water system design optimisation –ottawa case … · and more recent optimisation technology...
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Water System Design Optimisation – Ottawa Case Study and more recent Optimisation Technology Developments
Mark Randall-SmithTechnical Director, Mouchel Consulting
Dr Ed KeedwellUniversity of Exeter
14 October 2015
Contents
• Case study: Ottawa Water Supply Master Plan
• Application of Genetic Algorithms (GA) to the problem
• The case study experience: how did the GA perform?
• Overview of the history of Water Distribution Network optimisation
• Heuristics and Hyper-Heuristics: the next generation of EA’s
• Indicative Benefits
• Questions
Ottawa Optimisation Study
• Prepare update to Water Distribution System Master Plan using Optimisation Technology
• Develop a cost-effective system expansion plan to meet design criteria over an appropriate planning period
• Define system operations strategy
• Develop infrastructure phasing plan
Scope:
Objective:• Update Ottawa Master Plan design to
accommodate planned growth to 2031
City of Ottawa Water Distribution System75 Node Model
Conroy Tank
Stittsville Tank
Hurdman Bridge PS
Moodie Tank
Fleet Street PS
Innes Rd Tank
Ottawa Distribution System
Lemieux WPP
Britannia WPP
Orleans Reservoir
Carllington Reservoir
Ottawa South Reservoir & PS
Barrhaven Reservoir
Glen Cairn Reservoir
Design Options
• Expansion of Water Treatment Works• Expansion of all existing reservoirs• Reinforcement of major water main network in existing areas• Upsizing of major water mains that are expected to be rehabilitated in
the near future• Expansions at all major pump stations• New elevated storage tanks in Pressure Zones 2E, 3C and 3W• New trunk main alignments to feed the West, South, and East Urban
Communities• New pump stations to serve Pressure Zones 3W and 3C• Rezoning of some areas to improve pumping efficiency
… a vast number of complex combinations!
Evolutionary Algorithms - principles
• Complex water network design problems cannot satisfactorily be optimised using trial & error or linear solving techniques
• Genetic Algorithm (GA) functions apply evolutionary learning processes emulating natural selection: Selection, Reproduction, Mutation
• Repeated through successive generations, to generate progressively better solutions
• GA can identify optimal solutions after evaluating a very small proportion of the available combinations of individual component choices
-1-0.7
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-2 -1.7 -1.4 -1.1 -0.8 -0.5 -0.2 0.1 0.4 0.7
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Selection
• Trial solutions evaluated for ‘fitness’– Satisfaction of hydraulic
requirements– Cost
• Low overall cost = high fitness
• High fitness = greater probability of ‘reproducing’
Well suited
Selected
Poorly suited
Not selected
Parents 0 0 0 0 0 0 01 1 1 1 1 1 1
Offspring 1 1 1 0 0 0 0 0 0 0 1 1 1 1
Reproduction
Genetic Algorithm (GA) optimisation objective …
• Extensive development/ demand increase projected in peripheral Urban Communities (to 2031 horizon)
• Meet future demands at adequate pressure at least cost (NPV, capital + operational), for future maximum day demands at the planning horizon …
• … by finding the optimal configuration of:– Transmission mains– Storage facilities– Pumping stations
Optimisation inputs
• Required minimum pressure level – 40 psi (or maintain at current level where 40 psi cannot currently be achieved)
• Growth projections – expressed as nodal demands at the planning horizon (2031)
• Definition of potential feasible future components (transmission, pumping, storage)
• Cost models – capital, operational, energy
Reservoir Expansion
New Pump Station
Pump Station Expansion
WPP Expansion
Optimisation:Final Alternatives Set
Optimisation software: GAnet
GAnetOptimisation Configuration
settings
Project FileOptions, cost models,constraints & penalties
Epanet hydraulic solver
Epanet input fileNetwork base model
Evaluation fileSelections, flows, pressures, costs, penalties
Epanet output fileNetwork solution model
• Complexity and constraints hindered convergence on feasible solutions
• Preliminary results were sub-optimal– Inconsistent pipe size
selections– Residual constraint
violations
Preliminary Optimisation Conclusions
• Number & complexity of options needed reducing to improve the performance of the GA
Final Solutions – Revised Approach
• Reduction in number & complexity of options to improve GA performance– Omit options selected rarely or never in preliminary runs– Separate analysis of relatively independent zones
• ‘Customised’ decision variable configuration added – allow groups of dependent options to be treated as a single
decision• ‘Heuristic’ post-optimisation process built in to achieve pipe
size selection ‘smoothing’
Final 2031 Solution
Reservoir Expansion
New Pump Station
Pump Station Expansion
WPP ExpansionNew Elevated Tank
2011 Phasing Plan
2021 Phasing Plan
Final 2031 Solution
Ottawa Study: Final Outcome & Lessons Learnt
• Some significant challenges addressed and overcome• Strong, cost-effective solutions eventually identified• The study had a significant impact on the development of the
future Ottawa supply system• Successful outcome depended on engineering/ optimisation
‘partnership’– Lessons learned about GA limitations when applied to large,
complex ‘real world’ problems– Highly interactive relationship between the Canadian engineers
and optimisation team was key• Study helped to define desirable characteristics of future
optimisation approach and technology
Progression of WDN Optimisation Techniques
• Manual Design• Linear Programming/Non-Linear Programming• Meta-heuristics
– Genetic algorithms– Swarm optimisation methods
• Hyper-heuristics– ‘Optimising the optimiser’
Developments in technology
• Key developments in evolutionary algorithms are to bring engineering expertise into the optimisation process
• Embed ‘common sense’ heuristics into the EA through enhanced mutation operators– Adaptive local search– Pipe smoothing
• Develop new methods (hyperheuristics) to incorporate the above heuristics more intelligently
ALCO-GA Heuristic• ALCO-GA – Guides mutation by incorporating engineering expertise
– randomly selects a node– determines whether it is in deficit or surplus– ‘searches’ upstream/downstream to find the cause &
increments/decrements• Finds better, ‘engineering feasible’ solutions more quickly:
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0 500 1,000 1,500 2,000
Feas
ible
Pop
ulat
ion
(%)
Evaluations
SSGAALCO-GA (25%)ALCO-GA (50%)ALCO-GA (75%)ALCO-GA (100%)
Pipe Smoothing Heuristic• PSGA – Increases probability of smooth transition from WTW to network
extremities– randomly selects a pipe to mutate– determines the upstream pipe diameter– limits mutation to a maximum of the upstream pipe
• Finds better, ‘smoother’ solutions more quickly:
Hyperheuristics – The Future• Search the space of algorithms as well as solutions• ‘Optimise the optimiser’• Have the potential to incorporate any number of heuristics into the
optimisation process• Hyperheuristic methods will choose when to use certain heuristics
Hyperheuristics vs Metaheuristics
• Large industrial sized network problems (500+ pipes and 1100+ pipes)• 6 heuristics used in the hyperheuristic:
– LLH0: change only one pipe diameter randomly – LLH1: swap two pipe diameters at random – LLH2: increase a randomly selected pipe diameter by one pipe size. – LLH3: decrease a randomly selected pipe diameter by one pipe
size.– LLH4: ‘ruin’ several pipes and rebuild randomly. – LLH5: shuffle several pipes (i.e. makes several swaps).
Hyperheuristics vs Metaheuristics
27%
26%14%
19%
8%6%
Heuristic UtilisationLLH0 LLH1 LLH2 LLH3 LLH4 LLH5
Network EA Hyperheuristic
HH Long Run
A (500+ Pipes) £3,672,300 £3,932,692 £3,400,000
B (1100+ Pipes) £8,785,720 £8,478,026 £7,510,000
Feasible Network Cost
• Promising results in comparison with highly tuned EA on difficult industrial problems
• Heuristic utilisation reveals the best movements in search space
Optimisation is not just for design
• Optimisation techniques have been applied to other aspects of water distribution network management
• Have been used for:– Rehabilitation– Calibration– Operation (e.g. pump scheduling)– Rezoning– Etc.
Summary
• Evolutionary algorithms applied to a large-scale master planning project in the City of Ottawa
• With some engineering guidance, evolutionary algorithms can search the huge space of possible solutions in WDN optimisation
• Current research moving towards automated integration of engineering expertise and optimisation in the form of heuristics
• Heuristic-enhanced algorithms and hyperheuristics represent promising methods for achieving this
