Division of Industrial Electrical Engineering and Automation (IEA), Department of Biomedical Engineering, Lund University, Lund, Sweden.
IVL Swedish Environmental Research Institute, Stockholm, Sweden.
Water Environ Res. 2024 Jul;96(7):e11074. doi: 10.1002/wer.11074.
Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor-made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method. PRACTITIONER POINTS: Various methods can be used for automated data transfer between the physical system and a digital twin. Delays in the data transfer differ depending on implementation method. The digital twin objective determines the required simulation frequency. Implementation method should be chosen based on the required simulation frequency.
在过去十年中，数字孪生在各个领域引起了极大的兴趣。将传统的过程模拟模型引入（接近）实时被认为可以为许多行业的运营商、决策者和利益相关者提供有价值的见解。本文的目的是描述在水资源回收设施中实施数字孪生的两种方法，并强调和讨论它们的差异和适用情况，重点是从实际过程自动传输数据。案例 1 使用定制的基础设施在设施和数字孪生之间自动传输数据。案例 2 使用边缘计算实现快速自动数据传输。与两个系统中的模拟频率相比，从过程到数字孪生的数据传输延迟较低。可以使用这两种方法中的任何一种来实现所提出的数字孪生目标。案例 1 中的方法更适合模型参数的自动重新校准，尽管案例 2 中的方法存在解决方法。案例 2 中的方法非常适合软传感器等目标，因为它与 SCADA 系统集成并且延迟低。数字孪生的目标以及系统所需的延迟应指导方法的选择。从业者要点：可以使用各种方法在物理系统和数字孪生之间进行自动数据传输。数据传输的延迟取决于实施方法。数字孪生的目标决定了所需的模拟频率。应根据所需的模拟频率选择实施方法。
