BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium.
CAPTURE, Centre for Advanced Process Technology for Urban Resource Recovery, Ghent, Belgium.
Water Environ Res. 2024 Mar;96(3):e11016. doi: 10.1002/wer.11016.
Digital transformation for the water sector has gained momentum in recent years, and many water resource recovery facilities modelers have already started transitioning from developing traditional models to digital twin (DT) applications. DTs simulate the operation of treatment plants in near real time and provide a powerful tool to the operators and process engineers for real-time scenario analysis and calamity mitigation, online process optimization, predictive maintenance, model-based control, and so forth. So far, only a few mature examples of full-scale DT implementations can be found in the literature, which only address some of the key requirements of a DT. This paper presents the development of a full-scale operational DT for the Eindhoven water resource recovery facility in The Netherlands, which includes a fully automated data-pipeline combined with a detailed mechanistic full-plant process model and a user interface co-created with the plant's operators. The automated data preprocessing pipeline provides continuous access to validated data, an influent generator provides dynamic predictions of influent composition data and allows forecasting 48 h into the future, and an advanced compartmental model of the aeration and anoxic bioreactors ensures high predictive power. The DT runs near real-time simulations every 2 h. Visualization and interaction with the DT is facilitated by the cloud-based TwinPlant technology, which was developed in close interaction with the plant's operators. A set of predefined handles are made available, allowing users to simulate hypothetical scenarios such as process and equipment failures and changes in controller settings. The combination of the advanced data pipeline and process model development used in the Eindhoven DT and the active involvement of the operators/process engineers/managers in the development process makes the twin a valuable asset for decision making with long-term reliability. PRACTITIONER POINTS: A full-scale digital twin (DT) has been developed for the Eindhoven WRRF. The Eindhoven DT includes an automated continuous data preprocessing and reconciliation pipeline. A full-plant mechanistic compartmental process model of the plant has been developed based on hydrodynamic studies. The interactive user interface of the Eindhoven DT allows operators to perform what-if scenarios on various operational settings and process inputs. Plant operators were actively involved in the DT development process to make a reliable and relevant tool with the expected added value.
近年来，水务领域的数字化转型发展迅速，许多水资源回收设施建模人员已经开始从开发传统模型转向数字孪生 (DT) 应用。DT 近乎实时地模拟处理厂的运行情况，为操作人员和工艺工程师提供了强大的工具，用于实时场景分析和灾害缓解、在线过程优化、预测性维护、基于模型的控制等。到目前为止，文献中只有少数成熟的全规模 DT 实施示例，这些示例仅满足 DT 的一些关键要求。本文介绍了荷兰埃因霍温水资源回收设施全规模运行 DT 的开发情况，其中包括一个完全自动化的数据管道，结合详细的全工厂工艺模型和与工厂操作人员共同创建的用户界面。自动化的数据预处理管道提供了对经过验证的数据的连续访问，一个进水生成器提供了进水成分数据的动态预测，并允许预测未来 48 小时，曝气和缺氧生物反应器的高级分区模型确保了高预测能力。DT 每 2 小时运行一次近实时模拟。通过与工厂操作人员密切互动开发的基于云的 TwinPlant 技术，实现了 DT 的可视化和交互。提供了一组预定义的处理程序，允许用户模拟假设场景，例如工艺和设备故障以及控制器设置的更改。在埃因霍温 DT 中使用的先进数据管道和工艺模型开发以及操作人员/工艺工程师/经理在开发过程中的积极参与，使该孪生模型成为具有长期可靠性的决策的宝贵资产。实践者要点：为埃因霍温 WRRF 开发了全规模数字孪生 (DT)。埃因霍温 DT 包括一个自动化的连续数据预处理和调整管道。根据水动力研究，开发了工厂的全工厂机械分区过程模型。埃因霍温 DT 的交互式用户界面允许操作人员在各种操作设置和过程输入上执行假设情景。工厂操作人员积极参与 DT 开发过程，使该工具具有可靠且相关的特点，并具有预期的附加值。
