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-5291 Conference paper 2022 Bianucci M.; Merlino S.; Locritani M.; Paterni M. Monitoring Sea Current and Marine litter Transport using a low cost approaches 2022 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters, MetroSea 2022 - Proceedings 10.1109/MetroSea55331.2022.9950922 This paper describes how to use, in an innovative way, modern 'consumer' electronic technologies for wireless communication to achieve a marine monitoring system with low hardware costs, near-zero maintenance costs, and low power consumption, compared to systems based on single drifters with satellite communication. The development of newly developed oceanographic drifters that can be used for mesoscale monitoring of marine currents and for further applications (recovery of people/materials at sea, pollutant spills, marine litter dispersion) is described, together with much simpler but highly effective marine litter tracking systems for monitoring, in coastal waters, marine litter coming out of rivers, with open source systems and applicable with a citizen-science approach. Β© 2022 IEEE.
publications-5292 Book chapter 2022 Sweetapple C.; Salomons E.; Le Gall F.; Abid A.; Vamvakeridou-Lyroudia L.; Chen A.S.; van den Broeke J. Integrating Epanet and FIWARE for Development of Water Distribution System Digital Twins Springer Water 10.1007/978-981-19-1600-7_68 Digital Twins (DTs) have significant potential in the area of water distribution system (WDS) management. However, development of DTs can be complex, with the need for any hydraulic and/or quality model to be constantly paired with data and information from multiple sources in the physical world. Transmission, conversion, validation, storage and protection of this data are all important issues, and are currently complicated by a lack of standardization. However, standardization, along with interoperability and integration are fundamental features of a successful DT. This research, therefore, aims to provide a flexible solution for integrating hydraulic and quality WDS models with heterogeneous data sources by integrating the WDS simulation toolkit, OWA-EPANET 2.2, with the cloud-based context information platform, FIWARE. To achieve this, a new open source ‘Water Network Management’ FIWARE data model is first developed to enable management of all network data in a standardized, NGSI-LD format, based on JSON-LD serialization. Secondly, an interface is developed to: (a) translate existing EPANET model data into the requisite NGSI-LD format; (b) post this to a context information broker; (c) retrieve all data necessary to generate an up-to-date network model for simulation (capturing real-time network data supplied to the context broker from other sources); and (d) as and when required, run hydraulic and quality simulations of the network. Lastly, this FIWARE-integrated implementation of EPANET is demonstrated using a small case study WDS from the South West of England, where it utilises real-time data available from 91 smart meters in the network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
publications-5293 Article 2022 Lian B.; Zhu Y.; Branchaud D.; Wang Y.; Bales C.; Bednarz T.; Waite T.D. Application of digital twins for remote operation of membrane capacitive deionization (mCDI) systems Desalination 10.1016/j.desal.2021.115482 Digital Twins (DTs) have been developed for several pilot-scale membrane capacitive deionization (mCDI) units that are located in remote communities in China and Australia for desalination of brackish water and treated domestic wastewater. These pilot-scale mCDI units have a production capacity ranging from 5 to 50 m3/day and a water recovery rate of up to 85%. The mCDI DTs use Head-mounted Displays (HMDs) to facilitate the visualisation of transient real-time data and historical data from various sensors in the physical plants. The DTs contain device tag and sensor data display functions which greatly enhance the model functionality and user experience. By combining the DTs with Mixed Reality (MR) technology that blends elements of both Virtual Reality (VR) and Augmented Reality (AR), it was possible to use the DTs for remote control and remote operator training in an immersive environment. Our results suggest that more facile remote control and improved training outcomes could be achieved by use of DTs by the water industry compared to those achieved by conventional control and training methods. Β© 2021 Elsevier B.V.
publications-5294 Conference paper 2022 Lai W.; Zhang H.; Jiang D.; Wang Y.; Wang R.; Zhu J.; Chen Q.; Gao Y.; Li W.; Xie D. Digital Twin and Big Data Technologies Benefit Oilfield Management Society of Petroleum Engineers - ADIPEC 2022 10.2118/211116-MS The oil & gas industry has been value added from our digital assets since this new century, which helped our industry dig out more advanced algorithm, more robust logic to address the challenge from HPHT wells and deep-water wells. Nowadays the operators are facing much more challenges in oilfield management especially how to improve their decision efficiency and situation awareness. Thanks to the different sensors we deployed on oilfield from drilling to completion and production, tremendous data contributed to the digital asset we are having now. The digital twin makes oilfield management much easier than ever before, hundreds of wells' performance could be displayed in front of the decision maker or key management level of oil companies, and big data technique helps them get easy understanding of real time behavior on well construction progress, cost management, pain spot of each project. Combining these two methods, it is possible to have an up-to-date awareness of oilfield development status and perceptual intuition to very detail situations. There is a major operator manages over 200 wells per year and some of these wells are challenging exploration well with measured depth over 20000ft which requires experienced team to get the well to total depth, also a lot of shale gas wells with lateral intervals over 8000ft which demands intensive control of cost. All above operations or targets need be done under a safe and efficient way, then the management team taking digital twins to monitor the real time well status which help them get up to date information about whole oilfield status like drilling, completion, production and more. Big data analysis is also used to help enhance the decision- making efficiency and overcome puzzles that traditional method could not solved, like recommending the best practice way on well construction engineering parameters, or ROI (return on investment) assess. The oil company could achieve a better management level with less human resources and much more workload. By the advantages of digital twins and big data analysis, the oil company now managing more than 200 drilling rigs and 300 completion wells in the high efficiency way, and now involving the production wells into next phase digital construction target. Furthermore, considering develop an integrative digital twin of geology and engineering map which get whole formation and well construction more intuitive. Besides, it is proven that digital method like digital twins and big data technique could improve the skill of oilfield management significantly, which optimized the resource and expenditures investigated in modern oil and gas industry. Copyright Β© 2022, Society of Petroleum Engineers.
publications-5295 Article 2022 Semyachkov A.I.; Semyachkov K.Al. Digital model of groundwater technogenesis as an element of sustainable development of the urban environment; [Π¦ΠΈΡ„Ρ€ΠΎΠ²Π°Ρ_x008f_ ΠΌΠΎΠ΄ΠµΠ»Ρ_x008c_ Ρ‚ΠµΡ…Π½ΠΎΠ³ΠµΠ½ΠµΠ·Π° ΠΏΠΎΠ΄Π·ΠµΠΌΠ½Ρ‹Ρ… Π²ΠΎΠ΄ ΠΊΠ°ΠΊ Ρ_x008d_Π»ΠµΠΌΠµΠ½Ρ‚ устойчивого Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΡ_x008f_ городской среды] Sustainable Development of Mountain Territories 10.21177/1998-4502-2022-14-3-362-369 The purpose of this study is to create a digital twin of the sludge reservoir, which allows modeling and predicting the impact of technogenic formation on the sustainability of urban development, in particular, on the state of groundwater. The research methodology is to simulate groundwater based on numerical simulation using the ModTech 2.21 program. This program is designed to solve problems of various equations and partial derivatives that describe the geo-filtration of the environment, using a numerical method on a three-dimensional finite-difference grid. In order to assess the sustainable development of the urban environment in the city of Sterlitomak, a digital twin of the urban area was created in terms of groundwater. Sludge accumulator Β«White SeaΒ» is a unique man-made object located in the city of Sterlitomak and affecting groundwater due to infiltration losses of distiller fluid through the bed of the sludge reservoir. According to the simulation results, the infiltration losses of the SHBM are 3078 m3/day or 1123470 m3/year. The incoming part of the balance is formed due to the attraction of river runoff, infiltration nutrition, and the outgoing part is due to the discharge of groundwater into surface water. The model assessment was carried out in relation to the conditions of migration of the main pollutant component in the territory of the Β«White SeaΒ» - chlorides. With the help of the created digital model, it is possible to assess, predict and manage the situation in the urban environment in terms of groundwater. Digitalization through the use of intelligent solutions and innovations in the field of digital technologies makes it possible to understand the processes occurring in social, man-made, and natural systems at a new level. The next step in creating digital twins can be models of electronic maps of soil cover or vegetation, the state of the air basin, and more. The same models can be created on the basis of economic and social indicators. This is necessary for planning the sustainable development of the urban environment. Β© 2022 Authors. All rights reserved.
publications-5296 Article 2022 Pagani A.; Wei Z.; Silva R.; Guo W. Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics ACM Transactions on Internet Technology 10.1145/3461838 Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%-10% of the network nodes for known contaminant sources, and 50%-75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins. Β© 2021 Association for Computing Machinery.
publications-5297 Conference paper 2022 Carrera-Monterde A.; Gomez-Jauregui V.; Manchado C.; Otero C. Monitoring Industrial Plants from BIM Models with Extended Reality Lecture Notes in Mechanical Engineering 10.1007/978-3-030-92426-3_2 This project consists on creating a mobile application that, by means of Extended Reality (XR), allows the user to interact with any industrial plant, in our case a Wastewater Treatment Plant (WWTP). In order to make this possible, we have generated a digital twin of the facility from the BIM model. The user will be able to wander around the model immersed on it. The camera can be moved to explore the space by using, in VR, the mobile device’s gyroscope (to rotate) and a joystick (to move around) or by freely walking inside the building in AR. Its main purpose is to monitor the live status of the WWTP whether it is from a remote location using VR or inside the building using AR. In almost every equipment of the WWTP there is information from the computerized maintenance management systems (CMMS) and sensors uploading measurements to a server feeding the SCADA. The application will display this information in an intuitive 3D panel on top of each equipment once it is selected by the user. It will also be possible to interact with the model by moving equipment around or by taking measurements of the building on the device’s screen. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
publications-5298 Conference paper 2022 Ocaka A.; Briain D.O.; Davy S.; Barrett K. Cybersecurity Threats, Vulnerabilities, Mitigation Measures in Industrial Control and Automation Systems: A Technical Review 2022 Cyber Research Conference - Ireland, Cyber-RCI 2022 10.1109/Cyber-RCI55324.2022.10032665 Cyberattacks on Industrial Control and Automation Systems (ICAS) have significantly increased in recent years due to IT and OT convergence. Traditionally, ICAS were isolated systems running proprietary protocols on specialised software and hardware. However, to improve business processes and efficiency, ICAS vendors are adopting smart technologies such as Industrial Internet of Things (IIOT), Machine to Machine (M2M), Digital Twin, cloud computing, and Artificial Intelligence (AI). This integration presents new vulnerabilities in ICAS that can be exploited by threat actors. ICAS are utilised in critical infrastructure and widely used in power, nuclear plant, water, oil, natural gas, and manufacturing industries. Therefore, cyberattacks on these systems can pose a significant threat to humans and the environment, disrupt social services, cause financial losses, and threaten national security. Because of these threats, numerous mitigation measures are being implemented to protect ICAS from cyberattacks. However, security experience and expertise have demonstrated that we can never fully protect a system and one should never propose that their solution will fully protect. Rather one can claim that their solution/mitigation technique adds a layer to the defence in depth approach. This paper discusses the different cybersecurity standards and frameworks for ICAS, investigates the existing threats and vulnerabilities, and methods of securing ICAS Β© 2022 IEEE.
publications-5299 Conference paper 2022 Fernandez K.C.T.; Baldovino R.G.; Billones R.K.C. Digital Twinning to Predict Harvest Weight of Hydroponically Grown Romaine Lettuce 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022 10.1109/HNICEM57413.2022.10109418 As the world population continues to grow, pressure to improve food harvests further increases. This study aims to produce a digital twin of romaine lettuce being grown in a passive, Kratky style hydroponic system to help increase food harvests. Inputs include water pH, nutrient composition and concentration, amount of light received by the plant, and ambient temperature. Data from various studies measuring the effects of these parameters on the growth of lettuce was collected, combined and factored into a digital model through the use of regression modelling in an attempt to predict the harvest weight of a head of lettuce at the end of a standard hydroponic growing period of 5-6 weeks. The model's ideal conditions were predicted to be the use of nutrient treatment 3, a 16-hour photoperiod, an ambient temperature of 26 degrees Celsius, a water temperature of 20 degrees Celsius, and a water pH of 5. The model's predicted harvest of 121 grams per plant which was 4.9% larger than the previous best observed in reference studies. The methodology used also has the advantage of producing equations that will allow producers to estimate harvests without the need for specialized software. It is hoped that the methods used in this study can further be improved and applied to other types of crops to increase yields. Β© 2022 IEEE.
publications-5300 Conference paper 2022 Karimi M.A.; Arsalan M.; Shamim A. Digital Twin of Expensive Multiphase Flow Loop Test to Develop Next Generation of Production Technologies International Petroleum Technology Conference, IPTC 2022 10.2523/IPTC-22124-EA Multiphase flow is frequently encountered in upstream O&G industry that has significant impact on the development of numerous production technologies such as multiphase flowmeter. Before the deployment of these technologies in an oil/gas field, the technologies are tested in a multiphase industrial flow loop test that emulates multiphase test conditions. This paper presents a digital twin of 2-phase flow (oil & water) as a low cost alternative to expensive multiphase flow test. We have adopted backward strategy to design the digital twin of multiphase flow. At first, we characterized our proprietary microwave water-cut (WC) meter in an industrial flow loop in variable test conditions. Then, multiple digital models of the flow regimes were built and tested on our microwave WC meter. One of those models (rotated zigzag) was able to accurately predict WC sensor response over full WC range in oil continuous as well as water continuous flow conditions under varying salinity levels. Two sets of responses have been recorded and compared - first obtained from the industrial flow loop trials and second from our EM simulation model. Key microwave resonator parameters such as resonant frequency (f0) and quality (Q) factor have been compared under varying conditions. The comparison suggests that f0 & Q-factor give higher sensitivity against WC in oil continuous and water continuous flow conditions respectively. Moreover, WC sensor performance was also compared under varying salinity conditions in the range of 20, 000 ppm to 80, 000 ppm and digital twin is able to successfully predict the sensor response in these conditions as well. Significant amount of resources are spent on setting desired flow condition such as flow regime, WC and required salinity level. Our proposed digital twin model is able to emulate all of these multiphase flow conditions at negligible cost. It can help develop & test new production technologies without requiring to spend huge amount of money on lengthy, complex and expensive multiphase flow loop tests. Copyright Β© 2022, International Petroleum Technology Conference.