Application of EPANET for the Determination of Chlorine Dose

8/16/2019 Application of EPANET for the Determination of Chlorine Dose

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Application of EPANET for the determination of chlorine doseand prediction of THMs in a water distribution system

1, 1 1 2Jae Chan Ahn, *Su Won Lee, Kevin Y. Choi and Ja Yong Koo

1Waterworks Research Institute

Seoul Metropolitan GovernmentSeoul 143-820, Korea

2Department of Environmental Engineering

University of SeoulSeoul 130-743, Korea

Key Words: Chlorine residual, chlorine decay, EPANET, water distribution, modelling

*Corresponding author

Email: [email protected]

ABSTRACT

INTRODUCTION

The purpose of this study is to predict the level of chlorine residual and trihalomethanes (THMs)in a drinking water distribution system and to help operators to determine chlorine dose in a drinkingwater treatment plant (WTP) using EPANET 2.0. Water quality modelling was conducted by chlorine

bulk decay and THM formation from bottle tests. Chlorine decay tests for the finished water of theWTP were performed on a monthly basis for one year. As the result, a simple equation for the targetchlorine concentration in the finished water was derived by a multiple regression method in relationto initial chlorine concentrations, temperatures, total organic carbon and chlorine decay coefficients.The chlorine concentration after it leaves a WTP, before entering its distribution networks, is criticalin maintaining chlorine residual levels throughout the system. It is apparent that the chlorine concen-

-1tration of the finished water be maintained to be flexible to keep it at over 0.1 mg L throughout thesystem because chlorine consumption is different according to the season, flow and pipe networks.These chlorine values of the finished water are the target chlorine concentrations for determining the

chlorine dosage to the filtered water in a WTP. As the results of this study using a multiple regressionmodel for chlorine decay and EPANET, the target chlorine concentrations of the finished water were-1 -1 -1

set to 0.5 mg L in spring and fall, 0.55 mg L in summer and 0.45 mg L in winter. These values areslightly lower than the existing target values.

Water treatment has contributed to the preventionof waterborne disease and to safeguard human healthin drinking water. Chlorine disinfection is one of thekey factors in drinking water treatment processes.However chlorine disinfection may cause unpleasanttastes and odours due to the chlorine residual, corro-

sion of pipes, the formation of disinfection by-productssuch as trihalomethanes (THMs), among others.Therefore, management of the chlorine residual fromfinished water in a water treatment plant (WTP) toconsumers taps is critical to balance customer satis-faction and protection from pathogens. Understandingthe factors contributing to the loss of chlorine and theformation of disinfection by-products is a requisite tomaintain chlorine residual levels in distributionsystems.

Chlorine residual levels with a range of 0.1 to 0.4

-1mg L set by Seoul city as the government guideline indistribution systems are to meet the minimum level ofchlorine residual levels throughout the entire waterdistribution systems and to alleviate customer com-

plaints related to the odour of the disinfectant.Flow in distribution system affects water quality in

general and chlorine residual in particular. The conceptof water quality modelling in distribution system

networks was based on steady-state formulations in theearly 1980s, and then dynamic water quality modelswere developed in the mid-1980s. There are now manymodelling software packages available for hydraulicand water quality network analyses: EPANET, AQUIS,H2ONET, InfoWorks WS, MikeNet, Pipe2000,WaterGEM/WaterCAD and so on [1].

EPANET was developed in 1993 with the func-tions of modelling chlorine decay and THM formation[2]. Hydraulic modelling should be first developed for

predicting chlorine residual levels within a water

Sustain. Environ. Res., 22(1), 31-38 (2012) 31


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distribution system using EPANET. The water qualitymodel then predicts the chlorine residual in a distribu-tion system by inputting chlorine k and k in EPANET. b wThe chlorine concentration after the WTP decreases gradually due to many factors as the finished watergoes through the pipe networks. This phase of chlorineconsumption could be mainly divided by chlorine bulkdecay and wall decay. The bulk decay is due to chlo-rine consumption with organic and inorganic matters,and the wall decay is due to reactions with pipe mate-rials, biofilms on inner pipe walls and so on. The chlo-rine bulk decay rate can be obtained by experimentsand the wall decay rate can be also estimated by com-

paring the results from a simulated network modelwith the results measured in a distribution system.Kinetic models describing the decay of chlorine inEPANET have been developed, tested and evaluatedusing data collected in field sampling studies [3-6].

Chlorine disinfectant reacts with natural organicmatter present in raw waters, and generates by-prod-ucts including THMs. As THMs are potentially carcin-ogenic, there has been the establishment of maximumadmissible levels [7]. Several studies developing pre-dictive models of THM formation have been per-formed for a better understanding of THM formationand to help manage different strategies in distributionsystems. The formation of THMs is affected by variouswater quality parameters: organic matter, chlorinedose, pH, temperature, reaction time, bromide levelsand so on. Multiple regression analysis and artificialneural networks approaches have been used to developthe THM formation model with water quality param-eters [8-12].

It is important to understand hydraulic behaviourto investigate water quality variations in a distributionsystem. Clark et al. [13] predicted THMs by using anetwork model in a distribution system. THMs wereassumed to be conservative tracers for studying theeffects of hydraulic behaviour on water quality.Vasconcelos et al. [14] developed a kinetic model ofTHM formation by using nonlinear least squares re-gression. It was a first order growth model with alimiting concentration to be applied to EPANET.

The purpose of this study is to predict chlorineresidual and THM formation in a drinking water distri-

bution system and to help operators to determine thechlorine dose in a drinking WTP using EPANET 2.0.

MATERIALS AND METHODS

Water quality modeling was conducted by chlorine bulk decay and THM formation from the bottle testsunder controlled conditions [15]. Chlorine decay testsfor the finished water were performed monthly for oneyear. Samples for the chlorine bulk decay tests weretaken from the storage tank just before entering itsdistribution system. The 250 mL amber bottles screw-

capped with PTFE-lined septa were used for the tests.

The THM formation tests were simultaneously per-formed for the same sample when the chlorine bulktests were conducted.

Chlorine concentrations were measured by a pock-TM

et colorimeter (Pocket Colorimeter II, Hach, Love-land, CO). As the result, a simple equation for thetarget chlorine concentration in finished water wasderived by a multiple regression method in relation toinitial chlorine concentrations, temperatures, the totalorganic carbon (TOC) level and the bulk decay rate.THM concentrations were also predicted by EPANETin the distribution system. The THM concentrationswere measured by a purge and trap concentrator, aGC/MS, and TOC concentrations by a TOC analyzer(Sievers 820, GE Analytical Instruments, Boulder,CO).

RESULTS AND DISCUSSION

1. Hydraulic Modelling

6 3 -1 A WTP with a flow of 0.22 x 10 m d was usedfor this study. The WTP treats source water by con-ventional processes including prechlorination, coagu-lation/flocculation, sedimentation, rapid sand filtrationand post chlorination. The distribution system has twoservice reservoirs and five pumping stations. The dis-tribution system consists of two zones: a UM zone anda CD zone. The finished water is supplied to the UMzone by gravity, but to the CD zone, it is supplied half

by gravity and half by pumping because it is partially

in a hilly area.Customers water usage, pipe diameters, the ele-

vation and other related data were input to perform hy-draulic network modelling with EPANET 2.0. Thenetwork model consisted of 6,573 pipes and 4,522

junctions. The network model was simulated with a 24h demand pattern for an extended period simulation(EPS). The EPS is a tool for the simulation of distribu-tion system behaviour under time-varying demand andoperational conditions [1].

There was the smallest demand at 4 a.m., and thelargest demand was at 10 a.m. in the CD zone (Fig. 1).Pressure measurement has been used as one of the

hydraulic calibration methods [1]. Figure 2 shows themeasured and the predicted values of the pressures attwenty sampling locations throughout the study area.The average error value of the two was 33 kPa with asimilar pattern. Vasconcelos et al. [14] conductedhydraulic calibration by comparing the observed andthe predicted pressure by the EPANET model in astudy area. As the average error of the six samplinglocations was 25 kPa, it was smaller than that in thisstudy.

2. Modelling of Chlorine Residual

There have been several studies describing bulk

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decay in drinking water. Wable et al. [16] and Zhanget al. [17] found that chlorine consumption in treatedwater followed the equation of a first order reaction.

Vasconcelos et al. [14] implemented bottle tests for 11different waters and evaluated the performance of four

thkinetic models: a first order model, an n order model,a limited first order model and a parallel first ordermodel that Hass and Karra [18] applied to wastewatereffluents as follows:

Fig. 1. Demand Multiplier in the study area.

Fig. 2. Comparison of predicted and observed values.

(1)

(2)

(3)

(4)

thii) n order model

iii) limited first order model

iv) parallel first order model

-1where k is the chlorine bulk decay coefficient (d ), t is bthe residence time in the pipe (d), C is the initial chlo-0

-1rine residual (mg L ), C is the chlorine residual at time

n th -1 n-1t, n 1, k is the n order bulk decay constant (d mg b

2-n * -1

L ), C is the limiting chlorine concentration (mg L ),

i) first order model

-1k is the bulk decay constant for fast reactions (d ), b,fast

-1k is the bulk decay constant for slow reactions (d ), b,slowz is the ratio of fast to slow reactions (dimensionless).

The kinetic models were compared with the resid-ual sum of squared errors between the predicted and

observed values, and the adjusted coefficient of deter-2 thmination (R ). The n order model was the best fit forhalf of the cases while the parallel first order modelwas the best fit for the other half. Although the firstorder model was not best for these waters, the differ-ences between the model fits were very small.Vasconcelos et al. [6] applied the first order model tostudy water quality behaviour in water distributionsystems using EPANET. Powell et al. [19] examinedthe performance of six different kinetic models: a firstorder model, a second order model with respect tochlorine only, a second order model with respect to

thchlorine and another reactant, an n order model, a

limited first order model, and a parallel first ordermodel. The kinetic models were compared regarding2

the R values and average errors between the modelledresults and the observed results. The authors concludedthat it is generally reasonable to assume a first ordermodel for network modelling purposes.

Rossman et al. [4] and Vasconcelos et al. [6] sug-gested Eqs. 5 and 6 for a first order reaction for mod-elling chlorine residuals in distribution systems. Thereactions of chlorine consumption are divided by the

bulk decay and wall decay for application to waterquality modelling in distribution systems. While the k bvalues were determined by bottle tests based on a labo-

ratory, the k values were adjusted over a range ofwvalues, and the simulated EPANET results were com-

pared to the observed data at the sampling locations ofthe study area. The first order wall decay was charac-terized by a rate coefficient with units of length divid-ed by time, which was equivalent to a ratio of the mass

per unit volume to the mass per unit wall surface area per unit time. The relevant equations are

(5)

(6)

-1where k is the overall chlorine decay coefficient (d ),

-1k is the chlorine wall decay coefficient (m d ), k isw f the mass transfer coefficient between the bulk flow

-1and pipe wall (m d ), and R is the hydraulic radius ofHthe pipeline (m).

Chlorine bulk decay is affected by the initial chlo-rine concentration, TOC, and temperature in treatedwater [14,20-24]. k values are inversely proportional bto the initial chlorine concentration if the other waterquality parameters are fixed. However, it increasedwith the TOC and temperature. Vasconcelos et al. [14]reported Eq. 7 as a function of the TOC and tempera-

ture.

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Powell et al. [21] added a term for the initial chlo-rine concentration to Eq. 7, and extended it to Eq. 8.Al-Omari et al. [22] showed a logarithmic relationship

between the chlorine k and each water quality param- beter of the initial chlorine concentrations, TOC andtemperature through a study based on laboratory ex-

periments. The authors modified Eqs. 7 and 8, and2

suggested Eq. 9 with an R value of 0.99, obtaining theconstants by multiple regression analysis. The equa-tions are:

(7)

(8)

(9)

where the constants p and f are derived by optimisationto minimise the sum of the squared errors between the

predicted and observed k values. T is the temperature b-1

(°C), E is the activation energy (J mol ), and R is the-1 -1

ideal gas constant (8.31 J mol °C ).In this study, experiments were carried out to ob-

tain the k at the water temperatures when the samples bwere taken from the effluent just before entering intothe distribution system. Thirteen bulk tests were con-ducted on a monthly basis. As the effluent from theWTP took about 2 d to reach the entire supply area, the

bulk tests lasted over 50 h. The first order decay reac-tion model was applied to this study because the curve

for the bulk tests was good fit, and it is easy to handlethe first order model in EPANET [19]. The k values b

-1 2ranged from 0.18 to 0.41 d with an average R valueof 0.95, as shown in Fig. 3 and Table 1.

Vasconcelos et al. [6] reported that the k values b-1 2

ranged from a low of 0.082 d with an R of 0.85 to a-1 2

high of 17.7 d with an R of 0.99. Powell et al. [19]observed a wide range of values for k with an average b

2R of 0.95.

Water quality varies with temperature, rainfall,

Fig. 3. An example of chlorine bulk decay tests.

Table 1. Water quality and chlorine bulk decay

Year

2006

2007

Jun.

Jul.

Aug.

Sep.

Oct.

Dec.

Jan.

Feb.

Feb.

Apr.

Apr.

May

Jun.

Water Temp. (°C)

20

20

23

21

17

8

4

5

5

12

12

20

23

Initial chlorine,-1C (mg L )0

0.64

0.77

0.7

0.69

0.67

0.63

0.58

0.49

0.49

0.57

0.52

0.52

0.6

TOC-1 (mg L )

1.1

0.9

0.9

1

0.9

1

0.9

1.2

1.2

1.1

1.1

1.1

0.8

7

6.7

6.8

7.1

7

7.1

7.2

7.3

7.3

7.3

7.3

7

7.1

Conductivity-1(ìS cm )

147

146

120

142

153

164

143

168

168

159

159

161

159

Chlorine bulk-1decay, k (d ) b

0.3

0.2

0.23

0.25

0.19

0.19

0.18

0.24

0.25

0.28

0.28

0.41

0.27

0.98

0.94

0.96

0.96

0.95

0.95

0.92

0.94

0.92

0.96

0.87

0.99

0.98

to UM zone

to CD zone

to UM zone

to CD zone

to UM zone

to UM zone

to UM zone

Remarks

effluent to

UM zone

pH 2R Month

contaminants and so on, and it is also natural that thechlorine k depends on the water quality parameters. It bneeds to consider the variations of water quality pa-rameters for modelling chlorine residuals in distribu-tion systems.

Multiple regression analysis was performed to de-

velop a mathematical model of chlorine bulk decay.The dependent variables were the initial chlorine con-centration, temperature and TOC (Eq. 10). The inde-

pendent and dependent variables were first formed intonatural logarithms, and then the coefficients of thedependent variables were identified by multiple re-gression analysis. Figure 4 showed a good agreementfor k between the predicted and the observed values b

2with an R of 0.92.

(10)

Fig. 4. Predicted and observed values of k .b



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Vasconcelos et al. [6] tested and evaluated thekinetic rate equations of chlorine decay through fieldsampling studies using EPANET. The authors per-formed a study to find out the chlorine kinetics of bulkand wall decay in five water distribution systems withsampling locations from 8 to 31. The results of thestudy indicated that chlorine kinetics can be effectivelycharacterized as a combination of first order decay inthe bulk liquid and first order or zero order decay reac-tions at the pipe wall.

The k values were input and the k values were b wthen estimated through chlorine calibration that mini-mises the difference between the observed and the pre-dictive values. The k values ranged from 0.0 to 1.5 mw

-1d according to chlorine demand by the pipe wall.

Ahn et al. [24] conducted a study on the identifica-tion of k and k for chlorine residual management in a b wdistribution system using a network model. The par-

allel first order model was applied to the field study:fast reaction for the WTP to large service reservoirs,and slow reaction for the large service reservoirs to thedead-end area. The k values by the first order modelwwere obtained on a bimonthly basis by chlorine cali-

bration. The wall decay of ductile cast iron pipes(DCIP) lined with cement mortar was small at 0.02

-1 -1and 0.05 m d in March, but was larger at 0.2 m d inJuly. The wall decay of unlined gray cast iron in the

-1study area varied from 0.1 to 0.4 m d in November,

-1and from 0.2 to 0.7 m d in July.

Table 2 shows the input values for the simulationof chlorine using EPANET throughout the distribution

networks. The “initial chlorine” is the chlorine con-centration in the effluent of the WTP. When the initial

-1chlorine residuals were 0.63 mg L in the effluent for

-1the UM zone and 0.65 mg L for the CD zone with a

-1water temperature of 23 °C and a TOC of 1.0 mg L ,the chlorine k values calculated by Eq. 10 were 0.29 b

-1 -1d for the UM zone, and 0.27 d for the CD zone. Thechlorine k values were introduced from the previouswstudy by Ahn et al. [24] and were adjusted as shown inTable 3.

Table 2. Input values for chlorine simulation usingEPANET

Inputvalues

Initial chlorine,-1

C (mg L )0

Chlorine bulk-1

decay, k (d ) b

Chlorine wall-1

decay, k (m d )w

UM zone

0.63

CD zone

0.65

UM zone

0.29

CD zone

0.27

*DCIP

0.2

*CIP

0.3

PE, Copper, Steel

0.2

*DCIP: ductile cast iron pipes lined with cement mortar; CIP: gray cast iron pipes rehabilitated with epoxy resin; PE: polyethylene

Table 3. Input factors for the simulation of chlorine concentration

-1Effluent s chlorine conc. (mg L )

-1Bulk decay, k (d ) b

Wall decay, k w-1

(m d )

Spring and fall

(15 °C)

0.45

0.38

0.10

0.20

0.10

Summer

(25 °C)

0.50

0.40

0.20

0.30

0.20

Winter

(5 °C)

0.40

0.30

0.02

0.10

0.02

DCIPCIP

PE, Copper, Steel

The water quality model was calibrated by comparing the predicted values with the values of the chlo-rine residual measured at the nineteen sampling loca-tions in the field. As shown in Fig. 2, the absolute error

-1was 0.04 mg L . In the study of Vasconcelos et al. [6],the models showed the absolute errors in the range of

-10.05 to 0.15 mg L compared with the predicted andthe observed chlorine concentrations.

Figure 5a showed the distribution of chlorine con-centrations at all junctions in the distribution system.When the chlorine concentration in the effluent of the

-1 -1WTP was 0.63 mg L , junctions with 0.5 mg L ormore were 31% of the total, and those with more than

-10.4 mg L were 91% of the total. However, more than90% of the junctions in this study did not meet the

-1range of 0.1 to 0.4 mg L in chlorine residual levels.The range of chlorine concentration from 0.1 to 0.4

-1mg L has been set by Seoul city as the guideline for

chlorine residual levels in distribution systems toreduce customer complaints about the odour of thedisinfectant.

Fig. 5. Percentage of junctions according to chlorine andTHMs concentrations.

3. Modelling of THMs

THM formation is usually affected by the sourcewater quality, water temperature, TOC, residence timeand so on. It is high in summer according to the in-crease of chlorine consumption and temperature al-though other water quality parameters have no changes[8]. Ahn et al. [24] showed that THMs formed in labtests were similar to those in a distribution system.

THM formation was observed for the effluent ofthe WTP when chlorine bulk test was simultaneouslyconducted in August for the prediction of the level ofTHMs in the distribution system. THMs concentration

-1

in the effluent of the WTP was 42 ìg L at a water


100

80

60

40

20

0

100

80

60

40

20

0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0 10 20 30 40 50 60 70


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temperature of 23 °C.

A first-order growth reaction for THM formationwas developed by a nonlinear least squares regressionmethod in Eq. 11 [14], and the THM formation coeffi-cient was applied to EAPNET for the prediction ofTHMs in this study. The equation is

(11)

-1where k' is the THM formation coefficient (d ), t is theresidence time in the pipe (d), C is the initial THMs0

-1concentration (ìg L ), and C is the ultimate forma-max

-1tion potential (ìg L ).

The first-order growth coefficient and the ultimateformation potential obtained by nonlinear least squares

-1 -1regression were 0.18 d and 43.5 ìg L , respectively(Fig. 6). These values were applied to EPANET for the

prediction of THMs in the distribution networks.

Fig. 6. Comparison of predicted and observed THMsconcentration.

THMs

43

46

49

52

ìg L-1

Fig. 7. Contour plot of THMs in the distribution.

The result of the simulation with EPANET showed-1

that the concentration of 42 ìg L of THMs in the ef--1

fluent was increased to 52 ìg L at the location with-1

the longest residence time. The 10 ìg L of differencein the THM concentration was due to the residencetime in the distribution system. Figures 5b and 7 de-scribed THM concentrations at junctions in the distri-

bution system. The THM concentration at 27% of junc--1 -1

tions was 45 ìg L or less, and 99% below 50 ìg L .Most THMs were formed within the WTP before

being pumped to the distribution system, for the treatedwater had a long residence time for about 13 h in theclear well. On the other hand, its formation was notmuch in the distribution system with the short traveltime of about 2 d or less.

4. Target Chlorine Concentration of the EffluentAccording to Season

Chlorine concentration of the effluent of a WTP before entering to its distribution networks is a critical point to maintain chlorine residual levels throughoutthe distribution system. It is natural that chlorine con-centration of the effluent be changed to keep over 0.1

-1mg L of chlorine residual throughout the system, be-cause chlorine consumption is different according tothe season in bulk flow and pipe networks. Thesevalues of the effluent are the target chlorine concentra-tions to determine the chlorine dosage to the filteredwater in a WTP.

As shown in Fig. 5a, it was necessary to reducethe chlorine concentration in the distribution system bylowering the effluent s target chlorine concentration.The target concentrations of chlorine residual were seton a seasonal basis using EPANET.

The water demands in spring and fall, summer andwinter in the network model were 220,440, 234,860

3 -1and 206,020 m d , respectively. Chlorine k and k b w

values were calculated by Eq. 10 with the effluent schlorine concentration, water temperature and TOC.-1

The TOC concentration was assumed to 1 mg L atthis time. Chlorine k values were introduced from thew

previous study by Ahn et al. [24].As the results of simulations with a decrease in the

effluent s chlorine concentration little by little, the ef--1

fluent s chlorine concentrations were set at 0.45 mg L-1

in the spring and fall, 0.50 mg L in the summer and-1

0.40 mg L in the winter (Table 3). However withregard to the range of fluctuation in the chlorine dosingsystem, the target chlorine concentrations of the ef-

-1fluent were set to 0.5 mg L in the spring and fall, 0.55

-1 -1

mg L in the summer and 0.45 mg L in the winter(Table 4, Fig. 8). This study found that the 0.05-0.15

-1mg L concentration of chlorine can be reduced com-

pared with the existing target values using the multipleregression model for chlorine decay and the networkmodel simulations.

Table 4. Target chlorine concentrations of the effluent ona seasonal basis

Effluentchlorine

-1

conc. (mg L )

Existing target values

Result of this study

Spring

(15 °C)

0.60 ± 0.05

0.50 ± 0.05

Summer

(25 °C)

0.70 ± 0.05

0.55 ± 0.05

Fall

(15 °C)

0.60 ± 0.05

0.50 ± 0.05

Winter

(5 °C)

0.50 ± 0.05

0.45 ± 0.05



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(a) spring and fall

Chlorine

0.1

0.2

0.3

0.4

mg L-1

(b) summer

(c) winter

Chlorine

0.1

0.2

0.3

0.4

mg L-1

Chlorine

0.1

0.2

0.3

0.4

mg L-1

Fig. 8. Chlorine residual in the distribution system on aseasonal basis.

CONCLUSIONS

Water quality modelling was conducted by chlo-rine bulk decay and THM formation from the bottletests. As the result, a simple equation for chlorine dosewas derived by the multiple regression method inrelation to the initial chlorine concentrations, tempera-tures, TOC and k . b The chlorine concentration of the effluent in aWTP before entering into its distribution networks is a

critical point to maintain the chlorine residual levels

throughout the system. It is natural that chlorine con-centration of the effluent be changed to keep over 0.1

-1mg L of chlorine residual concentration throughoutthe system, because chlorine consumption is differentaccording to the season in bulk flow and pipe net-works. These effluent values are the target chlorineconcentrations for determining the chlorine dosage tothe filtered water in a WTP. As the results of the studyusing the multiple regression model for chlorine decayand EPANET, the target effluent chlorine concentra-

-1tions were set at 0.5 mg L in the spring and fall, 0.55

-1 -1mg L in the summer and 0.45 mg L in the winter.

-1This study found that the 0.05-0.15 mg L concentra-tion of chlorine can be reduced compared with theexisting target values using the multiple regressionmodel for chlorine decay and the network modelsimulations.

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Discussions of this paper may appear in the discus-sion section of a future issue. All discussions should

be submitted to the Editor-in-Chief within six monthsof publication.

Manuscript Received: November 5, 2009 Revision Received:

and Accepted:February 4, 2010

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Application of EPANET for the Determination of Chlorine Dose

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