Digital Twins: Reduce Operating Costs and the Risks of Unplanned Incidents

A digital twin provides a living representation of a physical network letting you test and query the network's behavior when conditions change. It enables you to make the right decisions in the real world.​

New ways to manage your networks

Digital twins take simulations to the next level. They combine models, comprehensive asset information, live operational data, and forecasts, giving decision-makers new ways to assess their networks.

The simplest digital twin could be of a rainwater tank with a depth sensor, which models the tank fill rate depending on rainfall, based on historical observations. Feed in the weather forecast, and the twin will show the response of the tank over time.

graphic showing the elements of a digital twin
satellite image of urban area with graphical overlay during flood event

It's for more complex, larger systems that digital twins deliver the greatest benefits. With networked systems, such as water distribution and wastewater collection systems, the twin can factor in the hydraulic connections between the elements and their geospatial positioning. Then add information about the physical assets - their age, construction materials, current condition and so on; usage patterns of consumers and businesses; weather conditions; and other factors.

A digital twin brings all this information together, giving you broad insights on which to base robust, defensible decisions.

See digital twins in action

The Smart Canal developed for Scottish Canals, Glasgow City Council & Scottish Water uses ICMLive to manage water levels in the Firth & Clyde canal to provide storage for surface water, helping release 110 hectares of historically undevelopable land for new development.

An award-winning project using ICMLive has helped Anglian Water prevent pollution incidents that threatened public safety and the environment. It creates alerts for operational problems such as blocked sewers and outfalls, so they can be cleared before there's customer or environmental impact.

At Bristol Water, IWLive Pro runs automatically every morning to predict flows and pressures. It can manage operations such as water quality incidents, in- and out-of-service boreholes, and rerouting and zoning of pumping stations.

See digital twins in action
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"Turning data into information, and information into decisions, will accelerate improvements in the water industry. This will be powered by increased adoption of water infrastructure asset management, adoption of digital twin technology and analytics of real-world data.”

VP of Innovation Innovyze

Ask yourself…

  • Can you optimize network operations to provide adequate performance?
  • Are required service levels being met right now – are they likely to be met in the hours and days ahead?
  • If not, are there any major service failures on the network identified or predicted?
  • What corrective action can you take to avoid predicted problems?
  • And if service level failure is inevitable, can you get staff on site quickly and warn the public in advance?

If the answers aren't all a firm 'yes', read on to discover the difference a digital twin could make.

Digital Twin Solutions

  • What is a Digital Twin for Water?

    A digital twin combines:

    1. Geospatial and digital data about assets – what they are, where, and how they're connected
    2. Observations, typically from sensors – such as how much it rains, and how fast the tank fills in response
    3. Performance data – whether static (data snapshots) or dynamic (continuous records, ideally over years)
    4. Analytics – the numerical engines used in models
    5. Visualization – graphics that aid decision-making, from the ops room to budget-holders to office-holders

    Read more about the Five Things >

    Screenshot showing water supply network software
  • Why Now?

    A combination of enough computing power; enough sensors with good remote comms; and rapid changes in population, land use, and climate, make now the right time to use digital twins.

    In the past, water engineers used models for one-off simulations – for example, to design the right size pipes for a new subdivision/development. Ten years ago models were updated every two to five years or even longer, and in between utilities relied on operator skill and knowledge to compensate for a lack of veracity in the model.

    Now, with parallel processing, fast computers and cloud facilities, digital models are truly representative. They can represent more of the real world, including assets and minor pumping stations, and can be updated continuously to take account of changes in natural and human activity. They can take in live network-monitoring data from sources such as SCADA. And they can use the increasing information available about customer demand and forecast weather.

    Information that was held in silos such as asset registries, water supply, wastewater, and flood control repositories can now be integrated and used.

    photo of a water pumping station with pumps, valves and pipes
  • Does it Really Work for Water and Wastewater?

    The term 'digital twin' has traditionally been used for plant rather than networks, with many sensors generating real-time data about the assets in the plant.

    Water and wastewater networks are a bit different. The sensors tend to be widely spaced (because of their cost and the challenges of retrieving data from remote locations). Some of the data is very far from real-time – pipe condition, for example, may be stable for many years. And historical data, not just real-time, is critically important to understanding and interpreting network behavior.

    But the scale and complexity of both the data and the physical structures it represents can be comparable, and the benefits are similar: maximizing network performance, customer service, cost effectiveness, and risk control.

    screenshot of dam break simulation superimposed on aerial photo of land
  • Combining the Three Types of Model

    The digital twin can combine three types of model.

    The static infrastructure model is mainly for asset management. It captures:

    • What you have (pipes, sewers, drains, pumps, valves, etc)
    • Where it is
    • How it is connected
    • How it was built
    • Inspection and survey history
    • What condition it is in

    The dynamic network model is needed for design, analysis of events, and planning. It:

    • Provides a hydraulic model of the flows, pressures and levels in the network
    • Is driven by demands on the network
    • Includes the response of control structures (pumps and valves)
    • Is calibrated and validated using monitored data (if possible)

    The real-time performance model is for everyday operational management. It:

    • Provides a dynamic model representing the performance of the network as it is now
    • Is validated against real-time monitoring
    • Predicts network performance in the hours and days to come
    • Gives warnings of potential service-level failures
    • Optimizes control actions
  • Making the Digital Twin

    The key to success through a digital twin is identifying its purpose first: framing the questions that you want to answer, so you end up with the right combination of tools, analysis, and visualization. Examples are capital improvement planning, risk modeling, and designing a water collection system with adequate pipe volumes for a new development.

    screenshot of InfoAsset Manager showing aerial photo plus H2S sensor data

  • Trusting the Results

    It's important that all stakeholders – not just modelers and engineers – trust the digital twin, and can rely on it giving the best picture of network performance:

    • What has happened
    • What is happening now
    • What will happen in the future

    So that the digital twin remains realistic, the model, data streams, simulations, analytic processes must be maintained, calibrated and validated.

    And visualizations must be presented appropriately for each audience, whether that's operations, management, IT, regulators, or officials. Fast, powerful graphics in 2D and 3D makes this far easier. Some tools are designed for non-specialists to use, including InfoAsset Online which gives the option of tailoring its browser views for particular roles.

    screenshot of InfoAsset Online, showing how the information displayed is tailored to a role
  • AI, Machine Learning, and Neural Networks

    It is possible to create a model or digital twin that's based entirely on data – instead of physics-based simulation models – but you must understand the shortfalls.

    Pure data-driven models, built using techniques such as neural networks, can be faster, but:

    • You may not know when their conclusions are determined from poor data
    • They give 'black box' answers that can't be interrogated – you can't check how they arrived at the answers
    • They need to have a lot of clean historical data to learn from
    • Meaningless pattern associations can emerge with data
    • There can be algorithmic bias based on the data

    Tried-and-tested physics-based simulation models excel. They offer:

    • Robustness
    • Accuracy
    • Results throughout the network
    • Stability
    • Predictive capabilities
    • Defensibility

    Machine learning and artificial intelligence (AI) show huge potential in combination with the simulation modeling process. Together they can improve on:

    • Calibrating and validating models
    • Interpreting data and model results
    • Validating machine learning results
    • Creating and interpreting warnings
    • Optimizing control actions

Digital Twins must be constructed with a clear goal in mind, and a real-world problem to solve

At its core, the Digital Twin for water is a problem-solving framework for complex, physics-driven, occurrences that may negatively impact the performance and delivery of the systems’ intended services. A successful Digital Twin should be built with the intention that it is useful, more so than it is technically accurate. It needs to be scalable both in its complexity and in the physical area that it is built to represent.

Discover more about the solutions

  • ON DEMAND WEBINAR

    Innovation in water supply networks and operations: Digital Twin

    Explore the value of the digital twin to show how a supply network will meet performance levels, and identify challenges before they become incidents

  • ON DEMAND WEBINAR

    Creating a Dynamic Sewer Master Plan with a Digital Twin

    Take the guesswork out of designing and analyzing sewer systems, with a digital twin. This webinar shows you how.

  • News

    Digital Twins are the Future

    The first SWAN Digital Twin Workshop debated near-real-time data, the ROI of digital twins, and more.

ON DEMAND WEBINAR

Innovation in water supply networks and operations: Digital Twin

Explore the value of the digital twin to show how a supply network will meet performance levels, and identify challenges before they become incidents

ON DEMAND WEBINAR

Creating a Dynamic Sewer Master Plan with a Digital Twin

Take the guesswork out of designing and analyzing sewer systems, with a digital twin. This webinar shows you how.

News

Digital Twins are the Future

The first SWAN Digital Twin Workshop debated near-real-time data, the ROI of digital twins, and more.

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"A Water Digital Twin is an integrated multiphysics, multiscale, probabilistic simulation of the assets of a water, wastewater, stormwater, or river system that uses the best available physical models, real-time sensor updates, historical performance data, machine learning/AI, etc., to replicate the life of its corresponding real world twin."

Colby Manwaring CEO, Innovyze