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Simulation and assessment of water supply network for specified districts at Najaf Governorate

  • Hassan Jaffar Al-Mousawey EMAIL logo and Basim Sh. Abed
Published/Copyright: February 24, 2023
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Journal of the Mechanical Behavior of Materials
From the journal Journal of the Mechanical Behavior of Materials Volume 32 Issue 1

Abstract

This study aims to simulate and assess the hydraulic characteristics and residual chlorine in the water supply network of a selected area in Al-Najaf City using WaterGEMS software. Field and laboratory work were conducted to measure the pressure heads and velocities, and water was sampled from different sites in the network and then tested to estimate chlorine residual. Records and field measurements were utilized to validate WaterGEMS software. Good agreement was obtained between the observed and predicted values of pressure with RMSE range between 0.09–0.17 and 0.08–0.09 for chlorine residual. The results of the analysis of water distribution systems (WDS) during maximum demand hours showed that the pumps unit capability cannot cover the high water demand during that time and resulted in a loss of pressure values, which were ranged between 0.2 and 2.1 bar. Moreover, the simulated results of the residual chlorine levels were within the permissible limits of 0.4–0.7 ppm, in different locations in the network. Providing good quality and adequate water supply is an important component for human life development. Modeling WDS is an efficient method of gaining a true understanding of the functioning of the network and determining the factors and conditions affecting the performance of the network.

Keywords: hydraulic analysis; WaterGEMS; water distribution; residual chlorine

1 Introduction

Water is one of the primary needs for a good life, every person deserves to have available clean drinking water. Providing good quality and adequate water supply is an important component for human life development. Providing an adequate supply of potable water ensures the well-being of the citizens and the optimum development of several industries [1,2].

One of the most significant sections of the city’s services systems is the water distribution system (WDS), which includes all of its components. It has become an important element in the establishment of good life [3]. However, the performance of these systems is sometimes overlooked until a major disruption or operational breakdown occurs. While failure events are unavoidable and often dramatic and costly, the inefficient operation of a WDS daily imposes significant economic, social, and environmental costs. In engineering, the behavior and control of any WDS, performance measurement is critical [4]. Throughout its entire life, a water supply system should be able to provide the appropriate amount of water for the expected loading circumstances while maintaining the desired residual pressures at all nodes at all times throughout the day [5]. Modeling WDS is used to gain a true understanding of the functioning of the network and to determine the factors and conditions affecting the performance of the network. Controlling the quantity of free residual chlorine in WDS is a critical issue for ensuring pathogen-free water consumption at the end-user tap. Modeling of water distribution networks is necessary to forecast the impact of physical and operational behaviors on hydraulic and water quality parameters [6]. WDS are typically designed and built to last for a long time, so variables that influence the system’s future performance must be considered. Population growth, the requirement for system expansion, pipe length, diameter, and pump capacity are only a few of these factors [7]. Many software is available for simulating distribution networks, and the most popular are Epanet, Loop, and WaterCAD. But WaterGEMS has the highest capabilities out of all the other software and was used in the current investigation [8].

Ekwule and Utsev studied the serviceability of the water distribution network of the federal university of agriculture in Makurdi using WaterCAD and WaterGEMS software, the study determined values of velocity, pressure, head loss, and flow rate. The study concluded that all nodes in the system had lower pressure values than the design pressure of 10 m, in addition, 15% of the network pipes exceeded the design velocity, and both the software can be used interchangeably since there were no statistical differences [9].

Agunwamba et al. carried out a study using WaterCAD and Epanet software and investigated the performance of the Wadata sub-zone water distribution network. The study found that the Wadata sub-water zone’s distribution system is insufficient under present demand. Research revealed that almost 12% of the velocities exceeded the adopted velocity, causing leaks and pipe breaks at various spots across the system. The study also concluded that Epanet software analysis produced higher values of pressure and velocity readings in roughly 60% of the cases tested [10].

Berhane and Aregaw constructed a hydraulic model for the water distribution in Wukro town, Ethiopia using WaterGEMS software. The highest pressure before optimization was 31.1 m and climbed to 38.1 m after optimization, while the lowest pressure during peak hour demand was 7.9 m and grew to 16 m after optimization. The study concluded that the WaterGEMS model is a potential solution for optimizing water distribution networks [11].

Kadhim et al. assessed the water distribution network of the Al-Karada area, Baghdad, Iraq. The study reviewed values of pressure and velocity assuming average daily demand of 350 liters/day per capita. The study concluded that the flow velocity is not excessive, and the pressures were within permissible ranges of 0.7–2.2 bar [12].

Beker and Kansal performed a study that aims to evaluate the hydraulic performance of the WDS in sections of Dire Dawa, Ethiopia (Zone-1), the study utilized WaterGEMS V8i software, the findings indicated that some nodes had higher pressure values, which might lead to water leaks in the system and the values of velocity, were below adequate limits. The study concluded that suitable actions, such as placing pressure control valves and altering pipe diameters in key sectors, can be undertaken to optimize the network hydraulic performance [13].

Mehta et al. using WaterGEMS V8i software evaluated the WDS in Punagam, Surat, in terms of pressure. The study concluded that pressure and velocity throughout the network are adequate and the system provides acceptable water to the network of the study area [14].

Mostafa et al. performed a water quality analysis on the 6th of October city using WaterCAD software and concluded that the values of residual chlorine among the network were above the lower limit; however, concentrations may fall below the allowable limits of 0.2 mg/L at the network ends on days where water age was high; this could increase the requirement for re-chlorinating stations along the system [15].

Al-Mamori and Al-Musawi conducted a study where EPANET software was used to model chlorine concentration at several network points in the water distribution network of Gukook city. Residual chlorine values ranged from 0.2 to 2 mg/L. The analysis concluded that chlorine concentration levels in the network were within the allowable standards [16].

The main purpose of this study is to assess the hydraulic performance of the water distribution network system of Al-Nidaa and Al-Milad Districts in Al-Najaf Governorate through field measurements and the use of WaterGEMS software. Moreover, it aims to study and analyze the chlorine decay in the distribution network.

2 Materials and methods

2.1 Study area

Al-Nidaa and Al-Milad districts are located in the northern section of Al-Najaf governorate as shown in Figure 1. Both districts are supplied by one network and have a combined surface area of 5.4 km2, population of 85,000 capita according to the 2021 local estimation of population, drinking water in Al-Najaf City is treated by the main water treatment project of Al-Najaf City. After treatment, water is pumped to the main pump station located approximately at the center of the city, from there, 7 mains distribute the water to the city, the network of the study area is supplied by a single main pipe of a 700 mm diameter, and two pumps pumping water to the specified network working alternatively for 8 h period. All the pipelines in the network are tested for 10 bar working pressure. For the current analysis, a water distribution network model was constructed that included main and secondary pipes, neglecting network laterals as shown in Figure 2. The total pipe length of the network is 42.1 km.

Figure 1 Area of study within Al-Najaf governorate.
Figure 1

Area of study within Al-Najaf governorate.

Figure 2 WDS of Al-Nidaa and Al-Milad districts.
Figure 2

WDS of Al-Nidaa and Al-Milad districts.

2.2 Field measurements and observation data

The data and plans for the network layout were gathered from the city authority of the water supply and other data regarding the pump supplying the network, and times of operation were obtained from the main pump station of Najaf City. Up to date plans and recent changes to the network were obtained from the department of water network maintenance. GIS plans were acquired from the GIS unit associated with the water department to accurately construct the model using WaterGEMS–ArcGIS integration. Pressure readings measurements were performed at the end-user taps in various locations in the network shown in Figure 3, using a glycerin-filled pressure gauge preceded by a valve used for air venting connected directly at the first 3/4-inch connection inside the household as shown in Figure 4; the measurements were performed at the end of the year 2021 during maximum demand hours. Water samples were collected from the network using clean sampling bottles for residual chlorine testing.

Figure 3 The locations of fieldwork measurements.
Figure 3

The locations of fieldwork measurements.

Figure 4 The field apparatus used for the pressure head measurements.
Figure 4

The field apparatus used for the pressure head measurements.

2.3 Determination of residual chorine coefficients

2.3.1 Bulk decay coefficient (K b)

Pipe features do not affect the bulk flow reaction coefficient. The chemical construction of water defines its properties. To determine its value, a laboratory test was conducted [15,17], the laboratory tests were conducted based on the following procedure:

  1. Collection of eight water samples from different sites in the network.

  2. Each water sample is then divided into 13 samples.

  3. Lovibond device was used to measure the residual chlorine.

The first sample was measured at time zero. Then, the other samples were measured successively at intervals of 1 h; the results were plotted against time as shown in Figure 5. This procedure was done to all eight samples of water to obtain the most consistent result.

Figure 5 First-order adjustment of the testing results.
Figure 5

First-order adjustment of the testing results.

Then, K b was calculated to be equal to −0.095/h, which equals 2.28/day and has a regression coefficient of (R 2 = 0.991).

2.3.2 Wall decay coefficient (K w)

The wall decay coefficient is determined by the state of the pipe wall and the lining material. It is influenced by the bulk flow interaction with the wall interface. The mass transfer coefficient, which is dependent on the monitored substance’s molecular diffusivity, affects the rate of bulk pipe flow. Chlorine diffusivity equals 1.44 × 10−9 m2/s in water temperature of 25°C. The network is made up of ductile iron pipes and PVC HDPE pipes, since the network is only 10 years old the value of K w for the PVC pipes is assumed to equal zero. The K w value for ductile iron pipes is assumed as K w = −4 mg/m2/day [18]. Then, following a trial-and-error process, the value of K w was modified until an acceptable value of fitness was reached. That value results in good agreement between the observed and simulated concentrations at different sampling sites.

2.4 Bentley WaterGEMS

Haestad Methods Inc., Cincinnati, Ohio, developed WaterCAD, a windows-based software. WaterCAD analyzes sets of equations that represent pressure and discharge in pipe networks using gradient techniques proposed by Todini and Pilati. The gradient technique makes use of a matrix formulation of network issues to fully utilize the computing capacity of modern computers [19]. WaterGEMS is the most recent version of the WaterCAD software, and it is a simple software for modeling or simulating WDS. Engineers and utilities can use WaterGEMS to assess, design, and enhance WDS operating in the matter of discharge, pressure head, constituent concentration analyses, and pump simulation, among other features. The Hazen–Williams head loss formula is being used in the current study to measure the amount of hydraulic head loss caused by friction with pipe walls. It is assumed that continuous pumping of water is supplying the distribution network to maintain the required amount of water and adequate pressure all over the distribution network. The attributes for the reservoir, pipes, junctions, valves, and pumps were accurately modeled, and the demand in the system was calculated on the basis of 350 L per capita per day. Then, the demand of each distribution network junctions based on the area served by the specific junction was estimated. Network information was entered including pipes diameters, lengths, diameters, roughness coefficients (major and minor losses coefficients), and pipe materials, as well as pump characteristics and valves. The network pipes have a total length of 42.1 km consisting of cement-lined ductile iron pipes with diameters of 700 and 600 mm and PVC Hdpe pipes with diameters of 225 and 160 mm. Hazen–Williams coefficient (C) was taken as 120 and 150 for the two types of pipes, respectively [4,20].

2.4.1 Model calibration

The calibration of a hydraulic model seeks to confirm the reliability of the model [21]. This step in the modeling process involves fine-tuning a model till it correctly matches field observations over a certain time period in such a way that will be used to anticipate system performance and assess alternative schemes. This means making minor changes to the input values to obtain output values that correctly represent the system [1,22]. A set of data was used to perform the calibration process of the distribution network.

Pressure heads values measured all over the water distribution network are used to calibrate the pressure head levels. A variety of variables could be responsible for the variance between simulated and field values. As a result, calibration may be achieved by only modifying internal pipe roughness values or nodal demand estimations until measured and simulated pressure head and flow values concur. This theory is based on the fact that pipe roughness coefficients and system demands are usually approximated, leaving room for inaccuracy, unlike pipe lengths and diameters that are measured directly. The calibration process was done during the maximum demand hours between 4–5 am. Root mean squared error was used in the comparison between the observed and the simulated results of pressure head values to show how close the two data sets were. Figure 6 shows the comparison between simulated and measured pressure values. On another side, the model was calibrated to acquire the approximate value for wall decay coefficient (K w), which was assumed to be equal to K w = −4 mg/m2/day (as was mentioned earlier), by trial and error, the value for K w that gave the most reasonable results that simulate the actual readings in the network was found to be K w = −2.3 mg/m2/day. The model calibration was conducted using the results of the constituent concentration tests of ten samples taken from different points in the network for modeling residual chlorine concentrations throughout the network during maximum demand hours. The simulated results were compared with the results of collected samples. Figure 7 illustrates the comparison between computed and field measured chlorine samples at maximum consumption hours.

Figure 6 Comparison between simulated and measured pressure heads along different junctions.
Figure 6

Comparison between simulated and measured pressure heads along different junctions.

Figure 7 Comparison between simulated and field-measured of the residual chlorine concentrations at different junctions.
Figure 7

Comparison between simulated and field-measured of the residual chlorine concentrations at different junctions.

2.4.2 Model verification

To ensure the accuracy of the simulation of the modeled distribution network, another set of field measurements were conducted to gather pressure head readings in multiple locations throughout the network using pressure gauges during the period of moderate water usage hours between that is between 10 and 11 am, and flowrate measurements were performed at the location of the pump station and some other locations using an ultrasound flowmeter, which would represent the water demand at the time of measurement. Moreover, residual chlorine concentrations were tested using the Lovibond device at multiple locations of the field measurements. All of these field measurements data were used to perform the verification process of hydraulic and residual chlorine simulation of the specified distribution network of the study area. Table 1 shows the comparison between field observations and simulated values, and the results of the verification process reveal a good agreement between these measurements due to insignificant difference and low value of RMSE of 0.164.

Table 1

Comparison between calculated and measured pressure heads at various nodes of WDS

Node ID Pressure calculated through simulation (bar) Pressure field measured (bar) RMSE
J-1697 1.96 1.9 0.164
J-1687 1.88 1.6
J-1695 1.868 1.65
J-1646 1.618 1.5
J-1699 1.426 1.2
J-1691 1.375 1.25
J-1701 1.341 1.2
J-1693 1.271 1.1
J-1689 1.225 1.25
J-1703 1.002 0.9

Table 2 illustrates the comparison between the observed and simulation values of the residual chlorine concentrations at different locations along the WDS; the results show that the difference between these values is insignificant and the simulation achieves close results to the observed values due to the small value of RMSE of 0.08. So, the hydraulic and residual chlorine simulation values can be confidently adopted to assess and analyze the WDS.

Table 2

Comparison between simulated and observed residual chlorine concentrations

Node ID Simulated residual chlorine concentrations (mg/L) Observed residual chlorine concentrations (mg/L) RMSE
J-1646 0.655 0.7 0.08
J-1697 0.633 0.65
J-1691 0.622 0.6
J-1695 0.614 0.55
J-1703 0.603 0.55
J-1687 0.593 0.65
J-1693 0.568 0.45
J-1689 0.565 0.5
J-1699 0.559 0.4
J-1701 0.539 0.45

3 Results and discussion

3.1 Pressure heads analysis

A hydraulic analysis (pressure heads analysis) of the distribution network was conducted using WaterGEMS software for the steady-state conditions during maximum water demand hours, during hours of moderate demands of water, and during the hours of low usage of water. Through the hours of maximum demand of water, it was found that during the operation of one pump, the water demand was exceeding the design operation capacity of the pump, mainly due to the increased water consumption and the continuous operation of domestic pumps inside households. This increase in demand causes the pump to reach the maximum discharge point, which results in a lower pumping head, most of the network pressure heads were below the acceptable values in multi-locations, especially at the end edges of the network, and the values of these heads are ranged between 0.2 and 1.4 bar, regardless some junctions in the network had acceptable values of water pressure of 1.5–2.1 bar. While the analysis of the simulation results shows that the pressure heads for hours of the moderate water demand are within allowable limits 1.5–2.6 bar, except for the far points in the network having values lower than 1.4 bar. For the third flow condition that represents the low water demand, the pressure heads acceded the designed level due to low usage of water and due to the domestic household tanks being full at this period thus resulting in decreased operation time of household pumps, pressure values were in the range of 1.6–3.1. From field observation, the majority of households have domestic pumps, which results in a drop in water pressure heads in the network, especially as the distance from the pumping station increases. A contour profile in Figures 8 and 9 shows the different ranges of pressures throughout the network for different flow conditions.

Figure 8 Contour map of pressure heads during maximum water demand.
Figure 8

Contour map of pressure heads during maximum water demand.

Figure 9 Contour map of the pressure heads during low water demand.
Figure 9

Contour map of the pressure heads during low water demand.

A proposed solution for the low pressures in the current network is to replace the current pump with a newer model (available locally) that has a pump capacity of 3,000 m3/h, which is double the current pump capacity of 1,500 m3/h. This improved pump will be able to provide the required pressure at the near-end junctions of the network. The simulation result of the proposed solution is shown below in Figure 10 where the lowest pressure value was 1.2 bar for maximum demand conditions.

Figure 10 Contour map of pressure heads with improved pump at maximum demand conditions.
Figure 10

Contour map of pressure heads with improved pump at maximum demand conditions.

3.2 Velocity analysis

Another hydraulic analysis (flow velocities analysis) was implemented for the distribution network system. The simulation analysis shows that during maximum water demand, the flow velocities ranged between 0.4 and 1.44 m/s for ductile iron pipes of 700, 600, and 500 mm. The velocities in the PVC-HDPE pipes of 225 mm ranged between 0.25 and 2.27 m/s, while the velocity values for 160 mm PVC-HDPE pipes ranged between 0.15 and 3.85 m/s. Noting that in the system consisting of a total of 115 pipes, only 6 pipes had velocities below 0.2 m/s. The simulation results revealed that the velocity in the main pipes is within the allowable limits at all times, while the velocity in the lateral pipes are exceeding the allowable limit in the low demand periods and decrease to be below the permissible limit in the dead flow junctions. The trend of velocity distribution for the lateral pipes was found in the distributary pipes due to the same reasons. Figures 11 and 12 show the velocity distribution for the distribution system for high and low water demand periods.

Figure 11 The ranges of velocities in the WDS at maximum water demand.
Figure 11

The ranges of velocities in the WDS at maximum water demand.

Figure 12 The ranges of velocities in the WDS at low water demand.
Figure 12

The ranges of velocities in the WDS at low water demand.

3.3 Residual chlorine analysis

Results for constituent simulation analysis showed that the values for residual chlorine concentrations were within the permissible limits of 0.4–0.7 ppm throughout the network during both testing periods. So, the results assure that the chlorine decay was within the standard limits and the tap water in the study area is safe for municipal uses. Figure 13 shows the distribution of the concentration of residual chlorine in the WDS.

Figure 13 Ranges of residual chlorine calculated in the WDS.
Figure 13

Ranges of residual chlorine calculated in the WDS.

4 Conclusions

The simulation of the network using WaterGEMS software was successful in terms of hydraulic performance and chlorine concentrations, where RMSE values ranged between 0.09–0.17 and 0.08–0.09 for the two simulations, respectively. The simulation showed that there is a shortage in water pressure values nearly at the end edges of the distribution network where the pressure heads drop below 1 bar especially during maximum demand hours due to the current one pump operation condition, while during low demand hours the network performance was adequate and water pressure values were between 1.6 and 3.1 bar, and the flow velocity in the different pipes of the distribution network is within the acceptable range. Although velocity increases or decreases due to demand change. The study concluded that the pump supplying the distribution network is of inadequate specifications, as well as it is incapable of running within its optimum range thus a drop in pressure heads occurs to provide the flow demanded. The simulation also showed that the values of residual chlorine were between the range of 0.4–0.7 ppm during both testing periods, which is within the allowable limits.

  1. Funding information: The authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Conflict of interest: The authors state no conflict of interest.

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Received: 2022-04-13
Revised: 2022-05-18
Accepted: 2022-05-23
Published Online: 2023-02-24

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  51. Sustainable high-strength lightweight concrete with pumice stone and sugar molasses
  52. Transient response and performance of prestressed concrete deep T-beams with large web openings under impact loading
  53. Shear transfer strength estimation of concrete elements using generalized artificial neural network models
  54. Simulation and assessment of water supply network for specified districts at Najaf Governorate
  55. Comparison between cement and chemically improved sandy soil by column models using low-pressure injection laboratory setup
  56. Alteration of physicochemical properties of tap water passing through different intensities of magnetic field
  57. Numerical analysis of reinforced concrete beams subjected to impact loads
  58. The peristaltic flow for Carreau fluid through an elastic channel
  59. Efficiency of CFRP torsional strengthening technique for L-shaped spandrel reinforced concrete beams
  60. Numerical modeling of connected piled raft foundation under seismic loading in layered soils
  61. Predicting the performance of retaining structure under seismic loads by PLAXIS software
  62. Effect of surcharge load location on the behavior of cantilever retaining wall
  63. Shear strength behavior of organic soils treated with fly ash and fly ash-based geopolymer
  64. Dynamic response of a two-story steel structure subjected to earthquake excitation by using deterministic and nondeterministic approaches
  65. Nonlinear-finite-element analysis of reactive powder concrete columns subjected to eccentric compressive load
  66. An experimental study of the effect of lateral static load on cyclic response of pile group in sandy soil
https://doi.org/10.1515/jmbm-2022-0233
Keywords for this article
hydraulic analysis; WaterGEMS; water distribution; residual chlorine
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BY 4.0

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  52. Transient response and performance of prestressed concrete deep T-beams with large web openings under impact loading
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