Digital twins for the water sector

‘Digital twins’ is the buzzword of recent years in many industries, including the water industry. The term is used to indicate digital representations (copies in some way) of physical systems. In some cases, it is merely used to relabel these things that we used to call models in the past, i.e. digital representations of some aspects of a technical system that can be used to predict its behaviour under specified conditions. This is also true for digital twins.

However, in the definition we like to apply, there are additional aspects:

  1. a significant and consistent part of a technical system is included in the model in a way that is sufficiently comprehensive for the intended purpose;
  2. the model is kept up to date with respect to its configuration and/or state through e.g. live sensor readings;
  3. (optionally) the digital twin is capable of influencing or controlling the real twin through actuators. This view is reflected in Figure 1.
Figuur 1: Schema van digitale tweeling met links de weergave van de operationele toepassingen (heden) en rechts de tactische en strategische toepassingen (wat-als- en toekomstscenario's).

Figure 1: Digital twin diagram, with the left side representing operational applications (present) and the right side tactical and strategic applications (what if and future scenarios).

In the context of this definition, we do see a wide range of applications, uses and added benefits for digital twins in water systems compared to traditional models. These can be broadly classified into two categories.

The first category encompasses operational applications of digital twins in order to monitor and control systems and detect anomalies in these (left-hand side of Figure 1). Examples include full (automatized) control of a water production facility or the detection of bursts in a distribution system.

The second category includes tactical/strategic applications such as scenario studies, training, incident simulation, systems design, etc. (right-hand side of Figure 1). For this class of applications, the term “predictive twin” is considered more appropriate. Obviously, there is no assimilation of live sensor data in such a predictive twin; rather, it is fed by simulated data based on scenarios. However, a functioning and validated digital twin of the current system should be used as its basis whenever possible.

KWR has been working on the development and investigation of several aspects and components of digital twins for (drinking) water systems, in particular distribution networks, but also other water systems, including water-energy systems.

Example 1: Digital Twin of a water distribution network

Water utilities commonly work with models of their water distribution network representing its behaviour on a specific day (for instance, an average or maximum demand day). The actual or current behaviour of the system is, however, expected to be different. Actual water demand, maintenance works, and the pipes in the network getting older, among many other factors, affect how the system is actually performing. With a digital twin, it is possible to bring all this information together and create a model that reacts to the live environment. Such a digital twin is also ideal for future or what-if scenario studies, and training purposes. The current application for a Dutch city aims at determining the impact of corona related measures – lockdown and increased working-from-home – and drought on water demand and how the water distribution network performs in these situations.

Figuur 2: concept van een digitale tweeling van een drinkwaterdistributienetwerk.

Figure 2: Concept of a digital twin of a drinking water distribution network. Water demand, maintenance and repair works, controls and pipe condition are updated based on data (including sensors, weather, mobility, politics, etc.) and models. This gives insight into the systems’ performance, resulting in better decisions on how to operate the system in the present and improving its design for the future.

Example 2: Digital Twin for Aquifer Storage and Recovery systems

Aquifer Storage and Recovery (ASR) of excess freshwater can play an important role in enhancing the sustainability of water use in the agricultural, industrial and drinking water sectors. However, ASR system users do not at present have a good sense of how much suitable freshwater is still available for recovery, as part of the injected water is lost due to mixing processes in the subsurface. As a result, the systems are frequently used sub-optimally. This motivated the development of a digital twin application based on numerical model calculations that comprises an online dashboard on which the most up-to-date field data is presented, together with the estimated current extend of the freshwater lens and predicted recoverable freshwater volume under various usage scenarios. The digital twin thereby enables users of ASR systems to make more strategic management decisions and decide when and where best to inject or extract the water to obtain higher recovery efficiencies. The study will also investigate the possibility of using the real-time field data incorporated in the digital twin for making more lightweight, rapid predictions of the recoverable volume, for example, based on machine learning techniques.

Example 3: Digital twin of WarmteStad Groningen

WarmteStad Groningen is an initiative to supply the city of Groningen with sustainable heat from multiple sources using a district heating network. They are currently expanding the system, combining multiple heat sources and heat storage options in the new Zernike heat plant. The combination of multiple (low) temperature sources and storage within heating networks is still relatively new. For this reason, there are (as yet) hardly any suitable, integrated design and operation models available. Currently, district heating companies have to draw on lessons learned in the operation of existing systems. In doing so, they encounter a variety of problems. As an example, testing different scenarios, such as a harsh winter or cloudy summer, is not possible over the short term, because the data are not available. KWR is developing a digital twin of the system to evaluate the design of the heating plant, the effects of different scenarios, designs and operational control, conduct ‘what-if’ analyses and optimize its operation for the future. This strategy is also useful for the design and operation of future, comparable systems with multiple sustainable sources, energy conversion stages and subsurface heat storage.

Example 4: A digital twin of the drinking water production plant

Within the joint research program, KWR and the drinking water companies are working on a road map to aid the development of a digital twin of drinking water production plants, as well as its corporation in the business. Such a transformation involves technical adaptations and investments, but also requires the organization to adopt the digital twin in its processes; only then the digital twin can be used to its fullest potential. The digital twin can help to monitor and investigate the performance of the different treatment steps and plants which allows a more evidence-based decision making. Moreover, when the digital twin (and its underlying models) mature further, it can be used for optimization and control of the different processes. Consequently, their operational efficiency does not solely rely on the operator. Also, this may lead to an improved and more stable water quality of the treated water and/or a reduction of the use of raw materials, energy and costs.

What can KWR do for you?

KWR has many experts with domain knowledge on all aspects of the human/urban water cycle. We also have technical experts that can develop representative models of all relevant aspects of the system. In fact, we have already done so for many system components. We also have experience in tying separate models/components together towards digital twins. This puts us in an excellent position to advise on the development of a digital twin, to provide technical/model components, to be part of a development process, or to actually build a digital twin.

For more information, please contact Ina Vertommen.