Simulator for NRW and Real Loss Estimation in Water Distribution Systems for Developing Countries

Transcription

1 IWA Water Loss Conference Water Loss 2018, Cape Town, South Africa Simulator for NRW and Real Loss Estimation in Water Distribution Systems for Developing Countries 7 th May, 2018 Parag Gurav, Avi Anthony Cornelio and Kawamoto Takashi Research & Development Center, Hitachi India Pvt. Ltd Hitachi, Ltd All rights reserved.

2 Contents 1. Introduction 2. Objective of WBS (Water Balance Simulator) 3. Methodology 4. Case study 5. Conclusion and future work Hitachi, Ltd All rights reserved.

3 Contents 1. Introduction 2. Objective of WBS (Water Balance Simulator) 3. Methodology 4. Case study 5. Conclusion and future work Hitachi, Ltd All rights reserved.

4 1.1 Introduction In India most of the water utilities operates water distribution intermittently * NRW of some cities in India Ahmedabad (Gujarat) 28% Jhansi (Uttar Pradesh) 40% Tumkur (Karnataka) 35% Kochi (Kerala) 39% map.comersis.com Many cities non revenue water (NRW) more than 20% (mainly due to leakage and apparent losses) Estimation of NRW is challenging due to uncertain and missing data (Use of assumptions reduces reliability of results) * Source - Water and sanitation service levels in cities of India and ) PAS CPET University, * PAS website 3

5 1.2 NRW estimation approach For estimation of NRW and real losses at initial stage most of utilities prefer IWA top down approach Data availability for components Q SIV,Q BAC, Q UAC, Q AWL defines challenges Authorized Consumption Q AC (Q BAC + Q UAC ) Billed Authorized consumption Q BAC Unbilled Authorized consumption Q UAC Billed Metered Consumption Billed Unmetered Consumption Unbilled Metered Consumption Unbilled Unmetered Consumption Revenue Water Q RW System input volume (Corrected for known errors) Q SIV Water Losses Q WL (Q SIV - Q AC ) Apparent losses Q AWL Real losses Q RWL (Q WL - Q AWL ) Unauthorized Consumption Customer Metering Inaccuracies and Data Handling error Leakage on Transmission and Distribution Mains Leakage and Overflows at Utility s Storage Tanks Leakage on Service Connections up to point of Customer metering Non Revenue Water Q NRW (Q SIV - Q BAC ) Sources: IWA and World Bank Institute. Estimated using available data 4

6 1.3 Issues and challenges for NRW and real loss estimation in India ESR Bulk flow meter Valve M Issues Issues Absence of bulk flow meter Billed Authorized consumption Q BAC No AMR, Manual meter reading Missing data due to non- working meter connections System input volume Q SIV Missing data due to malfunction of bulk flow meter Unbilled Authorized consumption Q UAC Apparent losses Q AWL Limited information and records Unauthorized consumption varies from area to area Poor meter replacement plan 5

7 Contents 1. Introduction 2. Objective of WBS (Water balance simulator) 3. Methodology 4. Case study 5. Conclusion and future work Hitachi, Ltd All rights reserved.

8 2.1 Objective of WBS (Water balance simulator) WBS designed to estimate NRW at initial stage with available data and infrastructure. And to analyze the effectiveness of solutions to reduce NRW. DMA Source Water supply data ESR Water Balance Simulator (WBS) IWA Water Balance Billing cycle data, consumer data Network data Data entry sheets Top down approach Missing data Estimation models Methodologies Inbuilt Model/Methodology Water supplied Apparent losses Billed water consumption Unbilled authorized consumption WB Results KPIs RW (m 3 ) NRW (m 3 ) Real loss (m 3 ) Apparent Losses (m 3 ) ILI Feasibility Analysis Revenue loss Prioritization of leaky DMA Analysis for effectiveness of solutions 7

9 2.2 WBS GUI view The main page shows solution tabs, IWA water balance table and KPIs. It shows name of utility and number of DMAs based on selected database. Slide 3.2 Slide 3.3 Data period Slide 3.1 Slide 3.4 8

10 Contents 1. Introduction 2. Objective of WBS (Water balance simulator) 3. Methodology 4. Case study 5. Conclusion and future work Hitachi, Ltd All rights reserved.

11 3.1 Estimation of missing water supply data Issue - Missing water supply (m 3 /day) data due to bulk flow meter malfunction Solution Estimate missing data based on model developed using available data Date Data inputs to WBS Supply Hours Weather Condition variables Water supply Volume (m 3 /day) Estimation method Model development Based on available data Available data Missing data Advantages - Considers seasonal variations Model is based on actual available data Simple to understand for water utility 10

12 3.2 Estimation of missing water consumption data Issue Missing data in billing cycle due to non-working meters Solution Estimate missing data based on consumption model developed using available data Connection number Data inputs to WBS Household Population Water Consumption per month (Billing cycle) Monthly model development based on working meter connection data Available data Missing data LPCD method Or Regression model with abnormal data detection Advantages - Considers actual monthly consumption data for model generation Simple to understand for water utility 11

13 3.3 Unbilled authorized consumption (UBAC) Issue - Estimation of unbilled authorized consumption Solution Standard methods Metered UBAC Government offices Government schools Gov. Hospitals UBAC Unmetered UBAC Drinking water truck tankers Firefighting truck tankers Decorative purpose Estimation method Metered UBAC 1) Comparison procedure in case of missing data - Consumption data of connections alike in size, hours of operation, type of use. Unmetered UBAC 2) Batch procedure Data- Truck tanker volume and number of tanker used 3) Discharge procedure Data- Flowrate and time (hr/ day/month) 12

14 3.4 Apparent loss Customer metering inaccuracies Issue Customer metering Inaccuracies Solution Estimate using meter error function and prioritization of connections Inputs Meter calibration data for sample meters Customer connection data Meter size, meter age, Billed consumption Error curve / function generation Prioritization of connections (Meter age, pressure in area) Meter error = f (size of meter, meter age, flowrate) Losses = f ( meter error, billed consumption) 13

15 Contents 1. Introduction 2. Objective of WBS (Water balance simulator) 3. Methodology 4. Case study 5. Conclusion and future work Hitachi, Ltd All rights reserved.

16 4.1 Case study - Utility 1 A small area in a network was selected for case study, data obtained for 3 months. Connection Details Number of connections 80 Connections with working meters 60% Domestic connections 98% Commercial connections 2% No bulk flow Meter 40 % non-working meter connections ESR Valve Water Supplied estimation Supply flowrate (Based on experimentation) and Supply hours (per month) 15

17 4.2 Data inputs to WSB Data sheet WB component data (SIV), Water supplied data (BAC) Billing cycle consumption, customer data (UBAC) Information about unbilled authorized consumption (CMI) Data for customer metering inaccuracies and data handling error (UC) Site survey data for unauthorized consumption Network data Data for UARL (Unavoidable Annual Real Losses) WBS 16

18 4.3 Billed authorized consumption estimation (BAC) BAC = Consumption by working meter connections + Estimated consumption by not working meter connections Water consumption estimation Meter Status Number of Connections Month 1 Domestic Commercial Total RW Normal XXX Non working Missing 17

19 4.4 Apparent loss component estimation Unauthorized consumption (UC) Site survey Consumer metering inaccuracies (CMI) Currently assumed meter calibration data Connection type Number of connections observed Domestic 3 Commercial 1 Consumption model for each month and UC details Customer data 18

20 4.6 NRW results The results for water balance components are shown in water balance table. The height of the column is automatically adjusted based on volume of component 19

21 4.7 NRW results ILI estimated based on URL and RL for selected data period 20

22 4.8 Comparison of DMAs example DMA Comparison can be performed based on selection of one of the KPIs. NRW volume (m 3 ), ILI and NRW % (for DMAs with same supply hours and similar connection numbers) Sample site (Utility x) DMA Comparison NRW volume (m 3 ) ILI NRW % 21

23 Contents 1. Introduction 2. Objective of WBS (Water balance simulator) 3. Methodology 4. Case study 5. Conclusion and future work Hitachi, Ltd All rights reserved.

24 5. Conclusion and future work In developing countries lack of minimal infrastructure causes issue of data availability to evaluate volume of NRW. Based on existing infrastructure and available data (billing for e.g) WBS is able to evaluate NRW with certain level of acceptable accuracy with the utility. The inbuilt estimation methods in WBS provides ease for user in water balance estimation Based on water balance results and DMA comparison study, WBS can help identify leaky area for prioritization and subsequent action For apparent loss estimation obtaining initial necessary data is challenging, in future work improvement of apparent estimation technique is considered. 23

25 IWA Water Loss Conference Water Loss 2018, Cape Town, South Africa END Simulator for NRW and Real Loss Estimation in Water Distribution Systems for Developing Countries 7 th May, 2018 Parag Gurav, Avi Anthony Cornelio and Kawamoto Takashi Research & Development Center, Hitachi India Pvt. Ltd Hitachi, Ltd All rights reserved.