Cetaqua, Barcelona, Spain.
Aigües de Barcelona, Empresa Metropolitana de la Gestió del Cicle Integral de l'Aigua, Barcelona, Spain.
Water Environ Res. 2024 Oct;96(10):e11139. doi: 10.1002/wer.11139.
This paper showcases the successful development and implementation of two Digital Twin prototypes within the Lab Digital Twins project, designed to enhance the efficiency and quality control of Aigües de Barcelona's drinking water network. The first prototype focuses on asset management, using (near) real-time data and statistical models, and achieving a 70% success rate in predicting pump station failures 137 days in advance. The second prototype addresses water quality monitoring, leveraging machine learning to accurately forecast trihalomethane levels at key points in the distribution system, and enabling proactive water quality management strategies, ensuring compliance with stringent safety standards and safeguarding public health. The paper details the methodology of both prototypes, highlighting their potential to revolutionize water network management. PRACTITIONER POINTS: Digital representation of assets and processes in the drinking water treatment network Early fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution network Reduction on monitoring time and incident response for target assets by means of Digital Twins Improvement in visualization, prediction, and proactive measures for asset management and water quality control Contribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations.
本文展示了在“实验室数字孪生项目”中成功开发和实施的两个数字孪生原型，旨在提高巴塞罗那自来水公司饮用水管网的效率和质量控制。第一个原型侧重于资产管理，使用（近）实时数据和统计模型，提前 137 天成功预测水泵站故障的成功率达到 70%。第二个原型涉及水质监测，利用机器学习准确预测分配系统中关键点的三卤甲烷水平，并实现主动水质管理策略，确保符合严格的安全标准，保障公众健康。本文详细介绍了这两个原型的方法，强调了它们在水网络管理方面的变革潜力。
饮用水处理网络中资产和流程的数字表示
早期资产故障检测和饮用水分配网络中三卤甲烷形成的预测
通过数字孪生实现目标资产的监测时间和事件响应减少
资产管理和水质控制的可视化、预测和主动措施的改进
对数字孪生及其在水网络运营中变革潜力的不断增长的知识的贡献。
