Outline
description
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The University of Adelaide, AustraliaSchool of Civil, Environmental & Mining Engineering
Water Systems and Infrastructure Modelling & Management Group
(WaterSIMM)
Using Genetic Algorithms to Optimise Network Design and System Operation Including Consideration of Sustainability
Professor Angus Simpson
Victorian Modelling Group24 March 2010
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Outline
1. Simulation of water distribution systems
2. Various formulations of equations
3. Todini and Pilati solution method
4. Genetic algorithm optimisation of water distribution system networks
5. Genetic algorithms for optimising operations of pumping systems
6. Optimising for sustainability
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My research interests
1. Optimisation of the design and operation of water distribution systems using genetic algorithms [including sustainability considerations (GHGs)]
2. Monitoring health and assessing condition of pipes non-invasively in water distribution systems using small controlled water hammer events
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My research interests
3. Steady state computer simulation analysis of water distribution systems - improving solvers and modelling of PRVs and FCVs
4. Water hammer modelling in pipelines
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The research team
• Total of 14• 9 academics (local and overseas)• 1 Research Post Doc.• 4 PhDs
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The research team - academics
• Prof. Angus Simpson• Prof. Martin Lambert (condition assessment and
genetic algorithms)• Prof. Holger Maier (genetic algorithms and
sustainability)• Dr. Sylvan Elhay – School of Computer Science,
The University of Adelaide (steady state solution of pipe networks)
• Prof. Lang White – School of Electrical and Electronic Engineering, The University of Adelaide (condition assessment)
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International collaboration
• Professor Caren Tischendorf – Head of Dept. of Mathematics and Computer Science, University of Cologne, Germany (steady state solution of pipe networks)
• Dr. Jochen Deuerlein – 3S Consult, Germany (steady state solution of pipe networks, correct modelling of PRVs, FCVs in water networks) – ex-University of Karlsruhe
• Dr. Arris Tijsseling – University of Eindhoven, The Netherlands (condition assessment)
• Prof. Wil Schilders – University of Eindhoven, The Netherlands (speeding up solution of nonlinear steady state pipe network equations)
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Components in a Water Distribution System
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Simulation of water distribution systems
• Solution of a set on non-linear equations for – flow (Q) and – pressure (head or hydraulic grade line – H)
• EPANET uses Todini and Pilati (1987) method – very fast
• There are many sophisticated commercially available software packages
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Assumptions
• Fixed demands – assumed to be not pressure dependent, for example - 100 houses aggregated to a node
• Various water demand loadings cases– Peak hour (hottest day in summer)– Peak day (extended period simulation usually
over 24 hours to check tanks do not run empty)– Fire demand loading cases– Pipe breakage cases
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Decisions to be made
• Diameters and locations of pipes• Location of pumps• Locations and setting of valves (PRVs, FCVs)• Locations and elevations of tanks• Operation of pumps – off-peak electricity rates• Minimising carbon footprint• Reliability considerations
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Design objectives
• For the economic cost component minimise sum of – Capital cost of the water distribution system
(say for a 100 year life)– Present value of pump replacement/refurbishment
(every 20 years)– Present value of pump operating costs (100 years)
)()()(),(11
pumpingNPVpcLdcPDC k
NPUMP
kkkk
NPIPE
kk
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Accounting for Time
• Present value analysis (PVA)
• Usually the discount rate i is selected to be cost of capital 6 to 8%
• For social projects, such as WDSs, a social discount rate should be used, for example, i = 1.4% (intergenerational equity)
tti
CPV
1
C: Payment/cost on a given future date
t = Number of time periods
i = Discount rate
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Design objectives
• Satisfy design criteria – for example–Minimum/maximum allowable pressures–Maximum allowable velocities–Tanks must not empty
NDEMANDSjNJitHtHH jijiji ,...,1;,...,1,,)( max,,
min,
NDEMANDSjNPitVtV jiji ,...,1;,...,1,,)( max,,
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Trial and error approach
• User has to choose - diameters of pipes, locations of tanks, pumps, and PRVs
• Engineering judgment and experience• Run simulation model for demand load cases• Check design criteria and compute cost• Adjust sizes of elements in response to
pressures• Rerun simulation model
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New York Tunnels problem
New York City Water Supply Tunnels
Brooklyn
Hillview ReservoirEL 340 ft
16
9 21
8
20
19
11
7
6
5
4
17 18
13
14
3
2City Tunnel No. 1
City Tunnel No. 2
Bronx
Queens
Richmond
Manhattan
10
12
15
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A very very large search space size
• Any one of the 21 existing pipes could be duplicated
• Choose from 16 allowable pipe sizes to meet demands
• Search space size = 1.43 x 1021
• Eliminate 99.99% of possible solutions by engineering experience (leaves 1.43 x 1017)
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A very very large search space size
• At 10,000 evaluations per second – can compute 3.15 x 1011 per year
• It will take 454,630 years to fully enumerate 0.01% of the total search space (only for 21 decision variables)
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A typical simple water distribution system
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Simulation – Solving for The Unknowns
q Q1 Q2 QNP T=
• Only consider systems with pipes and reservoirs • The unknown flow vector (10 pipes in examples)
• The unknown head vector (7 nodes in example) (gives pressures)
h H1 H2 H NJ T=
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A total of 17 unknowns Qs and Hs
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Continuity Equation of Flow at a Junction
• Flow In = Flow Out + Demand (or Withdrawal Discharge)
• where Qj = flow in pipe j (m3/s or ft3/s) NPJi = number of pipes attached to node i DMi = demand at the node i (m3/s or ft3/s) NJ = total number of nodes in the water distribution system
(excluding fixed grade nodes such as reservoirs)
Q j
j 1=
NPJi DMi+ 0=
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Pipe Head Loss Equations in Terms of Nodal Heads
H i Hk– r jQj Qjn 1–
=
• where• = nodal head at node i in the water distribution system (m or ft)
• rj = resistance coefficient for the pipe j depending on the head loss relationship (for example, Darcy–Weisbach or Hazen–Williams)
• Qj = flow in pipe j (m3/s or ft3/s)
• n = exponent of the flow in the head loss equation (Darcy–Weisbach n = 2 or Hazen–Williams n = 1.852)
Hi
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Four different non-linear formulations
• #1 Q-Equations• #2 H-Equations• #3 LF- Equations (Loop Flow Equations)• #4 Todini and Pilati H-Q Equations
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#1 - The Q-equations formulation (10 unknowns)
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The Q-equations (10 unknowns)
q Q1 Q2 QNP T=
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The Q-equations (Newton iterative solution technique)
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#4 -Todini and Pilati Q-H formulation
• Define topology matrices• Develop block form of equations• Use an analytic inverse of block matrices to
reduce matrix size from 17 unknowns to 7 unknowns (same as unknown heads H)
• Fast algorithm
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Todini and Pilati Q-H formulation
• Unknowns
h H1 H2 H NJ T=
q Q1 Q2 QNP T=
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Todini and Pilati Q-H formulation - Define topology matrices
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Todini and Pilati Q-H formulation - Define topology matrices
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Todini and Pilati Q-H formulation - continuity at nodes
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Todini and Pilati Q-H formulation – head loss equations for pipes
Note that later on theinverse of this matrix will give problems for zero flows
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Todini and Pilati Q-H formulation – head loss equations for pipes
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Todini and Pilati Q-H formulation–two sets of equations
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The Todini and Pilati equations
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Research Issues
• Improving solution speed• Making solution algorithms more robust – zero
flows cause Todini and Pilati method to fail – a regularization method has been developed to control the condition number
• An improved convergence criterion for stopping has been developed
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Research Issues
• Decomposing networks into trees, blocks and bridges to speed up analysis
• Growing typical networks that have correct mix of loops, links and junctions
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GENETIC ALGORITHMS FOR OPTIMISATION OF WATER DISTRIBUTION SYSTEMS
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Types of Evolutionary Algorithms
• Genetic algorithms (Holland 1976; Goldberg 1989)
• Ant Colony Optimisation (ACO)• Tabu search• Simulated annealing• Particle swarm optimisation (PSO)• Evolutionary strategy (Germany)
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History of genetic algorithms applied to water distribution systems
• Pioneered at the University of Adelaide by Laurie Murphy under my supervision in an honours project in 1990 and a PhD starting in 1991
• Initial focus was on the optimisation of the design of water distribution systems
• A spinoff company of Optimatics Pty Ltd formed by University of Adelaide in 1996 – operates in Australia, NZ, USA and UK (employs 20 people)
• Research focus is now on optimising operations and accounting for multiple objectives (sustainability, reliability)
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Genetic algorithm optimisation
• Population orientated technique (select a population size of say 500)
• Based on mechanisms of natural selection and genetics
• Selection, crossover and mutation operators produce new generations of designs
• Fitness of strings drives process• Uses EPANET type simulation model to
assess performance of all trial water distribution networks in each generation
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Creating a string from sub-strings - an example
BINARY CODING
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Chromosome of decision variables
• Choice tables are required for each decision variable
Chromosome
Existing pipe [3] (binary)00 = no change, e = 2.5 mm01 = clean/line, e = 0.3 mm10 = duplicate 306 mm11 = close the pipe
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Model Operation
Optimisation-Simulation Model Link
GA OPTIMISATION MODEL
HYDRAULIC SIMULATION MODEL
Configuration of water distribution and performance passes back and forth
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Simulate hydraulics of water distribution system
• Decode each string using the choice tables• Run a computer simulation model• Simulate demand loading cases
consecutively – peak hour, fire, extended period simulation
• Record any violation of constraints (e.g. pressures too low, velocities too high)
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Choices for the decision variables
A decoding lookup table Nominal Diameter
(mm)
Actual or Internal
Diameter (mm)
Binary Coding
Integer Coding
Roughness height (mm) for Darcy-Weisbach friction
factor
Unit Cost ($/m)
150 151 000 1 0.25 49 200 199 001 2 0.25 63 250 252 010 3 0.25 95 300 305 011 4 0.25 133 375 384 100 5 0.25 171 450 464 101 6 0.25 220 500 516 110 7 0.25 270 600 615 111 8 0.25 330
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Total cost and corresponding fitness of the string
• Total cost is the: example1. Real cost of the water distribution system
design
PLUS 2. A penalty pseudo-cost (or costs) if the
constraint(s) are not met– For example
=K*Maximum pressure deficitwhere K=$50,000 per metre
• Fitness is often taken as the inverse of the total cost
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Steps in a genetic algorithm optimisation
• Select a population size (e.g. N=100 or N=500)• Select a reproduction or selection operator• Select a probability of crossover (Pc)• Select a probability of mutation (Pm)
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A Simple Genetic Algorithm
ThisGeneration
N=500
Selection Crossover &
Mutation
The NextGeneration
Mating Pool
N=500
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Tournament selection
Randomly select pairs of chromosomes
versus
Evaluation of the fittest
Forming the Mating Pool
1 1 1 1 1
1 11 11 11
1 11 1
1 1
1
1111
1 1 1
11 1
1 11
1
1
11 1 1
1 11 11
1 1
11 1 1 1 1 1
11 1 1
1
1
11
1
00 0 0
0
0
0 0
00
00 0 0 0
00
0 00
0000 0
00000
00000
00000
0
00
0
0 0
0
0 0
0
1 11 1 11 1 000
65
112
94
83
98
143
87
130
Fittest strings win
Two sets of tournament selection are required
versus
versus
versus
FITNESS
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Crossover (one-point)
Chromosome A
Chromosome B Parents
Chromosome A`
Chromosome B`Offspring
1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1
1 1 11 1 1
1 1 1 1
0 0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0 0
Randomly select a crossover point
Interchange the tails
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The mutation operator
• Mutation occurs with a very small probability• One bit switches to a new value
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Produce many generations
• Continue to create a series of new generations (say 100,000 different networks)
• Repeat selection, crossover and mutation
• Increasingly fit solutions are generated
• The 10 or so lowest cost solutions must be remembered along the way
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Designs improve in each generationB
est C
ost
in E
ach
Gen
erat
ion
($ m
illio
n)
30
40
50
60
70
80
90
100
0 50,000 100,000 150,000 200,000
Number of Solution Evaluations
New York Tunnels Problem
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Optimisation of pumping plant operations
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Murray Bridge – operations optimisation
• United Utilities – Riverland water treatment plant project at Murray Bridge, South Australia
• GA optimisation minimises operations electricity costs by – maximizing off-peak pumping and – minimizing static pump head
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Background
Pumps
Elevated Storage
Water Treatment Plant Clear Water
Storage
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Case study – Murray Bridge system layout
White HillStorage Tank
(WHS)Off
Takes
Murray Bridge
Onkaparinga
Pipeline
Murray Bridge Water Treatment
Plant
Clear WaterStorage Tank (CWS)
3 Parallel Fixed Speed Pumps
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Traditional approach to control
• Trigger levels in storage tanks OR• Pump scheduling
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Controls based on trigger levels
Lower Trigger Level (Minimum Allowable Level)
Upper Trigger Level (Maximum Allowable Level)More Peak
Pumping than Necessary
Time
Tank
Lev
el
Peak Tariff Period
Tank not Full for Next
Peak Tariff Period
Tank not Full at Start of Peak Tariff Period
Off-Peak Tariff Period
7am 7am9pm
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Optimisation-Simulation Model Link
GA OPTIMISATION MODEL
HYDRAULIC SIMULATION MODEL
Operating policies for pumping system - trigger levels, schedules for
pumps turning on and turning off
Model operation
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Decision variables - operations optimisation at Murray Bridge
• Used a combination of real-value and integer value representation of the decision variables
• Four decision variable for final formulation:–Pump start time to fill tank–Pump stop time to drain tank to minimum level–Reduced upper trigger level for tank– Initial level in CWS tank
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A system controlled with original trigger levels and schedules
Lower Trigger Level (Minimum Allowable Level)
Upper Trigger Level (Maximum Allowable Level)
Time
Peak Tariff Period
Tank Full at Start of Peak Tariff Period
Off-Peak Tariff Period
7am 7am9pm
Start Primary Pump
Stop Primary Pump
Tank at Minimum Level at End of Peak Tariff Period
Tan
k L
evel
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A system controlled with both schedules and a reduced upper trigger level (1)
Time
Lower Trigger Level (Minimum Allowable Level)
Upper Trigger Level (Maximum Allowable Level)
Peak Tariff Period
Tank Full at Start of Peak Tariff Period
Off-Peak Tariff Period
7am 7am9pm
Start PumpReduced Upper Trigger Level
Tank at Minimum Level at End of Peak Tariff Period
Tan
k L
evel
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Tan
k L
evel
Lower Trigger Level (Minimum Allowable Level)
Upper Trigger Level (Maximum Allowable Level)
Time
Peak Tariff Period
Tank Full at Start of Peak Tariff Period
Off-Peak Tariff Period
7am 7am9pm
Start Pump
Reduced Upper Trigger Level Extended into Off-Peak Tariff Period
Tank at Minimum Levelat End of Peak Tariff Period
Switch Time
A system controlled with both schedules and a reduced upper trigger level (2)
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Modelled System - Original Trigger Levels
Lev
el (
m)
4
5
6
7
Daily electricity cost (averaged over 28 days) = $313.65
Time (hrs)
Daily peak pumping (averaged over 28 days): $286.05
Initial Level in CWS = 3.4m
Lower trigger level = 5.49m
Upper trigger level = 6.98m
0
1
2
3
8
11 am9 am7 am 7 am5 am3 am1 am11 pm9 pm7 pm5 pm3 pm1 pm
Daily off-peak pumping(averaged over 28 days): $27.60
WHS (m)
CWS (m)
WHS and CWS tank levels for first 24 hours of a 28-day simulation under fixed trigger level control for a 7.17 ML/D flow
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Optimised system - new approach
Initial Level in CWS = 3.675 m
Lower trigger level = 5.49 m
Reduced trigger level = 5.76 m
Upper trigger level = 6.98m
WHS and CWS tank levels after optimisation for a 7.17 ML/D flow
0
1
2
3
4
5
6
7
8
Time (hrs)
Lev
el (
m)
WHS
CWS
Pump on at 1:54:13 am
Peak Pumping: $189.51 Off-peak Pumping: $67.63
Total electricity cost = $257.14. Hence a $56.51 or 18.0%
11 am9 am7 am 7 am5 am3 am1 am11 pm9 pm7 pm5 pm3 pm1 pm
Switch time 2:15 am
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Results - savings from new approach
18.284.34378.76463.110
13.646.3293.02339.328
18.056.48257.16313.647.17
22.459.72207.13266.866
22.936.17121.57157.744
54.950.6141.6592.262
(%)($)Improved Controls
Current Controls
SavingsPumping Cost ($/Day) Daily
Demand (ML/Day)
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Accounting for Sustainability in the Design and Operation of Water Distribution Pumping Systems
![Page 71: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/71.jpg)
Research Objectives
• To construct a sustainability integrated multi-objective genetic algorithm optimisation model for the planning, design and evaluation of WDSs
• To explore the impacts different sustainability criteria (Greenhouse Gas emissions) will have on the results of WDS optimisation
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Aspects of Sustainability-
Social
Environmental
Economic Technical
1: Total cost of the system
2: GHG emissions
3:System reliability 4: Robustness of
Pareto-optimal Front
![Page 73: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/73.jpg)
Two of the Main Conflicting Objectives
Minimisation of thetotal life cycle system costs
Minimisation of thetotal life cycle system GHG emissions
![Page 74: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/74.jpg)
Higher CostLower GHG
Lower Cost
Higher GHG
Big pumpSmall pipe
Small pumpBig pipe
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Evaluating multi-objective optimisation results using a Pareto tradeoff curve
Cost
GHG
TradeoffCurve
![Page 76: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/76.jpg)
Determining life cycle economic costs
![Page 77: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/77.jpg)
Determining life cycle GHG emissions (no discounting i = 0% IPCC)
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Optimisation Framework
Generate Options
MO
GA
Simulation
Evaluation
Comparison & Selection
WDS Optimisation
Obje
ctives
Minimisation of total cost
Minimisation ofGHG emissions
Maximisation ofsystem reliability
Maximisation of robustness
of Pareto-optimal solutions
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Multi-objective optimisation using genetic algorithms• MOGA: NSGA-II Generate initial
population
Objective evaluation
Simulation models
Ranking
Generate global population
Non-dominated sorting
Crowding distance
Comparison & selection
Constraint handling
Generate child population
Crossover
Mutation
Stopping criteria met?
Stop
Yes
No
![Page 80: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/80.jpg)
Case Study• The network consists
of a lower reservoir (water source), one pump, one rising main and an upper reservoir
1,500,00095
1,500120100
Average peakday flow (L/s)Design life (years)
Design conditions of case 1Annual demand (m3)
Static head Pipe length
![Page 81: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/81.jpg)
Case Study• The aim
– to select the best combination of the pump size and pipe size
– deliver the minimum average peak-day flow
– minimise both the total cost and GHG emissions of the network during its design life
1,500,00095
1,500120100
Average peakday flow (L/s)Design life (years)
Design conditions of case 1Annual demand (m3)
Static head Pipe length
![Page 82: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/82.jpg)
Pareto optimal tradeoff curve – multi-objective optimisation i = 6%
6% discount rate for costs
A
B
G H
C D EF
87.0
91.0
95.0
99.0
103.0
4.3 4.7 5.1 5.5
System cost (M$)
GH
G
(k-t
onne)
$21.8/tonneCO2-e
$134/tonneCO2-e $371/tonne CO2-e
(300)
(450)(375)
6% discount rate for costs
A
B
G H
C D EF
87.0
91.0
95.0
99.0
103.0
4.3 4.7 5.1 5.5
System cost (M$)
GH
G
(k-t
onne)
$21.8/tonneCO2-e
$134/tonneCO2-e $371/tonne CO2-e
(300)
(450)(375)
Diameter for lowest cost solution
![Page 83: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/83.jpg)
Tradeoff for i = 1.4%
1.4% discount rate for costs
B
C DE
H
FG
88.5
89.5
90.5
91.5
92.5
10.1 10.3 10.5 10.7 10.9 11.1
System cost (M$)
GH
G
(k-t
onne
) $72.1/tonneCO2-e
$376/tonneCO2-e $1,468/tonne CO2-e
(375)
(450)
1.4% discount rate for costs
B
C DE
H
FG
88.5
89.5
90.5
91.5
92.5
10.1 10.3 10.5 10.7 10.9 11.1
System cost (M$)
GH
G
(k-t
onne
) $72.1/tonneCO2-e
$376/tonneCO2-e $1,468/tonne CO2-e
(375)
(450)
Lowest cost
Lowest GHGs
![Page 84: Outline](https://reader035.fdocuments.in/reader035/viewer/2022070407/5681433e550346895dafb3a6/html5/thumbnails/84.jpg)
Conclusions
• Evolutionary algorithm optimisation has application in design and operation of water distribution systems
• It is relatively easy to tack on an evolutionary algorithm optimisation onto existing simulation models
• Capital costs and operating costs can be reduced significantly
• Sustainability can be optimised