A Review of Performance and Robustness in Smart Predictive Digital Twins for Water Supply Systems

Research Summit ‘25 A Review of Performance and Robustness in Smart Predictive Digital Twins for Water Supply Systems Mariana Alão(a)(b), Ana Luísa Reis (a),António Andrade-Campos(a) (a) Department of Mechanical Engineering, TEMA - Centre for Mechanical Technology and Automation, LASI - Intelligent Systems Associate Laboratory, University of Aveiro (b) SCUBIC, Parque da Ciência e Inovação (a) Aveiro, Portugal; (b) Ílhavo, Portugal (a) [email protected]; [email protected]; [email protected] Abstract — Water Supply Systems (WSS) are critical systems that ensure water supply to our homes, agricultural and industry in sufficient quantity and quality. 1.13% of all energy consumed in Europe is used by WSS, of which at least 60% is consumed by pumping stations [1]. Therefore, it is essential to manage these systems in the most possible efficient way. However, the management of these systems is challenging although complex, due to the need of considering multiple variables, including pump speed, local energy production and dynamic daily tariffs. With the loss of specialised knowledge to operate these systems, due to the retirement of the workforce, Digital Twins (DT) can help in the decision-making process of WSS [2]. DT are digital replicas that can synchronise the physical and the virtual world in real time [3]. They have evolved with time and may contain other components, such as cloud computing, predictive analysis and optimisation model [4]. In the context of this work, a DT that contain these three components is considered a SPDT. Given the limited number of real-world applications of DT not only in the water sector but in other fields, this work aims to systematize and critically analyse existing evaluation frameworks and robustness-enhancing techniques. The results of this review reveal an absence of a complete strategy for the evaluation of SPDT not only in the water sector but also in other sectors. By synthesising current methodologies and identifying key limitations, this review provides foundations for the development of more robust DT implementation, leading to a larges acceptance in the industry. Keywords — Performance Evaluation; Robustness Enhancement; Smart Predictive Digital Twins; Water Supply Systems. ACKNOWLEDGMENTS This work is supported by the doctoral grant (Ref. 2024.04917.BDANA) financed by the Portuguese Foundation for Science and Technology (FCT), by the FEDER and Regional Operational Program of the Center Region (CENTRO2030) within project I-ReTiS-LeaksD&Op nº 17304 (CENTRO2030-FEDER01177300) and through the Portuguese Foundation for Science and Technology (FCT), supported by the Recovery and Resilience Plan (PRR), within project I-ReTiS-Leaks (2024.07270.IACDC). This paper was also supported by the project UID 00481 Centre for Mechanical Technology and Automation (TEMA). REFERENCES [1] - International Energy Agency. (2018). Energy, water and the Sustainable Development Goals: Excerpt from World Energy Outlook 2018. IEA. https://www.iea.org/reports/energy-waterand-the-sustainable-development-goals [2] - Kalyani, C., Pradnya, K., Samkit, C., & Patil, V. (2023). Regular and equal water supply system. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 15(01), 34-37. [3] – Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022). Digital Twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, 30, 100383. [4] – Segovia, M., & Garcia-Alfaro, J. (2022). Design, modeling and implementation of digital twins. Sensors, 22(14), 5396.