dtr

tes

id,

een

ds (

d d

methodology (RSM)). Alum (coagulant) and polymer (coagulant aid) were used to treat a water source E-mail: muzaffar@utm.my

Z. Abdul-MajidFaculty of Science,Universiti Teknologi Malaysia,81310 Johor-Bahru,Malaysia

A. SelamatS. I. Omar

residual aluminium (Brub & Dorea ) from drinking

inife

thalu

et al. ) and polyferric chloride (PFC) (Zhan et al. )as coagulants are actively being carried out. The impact ofpH towards coagulation has also been thoroughly investi-gated (Gregory & Carlson ; Brub & Dorea ;

hat the choice of coagulants used,tional pH applied in a coagulationnt contribution to the operational

ant. Therefore, it is important tons when carrying out coagulationr unnecessary dosage of the associ-

ated chemicals may be prevented.

496 IWA Publishing 2012 Water Science & Technology | 65.3 | 2012(Amirtharajah & OMelia ). By far, the most commonwater. As a coagulation process has the ability to eliminate

many pollutants from surface and drinking water, the suc-cess of the process has a direct impact on the reliability oftreatment plant operations and nal water quality.

The effectiveness of the coagulation process ishighly dependent on many factors, including dosage of coagu-lant and coagulant aids and also pH of the operation

Zhan et al. ).It is well established t

the dosing and the operaprocess make a signica

cost of the treatment pluse the optimum conditioprocess so that wastage owater (Lin et al. ) and natural organic matter (NOM)(Zhan et al. ) present in surface water. The process isalso capable of removing arsenic (Hering et al. ) andwhile the value for polymer was set constant at 0.020 mg/l. Through RSM optimization, the optimum

dose for alum was 7 mg/l and for polymer was 0.004 mg/l. Optimum pH for the coagulation

operation obtained through traditional methods and RSM was 7.6. The nal turbidity, pH after

coagulation and residual aluminium recorded were all within acceptable limits. The RSM method was

demonstrated to be an appropriate approach for the optimization and was validated by a further test.

Key words | coagulation, jar test, one-factor-at-a-time, response surface methodology,water treatment

INTRODUCTION

Coagulation is one of the most important processes in watertreatment. It is an effective method for the removal of col-

loidal particles in surface water. Many researchers haveapplied the coagulation process to treat highly turbid

coagulants used are alumsulphate (Fe2(SO4)3) and

Graham ). Studies oninorganics such as polydoi: 10.2166/wst.2012.561SAJ Holdings Sdn. Bhd.,P.O. Box 262,80350 Johor-Bahru,Malaysia

um sulphate (Al2(SO4)3), ferricrric chloride (FeCl3) (Jiang &

e performance of polymerizedminium chloride (PACl) (LinMalaysia. The optimum conditions for these factors were chosen when the nal turbidity, pH after

coagulation and residual aluminium were within 05 NTU, 6.57.5 and 00.20 mg/l respectively.

Traditional and RSM jar tests were conducted to nd their respective optimum coagulation conditions.

It was observed that the optimum dose for alum obtained through the traditional methodwas 12 mg/l,with very low pH and high aluminium concentration at Sri-Gading water treatment plant (WTP) F. YusofOptimizing the coagulation process in a

treatment plant comparison between

and statistical experimental design jar

M. Zainal-Abideen, A. Aris, F. Yusof, Z. Abdul-Maj

and S. I. Omar

ABSTRACT

In this study of coagulation operation, a comparison was made betw

for pH, coagulant and coagulant aid obtained from traditional metho

time (OFAT) method) and with central composite design (the standarrinking water

aditional

ts

A. Selamat

the optimum jar test values

an adjusted one-factor-at-a-

esign of response surface

M. Zainal-Abideen (corresponding author)A. ArisFaculty of Civil Engineering,Universiti Teknologi Malaysia,81310 Johor-Bahru,Malaysia

Batu-Pahat, Malaysia. Its water source is from the BekokRiver. For nearly a decade, the Bekok River has registered

497 M. Zainal-Abideen et al. | Optimizing coagulation process through traditional & RSM jar test Water Science & Technology | 65.3 | 2012Several methods have been adopted to determine the

optimum coagulation conditions such as zeta-potentialmeasurement (Morfesis et al. ) and jar tests. Of thesemethods, the jar test has been commonly used to determine

the best pH and dosage of the chemicals owing to its simpli-city. In a typical jar test practice, the operator determines thebest pH and chemical dosages by systematically changingthe level of the factor (i.e. pH, dosage) one step at a time

while holding the level of other factors constant. The levelof the factor that results in the best response (e.g. lowest tur-bidity value) is then selected and used in subsequent tests

which continue in the same manner for other factors.While this approach, termed as one-factor-at-a-time(OFAT), is rather straightforward, it suffers from shortcom-

ings that may lead to a wrong conclusion. In particular, itdoes not fully explore the whole experimental space tond the best factors conditions and is incapable of identify-ing the interaction effects resulting from the factors being

considered. Due to these reasons, the typical jar test practicecould have missed the actual best pH and dosage, which arepossibly hidden in the experimental space not covered by

the OFAT approach (Zainal-Abideen et al. ). Therefore,a statistically designed experimental approach is propo-sed for jar tests to overcome the shortcomings formerly

stated.Statistically designed experiments allow efciency and

are quite economical in that they require a relatively small

number of experiments but are still able to be analyzed bystatistical methods and result in valid and objective con-clusions. The statistical approach to experimental design iscrucial if meaningful conclusions are to be drawn from the

data. When the problem involves data that are subjectedto experimental errors, a statistical method is the only sys-tematic approach to analyse the data (Montgomery ).

The response surface method (RSM) is one example of thestatistical design of experiments. It is a collection ofmathematical and statistical techniques that are useful

for modelling and analyzing problems in which responses(or a response) of interest are inuenced by severalfactors or variables and in which the objective is to optimize

the responses. With RSM, the interaction betweenfactors and responses can also be determined (Montgomery).

RSM has been used in many optimization experiments.

Ahmad et al. () explained the effect and interactionbetween coagulant dose, occulant dose and pH when treat-ing palm oil mill efuent (POME) through a coagulation

occulation process combined with membrane separation

technology. Wang et al. () optimized the coagulationa low pH of less than 5.5 due to the acidicationprocess of acid sulphate soil within the river basin (Aris

et al. ).In identifying its best coagulation conditions, Sri-

Gading WTP has adopted an adjusted OFAT jar test

whereby only the coagulant dosing is systematicallyaltered and not the coagulant aid and pH of the coagu-lation. This situation is because the WTP is more

concerned about the amount of coagulant used as com-pared with any other chemicals as its usage isconsiderably higher than the others. For the record, the

daily average cost ratio of coagulant aid to coagulant atSri-Gading WTP is 1:240. Apart from the coagulant aid,the coagulation pH has also been kept constant duringthe actual jar test as the WTP focuses more on the pH

after coagulation. As long as the pH after coagulation iswithin 6.5 to 7.5 or around neutral, the initial pH forcoagulation is immaterial. Usually, this initial pH value

is set in the range of 7.28.0 and its mean value calculatedocculation process to achieve minimum turbidity and

sludge volume index (SVI) for paper-recycling wastewatertreatment. Pinzi et al. () used the RSM to optimize thetransesterication reaction for several types of vegetable

oils.Despite the application of the RSM in many experimen-

tal studies, its use in optimizing coagulation conditions withjar testing in water treatment is apparently lacking. Hence,

the objectives of the study were to obtain and to comparethe optimum coagulation conditions through traditionaljar testing conducted by Sri-Gading water treatment plant

(WTP) operators and the RSM. The variables thatwere considered include the coagulants and coagulant aiddosages, initial pH setting, nal turbidity, settling pH

after coagulation process and residual aluminium. As thecontrol of disinfection by-products is not a problem atthis WTP, NOM parameters were not included in thisinvestigation.

MATERIALS AND METHODS

Sri-Gading WTP

Sri-Gading WTP has a treatment capacity of 72,000 m3 ofwater a day and supplies water for public use infrom the last years historical data is 7.55.

Materials

Alum (liquid aluminium sulphate) and polymer (Nalclear8173 PULV) respectively were used as the coagulant and

coagulant aid at Sri-Gading WTP. To adjust the raw water

aluminium analysis. Turbidity was determined by a turbidity

The optimum conditions for jar tests were selected when

the coagulated water from Sri-Gading WTP achievedsettling pH between 6.57.5, turbidity below 5 NTU andresidual aluminium less than 0.20 mg/l through minimum

usage of alum, hydrated lime and polymer. According to

/l)

498 M. Zainal-Abideen et al. | Optimizing coagulation process through traditional & RSM jar test Water Science & Technology | 65.3 | 2012meter (HI 93703, Hanna Instrument). Residual aluminiumwas measured through Aluminon method (adapted from

Standard Methods) using a spectrophotometer (DR 5000,Hach Company, USA).

Table 1 | Water quality parameters for raw water sample from Sri-Gading WTP

pH Turbidity (NTU) Colour (TCU) Fe (mg/l) Al (mgpH to the desired pH, hydrated lime was used. Both alumand hydrated lime used were obtained from Damini Corpor-ation, Malaysia while polymer was delivered from Usaha

Kimia (Malaysia).

Experimental setup

Raw water sample collected from Sri-Gading WTP wasbrought to Universiti Teknologi Malaysia (UTM). This

sample was used to carry out traditional and statisticaldesigned (CCD RSM) jar test experiments at a benchscale. Characterization of water quality was carried out in

accordance with Standard Methods (APHA et al. ).Table 1 summarizes the water quality for the raw watersample.

Before proceeding to the jar test experiment (conductedusing a Chemix Floc-Tester Model CL-4), the initial pH ofthe water sample was adjusted by utilizing 1% hydratedlime solution. Jar testmixing speeds and timewere set accord-

ing to the WTP practice: rapid mixing of one minute at200 rpm; rapid mixing for 2 min at 100 rpm; slow mixingfor 3min at 50 rpm; slowmixing of 10 min at 25 rpm; settling

time of 30 min. Alum, followed by polymer, was added at thestart of the fast (200 rpm) mixing stage. The ranges of initialpH, alum and polymer concentration used in the jar tests

are explained below in the traditional jar test and RSM jartest subsections.

Following the 30 min settling period, the pH of the sol-

ution was measured with a pH meter (model 410, ThermoOrion) and samples were withdrawn 2 cm below the watersurface by using a 25 ml glass pipette (Hirschmann,Germany). Samples were taken for turbidity and residual4.04 5.46 30 0.52 1.75World Health Organization guidelines (), the upperlimit of turbidity for nished water is 5 NTU and theUSEPA sets the secondary maximum contaminant level in

drinking water for aluminium to be 0.20 mg/l (Sawyeret al. ).

Traditional jar test

Following the WTP practice, the alum dose was varied

between 818 mg/l for a water sample with turbiditybetween 5 to 10 NTU and the initial pH for jar test wasraised to 7.6. It is also the WTP practice to set the polymer

dosage constantly at 0.02 mg/l. Table 2 shows the initial pHand the dosages of alum and polymer used in theseexperiments.

RSM jar test

A three-factor CCD with four replicates at the centre points

was employed in designing the RSM jar test. In total, 18 runswere required in order to complete the experiment. Thisapproach is to t a quadratic surface which leads to optim-

ization as well as experimental error for this study(Multifactor RSM Tutorial ). Alum dosage (A), initialpH (B) and polymer dosage (C) used in this experimentwere the three independent variables in the coagulation pro-cess. The range and level of factors used are presented inTable 3. They were developed from the pH and coagulants

dosing historical data used in the jar test conducted dailyat the plant for the last 1-year period (Zainal-Abideenet al. ). The Design-Expert software (version 7.1, Stat-Ease, Inc., Minneapolis, USA) was used for regression and

graphical analyses of the data. The optimum values for thevariables were obtained in two ways: (i) by direct readingfrom RSM table (Table 4) and (ii) by carrying out the optim-

ization procedure in the Design-Expert guide (MultifactorRSM Tutorial ).

Temperature (WC) Mn (mg/l) NH3 (mg/l) TDS (mg/l)22.6 0.07 0.15 87

water sample. It can be clearly observed that all runs gave

suggested that they look directly at the RSM results shownin Table 4, as this table can identify one type of optimum

condition almost instantaneously.From Table 4, it is evident that almost all runs gave an

acceptable quality of coagulated water except for runs 3, 5

and 6 in which the coagulated sample did not meet therequirement in either settling pH or residual aluminium.Through direct reading of the RSM table (Table 4), run 12

Table 2 | Traditional jar test result

Factors Responses

RunAlum(mg/l)

InitialpH

Polymer(mg/l)

Turbidity(NTU)

SettlingpH

Residualaluminium(mg/l)

1 8 7.6 0.02 0.23 6.59 0.11

2 10 7.6 0.02 0.00 6.64 0.11

17 1 1 1 0.71 6.99 0.1918 0 0 0 0.61 6.72 0.09

499 M. Zainal-Abideen et al. | Optimizing coagulation process through traditional & RSM jar test Water Science & Technology | 65.3 | 2012acceptable results as the responses are within acceptableconditions. According to the WTP practice, the best con-dition for coagulation was chosen through direct reading

from Table 2 (i.e. run 3), where it gave the lowest turbidityvalue, its settling pH is near to neutral (7) and the residualaluminium is below 0.20 mg/l. Therefore, the chosen valuesof alum dosage, initial pH and polymer dosage obtained

through traditional jar test were 12 mg/l, 7.6 and 0.02 mg/lrespectively.

RSM jar test

The role of the traditional jar test carried out at Sri-Gading

WTP was to obtain the optimum coagulation conditionsalmost on the spot. If the RSM jar test is substituted forRESULTS AND DISCUSSION

Traditional jar test

Table 2 shows the results of the traditional jar test for the

3 12 7.6 0.02 0.00 6.80 0.12

4 14 7.6 0.02 0.42 6.62 0.14

5 16 7.6 0.02 0.29 6.87 0.14

6 18 7.6 0.02 0.28 6.64 0.13the traditional jar test at the WTP, it is difcult for the oper-ators to identify the optimum conditions immediately as the

results need to be analysed through the optimization pro-cedure in the Design-Expert guide. Therefore, withouteliminating the necessity to execute the optimization pro-

cedure to obtain the best coagulation conditions, it is

Table 3 | Experimental range and levels of central composite design

Range and levelsVariables 2 1 0 1 2

A, alum dosage (mg/l) 1 7 15.5 24 30

B, initial pH 6.5 6.9 7.5 8.1 8.5

C, polymer dosage (mg/l) 0 0.004 0.01 0.016 0.02Table 4 | CCD and response results for the study of three experimental variables in codedunits

Factors Responses

Run A B CTurbidity(NTU)

SettlingpH

Residualaluminium (mg/l)

1 2 0 0 1.17 7.00 0.182 0 0 0 0.71 6.73 0.07

3 1 1 1 0.63 6.70 0.314 0 0 2 0.13 6.79 0.095 1 1 1 1.07 6.64 0.266 0 2 0 1.80 6.33 0.147 2 0 0 1.26 6.58 0.17

8 1 1 1 1.03 6.68 0.07

9 0 0 2 0.72 6.67 0.08

10 1 1 1 0.00 6.71 0.1411 0 2 0 1.05 6.82 0.19

12 1 1 1 0.61 6.74 0.1313 0 0 0 0.64 6.75 0.08

14 0 0 0 1.00 6.77 0.10

15 1 1 1 0.87 6.88 0.1616 1 1 1 0.90 7.13 0.11was decided to be the optimum coagulation condition asits coagulated water had the third lowest turbidity and satis-ed other requirements. Although runs 4 and 10 producedeven lower turbidity coagulated water than run 12 and

met settling pH and residual aluminium conditions, they uti-lized higher alum dosages. The conditions for run 12 werealum dosage: 7 mg/l, initial pH: 6.9 and polymer dosage:

0.016 mg/l.In order to obtain the optimum coagulation conditions

through the optimization procedure in the Design-Expert

guide, the regression models formulated by the softwarerelating responses and factors in terms of coded factorsneed to be examined. Whenever necessary, statistically

insignicant terms were eliminated through backwardmethod and/or response transformation were carried out

2.33

.54

.80

; PRE

500 M. Zainal-Abideen et al. | Optimizing coagulation process through traditional & RSM jar test Water Science & Technology | 65.3 | 2012to form signicant models. The generated multipleregression equations are as follows:

Turbidity 1:00 0:14C 0:23C2 1

Settling pH 6:76 0:099A 0:063B 0:084C 2

1Residual Al

p 3:420:084A0:050B0:012C0:42AB

0:22BC0:40A20:36B2 3

The analysis of variance (ANOVA) for the regressionmodels (1), (2) and (3) are summarized in Table 5. A

model is signicant at the 95% condence level if theFisher F-test has a probability value (Prob> F) below 0.05.The lack of t (LOF) F-test describes the deviation ofactual points from the tted surface, relative to pure error(Anderson & Whitcomb ). A large value of Prob> Ffor LOF, possibly greater than 0.05, is preferred. A high R2

value is desirable and a reasonable agreement with adjustedR2 is crucial (Ghafari et al. ). Adequate precision (AP) isdened as a measure of the experimental signal to noise

ratio (Anderson & Whitcomb ); an AP that exceeds 4usually indicates that the model will give reasonable per-formance in prediction. The standard deviation (SD) andthe coefcient of variation (CV) are shown in Table 5.

PRESS, the prediction error sum of squares, is a measure

Table 5 | ANOVA results for response parameters in RSM jar test

Model LOFResponse F value Prob> F Prob> F R

Turbidity 3.82 0.0455 0.1164 0

Settling pH 5.63 0.0096 0.0050 0

Residual aluminium 6.03 0.0059 0.2995 0

LOF: lack of t; AP: adequate precision; SD: standard deviation; CV: coefcient of variationon how well the model for the experiment is likely to predictthe responses in a new experiment. The SD, CV and PRESS

values are preferred to be small (Montgomery ).The regression model (1) is a signicant model as its

Prob> F value is smaller than 0.05. In this particularwater sample, the second-order effect of polymer (C2) isthe only signicant model term. The function of polymeris to improve the effectiveness of coagulant in coagulationprocess. Long-chain charged synthetic and natural polymers

(polyelectrolytes) can act to destabilize colloids by forming abridge between one colloid and another. One charge site onthe long polymer can adsorb onto a site on one colloid,while the remainder of the polymer molecules extend intosolution. If the extended portion of the polymer becomes

attached to another colloid, then the two colloids are effec-tively tied together and form ocs (Sawyer et al. ). Theocs may then be easily removed through the occulation

process; hence reducing the turbidity of water.The Prob> F for LOF is large and this situation implies

that the LOF of the model is insignicant. Even though the

R2 value is relatively low, it is in good agreement withadjusted R2. It is worth pointing out that a good regressionmodel does not depend on a large value of R2 as R2 is notvery well suited to assess outcomes from planned exper-

iment (Myers & Montgomery ; Anderson &Whitcomb ). The AP, SD and PRESS for the regressionmodel (1) are satisfactory as the AP is greater than 4 and SD

as well as PRESS values are very small.The regression model (2) is also a signicant model with

a Prob> F value of 0.0096. The main effect of alum (A) andpolymer (C) dosage are the signicant model terms. Fromthe regression model, the addition of alum will reducethe pH. This may be explained by Al (III) ions hydrolysis

phenomenon to form soluble monomeric (Equation (4)) aswell as polymeric species, H ion and solid precipitateswhen alum is mixed with water (Jiang & Graham ).This will reduce the pH of treated water.

Al3 H2O! AlOH2 H 4

Adjusted R2 AP SD CV PRESS

76 0.2493 6.034 0.35 41.66 2.44

69 0.4499 8.040 0.13 1.92 0.44

85 0.6744 6.696 0.35 12.30 5.05

SS: prediction error sum of squares.The regression model (2) has a relatively good R2 value(0.5469) and is in good agreement with the adjusted R2. TheAP value was greater than 4 while the SD, CV and PRESS

values are small.A signicant model with Prob> F value of 0.0059 is pro-

duced to form regression model (3) when the response has

been transformed by inverse square root to provide abetter t. In this case, the two level interactions of alumdosage and initial pH (AB) as well as the second-ordereffect of alum dosage (A2) and initial pH (B2) are the

Traditional vs. RSM jar test

pH,

Figure 2 | Design-Expert plot; overlay plot for optimal region at polymer 0.004 mg/l.

501 M. Zainal-Abideen et al. | Optimizing coagulation process through traditional & RSM jar test Water Science & Technology | 65.3 | 2012signicant model terms. The Prob> F value of LOF is 0.2995and this nding indicates that the LOF is not signicant rela-

tive to the pure error. The R2 value is high (0.8085) and doesnot deviate far from the adjusted R2 value. The AP, SD, CVand PRESS values comply with the statistical conditions

listed previously. Diagnostic plots such as the predictedversus actual values (Figure 1) help us to judge if themodel is satisfactory. These plots indicate an adequate agree-

ment between real data and the outputs from the models.Desirable criteria were set up to obtain the optimum jar

test condition through RSM. The criteria were minimumdosages of alum and polymer; initial pH setting and settling

pH after coagulation were in the ranges of 6.98.1 and 6.57.5 respectively; turbidity must be minimum and below5 NTU and residual aluminium must be below 0.20 mg/l.

With multiple responses, the optimum condition is one at

Figure 1 | Design-expert plot; predicted vs. actual values plot for (a) turbidity, (b) settlingwhich all parameters simultaneously meet the said desirablecriteria. This result could be visualized graphically by super-

imposing the contours of the response surfaces of theregression models (1), (2) and (3) in an overlay plot. Graphi-cal optimization displays the area of feasible response values

in the factor space and the regions that do t the optimiz-ation criteria would be shaded (Ghafari et al. ). Theshaded area in Figure 2 shows the RSM optimum jar testcondition for the desirable criteria mentioned earlier.

One of the conditions in the region is displayed inTable 6. Experiments were conducted to determine whetherthe predicted results by the models are attainable. The exper-

iments were carried out in triplicate and the responsesexpressed in Table 6 were the mean of the three experimen-tal results. From these results, it is observed that the

responses predicted through the regression models andmeasured from the experiments were in close agreement.(c) 1=Residual aluminium

p of coagulated water sample through RSM jar test.The alum dose for optimum coagulation obtained throughthe traditional method was nearly double the one obtained

through the RSM jar test. For the polymer, its dosing inthe traditional jar test has always been set to be 0.02 mg/lregardless of the condition of the raw water. On the con-trary, for this particular raw water sample, it was observed

from the RSM experiment that it was not necessary to use0.02 mg/l of polymer. Instead, the optimum polymer dosesobtained from direct reading of RSM table and RSM optim-

ization were 0.016 and 0.004 mg/l, respectively.Nonetheless, the initial pH set for coagulation in the tra-

ditional jar test that produced coagulated water that satised

the WTP requirement was the same as the initial pH settingfor optimum coagulation acquired through RSM optimiz-ation. The pH setting obtained through direct reading of

RSM table was amongst the lowest. Although the water tur-bidity after applying the traditional method was lower than

clearly demonstrated through RSM.

Settling pH 7.00 7.21 0.02

502 M. Zainal-Abideen et al. | Optimizing coagulation process through traditional & RSM jar test Water Science & Technology | 65.3 | 2012RSM jar testing requires a greater number of runs com-

pared with the adjusted OFAT or traditional jar test. Shouldthe actual OFAT jar test is be implemented at Sri-GadingWTP, similar number of runs may have to be conducted asthe polymer dosing and the initial pH for coagulation

must also be varied. Nevertheless, the purpose of this inves-tigation is not to replace the traditional jar test by RSM but isthe RSM jar test, both methods gave water turbidity below1 NTU. The pH values after ocs settlement acquired

through both methods were lower than their initial pH set-tings and met the optimum condition. The residualaluminium concentrations from both traditional and RSM

jar tests were below 0.20 mg/l.

CONCLUSIONS

The quest for optimum coagulation operational pH, alum

and polymer dosages through RSM has been successful. Itproved that the RSM jar test generated lower optimumalum and polymer dosages than the traditional jar test and

was still able to produce comparable and acceptable qualityof coagulated water in terms of pH after ocs settlement, tur-bidity and residual aluminium. The interaction between

these factors and responses in coagulation process were

Residualaluminium (mg/l)

0.13 0.15 0.04

aThe measured values are expressed as means standard deviations of three indepen-dent experiments.Table 6 | Experimental and predicted values of the responses at the optimal levelspredicted by RSM

Optimal conditions Response Predicted Measureda

A 7 mg/l; B 7.6;C 0.004 mg/l

Turbidity(NTU)

0.64 0.83 0.20a part of an investigation to form an empirical relationship

between raw water quality parameters and the best coagu-lation conditions obtained through traditional and RSMmethods in term of pH and chemical dosing.

ACKNOWLEDGEMENTS

The authors thank Syarikat Air Johor (Holdings) Sdn. Bhd.for providing nancial support and giving permission to

work with Sri-Gading WTP in order to carry out this studysuccessfully.REFERENCES

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First received 8 November 2010; accepted in revised form 18 February 2011

503 M. Zainal-Abideen et al. | Optimizing coagulation process through traditional & RSM jar test Water Science & Technology | 65.3 | 2012

Optimizing the coagulation process in a drinking water treatment plant - comparison between traditional and statistical experimental design jar testsINTRODUCTIONMATERIALS AND METHODSSri-Gading WTPMaterialsExperimental setupTraditional jar testRSM jar test

RESULTS AND DISCUSSIONTraditional jar testRSM jar testTraditional vs. RSM jar test

CONCLUSIONSThe authors thank Syarikat Air Johor (Holdings) Sdn. Bhd. for providing financial support and giving permission to work with Sri-Gading WTP in order to carry out this study successfully.REFERENCES