A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/4/514 |
| _version_ | 1797475904443645952 |
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| author | Carlos A. Bonilla Ariele Zanfei Bruno Brentan Idel Montalvo Joaquín Izquierdo |
| author_facet | Carlos A. Bonilla Ariele Zanfei Bruno Brentan Idel Montalvo Joaquín Izquierdo |
| author_sort | Carlos A. Bonilla |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:51:24Z |
| format | Article |
| id | doaj.art-3eccdf5586fc4b71b50e88b04e3230d6 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T20:51:24Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-3eccdf5586fc4b71b50e88b04e3230d62023-11-23T22:33:10ZengMDPI AGWater2073-44412022-02-0114451410.3390/w14040514A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State EstimationCarlos A. Bonilla0Ariele Zanfei1Bruno Brentan2Idel Montalvo3Joaquín Izquierdo4Department of Civil, Environmental and Chemical Engineering, Faculty of Engineering and Architecture, University of Pamplona, Pamplona 543050, ColombiaFaculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università 5, 39100 Bolzano, ItalyFluing-Institute for Multidisciplinary Mathematics, Universitat Politècnica de València, 46022 Valencia, SpainFluing-Institute for Multidisciplinary Mathematics, Universitat Politècnica de València, 46022 Valencia, SpainFluing-Institute for Multidisciplinary Mathematics, Universitat Politècnica de València, 46022 Valencia, SpainWater 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.https://www.mdpi.com/2073-4441/14/4/514graph convolutional neural networksmachine learningstate estimationwater distribution systemhydraulic modelingdigital twin |
| spellingShingle | Carlos A. Bonilla Ariele Zanfei Bruno Brentan Idel Montalvo Joaquín Izquierdo A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation Water graph convolutional neural networks machine learning state estimation water distribution system hydraulic modeling digital twin |
| title | A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation |
| title_full | A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation |
| title_fullStr | A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation |
| title_full_unstemmed | A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation |
| title_short | A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation |
| title_sort | digital twin of a water distribution system by using graph convolutional networks for pump speed based state estimation |
| topic | graph convolutional neural networks machine learning state estimation water distribution system hydraulic modeling digital twin |
| url | https://www.mdpi.com/2073-4441/14/4/514 |
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