Enabling adoption of digital twins in the water industry

What are the use cases of Digital Twins? 

At a high level, when executed with quality data, digital twins will unlock the capability for remote monitoring and maintenance of infrastructure, predictive analytics and scenario planning. 

This lets water companies predict answers to the questions operational teams are already asking…  

• What if there was a sudden spike in demand? 
• What if there was unprecedented rainfall? 
• What if we used an alternative energy source? 

By deploying advanced data modelling techniques and leveraging machine learning algorithms, it is possible to simulate these kind of 'what-if' scenarios at the click of a button. 

Forecasting the impact of demand variations, infrastructure changes, or extreme weather events isn't a new concept, but modern computing power and advancements in AI allow rapid testing, with more accurate results. 

Where to start? 

Define Your Objectives  

In a sea of possibilities, aiming to address specific concerns will be essential to focus your initial project. Whether it's reducing non revenue water, improving climate resilience, or enhancing decision-making, having a clear vision will guide your implementation strategy. 

Assess Data Availability and Quality 

Evaluate the availability and quality of the data needed to build the digital twin. Identifying areas of your infrastructure that are in good shape from a data collection point of view could offer a low effort (relatively speaking!) entry point for a pilot project. 

Think Big, Start Small 

The end goal might be a replica of your whole infrastructure, but you should treat your digital twin like any other project. 

Choosing a specific asset for your initial implementation will be much less intimidating, allowing you test, learn to inform future phases, and you'll be able to realise the benefits much quicker. 

Can you start with just a single question in mind?  

Industry Collaboration 

There's huge appetite and value in knowledge sharing across the water industry. 

Developing open data standards and fostering collaboration between stakeholders can enable the integration of various systems, ensuring data compatibility and allowing for the exchange of information in real-time. 

By sharing data, lessons learned and best practices it's possible to collectively accelerate advancement of digital twins. 

Conclusion 

Digital Twin projects can be challenging. They require collaboration across operational teams to create, and new skills and knowledge. Investment can be significant, and returns hard to predict. 

However, businesses that have dipped their toe in the water are already starting to reap the rewards. 

Our own client, Northumbrian Water Group have already had success with pilot projects reducing leakage by 25%. 

Elsewhere in the UK, Anglian Water have implemented digital twins for the design, build and operation of water systems to improve their resilience, efficiency and incident management. 

Greek Water company DEYAK are another great example of early success, with their own implementation providing valuable insight of its supply network- leading to the speed and quality of leak repairs improving by 50%. 

Now is the time to start learning from some of these early ventures into digital twins, and embarking on your own small-scale digital twin projects that align with your current organisational goals.  

With evidence benefits of digital twins in actions, water companies can gain the confidence needed to tackle more ambitious digital twins, with a more resilient future for utilities in sight.