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

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Professor Graeme Dandy from the University of Adelaide presenting on Optimisation of Water Management at the Landscape Science Cluster Seminar, May 2009

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

    1. 1. Optimisation of Water Management Prof. Graeme Dandy School of Civil, Environmental and Mining Engineering University of Adelaide
    2. 2. Acknowledgement of Co-Researchers <ul><li>Prof Holger Maier </li></ul><ul><li>Postdocs: </li></ul><ul><ul><li>Matt Gibbs </li></ul></ul><ul><li>Postgrads: </li></ul><ul><ul><li>Abby Goodman </li></ul></ul><ul><ul><li>Dan Partington </li></ul></ul><ul><li>Honours students: </li></ul><ul><ul><li>Fiona Paton </li></ul></ul><ul><ul><li>John Baulis </li></ul></ul><ul><ul><li>Ben Staniford </li></ul></ul><ul><ul><li>Lisa Lloyd </li></ul></ul><ul><ul><li>Rebecca Tennant </li></ul></ul><ul><ul><li>Jason Nicolson </li></ul></ul><ul><ul><li>Liam Harnett </li></ul></ul>
    3. 3. Outline <ul><li>Background: Optimisation Models </li></ul><ul><li>Case Studies: </li></ul><ul><ul><li>Optimum planning of urban water systems (regional scale) </li></ul></ul><ul><ul><li>Resource optimisation framework for the Upper South East Region of SA </li></ul></ul><ul><ul><li>Optimum design of greywater reuse systems (cluster scale) </li></ul></ul><ul><li>Conclusions </li></ul>
    4. 4. Types of Models <ul><li>Descriptive </li></ul><ul><ul><li>How does the system behave? </li></ul></ul><ul><ul><li>What will be the consequences of certain actions? </li></ul></ul><ul><ul><ul><li>Simulation Models </li></ul></ul></ul><ul><li>Prescriptive </li></ul><ul><ul><li>What are the best actions to achieve a particular objective or set of objectives? </li></ul></ul><ul><ul><ul><li>Optimisation models </li></ul></ul></ul>
    5. 5. Methodology <ul><li>Systems approach </li></ul>Optimisation Module Selection of Alternative Simulation of Alternative Evaluation of Alternative Selection of Objectives Results Selection of Objectives Optimisation Module
    6. 6. Form of an Optimisation Model <ul><li>Choose values for a set of decision variables so as to maximise (or minimise) a particular objective </li></ul><ul><li>Subject to a set of constraints </li></ul>
    7. 7. Genetic Algorithm Optimisation
    8. 8. What Are Genetic Algorithms ? <ul><li>Guided search procedures that work by analogy to natural selection </li></ul><ul><li>Include embedded computer simulation </li></ul><ul><li>Each solution is represented by a string of numbers </li></ul><ul><li>Work with a population of solutions </li></ul><ul><li>Algorithm can run for any length of time </li></ul><ul><li>Can’t prove that you have reached the optimum solution </li></ul>
    9. 9. Typical GA string Distribution Network Pipe Material Distribution Network Pipe Diameters Pump Size Collection Network Pipe Material
    10. 10. Solution Cost ($ million) 30 40 50 60 70 80 90 100 0 50,000 100,000 150,000 200,000 Number of Solution Evaluations The GA conducts a directed search for optimal solutions Repeat Towards Convergence
    11. 11. Multi-Objective Optimisation
    12. 12. Multi-Objective Optimisation Pareto Optimal Front
    13. 13. Optimum Planning of Urban Water Systems (Regional Scale)
    14. 14. TEMPORAL SCENARIOS SUPPLY TYPE ALTERNATIVES
    15. 15. <ul><li>2020 – 72ML/day </li></ul><ul><li>2060 – 225ML/day </li></ul><ul><li>2100 – 300ML/day </li></ul><ul><li>0KL </li></ul><ul><li>< 30GL/yr </li></ul>Risk Based Performance Assessment
    16. 16. Risk Based Performance Assessment
    17. 17. Optimisation <ul><li>Objectives: </li></ul><ul><ul><li>Minimise present value of total system cost </li></ul></ul><ul><ul><li>Minimise greenhouse gas emissions </li></ul></ul><ul><li>Constraint: </li></ul><ul><ul><li>Availability of water from the Murray (30 GL/year) </li></ul></ul>
    18. 18. Optimisation <ul><li>Decision Variables: </li></ul><ul><ul><li>Capacity of desalination plant (ML/day) </li></ul></ul><ul><ul><li>Size of rainwater tanks for all households (kL) </li></ul></ul><ul><ul><li>Operating rules for the system </li></ul></ul>
    19. 19. Approximate trade-offs between supply types
    20. 20. Optimal Tradeoffs – Southern System
    21. 21. Breakpoint (250ML/day, 2KL) (251ML/day, 1.8KL) (248ML/day, 2.6KL) Optimal Tradeoffs – Southern System
    22. 22. $45/tonne $1000/tonne Breakpoint (250ML/day, 2KL) (251ML/day, 1.8KL) (248ML/day, 2.6KL) Optimal Tradeoffs – Southern System
    23. 23. <ul><li>Range depends on optimal rainwater tank size, </li></ul><ul><li>which depends on average yearly water supply per tank: </li></ul>Optimisation Process
    24. 24. Sensitivity Analysis of the Optimisation Process
    25. 25. Planned Extensions to this Research <ul><ul><li>Include more objectives (reliability, social factors) </li></ul></ul><ul><ul><li>Add more alternatives (e.g. stormwater reuse) </li></ul></ul>
    26. 26. Conclusions <ul><li>Future expansion of Adelaide’s water supply will use a combination of non-traditional sources including desalination, rainwater tanks and stormwater and wastewater reuse </li></ul><ul><li>Tradeoffs exist between the costs and environmental impacts of these sources </li></ul><ul><li>Multi-objective Optimisation can be used to quantify some of these tradeoffs </li></ul>
    27. 27. Resource Optimisation Framework for the Upper South East Region of SA
    28. 29. <ul><li>Dune and flat topology </li></ul><ul><ul><li>Very flat, slope of 1:6000 </li></ul></ul><ul><ul><li>Prone to flooding </li></ul></ul><ul><li>Flats cleared for agriculture, dunes contain wetlands of high conservation value </li></ul><ul><li>Area of 1 Million Ha </li></ul><ul><ul><li>40% affected by dryland salinity </li></ul></ul><ul><li>Over 640 km of groundwater drains installed </li></ul><ul><li>100 regulators throughout the region </li></ul>Case Study – Upper South East 110 km
    29. 30. Management Decisions <ul><li>Management decisions involve movement of available water </li></ul><ul><ul><li>Regulators in the drainage network allow water to be directed around the landscape </li></ul></ul><ul><li>Decisions are based on a number of considerations: </li></ul><ul><ul><ul><li>Water quantity </li></ul></ul></ul><ul><ul><ul><li>Water quality </li></ul></ul></ul><ul><ul><ul><li>Wetland priorities </li></ul></ul></ul><ul><li>Conflicting objectives: </li></ul><ul><ul><li>Manage dryland salinity </li></ul></ul><ul><ul><li>Maintain wetland biodiversity </li></ul></ul><ul><ul><li>Mitigate flooding </li></ul></ul>
    30. 31. Proposed Decision Support System <ul><li>A multidisciplinary approach is proposed to produce a dryland salinity decision support tool: </li></ul><ul><ul><li>Groundwater modelling </li></ul></ul><ul><ul><li>Rainfall-runoff modelling </li></ul></ul><ul><ul><li>Salt-transport modelling </li></ul></ul><ul><ul><li>Ecological modelling </li></ul></ul><ul><li>Models combined to produce an integrated modeling framework to assist management decisions </li></ul>
    31. 32. Integrated Modelling Framework groundwater response Regulator Decision Point shut open flow flow runoff, salinity rainfall evap. runoff, salinity runoff, salinity runoff, salinity rainfall evap. rainfall evap. rainfall evap. wetland response environmental conditions
    32. 36. Water Quantity Modelling <ul><li>Hydrologic model required </li></ul><ul><li>Good GIS information available: </li></ul><ul><ul><li>Drain, wetland and regulator locations </li></ul></ul><ul><ul><li>Catchment wide LiDAR elevation data are currently being processed </li></ul></ul><ul><li>Very sparse flow data records </li></ul><ul><ul><li>Historically not much data recorded </li></ul></ul><ul><ul><li>Drains installed since the late 1990s </li></ul></ul><ul><ul><li>Very little to measure in the last few years </li></ul></ul>
    33. 37. Hydrologic Modelling <ul><li>HEC-HMS adopted for modelling </li></ul><ul><li>Different models can be selected for each component of rainfall-runoff model </li></ul><ul><ul><li>Loss (Infiltration) </li></ul></ul><ul><ul><li>Transformation (Rainfall-Runoff) </li></ul></ul><ul><ul><li>Baseflow </li></ul></ul>
    34. 38. Hydrologic Modelling
    35. 39. Water Quality Modelling <ul><li>Salinity is very important variable to decision making process </li></ul><ul><li>No water quality models in HEC-HMS </li></ul><ul><li>CATSALT (Tuteja et al., 2003) to determine salt load </li></ul><ul><ul><li>Considers flow from groundwater and unsaturated zone separately </li></ul></ul><ul><li>Other considerations, such as evaporation in storages </li></ul>Q SW Q GW Unsaturated Saturated Q UW
    36. 40. Integrated Modelling Framework groundwater response Regulator Decision Point shut open flow flow runoff, salinity rainfall evap. runoff, salinity runoff, salinity runoff, salinity rainfall evap. rainfall evap. rainfall evap. wetland response environmental conditions
    37. 41. Groundwater Modelling <ul><li>Regional groundwater flow is relatively well understood </li></ul><ul><li>Local effects of drains on groundwater table largely unknown, and highly controversial </li></ul>
    38. 42. Groundwater Modelling <ul><li>Groundwater modelling to answer important questions, such as: </li></ul><ul><ul><li>What is the zone of influence of the drain? </li></ul></ul><ul><ul><li>Once a regulator is changed, how long will it take for the groundwater table to adjust? </li></ul></ul><ul><ul><li>What is the expected effect on the soil salinity? </li></ul></ul>
    39. 43. Integrated Modelling Framework groundwater response Regulator Decision Point shut open flow flow runoff, salinity rainfall evap. runoff, salinity runoff, salinity runoff, salinity rainfall evap. rainfall evap. rainfall evap. wetland response environmental conditions
    40. 44. Ecological Response to Salinity <ul><li>A further criterion on the management problem is to sustain the wetlands in the region </li></ul><ul><li>Project aims to answer questions such as: </li></ul><ul><ul><li>What are the impacts of elevated salinities on the health and survival of aquatic species? </li></ul></ul><ul><ul><li>How long can elevated salinities be tolerated? </li></ul></ul><ul><ul><li>How can we best use water from the drains to optimise wetland health and function? </li></ul></ul><ul><li>Field and laboratory studies to collect necessary data </li></ul><ul><li>Modelling to allow expected effects to be predicted </li></ul><ul><li>Decision making process can then make use of modelling results </li></ul>
    41. 45. Bayesian Network Modelling
    42. 46. Interaction Between Models Rainfall, Evaporation Current Conditions Groundwater Models Wetland Models Rainfall-Runoff Models Salt Transport Models Regulator Settings Catchment Routing Environmental Response Dryland Salinity Flooding Evaluate Option Simulation/Optimization
    43. 47. Summary <ul><li>Currently, the information required to tackle the problem of dryland salinity is incomplete </li></ul><ul><li>A multidisciplinary approach is proposed to adequately address the problem </li></ul><ul><ul><li>Water quality and quantity </li></ul></ul><ul><ul><li>Groundwater </li></ul></ul><ul><ul><li>Ecology </li></ul></ul>
    44. 48. Project Outcomes <ul><li>Integrated simulation model of the system </li></ul><ul><ul><li>Considering all aspects that affect regulator operation </li></ul></ul><ul><li>Optimisation component to determine optimal operating scheme </li></ul><ul><ul><li>Multi-Objective evolutionary algorithms </li></ul></ul>
    45. 49. Outcomes (2) <ul><li>Novel aspects include: </li></ul><ul><ul><li>Ungauged catchment model calibration </li></ul></ul><ul><ul><li>Groundwater modelling </li></ul></ul><ul><ul><li>Ecological modelling </li></ul></ul><ul><ul><li>Integrated catchment modelling </li></ul></ul><ul><ul><li>Optimisation and reliability aspects </li></ul></ul>
    46. 50. Summary <ul><li>Whereas simulation models can be used to assess the likely effects of various actions on a system, optimisation models are useful for providing guidance in identifying the best set of actions </li></ul><ul><li>Optimisation models require a clear definition of objectives </li></ul><ul><li>Multi-objective optimisation models can be used to assist in managing scarce water resources </li></ul>
    47. 51. Wastewater Treatment Wetland ASR House or Cluster Mains water Stormwater Total Urban Water Management Industry
    48. 52. Optimum Design of Greywater Reuse Systems (Cluster Scale)
    49. 53. Research Objectives <ul><li>Develop a new methodology for the planning of greywater reuse schemes in urban areas that considers their sustainability </li></ul><ul><li>Apply methodology to development in Streaky Bay </li></ul>
    50. 54. Methodology <ul><li>Systems approach </li></ul>Optimisation Module Selection of Alternative Simulation of Alternative Evaluation of Alternative Selection of Objectives Results Selection of Objectives Optimisation Module
    51. 55. Case Study Scale 01 9 houses Scale 02 19 houses Scale 03 47 houses Scale 04 68 houses Scale 05 117 houses Scale 06 147 houses
    52. 56. System Components <ul><li>Individual house reuse system </li></ul><ul><ul><li>Treatment system </li></ul></ul><ul><ul><li>Pump </li></ul></ul><ul><ul><li>Storage tank </li></ul></ul>
    53. 57. System Components <ul><li>Cluster scale reuse schemes </li></ul><ul><ul><li>Greywater collection network </li></ul></ul><ul><ul><li>Treatment system </li></ul></ul><ul><ul><li>Storage tank </li></ul></ul><ul><ul><li>Pump </li></ul></ul><ul><ul><li>Treated greywater distribution network </li></ul></ul>
    54. 58. Layout
    55. 59. Sustainability Objectives <ul><li>Sustainability: </li></ul><ul><ul><li>Environmental: Total energy consumption (GJ) </li></ul></ul><ul><ul><li>Economic: Present value of life cycle cost ($) </li></ul></ul><ul><ul><li>Social </li></ul></ul><ul><ul><li>Technical </li></ul></ul>
    56. 60. Simulation of Alternatives <ul><li>Many design variables </li></ul><ul><ul><li>Collection network material and pipe diameter </li></ul></ul><ul><ul><li>Distribution network material and pipe diameters </li></ul></ul><ul><ul><li>Greywater Treatment System </li></ul></ul><ul><ul><li>Pump Size </li></ul></ul><ul><li>However, we need to simulate each component </li></ul>
    57. 61. Results
    58. 62. Comparison of Results <ul><li>Case Study </li></ul><ul><ul><li>Between $3 and $6 per kL </li></ul></ul><ul><li>Rouse Hill (Sydney) </li></ul><ul><ul><li>Between $3 and $4 per kL </li></ul></ul>
    59. 63. Sensitivity Analysis <ul><li>Doubling the population density </li></ul><ul><li>Extending the available pipe materials and diameters </li></ul>
    60. 64. Sensitivity Analysis
    61. 65. Conclusions <ul><li>Cluster scale is more sustainable than individual household </li></ul><ul><li>Reuse schemes are more sustainable with: </li></ul><ul><ul><li>Increased population density </li></ul></ul><ul><ul><li>Network design standards that allow different pipe materials and diameters </li></ul></ul>
    62. 66. Further Work <ul><li>Include other objectives </li></ul><ul><ul><li>Ecological impacts </li></ul></ul><ul><ul><li>Reliability </li></ul></ul><ul><li>Include other options </li></ul><ul><ul><li>Rainwater tanks </li></ul></ul><ul><ul><li>Stormwater reuse </li></ul></ul><ul><ul><li>Blackwater reuse </li></ul></ul><ul><ul><li>Aquifer storage and recovery </li></ul></ul><ul><li>Apply to larger scales </li></ul>
    63. 67. Summary <ul><li>Whereas simulation models can be used to assess the likely effects of various actions on a system, optimisation models are useful for providing guidance in identifying the best set of actions </li></ul><ul><li>Optimisation models require a clear definition of objectives </li></ul><ul><li>Multi-objective optimisation models can be used to assist in managing scarce water resources </li></ul>

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