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 â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-4951 Conference paper 2024 Masud Rana S.M.; Uber J.; McCary J.; Keck J. A Greedy Search Algorithm for Closed Valve Analysis in Drinking Water Networks for Real-Time Model Development World Environmental and Water Resources Congress 2024: Climate Change Impacts on the World We Live In - Proceedings of the World Environmental and Water Resources Congress 2024 10.1061/9780784485477.112 Real-time analysis and operation of drinking water networks (DWN) are key to improving water quality throughout the network, reducing operational cost and improving emergency event response. Network calibration, (i.e., pump head and energy curve calibration, valve calibration, valve closure analysis, etc.) is an essential first step toward the development of a digital twin and real-time operational analysis. A typical drinking water network contains a large number of isolation valves (~103), many of which are often required to be manually closed to restrict or redirect flows (e.g., to perform maintenance operations on parts of the network). Some of these closed valves may remain closed (often unintentionally) without proper records and/or incorporation in a model. Such closed valves can become a major source of energy dissipation in the system and can potentially create water quality issues. Hillsborough County Water Resources Department (HCWRD) is adopting a state-of-the-art digital twin platform capable of real-time monitoring and analysis. As part of the development of the real-time network model, calibration steps were taken to improve model performance, and a valve closure analysis was performed when the real-time data indicated unusual energy loss in the system that could not be explained using a model without closed valves. The valve closure analysis was performed using a greedy search method utilizing real-time pressure data and network state information. Field exploration of the network performed by HCWRD discovered some of the actual closed valves very close to what was indicated by the valve closure analysis. Β© 2024 ASCE.
publications-4952 Article 2024 Enikeev R.M.; Topolnikov A.S.; Plaguta A.A.; Valiakhmetov L.V.; Zakirov V.F.; Silnov D.V.; Gibadullin A.R.; Petrenko S.N. The group optimization of operating modes of artificial lift oil wells Neftyanoe Khozyaystvo - Oil Industry 10.24887/0028-2448-2024-2-68-72 The article presents the algorithm of optimization of operating regimes of a group of oil wells, equipped by electrical submergible pumps and sucker rod pumps, with presence of limitations on the total liquid rate, oil rate and energy consumption. The optimization means the control of frequency of rotation of the submergible motor shaft, which enables torsion of pump impellers, or variation of number of swinging of pumping unit, which leads to change of speed of opening and closing of valve of a rod pump. In the case of periodic operation of the well the optimization is the variation of pumping and storage periods, which change each other during periodic turning on and off of the pump. During optimization the algorithm selects the combination of parameters of well operation in the way to achieve for wells in total the maximum oil rate at fixed liquid rate or energy consumption; minimum specific energy consumption or minimum total energy consumption at fixed oil or liquid rate. The specialty of the algorithm is the taking into account the change of the wells parameters, such as reservoir and bottomhole pressure, water cut, well head pressure and liquid volume rate in time, which enables to specify the effect of optimization for a horizon of several months accounting for interaction between wells and their influence on the ground infrastructure. Information is provided on pilot field tests of group optimization of wells at one of the fields of Bashneft-Dobycha LLC to diminish the specific energy consumption preserving the total oil rate. Β© 2024, Neftyanoe Khozyaistvo. All rights reserved.
publications-4953 Book chapter 2024 Bhunia G.S.; Shit P.K. Big Data Analysis for Sustainable Land Management on Geospatial Cloud Framework Environmental Science and Engineering 10.1007/978-3-031-38004-4_1 The advancements of the 1980s led to the creation of various important technologies, including GPS and satellite imagery, which allowed for the sustainable management of land resources. This must be done while preserving sustainable landuse systems and confronting concerns such as climate change, water scarcity, and the risk of increasing erosion and productivity due to extreme weather events. In several areas, geospatial Big Data analytics is transforming the way firms’ function. Although there are many research workson geographic data analytics and real-time data processing of massive spatial data streams in the literature, only a few have covered the entire geospatial big data analytics and geospatial data science project lifecycle. Because of the volume, pace, and variety of the data being analysed, big data analysis differs from typical data analysis. In comparison to conventional data analysis projects, geospatial data science initiatives are likely to be more difficult and require advanced technologies. The current study introduces a novel geographic big data mining and machine learning framework for geospatial data gathering, fusion, storage, management, processing, analysis, visualisation, and land resource modelling and evaluation. Any data science project that has a robust procedure for land resource data analysis and clear instructions for comprehensive analysis is always a positive. It also aids in estimating the amount of time and resources required early in the process to gain a good picture of the land resource challenges that must be overcome. Automation and the use of artificial intelligence (AI), the internet of things (IoT), drones, satellite imagers, and Big Data lay the foundation for a global β€_x009c_Digital Twin,β€_x009d_ which will aid in the development of site-specific conservation and management practices that will boost incomes and ensure the long-term sustainability of land use/land cover systems. Β© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
publications-4954 Article 2024 Shishlenin M.; Kozelkov A.; Novikov N. Nonlinear Medical Ultrasound Tomography: 3D Modeling of Sound Wave Propagation in Human Tissues Mathematics 10.3390/math12020212 The article aimed to show the fundamental possibility of constructing a computational digital twin of the acoustic tomograph within the framework of a unified physics–mathematical model based on the Navier–Stokes equations. The authors suggested that the size of the modeling area is quite small, sound waves are waves of β€_x009c_smallβ€_x009d_ disturbance, and given that a person consists of more than 60% water, human organs can be modeled using a liquid model, taking into account their density. During numerical experiments, we obtained the pressure registered in the receivers that are located on the side walls of the tomograph. The differences in pressure values are shown depending on the configuration of inclusions in the mannequin imitating internal organs. The results show that the developed technology can be used to probe the human body in medical acoustic tomographs and determine the acoustic parameters of the human body to detect neoplasms. Β© 2024 by the authors.
publications-4955 Article 2024 Swaminathan D.; Rajagopalan A.; Nidumolu V.; Alroobaea R.; Kotb H.; Aboras K.M.; Elrashidi A. ODTRA-Based Task Offload Optimization for Industrial Internet of Things: Improving Efficiency and Performance With Digital Twins and Metaheuristic Optimization IEEE Access 10.1109/ACCESS.2024.3385636 The Industrial Internet of Things (IIoT) is the recent innovation that had revolutionized the industries by enabling interconnected devices and systems to exchange intelligent data. However, implementing and operating such IIoT systems have various challenges. This article addresses those challenges pertained to task offloading in IIoT in which the resource-intensive tasks are transmitted and executed on remote cloud servers. To optimize the task offloading decisions this work propose the integration of Digital Twins, which are the computer-generated replicas of physical objects. By using the functionalities of Digital Twins along with real-time monitoring, and metaheuristic optimization algorithms this work presents a task offloading model for IIoT. Through this combined framework, the proposed model attempts to minimize the task execution time by considering the server capacity, bandwidth constraints, and device power consumption. The proposed Offloading with Digital Twins and Raindrop Algorithm (ODTRA) algorithm that is based on the water cycle metaphor and the Probabilistic Recursive Local (PRL) search algorithm had efficiently optimizes offloading performance which was proven through different experiment simulation and analysis. Β© 2013 IEEE.
publications-4956 Article 2023 Bonilla C.; Brentan B.; Montalvo I.; Ayala-Cabrera D.; Izquierdo J. Digitalization of Water Distribution Systems in Small Cities, a Tool for Verification and Hydraulic Analysis: A Case Study of Pamplona, Colombia Water (Switzerland) 10.3390/w15213824 Digitalization in water networks is essential for the future planning of urban development processes in cities and is one of the great challenges faced by small cities regarding water management and the advancement of their infrastructures towards sustainable systems. The main objective of this study is to propose a methodology that allows water utilities with limited budgets to start the path toward the digitalization and construction of the hydraulic model of their water distribution networks. The small city of Pamplona in Colombia was used as a case study. The work explains in detail the challenges faced and the solutions proposed during the digitalization process. The methodology is developed in six phases: an analysis of the cadastre and existing information, the creation and conceptualization of the base hydraulic model, the development of the topography using drones with a limited budget, an analysis of water demand, the development of a digital hydraulic model, and a hydraulic analysis of the system. The product generated is a tool to assess the overall performance of the network and contributes to the advancement of SDG-6, SDG-9, and SDG-11. Finally, this document can be replicated by other cities and companies with similar characteristics (e.g., limited size and budget) and offers an intermediate position on the road to digitalization and the first steps towards the implementation of a digital twin. Β© 2023 by the authors.
publications-4957 Conference paper 2024 Amir M.M.; Wei T.N.; Bahrim R.K.; Piah M.B.M.; Tewari R.D.; Kechut N.; Razak A.A.; Manaf N.A.; Castillano E. Digital Twin for Oil Rim Management Using Early Warning System and Exception Based Surveillance, Offshore Malaysia Offshore Technology Conference Asia, OTCA 2024 10.4043/34863-MS Effectively managing oil rim reservoirs poses significant challenges due to uncertainties in predicting oil rim movements. This leads to production losses, high gas-to-oil ratios (GOR), increased water cut, and the risk of losing the oil rim. To address these issues, a proof of concept for an early warning system was developed, utilizing innovative data analytics techniques and leveraging well data, such as pressure and temperature measurements, to detect changes in water cut as an indicator of oil rim movement. The workflow involves data extraction, pre-processing, regime detection, trend analysis, and exception-based surveillance with alerts. A selected oil-producing well with ample flow line pressure, temperature, and well test data was used to test the workflow. Pipesim modeling indicated a clear correlation between temperature and water cut, validated through sensitivity analysis at different GOR levels. The technology provides an integrated workflow covering data management, analytics, an event detection alert algorithm, and a visualization dashboard. It is not intended to replace existing oil rim management tools but serves as an additional decision support tool when reliable models are unavailable. The early warning system presented in this paper addresses the limitations of current methods by offering a proactive approach to detect fluid movements and mitigate production losses. By integrating novel data analytics techniques and utilizing readily available well data, operators can make informed decisions and take timely actions, improving the efficiency and effectiveness of oil rim management in petroleum operations. This technology serves as a valuable decision support tool in the absence of reliable models, ultimately enhancing the overall performance of oil rim management strategies. Copyright Β© 2024, Offshore Technology Conference.
publications-4958 Conference paper 2024 Zhang Z.; Yang W.; Wang L.; Liang T.; Liu Z. Construction and Simulation of Digital Twin Model and Electrolyzer Wide Power Adaptation Model for Alkaline Electrolytic Water Hydrogen Production Springer Proceedings in Physics 10.1007/978-981-99-8631-6_28 Combining the framework of digital twin and the working mechanism of electrolytic water hydrogen production, a digital twin model of alkaline electrolytic water hydrogen production is constructed, and the influence of electrolyzer cell voltage by temperature and pressure is analyzed according to the mathematical model of electrolytic water hydrogen production. In order to improve the adaptability of the electrolyzer of the hydrogen production system to the power fluctuation of renewable energy input, a wide power adaptation model with multiple electrolyzers sharing one set of gas-liquid handling device is proposed. The case study shows that by reasonably selecting the electrolyzer model of the hydrogen production system, the use of the wide power adaptation model can effectively adapt to the fluctuation of wind power and improve the adaptation capability of the electrolyzer to wide power fluctuations. Β© 2024, The Author(s).
publications-4959 Conference paper 2024 Pehlken A.; Davila R.M.F.; Dawel L.; Meyer O. Digital Twins: Enhancing Circular Economy through Digital Tools Procedia CIRP 10.1016/j.procir.2024.01.082 In the drive towards sustainable design, the push for products with greater longevity, reparability, and recyclability has never been more crucial. Central to this is the integration of eco-design principles within manufacturing processes. However, there is a gap: manufacturers lack both standardized processes and digital tools to support them, even though the promising digital product passport largely focuses on product lifespan. Key Performance Indicators (KPIs) are paramount, serving as benchmarks for both the manufacturing process and environmental sustainability of a product. These KPIs encompass factors like energy, water, compressed air, and material resource consumption. To emphasize the importance of these metrics, Europe is vulnerable to supply disruptions due to its high dependence on raw materials from non-EU countries. This paper discusses the state of the art of digital twins and presents a digital shadow - a comprehensive digital tool design to support manufacturers during the product design phase. Drawing from a case study in the automotive sector, this tool not only aligns with recycling and sustainability objectives but also mitigates risks associated with raw material dependencies. Β© 2024 Elsevier B.V.. All rights reserved.
publications-4960 Article 2024 Zaitsev A.A.; Murashev P.A.; Koryakovtsev A.V.; Tsymai D.V. Simulation Modelling of Technological Processes in Power and Utility Supply and Environmental Management at Chermk of Pao Severstal Metallurgist 10.1007/s11015-024-01629-1 The presented simulation model for technological processes in power and utility supply and environmental management at CherMK of PAO Severstal, based on AnyLogic software, was discussed. The primary objectives included forecasting the needs for various energy resources over a specified period, considering the strategic business plan, energy balances, and CO2 emissions calculator. The model also aimed to simulate the volumes of CO2 emissions for all equipment considered, calculate the price of energy resources, and estimate potential production losses. The simulation model incorporated various energy resources, such as natural gas, compressed air, oxygen, water, electric power, coke oven gas, blast furnace gas, and converter gas. Equipment operation was depicted based on the maintenance, repair, and operation (MOR) schedule. This developed simulation model serves as a tool for modeling variations in the volume of raw materials or finished products. It can be used to create portfolios for strategic investment programs, explore development scenarios, and determine optimal values for consumed and produced resources. Furthermore, it helps to assess the need to purchase appropriate resources and calculate economic indicators. The model allows energy balances to be generated, along with visualizing variations in CO2 emissions levels with the introduction of new production facilities. Β© Springer Science+Business Media, LLC, part of Springer Nature 2024.