efficient water distribution presentation
TRANSCRIPT
Modelling Toois Dinamis dan studi kasus di water gems. Misalnya scheduling pompa, hidran pembila san, darwin calibrator untuk deteksi kebocoran dan pengaturan katup dengan menghubungkan permintaan data secara real time
Pedro Pina, Water Industry Solution Architect, Bentley Systems
© 2008 Bentley Systems, Incorporated
Rangkuman
• Pandangan atas masalah • Alat seadanya • Features Update• Examples
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Masalah
Data
Report
Persiapan Input
Simulation/ Analysis
Verification
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Features
• Input/Integrasi Data- Pengembangan Model - SCADA Connect - Water Objects
• Simulation/Analsys - Model Calibration - Leakage Detection - Close Valve Detection - Pump Optimization - Data Logger Placement
4 © 2008 Bentley Systems, Incorporated
Input - Model Builder
CAD, GIS, TableData ModelBuilder WaterGems/CAD
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Connectivity Issues Pipes without end nodes
Pipes that do not connect but should
Pipes that appear to connect but are not
Pipes that cross without junctions
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Complex Demand Paterns
SCADA
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Input - SCADA Connect
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Input - Water Objects
• .NET Development Environment • Means of extending capability of model • Can do
- Pre-processing - Post-processing - Add engines
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Real Time modelling
Current Time
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Water Objects Examples
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Available Tools
• Commercially Available - Model Calibration - Leakage Detection - Closed Valve Detection - Pump Optimization - Pipe Renewal Planning and Optimization
• Prototypes (also available under conditions) - Fire Hydrant/Flushing optimization - Data logger optimization - Improved Pump Optimization
12 © 2008 Bentley Systems, Incorporated
Darwin Calibrator - Calibration Module Including in WaterGEMS - Addition in WaterCAD
• Theory of natural selection developed in the 70’s
• Applied to water systems in the 90’s
• Optimization through genetic algorithms
• Uses multiple field data sets to calibrate: Roughness, Demands and States
• It generates tests of successive populations
•Comparison of Field data: • The strong will survive-Pressures or gradients at nodes-Flows in pipes, pumps, and valves
13 © 2008 Bentley Systems, Incorporated
Leak Detection Model Overview
• Data-driven models - Apply data-driven modeling methods, e.g. statistics/
regression methods, artificial neural network, support vector machine etc.
- Purely based on data analysis (pressures and flows) to uncover new abnormalities as possible new pipe bursts
- Unable to detect the leaks that have already existed in the system
• Physics-based models - Simulate leakage through network hydraulic model - Model leakage as pressure dependent demand for known
leakages - Key is to predict the whereabouts and size of leaks
throughout a distribution system
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Tota
l Hea
d (m
)
Tota
l Hea
d (m
)To
tal H
ead
(m)
Tota
l Hea
d (m
)
Perfect Hydraulic Modeling 120.00
120.00 100.00
100.00 80.00
80.00 60.00
00:00 08:00
Model16:00
Field Test00:00
60.00 00:00 08:00 16:00 00:00
KALL2 Source
120.00 120.00
100.00 100.00
80.00 80.00
60.00 00:00 08:00 16:00 00:00 60.00
Model Field Test 00:00 08:00 16:00 00:00
Model Field Test
15 © 2008 Bentley Systems, Incorporated
Model Calibration Demand Leakage Actual HGL
100 gpm
Calibration Time
Pre Calibration
Post Calibration
80 gpm
Modelled HGL
HGL
80 gpm
A B C
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Leakage Hydraulics
HGL
Leakage is allocated to the node in a model Q1 =k1 (p1)^n
• Leakage Qi is pressure dependent, given as emitter flow as above • Ki is the emitter coefficient to be optimized as leakage indicator • Ki > 0 indicates a leakage at node i, while Ki = 0 indicate no leakage
at node i.
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Parameter Identification • Genetic Algorithm based network calibration/optimization
tool • Made up of a GA (Darwin Calibrator) and hydraulic
engine • Has three functionalities
- Demand based calibration - “Pressure Dependent” based calibration – Leakage Hotspot Detection
• The GA optimizes any combination of: - Nodal outflow (Consumption and/or Leakage) - Links roughness - Links operational status
• Attempts to generate nodal heads and flow rates that best matches recorded field data
18 © 2008 Bentley Systems, Incorporated
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Enable Active Water Loss Control
• Predict the location and size of water losses (both real and apparent)
• Guide field engineer to quickly locate leaky pipes and/or apparent water losses
Field personnel can focus on area(s) detected by Darwin Calibrator
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Integrated Framework Darwin Calibrator for Leakage Detection & Model Calibration
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Real DMA Leakage Detection • Darwin Calibrator for leakage detection• 28 observed pressure points and total
inflow into the system • Apply it to watersystems in UK
• Optimize emitter flow
• Predict leakage hotspots
• Minimize leak detection uncertainty
• Facilitate a better detection rate
• Narrow down leakage spots
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Case I Leak repaired, 10 l/s saving
Posi-Tect & field survey
Forest Farm
Leakage spots identified with Darwin Calibrator
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Case I (cont) Historical leak
Predicted leak
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Case I (cont) Historical leak
Predicted leak
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Case I (cont) Historical leak
Predicted leak
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Case I (cont)
Historical leak
Predicted leak
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Closed Valve Detection
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Pump Optimization
Tank Supply
PumpStation
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CSP Case Study (Wu, Woodward & Allen 2009) • DMZ system • 57 Ml/day• 11 pump
stations and 9 tanks
• Energy cost: £330K/year
• Recorded daily energy cost: £912
• Modeled daily energy cost: £923
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Energy Cost comparison Pump Existing controls Optimized controls
Pump utilizationID (%) Daily cost (£) Pump utilization (%) Daily cost (£)X2420052_ 100 181.99 100 181.73X2420014_ 40 142.11 41 120.51X2420075_ 42 201.95 37 141.19X2410361_ 50 31.99 42 22.65X2419963_ 50 31.99 42 22.65X241998C_ 26 7.92 31 5.18X2450024_ 40 37.35 21 13.87PILWTH 82 236.19 40 98.33NEWMRKT 23 111.63 22 88.98Total cost(£) 983.12 695.10
• Overall saving is 29% of original energy cost• By shifting pumping hours and increasing supply of 3.5 Ml/d from
gravity source
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Pipe Renewal Planning and Design Optimization
• Condition assessment tool
• Tool to rank pipe links based on several “aspects”
• Calculate a score for each aspect
• Combine scores for overall ranking
• Part of WaterGEMS, or WaterCAD add-on • Results display tools
31 © 2008 Bentley Systems, Incorporated
WorkflowPipe Score
Pipe Break History
BreakAnalysis
Normalized Break Score
System Inventory
Model
Fire Flow CriticalityAnalysis Analysis
Normalized NormalizedFire Score Criticality Score
Other Property Of Interest
Analysis
Normalized Score
Weighting
OverallScore
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Color Coding by Score
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Rehabilitation Optimization
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Available Tools
• Comercially Available - Model Calibration - Leakage Detection - Closed Valve Detection - Pump Optimization - Pipe Renewal Planning and Optimization
• Prototypes (also available under conditions) - Fire Hydrant/Flushing optimization - Data logguer optimization - Improved Pump Optimization
35 © 2008 Bentley Systems, Incorporated
GPU
Ad
vanc
emen
t
A New Paradigm… Heterogeneous
CPU Advancement Computing
Single-core Many-core
HomogeneousComputing
CP CPU U1 N
U Coh
Generalpurpose
Graphicsdriver
GPU
Memor
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Accelerated Modeling14001200
1000
800
600
400
200
0
2.84
1.51
48 hr 96 hr
CPU GPU
3.943.52
144 hr 192 hr
SpeedUp
1500
43.532.521.510.50
3.94 4
1000 2.46
500
014K Pipes
CPU
3
2
1
081K Pipes
GPU
SpeedUp Linear (SpeedUp)
Spee
dUp
Seco
nds
Spee
dUp
Seco
nds
37 © 2008 Bentley Systems, Incorporated
Com
puta
tion
Tim
e (m
in)
Data-Driven Model• Big data, big opportunity• Data ≠ information• Capture data relationships• Fast ANN modeltraining/calibration
765
Host CPU Neural Computing on GPU
Uploaddata to
GPU
800700600500400300200100
0On CPU
ReadfromGPU
14
On GPU 38
Pump Scheduling • Optimize pump operation • Minimize energy cost
Fitness
Solutio n
Respon se
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Accelerated Pump Scheduling
Fitness
Solutio n
Trained A Response
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Flushing Problem
• Opening hydrant changes head loss and flow velocity of pipes, which is useful - Greater the change, more helpful for the model
calibration - Changing velocity helps remove bad accumulations
in the pipe 41 © 2008 Bentley Systems, Incorporated• Very common operation in practice
Hydrant Selection - Find Best Hydrants To Open
• We don’t want to open all hydrants <- Limited number of hydrants should be opened
• Which one to open? -> Affect as much as possible pipes [Efficiency]
• How many to open? -> Require as few as possible [Cost]
• How much hydrant flow should be used? -> Smaller the better
42 © 2008 Bentley Systems, Incorporated
Case study • Use Hydrant Selection
Tool to find optimal combination of hydrants - A water system with 429
pipes - Currently 8 hydrants are
selected for flow testing, selected by experience
- Head loss change threshold: 0.1 m H2O (0.14psi)
- Hydrant Flow Range: 32 - 126, Interval: 4
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Sum of Pipe Lengths Comparison Current 8 Hydrants Optimum 8 Hydrants
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Affec
ted
Perc
enta
ge
Sum of Pipe Lengths / Total Pipe Lengths Comparison
Optimal VS Existing 8 90.00%
80.00%
70.00%
60.00%
50.00%
40.00% OptimalExisting 8
30.00%
20.00%
10.00%
0.00% 1 2 3 4 5 6 7 8 12 20
Number of open hydrants
• Optimal solution outperforms existing 8hydrant setting with even 2 hydrants
45 © 2008 Bentley Systems, Incorporated
Logguer Placement
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Logguer Placement
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Aknowlegments
• http://inside/bsw/AppliedResearch/Watertown/S itePages/Home.aspx
• Zheng Wu et al. 2011,2012,2114
48 © 2008 Bentley Systems, Incorporated