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The UK water sector is investing heavily in smarter infrastructure. In the next Asset Management Plan period (AMP7), company business plans will benefit from a combined investment of £51 billion to improve services, £13bn of which is allocated towards providing resilient services and improved environmental performance as part of a long-term strategy.
Sewer flooding is an increasingly common risk in the UK. In 2019, there were over 3,250 internal sewer flooding events in England and Wales, and almost 23,500 external flooding events of private land and gardens. Customers find internal sewer flooding incidents distressing, with the majority, who have been flooded, afraid to leave their homes for long periods of time, in case a flooding event occurs. How can emergent technologies like digital twins enable better planning and management?
Adopting Digital Twins
Digital twins are a great fit for water infrastructure bringing new pre-emptive management and operation to any building or asset. A digital twin is simply a data-driven digital representation of assets, processes or systems. A twin can provide an evolving picture of an asset’s current performance and weaknesses, and help identify where investment needs to be prioritised. Digital twins are more than just models or visualisations. They convert data from physical assets into valuable insights that can help operators to make strategic decisions and interventions, improve operations and help shape future plans and projects.
Reactive remediation to proactive interventions: a water system that learns (and predicts)
Water utilities spend millions around the world reacting to sewer flooding events which damage the environment and impact negatively on their customers. Currently they act as and when events occur and manage their network using large, complex hydraulic models. These are often constrained by computing resources and cannot run in real-time to provide usable predictions on the location and frequency of flooding events.
Digital twins allow users to make data-driven predictions powered by machine learning
Digital twins allow users to make data-driven predictions powered by machine learning (ML). Arup has developed digital twins which use ML to understand and learn from the behaviour of a specific system and predict the location of future flooding or pollution. Using sensors throughout the catchment, the Arup algorithm learns the behaviours of each sensor, the relationships between the sensors and how the weather impacts them. Our algorithm trains a bespoke set of ML models that is optimised for every sensor.
Digital twin ecosystem turning siloes of data into a web of sources
As tools, digital twins work best when as wide a range of information sources as possible interact within the system, allowing it to talk to other parts of an operator’s infrastructure. Arup has adopted the Centre for Digital Built Britain (CDBB)’s Gemini Principles when developing their approach, recognising the impact of secure interoperable use of data across their customer’s organisations.
Deploying IOTICS’ patented software in a hybrid cloud Arup created digital twin models (schemas) of public, private and third-party data. IOTICS platform enables a data mesh of digital twin and data interactions that provide secure interoperation of dynamic, historical, static and real-time data with Arup’s world-leading ML solution.
Impact & Scalability
Transforming operator capabilities
Arup’s digital twin solution, powered by its bespoke algorithm and IOTICS’ platform allows network operators to act pre-emptively, leading to reduced operational costs and greater efficiency. The combination of digital twin facilitated ML and federated twin and data interactions is enabling the water sector to meet their challenges ensuring regulatory compliance in terms of sewer flooding, managing pollution and network capacity to meet ever-increasing usage demands.
UK-innovation, universal application
However, this is not just a UK issue. Worldwide, ageing infrastructure remains a key challenge, combined with population growth and climate change. Rapid improvements in communication technology, lower costs of sensors and data storage, and advanced data analytics will see a shift from building infrastructure to managing capacity through a digital twin.
The digital twin models created by Arup and IOTICS are abstractions of the complexity of operators’ data estates. The approach models data sources and enables them to interact without costly and lengthy integrations or rearchitecting of operators’ internal data and systems. Virtualising the interaction allows the solution to scale as operators requirements grow, be adopted by any operator, and is source- and system-agnostic, driving the adoption and commercialisation of this emergent technology globally.
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