Data-driven digital twins drawing more attention in quest to bolster asset resilience, performance
As an integrated, digital representation of physical assets, a digital twin uses historical and current data – in near time – to help predict and optimise system performance.
It’s in constant dialogue with its physical counterpart – making it distinct from traditional models – and can be built on design criteria, calibrated using historical data, and updated in real-time with sensor data.
European utilities are seeing and beginning to embrace the value of digital twins, reflected in global critical human infrastructure leader Black & Veatch’s creation of one of the first water utility digital twins – for Anglian Water in England – in 2019.
The benefits come from a digital twin’s constant comparisons of the modelling predictions with actual operations, combining multiple internal and external data sources across the asset base with predictive analytical techniques served through multiple functional views. The result: improved insights that support better decisions, leading to better outcomes in the physical world.
For example, a digital twin can support water quality and supply teams by integrating weather and raw water quality data with asset availability from SCADA across the network, creating a predictive view of operating scenarios and performance deterioration, along with root-cause identification that allows users to intervene before a reactive event occurs. A digital twin also can help with maintenance activities, supporting better pre-planning of the work and overlaying maintenance work orders within the spatial models, leading to accurate identification of the asset’s location.
Outside of the operational phases of the asset lifecycle, a digital twin will be able to dynamically inform risk-based investment decisions.
Making data work
Putting the data that utilities gather to work requires a strategy that spans from data collection to data-driven decision-making. Responses to the 2021 Black & Veatch Strategic Directions: Water Report suggests that such strategies are not yet widely implemented in the U.S., although they are developing.
Because digital twins are of greatest value to utilities with a mature, integrated data programme, their current application is likely to be limited. A digital twin will only be as good as the data upon which it is based; without solid historical data to build the model and real-time performance data to continuously update it, a digital twin risks becoming a stranded asset.
A crawl-walk-run approach can yield compounding value to a maturing organisation, building upon foundational steps such as defining the data that best supports the utilities’ goals, thereby ensuring that this data is being captured and available in an accessible format. Analytics and visualisation tools are becoming much more readily available and easier to use, offering a good way of unlocking value and being an effective way of putting existing data to work. All of these steps are valuable in their own right, along the way laying the foundation for an effective digital twin programme.
It’s in constant dialogue with its physical counterpart – making it distinct from traditional models – and can be built on design criteria, calibrated using historical data, and updated in real-time with sensor data.
European utilities are seeing and beginning to embrace the value of digital twins, reflected in global critical human infrastructure leader Black & Veatch’s creation of one of the first water utility digital twins – for Anglian Water in England – in 2019.
The benefits come from a digital twin’s constant comparisons of the modelling predictions with actual operations, combining multiple internal and external data sources across the asset base with predictive analytical techniques served through multiple functional views. The result: improved insights that support better decisions, leading to better outcomes in the physical world.
For example, a digital twin can support water quality and supply teams by integrating weather and raw water quality data with asset availability from SCADA across the network, creating a predictive view of operating scenarios and performance deterioration, along with root-cause identification that allows users to intervene before a reactive event occurs. A digital twin also can help with maintenance activities, supporting better pre-planning of the work and overlaying maintenance work orders within the spatial models, leading to accurate identification of the asset’s location.
Outside of the operational phases of the asset lifecycle, a digital twin will be able to dynamically inform risk-based investment decisions.
Making data work
Putting the data that utilities gather to work requires a strategy that spans from data collection to data-driven decision-making. Responses to the 2021 Black & Veatch Strategic Directions: Water Report suggests that such strategies are not yet widely implemented in the U.S., although they are developing.
Because digital twins are of greatest value to utilities with a mature, integrated data programme, their current application is likely to be limited. A digital twin will only be as good as the data upon which it is based; without solid historical data to build the model and real-time performance data to continuously update it, a digital twin risks becoming a stranded asset.
A crawl-walk-run approach can yield compounding value to a maturing organisation, building upon foundational steps such as defining the data that best supports the utilities’ goals, thereby ensuring that this data is being captured and available in an accessible format. Analytics and visualisation tools are becoming much more readily available and easier to use, offering a good way of unlocking value and being an effective way of putting existing data to work. All of these steps are valuable in their own right, along the way laying the foundation for an effective digital twin programme.
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