A digital twin is not like a virtual copy of a sibling. Not unless you keep those around to run tests you would prefer not to do on yourself. Rather, this technology helps industries virtually testing their innovations and to simulate the effects of different scenarios, without having to experience it in real life. In water sector, Digital Twins helps water and wastewater utilities to operate more efficiently and cost-effectively, supporting decision making and helping unlock the maximum potential of infrastructure and human resources.
Digital Twins: meet your new virtual dummy
Digital twins are a virtual representation of a physical object or a system. They are revolutionising product and systems testing because they make it possible to simulate the effects of different scenarios, without any real-life testing. Digital twins are used across disciplines and industries. An engineer may, for example, want to know how tweaks to a car he is designing will affect a vehicle’s performance. A logistics manager for a supermarket chain might want to model how COVID lockdowns in an important supplier country might disrupt stocks. Both could create a virtual representation of the car/system to test a range of scenarios, all without ever having to build a car or do any field testing.
Digital twins can also monitor a physical asset in real-time. Connected sensors on the asset can collect real-time, real-world data that feeds into a virtual model. This allows for better monitoring and decision-making. Sensors on a company’s fleet of cars, for instance, inform the fleet manager which vehicles need servicing without active check-ups.
Digital Twin in the water sector
Digital twins hold tremendous opportunity for the water sector. The path to innovation is not always straightforward though. Christian Ziemer, Manager Business Development and Strategy, Water & Wastewater at Siemens AG, remembers that when he started working on digitalising the water sector with the Water German Partnership in 2014, stakeholders had not yet warmed up to the technology. “We were trying to find a utility that would agree to use a digital twin at its facility, but no one said yes,” he recalls. “They considered themselves already digitalised. They already had real-time information”. Christian was able to convince stakeholders that digital twins offer exciting new ways of testing “what if” scenarios and predicting performance.
Christian explained how a digital twin creates a virtual model of a water system and its connected assets. Its simulations can provide direction for optimising the operation of a pumping station or a wastewater treatment plant. This is what the University of Berlin is doing. Researchers there have centralised all the information about a pumping station in a digital twin. They use sensors collecting real-time data to detect and eliminate system failures, all without the need for on-site inspections.
Water and wastewater utilities can also benefit from having a digital twin at a plant level. Christian explains how the Upper Engadine wastewater treatment agency (Switzerland), used a digital twin, developed by Siemens, of their entire plant before they even built it. The twin did not only help them during the construction phase, but also streamlined operations and maintenance once the plant was up and running. Employees can use the digital twin to familiarise themselves with the plant, and test operating scenarios. A virtual model of the plant that is always up to date builds in more certainty and safety for the operations staff. They can test what will happen to the plant if a specific pump stops working or a valve gives out, so they can think ahead.
Christian is convinced that digital twins will inspire a holistic approach to water management and will improve decision-making. By creating a virtual representation of the whole water management system, digital twins connect different actors along the value chain, such as industries, households or the agricultural sector, with the environment. Aggregating data from different (parts of) digital twins can improve our understanding of the whole water system and the way its different parts connect. As such, it can look for integrated solutions that take into accounts the needs and challenges of different stakeholders. A system-wide digital twin could for instance simulate heavy downpour to see whether sewage systems will be able to cope, how drinking water safety might be threatened, and which areas of the cities might flood. It would also be able to show how interventions by one actor might impact others. Since digital twins collect large quantities of data, they can also feed larger systems models that can offer more generalised decision-making inputs across the water sector.
How does DWC uses the potential of Digital Twins
DWC’s own Francesco Fatone is using digital twins to develop an early warning system for the safe reuse of water in agriculture in Milan. CAP’s team installed sensors and meters in the Peschiera Borromeo wastewater treatment plant to monitor pathogens and water chemistry. UNIVPM uses machine learning to detect outliers and generate new insights and fill in field data gaps. The results help provide the digital twin, which is used to integrate soft sensors supporting early warning systems. The researchers use a digital twin of the plant to simulate hazardous events. This allows them to virtually assess risk and minimise it. Then, Francesco and his team also share data on health risks associated with the reuse of treated water with farmers and operators of water infrastructures, thus maximising the benefits of effective water reuse in productive agriculture.
Francesco is convinced that the use of the digital twin “not only informs stakeholders about the analysis and management of water reuse risks, but also raises awareness of the challenges of the water-energy-food-climate interface.” The next step in Milan is to develop a serious game to raise awareness of water reuse. This virtual tool will allow citizens to communicate about the benefits of water reuse, enabling citizens to better understand the complexity of the links between water availability, carbon emissions, energy consumption, food crop productivity and climate variability.