A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Water

卷:14

Carlos A. Bonilla¹  Ariele Zanfei²  Idel Montalvo³  Joaquín Izquierdo³  Bruno Brentan³ 

[1] Department of Civil, Environmental and Chemical Engineering, Faculty of Engineering and Architecture, University of Pamplona, Pamplona 543050, Colombia;

[2] Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università 5, 39100 Bolzano, Italy;

[3] Fluing-Institute for Multidisciplinary Mathematics, Universitat Politècnica de València, 46022 Valencia, Spain;

关键词: graph convolutional neural networks;    machine learning;    state estimation;    water distribution system;    hydraulic modeling;    digital twin;   

DOI  :  10.3390/w14040514

来源: DOAJ

【 摘 要 】

Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow rate and pressure data at some network pipes and nodes. This work proposes a state estimation methodology that enables one to infer the hydraulic state of the operating speed of pumping systems from these pressure and flow measurements. The presented approach suggests using graph convolutional neural network theory linked to hydraulic models for generating a digital twin of the water system. It is validated on two benchmark hydraulic networks: the Patios-Villa del Rosario, Colombia, and the C-Town networks. The results show that the proposed model effectively predicts the state estimation in the two hydraulic networks used. The results of the evaluation metrics indicate low values of mean squared error and mean absolute error and high values of the coefficient of determination, reflecting high predictive ability and that the prediction results adequately represent the real data.

【 授权许可】

Unknown