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Optimal use of chlorine in water distribution networks based on specific locations

of booster chlorination: Analysing Conditions in Mexico.

Hernndez Cervantes, Daniel1; Mora Rodrguez, Jess2; Delgado Galvn, Xitlali2;

Ortz Medel, Josefina2; Jimnez Magaa, Martn Rubn3

1 Hydraulics Engineering Student. Universidad de Guanajuato. Av. Jurez No. 77,

Centro, 36000, Guanajuato, Mexico. 2 Geomatics and Hydraulics Engineering Department. Universidad de Guanajuato. Av.

Jurez No. 77, Centro, 36000, Guanajuato, Mexico. jesusmora@ugto.mx, 3 Hydraulics Department. Facultad de Estudios Superiores de Aragn, Universidad

Nacional Autnoma de Mxico. Av. Rancho Seco S/N, Colonia Impulsora,

Nezahualcyotl, Edo. de Mxico, 57130.

Abstract

Water distribution networks (WDN) could present problems of pathogen intrusion that

affect consumers health. One solution to diminish this risk is to add more disinfectant to the water at the Drinking Water Treatment Plant (DWTP). However, this increases

the cost of water treatment and may also cause the formation of trihalomethanes

(THMs). Mexico has the largest bottled water market in the world. Also, most houses

are built with individual storage containers due to intermittent service, which generates

a greater residence time of the water before consumption or use. This paper an

alternative to the Water Distribution Network Managers (WDNM) to guarantee the

minimum disinfection along the WDN and diminish the use of disinfectant at the

DWTP considering the conditions of consume and use of water in Mexico. The

proposal is a numerical routine based on Genetic Algorithms to obtain scenarios where

free chlorine maintains the minimum permissible concentration throughout the day. In

addition, the WDNM could optimize the cost of water production by controlling the

optimal use of disinfectant by proposing sites of booster stations of chlorine (BSC). The

results show that chlorine use could be reduced by 32%, therefore guaranteeing the

chlorine concentration limits along the WDN.

Keywords: Drinking water quality, extended period, free residual chlorine, genetic

algorithms.

Corresponding author: Mora Rodrguez, Jess. jesusmora@ugto.mx

INTRODUCTION

In recent years, a large number of Water Distribution Networks (WDN) have reached

their planned lifetime (Francisque et al., 2014; Lei J. and Sgrov, 1998). Pipes, tanks,

and accessories have suffered damage due to normal and abnormal operation. WDN are

vulnerable to pathogen intrusion, according to the type of operation and maintenance

(Mora et al., 2012; Le Chevallier et al., 2003). Specifically, pathogen microorganisms

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affect consumers health in the short term. The preservation of microbiological water quality in WDN is one of the most complex technological issues for water suppliers due

to the use of disinfectants, characteristics of the water, and conditions of the network.

Therefore, numerical quality models are necessary tools for operating and maintaining

water quality.

Optimal water quality related to microorganisms is achieved when the disinfection

process treats the water in the Drinking Water Treatment Plant (DWTP). Disinfectants

are mainly used to ensure the inactivation of microorganisms (Geldreich, 1996) that

could be present in the water from supply sources, and grow throughout the network

(Figure 1). The principal objective is to prevent gastrointestinal disease due to drinking

contaminated water, although the addition of chlorine may result in Disinfection By-

Products (DBPs). The DBPs are a consequence of the added chlorine reacting with

organic and/or inorganic substances in the bulk water (Sadiq and Rodrguez, 2004). In

the case of the chlorine, one of the DBPs formed are Trihalomethanes (THMs). Diverse

species of THMs have been linked to carcinogenic effects on human health (Chowdhury

et al., 2009). In many countries, the maximum accepted concentration of THMs varies

from 0.08 to 0.25 mg/L (Sadiq and Rodrguez, 2004).

Figure 1. Presence of microorganisms in pipes (Based on Knobelsdorf et al., 1997).

The World Health Organization recommends a minimum residual concentration of 0.5

mg/L of free chlorine over 30 minutes of contact time at a maximum pH of 8.0 for

terminal chlorination. Free chlorine residual concentration must be maintained

throughout WDN at a level between 0.2 to 0.5 mg/L at the points of delivery (WHO,

1997). In the case of Mexico, the MWDM, require that disinfection be maintained

according to the Official Mexican Standard (NOM-127-SSA1-1994 or NOM-127),

established by the Ministry of Health. According to NOM-127, the range of free

chlorine must be between 0.20 mg/L and 1.50 mg/L.

In Mexico, there are 2,457 municipalities and one Federal District, each one with a

separate WDNM. The Mexican Institute of Water Technology (IMTA, initials in

Spanish) implemented a system of management indicators of a WDNM (IMTA, 2014).

The indicators are register annually and there are records from 2002 to 2013. There are

two indicators related to intermittent water supply. The first one is whether the

consumers tap has continuous water supply. During the eleven years of implementation, 90 WDNM collected information on this indicator as an annual mean

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and reports that 41% of consumers taps have 100% continuous water supply. However, 8% of WDNM reported that there was a continuous water supply for less than 10% of

the year on average. Based on the yearly average, 73.5% of the consumers tap have continuous water supply. The second indicator is hours of service per day, based on

information from 60 mean annual WDNM. Where service is not continuous, the mean

number of hours of service per day is 10.6 hours. However, 45% of the WDNMs

studied had an average daily service between 12 and 23.46 hours. Whereas, 12% of

those studied had a daily average between 4 and 1.5 hours of water service.

The inconsistent operation of water services does not guarantee disinfection throughout

the networks. Therefore the WDNMs increase the amount of chlorine used in the

DWTP to maintain disinfection limits within those stipulated by NOM-127, with the

risk of producing THMs. Therefore, most city-dwellers in Mexico have turned to

consuming bottled water (Greene, 2014). In fact, the Mexican bottled water industry is

the largest in the world (Jaffee and Newman, 2013).

Another important consequence related to the inconsistent operation of the WDN is that

the majority of houses have an individual storage container (Omisca, 2011). The use of

the individual storage container is mainly due to intermittent service. However, in most

cases, new-builds include an individual storage container despite the existence of

continuous service. In fact, the majority of homes have at least one container, which has

consequences for the lifetime of free chlorine, and therefore drinking water quality.

Taking the conditions of consume and use of water in Mexico, this paper proposes

maintaining the minimum chlorine concentration by optimizing the amount of chlorine

used and thereby guaranteeing the reduction of problems of THMs generation and

gastrointestinal disease incidence. This study shows that the use of chlorine could

decrease up to 37%, and the disinfectant concentration remains more uniform along the

WDN during the 24 hours of water consumption. Ultimately the cost of producing

drinking water is reduced.

CHLORINATION IN WDN

The main disinfectants used in WDNs include free chlorine, chloramines, ozone,

chlorine dioxide and ultraviolet light (Propato et al., 2004). Free chlorine is one of the

most effective agents against inactive bacteria and other pathogens due to its residual

effect of disinfection along the entire WDN (Geldreich, 1996). In Mexico, free chlorine

is the most widely used disinfectant due to its effectiveness along the WDN

(CONAGUA, 2013). However, when chlorine gets in contact with water, it reacts in

different processes and chlorine concentration tends to decrease.

Decay mechanism of chlorine and booster chlorination stations

Chlorine concentration decreases as a function of the characteristics of microorganisms,

such as their state and their mixture with dissolved matter, besides other factors such as

temperature and pH (Geldreich, 1996). The Chlorine decay curve describes the

evolution of chlorine in contact with water (Figure 2). When chlorine comes into

contact with water, it generates a reaction with reducing compounds, these substances

can be dissolved or suspended. The compounds that act with chlorine are hydrogen

sulfide, manganese, iron, and nitrites (AEAAS, 1984). The additional chlorine begins to

react with organic matter and organic chlorine compounds are produced from this

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reaction. Organic chlorine does not have the ability to disinfect and generates a

characteristic odour and flavour. The chlorine continues to react with reducing

substances, organic matter, and ammonia. Finally, the additional chlorine will remain as

free chlorine available which is a very active disinfectant. After this point, all the

nitrogen compounds have been destroyed and, therefore, any further addition of

chlorine causes an increase in the level of free chlorine in the water (AEAAS, 1984).

Figure 2. Chlorine decay curve (AEAAS, 1984).

According to Castro (2003), loss of residual chlorine concentration throughout a WDN

is due to several separate mechanisms. Table 1 shows the diverse types of reactions and

some related reaction coefficients (Phillip, 2003; Al-Jasser, 2007). These values depend

on multiple variables, and they could vary according to the local conditions of every

study. Ozdemir and Erkan (2005) related the decay of chlorine to the lifetime of water

in the network, the quality of the treated water and the age of the pipes. The

effectiveness of disinfection and microorganism resistance depend on water pH, the

concentration of disinfectant, and the contact time.

Table 1. Range of chlorine reaction coefficients from diverse authors

Type of reaction Minimum values for

Reaction Coefficients

Maximum values for

Reaction Coefficients

By chlorine reaction in the

bulk water, bacteria, and other

microorganisms.

0.09 0.12 d-1 1.38 1.52 d-1

By chlorine reaction on the

pipe wall. 0.03 0.04 m/d 1.34 1.52 m/d

Alcocer et al. (2004) mentioned that the lowest concentrations could occur in zones with

low velocity and in storage tanks, but not necessarily in the farthest zones from the

DWTP. Therefore, chlorine decays once introduced into the WDN, and there is a risk

that the network could be unprotected in certain zones with the corresponding risk to

consumers health. Booster chlorination stations (BCS) are an alternative to reduce this risk.

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The BCS (Figure 3) are installed at critical locations (Parks and VanBriesen, 2009).

Specifically where the free chlorine concentration is below the minimum level

according to the standards (Islam et al., 2013). Using BCS, MWDS can guarantee

disinfection with the minimum concentration and a more uniform disinfectant along the

WDN (Boccelli et al., 1998).

Figure 3. Typical booster chlorination station (based on PAHO, 2004).

To reduce the risk of pathogen microorganisms along the WDN, we propose the

installation of BCS in strategic locations to maintain the minimum permissible chlorine

concentration. Besides reducing risk, the Genetic Algorithms (GA) model proposes an

optimal use of chlorine while taking into consideration the following conditions: a)

Avoiding higher concentrations, thereby reducing the possibility of generating THMs.

b) The GA must guarantee the optimal range from 0.20 to 0.50 mg/L of free residual

chlorine. c) Saving costs of drinking water production related to a minimum use of

disinfectant and proposing BCS along the WDN. d) Considering the individual storage

containers and many people in Mexico do not drink the WDN water, the chlorine

concentration may be kept closer to the lower permissible limit.

OPTIMAL BOOSTER DISINFECTION MODEL

In this paper, it is propose a numerical routine based on GA to obtain the optimal

quantity and locations of BCS, considering the minimum investment costs and reducing

the use of chlorine during the operation of the WDN, and maintaining the free chlorine

in the range from 0.20 to 0.5 mg/L (WHO, 1997). Every node of the network is

analysed the last 24 hours of consumption of 72 hours of simulation, in order to ensure

the efficient use of the disinfectant. The algorithm will establish the optimal scenario for

the efficient use of disinfectant considering the mentioned criteria.

Genetic Algorithms

GA are adaptive methods that can be used to solve specialized problems of search and

optimization (Beasley et al., 1993). The basic algorithm is comprised of the following

steps:

1. Randomly generate an initial population.

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2. Calculate the fitness of each individual.

3. Selection (sample) on the basis of individual aptitude.

4. Apply genetic operators (crossover and mutation) to generate the next

population.

5. Cycle over many generations until some condition is satisfied.

GA uses a direct analogy with natural selection (Holland, 1992). GA are applied to

populations of individuals. Each individual represents a feasible solution to a given

problem. Each individual obtains a score depending on the fitness of the solution for a

particular problem. In nature, the score of each individual is equivalent to the

effectiveness of an organism to compete for certain resources. The greater the fitness of

an individual, the more likely it will be selected to reproduce, crossing its genetic

material with another individual selected in the same way. This crossover will produce

new individuals, which share some of the characteristics of their parents. The lower the

fitness of an individual the less likely they are to be selected for reproduction and,

therefore its genetic material is not passed down to successive generations and then

disappears from the gene pool.

Using this method, it is produced a new population of possible solutions. This

population replaces the previous one, and the properties of this new generation must

contain a higher proportion of good features in comparison with the previous

population. If the GA has been well designed, the population will converge towards the

optimal solution for the problem.

In the numerical routine, the optimization increases with the number of generations.

When you have a large number of nodes, it tends to increase the number of individuals

to maintain the diversity of individuals and can perform searches on those who are

improving their fitness. Using GA significantly reduces the number of simulations to

find a better option in terms of limited use of chlorine.

Optimal locations of BCS by GA

This paper focuses on finding the minimum number of BCS necessary to maintain the

concentration of free chlorine, reducing the costs of production due to the disinfection,

maintaining water quality within the NORM-127 and considering the conditions of

consume and use of water in Mexico. Besides, human health must be guaranteed

meaning that the level of disinfection will never be under 0.20 mg/L and the

concentration near to the DWTP is going to maintain the concentration of free chlorine

around the value of 0.50 mg/L as proposed by the WHO in 1997.

The algorithm considers that every node of the WDN represents a BCS by providing a

value of additional supply concentration of chlorine. The concentration values provided

from the BCS are the variables for the GA. The simulation time depends on three

factors: a) the number of variables for each individual, b) the methods including on the

GA process: crossover, selection, mutation and recombination, and c) number of

generations to evaluate. In this case, eight values are proposed for the free chlorine

concentration between 0.2 and 1.5 mg/L (Table 2). The first value, zero, indicates that it

is not necessary to install a BCS at the corresponding node. A binary code is used to

represent the concentration chlorine values. For each of the eight different chlorine

booster supply values there is a corresponding binary code, which is three characters

long (Table 2).

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Table 2. Binary code for the values of chlorine booster supply.

Values of chlorine

booster supply

(mg/L)

Binary value

Values of

chlorine booster

supply

(mg/L)

Binary value

0.25 0000 0.70 1000

0.00 0001 0.00 1001

0.40 0010 0.80 1010

0.00 0011 0.00 1011

0.50 0100 1.00 1100

0.00 0101 0.00 1101

0.60 0110 1.20 1110

0.00 0111 1.50 1111

Fitness function

The fitness function is proposed to determine the effectiveness of the solutions

generated by the algorithm. A higher value of the fitness function represents the best

solution for an individual for the use of BCS. Three main aspects to obtain an optimal

solution are: a) maintain the free chlorine concentration in the range established by the

NOM-127 in all network nodes, b) minimize the number of BCS, as it implies a low

investment cost and, c) maintain the adequate concentration of chlorine in order to

obtain the values of free chlorine proposed along the entire network during the last 24

hours of simulation. According to these conditions, the fitness function is presented in

equation [1].

[1]

Where:

= Minimum chlorine concentration. = Maximum chlorine concentration. = Mean chlorine concentration for the node i. = Booster chlorine disinfection installation cost = Penalization cost due to the range concentration out of the NOM-

127 range cmin, cmax.

= Concentration of chlorine out of the range of the standard limits of the node i (cmin, cmax)

= Number of nodes of the network.

The value of the fitness function increases when the concentration at all the nodes

approaches the minimum concentration allowed by NOM-127 during the simulation. In

addition, the fitness function tends to decrease accordingly when: a) a large number of

BCS are proposed by an individual on the GA and, b) the concentrations in the nodes

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are out of range of the NOM-127. Finally, the standard deviation implemented in the

fitness function is focused on the mean concentrations of the nodes near to the

minimum permissible value of 0.2 mg/L.

APPLICATION OF THE NUMERICAL ROUTINE ON A WDN

The model network used in the simulations is an example network from the EPANET

program. The net3.net shown in Figure 4 was selected considering the complexity of its

structure and on the operation. The net3.net contains the following components

(Rossman 2000):

2 reservoirs

3 tanks

2 pumps

117 pipes

92 nodes (5 nodes with its ID for the discussion)

1 general demand pattern and other 4 to certain nodes

Figure 4. Network model net3.net

The optimization algorithm based GA was programmed in MATLAB. The algorithm

solves the hydraulic and quality WDN calling the EPANET software from MATLAB.

The hydraulic model was simulated with the equation of Hazen-Williams on the English

system of units. The roughness coefficients are from 110 to 199 for 0.2 to 2.5 metre

diameter pipes. The total length of the network is 65,748 metres. The total base demand

on the network is 0.192 m3/s. The analysis of the free chlorine was simulated over an

extended period of 72 hours with a quality time step of 5 minutes. Both model reactions,

bulk and wall, used in the simulation are first order.

Lake

River

Tank 1

Tank 2

Tank 3

101

123

131

219

243

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The added input values to simulate water quality were bulk and wall decay coefficients

and initial chlorine concentrations for the reservoirs. In relation to optimization of

disinfectant use, the scenarios proposed address the range of the chlorine reaction

coefficients (CRC) obtained from the literature. In order to visualize the state of the

network in diverse conditions of CRC, were propose aleatori values from table 1:

scenario A with the minimum CRC, scenario B with values arround 15% of the

maximun CRC and scenario C with values between 20 to 25% of the maximum CRC

reported on the literature (Table 3). Only 25% of the maximum values were applied

because simulations made with more CRC generates doses of chlorine above the NOM-

127 standards in a huge part of the network during the simulations. The initial chlorine

concentration was selected in every scenario according to the limits of NOM-127 for

chlorine in the WDN. The nodes near to the sources have a chlorine concentration of 1.5

mg/L for 13 hours in scenarios B and C, and 15 hours in scenario A. However, the

critical nodes have concentrations below 0.20 mg/L for 10 hours in all three scenarios.

Table 3. Scenarios Proposed for optimizing use of chlorine.

Source chlorine

concentrations Reaction coefficients

Scenario River

(mg/L)

Lake

(mg/L)

kb

(1/d)

kw

(m/d) Purpose

A 1.50 1.50 0.120 0.04 WDN with minimal (A)

medium (B) and critical (C)

chlorine reaction conditions.

The 3 scenarios with

necessary initial concentration

for regular operation and

compliance standards.

B 1.69 1.62 0.233 0.21

C 1.79 1.68 0.350 0.32

The extended period simulations have a total duration of 3 days, focusing on the

analysis of the nodes concentrations between 48 and 72 hours. The objective of

analysing only the last 24 hours is to observe the scenarios that have reached

equilibrium in terms of quality variables. Chlorine concentrations are also better

adjusted to cyclical behaviour when demand patterns are taken into consideration.

RESULTS AND DISCUSSION

The proposed GA to obtain the optimal scenarios were simulated between 1,200 and

2,500 individuals and stopped after no change in fitness was observed for minimum 25

generations (Figure 5). The best fitness functions of the three scenarios were obtained

from 50 to 100 generations.

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Figure 5. Evolution of GA on the numerical routine.

The optimized scenarios A, B, and C consider an initial concentration between 0.50

mg/L and 0.59mg/L from both sources of water (Table 4). With this optimized initial

condition, the numerical routine obtained the result of specific locations of BCS with

the doses shown in Table 4.

Table 4. Comparison between initial scenarios and optimized scenarios using BCS.

Total chlorine used on the optimized scenarios was reduced by 37.7%, 10.9% and 2.1%

for Scenarios A, B and C respectively. In the optimized scenarios, the chlorine

concentrations throughout the network are within the range recommended by the WHO

in 1997. The limits in all simulations result in a better control of the use of chlorine in

Scenarios

chlorine doses Location of booster chlorination stations Total

chlorine

used

(mg/L)

River

(mg/L)

Lake

(mg/L)

Node #

dose (mg/L)

A 1.50 1.50 ------------ 3.00

A

(optimized) 0.50 0.50

Node 20 Node 241 Tank 2 1.87

0.32 0.30 0.25

B 1.69 1.62 ------------ 3.31

B

(optimized) 0.56 0.53

Node 127 Node 211 Tank 1 Tank 2 2.95

0.56 0.60 0.25 0.45

C 1.79 1.68 ------------ 3.47

C

(optimized) 0.59 0.55

N 20 N 40 N 50 N 131 N 145 N 211 3.40

0.50 0.21 0.40 0.25 0.30 0.60

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the network, ensuring lower chlorine usage for water disinfection considering the

operating conditions and water uses in Mexico. On the Figure 6, it is show the three

initial scenarios (left hand) and the three optimized scenarios with the BCS (Right

hand). It were graphically 5 nodes during the 72 hours of simulation, the nearest nodes

of consume from the sources (nodes 101 and 123) and the nodes which have the low

values of free chlorine during the last 24 hours of simulation (nodes 131, 219 and 243).

On the graphics are remark the 24 hours of study. On the Initial scenarios the nodes near

to the sources have the maximum concentration according to the NOM-127 (1.5 mg/L)

and for the same hours the critical nodes have values under the NOM-127 (0.20mg/L).

On the optimized scenarios the 5 nodes have values between 0.5 and 0.20 mg/L during

the last 24 hours of simulation. Only on the scenario C, the node 243 have

concentrations under 0.20mg/L during 2 hours. However on the same scenario two of

the three critical nodes have concentration under the same value.

Figure 6. Range of chlorine in the critical nodes, before and after optimization.

The hour 50 of simulation is the one were the consume on the network is the maximum

(practically the double of the base demand), the configuration of chlorine for the three

scenarios along the networks is show on the Figure 7. The scenarios A, B and C are

compare with the optimized results considering the BCS. On the left hand are the initial

scenarios with range of chlorine from 0 to 1.80 mg/L. It is observe that the maximum

limit of the NOM-127 is achieve on the nodes of consume near to the sources. Even so,

on the south zone there are nodes with concentration under the NOM-127. On the right

hand, the scenarios optimized have range from 0 to 0.60mg/L. It is observe that the

values below 0.20mg/L disappear. In order to maintain the concentrations on the range

of the WHO (1997), there is a BCS with a dose of 0.60mg/L on the south of the

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scenarios B and C optimized. The free chlorine concentration maintains from 0.20 to

0.50mg/L.

Figure 7. Chlorine concentration with the maximum consume of simulation.

The hour 68 of simulation is the one with the minimum consume, practically the 64% of

the base demand. The configuration of chlorine along the network is show on the Figure

8. On the left hand, the range of chlorine concentration is 0 to 1.80mg/L for the initial

scenarios. The northeast zones have nodes with concentrations below 0.20mg/L. In this

zone it was observe that the nodes maintain that concentration during 10hrs. On the

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right hand the scenarios optimized with the BCS, have a range concentration from 0 to

0.60mg/L. The northeast zone diminished the nodes with concentrations below 0.20,

only the node 243 have a concentration around 0.10 to 0.20mg/L during 2 hours of

simulation on the scenarios B and C. The concentrations are on the range of 0.20 to 0.50

mg/L on the nodes of consume and only on the nodes proposed like CBS have values

over 0.50mg/L.

Figure 8. Chlorine concentration with the minimum consume of simulation.

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CONCLUSIONS

Maintaining chlorine concentrations within the standards for drinking water is a

complex concern. It requires optimal infrastructure of the WDN and operation by

MWDS, besides taking many water samples along the network. The operating

conditions of some WDN in Mexico do not guarantee safe drinking water at the

consumers taps and the consumers do not drink water from the WDN. Therefore, in this paper we propose a numerical routine based on GA to optimize the use of chlorine

by instigating BCS and saving on the cost of water production.

The validation of the GA in the network net3.net, with a diverse range of chlorine

reaction coefficients, shows that the doses could decrease reaching up to 37.7%

considering the minimum values of reaction coefficients describe on the literature. It is

observe a decrement on the savings of chlorine when the reactions coefficients increase.

The proposal of BCS with a minimum dosage led the MWDS to maintain chlorine

concentrations within the range of 0.50 to 0.20 mg/L, avoiding the excessive use of

chlorine. These concentrations are enough to eliminate pathogenic microorganisms. We

applied GA to find the optimum BCS locations, which we propose as a solution to have

the lowest concentration of residual chlorine in the network and at the same time

provide sufficient drinking water services to consumers that have individual storage

containers and in general do not drink water from the network.

The GA numerical routine with the fitness function proposed in this paper was validated

in order to obtain scenarios with more stable concentrations of chlorine over the 24 hour

analysis period. We proposed optimized scenarios for WDNs under specific operating

conditions and water uses in Mexico considering that this results generates a better

distribution of the disinfectant on the network and controlling the doses for the

operations and uses of water on that country.

ACKNOWLEDGEMENTS

The research was made with the financial support by the Universidad de Guanajuato, to

the Projects: DAIP: 20113.2013 and DAIP: 391/2014. The authors would like to thank

the Direction to Support Research and Postgraduate of the University of Guanajuato for

the English revision.

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