Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID ▲ Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-3651 article 2023 Branchaud, D. and Seo, L. and Petheram, C. and Fu, Q. and Watson, I. Advancing environmental management through digital twin technology: A demonstration and future outlook for land and water resource development in Australia MODSIM2023, 25th International Congress on Modelling and Simulation. 10.36334/modsim.2023.branchaud : Modern environmental management, planning and granting of social license demand innovative technological solutions to navigate their inherent complexities. In this presentation, we will demonstrate the state-of-the-art preliminary implementation of a comprehensive Digital Twin technology developed to model, visualize, and effectively manage varying scales and types of potential greenfield land and water resource developments in key catchment areas across Australia. Our focus incorporates not only Digital Twin of landscapes and potential irrigated areas but also critical infrastructure such as road networks and human communities. The virtual representation encompasses an array of potential greenfield water resource developments including large engineered dams, earth embankment farm-scale gully dams, off-stream storage, groundwater bores, instream weirs, and managed aquifer recharge systems. Our technology provides dynamic and interactive visualization, allowing users to experience changes in river flow under diverse development scenarios and seasons, potential off-stream impacts, and temporal changes.
publications-3652 article 2023 ιƒ­, ζ_x008c_― The Role of Digital Twin Technology in Smart Water Conservancy Construction Open Journal of Soil and Water Conservation 10.12677/ojswc.2023.114004 Digital twin technology plays a crucial role in addressing scientific decision-making challenges in β€_x009c_smart water managementβ€_x009d_, aiding in the scientific decision-making transition from β€_x009c_informa-tion-based water managementβ€_x009d_ to β€_x009c_smart water managementβ€_x009d_. This article explores the background of digital twin basins and the overall framework of smart basins, analyzing the significant
publications-3653 article 2022 Granados, Carlos Alexis Bonilla and Granados, Carlos Alexis Bonilla and Zanfei, Ariele and Zanfei, Ariele and Brentan, Bruno Melo and Brentan, Bruno and Montalvo, Idel and Montalvo, Idel and Izquierdo, Joaqun and Izquierdo, JoaquΓ­n and Izquierdo, JoaquΓ­n A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation Water 10.3390/w14040514 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.
publications-3654 article 2022 Henriksen, Hans Jørgen and Schneider, Raphaël and Koch, Julian and Koch, Julian and Ondracek, Maria and Troldborg, Lars and Seidenfaden, Ida Karlsson and Kragh, Søren Julsgaard and Bøgh, Eva and Stisen, Simon A New Digital Twin for Climate Change Adaptation, Water Management, and Disaster Risk Reduction (HIP Digital Twin) Water 10.3390/w15010025 The paper analyzes the national DK-model hydrological information and prediction (HIP) system and HIP portal viewed as a ‘digital twin’ and how the introduction of real-time dynamic updating of the DK-model HIP simulations can make room for plug-in submodels with real-time boundary conditions made available from an HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges were addressed as part of the Joint Governmental Digitalization Strategy 2016–2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction. This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terabytes of hydrological model data to the portal, with robust calibration methods and hybrid machine learning (ML) being key parts of the deliverables. This paper discusses the challenges and potentials of further developing the HIP digital twin with ‘plug-in digital twins’ for local river basins, including feedback to the national level.
publications-3655 article 2023 Liu, Wangjiayi and Guan, Guanghua and Tian, Xuemin and Cao, Zi-Jun and Chen, Xiaonan Exploiting a Real-Time Self-Correcting Digital Twin Model for the Middle Route of the South-to-North Water Diversion Project of China Journal of water resources planning and management 10.1061/jwrmd5.wreng-5965 Real-time monitoring and forecasting are essential to ensure an on-time and on-demand supply of water diversion projects. However, water transfer systems currently lack spatiotemporal data in a dense resolution, failing to monitor real-time conditions and test plausible scenarios. To address the problem, this paper proposes a novel digital twin framework. It includes a real-time self-correcting model, which combines (1) a hydraulic solver using the one-dimensional Saint-Venant equations; and (2) a method updating hydraulic states driven by field observed data. This framework consists of four phases: preparation, warming up, tuning, and monitoring and predicting. Particularly in monitoring and predicting, an identification method for diagnosing abnormal events is also proposed as one of the functions of the twin model. The model shows beyond 98\% similarity to reality based on the metric similarity (S) proposed in this paper on both of two real-world scenarios: a large flow scenario and a normal one. The deviation is generally lower than 5 cm for water level 2 m3/s for discharge. The abnormal situation diagnosis method also provides timely fault detection for daily scheduling. It is anticipated that this framework can be a powerful tool to estimate current canal states and predict change trends, further ensuring the security and efficiency of operations for large-scale water diversion projects.
publications-3656 article 2023 Ostfeld, Avi and Abhijith, Gopinathan R. Digital Twin for Water Distribution Systems Managementβ€”Towards a Paradigm Shift Journal of Pipeline Systems Engineering and Practice 10.1061/jpsea2.pseng-1486
publications-3657 article 2023 Mohammed, Mazin Abed and Lakhan, Abdullah and Abdulkareem, Karrar Hameed and Ghani, Mohd Khanapi Abd and Marhoon, Haydar Abdulameer and Kadry, Seifedine and Nedoma, Jan and Martínek, Radek and Garcia-Zapirain, Begonya Industrial Internet of Water Things Architecture for Data Standarization Based on Blockchain and Digital Twin Technology Journal of Advanced Research 10.1016/j.jare.2023.10.005 The Industrial Internet of Water Things (IIoWT) has recently emerged as a leading architecture for efficient water distribution in smart cities. Its primary purpose is to ensure high-quality drinking water for various institutions and households. However, existing IIoWT architecture has many challenges. One of the paramount challenges in achieving data standardization and data fusion across multiple monitoring institutions responsible for assessing water quality and quantity. Additionally, ensuring data security and interoperability among the sensors and nodes within the IIoWT architecture remains a critical concern. This paper introduces the Industrial Internet of Water Things System for Data Standardization based on Blockchain and Digital Twin Technology. The main objective of this study is to design a new IIoWT architecture where data standardization, interoperability, and data security among different water institutions must be met. We devise the digital twin-enabled cross-platform environment using the Message Queuing Telemetry Transport (MQTT) protocol to achieve seamless interoperability in heterogeneous computing. In water management, we encounter different types of data from various sensors. Therefore, we propose a CNN-LSTM and blockchain data transactional (BCDT) scheme for processing valid data across different nodes. Through simulation results, we demonstrate that the proposed IIoWT architecture significantly reduces processing time while improving the accuracy of data standardization within the water distribution management system. Overall, this paper presents a comprehensive approach to tackle the challenges of data standardization and security in the IIoWT architecture. Based on Blockchain and Digital Twin Technology, the proposed system offers a promising solution for efficient water management in smart cities, ensuring users’ reliable delivery of high-quality drinking water.
publications-3658 article 2023 Brahmbhatt, Parth and Maheshwari, Abhilasha and Gudi, Ravindra D. Digital Twin assisted decision support system for quality regulation and leak localization task in large-scale water distribution networks Digital Chemical Engineering 10.1016/j.dche.2023.100127 Effective water resource management is essential in large metropolitan cities. Digital Twins (DT), supported by IIoT and machine learning technologies, provide opportunities for real-time prediction and optimization for effective decision-making in water distribution systems. A framework for the digital twin of the Water Distribution Network (WDN) is developed in this paper to achieve higher operational efficiency using ‘WNTR’, the Python-based library of EPANET. All computational experiments and methods were validated on the benchmark hydraulic C-TOWN network (Ostfeld et al., 2011). The hydraulic parameters and quality parameters of the DT model for the water network were calibrated using the Differential Evolution (DE) algorithm. The calibrated DT served as a real-time proxy to generate simulation data, which is used for two different applications in large-scale water networks: (i) Disinfectant dosage regulation task using booster stations and (ii) pipe leakage localization task. The calibrated DT was utilized to estimate the optimal disinfectant dosing rates, ensuring water quality control within an acceptable range using optimization. The results highlight the effectiveness of the neural network and real-time optimization strategy to achieve the optimal dosing rate. For the leakage localization task, the Graph Convolution Networks (GCN) based neural network trained on the DT was found to predict leakage location very accurately.
publications-3659 article 2023 Pallavicini, Jacopo and Fedeli, Matteo and Scolieri, Giacomo Domenico and Tagliaferri, Francesca and Parolin, Jacopo and Sironi, Selena and Manenti, Flavio Digital twin-based optimization and demo-scale validation of absorption columns using sodium hydroxide/water mixtures for the purification of biogas streams subject to impurity fluctuations Renewable Energy 10.1016/j.renene.2023.119466 This paper aims to validate a demo scale plant scrubber technology through experimental campaign and development of a digital twin. Thus, it is useful to evaluate the H2S absorption process in a biogas production plant for analysis and optimization purposes. The absorber unit removes H2S through the chemical absorption via sodium hydroxide (NaOH) as wet agent (30\% w/w). The column treats 300 Nm3/h of biogas, whose inlet H2S concentration ranges from 1000 to 3000 ppm. Field measurements are conducted to investigate the H2S removal efficiency. An experimental dataset is collected, processed and used as input on Aspen PLUS suite to develop the digital twin. This model is helpful to generate a large dataset and simulate operating conditions different from the demo-scale plant. The process simulation is then exploited to perform a sensitivity analysis to figure out main variables influencing the H2S removal efficiency. Operating conditions such as H2S concentration, soda concentration and flowrate, temperature, and freshwater flowrate are perturbed in the sensitivity analysis. NaOH flowrate and its concentration are the variables with the biggest impact on the process. In detail, the highest efficiency performance was obtained using 50\% NaOH solution with a flowrate higher than 8 kg/h.
publications-3660 article 2023 Cheng, Hang and Fei, J. and Wen, Jianfeng and Tu, S.T. Predictive Maintenance of Alkaline Water Electrolysis System for Hydrogen Production Based on Digital Twin International Conference on Computational & Experimental Engineering and Sciences 10.32604/icces.2023.09663 Alkaline water electrolysis system for hydrogen production has the characteristics of complex structure, fault coupling and state nonlinearity, coupled with the restriction by many factors such as data acquisition methods and analysis methods. The operation status cannot be fully characterized through current monitoring information. In order to solve the problems in health status assessment in the operation of alkaline water electrolysis system, a digital twin-driven predictive maintenance method is put forward to achieve the real-time monitoring of operation status and prediction of remaining useful life. In the study, a multi-disciplinary simulation model of the alkaline electrolysis system and a physical degradation model of the electrolyzer are established. Meanwhile, the data-driven fault diagnosis and the life prediction algorithm are constructed by using the deep learning method. Finally, the two are fused by the particle filtering algorithm and transfer learning to realize predictive maintenance of alkaline water electrolysis system. Results indicate that, in contrast with the single model-based method or the data-driven method, the predictive method based on digital twin has higher prediction accuracy, which overcomes the problems of inconsistent models and poor adaptability of data algorithms. For the fault diagnosis of the alkaline water electrolysis system, the fault diagnosis model is trained based on digital twin simulation data, and then transferred to the data collected by actual sensors by the transfer learning method. The diagnosis accuracy reaches 90\%, indicating that the method is able to relatively better diagnose the fault in the operation of the alkaline water electrolysis system for hydrogen production. The predictive maintenance method based on digital twin proposed in this paper also provides an effective solution for other complex equipment.