Distribution Network Assessment Using EPANET

Distribution Network Assessment using EPANETfor Intermittent and Continuous Water Supply

Sanjeeb Mohapatra & Aabha Sargaonkar &Pawan Kumar Labhasetwar

Received: 2 July 2013 /Accepted: 29 May 2014 /Published online: 29 June 2014# Springer Science+Business Media Dordrecht 2014

Abstract Drawbacks of intermittent water supply system and inability to shift to continuoussupply mode is the main challenge in developing countries. The suitability of the infrastructurelaid over past two to three decades to meet the 24/7 demand of todays population is the issuefor many water mangers. The present study addresses this issue using EPANET software for apilot study area in Nagpur city, India.

GIS maps, field survey data, remote sensing data and in-situ measurements of pressure andwater quality are used in model simulation study. Total 96 artificial reservoirs are inserted intothe network which replicate the end-user practices of excess water withdrawal. Reservoirs areassumed connected to damand nodes with equivalent diameter pipes for intermittent supplysimulation. For continuous supply, demand multipliers are derived using Monte Carlo simu-lation. Bulk decay coefficient 0.17 day1 for residual chlorine is used in water qualitysimulation. Simulation scenario of intermittency indicates existing network is not suitable tomaintain desired headloss, and pressure in most of the pipes is very low (

1 Introduction

Performance of water distribution system (WDS) to consumers satisfaction is the major challengefor water authorities all over the world. In developing countries, urban water supplies are mostlyintermittent, typically ranging from 24 h in a day (Ingeduld, et al., 2006). In India, water supplyrate varies from 16 to 300 l per capita per day (lpcd) depending on the locality and the economicstatus (Singh and Turkiya 2012). Due to Pressure/head dependent hydraulics rather thanDemand driven, consumers in low pressure zone opt for illegal tapping and storage in under-ground pits to fulfill their demands (Ingeduld, et al., 2006). This often exerts excess demand andincreases recontamination probability at the consumer end (Thompson, et al., 2000; Karadirek,et al., 2012). Intermittent water supplies also pose high water quality risk due to contaminantingress into the pipes ofWDS during non-supply hours (Mermin, et al., 1999; Kelkar, et al., 2002).

The hydraulic integrity problems of intermittent supplies are not only of concern to the localsbut also toWHO - reporting about 1.6 million children, less than 5 years of age die every year dueto contaminated water supply (WHO 2003, UNICEF/WHO, 2006). Water Boards and public atlarge, therefore, demand continuous water supply to ensure water safety. However, it is imperativethat the proposal of shifting from intermittent to continuous mode has many challenges - mainlyabout the suitability of the infrastructure laid over past two to three decades to meet the 24/7demand of todays population i.e. hydraulics concern (pressure and flow) and water quality concern(residual chlorine and water age).

Water quality problems within WDS include interactions between the pipe wall and thewater, and reactions within the bulk water itself (Brown, et al., 2011). Depending on the flowrate, finished water quality, pipe material and deposited material (i.e., sand, iron, manganese),the transformations of various constituents proceed to a greater or lesser extent. In addition,corrosion of pipes and chlorine disinfection may cause unpleasant tastes and odors due tochlorine residual (Al-Jasser, 2007; Mutoti, et al., 2007). Formation of disinfection by-productssuch as trihalomethanes (THMs) pose major quality concern from public health point of view(Brown, et al., 2011). Therefore, management of chlorine residual and water age from finishedwater in a water treatment plant (WTP) to consumers taps is critical to balance to provideprotection from pathogens and customer satisfaction.

To address the issues of water safety, many cities in India are implementing Water Safety Plan(WSP) through Ministry of Urban Development prior to implementation of 24/7 water supply(Sargaonkar, et al., 2010; Sargaonkar et al. 2013). Present study is undertaken as a part of proposedproject for quantification of exisiting intermittent and proposed continuous supply scenario for apilot area in Nagpur city, India. Mathematical model EPANET (Rossman, 2004) is setup for hydraulics and water quality simulation. To aid in decision making, results areevaluated for suitability of the network to implement 24/7. The paper presents the details ofmodel simulation study where results indentify the areas with low pressure, less residualchlorine and high water age. Model calibration, field verification and predictions are discussed.Lastly, a possibility of technological improvement such as use of sensors with renewable energyresource for on-line monitoring of water quality in WDS is also explored.

2 Materials and Methods

Figure 1 presents the location map of pilot study area, Untkhana. Approximately 4,630consumers in the pilot area receive water supply for about 1 to 2 h a day (from 6:00 a.m. to7:30 a.m.). The water distribution network in 0.5 km2 area is quite old, some of the pipes arelaid since 1980. There are 172 pipes with 146 nodes. Water is supplied from Elevated Service

3746 S. Mohapatra et al.

Reservoir (ESR). Pipe material is of Mild Steel (MS), Cast Iron (CI), Galvanized Iron (GI) andDuctile Iron (DI) and pipe diameter range from 75 to 700 mm. Feeder main is 6.4 km in lengthwith 700 mm diameter, while distribution main is 11.44 km in length with 400 mm diameter.Service connections are 5m in length and 15 mm diameter.

For model application, the water supply network map, node map and elevation map weregenerated in ArcGIS10.1 and georeferenced using WGS84 UTM-Zone 43 projection. Hydro-Gen extension to ArcView was used to create the .inp format files for pipe map, route map andelevation polygon map shape files to set up hydraulic model EPANET and HydroGen(Hydrogen 2000, Corte and Sorensen 2013).

2.1 Continuous Supply Simulation Model

EPANET, in general operates under Demand-driven assumption - wherein, the nodaldemands are assigned fixed values and the problem is to find pipe flows and nodal pressuresthat are hydraulically consistent with the nodal demands (Rossman, 2004). For this, hydraulichead loss within the pipes is calculated using the Hazen-Williams formula

hL 4:727C1:852d4:871q1:852L 1where, hL is the head loss (ft), C is the hazen-williams roughness coefficient, d is the pipediameter (ft), q is the flow rate (cfs), L is the pipe length (ft).

2.1.1 Thiessen Polygons

To estimate nodal demands, Thiessen polygons were generated considering node shape file asinput in ArcMap 10.1 (Fig. 1). Number of houses in each polygon were counted manually byoverlay analysis of Thiessen polygon map and IKONOS ortho-rectified image (1 m resolution)of the study area. Nodal demands are then estimated considering average 5 persons in each

Fig. 1 Pilot study area, Untkahana and house locations inside thiessen polygons

Distribution Network Assessment using EPANET 3747

house (Global Scintific, 2011) and the water supply rate of 155 lpcd (135 lpcd+15%unaccounted flow) as per the water supply standards in India (CPHEEO, 1999).

2.1.2 Diurnal Pattern

The diurnal water demand, an important input for EPANET is estimated using Monte CarloSimulation (MCS) technique (Computational Science Education Project, 1995) as detailed below:

A time series distribution of water demand is generated based on socioeconomic data in India(Jethoo andPoonia 2011). The distribution gives amount ofwater consumed for day-to-day activitiesviz. drinking, bathing, sanitation and hygiene (cloth washing, toilet flushing, utensils and housecleaning), gardening and cooking in different time series 1, 2,,6 which indicate different time slotsin a day i.e. 06, 610,, 2024 h, where maximum of slot is included in the corresponding slot(Table 1). In order to model the randomness in hourly water demand, 10,000 realizations of randomnumbers are generated in right open set [0, 0.5) and a sample of size N=24 is drawn. The samplerepresents a set of multipliers to estimate the hourly water demand pattern. For example, for drinkingpurpose first six randomnumbers aremultipliedwith (1 lit) demand in first time series (1 to 6 h), nextfour random numbers are multiplied with (1 lit) demand in second time series (7 to 10 h), and so on.Sum of hourly demand for drinking purpose is ensured to be approximately 5 l. The same set ofrandom numbers is used to generate hourly water demand pattern for other activities. A set ofrandom numbers is considered acceptable for which per capita per day water demand is 135 l as perBureau of Indian Standards (BIS:1172, 1993). The number of samples to be drawn depends uponthe convergence achieved for total demand and thereby for BIS standards.

It is important to ensure that the random pattern is autocorrelated/or follows long-term memoryprocess. This is verified through rescaled range (R/S) analysis that relates to Hurst exponent (H),also known as the index of dependence or the index of long-range dependence. The parameter(0

reservoir is the sum of the elevation at a particular node and the pressure head. Terminal nodesi.e. the nodes connected to the pipe outside the area are replaced by artificial reservoirs withsuitable heads. Accordingly, the pressure driven demand in intermittent simulation is definedas (Ingeduld, et al., 2006)

Qi QmaxHiHminHmaxHmin

r2

where, Hi is the pressure at a node i, Hmin,i is the minimum required pressure at a node i, Hmax,iis the maximum pressure at a node i (defined as pressure above the estate height), Qi is thedemand at a node i, Qmax,i is the maximum demand at a node i.

Accordingly, pressure dependent flow conditions are:

Intermittent flow conditions Scenario in the present case

Pressure Condition Demand (flow) Pressure Condition Demand (flow)

HiH defined,i Qi=f(H)

H min,i

2.3 Water Quality Simulation Model

Bulk and wall decay of chlorine in EPANET is wriiten as total chlorine demand (Warton, et al.,2006)

C C0ekt 4

k kb kwk fR kw k f 5

where, Co and C are the initial and final concentrations of chlorine, k is the overall chlorinedecay coefficient (d1), kb is the chlorine bulk decay coefficient (d

1), kw is the chlorine walldecay coefficient (m d1), kf is the mass transfer coefficient between the bulk flow and pipewall (m d1), and R is the hydraulic radius of the pipe (m).

2.4 Model Set up and Parameter Estimation

For model set up, .inp format files were imported in EPANET and edited for deletion ofduplicate nodes, addition of artificial reservoirs, defining ESR as source tank, ESR andreservoir levels and model parameters. The baseline data with respect to pipe age, materialand diameter obtained from water authority was used to assign suitable pipe roughness for theworst case scenario with reference to literature (Walski, 2003, Tamminen, et al., 2008).

Laboratory experiments were carried out to determine the chlorine bulk decay constant (kb).As per the methods of APHA, et al. (2001) and Mutoti, et al. (2007) 4500-Cl G, 8L sample oftreated water supplied to the pilot study area was used to dose with sodium hypochlorite (t=2min) followed by ammonium chloride addition at 3:1 mass ratio of chlorine to nitrogen. The

Fig. 2 Model set up for intermittent simulation with artificial reservoir


experiment to determine all combined chlorine measurements (details not given) usingspectrophotometer reported as total chlorine was repeated several times and the experimentaldata which showed good statistical correlation coefficient (0.995) was considered. Theestimated decay coefficient of 0.17 d1 was used in model simulation. The wall decaycoefficient in the range 5 to 23 d1 reported in the literature was used in the model settingand calibrated during subsequent simulations (Brown, et al., 2011).

Pipe roughness and leakage coefficient were the calibration parameters for hydraulic model.To exactly locate the leaking pipes and corresponding nodes in the study area, visible leakswereidentified during field survey with hand held GPS. For model set up, an estimate of approxi-mately 4.6 lps leakage (as reported by water authority), was equally distributed at the leakagepoints, defining emitter coefficient of 0.6. pH, temperature, pressure, flowrate and residualchlorine concentration at the consumer-end were measured in-situ for model calibration.

For continuous water supply simulation, the treatment plant was represented by an artificialreservoir supplying water to the ESR during 6 PM to 9 AM. This was defined by formulatingsimple control based statements. For intermittent simulation initial pipe charging process wasneglected and model simulations for the specific hour were obtained. Similarly, graduation ofequivalent pipe size connecting distribution network and artificial storage reservoir is ignoreddue to the complexity involved during model handling. For water quality simulations,completely mixed tank with initial chlorine concentration of 1 ppm was considered. The timesteps used in simulation are 5 min for hydraulics and 1 s for water quality. The total durationfor intermittent and extended period simulations, (both hydraulics and water quality), weredecided during model calibration. The results of hydraulic and water quality simulation forintermittent and continuous water supply, and water age analysis for continuous water supplyare discussed below.

3 Results and Discussion

Figure 3 presents the hourly water demand pattern estimated using MCS. Rescaled rangeanalysis indicated that the Hurst exponent for different activities ranges from 0.592 to 0.868and for total demand 0.974 (Fig. 3). This confirms that the estimated daily demand pattern hasfractal characteristics (Zhi-Guang and FaC Ren-Qiang 2009).

During model calibration for intermittent hydraulics, worst case values of Hazen Williamssroughness coefficient were considered initially due to old pipe network in the pilot area, whichimproved significantly during subsequent simulations. The calibrated roughness values fordifferent pipe materials are presented in Table 2. Comparison of simulated pressure and flowrate values with the observed pressure and flowrate values recorded at strategic locations in thestudy area show correlation coefficient 0.976 and 0.978, respectively. Similarly, the correlationcoefficient between observed and simulated residual chlorine level at eight locations in thestudy area was 0.85 (Fig. 4).

3.1 Hydraulics of Intermittent and Continuous Water Supply

During intermittent supply simulation, the flow velocities in the transmission main (pipe id 80)and distribution mains (pipe id 100) are 1.57 and 3.61 m/sec, respectively, while duringcontinuous supply, the respective peak hour velocities are lesser by 8.9% and 8.5%. Thisindicates pressure dependent higher flow rate in both the pipes. This is likely to be the causefor high friction loss leading to more maintenance or wear-tear. Similarly, the unit headlossvalues in these pipes for intermittent supply are 3.25 and 28.52 m/km, respectively, while


during peak hour continuous supply, the respective values are lesser by 16 and 12.8%. In boththe supply conditions, the unit head loss in the distribution mains is much higher than therecommended value of 4m/km (CPHEEO, 1999). In addition, the unit head loss in other 15%pipes in the network (with diameter ranging from 100 to 150 mm), also exceeds therecommended value during intermittent supply. This has resulted in frequent replacement ofnetwork pipes since last 30 years, exerting recurring economic burden on water board.

Pressure condition in the network during intermittent supply is less than 1 m at 39 nodes outof 146 (Figs.5a to c). The houses connected to these nodes hardly receive any water asobserved during field survey. This is the major cause for water theft through illegal pumping.In areas where the pressure condition is satisfied (i.e. pressure>1m), simulation indicatescontinuous flow of water to the artificial reservoirs and hence water demand is likely to be highdue to no fixed demand pattern.

If water supply is made continuous within the same network, then majority of the areawould receive water with pressure less than 7 m during peak demand hour. In the North-Eastof pilot area (away from ESR), pressure would be relatively higher than the central part(Fig.5c), mainly due to simple network, low demand and uniform pipe diameters. Frequencyanalysis indicates pressure would be less than 7 m at 45% nodes during peak hours and at 30%nodes during lean hours. Low pressure would prevail mostly in the central part of the network.

Fig. 3 Demand multiplier and diurnal pattern of water demand for various activities in a day with Hurstexponent

Table 2 Pipe materials and Hazen Williams roughness coefficient

Pipe Material Year of Installation Roughness Before Calibration Roughness After Calibration

CI 1980 75 122

CI 2003 to 2011 130 141

DI 2003 to 2011 140 140

GI 2003 120 120

MS 1980 90 122


3.2 Water Quality Simulation

For intermittent as well as continuous supply, residual chlorine concentration in the networkremains below WHO recommended standard of 0.2 ppm at almost all the nodes (Fig.6a), exceptfor the pipes replaced during 2003 to 2011 where it ranges from 0.2 to 0.5 ppm (Fig.6b). Nodesnearer to ESR i.e. 122, 123 and 152, show residual chlorine concentration greater than 0.2ppm, dueto immediate withdrawal of water from 400mm diameter pipe and less chlorine decay in largerdiameter pipes (Fig.6b and c). Low concentrations are attributed to high velocities at peak hourswhich help efficient transport towards the pipe wall (Powell, et al., 2000) and CI pipes (around 152pipes) in the network that react readily with chlorine. Approximately, 98.83 and 98.62% chlorinedecay occurs in the pipe wall during intermittent and continuous water supply, respectively. Similarfindings are reported in studies for CI pipes (Hallam, et al., 2002; Tamminen, et al., 2008, Ramos,et al., 2010). Low residual chlorine adds to microbial contamination in the network.

3.3 Water Age Analysis

For continuous supplymode, water age in the networkwould be less than 48 h except at dead ends,where it is less than 3 days. At dead ends residual chlorine decays due to aging effect, indicatingdeterioration of water quality (Figs. 6 & 7), as reported in the literaute (AWWA, AWWARF 1992).

Although the intermittent flow velocity ranges from 0 to 3.56 m/sec, the dominant velocityat consumer end is less than the self-cleansing velocity 0.6 m/sec (CPHEEO, 1999). Numberof pipes experiencing such low velocity are 114. This can contribute to deposition of soilparticles into the pipes due to inefficient self-cleansing velocity. In pressureized system, thiscondition becomes more acute due to accumulation of contaminants at dead ends.

3.4 Scope for Further Studies/Temperature Measurement

In reality, the temperature in the underground pipes tends to be constant relative to airtemperature. The temperature difference between the two might range for several degrees inhot climatic regions. Thus, thermoelectric generator which works on Seebeck effect is asuitable option to harvest energy to power the sensors used in monitoring WDS. Thispossibility was studied by measuring the water temperature in WDS of the pilot area and air

Fig. 4 Comparison of observed and simulated pressure and residual chlorine at selected nodes


temperature data collected from the Weather Spark (Vector Magic) during the hot summermonths of the city i.e. March, April and May, 2013 (Vector & Weather Spark beta, 2013). Thethermoelectric power is evaluated considering the water-air temperature difference and theparameters of the commercial Bi2Te3, thermoelectric device of 40404.2 mm with a matrixof 127 thermoelectric couples (N) inside (Niu, et al., 2009).

Considering, the Seebeck coefficient of each thermoelectric couple =0.0002 V/K, thetotal Seebeck coefficient of the device is S=2N i.e. 0.05 V/K and the internal resistance (R)as 2 (ohm). The output power for difference in temperature (t) is determined according toequation (Ye and Soga 2012)

P UR R

2R U

2

4R S

2T2

4R6

where, U is the Seebeck voltage.

Fig. 5 Pressure distribution (a) intermittent simulation, (b) continuous simulation-peak hour (c) continuoussimulation - lean period


The evaluated power which is directly proportional to temperature deifference varies from2.8 to 118 mW (Fig. 8) and varies for various months in the order as March < April < May.Since the sensors require 10 mW range of power, the extra output can be stored to power thesensors for on-line monitoring of WDS during non harvesting hours.

4 Conclusion and Recommendation

Present study describes the application of EPANET to intermittent supply system usingartificial reservoir approach. Study reveals that presently distribution network in the studyarea is not suitable to maintain the desired head loss especially, in the mains. It is necessary toinstall another pipe in parallel or replace the existing mains with larger diameter pipe to reducefrequent maintenance of the network. Low pressure, high water age, and intermittent supplyare the main concerns in the network. Thus, methodology is useful to evaluate the suitability of

Fig. 6 Spatial distribution of chlorine (a) intermittent simulation (b) continuous simulation -peak hour (c)continuous simulation - lean period


network to implement 24/7. However, it is difficult to implement artificial reservoir concept forlarge network.

The study presents an approach to hourly demand pattern estimation based on MCS. Thehourly demand shows persistent behavior (0.5

In the present scenario, improving residual chlorine concentration at consumer end throughchlorine boosting at critical locations is necessary to reduce water quality problems and healthimpacts in the study area. This particular information is very essential for local waterauthorities.

Acknowledgment The authors are thankful to The Director, CSIR-NEERI for consistent support and encour-agement to undertake the work. Authors are also thankful to Dr. R.A. Sohony and Er S. R. Watpade for theirsupport and guidance during the work. The assistance received from Mr. Swapnil Kamble is acknowledged withthanks.

Annexure I

R/S Range analysis

1. Time series

Let X=X1,X2,X3,.....,Xn, where n=24 be the full time series of hourly water demand for aparticular activity (say drinking).

Divide this into partial time series of length 12, 6, and 3 h and calculate mean and standarddeviation for each series as:

mt 1n ni1Xi

S t 1

nni1 Ximt 2

r9>=>; A 1

where, n=24 for full and n=12,6,3 for partial series and t=1 to 4

2. Mean-deviates

Deviation of hourly demand from mean value is computed for each of the full and partialseries as :

Yi;t Xi;nmt; A 2

where Xi,n represents Xi belonging to full (n=24) or partial time series n=12,6,3 withcorresponding mean mt t=1 to 4

Cumulative deviate series is computed as:

Zi;t ni1Yi;t for n 24; 12; 6; 3 and t 1; 2; 3; 4 A 3

In this case, total number of series (full and partial) are four (for one activity drinking), sowe get 4 cumulative deviate series.

3. Rescaled range estimate

Maximum and minimum values of each cumulative deviate series are estimatedZt,max=max(Zi,t) and Zt,min=min(Zi,t) to compute range as

R t Zt;maxZt;min;where t 1; 2; 3; 4 A 4


Finally, rescaled range is calculated as R t S t for all the four series. A power law function is

fitted to the logarithm of E R n S n h i

and Log(n) as :

ER n S n

CnH where; n 24; 12; 6; 3 A 5

Slope of the straight line gives the Hurst exponent (H). The complete procedure forcalculation of R/S range and fitting the power law model as a function of Log n is performedfor each activity and for total water demand data.

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Fifth international conference on natural computation IEEE


Distribution Network Assessment using EPANET for Intermittent and Continuous Water SupplyAbstractIntroductionMaterials and MethodsContinuous Supply Simulation ModelThiessen PolygonsDiurnal Pattern

Intermittent Supply Simulation ModelEquivalent Pipe Diameter

Water Quality Simulation ModelModel Set up and Parameter Estimation

Results and DiscussionHydraulics of Intermittent and Continuous Water SupplyWater Quality SimulationWater Age AnalysisScope for Further Studies/Temperature Measurement

Conclusion and RecommendationAnnexure IR/S Range analysis

References

Distribution Network Assessment Using EPANET

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