisms: charge neutralization and a sweep-floc mechanism. The charge neutralization may due to a specific chemical reaction between positively charged coagulants and negatively charged colloids and natural organic matter or from the shielding of the negatively charged sites, resulting in precipitation. A narrow pH range (4-5.5) is feasible for coagulation by charge neutralization. The sweep-floc mechanism occurs in the range of pH 6-8 where the conditions are suitable for the rapid formation of amorphous solidphase Al(OH)3(s). During the sweep-floc process, removal of turbidity and natural organic matter takes place by adsorption on the precipitation of Al(OH)3(s) (15). Al-Malack and Anderson (16) have shown the effect of using alum, polyaluminum silicate (PASS), and lime as coagulants on the performance of cross-flow microfiltration of domestic wastewater. The coagulants were added to the circulation tank at the beginning of each run. Doses of 20120 mg/L of alum were used at pH 7. The results showed 50% improvement in flux values as compared to direct filtration without coagulant. Vigneswaran and Boonthanon (17) showed that crossflow microfiltration with in-line flocculation reduced the clogging of membranes, thus leading to higher quality water at an economic filtration rate. The filtration rate can be increased by more than 200% by adopting in-line flocculation. The coagulation-flocculation process is influenced by various factors such as the type and dosage of coagulant (18-20), the type and dosage of flocculent or coagulant aid, pH (21, 22), mixing speed and time (23), temperature, and retention time (24). These factors were studied to determine the effect of these factors in treating POME, and the optimum values were obtained using a response surface methodology.

Response Surface Methodology (RSM) Optimizing the significant parameters in the coagulationflocculation process through the classical method involves the changing of one variable at a time while fixing all other variables at one level and studying the effect of the variable on the response. This is an extremely time-consuming, expensive, and complicated process for a multi-variable system. To overcome this difficulty, statistical experimental design techniques using the response surface methodology (RSM) for the study of POME treatment are applied. The RSM technique can improve product yields and provide closer confirmation of the output response toward the nominal and target requirements. This statistical experimental design is widely used in various fields such as in biochemistry, as reported by Adinarayana et al. (25); performance of coated carbide tools in material processing, as reported by Noordin et al. (26); for the interactions of metal ions with crude kaolin particles, as reported by Zaman et al. (27); the study on speciation of Cr(VI)/Cr(III) in environmental water, as reported by Massumi et al. (28); and the study to optimize the preparation of activated carbons for use in water treatment, as reported by Bacaoui et al. (29). The current study is an attempt to optimize the coagulation-flocculation process using jar tests, a valuable tool to evaluate the efficiency of the treatment process. The parameters that need to be optimized are coagulant dosage, flocculent dosage, and pH. A standard RSM design called a central composite design (CCD) is selected for the optimization. The choice of CCD is justified by a number of advantages such as: (i) It can be run sequentially, which means that it can be naturally partitioned into two subsets of points. The

TABLE 1. Experimental Range and Levels of the Independent Variables range and levels variables

-2

-1

0

1

2

A, coagulant dosage (mg/L) 0 7500 15 000 22 500 30 000 B, flocculent dosage (mg/L) 0 125 250 375 500 C, pH 2 4 6 8 10

first subset estimates the linear and two factor interaction effects while the second subset estimates the curvature effects. The second subset need not to be run when analysis of the data from the first subset points indicates the absence of significant curvature effects. (ii) The CCD method is efficient and flexible, providing sufficient information on the effects of variables and overall experimental error with a minimum number of experiments. The availability of several varieties of CCDs enables usage under different experimental regions of interest and operability. The objective of the current study is to obtain the optimum values of the process parameters that result in the maximum water recovery with minimum turbidity through the RSM.

Materials and Methods The experiments were carried out in a bench scale using a jar test apparatus, and statistically designed experiments were used to optimize three variables in the coagulationflocculation process, including the coagulant dosage, flocculent dosage, and pH. Modified industrial grade alum (Envifloc-40L) and flocculation agent (Profloc 4190) were obtained from Envilab Sdn. Bhd. and Exotic Chemicals Sdn. Bhd. Malaysia, respectively. In every test, 500 mL of POME sample (collected from United Palm Oil Mill, Sg. Kecil Nibong Tebal, Penang, Malaysia, and cooled to room temperature) was used. After the coagulant was added with the dosage varying from 0 to 30 000 mg/L, the pH of the sample was adjusted by adding NaOH within the range of 2-10, and then the flocculent agent was added with dosage varying from 0 to 500 mg/L as per the experimental design. The sample was mixed rapidly at 200 rpm for 30 s, followed by slow mixing at 30 rpm for 20 min, and the sample was left to settle for 30 min before being filtered to separate the sludge and the supernatant. In all the tests, turbidity and supernatant or water recovery were measured as the responses. Turbidity was determined by a turbidity meter, model WTW Turb 350 IR, while water recovery was measured by collecting all the filtered supernatant in a graduated cylinder. A 23 full-factorial design of CCD with six replicates at the central points was employed to fit the second-order polynomial models and to obtain an experimental error for this study. The range and levels of experimental variables investigated in this study are presented in Table 1. Twenty runs were required for a complete set of the experimental design and are shown in Table 2. The Design Expert software (version 6.0, Stat-Ease, Inc., Minneapolis, MN) was used for regression and graphical analyses of the data. The central values (zero level) chosen for the experimental design were as follows: coagulant dosage, 15 000 mg/L; flocculent dosage, 250 mg/L; and pH 6. Further details in developing the regression equation can be found in the Supporting Information. The optimum values of selected variables were obtained by solving the regression equation and also by analyzing the response surface contour plots (28).

Results and Discussion A preliminary study on the effect of type and dosage of coagulant, type, and dosage of flocculent or coagulant aid, VOL. 39, NO. 8, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. CCD for the Study of Three Experimental Variables in Coded Units factor run no.

coagulant dosage (A)

flocculent dosage (B)

pH (C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-1 1 0 -1 -1 0 -1 1 0 1 1 0 2 0 0 0 0 0 0 -2

1 -1 0 1 -1 0 -1 1 0 1 -1 0 0 0 0 0 -2 0 2 0

-1 1 0 1 1 0 -1 -1 0 1 -1 0 0 2 0 0 0 -2 0 0

TABLE 4. ANOVA for Response Surface Quadratic Modela source block model A B C A2 B2 C2 AB AC BC residual lack of fit pure error cor total SD mean CV PRESS a

Prob > F

sum of squares DF mean square F value 0.013 7.951 1.396 0.170 0.408 1.617 0.644 3.723 0.116 1.276 0.170 0.65 0.64 0.01 8.61

1 9 1 1 1 1 1 1 1 1 1 9 5 4

0.013 0.883 1.396 0.170 0.408 1.617 0.644 3.723 0.116 1.276 0.170 0.072 0.127 0.003

12.296 0.0005b 19.428 0.0017 2.367 0.1583 5.678 0.0410 22.499 0.0011 8.958 0.0151 51.812 F

block model A B C A2 B2 C2 AB AC BC residual lack of fit pure error cor total

27.648 2311.010 26.522 406.022 607.622 51.334 609.034 405.904 156.645 0.845 6.845 659.59 636.66 22.93 2998.25

SD mean CV PRESS

8.56 68.74 12.45 6913.00

a

1 9 1 1 1 1 1 1 1 1 1 9 5 4 19

Response: water recovery.

27.648 256.779 26.522 406.022 607.622 51.334 609.034 405.904 156.645 0.845 6.845 73.288 127.332 5.732

R2 adj R2 pred R2 adeq precision b

3.504 0.362 5.540 8.291 0.700 8.310 5.538 2.137 0.012 0.093

0.0379b 0.5623 0.0430 0.0182 0.4243 0.0181 0.0431 0.1778 0.9168 0.7669

22.212 0.0051

0.7780 0.5559 -1.3271 6.0877

Significant.

significant factors and were selected as the variable factors for optimization. The three-level experiments were carried out according to the CCD experimental plan (Table 2), and the average value of turbidity and water recovery as the responses obtained from the experiments are shown in Table 3. The results were further analyzed using Design Expert software. Testing of the fit summary output revealed that the quadratic model is statistically significant for both responses; therefore, it was used for further analysis. ANOVA Analysis. The results of the second-order response surface model in the form of analysis of variance (ANOVA) for turbidity and water recovery are shown in Tables 4 and 5, respectively. The ANOVA values for turbidity responses based on log10 values were used since the turbidity values are very large. From the analysis, the quadratic regression model demonstrates that the model is highly significant as the Fisher F-test (Fmodel, mean square regression/mean square residual ) 12.296) with a very low probability value [(Pmodel > F) ) 0.0005]. The main effect of coagulant dosage (A) and pH (C); the second-order effect of coagulant dosage (A2), flocculent dosage (B2), and pH (C2); and the two-level

FIGURE 1. DESIGN-EXPERT plot. Predicted vs actual data for turbidity (log10).

FIGURE 2. DESIGN-EXPERT plot. Predicted vs actual data for water recovery.

interactions of coagulant dosage and pH (AC) are the significant model terms. Other model terms are not significant. The fit of the model was checked by the determination coefficient (R2). In this case the value of the determination coefficient (R2 ) 0.9248) indicates that only 7.52% of the total variation is not explained by the model. The value of the adjusted determination coefficient (adjusted R2 ) 0.8496) is also high to advocate a high significance of the model (31). A higher value of the correlation coefficient (R ) 0.9617) justifies an excellent correlation between the independent variables. Simultaneously, a relatively low value of the coefficient of variation (CV ) 13.75%) indicates good precision and reliability of the experiments (32). Results of ANOVA in Table 5 (response: water recovery) show that the model is significant. While the R2 value of the quadratic model (R2 ) 0.7780) is not as high as that of the model for turbidity, the R2 value is better as compared to other models. Therefore, the quadratic model was selected. The main effect of flocculent dosage (B) and pH (C) and the second-order effect of flocculent dosage (B2) and pH (C2) are significant model terms. The following regression equations are the empirical models in terms of coded factors for: (a) turbidity (log10):

turbidity (log10) ) 1.274 - 0.295A - 0.103B - 0.160C + 0.260A2 + 0.164B2 + 0.394C2 - 0.120AB 0.399AC - 0.146BC (3) (b) water recovery:

water recovery ) 74.91 - 1.288A + 5.038B - 6.162C + 1.462A2 - 5.038B2 - 4.112C2 + 4.425AB 0.325AC + 0.925BC (4) The predicted versus actual plots for turbidity and water recovery are shown in Figures 1 and 2, respectively. The observed points on both of these plots reveal that the actual values are distributed relatively near to the straight line in both cases. Contour plots of the RSM are drawn as a function of two factors at a time, holding all other factors at fixed levels (normally at the zero level). Those plots are helpful in understanding both the main and the interaction effects of

FIGURE 3. DESIGN-EXPERT plot. 3D surface graph of turbidity showing the effect of coagulant dosage and pH. these two factors. The 3D surface graph, pH versus coagulant dosage in Figure 3, shows that a significant mutual interaction occurs between coagulant dosage and pH for turbidity as a response. It is clear from the figure that the turbidity reduces at the condition where coagulant dosage is within the range of 15 000-30 000 mg/L and pH range from 6 to 8. It was reported by Fountain (33) that at the optimum alum dosage, coagulation occurs generally between pH 6 and 7.8. At this state, metal coagulants such as aluminum or iron salts require sufficient alkalinity for proper hydrolysis to occur, whereby the insoluble hydroxide is formed and precipitation leads to the reduction in turbidity (33). The obtained results were also supported by the sweep-floc mechanism that appears within the range of pH 6-8, which was suitable for the rapid formation of amorphous solid-phase Al(OH)3(s). The removal VOL. 39, NO. 8, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. DESIGN-EXPERT plot. Contour plot of turbidity showing the effect of coagulant dosage and flocculent dosage. of turbidity occurred by adsorption on the precipitated Al(OH)3(s) (15). Figures 4 (flocculent dosage vs coagulant dosage) and 5 (pH vs flocculent dosage) show the response surface for turbidity. Both figures demonstrate a symmetrical mound shape contour with the maximum response occurring within the central contour meaning that the slope of each contour curve of every variable is almost independent of the concentration of the other (32). Flocculent dosage has no interaction with coagulant dosage and pH, which was apparent from the relative circular nature of the contour curves. The 3D surface graphs and contour plots for water recovery are shown in Figures 6-8, respectively. The saddle contour in the 3D surface graphs in Figures 6 and 7 indicate that the water recovery percentage increases at the center of the region, which involves the interaction between coagulation dosage with flocculent and coagulant dosage with pH. The pattern of both graphs indicates that the highest percentage of water recovery is obtained generally at the intersection of zero levels of all of the factors. The mechanism of coagulation and flocculation occurs once sufficient coagulant has been dispersed to achieve optimum destabilization of the colloidal particles and the flocculent allowed particles to agglomerate into larger flocs (33, 34). The high molecular weight synthetic polymer used as a flocculent in this treatment has a number of ionized or potentially ionisable functional groups within its structure. The destabilization of particles by large polymeric molecules involves the bridging of particles by the polymer chain, hence forming larger structural units that are readily separated from the aqueous dispersing medium (35). This process results in an easier filtration process where good quality and a higher quantity of water can be obtained. The flocculent dosage and pH interaction shown in Figure 8 indicate another symmetrical mound shape contour with 2832

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FIGURE 5. DESIGN-EXPERT plot. Contour plot of turbidity showing the effect of flocculent dosage and pH.

FIGURE 6. DESIGN-EXPERT plot. 3D surface graph of water recovery showing the effect of coagulant dosage and flocculent dosage. the maximum response occurring within the central contour. The sweep-floc mechanism occurred here to accommodate

FIGURE 9. DESIGN-EXPERT plot. Overlay plot for optimal region.

FIGURE 7. DESIGN-EXPERT plot. 3D surface graph of water recovery showing the effect of coagulant dosage and pH.

reveals that the flocculent dosage has little interaction with pH. Optimization Analysis. The overlay plot was generated by superimposing the contours for the various response surfaces. By defining the desired limits of the turbidity and water recovery, the shaded portion of the overlay plot defined the permissible values of the dependent variables as shown in Figure 9. The optimum values of selected variables were obtained by solving the regression equations. The optimum values of the test variables in actual were as follows; coagulant dosage ) 15 000 mg/L, flocculent dosage ) 300 mg/L, pH ) 6 while the responses predicted were turbidity ) 19 NTU (1.2794 in log10) and water recovery ) 76%. A verification of the results using the set of optimized parameters was accomplished by performing the experiments incorporating the optimized variables. The experiments were conducted in triplicate. The average turbidity value obtained through the experiment was 20 NTU, and the average water recovery percentage obtained was 78%. These experimental findings were in close agreement with the model prediction.

Acknowledgments The authors gratefully acknowledge Yayasan FELDA for their financial support for this research. The authors also thank the United Oil Palm Industry, Nibong Tebal, Pulau Pinang, for providing the sample of POME throughout this research study.

Supporting Information Available Further details of response surface methodology and additional diagnostic plots. This material is available free of charge via the Internet at http://pubs.acs.org.

Nomenclature A

first factor or input variable, coagulant dosage (mg/L)

B

second factor or input variable, flocculent dosage (mg/L)

C

third factor or input variable, pH

FIGURE 8. DESIGN-EXPERT plot. Contour plot of water recovery showing the effect of flocculent dosage and pH.

adeq precision

the separation of the solid and liquid phase in the coagulation process. The relative circular nature of the contour curve

cor total

adj

R2

adequate precision adjusted R2 totals of all information corrected for the mean

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CV

coefficient of variation

DF

degree of freedom

e

error term

pred R2

predicted R2

Prob > F

proportion of time or probability expected to obtain the stated F value

PRESS

predicted residual error sum of squares

R2

determination coefficient

SD

residual standard deviation

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Received for review February 8, 2004. Revised manuscript received January 18, 2005. Accepted January 21, 2005. ES0498080