Non-revenue water mathematical model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi water utility (Batumi, Georgia)

Título del Trabajo Fin de Máster: Non-Revenue Water Mathematical Model as a tool for the establishment of water losses management in utilities in developing areas. Application in Batumi Tskali (Batumi, Georgia). Intensificación: HIDRÁULICA URBANA Autor: JOSÉ FRANCISCO PONS AUSINA Director: Dr. FRANCISCO ARREGUI DE LA CRUZ Codirector: Dr. JAVIER SORIANO OLIVARES Fecha: SEPTIEMBRE de 2014 Título del Trabajo Fin de Máster: Non-Revenue Water Mathematical Model as a tool for the establishment of water losses management in utilities in developing areas. Application in Batumi Tskali (Batumi, Georgia). AUTOR: PONS AUSINA, JOSE FRANCISCO Tipo Director Codirector 1 Codirector 2 Tutor A☐ ☐☐ ☐ B   FRANCISCO ARREGUI JAVIER SORIANO Lugar de Realización Fecha de Lectura VALENCIA SEPTIEMBRE , 2014 Resumen : La empresa pública de abastecimiento de agua "Batumi Tskali" en la ciudad de Batumi, en Georgia, había estado experimentando la pérdida de agua de hasta un 85% debido al deterioro de la red, que se ha estado utilizando desde la década de 1800. Además, durante la era soviética no se realizó ningún tipo de mantenimiento, lo que contribuyó al deterioro de las tuberías e demás infraestructura hidráulica. En 2006, Batumi Tskali fue liquidado para formar una nueva empresa municipal del agua, con el apoyo de la consultora alemana MACS Energy and Water, que fiscaliza el programa en curso de rehabilitación de las infraestructuras con fondos del Banco Alemán para el desarrollo y los programas de rehabilitación (KfW). Una de las herramientas más importantes para hacer frente al problema de las pérdidas de agua y establecer una gestión adecuada es el modelo matemático de la red. La intención principal de la tesina es presentar la metodología seguida para implementar un programa de reducción de pérdidas de agua en la empresa pública suministradora utilizando las diferentes etapas y los datos necesarios en la construcción del modelo matemático del sistema rehabilitado. Por otra parte, el objetivo principal es la construcción de un modelo matemático de agua no facturada, donde incluir el modelado de las pérdidas de agua. Abstract: Georgian water utility "Batumi Tskali" had been experiencing water loss of up to 85% from its deteriorating network, some of which that has been in place since the 1800s, with poor maintenance during the Soviet era contributing to pipe deterioration. In 2006, Batumi Tskali was liquidated to form a new municipal water utility, with support from German consultancy MACS Energy and Water, leading to the implementation of an ongoing infrastructure rehabilitation program borne by KfW (German Bank for developing and rehabilitation programs). One of the most important tools to cope with the problem of water losses and establish a proper management is the Mathematical Model of the network. The aim of the Master thesis is to present the followed methodology to implement a water losses reduction program at the water utility using the different stages and data needed when building the Mathematical Model of the rehabilitated system. Furthermore, the main objective is to build a Non-revenue model where include the modeling of the water losses. Resum: L'empresa pública d'abastiment d'aigua "Batumi Tskali" a la ciutat de Batumi, a Geòrgia, havia estat experimentant la pèrdua d'aigua de fins a un 85% a causa del deteriorament de la xarxa, que s'ha estat utilitzant des de la dècada de 1800. A més, durant l'era soviètica no es va realitzar cap tipus de manteniment, el que va contribuir al deteriorament de les canonades i altres infraestructures hidràuliques. El 2006, Batumi Tskali va ser liquidat per formar una nova empresa municipal d'aigua, amb el suport de la consultora alemanya MACS Energy and Water, que fiscalitza el programa en curs de rehabilitació de les infraestructures amb fons del Banc Alemany per al desenvolupament i els programes de rehabilitació (KfW). Una de les eines més importants per fer front al problema de les pèrdues d'aigua i establir una gestió adequada és el model matemàtic de la xarxa. La intenció principal de la tesina és presentar la metodologia seguida per implementar un programa de reducció de pèrdues d'aigua a l'empresa pública subministradora utilitzant les diferents etapes i les dades necessàries en la construcció del model matemàtic del sistema rehabilitat. D'altra banda, l'objectiu principal és la construcció d'un model matemàtic d'aigua no facturada, on incloure el modelatge de les pèrdues d'aigua. Palabras clave: Agua no facturada, modelo matemático, fugas, balance hídrico, distritos de medición Key words: Non-revenue water, mathematical model, leakage, water balance, district metering areas Paraules Claus: Aigua no facturada, model matemàtic, fugues, balanç hídric, districtes de medició Non-Revenue Water Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). Author: JOSÉ FRANCISCO PONS AUSINA Director: Dr. FRANCISCO ARREGUI DE LA CRUZ Co-director: Dr. JAVIER SORIANO OLIVARES Date: 2014 September Als meus pares Agradecimientos: Me gustaría agradecer a las siguientes personas la ayuda prestada para la realización de este trabajo: Al Dr. Thomas Schiller y a Matthias Hitzel, directores generales de MACS Energy&Water, gracias por su confianza. A Batumi Tskali, especialmente a sus directores Janne, Temur y Aleko, y al equipo de agua no facturada, Zura, Nugzar, Sulkhan y Giorgi. Gracias por llevar a cabo siempre lo que les he pedido, sin importarles la hora ni el día. Al Dr. Francisco Arregui de la Cruz por creer en mí y al Dr. Javier Soriano Olivares por su amistad y ayuda siempre. A mi familia por su gran apoyo durante mis ausencias. მინდა მადლობა მოვახსენო ბატონ ჯანსუღ ვარშალომიძეს (გენერალური დირექტორი), ბატონ თეიმურაზ ბედინაძეს (ფინანსური დირექტორი), ბატონ ალექსანდრე მიქელაძეს (ტექნიკური დირექტორი), არაშემოსავლიანი წყლის ჯგუფის წევრებს - ზურას, ნუგზარს, გიორგის და შპს." ბათუმის წყალის" ყველა თანამშრომელს, ასევე ბატონ ზაურ ფატლაძეს და ბატონ ჯაბა ტუღუშს (პასი) ჩემი ბათუმში ყოფნის და მუშაობის დროს გაწეული უდიდესი დახმარებისთვის. ასევე გულწრფელი მადლობა მინდა გადავუხადო ჩემს მაქსის ბათუმის ოფისის თანამშრომლებს: ლია დავითაძეს, ნანა მაზმიშვილს, ანჟელა და სირა ოვანესიანებს, დიანას, ლევან სარჯველაძეს, მატიას ჰითცელს და ჰარალდ ვალდიქსს მხარდაჭერისთვის და თანადგომისთვის NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 1 INDEX 1 BLACKGROUND ................................................................................................... 8 2 INTRODUCTION .................................................................................................. 10 2.1 Water losses in developing areas: the case of Batumi, Georgia ...................... 10 2.2 Objectives ........................................................................................................ 10 2.3 State of the art .................................................................................................. 11 2.4 General overview ............................................................................................. 12 3 MATERIAL AND METHODS.............................................................................. 13 3.1 Batumi water system ........................................................................................ 13 3.1.1 Introduction .............................................................................................. 13 3.1.2 Existing situation at the beginning of the project ..................................... 13 3.1.3 New system design ................................................................................... 17 3.1.4 Network division into district metered areas ............................................ 20 3.2 Initial audit and work plan ............................................................................... 23 3.2.1 Introduction .............................................................................................. 23 3.2.2 Water balance ........................................................................................... 23 3.2.3 Measurement plan..................................................................................... 30 3.2.4 Mathematical model ................................................................................. 38 3.3 Water balance .................................................................................................. 41 3.3.1 Introduction .............................................................................................. 41 3.3.2 Comprehensive Overview ........................................................................ 41 3.3.3 IWA Water Balance.................................................................................. 50 3.4 NRW Mathematical Model .............................................................................. 53 3.4.1 Introduction .............................................................................................. 53 3.4.2 Including NRW in the Mathematical model............................................. 54 3.4.3 NRW distribution ..................................................................................... 57 3.4.4 NRW modeling case 1: correction with a demand factor ......................... 59 3.4.5 NRW modeling case 2: constant leak flow .............................................. 61 3.4.6 NRW modeling case 3: leak flow using emitters ..................................... 62 3.4.7 Comparison between the three methods ................................................... 68 3.4.8 Supplying from one reservoir hypothesis study ....................................... 70 3.4.9 Model calibration ...................................................................................... 73 3.5 DMAs implementation .................................................................................... 79 3.5.1 Introduction .............................................................................................. 79 3.5.2 Isolation strategy ...................................................................................... 79 3.5.3 Temporary DMAs study ........................................................................... 86 3.5.4 Definitive DMAs .................................................................................... 106 3.5.5 Zero pressure tests .................................................................................. 107 3.6 Managing information system ....................................................................... 113 3.6.1 Introduction ............................................................................................ 113 3.6.1 Batumi Tskali MIS ................................................................................. 116 4 APPLICATION TO DMA 1-2-3: MODELING AND SIMULATION ............... 118 4.1 Introduction .................................................................................................... 118 4.2 DMA isolation modeling ............................................................................... 119 4.3 Unbilled unmetered consumption investigation ............................................ 120 4.4 Leakage and Overflows at Utility's Storage Tanks ........................................ 121 4.5 Large consumption identification and allocation at the model ...................... 122 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 2 4.6 Water balance ................................................................................................ 123 4.7 NRW modeling .............................................................................................. 126 4.8 Consumption pattern ...................................................................................... 128 4.9 Pressure management .................................................................................... 128 4.10 Simulation and results ................................................................................ 130 5 CONCLUSIONS .................................................................................................. 137 6 REFERENCES ..................................................................................................... 138 LIST OF TABLES Table 1: Data Billing Area. .......................................................................................... 26 Table 2: Correspondence BA-DMA ............................................................................ 27 Table 3: Flow measurements plan .............................................................................. 29 Table 4: Pressure measurements plan ....................................................................... 29 Table 5: NRW Distribution.......................................................................................... 54 Table 6: Consumption modelled types by EPANET ................................................... 54 Table 7: Constant Consumption modeling ................................................................. 55 Table 8: Time-Varying Consumption modeling ........................................................... 55 Table 9: Demand Patterns ......................................................................................... 55 Table 10: Emitter node properties .............................................................................. 56 Table 11: Elements modelled by emitters .................................................................. 56 Table 12: Flow injection difference between Model and Measurements Plan .............. 58 Table 13: NRW distribution ......................................................................................... 59 Table 14: Demand factor calculation ........................................................................... 59 Table 15: Results demand factor correction ................................................................ 60 Table 16: Constant leak flow distribution ..................................................................... 61 Table 17: Results demand factor correction ................................................................ 62 Table 18: Allocation of leakage flow for emitter calculation ......................................... 62 Table 19: Pressure dependent and non-dependent flows ........................................... 63 Table 20: Allocation pressure non-dependent leakages flow ....................................... 63 Table 21: Emitter coefficient calculation ...................................................................... 64 Table 22: Results emitter coefficient ........................................................................... 65 Table 23: New NRW distribution for emitter calculation ............................................... 65 Table 24: New pressure dependent and non-dependent flows .................................... 65 Table 25: New allocation pressure non-dependent leakages flow ............................... 66 Table 26: New emitter coefficient calculation .............................................................. 66 Table 27: Results emitter coefficient ........................................................................... 67 Table 28: Injected flow calculation during lower consumption time.............................. 76 Table 29: Demanded flow calculation during lower consumption time ......................... 77 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 3 Table 30: Leakage flow calculation during lower consumption time ............................ 77 Table 31: Daily leakage flow estimation ...................................................................... 77 Table 32: Daily leakage flow estimation ...................................................................... 78 Table 33: DMAs isolation strategy............................................................................... 85 Table 34: Temporary DMA’s information ..................................................................... 92 Table 35: Summary Water Balance DMA’s ................................................................. 92 Table 36: Calculation NRW data DMA Bartskhana ..................................................... 94 Table 37: NRW IWA terms DMA Bartskhana .............................................................. 95 Table 38: Calculation NRW data DMA P2B ................................................................ 97 Table 39: NRW IWA terms DMA P2B ......................................................................... 98 Table 40: Calculation NRW data DMA P2B-P2D1 .................................................... 100 Table 41: NRW IWA terms DMA P2B-P2D ............................................................... 101 Table 42: Calculation NRW data DMA Injalo ............................................................. 103 Table 43: NRW IWA terms DMA Injalo ..................................................................... 104 Table 44: NRW balance on temporary DMAs ........................................................... 105 Table 45: DMAs isolation status, zero pressure tests ................................................ 110 Table 46: DMA 1-2-3 Bulk meters metering .............................................................. 120 Table 47: Leakage reservoirs .................................................................................... 121 Table 48: Leakage reservoirs DMA 1-2-3 part .......................................................... 121 Table 49: Base demand nodes DMA 1-2-3 ............................................................... 123 Table 50: DMA 1-2-3 water balance .......................................................................... 124 Table 51: DMA 1-2-3 IWA balance, NRW ................................................................. 125 Table 52: DMA 1-2-3 P dependant and non-dependant flows ................................... 126 Table 53: DMA 1-2-3 P dependant and non-dependant flows ................................... 126 Table 54: DMA 1-2-3 emitter coefficients calculation ................................................ 127 Table 55: DMA 1-2-3 total inflow, outflow and consumption ...................................... 134 Table 56: DMA 1-2-3 consumption vs billing ............................................................. 134 Table 57: DMA 1-2-3 PRV consumption reduction estimation ................................... 136 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 10 2 INTRODUCTION 2.1 Water losses in developing areas: the case of Batumi, Georgia Georgian water utility "Batumi Tskali" had been experiencing water loss of up to 85% from its deteriorating network, some of which that has been in place since the 1800s, with poor maintenance during the Soviet era contributing to pipe deterioration. Furthermore, the absence of awareness of the cost and value of water had complicated the implementation of a proper water losses program in the utility. In 2006, Batumi Tskali was liquidated to form a new municipal water utility, with support from consultancy MACS Energy and Water, leading to the implementation of an ongoing infrastructure rehabilitation program borne by KfW. The technical support which is being given to Batumi Tskali focuses on the establishment of water loses control program. On that framework, the main idea of this Master Thesis was using the process needed to build the mathematical model of the system to introduce in the utility a strategy to control the nonrevenue water (NRW). The NRW control program is based on the IWA methodology and nowadays is still an ongoing process. However, the main steps have been made on almost the entire network. On the next chapters we will define the system and present the steps to implement the water losses control program from the beginning 2 years ago until now. It is needed to understand that we are presenting and on-going process not yet finished and also think about the system improvement progression, which was formed by two completely different parts when we arrived (old and new network). These parts were treated separately for calculations, as we will present on the chapter regarding the system definition and concept. 2.2 Objectives The main objectives and contributions we expect to reach with the thesis are the following: - NRW mathematical model of the system: water losses modelling and simulation. -Obtaining a procedure to consider the NRW mathematical model as a tool for implement a water losses control management program in water utilities in developing areas. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 11 2.3 State of the art The control of the non-revenue water (NRW) is one of the most important issues that need to be managed in developing areas. It would be possible to use the process to build a NRW mathematical model in one developing area as a tool for achieving a correct water losses management, and link each step of the procedure with the reduction in NRW observed. Some of the methods for leakage control which are mentioned in the article “Methods and Tools for Managing Losses in Water Distribution Systems” (Harrison E. Mutikanga et al. 2013) have been consulted, in order to know the existing leakage control methods and its results. One of the conclusions of that research paper states that solving problems in water distribution systems in developing countries demand unique tools and methods for water loss control that require further research, due to its peculiar technical characteristics. Additionally, another conclusion reports that whereas the IWA/AWWA performance indicators provide a good foundation, they are insufficient for international water loss benchmarking (McKenzie et al. 2007) and not directly applicable to most water distribution systems in developing countries. The quantity of water lost, or NRW, is a measure of the operational efficiency of a water distribution system (Wallace 1987), and high levels of NRW are indicative of poor governance (McIntosh 2003) and poor physical condition of the water distribution system (Male et al. 1985). Regarding this fact, tools and methods have been developed to implement losses control programs in the water utilities. Hydraulic simulation models can be used to evaluate leakages in water distribution networks. Cheung and Girol (2009) in Brazil studied that night flow analysis, coupled with leakage hydraulic analysis, has proven to be a valuable tool for leakage estimation even in networks of irregular water supply, which could be the case of networks in developing areas. The network hydraulic model has been well developed and applied to water distribution system analysis in the last three decades (AWWA 2005). For leakage management, the hydraulic model can be used for many purposes, including network zoning (Awad et al. 2009; Sempewo et al. 2008), pressure managing planning for leakage control (Burrows et al. 2003; Tabesh et al. 2009; Ulanicki et al. 2000) and leakage modeling as pressure-dependent demand (Almandoz et al. 2005; Germanopoulos 1985; Giustolisi et al. 2008; Wu et al. 2010). Furthermore, there are some research papers focusing on the nonphysical water losses. The Asian Development Bank (ADB) estimates that 50-65% of NRW in Asian water utilities is due to apparent losses (McIntosh 2003). To minimize these losses, many researchers have developed tools and methodologies for water meter replacement based on meter testing, economic optimization, and operational research techniques (Arregui et al. 2011; Lund 1988; Noss et al. 1987; Yee 1999). Arregui et al. (2006) stated that metering NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 12 inaccuracies could be minimized by integrated meter management policies and strategies. Moreover the high unauthorized water consumption, that is common in developing areas, requires not only engineering solutions but sociocultural approaches, which have been reported as the major drivers in reducing NRW in some Asian cities (Luczon and Ramos 2012). Regarding the importance of the water utilities management performance, benchmarking studies on water loss management using partial methods have been reported in various countries (South Africa, Seago et al. 2004; Austria, Koelbl et al. 2009b; Portugal, Marques and Monteiro 2003). 2.4 General overview The following flow chart presents the overview of the process with the aim of giving a general idea about the methodology. Figure 3: Methodology overview INFO 1: CAD, GIS, BILLING, WATER BALANCE 2: SYSTEM; MODEL 3: BILLING, WATER BALANCE 4: BILLING, WATER BALANCE 5: Q&P METERING NRW MATHEMATICAL MODEL PHASES 1. DATA COLLECTION 2. NETWORK SKELETISATION 3. RECORDED CONSUMPTION ANALYSIS * MODEL LOADING * "BIG CUSTOMERS" INDENTIFICATION * CONSUMPTION TYPE 4. UNRECORDED CONSUMPTION ANALYSIS * P-DEPENDENT CONS *NO P-DEPENDENT CONS *EMITTER COEFFICIENTS 5. SIMULATION WATER LOSSES MANAGEMENT 1. SYSTEM UPDATING AND VERIFICATION 2. DMA AND MACRO-METERING IMPLEMENTATION 3. MICROMETERING, CONSUMPTION PATTERNS 4. NRW CALCULATION AND DISTRIBUTION 5. ACTIVE LEAKAGE CONTROL NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 13 3 MATERIAL AND METHODS 3.1 Batumi water system The aim of this chapter is to explain what the situation of the water system in Batumi was from the beginning of the project until the last agreed update to implement the DMAs as a part of the losses control program. 3.1.1 Introduction As explained at the introduction of this document, the project is still on-going and nowadays the scope of work is to finish with the establishment of the DMAs and with the installation of individual water meters. On the other hand, the network on some parts of the city, less inhabited, are still under reconstruction. It will be finished with the last phase of the project. Anyway, those areas are already included into the losses control program. In order to have a better idea of the project, we will summarize the different rehabilitation project phases to make it clear. On the next sub-chapters the existing situation at the beginning and the new system design will be presented. Furthermore, the agreed network division into DMAs concept will be also explained and detailed. 3.1.2 Existing situation at the beginning of the project 3.1.2.1 General layout The general layout of the Batumi water supply system was a result of historical development, changing decision criteria; changing strategies between surface water supplied by gravity and pumped well field water. The distribution system had a total official length of 300 km, however according to the drawings available, the Consultant found the complete system’s length with 201 km including transmission mains, and a length of 153 km without TMs. The water from the two main sources, Chakvi and Chaisubani, enters from the east and feeds directly into the distribution system. There is no reservoir volume available to buffer demand peaks, which depend rather on operation hours and capacities of the individual booster pumps than on consumption patterns. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 14 3.1.2.2 Valves and hydrants The total number of line valves was unknown, most of the visible line valves were leaking. However, in some areas Batumi Tskali operated line valves to open distribution to specific areas during the night only, and closed these connections in the morning. The number of hydrants varied according to different sources between 80 (Batumi Tskali) and 68 (fire brigade). The specific hydrant density was thus about 0.4 hydrants per km of the distribution system. 3.1.2.3 Pipe material and age The material consisted mainly of steel pipes and cast iron pipes. The oldest parts were constructed around 1908 and the newest parts from 1970 to 1980. Materials like HDPE were so far just used in a few extensions and for private house connections. Also GAZ pipes were used for this purpose. 3.1.2.4 Service connections Officially there were 18.750 registered service connections. In fact there were many more connections to main pipes, since numerous apartments had ‘private connections’, equipped with booster pumps connected somehow to the distribution system. These connections were not registered as they were installed privately without following any standards or regulations. Almost 100 percent of the service connections were made of steel pipes. Physical connections to the main pipes are made by welding, threading and a variety of improvised methods. 3.1.2.5 Booster pumping There were 215 “official booster” pumping stations installed in the distribution system. The purpose was to increase the operational pressure in the service connections. Batumi Tskali was responsible for operating, maintaining and paying electricity bills for these “official booster pumping stations”. In general these BPS were in a very poor condition and subjected to frequent repairs. Operating time was usually 12 hours per day, in the morning between 8 and 12 hours, and in the afternoon/evening between 15 and 23 hours. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 15 These BPS were connected directly to the distribution system using the network as a suction tank (the network volume is about 35.600 m3) and directly delivered into the house installation. Typically neither pressure surge tanks were installed nor any kind of operation control. Only very few buildings were equipped with roof tanks. 3.1.2.6 Consumption metering Batumi Tskali reported approximately 1.000 working water meters; all of them installed for legal entities. There was no metering on private consumption at all. Given the official figure of 18.750 service connections only 5% of the connections were metered, all others were calculated according to the pertinent standard consumption per user group. 3.1.2.7 Present service conditions A pressure monitoring program recording service pressure for at least 24 hours was carried out by installing automatic pressure loggers equally distributed over the system. The results can be summarized as follows: ⇒ ⇒⇒ ⇒ Maximum pressure was around 2 bar, only in one point a pressure of up to 4 bar was measured ⇒ ⇒⇒ ⇒ Minimum pressure in all districts was below 0.5 bar, even negative pressures were measured (created by the booster pumps) ⇒ ⇒⇒ ⇒ The pressure ranged in all districts most of the time below 0,7 bar 3.1.2.8 Present operation and maintenance of the system Maintenance of the system was limited to emergency repairs on main pipes and service connections. Batumi Tskali staff had been trained on leak detection and basic leak repair and tried to keep up with repairing detected leaks. 3.1.2.9 Detailed inventory of the existing distribution system A detailed inventory of the existing distribution system was carried out, based on the existing GIS system as well as on own field assessments. The complete first design of the system had been divided into 4 zones, which are foreseen to be rehabilitated in 3 phases. The inventory of the existing system had been divided into those 4 zones accordingly. These zones respectively phasing is shown in the map below. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 16 Figure 4: Existing system and rehabilitation phases 3.1.2.10 House installations House installations in apartment blocks followed the Russian design standards. Every apartment had up to four vertical pipes supplying kitchen, toilet and bathrooms separately. Horizontal rings in the apartments were very rare. All house installations were made of steel pipes and leaking rising mains were found seldom. Household taps and sanitary appliances were normally in poor condition and leaking, not closing properly and some older toilet bowl models need repeated flushing to finally discharge. All this contributed to the high specific consumption. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 17 3.1.3 New system design 3.1.3.1 Project target for the new water supply system The targets for rehabilitation of the system including house connections can be summarized as follows: All buildings shall be connected to a functioning water supply network, providing: ⇒ ⇒⇒ ⇒ 24 h continuous supply ⇒ ⇒⇒ ⇒ Sufficient water quantity for the 2025 demand, at an assumed max. per capita domestic specific demand of 120 l/c·d, and an overall per capita demand of 313 l/c·d ⇒ ⇒⇒ ⇒ Sufficient service pressure to operate the system without booster pumps up to the 9th floor ⇒ ⇒⇒ ⇒ Water in quality in line with Georgian Standards Further, ⇒ ⇒⇒ ⇒ All house connections shall allow metered water billing (condominium based) ⇒ ⇒⇒ ⇒ The loss level in the system shall be below 20% ⇒ ⇒⇒ ⇒ The system shall be compatible with the future overall layout of the entire city system, which requires at this stage already dimensioning of the entire future city system For balancing the demand, two news reservoirs will be constructed at Salibauri and Injalo hills. 3.1.3.2 General design On the next image we will be able to see the initial idea of the new network design. The network was divided into 4 different areas as explained, to build one behind the other, disconnecting the customers from the old system and supplying each area from the built new system. The rehabilitation is being done in three phases, nowadays the phase III is on the tender review stage. Under phase III will be built the network on some less inhabited areas at the moment, but with prevision of growing up in a short time. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 18 Figure 5: General design new system NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 19 3.1.3.3 New water supplying system Following a schematic chart is presented in order to define each element which forms part of the new water supplying system. Figure 6: New system scheme As we can see on the figure, the system is being supplied by three water sources: ⇒ ⇒⇒ ⇒ Chakvi Treatment Plant: this plant takes the water from the river Chakvi and feeds the system by gravity. It is possible to feed both reservoirs by Chakvi line, which is the main transmission pipe crossing all the city and villages from Chakvi treatment plant. ⇒ ⇒⇒ ⇒ Chaisubani Treatment Plant: in this case the plant takes water from the river Chaisubani feeding the system also by gravity. Normally supplying Salibauri reservoir. ⇒ ⇒⇒ ⇒ Mejinistkali Pumping Station: this is a yield with several wells situated on the bottom of the city. Normally supplying Injalo reservoir by pumping (some hours). ⇒ ⇒⇒ ⇒ Salibauri reservoir: inflows from Chakvi or from Chaisubani treatment plants. ⇒ ⇒⇒ ⇒ Injalo reservoir: inflows from Chakvi TP and from Mejinistkali PS. Both reservoirs are situated on hills which have the same height, so being Batumi a flat city each area of the system supplied by one or by the other reservoirs has the same pressure. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 26 On the other hand, the Billing Department of the water company was asked to start dividing the customers into the following categories shown on the table. CATEGORIES Customers with/without Water Meter Total Householders (Domestic) Smalle r Legal Entities (Commercial) Large ger Legal Entities (Industrial) Public entities Table 1: Data Billing Area. It is needed to explain a bit how the situation was at that moment. As Georgia was a soviet republic not long ago, they were not used to pay for water. Due to this, water consumption was not metered when speaking about micro-metering and badly the macro-metering. When the water infrastructure rehabilitation project started, after the soviet era, the Utility divided the city into Billing Areas, and the Billing Department started charging the equivalent of a consumption of 120 L/capita/day. This was planned to be provisional while building the new network and installing water meters to each customer. The true is that the real consumption per capita was at that moment around 700-800 L/cap/day. This issue is better explained when speaking about the IWA water balance implementation through the monthly comprehensive overviews. In order to plan the measurements to implement the water balance the new network was divided into DMAs. As Batumi is a flat city, it was agreed to follow the structure of billing areas to convert those areas in District Metered Areas like showed on the table. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 27 The DMA implementation will be widely explained on the correspondent chapter. Billing Area (BA) DMA Billing Area (BA) DMA BA 1 DMA 1-2-3 BA 11 DMA 11a BA 2 DMA 11b BA 3 BA 12 DMA 12a BA 4 DMA 4-5 DMA 12b BA 5 BA 13 DMA 13 BA 6 DMA 6 BA 14 DMA 14 BA 7 DMA 7-8 BA 15 DMA 15 BA 8 BA 16 DMA 16 BA 9 DMA 9 DMA 18 BA 10 DMA 10 DMA 19 DMA 20 BA 17 DMA 17 Table 2: Correspondence BA-DMA 3.2.2.2 Working plan In order to be able to improve the input data to be used for the water balance, it was planned to work on the next points: ⇒ ⇒⇒ ⇒ Flow meters installation at each inflow point: We needed to know the inflow water in the system from Chakvi, Chaisubani and Mejini Tskali. At that moment there were installed flow meters in Chakvi and Mejini Tskali, but inflow from Chaisubani was estimated. We could also obtain flow measurements from the reservoirs, Salibauri and Injalo, in order to know which part of the water from Chakvi, Chaisubani and Mejini Tskali supplied the reservoirs and which part was consumed by villages and settlements or consumed by old network illegal connections, or also lost by leakages. ⇒ ⇒⇒ ⇒ Each water use must have a water meter/ flow meter installed: This point was really difficult to reach, although the more new network built the more water meters installed. At that moment the system outflows measurements from Chakvi main were being made by 48 hours per month and that results were extrapolated to the entire month. The optimum would be measure it continuously to be able to improve the water balance and use it to know the Modulation Curves. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 28 ⇒ ⇒⇒ ⇒ Consumption Modulation Curves: Obviously, the consumption in a water supply is not constant throughout the day. The next image shows as an example a possible flow evolution curve, injected in a water supply over 24 hours a day. Figure 11: Modulation Curve The curve represents the Modulation Coefficient c (t), i.e. the relationship between average flow of each time of day, Q (t), and the average daily flow, Qad: =  This modulation curve will change from day to day (it is not the same a Tuesday than a Saturday) and it will also change throughout the year (it’s not the same a July day than a February day). In an existing network must be measured every injected or consumed flow (not just billed). With all this information we can establish average water provision, peak ratios, night flow and modulation curves for any day of the year. The more the infrastructure in water meters or flow meters, the more information is available. Analysis of these data is a prerequisite for using the mathematical model and obtains interesting results. The more reliable the input data are, the better the results. In order to start having more reliable data, a measurement plan was agreed and implemented while the works on the network continued. The initial plan is presented on the next page. These initial results were very useful to calibrate the first version of the mathematical model and also to start knowing consumption patterns and improving the water balance accuracy. 0 0,5 1 1,5 2 1 3 5 7 9 11 13 15 17 19 21 23 Modulation Coeffcient c(t) t (h) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 29 Table 3: Flow measurements plan Table 4: Pressure measurements plan MEDITION MEDITION FFM MFM MFM MFM MFM MFM MFM MFM FFM MEDITION MEDITION FFM FFM FFM WM MFM FFM FFM FFM FFM MFM FFM WMBARTSKHANA P.S. (output) CONTINUOSLY Mobile Flow Meter Water Meter SALIBAURI (output) CONTINUOSLY SUBSCRIBERS CONTINUOSLY INJALO (output) CONTINUOSLY INJALO (input) CONTINUOSLY Fix Flow Meter CHAISUBANI: PIPE 600 1 WEEK PER MONTH (7 DAYS) MEJINI TSKALI CONTINUOSLY INFLOW BATUMI TSKALI NETWORK PERIODICITY OUTFLOW BATUMI TSKALI NETWORK PERIODICITY SALIBAURI (input) CONTINUOSLY DMA's CONTINUOSLY 1 WEEK PER MONTH (7 DAYS) 1 WEEK PER MONTH (7 DAYS) FLOW MEASUREMENTS PLAN OLD NETWORK INFLOW BATUMI TSKALI NETWORK PERIODICITY OUTFLOW BATUMI TSKALI NETWORK PERIODICITY CHAKVI CHAISUBANI: 1 WEEK PER MONTH (7 DAYS) 1 WEEK PER MONTH (7 DAYS) FLOW MEASUREMENTS PLAN NEW NETWORK CONTINUOSLY 1 WEEK PER MONTH (7 DAYS) 1 WEEK PER MONTH (7 DAYS) 1 WEEK PER MONTH (7 DAYS) CONTINUOSLYBARTSKHANA P.S. (input) PIPE 400 PIPE 350 PIPE 600 MAKHINJAURI SPUTNIK SKOLA OTHER CONNECTIONS OLD NETWORK MEDITION MEDITION MPDL FPDL MPDL FPDL MPDL Mobile Pressure Data Logger FPDL FPDL Fix Pressure Data Logger FPDL INJALO CONTINUOSLY BARTSKHANA P.S. CONTINUOSLY BARTSKHANA P.S. 1 WEEK PER MONTH (7 DAYS) DMA's CONTINUOSLY SKOLA 1 WEEK PER MONTH (7 DAYS) SALIBAURI CONTINUOSLY PRESSURE MEASUREMENTS PLAN OLD NETWORK PERIODICITY NEW NETWORK PERIODICITY NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 30 3.2.3 Measurement plan 3.2.3.1 Introduction As we explained above, the measurements plan was created at the beginning of the losses control program implementation because the necessity to have a more accurate water balance to build and calibrate the mathematical model. It is worth to say that the metering points have changed along with the construction of the network and with the connection of the rehabilitated areas to the new supplying system. In each time we adjust the measurements to we need at that moment. At the beginning we had to divide the system into two: old system (the old areas supplied by Chakvi main, with lots of leakages and illegal connections) and the new one with the customers connected to the new system. Later, with most of the city connected to the new system and with the ongoing installation of individual water meters, we left the old system out of our water balance. From that moment, Chakvi main started to be rehabilitated to cut the leakages and also to disconnect from the system the illegal connections. Onwards, Chakvi main will only be used to supply the reservoirs with flow from Chakvi treatment plant. That situation will arrive when finishing the phase III, as nowadays a few DMAs are still connected to Chakvi main and supplied directly from Chakvi treatment plant and not from the reservoirs. In any case, those areas which will be rehabilitated under phase III have not so much connections nowadays. We are presenting the results of the measurements plan in our monthly and quarterly reports, and using it to calculate the water balance and calibrate the model. So, we think that the best option to show the results we are obtaining is to present the measurements of the last quarterly report before writing this Master Thesis. 3.2.3.2 Flow measurements At this stage of the program, we are using flow data from the following metering points: ⇒ ⇒⇒ ⇒ Outflow meters of the reservoirs. ⇒ ⇒⇒ ⇒ DMAs flow meters. We also know the water produced each month and have it online, as we will explain later when presenting the Managing Information System (MIS). NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 31 Regarding the water consumption, the next results from the reservoirs were observed during the last quarter. In first place the results of Salibauri reservoir. Figure 12: Salibauri average 24 h flow evolution in March Figure 13: Salibauri average 24 h flow evolution in April Figure 14: Salibauri average 24 h flow evolution in May 0,00 200,00 400,00 600,00 800,00 1000,00 1200,00 1400,00 1600,00 1800,00 2000,00 0:00:00 1:00:29 2:00:58 3:01:26 4:01:55 5:02:24 6:02:53 7:03:22 8:03:50 9:04:19 10:04:48 11:05:17 12:05:46 13:06:14 14:06:43 15:07:12 16:07:41 17:08:10 18:08:38 19:09:07 20:09:36 21:10:05 22:10:34 23:11:02 Q (m3/h) Salibauri Reservoir March Inlet Burstkana P.S. Inlet Chaisubani Outlet 0,00 200,00 400,00 600,00 800,00 1000,00 1200,00 1400,00 1600,00 1800,00 0:00:00 1:00:29 2:00:58 3:01:26 4:01:55 5:02:24 6:02:53 7:03:22 8:03:50 9:04:19 10:04:48 11:05:17 12:05:46 13:06:14 14:06:43 15:07:12 16:07:41 17:08:10 18:08:38 19:09:07 20:09:36 21:10:05 22:10:34 23:11:02 Q (m3/h) Salibauri Reservoir April Inlet Burstkana P.S. Inlet Chaisubani Outlet 0,00 200,00 400,00 600,00 800,00 1000,00 1200,00 1400,00 1600,00 1800,00 2000,00 0:00:00 1:00:29 2:00:58 3:01:26 4:01:55 5:02:24 6:02:53 7:03:22 8:03:50 9:04:19 10:04:48 11:05:17 12:05:46 13:06:14 14:06:43 15:07:12 16:07:41 17:08:10 18:08:38 19:09:07 20:09:36 21:10:05 22:10:34 23:11:02 Q (m3/h) Salibauri Reservoir May Inlet Burstkana P.S. Inlet Chaisubani Outlet NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 32 As we can see on the charts, the minimum consumption occurs during night time with a value around 1.000 m3/h (still high) and the maximum is growing each month from 1.400 m3/h in March until 1.800 m3/h in May. The inlet from Chaisubani oscillates between 1.200 and 1.400 m3/h and the inlet from Burthkana pumping station is variable according with the water height in Chakvi. In any case this is less important than Chaisubani inlet to Salibauri. On the other hand, the difference between the night time consumption and the day time one, in comparison with the average consumption, is starting to be different, broking the observed tendency last months. This is a good signal of NRW reduction because DMAs isolation. The consumption patterns observed each month and the average are presented as follows. Figure 15: Salibauri consumption pattern during the quarter Figure 16: Salibauri consumption pattern in average As we have commented above, the consumption pattern slightly has broken the usual shape we are observing along the last months. Maximums consumption peaks are 20% higher than the average consumption and minimums ones are 30% lower. 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Salibauri Consumption Pattern March14 April14 May14 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Salibauri Consumption Pattern C (t) C (t) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 33 In second place, the presentation of Injalo reservoir results. Figure 17: Injalo average 24 h consumption in March Figure 18: Injalo average 24 h consumption in April Figure 19: Injalo average 24 h consumption in May In the case of Injalo, the minimum consumption occurs during night time with a value around 1.000 m 3 /h as in Salibauri and the maximum is higher in March with around 2.000 m 3 /h. So, during March Injalo was supplying the network more than Salibauri. The other months, April and May, the maximums were similar to Salibauri. 0,00 500,00 1000,00 1500,00 2000,00 2500,00 3000,00 3500,00 4000,00 0:00:00 1:00:29 2:00:58 3:01:26 4:01:55 5:02:24 6:02:53 7:03:22 8:03:50 9:04:19 10:04:48 11:05:17 12:05:46 13:06:14 14:06:43 15:07:12 16:07:41 17:08:10 18:08:38 19:09:07 20:09:36 21:10:05 22:10:34 23:11:02 Q (m3/h) Injalo Reservoir March Inlet Chakvi Inlet MejinisTskali Outlet 0,00 200,00 400,00 600,00 800,00 1000,00 1200,00 1400,00 1600,00 1800,00 0:00:00 1:00:29 2:00:58 3:01:26 4:01:55 5:02:24 6:02:53 7:03:22 8:03:50 9:04:19 10:04:48 11:05:17 12:05:46 13:06:14 14:06:43 15:07:12 16:07:41 17:08:10 18:08:38 19:09:07 20:09:36 21:10:05 22:10:34 23:11:02 Q (m3/h) Injalo Reservoir April Inlet Chakvi Inlet MejinisTskali Outlet 0,00 200,00 400,00 600,00 800,00 1000,00 1200,00 1400,00 1600,00 1800,00 0:00:00 1:00:29 2:00:58 3:01:26 4:01:55 5:02:24 6:02:53 7:03:22 8:03:50 9:04:19 10:04:48 11:05:17 12:05:46 13:06:14 14:06:43 15:07:12 16:07:41 17:08:10 18:08:38 19:09:07 20:09:36 21:10:05 22:10:34 23:11:02 Q (m3/h) Injalo Reservoir May Inlet Chakvi Inlet MejinisTskali Outlet NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 34 The inlet from Chakvi oscillates between 1.400 and 1.500 m3/h (the chart of March is registering one mistake at the end of the month) and the inlet from Mejinistkali presented values between 1.600 – 2.000 m3/h. Respecting consumption patterns, we can see the increase on the maximum during March. Regarding the value of the minimum is similar to past periods. Figure 20: Injalo consumption pattern during the quarter Figure 21: Injalo consumption pattern in average Finally, the flow measurements at the DMAs are being done by flow meters installed as showed on the design map. As it will be presented later when speaking about DMA implementation and about the MIS, each flow meter has installed a transmitter which sends the flow data each 15 minutes to the water utility server. Due to this, we can know the flow in each device consulting the MIS on the website. On the other hand, we implemented on the MIS the calculation of the monthly flows in each flow meter in order to collect automatically the data. 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Injalo Consumption Pattern March13 April14 May14 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Injalo Consumption Pattern C (t) C (t) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 35 As having electricity cuts is still quite likely, the NRW team (water utility staff trained by us on NRW issues) is also getting the data from the flow meters via usb each month. Following, examples of some flow meters data. DMA 17 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 42 Outlet Chakvi TP m 3 2.704.689 Water Production m 3 3.557.596 Outlet Chaisubani TP m 3 498.720 Inlet reservoirs m 3 1.980.263 Outlet Mejinistskali m 3 354.187 Inlet DMA17 m 3 191.029 Outlet Salibauri m 3 972.265 m 3 1.386.304 Outlet Injalo m 3 1.007.998 % 39% Input Data Distribution Water Balance Consumption+ Leakages Chakvi Inlet m 3 392.069 Chakvi Inlet m 3 884.297 Chaisubani Inlet m 3 728.064 MejiniTskali m 3 354.187 Total m 3 1.120.133 Total m 3 1.238.484 Inlet Salibauri Inlet Injalo DMAs Water Balance DMA DMA 6 DMA 7-8 DMA 9 DMA 10 DMA 11a DMA 11b FM measuring DMA Inflows ID FM09; FM16A; FM17 FM18;FM19 FM26;FM30A FM20;FM25 FM08;FM15 FM16 FM measuring DMA Outflows ID FM18 FM19;FM24;FM26 FM16A;FM17 Inflow metered m 3 63.746 142.159 365.017 120.705 20.041 Outflow metered m 3 65.382 76.777 Input Volume per DMA m 3 -1.636 142.159 365.017 43.928 20.041 FM11 FM30A 429.411 186.384 243.026 DMA 1-2-3 FM36;FM10 FM08;FM11 651.337 429.411 221.926 DMA 4-5 DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 FM13 FM29;FM30 FM28 FM21 FM22 FM12 FM23 FM27 FM24 FM31;FM29 FM20 FM23;FM28 ; FM25 FM27 51.831 224.687 66.009 946.373 191.029 56.474 43.576 59.866 117.313 51.831 224.687 -43.576 6.143 829.060 191.029 56.474 1 ) 2 ) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 43 DMA 6 DMA 7-8 DMA9 DMA 10 DMA 11a DMA 11b Population - Capita - Inhabitants Single No. 2230 1713 967 332 17 1406 Italian No. 2315 1338 874 99 55 735 Block No. 2157 2829 4509 4892 95 1391 Total Householders (Domestic) No. 6.702 5.880 6.350 5.323 167 3.5329.339 DMA DMA 1-2-3 DMA 4-5 372 5273 3694 658 2798 7926 11.382 DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 828 36 4 59 17 134 127 404 483 299 15 383 151 1417 430 4269 4898 9210 4208 2185 2.649 949 4.572 4.972 9.227 4.725 2.463 Number of Customers Single No. 1167 917 477 258 7 775 Italian No. 1447 846 502 109 25 367 Block No. 1353 1772 2440 2456 30 640 Total Householders (Domestic) No. 3.967 3.535 3.419 2.823 62 1.782 Smaller Legal Entities (Commercial) No. 257 173 161 83 2 98 Bigger Legal Entities (Industrial) No. 2 3 2 Public entities No. 7 9 3 1 1 4 Total=H+C+I+P No. 4.231 3.717 3.585 2.907 68 1.886 5.726 7.062 7.5876.497 499 26 739 3 29 234 3074 2418 340 1631 5091 429 15 4 46 283 140 88 107 92 76 290 178 192 6 67 73 152 14 6 140 630 189 3110 4672 3696 1934 121 929 1.349 382 3.306 4.724 350 3.909 2.174 242 98 1.145 29 2 96 45 13 145 47 3 45 1 1 3 1 17 3 6 7 1 9 1.382 385 3.419 4.772 363 4.060 2.229 246 98 1.199 3 ) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 44 Number of Customers Watermeters Single No. 1348 1041 555 276 1 36 Italian No. 1182 732 299 66 63 Block No. 452 611 366 373 60 Total Householders (Domestic) No. 2.982 2.384 1.220 715 1 159 Smaller Legal Entities (Commercial) No. 256 173 161 83 2 96 Bigger Legal Entities (Industrial) No. 2 3 2 Public entities No. 7 9 3 1 1 4 Total=H+C+I+P No. 3.245 2.566 1.386 799 7 261 3.673 935 737 3.831 3.308 4.442 363 1230 1715 231 2507 3 29 498 25 427 1 138 295 132 1 107 90 87 159 195 166 43 63 13 14 6 77 337 1224 2068 162 47 119 254 923 1.420 2.372 338 357 61 240 96 418 29 2 96 44 13 144 47 3 45 1 1 3 1 16 3 6 7 1 9 956 3 1.532 2.419 351 507 116 244 96 472 DMA 6 DMA 7-8 DMA9 DMA 10 DMA 11a DMA 11b Billed Metered Consumption Single m 3 32802 23100 15470 6735 16 994 Italian m 3 17940 10629 4807 1250 767 Block m 3 5231 7821 4572 3370 402 Total Householders (Domestic) m 3 55.973 41.549 24.849 11.355 16 2.163 Smaller Legal Entities (Commercial) m 3 3378 5097 5320 1521 114 1556 Bigger Legal Entities (Industrial) m 3 2063 581 2563 Public entities m 3 1388 1878 752 84 111 51 Total=H+C+I+P m 3 60.739 48.525 32.984 12.960 822 6.333 DMA DMA 1-2-3 DMA 4-5 40.985 57.266 40.374 3019 28474 9492 6805 17122 16447 25122 15340 2386 14407 2485 83.834 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 45 DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 9384 2082 7671 2917 4 3081 3337 2053 3357 2302 1951 995 980 203 228 150 1459 4101 6588 8214 1260 253 1341 2879 16.842 8.890 12.247 8.666 5.157 460 4.650 3.487 6.391 616 7 5293 2498 490 4907 2586 50 2845 51 325 193 275 1600 968 812 986 16 466 17.702 282 15.783 15.713 9.156 10.875 4.357 4.716 3.487 9.702 DMA 6 DMA 7-8 DMA9 DMA 10 DMA 11a DMA 11b SCENARIO 1 Billed Unmetered Consumption (120 L/capita/day) Single m 3 259 166 338 54 61 6812 Italian m 3 565 86 803 126 2556 Block m 3 4766 6847 14602 16279 342 4399 Total Householders (Domestic) m 3 12.679 7.099 15.743 16.333 529 13.767 Smaller Legal Entities (Commercial) m 3 4 650 Bigger Legal Entities (Industrial) m 3 Public entities m 3 Total=H+C+I+P m 3 12.683 7.099 15.743 16.333 529 14.417 DMA DMA 1-2-3 DMA 4-5 24.5258.936 40 52 33 22864 43 1458 7394 230 1346 8.896 24.440 DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 695 76 14 7 14 792 104 551 900 601 1512 324 2311 1397 12776 17172 32202 15030 6469 3.557 2.372 13.392 17.179 32.216 17.334 6.898 6 6 129 3.557 2.372 13.521 17.185 32.223 17.334 6.898 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 46 DMA 6 DMA 7-8 DMA9 DMA 10 DMA 11a DMA 11b SCENARIO 2 Scenario 2 L/cap/day= 700 120 5,83 <---ratio Billed Unmetered Consumption (700 L/capita/day) Single m 3 1512 966 1974 315 357 39737 Italian m 3 3297 504 4683 735 14910 Block m 3 27804 39942 85176 94962 1995 25662 Total Householders (Domestic) m 3 32.613 41.412 91.833 95.277 3.087 80.309 Smaller Legal Entities (Commercial) m 3 23 3792 Bigger Legal Entities (Industrial) m 3 Public entities m 3 Total=H+C+I+P m 3 32.636 41.412 91.833 95.277 3.087 84.100 DMA DMA 1-2-3 DMA 4-5 252 1344 8505 7854 43134 133371 51.891 142.569 233 303 193 52.124 143.065 DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 4053 441 84 42 84 4620 609 3213 5250 3507 8820 1890 13482 8148 74529 100170 187845 87675 37737 20.748 13.839 78.120 100.212 187.929 101.115 40.236 36 36 750 20.748 13.839 78.870 100.248 187.965 101.115 40.236 DMA 6 DMA 7-8 DMA9 DMA 10 DMA 11a DMA 11b Total Consumption = Revenue Water (120 L/capita/day) Single m 3 33061 23266 15808 6789 77 7806 Italian m 3 18505 10715 5610 1250 126 3323 Block m 3 9997 14668 19174 19649 342 4801 Total Householders (Domestic) m 3 61.564 48.649 40.592 27.688 545 15.930 Smaller Legal Entities (Commercial) m 3 3382 5097 5320 1521 114 2206 Bigger Legal Entities (Industrial) m 3 2063 581 2563 Public entities m 3 1388 1878 752 84 111 51 Total=H+C+I+P m 3 66.333 55.624 48.727 29.293 1.351 20.750 DMA DMA 1-2-3 DMA 4-5 25162 14459 15340 2386 2518 92.769 81.791 3062 7035 29932 39310 49.881 64.814 18469 16886 4 ) 5 ) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 47 DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 10079 76 14 2089 7671 2931 796 3081 3337 2157 3908 900 2903 1951 995 980 1715 228 150 1783 6412 1397 19364 25386 33462 15283 1341 9348 20.399 2.372 22.282 29.426 8.666 37.373 17.794 4.650 3.487 13.289 616 7 5293 2504 490 4913 2586 50 2845 51 325 193 275 1729 968 812 986 16 466 21.259 2.654 29.304 32.898 9.156 43.098 21.691 4.716 3.487 16.600 DMA 6 DMA 7-8 DMA9 DMA 10 DMA 11a DMA 11b Total Consumption = Revenue Water (700 L/capita/day) Single m 3 34314 24066 17444 7050 373 40730 Italian m 3 21237 11133 9490 1250 735 15677 Block m 3 33035 47763 89748 98332 1995 26064 Total Householders (Domestic) m 3 88.586 82.961 116.682 106.632 3.103 82.471 Smaller Legal Entities (Commercial) m 3 3401 5097 5320 1521 114 5348 Bigger Legal Entities (Industrial) m 3 2063 581 2563 Public entities m 3 1388 1878 752 84 111 51 Total=H+C+I+P m 3 93.375 89.937 124.817 108.237 3.909 90.433 DMA DMA 1-2-3 DMA 4-5 2678 135.958 200.331 3271 8149 36979 24976 52626 149818 92.876 182.943 25355 14710 15340 2386 DMA 11b DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 40730 13437 441 84 2124 7671 3001 4624 3081 3337 2662 15677 6570 5250 5809 1951 995 980 9023 228 150 3349 26064 17583 8148 81117 108384 189105 87928 1341 40616 82.471 37.590 13.839 87.010 112.459 8.666 193.086 101.575 4.650 3.487 46.627 5348 616 7 5293 2534 490 4943 2586 50 2845 2563 51 325 51 193 275 2350 968 812 986 16 466 90.433 38.450 14.121 94.653 115.961 9.156 198.841 105.472 4.716 3.487 49.938 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 48 Inflows and Outflows DMA DMA 6 DMA 7-8 DMA 9 DMA 10 DMA 11a DMA 11b FM measuring DMA Inflows ID FM09;FM16A; FM17 FM18;FM19 FM26;FM30A FM20;FM25 FM08;FM15 FM16 FM measuring DMA Outflows ID FM18 FM19;FM24;FM26 FM16A;FM17 Inflow metered m 3 63.746 142.159 365.017 120.705 20.041 Outflow metered m 3 65.382 76.777 Input Volume per DMA m 3 -1.636 142.159 365.017 43.928 20.041 DMA 4-5 FM11 FM30A 429.411 221.926 FM36;FM10 FM08;FM11 651.337 429.411 186.384 243.026 DMA 1-2-3 DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 FM13 FM29;FM30 FM28 FM21 FM22 FM12 FM23 FM27 FM24 FM31;FM29 FM20 FM23;FM28; FM25 FM27 51.831 224.687 66.009 946.373 191.029 56.474 43.576 59.866 117.313 51.831 224.687 -43.576 6.143 829.060 191.029 56.474 SCENARIO 1 Non-Revenue Water = Input Volume - Revenue Water (120 L/capita/day) DMA DMA 6 DMA 7-8 DMA 9 DMA 10 DMA 11a DMA 11b Total Revenue Water m 3 66.333 55.624 48.727 29.293 1.351 20.750 % Revenue Water % -4054% 39% 13% 67% 7% #DIV/0! Total Non-Revenue Water m 3 -67.970 86.535 316.290 14.635 18.690 -20.750 % Non-Revenue Water % 4154% 61% 87% 33% 93% #DIV/0! DMA 1-2-3 92.769 41,80% 129.157 58,20% DMA 4-5 81.791 33,66% 161.235 66,34% DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 21.259 2.654 29.304 32.898 9.156 43.098 21.691 4.716 3.487 16600 41% #DIV/0! 13% -75% 149% 5% 11% 8% #DIV/0! #DIV/0! 30.572 -2.654 195.384 -76.474 -3.013 785.962 169.338 51.758 -3.487 -16.600 59% #DIV/0! 87% 175% -49% 95% 89% #DIV/0! #DIV/0! 7 ) 6 ) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 49 Comprehensive overview structure (only green cells are available to input data): 1) Water production data, network distribution and reservoirs inlets. 2) DMAs water balance: flow meters readings. 3) Billing data: population, customer type*, nº customers/DMA, nº customers with water meter/DMA, billed metered consumption, billed unmetered consumption (scenario 1). 4) Billed unmetered consumption (calculation scenario 2). 5) Total consumption: revenue water per DMA (both scenarios). 6) DMA inflows and outflows (from point 2). 7) Non-revenue water per DMA (both scenarios). *Customer type: householders (domestic consumption) are divided in single houses, blocks (apartment buildings) and Italian yards (typical houses disposition around a common courtyard). SCENARIO 2 Non-Revenue Water = Input Volume - Revenue Water (700 L/capita/day) DMA DMA 6 DMA 7-8 DMA 9 DMA 10 DMA 11a DMA 11b Total Revenue Water m 3 93.375 89.937 124.817 108.237 3.909 90.433 % Revenue Water % -5706% 63% 34% 246% 20% #DIV/0! Total Non-Revenue Water m 3 -95011,20185 52.223 240.200 -64.309 16.132 -90.433 % Non-Revenue Water % 5806% 37% 66% -146% 80% #DIV/0!38,74% 17,57% DMA 1-2-3 DMA 4-5 135.958 200.331 61,26% 82,43% 85.968 42.696 DMA 12a DMA 12b DMA 13 DMA 14 DMA 15 DMA 16 DMA 17 DMA 18 DMA 19 DMA 20 38.450 14.121 94.653 115.961 9.156 198.841 105.472 4.716 3.487 49.938 74% #DIV/0! 42% -266% 149% 24% 55% 8% #DIV/0! #DIV/0! 13.381 -14.121 130.034 -159.537 -3.013 630.219 85.557 51.758 -3.487 -49.938 26% #DIV/0! 58% 366% -49% 76% 45% 92% #DIV/0! #DIV/0! NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 50 The reason to use two scenarios is because nowadays the billing of the customers without water meter is not representing the reality of the consumption. Then, the Scenario 1 is calculated with the assumption of consumption of 120 L/cap/day which the water utility is billing to the customers. The Scenario 2 tries to represent the real consumption, taking into account some measurements done for example at the multi-apartment buildings which have not individual water meters at this moment. On those measurements we can notice the reality of the large consumption and calculate the Scenario 2 accordingly. 3.3.3 IWA Water Balance The last part of the comprehensive overview uses the data from Scenario 1 and Scenario 2 to calculate the IWA water balance of the entire system. Despite we are implementing the DMAs as explained before, it is not possible at the moment to calculate the IWA water balance per DMA, which is our next target. So, at the moment, we are calculating it for the system as a whole. Below we are presenting the balance as it appears on the comprehensive overviews, in this case calculated for Scenario 2. Finally, for the terms which form the non-revenue water, we are trying to accurate them increasingly. At the moment we are calculating some of them by random measurements and estimating the others. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 51 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 Metering Inaccuracies and Data Handling Errors Leakage on Transmission and/or Distribution Mains Leakage and Overflows at Utility's Storage Tanks Leakage on Service Connections up to Point of Customer Metering Real Loses=Leakage on Transmission+ Leakage and Overflows at Tanks + Leakage on Service Connection Apparent Losses = Unaothorized Consumption + Metering Inaccuracies and Data Handling Errors 220.640 365.436 137.900 unbilled Unmetered Consumption Authorized Consumption = Billed Auth. Cons. + unbilled Auth. Cons. Water Losses = Sistem Input Volume - Authorized Consumption Unauthorized consumption 2.171.292 395.233 1.086.557 1.481.790 689.502 34.475 68.950 103.425 68.950 Sistem Input Volume=Inflow from Reservoirs Salibauri and Injalo Billed Metered Consumption = Sum of Billed Metered Consumption per DMA Billed Unmetered Consumption = Sum of Billed Unmetered Consumption per DMA Revenue Water = Sum of Billed Metered Consumption per DMA + Sum of Billed Unmetered Consumption per DMA = Billed Authorized Consumption Non-revenue Water = SIV - RW unbilled Metered Consumption 1.585.215 586.077 275.801 89.635 13.790 unbilled Authorized Consumption = unbilled Met.Cons + unbilled Unmet.Cons. km 9 No. 42968 km 201 No./km 4774 m 55 L/day 2.175.877 m 3 163.588 % 23,5% 0,075 % Non-Revenue Water Lm = Length of mains Nc = Number of service connections, Nc>5000 Lp = Total length of private pipe, property line to customer meter (km) DC = Density of connections / km mains, DC> 20 Unavoidable Annual Real Losses (UARL) = (18 x Lm + 0.8 x Nc + 25 x Lp) x P Real Losses = CARL = Water Losses - Apparent Losses Infrastructure Leakage Index = ILI = CARL / UARL P = Average Pressure, P>25m NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 58 Figure 27: Flow injection 24 h distribution from Salibauri (left) and Injalo (right) There is a difference between the amount of water injected in the network calculated by the model and registered by the flow meters in Salibauri and Injalo. PERIOD FLOW INJECTION (m 3 ) MODEL 915.234 MEASUREMENTS PLAN 874.792 DIFFERENCE 40.442 Table 12: Flow injection difference between Model and Measurements Plan This difference would decrease in the next simulations due to the calibration of the model. It can be explained as we are using the total inflow divided between the consumption nodes of the model, and in the reality in this moment, the inflow is supplying only some parts of the network, not the entire network. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 59 To calibrate the model, the difference between flow injection metered in the reservoirs in the calculation period and the injected modelled flow will be considered as NRW. The distribution of the NRW is done like it was explained, as we can see as follows. NRW DISTRIBUTION (m 3 ) Leakages 45% 18.199 Measurement errors 18% 7.280 Illegal connections 4% 1.618 Unknown 33% 13.346 Table 13: NRW distribution Once NRW has been distributed, the next step is to apply it to the model using the three methods explained previously. 3.4.4 NRW modeling case 1: correction with a demand factor With this method we are adjusting the volumetric losses into the registered water, and resimulating the system. The Demand Factor will be calculated as follows: injected registered d F∀ ∀ = The next table shows the calculation of the demand factor depending on the scenario. Fd Calculation Scen ario 2 (300 L/cap/d) Injected Water (m 3 ) 915.234 Registered Water (m 3 ) 874.792 Fd 0,96 Table 14: Demand factor calculation The simulation with this demand factor affecting to the consumption curve leads to the adjustment between injected and registered water, as expected. However, EPANET presents problems of unbalanced system during some hours of the simulation due to the apparition of negative pressures. The next figure shows the reservoir’s outflow distribution in this case. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 60 Figure 28: Reservoirs flow injection 24 h distribution, case 1 The correction between the injected and registered water is done by the application of the factor, as the next table shows. DEMAND FACTOR CORRECTION (m 3 ) Injected Wat er (m 3 ) 878.628 Registered Water (m 3 ) 874.792 Difference (m 3 ) 2.354 Table 15: Results demand factor correction The model calculates the adjustment as a result of the reduction of the peaks of consumption in the nodes due to the consumption pattern. This procedure could reduce the leakages during the night time (higher pressure) but it can also reduce the demanded consumption during day time. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 61 3.4.5 NRW modeling case 2: constant leak flow In this case we will consider volumetric losses as constants and independent of the time at which they occur. It means that the leak flow will be constant and proportional to the base demand in each node. As we divided the total consumption between the consumption nodes of the model as a first approximation for this simulation, the base demand will be the same in each node, and in consequence, the leak flow will also be the same. To simulate this case, we have to use a constant modulation curve (same consumption pattern in each hour). The NRW flow distribution in the model has been done as follows: CONSTANT LEAK FLOW DISTRIBUTION Registered Water (m 3 ): 874.792 Base Demand: 0,296 L/s NRW (m 3 ): 40.442 NRW Flow: 0,014 L/s Total Demand: 0,309 L/s Table 16: Constant leak flow distribution In this case the simulation doesn’t present any error message. The outflow distribution by hour from the reservoirs is showed in the next image. Figure 29: Reservoirs flow injection 24 h distribution, case 2 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 62 The results obtained modeling NRW as a constant flow are presented following. CONSTANT LEAK FLOW (m 3 ) Injected Water (m 3 ) 876.052 Registered Water (m 3 ) 874.792 Difference (m 3 ) 1.259 Table 17: Results demand factor correction The results show that the adjustment between injected and registered water has been higher than in the previous case. However, this method model NRW as constant during the 24 hours of the day and not depending on the local pressure in the network. 3.4.6 NRW modeling case 3: leak flow using emitters The last model that we are presenting to model NRW is based in considering part of the leak flow as pressure dependent, and modeling the other part as flow not registered but consumed. The pressure dependent flow represents the leakages, with different value depending on the time of the day. The NRW which is not considered as pressure dependent represents the water that is consumed but not metered. This part follows the same consumption pattern as the registered flow. The leakage flow is divided using emitters at the nodes and calculated at time of lower consumption and higher pressure (night minimum flow). The other part of the flow is assigned constant or proportional to consumption demand in every moment. In the following table we can observe the distribution of NRW which will be used to calculate the emitters’ coefficient, following the AEAS research explained above. Leakages (m 3 /d) Distribution by AEAS research and allocation (m 3 /d) 1.305 Leakages 45,00% 67,16% 876 Measurement errors 18,00% 26,87% 350 Illegal connections 4,00% 5,97% 78 Unknown 33,00% Table 18: Allocation of leakage flow for emitter calculation NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 63 The last term is allocated into the first three ones proportionally, to establish the flow values which are not pressure dependent and the dependent ones. m3/d L/s Leakage Flow 1.305 15,10 Pressure dependent Flow 876 10,14 Non Pressure dependent Flow 428 4,96 Table 19: Pressure dependent and non-dependent flows The next step is to divide the non-dependent flow proportionally between the consumption nodes of the model. In our case, to make this first simulate, the model’s consumption nodes have the same basis demand, so the pressure non-dependent flow will be the same for all nodes. On the other hand, the pressure dependent flow will be used to calculate the emitter’s coefficients, as showed in the next equation.  =  · √ The pressure term is calculated in each node at the instant of lower flow (night time) which presents the higher pressure value. The following tables show the calculations explained previously applied to one node of each DMA (only to show briefly how it was made because the model has 1105 consumption nodes). Node Q BasisDemand (L/s) Q PressureNon-dependent (L/s) Q total (L/s) DMA1 - 2 - 3 0,30 0,0045 0,3045 DMA4 - 5 0,30 0,0045 0,3045 DMA6 0,30 0,0045 0,3045 DMA7 - 8 0,30 0,0045 0,3045 DMA9 0,30 0, 0045 0,3045 DMA10 0,30 0,0045 0,3045 DMA11a 0,30 0,0045 0,3045 DMA11b 0,30 0,0045 0,3045 DMA12a 0,30 0,0045 0,3045 DMA12b 0,30 0,0045 0,3045 DMA13 0,30 0,0045 0,3045 DMA14 0,30 0,0045 0,3045 DMA15 - 16 0,30 0,0045 0,3045 DMA17 0,30 0,0045 0,3045 DM A20 0,30 0,0045 0,3045 Table 20: Allocation pressure non-dependent leakages flow NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 64 Node Q Pressure-dependent (L/s) P (mca) √  Ec DMA1 - 2 - 3 0,01 60,44 7,77 0,0012 DMA4 - 5 0,01 60,50 7,78 0,0012 DMA6 0,01 60,32 7,77 0,0012 DMA7 - 8 0,01 58,90 7,67 0,0012 DMA9 0,01 60,98 7,81 0,0012 DMA10 0,01 59,96 7,74 0,0012 DMA11a 0,01 60,82 7,80 0,0012 DMA11b 0,01 54,93 7,41 0,0012 DMA12a 0,01 61,93 7,87 0,0012 DMA12b 0,01 50,73 7,12 0,0013 DMA13 0,01 61,15 7,82 0,0012 DMA14 0,01 61,06 7, 81 0,0012 DMA15 - 16 0,01 58,40 7,64 0,0012 DMA17 0,01 54,93 7,41 0,0012 DMA20 0,01 60,63 7,79 0,0012 Table 21: Emitter coefficient calculation Once obtained the value of the Emitter Coefficient it is updated in each consumption node and the simulation can be done. The outflow distribution by hour from the reservoirs with this method is showed in the next image. Figure 30: Reservoirs flow injection 24 h distribution, case 3 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 65 The results obtained in this case are presented on the next table. EMITTER COEFFICIENT (m 3 ) Injected Water (m 3 ) 940.210 Registered Water (m 3 ) 874.792 Difference (m 3 ) 65.418 Table 22: Results emitter coefficient Although the difference between injected and registered water is higher than the previous methods, in this case the simulation is more realistic. Indeed, it is needed to find the percentages of NRW which are dependent and non-dependent of the pressure. During the next months, when most of the customers will be metered by water meter and the DMAs in use, we will start a campaign to calculate more properly the values of the NRW terms. In this manner the percentage of NRW which is due to leakages could be calculate more accurately. The calibration of the model and the updated of the consumption nodes with new values of the emitter coefficients will allow reduce each time the observed difference. At the moment, we are going to increase the percentage of NRW pressure-dependent, in order to reduce the consumption which is non-pressure dependent. This is achieved converting the percentage due to unknown (33%) into leakages (45+33=78%), as the next table shows. Leakages (m 3 /d) NRW Distribution (m3/d) 1.305 Leakages 45,00% 78 % 1.018 Measurement errors 18,00% 18,00% 235 Illegal connections 4,00% 4,00% 52 Unknown 33,00% Table 23: New NRW distribution for emitter calculation And the flows calculation is changed due to the new distribution, as follows. m3/d L/s Leakage Flow 1.305 15,10 Pressure dependent Flow 1.018 11,78 Non Pressure dependent Flow 287 3,32 Table 24: New pressure dependent and non-dependent flows NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 66 Following, the tables with the calculations of consumption and emitter coefficient are repeated with the new values. Node Q BasisDemand (L/s) Q PressureNon-dependent (L/s) Q total (L/s) DMA1 - 2 - 3 0,30 0,0030 0,3030 DMA4 - 5 0,30 0,0030 0,3030 DMA6 0,30 0,0030 0,3030 DMA7 - 8 0,30 0,0030 0,3030 DMA9 0,30 0,0030 0,3030 DMA1 0 0,30 0,0030 0,3030 DMA11a 0,30 0,0030 0,3030 DMA11b 0,30 0,0030 0,3030 DMA12a 0,30 0,0030 0,3030 DMA12b 0,30 0,0030 0,3030 DMA13 0,30 0,0030 0,3030 DMA14 0,30 0,0030 0,3030 DMA15 - 16 0,30 0,0030 0,3030 DMA17 0,30 0,0030 0,3030 DMA20 0,30 0,0030 0 ,3030 Table 25: New allocation pressure non-dependent leakages flow Node Q Pressure-dependent (L/s) P (mca) √  Ec DMA1 - 2 - 3 0,0107 60,4400 7,7743 0,001371 DMA4 - 5 0,0107 60,5000 7,7782 0,001370 DMA6 0,0107 60,3200 7,7666 0,001372 DMA7 - 8 0,0107 58,9000 7,6746 0,001389 DMA9 0,0107 60,9800 7,8090 0,001365 DMA10 0,0107 59,9600 7,7434 0,001376 DMA11a 0,0107 60,8200 7,7987 0,001367 DMA11b 0,0107 54,9300 7,4115 0,001438 DMA12a 0,0107 61,9300 7,8696 0,001354 DMA12b 0,0107 50,7300 7,1225 0,001496 DMA13 0,0107 61,1500 7,8198 0,001363 DMA14 0,0107 61,0600 7,8141 0,001364 DMA15 - 16 0,0107 58,4000 7,6420 0,001395 DMA17 0,0107 54,9300 7,4115 0,001438 DMA20 0,0107 60,6300 7,7865 0,001369 Table 26: New emitter coefficient calculation NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 67 The simulation with the new values presents a flow distribution from the reservoirs as showed in the next image. Figure 31: Reservoirs new flow injection 24 h distribution, case 3 The results obtained in this case are presented on the next table. EMITTER COEFFICIENT (m 3 ) Injected Water (m 3 ) 887.553 Registered Water (m 3 ) 874.792 Difference (m 3 ) 12.761 Table 27: Results emitter coefficient As we can see, the results got better considering more percentage of pressure-dependent flow. This hypothesis has to be evaluated and estimated in a correct way to achieve the more realistic calibration of the model. Regarding this point, and as we commented various times, during the next months we should elaborate a plan to estimate the different terms which IWA defines as the parts of NRW. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 74 3.4.9.1.1 Pressure evolution study Regarding the pressure, the model replicate as happens in the reality as we can see in the next chart in comparison with our measurements plan. Figure 42: 24 hour pressure evolution in the measuring points In the graph the 4 points of our measurement plan are represented by model’s nodes, as follows: • Pink line: Varshanidze 54. • Red line: Theatre. • Green line: Sheraton. • Blue line: Pushkini 17. Pressure values have a maximum during night time (0 to 5 h) and have a minimum during day time when the consumption is high (8 to 18 h). Night time pressure values are directly related with leakage flows and the knowledge of the minimum flow consumed at night allows estimating losses. Regarding this fact, following we are presenting a brief study which is a useful tool to calibrate the NRW mathematical model. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 75 3.4.9.1.2 Night flow study Although this method requires having the network divided in DMA’s in operation, we are going to present a short study using the information from the flow registered in Salibauri and Injalo by the measurements plan and the flow injected by the model. During the next months, we will repeat the method applied to isolated DMA’s which have metered inflow and customers with water meters. Following are presented the values of the 24 hour based outflow of Salibauri and Injalo, which were got with the model. Figure 43: Outflow Salibauri and Injalo NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 76 If we pay attention to the hours when the pressure is the maximum which we have already known, 0 to 5 h, the lower consumption is made at 3:00 h. On the other hand, the consumption pattern which is applied to the basis demand in the model presents the next shape. Figure 44: Consumption pattern Using average measurements plan data and the model we can estimate the leakage flow as showed on the next tables. time (h) Salibauri outf .(l/s) Salib auri outf. (m 3 /h) Injalo outf. (l/s) Injalo outf. (m 3 /h) Injected Flow (m 3 /h) 0:00 134,48 484,13 136,73 492,23 976,36 1:00 133,00 478,80 134,98 485,93 964,73 2:00 131,46 473,26 133,2 9 479,84 953,10 3:00 129,92 467,71 131,60 473,76 941,47 4:00 133, 17 479,41 134,73 485,03 964,44 5:00 137,99 496,76 139,48 502,13 998,89 Table 28: Injected flow calculation during lower consumption time NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 77 time (h) Basis demand nodes (l/s) Basis demand nodes (m 3 /h) Consumption pattern coefficient Demanded Flow (m 3 /h) 0:00 320,45 1153,62 0,8 922,90 1:00 320,45 1153,62 0,8 922,90 2:00 320,45 1153,62 0,8 922,90 3:00 320,45 1153,62 0,7 807,53 4:00 320,45 1153,62 0,75 865,22 5:00 320,45 1153,62 0,8 922,90 Table 29: Demanded flow calculation during lower consumption time time (h) Injected Flow (m 3 /h) Demanded Flow (m 3 /h) Leakage Flow (m 3 /h) 0:00 976,36 922,90 53,46 1:00 964,73 922,90 41,83 2:00 953,10 922,90 30,20 3:00 941,47 807,53 133,94 4:00 964,44 865,22 9 9,23 5:00 998,89 922,90 76,00 Table 30: Leakage flow calculation during lower consumption time Once obtained the leakage flow during the minimum night time flow, it is possible to get the total leakage volume, as this will be the extrapolation of the leakage flow during the minimum night time flow, affected by a multiplier factor. That flow multiplier called Hour to Day Factor (HDF) has a value between 18 and 22 in most hydraulic systems, according to the literature. In our case, we are going to use the average between these 2 coefficients to get our estimated leakage flow in the network. time (h) Leakage Flow (m 3 /h) Daily Leakage Flow (m 3 ) 3:00 133,94 2.678,76 Table 31: Daily leakage flow estimation NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 78 Finally, we are going to calculate the daily injected flow to compare it with the estimated daily leakage flow. time (h) Salibauri outflow (l/s) Salibauri outflow (m 3 /h) Injalo outflow (l/s) Injalo outflow (m 3 /h) 0:00 134,48 484,13 136,73 492,23 1:00 133,00 478,80 134,98 485,93 2:00 131,46 473,26 133,29 479,84 3:00 129,92 467,71 131,60 473,76 4:00 133,17 479,41 134,73 485,03 5:00 137,99 496,76 139,48 502,13 6:00 160,21 576,76 162,05 583,38 7:00 188,76 679,54 191,08 687,89 8:00 203,06 731,02 205,54 739,94 9:00 212,60 765,36 215,16 774,58 10:00 211,06 759,82 213,45 768,42 11:00 206,36 742,90 208,51 750,64 12:00 200,07 720,25 201,95 727,02 13:00 190,62 686,23 192,16 691,78 14:00 184,32 663,55 185,62 668,23 15:00 184,36 663,70 185,54 667, 94 16:00 182,76 657,94 190,33 685,19 17:00 177,50 639,00 189,26 681,34 18:00 172,34 620,42 188,09 677,12 19:00 168,85 607,86 188,44 678,38 20:00 167,15 601,74 190,20 684,72 21:00 157,07 565,45 184,34 663,62 22:00 139,98 503,93 172,69 621,68 23:00 1 24,32 447,55 162,80 586,08 Total/Reservoir 11.633,00 12.338,00 Total 23.971 m 3 Table 32: Daily leakage flow estimation As we can see, in these conditions, the leakage flow (NRW) is approximately 11%. The NRW percentage estimated with the Night Flow Method will be useful to calibrate the model although we estimated the Hour to Day Factor. As we said previously, we will repeat the NFM applied to DMA’s where we will know exactly the inflow and also the billing data, to get more accurate values and achieve a better calibration of the model. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 79 3.5 DMAs implementation 3.5.1 Introduction The implementation of the district metering areas started some time ago with the civil and electrical works needed to build the metering chambers and to install the electrical boxes near it. Between, it was designed a strategy to achieve the isolation of each area and have it documented. Nowadays, most of the DMAs are isolated or it is possible to isolate them. While the works were going on, there was the possibility to study 4 isolated areas at the end of the phase II. On this chapter we are going to develop this bullets mentioned as an introduction. 3.5.2 Isolation strategy It was designed using the information we had from maps and GIS system. Actually, there were so many errors on that info, so we made profit of the process to update it correctly. Finally, we did a database linked to a GIS with this information:  Valve designation and DMA  DN pipe  Correct status of the valve for isolation  Observations  Location  Valve next to flow meter and transmitter or not After the location and checking of the state of each valve, repairing and replacing some of them, we finalize the database and proceed to isolation following the strategy, which is presented on the next page. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 80 DMA VALVE DESIGNATION PIPE DN CORRECT STATUS OBSERVATIONS LOCATION FM TRANS 17 V1 PE 225 Open Connected to Chakvi line (1000) Tbeti St. FM12 YES 17 V2 ST 300 Closed Supplying DMA 17 without MC nor FM. Connected to Chaisubani line (600) Boundaries DMAs 17 and 12b 12b V3 ST 300 Closed Supplying DMA 12a without MC nor FM Connected to Chaisubani line (600) Boundaries DMAs 17 and 12b 12a V4 ST 500 Open Inlet DMA 12a Noneshvili St. FM13 YES 12a V5 PE 110 Closed End pipe inside DMA 12a Boundaries DMAs 12a and 12b 11a V6 ST 200 Open Inlet DMA 11a (under Phase III) Connected to Chakvi line Gogoli Noneshvili Sts. FM15 YES 11a V7 PE 225 Open Inlet DMA 11a Gogebashvili Makasaria junction Sts. FM08 11b V8 ST 250 Open Inlet DMA 11b (under Phase III) Connected to Chakvi line Gogoli Mayakoski juction Sts. FM16 11b V9 ST 500 Open Inlet DMA 11b (under Phase III) Inlet DMA 6 Connected to Injalo Outlet Mayakoski Dadiani juction Sts. FM16A 1-2-3 V10 PE 225 Open Inlet DMA 1-2-3 Mayakoski Shavsheti junction Sts. FM36 YES 1-2-3 V11 ST 600 Open Inlet DMA 1-2-3 Tsereteli Imedashvili junction Sts. 1-2-3 V12 PE 355 Closed End pipe inside DMA 1-2-3 Tsereteli Imedashvili junction Sts. 1-2-3 V13 ST 600 Open Inlet DMA 1-2-3 Tsereteli Imedashvili junction Sts. FM10 YES 1-2-3 V14 PE 110 Closed Connected to DMA 4-5 Chavchavadze Asastiani junction Sts. 1-2-3 V15 PE 110 Closed Connected to DMA 45 Asiatini zubalashvili junction Sts. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 81 DMA VALVE DESIGNATION PIPE DN CORRECT STATUS OBSERVATIONS LOCATION FM TRANS 1-2-3 V16 PE 160 Closed Connected to DMA 4-5 Asiatiani Gorgasali junction Sts. 1-2-3 V17 PE 110 Closed Connected to DMA 4-5 Asiatiani Farnavaz Meve junction Sts. 1-2-3 V18 PE 110 Closed Connected to DMA 4-5 Asiatiani Farnavaz Meve junction Sts. 1-2-3 V19 PE 225 Closed Connected to DMA 4-5 Asiatiani Era junction Sts. 1-2-3 V20 PE 110 Closed Connected to DMA 4-5 Asiatiani Memed Abashidze junction Sts. 1-2-3 V21 PE 110 Closed Connected to DMA 4-5 Asiatiani Kldiashvili junction Sts. 1-2-3 V22 PE 110 Closed Connected to DMA 4-5 Asiatiani Rustaveli junction Sts. 1-2-3 V23 PE 110 Closed Connected to DMA 4-5 Rustaveli Vaja Pshavela junction Sts. 1-2-3 V24 PE 110 Closed Connected to DMA 4-5 Rustaveli 26 Maisi junction Sts. 1-2-3 V25 PE 160 Closed Connected to DMA 4-5 Rustaveli 26 Maisi junction Sts. 1-2-3 V26 PE 110 Closed Connected to DMA 4-5 Rustaveli 26 Maisi junction Sts. 1-2-3 V27 PE 110 Closed Connected to DMA 4-5 Hihoshvili 26 Maisi junction Sts. 1-2-3 V28 PE 110 Closed Connected to DMA 4-5 Hihoshvili 26 Maisi junction Sts. 1-2-3 V29 PE 160 Closed Connected to DMA 4-5 Hihoshvili Melikishvili junction Sts. 1-2-3 V30 PE 355 Closed Connected to DMA 45 Hihoshvili Boundary DMAs 1-2-3 and 4-5 4-5 V31 ST 600 Open Inlet DMA 4-5 Chavchavadze Vaja Pshavela junction Sts. FM11 4-5 V32 PE 110 Closed Connected to DMA 13 Near Griboedovi Era junction Sts. 13 V33 ST 500 Open Inlet DMAs 13 and 9 Abuseridze Griboedovi junction Sts. FM30 YES 13 V34 ST 500 Open Inlet DMA 13 Chavchavadze Griboedovi junction Sts. FM30A 13 V35 PE 110 Closed Connected to DMA 9 Near Abuseridze Javahishvili junction Sts. 13 V36 PE 160 Closed Connected to DMA 9 Abuseridze Loria junction Sts. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 82 DMA VALVE DESIGNATION PIPE DN CORRECT STATUS OBSERVATIONS LOCATION FM TRANS 13 V37 PE 225 Closed Connected to DMA 45 Abuseridze Gudiasvili junction Sts. 13 V38 PE 225 Open Inlet DMA 14 Abuseridze General Abashidze junc. Sts. FM29 YES 13 V39 PE 225 Closed Connected to DMA 14 General Abashidze Pirosmani junc. Sts. 13 V40 PE 160 Closed Connected to DMA 14 Inasaridze General Abashidze junc. Sts. 13 V41 PE 110 Closed End pipe inside DMA 13 Inasaridze General Abashidze junc. Sts. 13 V42 PE 355 Closed Connected to DMA 14 Boundary NW DMAs 13 and 14 13 V43 PE 225 Closed Connected to DMA 14 Boundary NW DMAs 13 and 14 13 V44 PE 160 Closed Connected to D MA 14 Boundary NW DMAs 13 and 14 6 V45 ST 500 Open Inlet DMA 6 (from DMA 11b) Eristavi Bagriatoni junction Sts. 6 V46 PE 355 Open Inlet DMA 7-8 Eristavi Bagriatoni junction Sts. 6 V47 ST 200 Open Inlet DMA 6 (under Phase III) Connected to Chakvi line Eristavi Shavsheti junction Sts. FM17 6 V48 PE 110 Closed Connected to DMA 11b Meshki Eristavi junction Sts. 6 V49 PE 110 Closed End pipe inside DMA 6 Mitisdziri Sulaberidze junction Sts. 6 V50 PE 110 Closed Connected to DMA 7-8 Mitisdziri Boundaries SW DMAs 6 and 7-8 6 V51 PE 110 Closed Connected to DMA 78 Mitisdziri Boundaries SW DMAs 6 and 7-8 6 V52 ST 500 Open Inlet DMA 7-8 Mitisdziri Asatiani junction Sts. FM18 YES 6 V53 PE 355 Open Inlet DMA 6 Tsereteli Maiakovski junction Sts. FM09 YES NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 83 DMA VALVE DESIGNATION PIPE DN CORRECT STATUS OBSERVATIONS LOCATION FM TRANS 6 V54 PE 225 Closed Connected to DMA 78 Chavchavadze Vaja Pshavela junction Sts. 6 V55 PE 160 Closed Connected to DMA 7-8 Pushkini Asatiani junction Sts. 6 V56 PE 110 Closed Connected to DMA 7-8 Giorgi Brtskinvale Asatiani junction Sts. 6 V57 PE 225 Closed Connected to DMA 7-8 Giorgi Brtskinvale Asatiani junction Sts. 6 V58 PE 110 Closed Connected to DMA 7-8 Asatiani Boundery W DMAs 6 and 7-8 6 V59 PE 110 Closed Connected to DMA 7-8 Asatiani Boundery W DMAs 6 and 7-8 6 V60 PE 225 Closed Connected to DMA 7-8 Asatiani Boundery W DMAs 6 and 7-8 6 V61 PE 110 Closed Connected to DMA 7-8 Asatiani Boundery W DMAs 6 and 7-8 6 V62 PE 110 Closed Connected to DMA 7-8 Asatiani Boundery W DMAs 6 and 7-8 6 V63 PE 225 Closed Connected to DMA 7-8 Asatiani Bagrationi junction Sts. 6 V64 PE 110 Closed Connected to DMA 7-8 Asatiani Boundery W DMAs 6 and 7-8 6 V65 PE 110 Closed Connected to DMA 7-8 Asatiani Boundery W DMAs 6 and 7-8 7-8 V66 PE 355 EXT Closed Connected to DMA 9 Bagrationi Selim Khimshiashvili junct. 7-8 V67 PE 355 Closed Connected to DMA 9 Komakhide Selim Khimshiashvili junct. 7-8 V68 PE 160 Closed Connected to DMA 9 Komakhide Selim Khimshiashvili junct. 7-8 V69 PE 355 Closed Connected to DMA 9 Giorgi Brtskinvale Selim Khimshiashvili NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 90 Figure 48: Temporary DMA “P2B-P2D1 area” NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 91 Figure 49: Temporary DMA “Injalo area” NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 92 On the next table we summarized the characteristics of the temporary DMA’s. Table 34: Temporary DMA’s information Before the explanation of each DMA, the next table shows a summary of the water balance done in those areas. Water Balance (average period) Temporary DMA Inflow (measurements) m 3 Outflow (billing) m 3 "Salmon" (Bartskhana) 235.260,61 27.67 8,58 "Green" (P2B) 13.860,01 13.743,50 "Blue" (P2B+P2D1) 192.055,43 172.314,83 "Yellow" (Injalo) 135.743,64 106.250 Table 35: Summary Water Balance DMA’s The structure of the following sub-points is the same for each of the four DMA. Our aim is to present tables and graphs with the results obtained from data processing and NRW calculation. In each one of them we can know the supplied water, the consumption and the amount of NRW during the studied period. NRW is presented divided into its different terms, as usual on the monthly Comprehensive Overviews. "Blue" P2B-P2D1 Measurements from chamber located at CP11 (intersection Maiakovski-Shavsheti Streets). It's not a closed area. We needed to do the following operations to isolate it: 1) Close valve at Pushkini Street ( node 321 Pushkini Griboedovi junction) 2) Close valve at Groboedovi street (on DN225 pipe at Griboedovi Chavchavadze junction) "Salmon" Bartskhana Measurements from chamber located at Noneshvili Street (Noneshvili Gogoli Junction). It's a closed area where the measurements were done installing a flow meter. "Green" P2B Measurements from chamber located at CP11 (intersection Maiakovski-Shavsheti Streets). It's a closed area where the measurements were done installing a flow meter. DMA (drawing color) AREA ISOLATION "Yellow" Injalo It's a closed area supplied by Injalo Reservoir (inflow measured with fix flow meter) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 93  Bartskhana area temporary DMA The inflow in this area during the measurement days oscillates around 400 m 3 /h during day time and 250 m 3 /h during night time as is showed on the next graph. Figure 50: Supplied Water DMA Bartskhana Although we noticed this diminution of supplied water during night hours, we still think that the amount of night flow is very high. The explanation to this fact could be the number of multi apartment buildings which are not metered with individual water meters yet. Due to past experiences Batumi Tskali had started installing individual water meters in a few buildings already. Those building have the characteristic of deteriorated internal piping and hence very high “consumption” due to a minimum maintenance of its water devices, presenting high intern leaks. The next figure shows the consumption pattern which follows a usual domestic consumption shape, with differences between day and night hours. The difference in consumption between working days and weekends that could be expected, in the case of Batumi is inexistent or not representative. 0,00 50,00 100,00 150,00 200,00 250,00 300,00 350,00 400,00 450,00 10-12-12 0:00 11-12-12 0:00 12-12-12 0:00 13-12-12 0:00 14-12-12 0:00 15-12-12 0:00 16-12-12 0:00 17-12-12 0:00 m3/h time NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 94 Figure 51: Consumption pattern DMA Bartskhana Regarding NRW, we are going to present the obtained results as follows. Table 36: Calculation NRW data DMA Bartskhana 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 37.365 20.758 93.412 78.881 72.654 24.910 35.289 62.968 172.293 6.774 41.516 20.905 27.679 207.582 10.379 Leakage and Overflows at Utility's Storage Tanks Leakage on Service Connections up to Point of Customer Metering Real Loses=Leakage on Transmission+ Leakage and Overflows at Tanks + Leakage on Service Connection Apparent Losses = Unaothorized Consumption + Metering Inaccuracies and Data Handling Errors 235.261 unbilled Unmetered Consumption unbilled Authorized Consumption = unbilled Met.Cons + unbilled Unmet.Cons. Authorized Consumption = Billed Auth. Cons. + unbilled Auth. Cons. Water Losses = Sistem Input Volume - Authorized Consumption Metering Inaccuracies and Data Handling Errors Leakage on Transmission and/or Distribution Mains Sistem Input Volume Billed Metered Consumption Billed Unmetered Consumption Revenue Water = Sum of Billed Metered Consumption + Sum of Billed Unmetered Consumption = Billed Authorized Consumption Unauthorized consumption Non-revenue Water = SIV - RW unbilled Metered Consumption t (d) C ( t ) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 95 Table 37: NRW IWA terms DMA Bartskhana 40% 27.679 12% 172.293 Real Losses System input volume Authorized Consumption Billed Authorized Consumption 62.968 Unbilled Authorized Consumption 73% 27% 35.289 15% Water Losses 235.261 100% 93.412 Apparent Losses 78.881 34% 24.910 11% Unauthorized Consumption 37.365 16% Leakage on Transmission and/or Distribution Mains 72.654 31% Leakage and Overflows at Utility's Storage Tanks Billed Metered Consumption 6.774 3% Billed Unmetered Consumption 20.905 18% Unbilled Metered Consumption 10.379 4% Unbilled Unmetered Consumption 0% Leakage on Service Connections up to Point of Customer Metering 12% Non Revenue Water 207.582 88% 9% 41.516 0 Metering Inaccuracies and Data Handling Errors 20.758 9% Revenue Water 27.679 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 96  P2B area temporary DMA The inflow in this area during the measurement days oscillates between 30 m 3 /h during day time and 10 m 3 /h during night time, having a minimum of 5 m 3 /h. Figure 52: Supplied Water DMA P2B The consumption pattern shows a domestic consumption shape, with peaks of consumption in the mornings, noon and evening, as usual. Figure 53: Consumption pattern DMA P2B 0,00 5,00 10,00 15,00 20,00 25,00 30,00 10-12-12 0:00 11-12-12 0:00 12-12-12 0:00 13-12-12 0:00 14-12-12 0:00 15-12-12 0:00 16-12-12 0:00 17-12-12 0:00 18-12-12 0:00 m3/h time 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 t (d) C ( t ) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 97 The observed minimum night flow in the night of Friday 14 th of December can’t be explained as something that happened due to human factors. It was explained as a supply problem in Salibauri reservoir. This fact was checked with the results of December Measurements Plan. Regarding NRW calculation we obtain the next results in this DMA. Table 38: Calculation NRW data DMA P2B m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 Sistem Input Volume 13.860 Billed Metered Consumption 6.071 Billed Unmetered Consumption 7.673 Revenue Water = Sum of Billed Metered Consumption + Sum of Billed Unmetered Consumption = Billed Authorized Consumption 13.744 Non-revenue Water = SIV - RW 117 unbilled Metered Consumption 6 unbilled Unmetered Consumption 14 unbilled Authorized Consumption = unbilled Met.Cons + unbilled Unmet.Cons. 20 Authorized Consumption = Billed Auth. Cons. + unbilled Auth. Cons. 13.764 Water Losses = Sistem Input Volume - Authorized Consumption 97 Unauthorized consumption 23 Metering Inaccuracies and Data Handling Errors 21 Leakage on Transmission and/or Distribution Mains 41 Leakage and Overflows at Utility's Storage Tanks Leakage on Service Connections up to Point of Customer Metering 12 Real Loses=Leakage on Transmission+ Leakage and Overflows at Tanks + Leakage on Service Connection 53 Apparent Losses = Unaothorized Consumption + Metering Inaccuracies and Data Handling Errors 44 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 98 Table 39: NRW IWA terms DMA P2B System input volume Authorized Consumption Billed Authorized Consumption Billed Metered Consumption 99% 7.673 Unbilled Unmetered Consumption 0% Revenue Water 6.071 44% 13.744 Billed Unmetered Consumption 13.744 99% 55% 13.764 Unbilled Authorized Consumption Unbilled Metered Consumption Non Revenue Water 99% 6 0% 20 14 0% Water Losses Apparent Losses Unauthorized Consumption 23 0% 13.860 44 Metering Inaccuracies and Data Handling Errors 100% 0% 21 53 0 0% 0% 117 0% 1% Real Losses Leakage on Transmission and/or Distribution Mains 97 41 1% 0% Leakage and Overflows at Utility's Storage Tanks Leakage on Service Connections up to Point of Customer Metering 12 0% NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 99  P2B-P2D1 area temporary DMA The inflow in this area during the measurement days oscillates between 350 m 3 /h during day time and 200 m 3 /h during night time, having a minimum of 50 m 3 /h. Figure 54: Supplied Water DMA P2B-P2D1 The consumption pattern is similar to the other ones that we are obtaining in Batumi. Figure 55: Consumption pattern DMA P2B-P2D1 0,00 50,00 100,00 150,00 200,00 250,00 300,00 350,00 400,00 10-12-12 0:00 11-12-12 0:00 12-12-12 0:00 13-12-12 0:00 14-12-12 0:00 15-12-12 0:00 16-12-12 0:00 17-12-12 0:00 18-12-12 0:00 m3/h time 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 t (d) C ( t ) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 106 3.5.4 Definitive DMAs The implementation of the district metered areas in the new network was based in two mains actions: isolation and water metering in each area. The isolation strategy has been explained above. On the other hand, as commented previously, flow meters and transmitters were installed in order to fulfil the other main action: metering. The electrodes of the ultrasonic flow meters were installed on the pipes using the underground metering chambers built for that. Near the metering chambers, the flow meters were installed inside the electrical boxes together with the transmitters and antennas. The electrodes were connected to the flow meters using underground protective tubes between the metering chamber and the electrical box. The NRW team was trained to install, configure and use the devices. The next photos show the process. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 107 3.5.5 Zero pressure tests After a reasonable time when finishing the installation of the devices and isolation strategy in order to see the network performance on these new conditions, we checked the isolation by zero pressure tests. In order to check the correct isolation of DMAs, it was decided to do “zero pressure tests” in each isolated area under Phase II. The procedure to realize these tests is explained following: during night time NRW team closed each inlet/outlet present on the DMA we were working on that moment. Before the closing maneuver we installed pressure loggers inside the DMA. Once valves were closed, we waited some time to see the pressure going down until values around 0 bar, the signal that those areas were totally isolated and not connected to any water source. The results of the tests were satisfactory. Main DMAs under Phase II are isolated and ready for operation. Some small issues occurred as problems with the mechanism of some valves which impeded the maneuvering but it was solved rapidly. Rest of DMAs under Phase III or the ones partly connected to DMAs under Phase III will be better tested during next months. Anyway, a detailed study of the state of valves and connections was already done. The followed strategy and results of the tests are presented on the next table. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 108 1-2-3 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 1-2-3 V10 PE 225 Open Inlet DMA 1-2-3 Mayakoski Shavsheti junction Sts. Closed 1-2-3 V13 ST 600 Open Inlet DMA 1-2-3 Tsereteli Imedashvili junction Sts. Closed 4-5 V33 ST 600 Open Outlet DMA 1-2-3 Tsereteli Imedashvili junction Sts. Closed Isolated 4-5 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 4-5 V33 ST 600 Open Inlet DMA 4-5 Chavchavadze Vaja Pshavela junction Sts. Closed 13 V37 ST 500 Open Outlet DMA 4-5 Chavchavadze Griboedovi junction Sts. Closed Isolated 6 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 6 V47 ST 500 Open Inlet DMA 6 (from DMA 11b) Eristavi Bagriatoni junction Sts. Closed 6 V49 ST 200 Open Inlet DMA 6 Eristavi Shavsheti junction Sts. Closed 6 V54 PE 355 Open Inlet DMA 6 Tsereteli Maiakovski junction Sts. Closed 11b V9 ST 500 Open Inlet DMA 6 (via DMA 11b) Connected to Salibauri Mayakoski Dadiani juction Sts. Closed Isolation Phase III (Connected to Chakvi line) 7-8 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 6 V48 PE 355 Open Inlet DMA 7-8 Eristavi Bagriatoni junction Sts. Closed 6 V51 ST 500 Open Inlet DMA 7-8 Mitisdziri Asatiani junction Sts. Closed Isolation Phase III 9 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 10 V89 ST 400 Open Inlet DMA 9 Lermontovi Bagrationi junction Sts. Closed 13 V37 ST 500 Open Inlet DMA 9 Chavchavadze Griboedovi junction Sts. Closed Isolation Phase III 10 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 10 V79 PE 160 Open Inlet DMA 10 Mtisdziri Melikishvili junction Sts. Closed 10 V80 PE 110 Open Inlet DMA 10 Mtisdziri Melikishvili junction Sts. Closed NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 109 10 V92 ST 400 Open Inlet to DMA 10 Sulxan-saba Orbeliani Gen. Abashidze Closed Isolation Phase III (Connected to Chakvi line) 11a DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 11a V6 ST 200 Open Inlet DMA 11a Gogoli Noneshvili Sts. Closed 11a V7 PE 225 Open Inlet DMA 11a Gogebashvili Makasaria junction Sts. Closed Isolated Phase III (Connected to Chakvi line) 11b DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 11b V8 ST 250 Open Inlet DMA 11b Gogoli Mayakoski juc tion Sts. Closed Isolation Phase III (Connected to Chakvi line) 12a DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 12a V4 ST 500 Open Inlet DMA 12a Noneshvili St. Closed 12a V5 PE 110 Closed Valve is open suppling DMA12a (Phagava Street) and part of DMA12b Connected by BTS Boundaries DMAs 12a and 12b Closed Isolated 12b DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test Isolation Phase III 13 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 13 V36 ST 500 Open Inlet DMA 13 Chavchavadze Griboedovi junction Sts. Closed Isolated 14 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 13 V40 PE 225 Open Inlet DMA 14 Abuseridze General Abashidze junc. Sts. Closed 16 V104 ST 500 Open Inlet DMA 14 Agmashenebeli (Hopa Junction) Closed Isolated 15 & 16 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 15 V98 ST 600 Open Inlet DMA 15&16 Khakhuli Gagarin junction Sts. Closed NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 110 Isolation Phase III 17 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 17 V1 PE 225 Open Inlet DMA17 Tbeti St. Closed Isolated (Connected to Chakvi line) 18 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 18 V107 PE 225 Open Inlet to DMA 18 Khakhuli Leonidze junction Sts. Closed Isolation Phase III 19 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test Isolation Phase III 20 DMA VALVE DESIGNATION DN PIPE VALVE CORRECT STATUS FUNCTION / ISOLATION LOCATION 0 P test 10 V94 PE 355 Open Inlet to DMA 20 Sulxan-saba Orbeliani Gen. Abashidze Closed Isolation Phase III Table 45: DMAs isolation status, zero pressure tests As we can see on the table, most of the DMAs under Phase II are totally isolated. Rest of DMAs under Phase II connected to DMAs under Phase III is mostly isolated and will be tested during next months (as some of its borders are connected to DMAs under Phase III and on isolation process). The next image shows DMAs totally isolated and yet tested. Figure 58: Isolated tested DMAs NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 111 Following, some pictures showing the process of “zero pressure tests”. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 112 During the “zero pressure tests” time, NRW team was trained using new leakage detection equipment which they will continue using during the valve identification. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 113 3.6 Managing information system 3.6.1 Introduction In order to fulfill the targets set in the business plan, while optimizing the performance of Batumi Tskali and sustaining the service quality provided to the population of Batumi, it was required an adequate management Information System (MIS). The MIS should combine the storage and computing of generated customer, financial and technical information while allowing the management proper decision making and business control. It furthermore has to enable the utility management and board to carry out proper monitoring and guidance of the business development. Such an MIS for a water utility has to consider the planning and documentation of works as well as reporting and monitoring of the performance. The creation of reliable information and communication routines for the different departments of the utility as well as the involved stakeholders is one of the key requirements for of the whole project. ⇒ ⇒⇒ ⇒ Sub-Tasks to be carried out: It is our goal to assist the utility in a radical turnaround by following a change management approach. For this purpose, we started by supporting the management to base their decision making on a holistic assessment of the problem and on reliable, adequate MIS information. For instance, with the help of real time payment information by customers, targeted activities of customer communication or payment enforcement to boost revenue generation and improvement of collection efficiency are possible. Furthermore without reliable and timely information about the water production and consumption patterns, proper assurance of supply quality and planning for peak demand will be impossible. As a result Batumi Tskali management will be able to regain the initiative for the development of the utility as well as the ownership of actions and responsibilities essential for the operation of a water supply system and adequate service provision. ⇒ ⇒⇒ ⇒ Assessment of MIS features At the beginning of the process we will carry out an assessment of the MIS features currently used. The main focus hereby should be on the customer database, the billing system, the accounting system and other MIS modules in use. Consequently, their functionality will be contrasted vis-à-vis the requirements of the current system as well as the potential for optimization. According to our understanding the following figure 10 depicts the MIS modules which might be desirable. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 114 Figure 59: MIS modules After the assessment, we proposed a course of action in close coordination with the management, and support the realization of the utility MIS to achieve the envisioned objectives. With unpredictable developments in mind, the MIS should be organized in a modular structure as this allows incremental development while updating and exchanging modules if the need arises. The main modules are as follows: ⇒ ⇒⇒ ⇒ The customer database and billing module In a first step, the module for billing and the customer database of the utility should be verified in an active process of the billing department. Together with the billing utility staff, we developed and implemented a procedure for database verification as an important step to eradicate illegal connections and legalize all customers. Moreover, a thorough verification will clean the database from obsolete information such as faulty and non-updated names and addresses as well as new or changed connections, new inhabitants, etc. ⇒ ⇒⇒ ⇒ The asset management (network and facilities) Asset management should be depicted in a MIS module which runs with the help of GIS. The utility GIS data should be as broad as possible and updated as frequently as possible. Hence, all new features, facilities and equipment will be reflected in terms of material, year of construction, performance, energy consumption (if any) and geographical location. Based on the data of this module, adequate asset management, repair, maintenance and replacement will be organized. The utility SCADA will be linked to the asset management module as Batumi Tskali MIS Customer database and Billing module Operation and maintenance module O&M budget monitoring Real time Payment module Accounting System Warehouse management module Reporting Asset management module Customer communication and complaint management module Network management optimzation NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 115 respective information can be properly used by the relevant departments in the most appropriate and non-redundant manner. ⇒ ⇒⇒ ⇒ Warehouse management Warehouse management is of critical importance for network utilities, as spare parts and O&M material are necessary for daily O&M. Moreover, it signifies an important cost aspect of the utility and is also critical to avoid loss of material and spare parts due to inaccurate inventory. In order to optimize warehouse management and monitor the usage of materials and spare parts, a proper warehouse module linked to the accounting software is necessary. ⇒ ⇒⇒ ⇒ O&M budget monitoring system Budget monitoring for O&M is fundamental for the utility regarding the planning and implementation of O&M work. Instead of drafting daily, weekly or monthly hard copy reports, the data should be collected in real time in the O&M module, allowing using the most actual data for decision-making of relevant actions for the O&M teams. Moreover, any O&M cost can be attributed to the O&M of facilities (cost places) by line items, thus allowing proper documentation and reporting. Analytic reports about the dynamic of O&M costs/supply areas can be instrumental in the process of decision-making. ⇒ ⇒⇒ ⇒ The network management optimization module The network management optimization module is an important tool to manage the District Metered Areas and Non-Revenue Water reduction works. This module will synthesize the utility’s network and water facility SCADA, DMA maps and flow and consumption monitoring making a real time monitoring of the utility network performance possible. The overall MIS structure and specific functionalities of the MIS module features will be discussed in the framework of MIS assessment with key stakeholders. In this context, we will link the technical and institutional capacities of the utility by applying the change management index. This will assure that in parallel, the identified training needs will be focused on staff capacity and institutional development. ⇒ ⇒⇒ ⇒ GIS system assessment A coherent, reliable and factually correct GIS database is vitally important for the successful implementation of the project. The transition from legacy systems to a modern GIS solution will create the necessary preconditions for ensuring that the asset data of the utility are geographically, descriptively and temporarily accurate. In order to achieve this, the existing GIS database shall be put through the following processes:  Identification and evaluation of existing data model, design of a new data model if necessary.  Identification of temporal granularity level of existing data.  Checks on data consistency, geographical and topological correctness, data projection, existence and completeness of attributes.  Analysis of address data, identification of gaps and duplicates and establishment of non-amlargeuous relationships between user accounts, addresses and spatial parcels. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 122 4.5 Large consumption identification and allocation at the model As presented above when describing the NRW modeling, the emitter coefficients were calculated the same for every consumption node on the model. When achieving the isolation of DMAs and the implementation of the micro-metering by individual water meters we should identify the “large customers” on each DMA. Once identified the large consumption should be allocated on the nearest consumption node in the model charging the consumption, and calculate the specific emitter coefficient for that node. Using the stored information in the MIS we can identify the large customers on the DMA, as show the next image. Figure 67: DMA 1-2-3 large customer identification on the MIS The first three columns show the location and the identification of the consumption, and the last one is the consumption measured in m 3 . In this case we considered as a large customer the consumption over 500 m 3 /month. With this information the next step was the location of the customers and their allocation on the nearest consumption node at the model, as presented on the next image. Figure 68: DMA 1-2-3 large customer allocation at the model NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 123 The next table show the base demand at the nodes of DMA 1-2-3, where we separated the nodes with the large customers specific demand and the rest of the nodes with equal base demand which represents the total inflow at the DMA less the consumption of the large customers. Table 49: Base demand nodes DMA 1-2-3 4.6 Water balance On this paragraph the water balance of DMA 1-2-3 during the calculation month, June, will be defined. As explained when presenting NRW modeling, we need to know the distribution of the consumption in order to identify what part of the water losses are real losses, which are pressure dependant. The table on the next page show the water balance applied to DMA 1-2-3 during the month of June. The NRW terms distribution was done as follows: ⇒ ⇒⇒ ⇒ Unbilled Metered Consumption: as explained above, a measurement campaign with bulk meters was performed to quantify this amount of water. It resulted to be the 56% of the total monthly NRW. ⇒ ⇒⇒ ⇒ The leakage and overflows at the reservoirs was calculated as presented above. This quantity supposed the 25% of the total monthly NRW, but it was deleted of the DMA balance as the DMA has not any reservoir. We only took into account this percentage to quantify the percentages of the other two terms of real losses. ⇒ ⇒⇒ ⇒ Leakages on transmission mains and connections were estimated as 20% of NRW (10% one term and 10% the other one). The reason is because following AEAS orientations (figure 24), the real losses are around 45% of the total NRW. So, in this case we supposed 25% of leakages on the reservoirs (part applied to DMA 1-2-3), and the other 20% on the pipes. Inflow (m 3 /mes) Consumption (m 3 /mes) Inflow (l/s) Consumption (l/s) DMA 1-2-3 194.458,00 72,60 Big customers: 1 6.108,00 2,28 2 5.801,00 2,16 3 3.350,00 1,25 4 3.247,00 1,21 5 2.702,00 1,01 6 1.237,00 0,46 7 1.059,00 0,39 8 1.054,00 0,39 9 986,00 0,37 10 916,00 0,34 0,42 l/s node 172 nodes 220.918,00 m3 total L arge Customers: NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 124 ⇒ ⇒⇒ ⇒ Metering Inaccuracies and Data Handling Errors are estimated as 18% of the total of NRW, as indicated by AEAS (figure 24). ⇒ ⇒⇒ ⇒ Unauthorized consumption was estimated with 5% of the NRW because the DMA is situated at the touristic part of Batumi, the richest and more maintained one. When simulating the entire network we are using a percentage of 40% of the total NRW. ⇒ ⇒⇒ ⇒ Unbilled Unmetered Consumption are estimated as a 1% of NRW by subtracting from the total the other terms. Table 50: DMA 1-2-3 water balance Once realized the NRW distribution we proceed to calculate it with the IWA water balance, resulting to be 28% as show the next table. m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 m 3 - Sistem Input Volume (inflow DMA 1-2-3) Billed Metered Consumption = Sum of Billed Metered Consumption DMA 1-2-3 Billed Unmetered Consumption = Sum of Billed Unmetered Consumption DMA 1-2-3 Revenue Water = Sum of Billed Metered Consumption DMA 1-2-3 + Sum of Billed Unmetered Consumption DMA 1-2-3 = Billed Authorized Consumption Non-revenue Water = Inflow - Revenue Water Unbilled Metered Consumption 193.891 27.027 3.131 11.272 6.262 Unbilled Authorized Consumption = Unbilled Met.Cons + Unbilled Unmet.Cons. Leakage on Service Connections up to Point of Customer Metering Real Loses=Leakage on Transmission+ Leakage and Overflows at Tanks + Leakage on Service Connection Apparent Losses = Unaothorized Consumption + Metering Inaccuracies and Data Handling Errors 12.524 14.403 6.262 Unbilled Unmetered Consumption Authorized Consumption = Billed Auth. Cons. + unbilled Auth. Cons. Water Losses = Inflow - Authorized Consumption Unauthorized consumption 220.918 83.834 74.463 158.297 62.621 34.968 626 35.594 Metering Inaccuracies and Data Handling Errors Leakage on Transmission and/or Distribution Mains Leakage and Overflows at Utility's Storage Tanks NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 125 Table 51: DMA 1-2-3 IWA balance, NRW 3% 100% 7% System input volume Authorized Consumption Billed Authorized Consumption 193.891 Unbilled Authorized Consumption 88% 35.594 16% 158.297 220.918 14.403 72% Real Losses 26.927 12% 12.524 6% Water Losses Apparent Losses Billed Metered Consumption 83.834 28% 62.521 Non Revenue Water 72% 158.297 38% Revenue Water 2,8% Leakage and Overflows at Utility's Storage Tanks 74.463 34% Unbilled Metered Consumption 34.968 3.131 626 0% Unauthorized Consumption 1% Metering Inaccuracies and Data Handling Errors 11.272 5% Leakage on Transmission and/or Distribution Mains 6.262 Unbilled Unmetered Consumption Billed Unmetered Consumption 16% Leakage on Service Connections up to Point of Customer Metering 6.262 NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 126 4.7 NRW modeling As explained on point 2.4.6 we will proceed modeling the NRW considering part of it as pressure dependent. The other part non-pressure dependent will be included as consumed water, following the consumption pattern. The leakage flow is divided using emitters at the nodes and simulated, having maximum at night time with lower consumption and higher pressure. The other part of the flow is assigned constant or proportional to the base demand. The next table presents the partition on pressure dependant and non-dependant, as explained on the water balance for DMA 1-2-3. m 3 /d L/s NRW 2.020 23,40 Pressure dependent Flow 404 4,67 Non Pressure dependent Flow 1.616 18,7 Table 52: DMA 1-2-3 P dependant and non-dependant flows Following the methodology explained on point 2.4.6 the next step is to divide the non-P dependant flow between the consumption nodes of DMA 1-2-3, taking into account that we have identified large customers and calculated its particular base demand. For the other consumption nodes the base demand is the same as calculated above. The next table shows the sum of this non-P dependant flow to the demand basis of the nodes. Node Q BaseDemand (L/s) Q Non-P dependent (L/s) Q total (L/s) 172 “ N ormal customers” nodes 0,42 0,10 0,52 10 “ Large customers” nodes 1 2,28 0,10 2,38 2 2,1 6 0,10 2,26 3 1,25 0,10 1,35 4 1,21 0,10 1,31 5 1,01 0,10 1,11 6 0,46 0,10 0,56 7 0,39 0,10 0,49 8 0,39 0,10 0,49 9 0,37 0,10 0,47 10 0,34 0,10 0,44 Table 53: DMA 1-2-3 P dependant and non-dependant flows NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 127 On the other hand, the pressure dependent flow will be used to calculate the emitter’s coefficients, as presented on the methodology, using the next equation.  =  · √ The pressure term is calculated in each node at the instant of lower flow (night time) which presents the higher pressure value. Results presented on the next table. Node Q P-dependent (L/s) P (mca) √  Ec 172 “Normal customers” nodes 0,03 50 7,0 7 0,004 2 10 “ Large customers” nodes 0,03 50 7,0 7 0,004 2 Table 54: DMA 1-2-3 emitter coefficients calculation The pressure value used applied to the “normal customers” was an average extracted from the next pressure contour plot, consulted for the night time with higher pressure. Figure 69: P contour plot from the model For the “large customers” the pressure value was consulted at each node. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 128 4.8 Consumption pattern The consumption pattern used to this last update of the model and simulation was the correspondent to the last quarter. Previously we have presented the consumption patterns for Salibauri and Injalo separately. As the network normally is being supplied half and half by each reservoir, the consumption pattern presented as follows is the average between them. Figure 70: Consumption pattern, simulation DMA 1-2-3 4.9 Pressure management One of the next most important steps to fight against leakages is to agree with the water utility management the installation of pressure reduction valves (PRV) working during night time. As observed on the consumption pattern the night consumption on the network is still high. On this chapter we are presenting how to model the installation of PRV applied to the DMA 12-3, in order to check the results which can be extrapolated to the rest of the network. Pressure reduction valves are automatic control valves which function is to keep constant the value of the pressure downstream of its installation point. Likewise, limiting the pressure, the valve can limit the instantaneous flow supplying the area downstream and also limit the leakages. The PRVs operating conditions are the following: ⇒ ⇒⇒ ⇒ Totally open: when the pressure value exiting the valve is lower than the set pressure value. ⇒ ⇒⇒ ⇒ Partially open: if the pressure value exiting the valve rises the set pressure value, the valve closes partially introducing higher head losses to get the set value. ⇒ ⇒⇒ ⇒ Closed: if for any reason the pressure value exiting the valve is higher than the entering one, then the valve closes avoiding the flow circulation. 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Consumption Pattern t ( h ) C ( t ) NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 129 In our case we have introduced two PRV on the mathematical model, one acting in each reservoir. The installation points are situated on the outflow mains of the reservoirs when entering the city, in order to be at similar height at the consumption nodes. Figure 71: PRV location Once the valves were modelled we proceeded to simulate the network with the new elements to check the conditions. The first setting value for the PRVs was 5 mwc lower than the node downstream. The results will be presented on the next point. Figure 72: PRV setting and simulation NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 130 4.10 Simulation and results First, we are going to analyse the inflows and outflows of DMA 1-2-3 in order to know the consumption and present results. After that, we will compare the obtained results when modeling and simulating the pressure reducing valves. DMA 1-2-3 has two inflows and two outflows as we can see on the network schema. Each one is metered by one flow meter sending the readings each 15 minutes via transmitters to the MIS, as explained above. Figure 73: DMA 1-2-3 flow meters, schema network NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 131 The inflows to the DMA during a 24 h simulation are presented on the next images. The green line corresponds to FM 10 and the red one to FM 36. As we can notice most of the inflow is entering at this moment through one of the system main distribution pipes, where FM 10 is installed. Figure 74: DMA 1-2-3 inflows evolution Figure 75: FM10 and FM36 24 h measurement NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 138 6 REFERENCES  Almandoz, J., Cabrera, E., Arregui, F., Cabrera, E., Jr., and Cobacho, R. (2005). “Leakage assessment through water distribution network simulation.” J. Water Resour. Plann. Manage, 131, 458–466.  American Water Works Association (AWWA). (2005). Computer modeling of water distribution systems—Manual of water supply practices, M32, 2nd Ed., American Water Works Association, Denver.  Arregui, F., Cabrera, E., Jr., and Cobacho, R. (2006). Integrated water meter management, IWA Publishing, London.  Arregui, F. J., Cobacho, R., Cabrera, E., Jr., and Espert, V. (2011). “Graphical method to calculate the optimum replacement period for water meters.” J. Water Resour. Plann. Manage., 137(1), 143–146.  Awad, H., Kapelan, Z., and Savic, D. A. (2009). “Optimal setting of time modulated pressure reducing valves in water distribution networks using genetic algorithms.” Integrating water systems, J. Boxall and C. Maksimovic, eds., Taylor and Francis, London, 31–37.  Burrows, R., Mulreid, G., and Hayuti, M. (2003). “Introduction of a fully dynamic representation of leakage into network modelling studies using EPANET.” Proc., Int. Conf. on Advances in Water Supply Management, C. Maksimovic, D. Butler, and F. A. Memon, eds., Swets & Zeitlinger, Lisses, The Netherlands, 109–118.  Cheung, P. B., and Girol, G. V. (2009). “Night flow analysis and modeling for leakage estimation in a water distribution system.” Integrating water systems, J. Boxall and C. Maksimovic, eds., Taylor and Francis, London.  Germanopoulos, G. (1985). “A technical note on the inclusion of pressure dependent demand and leakage terms in water supply network models.” Civ. Eng. Environ. Syst., 2(3), 171–179.  Giustolisi, O., Savic, D., and Kapelan, Z. (2008). “Pressure-driven demand and leakage simulation for water distribution networks.” J. Hydraul. Eng., 134(5), 626–635.  Koelbl, J., Mayr, E., Theuretzbacher-Fritz, H., Neunteufel, R., and Perfler, R. (2009b). “Benchmarking the process of physical water loss management.” Proc., 5th IWA Water Loss Reduction Specialist Conf., IWA, Hague, The Netherlands, 176–183.  Luczon, L. C., and Ramos, G. (2012). “Sustaining the NRW reduction strategy: The Manila Water Company territory management concept and monitoring tools.” Proc., 7th IWA Water Loss Reduction Specialist Conf., IWA, Hague, The Netherlands (Feb. 26– 29, 2012).  Lund, J. R. (1988). “Metering utility services: Evaluation and maintenance.” Water Resour. Res., 24(6), 802–816.  Male, J. W., Noss, R. R., and Moore, I. C. (1985). Identifying and reducing losses in water distribution systems, Noyes Publications, Saddle River, NJ. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 139  Marques, R. C., and Monteiro, A. J. (2003). “Application of performance indicators to control losses-results from the Portuguese water sector.” Water Sci. Technol. Water Supply, 3(1/2), 127–133.  McIntosh, A. C. (2003). Asian water supplies: Reaching the urban poor, Asian Development Bank, Manila, the Philippines.  McKenzie, R., Seago, C., and Liemberger, R. (2007). “Benchmarking of losses from potable water reticulation systems: Results from IWA task team.” Proc., 4th IWA Specialised Water Loss Reduction Conf., IWA, Hague, The Netherlands, 161–175.  Mutikanga, H. E., Sharma, S., and Vairavamoorthy, K. (2009). “Water loss management in developing countries: Challenges and prospects.” J. Am. Water Works Assoc., 101(12), 57–68.  Mutikanga, H. E., Sharma, S. K., and Vairavamoorthy, K. (2011a). “Assessment of apparent losses in urban water systems.” Water Environ. J., 25(3), 327–335.  Mutikanga, H. E., Sharma, S. K., and Vairavamoorthy, K. (2011b). “Investigating water meter performance in developing countries: A case study of Kampala, Uganda.” Water SA, 37(4), 567–574.  Mutikanga, H. E., Sharma, S. K., and Vairavamoorthy, K. (2011c). “Multi-criteria decision analysis: A strategic planning tool for water loss management.” Water Resour. Manage., 25(14), 3947–3969.  Mutikanga, H. E., Vairavamoorthy, K., Sharma, S. K., and Akita, C. S. (2011d). “Operational tools for decision support in leakage control.” Water Pract. Technol., 6(3), 10.2166/wpt.2011.057.  Noss, R. R., Newman, G. J., and Male, J. W. (1987). “Optimal testing frequency for domestic water meters.” J. Water Resour. Plann. Manage., 113(1), 1–14.  Seago, C., Bhagwan, J., and McKenzie, R. (2004). “Benchmarking leakage from water reticulation systems in South Africa.” Water SA, 30(5), 25–32.  Tabesh, M., Asadiyani, Y., and Burrows, R. (2009). “An integrated model to evaluate losses in water distribution systems.” Water Resour. Manage., 23(3), 477–492.  Ulanicki, B., Bounds, P. L. M., Rance, J. P., and Reynolds, L. (2000). “Open and closed loop pressure control for leakage reduction.” Urban Water, 2, 105–114.  Wallace, L. P. (1987). Water and revenue losses: Unaccounted for water, American Water Works Association, Denver.  Yee, M. D. (1999). “Economic analysis for replacing residential meters.” J. Am. Water Works Assoc., 91(7), 72–77. NRW Mathematical Model as a tool for the establishment of water losses management in water utilities in developing areas. Applying in Batumi Water Utility (Batumi, Georgia). 140