Prof Graeme Dandy at the Landscape Science Cluster Seminar, May 2009

Optimisation of Optimisation of Water ManagementWater Management

Prof. Graeme DandyProf. Graeme DandySchool of Civil, Environmental and School of Civil, Environmental and

Mining EngineeringMining EngineeringUniversity of AdelaideUniversity of Adelaide

Acknowledgement of Co-ResearchersAcknowledgement of Co-Researchers

Prof Holger MaierProf Holger Maier Postdocs:Postdocs:

Matt GibbsMatt Gibbs Postgrads:Postgrads:

Abby GoodmanAbby Goodman Dan PartingtonDan Partington

Honours students:Honours students: Fiona PatonFiona Paton John BaulisJohn Baulis Ben StanifordBen Staniford Lisa LloydLisa Lloyd Rebecca TennantRebecca Tennant Jason NicolsonJason Nicolson Liam HarnettLiam Harnett

OutlineOutline

Background: Optimisation ModelsBackground: Optimisation Models Case Studies:Case Studies:

Optimum planning of urban water systems Optimum planning of urban water systems (regional scale)(regional scale)

Resource optimisation framework for the Resource optimisation framework for the Upper South East Region of SAUpper South East Region of SA

Optimum design of greywater reuse systems Optimum design of greywater reuse systems (cluster scale)(cluster scale)

ConclusionsConclusions

Types of ModelsTypes of Models

DescriptiveDescriptive How does the system behave? How does the system behave? What will be the consequences of certain What will be the consequences of certain

actions?actions?Simulation ModelsSimulation Models

PrescriptivePrescriptive What are the best actions to achieve a What are the best actions to achieve a

particular objective or set of objectives?particular objective or set of objectives?Optimisation modelsOptimisation models

MethodologyMethodology

Systems approachSystems approach

Optimisation Module

Selection of Alternative

Simulation of Alternative

Evaluation of Alternative

Selection of Objectives

Results


Optimisation Module

Form of an Optimisation ModelForm of an Optimisation Model

Choose values for a set of decision Choose values for a set of decision variables so as to maximise (or minimise) variables so as to maximise (or minimise) a particular objectivea particular objective

Subject to a set of constraintsSubject to a set of constraints

Genetic Algorithm Genetic Algorithm OptimisationOptimisation

What Are Genetic AlgorithmsWhat Are Genetic Algorithms ??

Guided search procedures that work by Guided search procedures that work by analogy to natural selectionanalogy to natural selection

Include embedded computer simulationInclude embedded computer simulation Each solution is represented by a string of Each solution is represented by a string of

numbersnumbers Work with a population of solutionsWork with a population of solutions Algorithm can run for any length of timeAlgorithm can run for any length of time Can’t prove that you have reached the Can’t prove that you have reached the

optimum solutionoptimum solution

Typical GA stringTypical GA string

Distribution Network Pipe

MaterialDistribution

Network Pipe Diameters

Pump Size

Collection Network Pipe

Material

So

luti

on

Co

st

($ m

illi

on

)

30

40

50

60

70

80

90

100

0 50,000 100,000 150,000 200,000

Number of Solution Evaluations

The GA conducts adirected search foroptimal solutions

Repeat Towards Convergence

Multi-Objective Multi-Objective OptimisationOptimisation

Multi-Objective OptimisationMulti-Objective Optimisation

Pareto Optimal Front

Optimum Planning of Optimum Planning of UrbanUrban

Water Systems Water Systems (Regional Scale) (Regional Scale)

TEMPORAL SCENARIOS

SUPPLY TYPE ALTERNATIVES

Run simulation

model

Check constraints

Output results

Water Simulation Model (WaterCress)

Calculate: - Reliability - Resilience - Vulnerability

2020 – 72ML/day 2060 – 225ML/day 2100 – 300ML/day

0KL < 30GL/yr

Risk Based Performance Assessment

0

10

20

30

40

50

60

70

80

90

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060Date

Wat

er S

up

ply

(G

L/y

r)

MypongaReservoir

Happy ValleyReservoir

River Murray

DesalinationPlant

MaximumRiver MurrayFlow

ReliabilityResilience

(years-1)Vulnerability

(GL)

225ML/day 0KL 85% 0.63 26.0

Risk Based Performance Assessment

OptimisationOptimisation

Objectives:Objectives: Minimise present value of total system costMinimise present value of total system cost Minimise greenhouse gas emissionsMinimise greenhouse gas emissions

Constraint:Constraint: Availability of water from the Murray (30 Availability of water from the Murray (30

GL/year)GL/year)

OptimisationOptimisation

Decision Variables:Decision Variables: Capacity of desalination plant Capacity of desalination plant

(ML/day)(ML/day) Size of rainwater tanks for all Size of rainwater tanks for all

households (kL)households (kL) Operating rules for the systemOperating rules for the system

0

1

2

3

4

5

6

7

0 1 2 3 4Cost ($/kL)

GH

G e

mis

sio

ns

(kg

CO

2-e

/kL

)

Desalination PlantRainwater TankRiver MurrayHappy ValleyMyponga

Approximate trade-offs between supply types

The 2060 Pareto Front

21.76

21.78

21.80

21.82

21.84

6.990 7.000 7.010 7.020

Cost ($2007 billion)

GH

G e

mis

sio

ns

(Meg

ato

nn

es o

f C

O2-e

)

Optimal Tradeoffs – Southern SystemOptimal Tradeoffs – Southern System


21.76

21.78

21.80

21.82

21.84

6.990 7.000 7.010 7.020


GH

G e

mis

sio

ns

(Meg

ato

nn

es o

f C

O2-e

)

Breakpoint (250ML/day, 2KL)

(251ML/day, 1.8KL)

(248ML/day, 2.6KL)



21.76

21.78

21.80

21.82

21.84

6.990 7.000 7.010 7.020


GH

G e

mis

sio

ns

(Meg

ato

nn

es o

f C

O2-e

)

$45/tonne

$1000/tonne

Breakpoint (250ML/day, 2KL)

(251ML/day, 1.8KL)

(248ML/day, 2.6KL)


Range depends on optimal rainwater tank size, which depends on average yearly water supply per tank:

Optimisation Process

Average yearly water supply

per tank

1KL 24KL $3.08/KL 0.96kgCO2-e/KL

20KL 48KL $3.27/KL 3.34kgCO2-e/KL

0 200 400 600

Lifetime of the Desalination Plant

Lifetime of Rainwater Tanks

Climate Change Impacts

River Murray Supply Constraint

Demand

Social Discount Rate

Economic Discount Rate

247.1

241.9

247.8

206.7

168.0

245.5

247.7

251.1

251.1

287.0

296.0

512.0

251.0

251.6

Desalination Plant Size (ML/day)

0 2 4 6

Lifetime of the Desalination Plant

Lifetime of Rainwater Tanks

Climate Change Impacts

River Murray Supply Constraint

Demand

Social Discount Rate

Economic Discount Rate

1.8

1.8

0.9

1.1

1.8

1.7

1.7

3.1

5.3

2.9

3.2

2.9

3.6

2.8

Tank Size (KL)

Sensitivity Analysis of the Optimisation

Process

Planned Extensions to this ResearchPlanned Extensions to this Research

•Include more objectives (reliability, social factors)•Add more alternatives (e.g. stormwater reuse)


Future expansion of Adelaide’s water Future expansion of Adelaide’s water supply will use a combination of non-supply will use a combination of non-traditional sources including desalination, traditional sources including desalination, rainwater tanks and stormwater and rainwater tanks and stormwater and wastewater reusewastewater reuse

Tradeoffs exist between the costs and Tradeoffs exist between the costs and environmental impacts of these sourcesenvironmental impacts of these sources

Multi-objective Optimisation can be used Multi-objective Optimisation can be used to quantify some of these tradeoffsto quantify some of these tradeoffs

Resource Optimisation Resource Optimisation Framework for the Upper Framework for the Upper South East Region of SASouth East Region of SA

110 km

Dune and flat topologyDune and flat topology Very flat, slope of 1:6000Very flat, slope of 1:6000 Prone to floodingProne to flooding

Flats cleared for agriculture, Flats cleared for agriculture, dunes contain wetlands of high dunes contain wetlands of high conservation valueconservation value

Area of 1 Million HaArea of 1 Million Ha 40% affected by dryland 40% affected by dryland

salinitysalinity Over 640 km of groundwater Over 640 km of groundwater

drains installeddrains installed 100 regulators throughout the 100 regulators throughout the

regionregion

Case Study – Upper South EastCase Study – Upper South East

Management DecisionsManagement Decisions

Management decisions involve movement of available waterManagement decisions involve movement of available water Regulators in the drainage network allow water to be Regulators in the drainage network allow water to be

directed around the landscapedirected around the landscape Decisions are based on a number of considerations:Decisions are based on a number of considerations:

• Water quantityWater quantity• Water qualityWater quality• Wetland prioritiesWetland priorities

Conflicting objectives:Conflicting objectives: Manage dryland salinityManage dryland salinity Maintain wetland biodiversityMaintain wetland biodiversity Mitigate floodingMitigate flooding

Proposed Decision Support SystemProposed Decision Support System

A multidisciplinary approach is proposed to A multidisciplinary approach is proposed to produce a dryland salinity decision support tool:produce a dryland salinity decision support tool: Groundwater modelling Groundwater modelling Rainfall-runoff modelling Rainfall-runoff modelling Salt-transport modelling Salt-transport modelling Ecological modellingEcological modelling

Models combined to produce an integrated Models combined to produce an integrated modeling framework to assist management modeling framework to assist management decisionsdecisions

Integrated Modelling FrameworkIntegrated Modelling Framework

groundwaterresponse

RegulatorDecision

Point

shut openflow flow

runoff,salinity

rainfallevap.

runoff,salinity

runoff,salinity

runoff,salinity

rainfallevap.

rainfallevap.

rainfallevap.

wetlandresponse

environmentalconditions

Water Quantity ModellingWater Quantity Modelling

Hydrologic model requiredHydrologic model required Good GIS information available:Good GIS information available:

Drain, wetland and regulator locationsDrain, wetland and regulator locations Catchment wide LiDAR elevation data are currently Catchment wide LiDAR elevation data are currently

being processedbeing processed Very sparse flow data recordsVery sparse flow data records

Historically not much data recordedHistorically not much data recorded Drains installed since the late 1990sDrains installed since the late 1990s Very little to measure in the last few yearsVery little to measure in the last few years

Hydrologic ModellingHydrologic Modelling

HEC-HMS adopted for modellingHEC-HMS adopted for modelling Different models can be selected for each component of Different models can be selected for each component of

rainfall-runoff modelrainfall-runoff model Loss (Infiltration)Loss (Infiltration) Transformation (Rainfall-Runoff)Transformation (Rainfall-Runoff) BaseflowBaseflow

Hydrologic ModellingHydrologic Modelling

Water Quality ModellingWater Quality Modelling

Salinity is very important variable to decision making Salinity is very important variable to decision making processprocess

No water quality models in HEC-HMSNo water quality models in HEC-HMS CATSALT (Tuteja et al., 2003) to determine salt loadCATSALT (Tuteja et al., 2003) to determine salt load

Considers flow from groundwater and unsaturated Considers flow from groundwater and unsaturated zone separatelyzone separately

Other considerations, such as evaporation in storages Other considerations, such as evaporation in storages QSW

QGW

Unsaturated

Saturated

QUW


groundwaterresponse

RegulatorDecision

Point

shut openflow flow

runoff,salinity

rainfallevap.

runoff,salinity

runoff,salinity

runoff,salinity

rainfallevap.

rainfallevap.

rainfallevap.

wetlandresponse


Groundwater ModellingGroundwater Modelling

Regional groundwater flow is relatively well Regional groundwater flow is relatively well understoodunderstood

Local effects of drains on groundwater table largely Local effects of drains on groundwater table largely unknown, and highly controversialunknown, and highly controversial

Groundwater ModellingGroundwater Modelling

Groundwater modelling to answer important Groundwater modelling to answer important questions, such as:questions, such as: What is the zone of influence of the drain?What is the zone of influence of the drain? Once a regulator is changed, how long will it take Once a regulator is changed, how long will it take

for the groundwater table to adjust?for the groundwater table to adjust? What is the expected effect on the soil salinity?What is the expected effect on the soil salinity?


groundwaterresponse

RegulatorDecision

Point

shut openflow flow

runoff,salinity

rainfallevap.

runoff,salinity

runoff,salinity

runoff,salinity

rainfallevap.

rainfallevap.

rainfallevap.

wetlandresponse


Ecological Response to SalinityEcological Response to Salinity

A further criterion on the management problem is to sustain A further criterion on the management problem is to sustain the wetlands in the regionthe wetlands in the region

Project aims to answer questions such as:Project aims to answer questions such as: What are the impacts of elevated salinities on the health What are the impacts of elevated salinities on the health

and survival of aquatic species?and survival of aquatic species? How long can elevated salinities be tolerated?How long can elevated salinities be tolerated? How can we best use water from the drains to optimise How can we best use water from the drains to optimise

wetland health and function? wetland health and function? Field and laboratory studies to collect necessary dataField and laboratory studies to collect necessary data Modelling to allow expected effects to be predictedModelling to allow expected effects to be predicted Decision making process can then make use of modelling Decision making process can then make use of modelling

resultsresults

Bayesian Network ModellingBayesian Network Modelling

Interaction Between ModelsInteraction Between Models

Rainfall,Evaporation

CurrentConditions

Groundwater Models

Wetland Models

Rainfall-Runoff Models

Salt TransportModels

Regulator Settings

Catchment Routing

Environmental Response

Dryland Salinity

Flooding

Evaluate OptionSimulation/Optimization

Summary Summary

Currently, the information required to Currently, the information required to tackle the problem of dryland salinity is tackle the problem of dryland salinity is incompleteincomplete

A multidisciplinary approach is proposed to A multidisciplinary approach is proposed to adequately address the problemadequately address the problem Water quality and quantityWater quality and quantity Groundwater Groundwater EcologyEcology

Project OutcomesProject Outcomes

Integrated simulation model of the systemIntegrated simulation model of the system Considering all aspects that affect Considering all aspects that affect

regulator operationregulator operation Optimisation component to determine Optimisation component to determine

optimal operating schemeoptimal operating scheme Multi-Objective evolutionary algorithmsMulti-Objective evolutionary algorithms

Outcomes (2) Outcomes (2)

Novel aspects include:Novel aspects include: Ungauged catchment model calibrationUngauged catchment model calibration Groundwater modellingGroundwater modelling Ecological modellingEcological modelling Integrated catchment modellingIntegrated catchment modelling Optimisation and reliability aspectsOptimisation and reliability aspects

SummarySummary Whereas simulation models can be used to Whereas simulation models can be used to

assess the likely effects of various actions on a assess the likely effects of various actions on a system, optimisation models are useful for system, optimisation models are useful for providing guidance in identifying the best set of providing guidance in identifying the best set of actionsactions

Optimisation models require a clear definition of Optimisation models require a clear definition of objectives objectives

Multi-objective optimisation models can be used Multi-objective optimisation models can be used to assist in managing scarce water resourcesto assist in managing scarce water resources

Wastewater Treatment

Wetland

ASR

Industry

House or Cluster

Mains water

Stormwater

Total Urban Water ManagementTotal Urban Water Management

Optimum Design of GreywaterOptimum Design of Greywater

Reuse Systems (Cluster Scale) Reuse Systems (Cluster Scale)

Research ObjectivesResearch Objectives

1.1. Develop a new Develop a new methodology for the methodology for the planning of greywater planning of greywater reuse schemes in urban reuse schemes in urban areas that considers areas that considers their sustainabilitytheir sustainability

2.2. Apply methodology to Apply methodology to development in Streaky development in Streaky BayBay

MethodologyMethodology

Systems approachSystems approach

Optimisation Module

Selection of Alternative

Simulation of Alternative

Evaluation of Alternative


Results


Optimisation Module

Case Case StudyStudyScale 019 housesScale 0219 housesScale 0347 housesScale 0468 housesScale 05117 housesScale 06147 houses

System ComponentsSystem Components

Individual house reuse systemIndividual house reuse system Treatment systemTreatment system PumpPump Storage tankStorage tank

System ComponentsSystem Components

Cluster scale reuse schemesCluster scale reuse schemes Greywater collection networkGreywater collection network Treatment systemTreatment system Storage tankStorage tank PumpPump Treated greywater distributionTreated greywater distribution

networknetwork

LayoutLayout

Sustainability ObjectivesSustainability Objectives

Sustainability:Sustainability: Environmental: Total energy consumption Environmental: Total energy consumption

(GJ)(GJ) Economic: Present value of life cycle cost ($)Economic: Present value of life cycle cost ($) SocialSocial TechnicalTechnical

Simulation of AlternativesSimulation of Alternatives

Many design variablesMany design variables Collection network material and pipe diameterCollection network material and pipe diameter Distribution network material and Distribution network material and pipe pipe

diametersdiameters Greywater Treatment SystemGreywater Treatment System Pump SizePump Size

However, we need to simulate each However, we need to simulate each componentcomponent

21

23

25

27

29

10,000 12,000 14,000 16,000NPV per Household ($)

Tota

l Ene

rgy

per H

ouse

hold

(GJ)

1

2

3

4

5

6

21

23

25

27

29


Tota

l Ene

rgy

per H

ouse

hold

(GJ)

1

2

3

4

5

6

ResultsResults

Comparison of ResultsComparison of Results

Case StudyCase Study Between $3 and $6 per kLBetween $3 and $6 per kL

Rouse Hill (Sydney)Rouse Hill (Sydney) Between $3 and $4 per kLBetween $3 and $4 per kL

Sensitivity AnalysisSensitivity Analysis

Doubling the population densityDoubling the population density

Extending the available pipe Extending the available pipe

materials and diametersmaterials and diameters

17

19

21

23

25

27


Tota

l Ene

rgy

per H

ouse

hold

(GJ)

Sensitivity AnalysisSensitivity Analysis


Cluster scale is more sustainable than Cluster scale is more sustainable than individual householdindividual household

Reuse schemes are more sustainable Reuse schemes are more sustainable with:with: Increased population densityIncreased population density Network design standards Network design standards

that allow different pipethat allow different pipematerials and diametersmaterials and diameters

Further WorkFurther Work

Include other objectivesInclude other objectives Ecological impactsEcological impacts ReliabilityReliability

Include other optionsInclude other options Rainwater tanksRainwater tanks Stormwater reuseStormwater reuse Blackwater reuseBlackwater reuse Aquifer storage and recoveryAquifer storage and recovery

Apply to larger scalesApply to larger scales

SummarySummary Whereas simulation models can be used to Whereas simulation models can be used to

assess the likely effects of various actions on a assess the likely effects of various actions on a system, optimisation models are useful for system, optimisation models are useful for providing guidance in identifying the best set of providing guidance in identifying the best set of actionsactions

Optimisation models require a clear definition of Optimisation models require a clear definition of objectives objectives

Multi-objective optimisation models can be used Multi-objective optimisation models can be used to assist in managing scarce water resourcesto assist in managing scarce water resources

Prof Graeme Dandy at the Landscape Science Cluster Seminar, May 2009

Technology

Transcript of Prof Graeme Dandy at the Landscape Science Cluster Seminar, May 2009