A digital twin is a virtual model that serves as a replica of a physical object, and which is designed to simulate its attributes and performance. To build this kind of model, you first need to gather data from the physical object in question. Sensors can produce information about the object’s performance that is then combined with geometric data gathered via technology like lidar, photogrammetry, laser scanning, time-of-flight camera or physical measurement. This data is processed to create an accurate, virtual representation--or digital twin--of the object. Once built, sensor data continues to update the digital twin in real time, helping it mirror to the original object’s state as much as possible.
Digital twins are unique in that they can support a two-way flow of information, receiving useful data from the object sensors and then produces insights that can be used to manage the object itself.
According to water industry expert Brian Vu, senior practice solutions manager at Xylem, “digital twins represent the single largest technological breakthrough we’ve seen in wastewater this lifetime.”
Digital twins are helpful for modeling complex and hard-to-reach assets like wastewater infrastructure. These models are utilized in collection system and wastewater treatment plant management to monitor an asset’s performance, run different simulations and/or complete various analyses. Digital twin technology provides operators with the knowledge necessary to improve their systems’ performance, helping them make more informed decisions about risk reduction and other types of improvements.
Digital twins are particularly useful when artificial intelligence (AI) such as machine learning is applied to them. Machine learning takes the approach of letting computers learn for themselves through experience. Machine learning models require data input—the more, the better—to begin working autonomously. Using this data, a computer can learn to find patterns or make predictions over time. Integrating machine learning, then, means that the digital twin technology can eventually start to improve itself without worker intervention.
The wastewater sector is already making effective use of this advanced technology. For example, digital twins make training simpler for new and experienced wastewater workers alike. These models allow workers to view and interact with assets in a way that would be difficult or impossible in the real world, furthering workers’ understanding of how these assets function.
Similarly, treatment plant design and construction also benefit from digital twin integration. Feedback from AI helps bring design inefficiencies to the forefront, enabling engineers to adjust their plans when necessary. Construction projects benefit from this feedback in the form of lowered development costs and fewer wasted resources.
Because they store knowledge and patterns gained over time, digital twins also simplify the employee turnover process. These models ensure that freshman workers have access to legacy information and can pick up where previous employees left off.
Like many other advanced technologies, the advantages of digital twins are expected to become even greater in combination with virtual reality and augmented reality. AR/VR could allow wastewater workers to interact with digital 3D models in an immersive environment that furthers their research and testing capabilities.
--
While not every municipality currently has the training and resources necessary to incorporate digital twin technology into its toolbox, modern asset management software such as WinCan software is making this implementation easier than ever. Through WinCan, engineers can build the 3D models that digital twins depend on. To learn more about WinCan, schedule a free demo with one of Envirosight’s experts today.