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Quantifying Uncertainty to Support Sustainable Planning and Management of Water Supply Infrastructure. Alireza Yazdani Post-Doctoral Research Associate Department of Civil & Environmental Engineering Rice University Presented at: SAMSI Uncertainty Quantification Transition Workshop

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Quantifying Uncertainty to Support Sustainable Planning and Management of Water Supply Infrastructure

Alireza Yazdani

Post-Doctoral Research Associate

Department of Civil & Environmental Engineering

Rice University

Presented at:

SAMSI Uncertainty Quantification Transition Workshop

May 22nd, 2012

slide2

NETWORK TOPOLOGY

  • UNCERTAINTY QUANTIFICATION
  • SYSTEM PERFORMANCE EVALUATION
  • SUSTAINABLE WATER SUPPLY MANAGEMENT
water supply infrastructure
Water Supply Infrastructure
  • Water Distribution Systems (WDS) are large complex networks of multiple interdependent nodes (e.g. reservoirs, fittings, fire hydrants) joined by links (e.g. pipes, valves, pumps).
  • Main system components:
    • Source
    • Treatment
    • Transmission
    • Storage
    • Distribution

A hypothetical network representation

the problem
The problem
  • The US Water infrastructure is old, fragile and inadequate in meeting the increasing demand for water.
    • Last year’s Texas drought resulted in a spike in water main breaks (CBS local, Aug 2011).
  • Existing centralized networks, suffer from high water age, bio-film growth, pressure loss and high energy consumption.
  • There is currently an underinvestment (~ $108.6 Billion).
  • Source: (EPA, 2006 Committee on Public Water Supply Distribution Systems: Assessing and Reducing Risks, National Research Council, and 2009 Report Card for America’s Infrastructure)
slide5

2009 ASCE Report Card for America’s Infrastructure

America\'s Infrastructure G.P.A. = DA = Exceptional B = GoodC = MediocreD = Poor F = Failing

sustainability
Sustainability
  • A sustainable Water Supply System is one that supplies anticipated demands over a sensible time horizon without degradation of the source of the supply or other element’s of the system’s environment.*
    • Criteria:
  • Reliability:
    • adequate flow and pressure, availability of the physical components
  • Water Quality:
    • Acceptable water age and chemical contents
  • Efficiency:
    • leakage management, operational efficiency and environmental impacts
  • Achieving sustainability requires integrated analysis and optimization of performance criteria while dealing with uncertainties in the data/model/natural environment

* Water Distribution Systems (2011), D. Savic, J. Banyard (Eds.), ICE Press.

slide7

Reservoir and treatment facilities

Adequacy (quality/quantity):

How does water taste there?

Is the pressure sufficient?

Reliability:

what if these pipes break together?!

Efficiency:

what is the cost/impacts of getting water here?

A slightly reconfigured EPANET representation of Colorado Springs WDS

uncertainty and decision making
Uncertainty and Decision Making
  • Reducible ( epistemic) uncertainty: Resulting from a lack of information in model about the system, typically reduced through inspection, measurement or improving the analogy between the abstract model and real system
  • Irreducible (aleatoric) uncertainty: Natural randomness in a process, usually described by probabilistic approaches

Not to be absolutely certain is, I think, one of the essential things in rationality.

Bertrand Russell

Image taken from: S. Fox (2011), Factors in ontological uncertainty related to ICT innovations, I. J. Manag. Proj. Busin, 4 (1), 137-149.

examples from water supply engineering
Examples from Water Supply Engineering
  • Model (e): inability to represent true physics of the system and its behaviour
  • Data (e): measurement error, inconsistent/inaccurate/inadequate data
  • Operation (e): related to the system construction, design, equipments, deterioration, maintenance
  • Natural (a): unpredictability of nature and its impacts on the system
  • Determining the pipe size, tank diameter, network topology at design stage
  • Placement of sensors/control valves to monitor water quality
  • Prediction of the physical components failure rates and evaluating failure consequences
  • Estimating water weekly/monthly/yearly water demand to support normal/peak consumption
  • Assessing the impacts of climate/demographical changes on resources
slide10

WDS Performance is largely affected by network topology

  • Uncertainty in system performance due to the unknown/unpredictable parameters may be reduced through studying topology.

Source unavailable

Reservoir

Reservoir

Pipe Break/Contaminant Ingress

Tank

  • Reliability: how often the system fails (in quantity or quality terms).
  • Vulnerability: how serious the consequences of the failure may be.
  • Resiliency: how quickly the system recovers from failure.
the need and practicality
The Need and Practicality
  • Centralized treatment/operation
    • water quality deterioration
    • cost of wastewater collection
    • high energy loss
  • Decentralized treatment
    • shorter pipe lengths
    • improved water quality?
    • more efficient?

Image from D. Kang, K. Lansey, Scenario-based Robust Optimization of Regional Water/Wastewater Infrastructure,doi:10.1061/(ASCE)WR.1943-5452.0000236

network topology models
Network topology models
  • Random networks:
    • Random degree distribution (equal connectivity likelihood)
    • Network equally vulnerable to failures/attacks (typical nodes)
    • Examples: spatial networks (no hubs, large diameter)
  • Small worlds:
    • Gaussian or exponential degree distribution
    • Large networks with low path lengths and high clustering
  • Scale free networks:
    • Scale-free networks/power law degree distribution
    • Many low degree nodes with very few highly connected hubs
    • Robust against random component failures yet fragile under targeted attacks on the hubs
slide14

Image: Albert, Barabasi and Bonabeau, (2003), Scale-free Networks, Scientific American, 288, 50-59.

case studies
Case studies

Colorado Springs (CS), USA

City of Houston (COH), USA

Richmond Yorkshire Water (RYW), UK

generalized connectivity an example of reducing model uncertainty at the fine scales
Generalized connectivity(an example of reducing model uncertainty at the fine scales)

W1=1

W1=0.5

W2=0.5

W3=0.3

W3=0.3

W3=0.3

W3=0.3

Demand-adjusted entropic degree (DAED)* combines topology and physics by incorporating the number of links attached to a node, the capacity of the link connections and the way they are distributed while taking into account the demand for water at each node.

d=0.4

i

i

i

i

W3=0.6

W3=0.2

* A. Yazdani, P. Jeffrey (2012), Water Resour. Res., doi:10.1029/2012WR011897, in press

summary and conclusions
Summary and Conclusions
  • The analysis of WDS topology:
    • Reduces model uncertainty and offers a computationally inexpensive and less data-dependent simplified approach
    • Helps quantifying vaguely understood qualities such as redundancy, optimal-connectivity and fault-tolerance
    • Supports development and comparison of the alternative design and operation (e.g. Decentralized) scenarios
  • The UQ via studying interactions of system topology and performance (hydraulic reliability, energy use, water quality) provides theoretical support for finding sustainable solutions for water infrastructure systems planning and management (rehabilitation/design/expansion problems).
  • Due to the WDS specifications, data and model uncertainties, and hydraulic complexities, advanced UQ techniques (e.g. spectral methods, multiple regression and survival analysis and non-parametric statistics) have a special place in the realistic analysis of WDS vulnerability/sustainability.
ongoing and future work
Ongoing and future work
  • Performance analysis and comparison of the centralized, decentralized and hybrid layouts in terms of water quantity and quality
  • Analysis of historical failure data to develop component/system failure rate models serving reliability analysis
  • Investigating the role of network topology (in the presence or absence of shut off valves) in facilitating mass transport/preventing the spread of contaminants within the system validated by the EPANET models
acknowledgements
Acknowledgements
  • Rice University Shell Centre for Sustainability
  • SAMSI for the travel support
  • Dr. Leonardo Duenas-Osorio and Dr. Qilin Li of Rice University Civil and Environmental Engineering
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