Enhancing efficiency and quality control: The impact of Digital Twins in drinking water networks.

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 networkEarly fault detection in assets, and predictions of trihalomethane formation in the drinking water distribution networkReduction on monitoring time and incident response for target assets by means of Digital TwinsImprovement in visualization, prediction, and proactive measures for asset management and water quality controlContribution to the growing knowledge on Digital Twins and their potential to revolutionize water network operations [ABSTRACT FROM AUTHOR]