Design and Analysis of a Smart Water Distribution Network System in Jaipur, Rajasthan

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Design and Analysis of a Smart Water Distribution Network System in Jaipur, Rajasthan

Jagrat Jaggi1

1 MBA in Symbiosis Institute of Management Studies, Khadki, Pune

Hari Kaushik N2

2 PGADM in Real Estate and Construction Management, Symbiosis, Pune

Abstract:- The primary goal of this research paper is to study and design requirements for a water distribution system using EPANET to allow a fair supply of water to customers at adequate pressure head to the required place in Jaipur, Rajasthan’s Central Government colony. The design limitations include a water supply of at least 135 lpcd with adequate pressure head and budget constraint. To improvise the design service life span and distribution network reliability considerations, consideration has been given to high-grade cast iron pipe. To satisfy the demand requirement, the distribution network must be intended to decrease hydraulic losses. Different distribution network models have been simulated and analysed to meet the minimum 12 m pressure head and velocity in the range of 06 to 23 m / s and in some cases to meet the peak water demand hour for water supply. In addition, entropy tests of these tubes were carried out to determine the optimum positioning of sensors in the network.

Keywords: Water distribution system, EPANET, network, constraints

1. INTRODUCTION

    1. Importance of Water

      Providing adequate amount of clean water has been one of the most important issues in human history. Most of ancient civilizations flourished near water sources. With increasing population needs, the challenges to demand the incremental water supply demand substantially increased. Ancient Romans used to deliver water over long distance using aquaducts (Pitroda, 1993).

      New generation water supply system is essentially consisting of infrastructures such as collection centre, storage tanks and distribution network. With reducing & unpredictable pattern of rainfall and lack of perennial sources of water in India, there is an emerging need to design a distribution network with minimal hydraulic losses. Multiple studies were conducted in this domain; however, in most of the cases it has been found that they are site-specific in nature. Hence, it is need to conduct studies at the design phase of the project to eliminate any bottlenecks & validate the site-specific studies conducted by other eminent researchers, thereby effective meet the water supply demand.

    2. Water Distribution Network

      The main objective of WDN is to ensure that distribution network meets the flow rate and pressure requirement of water supply at the consumer need.

      Good distribution system requirements

      1. Water quality in the distribution tubes should not deteriorate.
      2. It should be able to supply water with adequate pressure head at all planned locations.
      3. It should be able to supply the required quantity of water during firefighting.
      4. The design should be such that during the repair of any portion of the scheme no customer would be without water supply.
      5. It is preferable to lay all distribution pipes one meter away or above the sewer lines.
      6. It should be watertight enough to maintain losses to a minimum due to leakage.

1.2.1 EPANET

    1. Environmental Protection Agency (EPA) has developed an open source software EPANET to designing and analyze the water supply model across the globe. The main purpose of EPANET is as mentioned below (EPANET, 2019):
      1. Determining diameters of pipes to be used.
      2. Determining the improvements and extensions the network needs.
      3. Determining the location for installation of tanks, valves and pumps.
      4. Studying chlorines behaviour and the necessity to establish second chlorination points.
      5. Altering source utilization within multiple source systems
      6. Use the formulas of Hazen-Williams, Darcy Weisbach, or Chezy-Manning to compute friction head loss.
      7. Modelling the age of water throughout a network
              1. Steps in using EPANET

                In order to accurately mode the water supply distribution network in EPANET, hydraulic engineer across the globe uses following steps as mentioned below (EPANET, 2019);

                1. Drawing a distribution system network representation or importing a fundamental network description placed in a text file.
                2. Editing the characteristics of the system objects.
                3. Description of the operation of the scheme.
                4. Select a number of alternatives for assessment.
                5. Select a number of alternatives for assessment. / water quality.
                6. Looking at the analytical outcome.
                    1. Smart City

                      A smart city is an urban development project involving the integration of various ICT solutions in a safe manner to manage the assets of a city such as local information systems, schools, libraries, transport systems, hospitals, power plants, water supply networks, waste management, law enforcement, and other community facilities. The objective of building a smart city is to improve the quality of life through the use of technology to improve service efficiency and meet the needs of residents (Guestrin et. al., 2005).

                      The Smart Cities Mission is one of the latest central government initiatives of the BJP and aims to set examples that can be replicated within and outside the Smart City, catalyzing the development of similar Smart Cities in different regions and parts of the country (Sathyanathan, 2016).

                      The core infrastructure elements in a smart city would include:

                      • Adequate water supply,
                      • Secure energy supply,
                      • Sanitation, including solid waste management,
                      • Efficient urban mobility and government transport,
                      • Affordable housing, particularly for the poor,
                      • Robust IT connectivity and digitalization,
                      • Good governance, in particular e-governance and citizen involvement,
                      • Sustainable environment,
                      • Safety and security of citizens, particularly women, children and the elderly, and
                      • Health and education.
                      • Area-based urban development (Greenfield development) strategic elements plus a pan-city initiative in which Smart in the Smart.
            1. Water Distribution Network Problems

              It well conceived fact that risk of failure of existing WDN increases with over-aging of the components which invariably influences the magnitude of hydraulic losses. In reality, hundreds of kilometers of globe-wide tubes are upgraded or substituted every year in an effort to decrease water loss owing to pipe bursts. The proportion of drinking water placed in a WDN that does not find its way to paid clients or unbilled approved users can be described as water losses. Losses of water are widely categorized as: obvious loss of water and actual loss of water. The former category relates to non-physical losses in utilities that are consumed but not correctly measured, accounted for or

              paid for. Whereas the latter category relates to the distribution system’s physical water losses, including pipe breaks and leaks (Christodoulou, 2015). Supervisory control and data acquisition systems (SCADA) and the use of sensors strategically situated across the network are now mainly used to monitor real-time. Such sensory placement’s primary objective s to maximize their sensing efficiency and also to limit their deployment and operating costs.

            2. Water Sensors

              A water sensor is a transducer system which is essentially contact based sensor which measures the measurable such as presence of water in pipe and discharge, velocity & entropy using Active and Passive Water Sensor respectively.

            3. Types of Water Detection Sensors
              1. Detectors of spot leakage

                Spot leak detectors are often used in fields such as drip pans, floor drains, or where water tends to converge in confined fields. Two samples stretch towards the ground, generally with a support bracket adjustable to suit distinct concentrations of water detection. While spot detectors are economic for particular apps, they are less appropriate for tracking wide open spaces.

              2. Hydroscopic Tape-Based Sensor

                Hydroscopic tape-based (HTB) sensors often attach water- sensitive tape to constructions such as water containers and tubes that are susceptible. An alarm is activated when subjected to water or humidity. Providing highly sensitive and considerable coverage, these detectors require optimal environments as elements like condensation can trigger false alarms.

              3. Rope-Style Sensor

        Leak detection cables (rope-style sensors) are designed to cover very large open areas. These wires can also be directly attached to water supply and return lines, making them suitable for multi-leak point coverage of big rooms. Conceptually, all leak detection cables are based on two sensing wires running concentrically around the cable. Conductive fluid acts as a switch between both wires connecting the circuit between them.

        These detection wires may have variable amounts of flexibility in bending, reactive reliability, and susceptibility to false alarms depending on material and quality, as dust and dirt may build up over time and act as an insulator. Individual wires are often put where they feed into a multi- zone control system, allowing users to identify the problem zone. Some devices allow reading over the cable map board along the pipeline, which enables us to identify the region of the leakage. Therefore, water sensors are mainly used to measure the extent of water leakage, water theft and unaccounted for water use.

            1. Overview of the Site under Investigation

              The site considered in this research study is situated alongside NH-8 in Chop close Jaipur (Rajasthan) about 40 kilometers from Jaipur. The weather of the site is warm and dry (Smart Cities, 2019).

              The site consists of 2 types of quarters, namely Type-II and III quarters. There are about 50 and 20 rooms in Type-II and III quarters respectively. Hence, the formers water

              distribution network is relatively larger and more complex. Figure 1 demonstrates the site map and the network layout of the water allocation.

              Fig. 1. Study Area
              Fig. 2. Methodology for Designing WDNFig. 3. The methodology adopted for conversion of WDN into Smart WDN
              Fig. 2. Methodology for Designing WDNFig. 3. The methodology adopted for conversion of WDN into Smart WDN

               

              1. METHODOLOGY The methodology adopted for the project is as shown below;

                Realistic Design Constraints

                In this project the following realistic design constraints are to be considered and work accordingly to overcome these constraints.

                • Economic Constraints: The design of the WDN should be done in such a way that the estimation of the system should satisfy the budget.
                • Environmental Constraints: The particular site considered in this project is located near Jaipur, Rajasthan. Hence, system should be efficient and no water loss should take place. We have to keep in mind that sensors should be heat resistant due to very hot climate.
                  • Sustainability Constraints: The design of the system should be done in such a way that it has minimal effect on the environment and reduces water losses.
                  • Health and Safety Constraints:

        Pure and hygienic water is supplied to the entire colony and thereby improving the health status of the colony.

        1. DESIGN ANALYSIS

          3.1 System Information for the Network

          In order to carry out the analysis and simulation of WDN, the information required are water demand of each building, nodal elevation and digitalized plan of the colony.

              1. Obtaining Water Demand

                The building’s water requirement was reached in accordance with IS 1172:1993 (Pitroda, 1993). Water usage per construction in terms of liters per head per day (l

                / h / d) is as follows:

                • Houses- 135 l/h/d
                • Office- 45 l/h/d

                  The product of population of each block with the per capita demand gives the water demand for that respective building.

                  Water Demand= Population × Per Capita Demand

                  (1)

                  The water demand calculated for each nodes of the tanks of each building in terms of cubic meters per hour (m³/hour) & elevation data for all the nodes in the layout is shown in Annexure-I & II and same is not shown for sake of brevity

            1. HYDRAULIC MODELLING AND SIMULATION

              In QGIS 2.8.7, the facility shape files, pipeline sketches and system boundaries are opened for the entire campus. The*.shp file is transformed to the picture bitmap (.bmp) file used in EPANET Software as a background. This allows us to assign prefixes to the junctions and pipes and obtain a working model of the system in EPANET. The intersections are established with their corresponding base requirements in front of each construction. These junctions are combined with variable length tubes. The length of the pipe lines between the nodes was determined and integrated in EPANET using Google Earth. The pumps were mounted in the network scheme with adequate pressure head to fulfill the necessary water requirement. The next step is to assign water requirement and elevation to the distinct nodes along with pipe features such as pipe length, pipe diameter and roughness coefficients to the corresponding tubes after a working model of the WDN scheme is produced in EPANET. (Santiago, 2011).

              The pumps were mounted in the network scheme with adequate pressure head to fulfill the necessary water requirement. The next step is to assign water requirement and elevation to the distinct nodes along with pipe features such as pipe length, pipe diameter and roughness coefficients to the corresponding tubes after a working

              model of the WDN scheme is produced in EPANET. The maximum demand for water happens twice a day, from 6 9am and 69pm, and is especially pronounced in university hostels (Strategies for smart cities mission, 2019).

              • The next steps are to model the water distribution network using EPANET:
              • Draw a distribution system network representation or import a fundamental network description.
              • Edit the characteristics of the system component objects. It involves editing the characteristics and entering the necessary information in different objects such as reservoirs, pipes, nodes and intersections.
              • Describe the operation of the scheme.
              • Choose a number of options for assessment.
              • Analysis of hydraulic / water quality.
              • See the evaluation outcomes.
            2. Findings of EPANET Analysis:

              During the simulation, changes were noted at different nodes at each hour in the chosen parameters such as flow, velocities, head and water pressure. The water distribution network comprises of 111 tubes with 1 pump and 1 source reservoir and 110 intersections. The tubes used were 32 mm, 50 mm, 65 mm,80 mm in diameters that were best suited after approaching the solution heuristically for elevated head values.

              1. Junction Report

                Junctions are network points where connections come together and where water enters or leaves the network. The fundamental input information needed for intersection are:

                • Higher than a reference (generally mean sea level)
                • Water demand

                  The output results computed for junctions at all time periods of a simulation are:

                • Hydraulic head
                • Actual Demand
                • Pressure

                  The following Fig 4. shows the proposed WDN plotted in EPANET 2.0

                  Fig 4. Proposed Water Distribution Network

                  The result of the simulation in form of Actual demand, Head & Pressure in Node-ID manner & base demand to actual demand ratio of the end nodes is given in the following Annexure-III & IV and same is not shown for sake of brevity.

            3. PLANNING FOR CONVERSION OF WDN INTO SMART WDN

              The description of the methods of optimization and deployment of sensors are given below:

              1. Entropy

                Entropy (normal symbol S) in thermodynamics is a measure of the amount of particular realizations or microstates that can represent a thermodynamic system in a defined state set by macroscopic factors. Most individuals know entropy within a macroscopic scheme as a measure of molecular disorder.

                Second Thermodynamics Law says that entropy will either boost or stay the same in any cyclic process. (Christodoulou, 2015)

                Change in entropy can be defined as equation (2)

                =

                (2)

                Mathematically, entropy can be expressed as the product of the probability mass function (Px) of a variable x, times the natural logarithm of the inverse of the probability (Christodoulou and Deligianni, 2010).

                It is expressed by the following Equation (3),

                = Px ln ( 1 )

                Where,

                Px- Probability Mass Function

                Three are of specific significance among the main characteristics of entropy: sub-additivity, maximum, and equivocation.

                • Sub-additivity denotes that the value of a function for the sum of two components is always equal to or below the sum of the orders of the function for each component.
                • Maximality The entropy function, H (p1, p2, …, pn), requires the highest value when all admissible results have equal probabilities (p1 = p2 = … = pn). In other words, for the equi-probability distribution of possible results, maximum uncertainty is achieved.
                • Equivocation In impact, is the conditional entropy of one random variable against another, and quantifies the remaining entropy (i.e., uncertainty) of the random Y variable, since the value of another random X variable is known.
              2. A Closer Look at Equivocation

          Mathematically speaking, misunderstanding is called Y entropy conditional on X. It is written in the manner in which the following equation sequence can be used (Eqn. 4,5,6)

          (|) = [()(| = )

          (4)

          = ()[(|) 1

          (|)

          (5)

          (3)

          = [(, ) () ]

          (,)

          3.5 Sensor Placement and Optimization

          (6)

          concentrations of these arcs, thus helping to avoid clustering detectors in just a few areas of the network. Furthermore, in order to take into account the overlap in sensing radii of sensors placed at the end nodes of an arc and/or segment lengths shorter than the sensor radius, the

          Since entropy is deemed a good measure of the order and

          stability of a system, maximizing its importance when a system is in a state of “equiprobability,” a greater degree of entropy should also show a more balanced system with regard to sensed data. The issue of sensor-placement optimization could therefore be re-stated as one in which sensor locations are sought as to maximize system entropy (Saminu et. Al., 2013).

          The entropy equation (2) describes the word of likelihood

          value of ri used in equation (6) is taken as the minimum between the segment length (Li) and the sensor radius (xi) (for one sensor) of the sensor., or the combined sensing radii (in the case of two sensors) using equation (8, 9)

          ri=min{xi;Li}

          (8)

          = [ ln ()]

          (px) as a statistical measure of the sensing radius ratio of a sensor over the network’s complete length. Therefore, for a

          (9)

          single-type sensor, the complete network entropy, Ht, would become

          = [ ln ()]

          Where j is the index of the sensor type ; nr is the number of different types of sensors used in the project ; ri, j is the number of sensor type units j used on node I ;nt is the total number of sensors in the network and rT, j is the total

          (7)

          number of sensor type j units used in the network. The main objective is therefore to maximize the entropy of the

          It should be noted that although the px = r / LT definition above is consistent with classical probability properties when the sensors cover the entire length of the network. On the contrary, if they don’t and so it’s not mathematically correct, it doesn’t comply. A alternative to this issue is to redefine the size of the previous ratio as r / L based on the length of the arc and not the length of the network. The value of Px is now taken as the proportion of the sensor radius over the length of the detected network arc, and the complete entropy of the structure can be calculated by summing the entropy values for each arc. This definition also adheres to the reality that sensors at junctions have numerous arcs and the same add to the entropy

          network, subject to an allowable maximum number of sensors (of a specified sensing radius) or to maximize entropy equivalently while minimizing the number of sensors used.

          In the sample arc shown in Fig, for instance. 5. Suppose we denote a sensed node by a filled circle and a node without a sensor as an empty circle, the proposed entropy-based sensor positioning technique with a length higher than the total of the sensing radii, the complete entropy generated by the arc setup can then be calculated on the basis of the above definition of entropy.

          Fig. 5. Proposed entropy-based sensor placement method

          )

          )

           

          = min(2;) × ln (min(2;)

          = 2 × ln (2)

          = min(2;) × ln (min(2;)

          = × ln () = 0

          (10)

          (11)

          )

          )

           

          In the case of an arc length shorter than the sum of the sensing radii of the two nodal sensors, the total pipe entropy is taken as

          Suppose, for a pipe of length 300 meters, one sensor be used (at node ni) with an assumed sensing radius of 200 meters, then the entropy for pipe(ni, nj) is computed as seen in Fig. 6.

          Fig. 6. Proposed numerical entropy-based sensor placement method

          )

          )

           

          = 200 ln (200

          = 0.270

          Sensors are to be placed in the network which will have to

          300 300

          (12)

          satisfy both uniformity and efficiency during deployment. The sensors will have to cover maximum area and they

          If two sensors are used (at nodes ni and nj) then the entropy is computed to be

          must not overlap at any place because makes them inefficient and complicates the coding as two sensors are covering the same node.

          )

          )

           

          = 300 ln (300

          = 0.00

          300 300

          (13)

          The data is fed into Microsoft Excel and the entropy is calculated accordingly, the data is then compiled and

          It should be observed that the technique demonstrates preference in both end-nodes for a single sensor relative to sensors, and that the suggested entropy-maximization strategy is great inmore complex sensor schemes. One in which is of equal value the data produced and/or distributed among its components. Therefore, the issue of sensor-placement optimization could be restated as one in which sensor locations are attempted to maximize the entropy of the scheme.

          thereafter the network is heuristically approached in order to find the uniformity in deployment of the sensors to increase the efficiency. The efficiency of a water distribution network is thus calculated mathematically without the need of educated guesses thus eliminating the trial and error method usually practiced.

          Fig. 7. EPANET layout of the water distribution network with diameter and pipe length

          This method was applied using Microsoft Office Excel for the water distribution network of the site. Two sensor types (Arc length 10 m, 20 m) were used and the best possible

          sensor configuration was adapted on the basis of highest entropy received. Annexure-V shows the calculation of entropy for all the pipe configurations in the water

          distribution network. Entropies of the pipelines were calculated and optimized; it was found that deployment of

          7 sensors would be ideal for managing the water

          distribution network. The following nodes as shown in Table 1 were selected based on the entropy parameters and location of the nodes:

          Table 1: Nodes selected for placement of sensors in the WDN

          S. NoNode Number
          11
          24
          36
          424
          534
          656
          759

          At node 4, sensor of arc radius of 20 m has to be placed. All the other nodes are to be fitted with sensors of arc radius of 10 m. The sensors have been optimised as necessary and have been evenly spread out as well. The sensors deployed can trigger alarm in case of an anomaly noted in the working of the network in real time and it can

          pin point the nodes which is malfunctioning. This greatly simplifies the maintenance activities in the future and cuts down the cost significantly in a long run. The sensors will have to cover maximum area and they must not overlap at any place as it will be rendered inefficient and complicates the coding as two sensors are covering the same node.

          Fig. 8. Schematic layout of the proposed sensor placement in the water distribution network
        2. CONCLUSION

This research study focused on analysing the water distribution network and identifying deficiencies in design, execution and use, as well as converting the current network into an intelligent water distribution network. At the end of the analysis, all factors such as water pressure, velocity, flow rate across all intersections were discovered to be good enough in the study region to provide sufficient equitable supply. One of the biggest issues in the distribution scheme is the existence of dead ends. Looping to link the dead ends is a suggested technique for increasing system reliability and providing steady water flow in pipes. System simulations disclosed that few pumps need to be installed to provide the water distribution system with adequate pressure head and velocity. All of the distribution system’s hydraulic limitations were minimized to a maximum extent. For the supply of the required quantity of water, the minimum pressure head and water

demand are ensured even during the peak hour. Using mathematical entropy approached using greedy search algorithm, the scheme was optimized for sensor deployment. The sensors were implemented in regions with important entropy and optimized throughout the network for standardized deployment. The assessment demonstrates that it is possible to provide sufficient water to the entire study area network at all intersections and speeds in all tubes. For brevity purposes, all the relevant data required for the analysis were collected in Annex I to V.

The following conclusions can be drawn from the study:

  • The supplied quantity of water is sufficient enough to easily satisfy the water demand of the entire colony as seen in Annexure-IV, where the Actual Demand to Base Demand ratio remains more than 1 in all the end nodes.
  • The residual pressure at all the nodes is found to be greater than 12 m. Hence, the flow can occur

easily and the water reaches the tank present at height of 10 m.

  • The assumed pipe diameters are sufficient to withstand for the pressure for the entire network.
  • The velocity in the pipe network is sufficient, i.e., in the range of 0.6 m/s to 2.5 m/s as prescribed in BIS (IS 2065:1983).
  • The system can be converted into a smart water distribution network by deploying the sensors.
  • The location of sensor deployment was pin pointed using a mathematical approach that led to an effective way to find the location of maximum efficiency.

REFERENCES

  1. Christodoulou, S. E., Smarting up water distribution networks with an entropy-based optimal sensor placement strategy, Journal of Smart Cities, Vol.1 (1), 2015, 4758.
  2. Christodoulou, S. and Deligianni, A., A neurofuzzy decision framework for the management of water distribution networks. Water Resources Management, vol.24 (1), 2010, 139156.
  3. EPANET Software- https://www.epa.gov/water- research/epanet (Accessed on 16.06.2019)
  4. Guestrin, C., Krause, A. and Singh, A. P., Near-optimal sensor placements in Gaussian processes, Proceedings of the 22nd International Conference on Machine Learning, 2005, 265272.
  5. IS 2065-1983 (Code of practise for water supply in buildings)- http://www.questin.org/sites/default/files/standards/is.2065.198 3.pdf (Accessed on 16.06.2019)
  6. Ministry of Urban Development Government of India: Smart Cities- http://smartcities.gov.in/upload/uploadfiles/files/SmartCityGui delines(1).pdf (Accessed on 16.06.2019)
  7. Pitroda S. G., Code of Basic Requirements for Water Supply, Drainage and Sanitation (IS 1172:1993), Bureau Of Indian Standards, 1993, 1-21
  8. Sathyanathan, R.,Hasan, M. and Deeptha, V.T., Water Distribution Network Design for SRM University using EPANET, Asian Journal of Applied Sciences, Vol.4(3), 2016, 669-679
  9. Saminu, A., Abubakar, Nasiru, Sagir, L., Design of NDA Water Distribution Network Using EPANET, International Journal of Emerging Science and Engineering, Vol. I(9), 2013, 5-9.
  10. Santiago, A., EPANET and Development- How to calculate water networks by computer, Arnalich Publications, 2011.
  11. Strategies for smart cities mission (Government of India)- http://smartcities.gov.in/content/innerpage/strategy.php (Accessed on 16.06.2019)

ANNEXURE-I

Node IDBase Demand (m³/hour)Node IDBase Demand (m³/hour)Node IDBase Demand (m³/hour)
JN-20T230.278T240.278
JN-30T170.278T220.278
JN-10T180.278T340.278
JN-150T190.278T350.278
JN-40T200.278T360.278
JN-140T21

 

0.278T370.278
JN-130JN-450T380.278
JN-90JN-480T390.278
JN-80JN-590T400.278
JN-100JN-460T410.278
JN-110JN-470T300.208
JN-50JN-250T310.208
JN-60JN-440T320.208
JN-70JN-260T330.208
JN-120JN-270JN-490
T10.139JN-280JN-500
T20.139JN-290JN-510
T30.139JN-300JN-520
T40.139JN-310JN-530
T50.069JN-320JN-550
T60.069JN-330JN-560
T100.139JN-340JN-570
T90.139JN-430JN-580
T80.139JN-420T420.278
T70.139JN-410T430.278
JN-160JN-400T440.278
JN-180JN-390T450.278
JN-170JN-380T460.278
JN-190JN-370T470.278
JN-200JN-360T480.278
JN-210JN-350T490.278
JN-220T290.278JN-540
JN-230T280.278T500.139
JN-240T270.278T510.139
T140.278T260.278T160.278
T150.278T250.278U.G. SumpN/A
Node IDElevation (m)Node IDElevation (m)Node IDElevation (m)
Jn-2100T23115T24115
JN-3100T17115T22115
JN-1100T18115T34115
JN-15100T19115T35115
JN-4100T20115T36115
JN-14100T21115T37115
JN-13100JN-45100T38115
JN-9100JN-48100T39115
JN-8100JN-59100T40115
JN-10100JN-46100T41115
JN-11100JN-47100T30115
JN-5100JN-25100T31115
JN-6100JN-44100T32115
JN-7100JN-26100T33115
JN-12100JN-27100JN-49100
T1113JN-28100JN-50100
T2113JN-29100JN-51100
T3113JN-30100JN-52100
T4113JN-31100JN-53100
T5105JN-32100JN-55100
T6105JN-33100JN-56100
T10113JN-34100JN-57100
T9113JN-43100JN-58100
T8113JN-42100T42115
T7113JN-41100T43115
JN-16100JN-40100T44115
JN-18100JN-39100T45115
JN-17100JN-38100T46115
JN-19100JN-37100T47115
JN-20100JN-36100T48115
JN-21100JN-35100T49115
JN-22100T29115JN-54100
JN-23100T28115T50115
JN-24100T27115T51115
T14115T26115T16115
T15115T25115U.G.Sump100

ANNEXURE- III

Node IDActual DemandBase DemandActual Demand/ Base Demand RatioNode IDActual DemandBase DemandActual Demand/ Base Demand Ratio
T10.140.1391.01T220.280.2781.01
T20.140.1391.01T340.280.2781.01
T30.140.1391.01T350.280.2781.01
T40.140.1391.01T360.280.2781.01
T50.070.0691.01T370.280.2781.01
T60.070.0691.01T380.280.2781.01
T100.140.1391.01T390.280.2781.01
T90.140.1391.01T400.280.2781.01
T80.140.1391.01T410.280.2781.01
T70.140.1391.01T300.210.2081.01
T140.280.2781.01T310.210.2081.01
T150.280.2781.01T320.210.2081.01
T230.280.2781.01T330.210.2081.01
T170.280.2781.01T420.280.2081.35
T180.280.2781.01T430.280.2081.35
T190.280.2781.01T440.280.2081.35
T200.280.2781.01T450.280.2781.01
T210.280.2781.01T460.280.2781.01
T290.280.2781.01T470.280.2781.01
T280.280.2781.01T480.280.2781.01
T270.280.2781.01T490.280.2781.01
T260.280.2781.01T500.140.1391.01
T250.280.2781.01T510.140.1391.01
T240.280.2781.01T160.280.2781.01

ANNEXURE-V

Start Node NumberEnd Node NumberLengthMinimum length (x,10)Minimum length (x,20)Entropy for 10 m arc sensorEntropy for 20 m arc sensor
Sump11210120.15190
231710170.31210
3154310200.33920.356
347210200.27410.3558
494210200.34160.3533
9844400
9141310130.20180
455810200.3030.3671
5644400
5101310130.20180
6113410200.35990.3121
874710200.32920.3635
61244400
7T133300
10T211100
10T433300
11T311100
11T633300
12T511100
14T1011100
14T933300
13T811100
13T733300
8131310130.20180
23242110200.35330.0464
151666600
16171210120.15190
171888800
18191210120.15190
192066600
20211210120.15190
212288800
22231210120.15190
24452010200.34650
48592010200.34650
45461210120.15190
464788800
7481210120.15190
252666600
26271210120.15190
272888800
28291210120.15190
293066600
30311210120.15190
313288800
32331210120.15190
333455500
444366600
43421210120.15190
424188800
41401210120.15190
403966600
39381210120.15190
383788800
37361210120.15190
363555500
45T302510200.36650.1785
46T312510200.36650.1785
47T322510200.36650.1785
48T332510200.36650.1785
59491610160.29370
49501210120.15190
505166600
51T472510200.36650.1785
50T482510200.36650.1785
51521210120.15190
52T462510200.36650.1785
49T492510200.36650.1785
525333300
535466600
54502510200.36650.1785
545566600
555666600
565766600
575866600
53T452510200.36650.1785
55T442510200.36650.1785
58T422510200.36650.1785
57T512510200.36650.1785
T43562510200.36650.1785
35583510200.35790.3197
15342510200.36650.1785
343555500
24252010200.34650
254455500
44592010200.34650
43T342510200.36650.1785
42T352510200.36650.1785
41T362510200.36650.1785
40T372510200.36650.1785
39T382510200.36650.1785
38T392510200.36650.1785
37T402510200.36650.1785
36T412510200.36650.1785
26T292510200.36650.1785
27T282510200.36650.1785
28T272510200.36650.1785
29T22510200.36650.1785
30T252510200.36650.1785
31T242510200.36650.1785
32T232510200.36650.1785
33T222510200.36650.1785
6T142510200.36650.1785
17T152510200.36650.1785
18T162510200.36650.1785
19T172510200.36650.1785
20T182510200.36650.1785
21T192510200.36650.1785
22T202510200.36650.1785

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