Utilities Digital Solutions Europe

TECH DIVE: Digital twins in water, what progress are we seeing?

Monday, 16 October 2023

In the age of digital transformation, the concept of a digital twin has taken centre stage across various industries, including water management. From theory to implementation, what progress has there been? In this article, we explore the progress made in leveraging digital twins for water management in key projects across the globe with comments from experts.

Understanding digital twins in water management

A digital twin for water management is much more than a static 3D model or a single software platform. It involves the integration of various technologies, including sensors, IoT (Internet of Things) devices, data analytics, and advanced modeling techniques. These components work together to create a dynamic, real-time representation of a water system, be it a treatment plant, distribution network, or a natural water body.

Speaking to Aquatech Online, Richard Vestner from software company Bentley Systems says: “The components of a successful digital twin strategy will always be directly linked to the effectiveness and alignment to the strategic goals of the organisation. 

“Utilities face different challenges related to economic sustainability, infrastructure resilience and rehabilitation as well as operational efficiency. The focus of the digital twin should be based on value to the company. Success depends on situational context, data availability, system interoperability, proactive processes, people readiness for change and knowledge capture of retiring experts. 

“From reduction of non revenue water and reduction of water intake to energy optimisation and reduction of carbon emissions, we need infrastructure intelligence to adapt the digital twin based on value creation and ROI.”


Global trends

In the realm of digital twin technology, a noticeable global trend is the increasing enthusiasm for integrating IoT sensors with digital twins. Furthermore, there is a growing emphasis on merging GIS data with digital twins to gain valuable spatial insights. 

Recently, there has been a surge in the adoption of AI and machine learning for predictive analytics within this domain. These advancements offer numerous advantages to utilities, including faster leak detection, the ability to predict infrastructure failures, optimized energy consumption, and informed data-driven decision-making. As a result, these innovations lead to cost savings and enhanced service delivery.

Vestner adds: “Multiple system and technology integrations are needed because all of the data is interconnected. A siloed way of working causes data sharing gaps, inefficiency in operations and makes it very hard to comply with standards. If we do not understand what our data is telling us, we might miss the important advantage of the integrated digital information insights and sharing in agile workflows.”

Projects leading the way 

While Valencia, Spain is largely seen as a flagship digital twin project, there are regular examples of successful implementation of digital twins from a whole project lifecycle, including treatment, distribution right up to the customer experience, according to Shayna Ramboz from water industry membership organisation SWAN tells Aquatech Online.

Some examples of note include in Singapore where the PUB implemented Royal HaskoningDHV's Aquasuite software at the Ulu Pandan Water Reclamation Plant's Integrated Validation Plant, which has a treatment capacity of 12,500 m3/day. 

“Over a two-year trial period, this software successfully validated significant reductions in energy consumption during water reclamation processes, minimised chemical usage, and reduced the reliance on manpower, all while enhancing water quality,” she says. 

Optimising water distribution in Spain

Similarly, the Tarragona Water Consortium in Spain adopted digital twins to optimise its water distribution system, Ramboz says.

“By integrating real-time sensor data with a hydraulic model, it can conduct live simulations to predict the system's behavior under various conditions. This proactive approach has resulted in early warnings for potential issues, facilitating timely maintenance and minimising service disruptions. Moreover, it has had a positive impact on water quality and has led to reduced energy consumption,” she says.

Last year a roadmap was launched by SWAN, Brown & Caldwell and multiple partners to help guide water’s digital twin journey.

Additionally, Danish water company VCS Denmark developed digital twin technology that integrates data from diverse sources, including sensors, SCADA systems, GIS, and customer feedback. “This comprehensive overview of its networks has empowered it to optimise operations, enhance service delivery, and explore innovative data-driven decision-making processes,” she concludes.

Similar success stories can be found with Anglian Water and Global Omnium, who have also embraced digital twins to refine their operations, resulting in reduced operational costs, energy savings, and improved customer satisfaction, Shamboz highlights. 


Brazil’s Sabesp project 

Vestner highlighted Brazillian water and waste management company Sabesp’s project as a great digital twin case study. 

Sabesp saw advancements in the availability of technologies that generate data for monitoring the supply system, which meant much greater volumes of data than they were able to use and, consequently, it needed a way to manage and automate data analysis.

The Sabesp digital twin centralises and integrates corporate data, facilitating qualitative and quantitative analysis of remote field sensor performance, equipment failures, pressure anomalies, network leaks, and water balance by district of measurement and control (DMC). It identifies inconsistencies in DMC limits recorded in the corporate GIS system, confirms measurement failures early to prevent misinterpretation of results, analyzes pumping systems' energy performance, and supports real-time hydraulic simulation, operational forecasting, and emergency response planning.

The performance pattern analysed is defined through machine learning algorithms that clean the historical data collected and determine upper and lower value ranges, creating confidence intervals, illustrated by the gray areas depicted below.

Digital twins in Greece

DEYAK Water Utility in Kosani, Greece is also a case study to mention. DEYAK recently implemented a water digital twin to gain useful insights to improve the performance and operations of its entire water supply network. The goal was to reduce non-revenue water, control pressure management, conduct active leak control, as well as improve speed and quality of repairs and asset management. 

Dr. Konstantinos Gkonelas, hydraulics technical manager at DEYAK told Aquatech Online: “The implementation of a digital twin helped us gain useful insights to improve the performance and operations of our entire water supply network. All main strategies regarding reducing non-revenue water were improved, pressure management, active leak control, speed and quality of repairs and asset management. 

“As a result, man hour times for pressure management were reduced by 40 per cent and the speed and quality of repairs on new reported and unreported leaks was improved by 50 per cent due to automatic location of isolation valves and the use of genetic algorithms that helped to identify them.”

Challenges when adopting digital twin technology 

Digital twins of course don’t come without their challenges, yet Vestner adds: “Challenges always depend on the way you see changes. Either you see digital twins as an opportunity to learn and grow or you see them as a problem that will only give you more work on top of the work you are already asked to do.

“A Digital Twin, by design, will break down data and departmental silos. Supporting a mindset change, because it obligates people to interact, encourages collaboration and communication, ultimately with the goal of faster and more accurate decisions and alignment to the same company goals.”

Gigi Karmous-Edwards, founder and co-chair of the SWAN Digital Twin Working Group agrees.

“The success of digital twins in the water sector relies heavily on the ability to integrate diverse technologies and data sources. This integration transforms raw data into actionable insights, optimising operations, enhancing reliability, and ensuring the longevity of critical water infrastructure.”


Learnings from Valencia, Spain

Valencia, Spain, has emerged as a global leader in the integration of digital twin technology into water management, setting an example for municipalities worldwide. At the helm of this transformative initiative is Pilar Conejos, the digital twin manager at Idrica, who brings over two decades of experience in hydraulic engineering and water management.

Speaking to Aquatech Online, Conejos adds that the achievements and collaborative efforts that have shaped Valencia's success story. 

The heart of Valencia's digital twin endeavor lies in its holistic approach. As Conejos aptly describes: “The primary goal was to operate the water network efficiently while safeguarding water resources and mitigating environmental impact. With early detection of potential issues, the system ensures a stable water supply and preserves this vital resource.”

Crucially, the success of Valencia's digital twin project is attributed to collaboration between various stakeholders. Local administrations, government agencies, local utilities, and technology partners have played pivotal roles in promoting and implementing this innovative solution.

“A digital twin is an evolving ecosystem that encompasses multiple technologies, and partnerships with technology experts are essential to selecting and integrating the most advanced digital solutions,” Conejos adds.

Real-time data monitoring and analysis

Valencia's digital twin system relies on real-time data monitoring and analysis, enabling the city to proactively respond to water-related challenges such as droughts, flooding, and water quality issues. This capability ensures informed decision-making, efficient resource allocation and timely intervention to address potential crises, aligning with the city's sustainability goals.

The economic and environmental benefits realised through the digital twin implementation are substantial. Valencia's digital twin not only optimises water infrastructure operation but also aligns with broader smart city initiatives. It enhances efficiency and reduces waste, contributing to sustainability objectives and long-term environmental conservation.

For municipalities aspiring to replicate Valencia's success, Conejos provides valuable advice. She emphasizes starting with specific goals and objectives, building a robust architecture, and progressively expanding the digital twin's use cases. “This scalable approach allows cities to tackle a wide range of water management challenges effectively.”

Looking ahead, Valencia envisions further expansion of digital twin applications throughout the entire water cycle. Multiple interconnected digital twins will form a virtual system that optimises water management at every stage.

The future

As the industry continues to face increasing challenges that impact public health and safety, along with the continuing advancements in digital twin technologies, it’s foreseeable that more utilities will adopt this approach to gain a more intelligent and resilient water network. 

SWAN’s Ramboz adds: “Digital twins are an ongoing journey for utilities, not a checkmark destination.”

Meanwhile Vestner says he believes the future of digital twins in the water sector is much more connectedness between the other services and industries within cities, to work in an agile and efficient way. “The digital twin will become much more automatic, with real-time control systems and AI and ML playing a central role in the accuracy and effectiveness of the outcomes.”

He believes that cities will become more resilient and water-wise. Industry and technology will evolve in order to achieve the goals focused on transition towards circular economy models that encompass water reuse, resources recovery, renewable energy production and the digital transformation of operations.

The concept of digital twins in water management has evolved beyond theoretical discussions into practical implementations that deliver tangible benefits. These projects, including Valencia's flagship initiative and Singapore's smart water grid, showcase the progress made in harnessing digital twin technology to enhance water resource management.

However, it's important to emphasize that digital twins are not one-size-fits-all solutions; they require careful planning, integration of various data sources, and continuous innovation. As the water sector continues to embrace digital transformation, we can expect to see even more impressive advancements in the coming years, ultimately leading to more efficient, sustainable, and resilient water management systems worldwide.

Key components for a digital twin strategy 

Gigi Karmous-Edwards founder and co-chair of the SWAN Digital Twin Working Group shares the key components of a successful digital twin strategy for water infrastructure projects.

Clear Objectives and Scope Definition: Before starting on the development of a digital twin, it's imperative to define what you want to achieve, ie. your objectives. Whether it's predictive maintenance, real-time monitoring, or optimizing operations, having clear objectives will guide the development process.

High-Quality Data: A digital twin is only as good as the data it receives. Ensuring accurate, timely, and comprehensive data is available for integration into the model during simulation. This includes data from sensors, historical records, SCADA, GIS, etc.. This may be the biggest hurdle I still hear from utilities. As more and more utilities start to embrace the use of LLM, and start to utilise them for data cleansing and analysis, which helps increase data quality.

Integrated Systems: Integration ensures that data flows smoothly and that the twin can provide real-time insights. Data integration is critical and sometimes a lengthy process if the involved data bases and/or application do not have APIs. Utilities should demand easy access to data collected or produced in the applications via APIs. This will shorten and simplify the time for the DT deployment.

Advanced Analytics and Machine Learning: To move beyond simple monitoring, digital twins should incorporate advanced analytics capabilities. Machine learning algorithms can predict failures, optimise operations, and provide actionable insights that traditional systems might miss.

Scalability: As water infrastructure grows and evolves, the digital twin should be able to accommodate new data sources, increased data volume, and more complex analytics. Digital Twins continue to evolve to bring more valuable insights as utilities introduce more data and more accurate models.

User-friendly Interface: The end-users, whether they are engineers, operators, or managers, should be able to interact with it easily. This means intuitive dashboards, clear visualisations, and user-friendly controls.

Cybersecurity: Ensuring that they are secure from cyber threats is paramount. This includes secure data transmission, storage and access controls.


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