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	Abstract
publications-5181	Conference paper	2022	Troupiotis-Kapeliaris A.; Zygouras N.; Kaliorakis M.; Mouzakitis S.; Tsapelas G.; Artikis A.; Chondrodima E.; Theodoridis Y.; Zissis D.	Data Driven Digital Twins for the Maritime Domain	Progress in Marine Science and Technology	10.3233/PMST220087	Digital twins are computational models that replicate the structure, behaviour and overall characteristics of a physical asset in the digital world. In the maritime domain, conventional approaches have relied on mathematical modeling (e.g., linearised equations of motion) and heavy computations for estimating ship resistance and propulsion, seakeeping and maneuverability and overall hull form optimization, treating the vessel as a point body. For instance, the ability to predict a vessel's future track in confined or congested waters presents a significant challenge due to the fact that as time passes, these models often fall out of sync with their digital counterparts due to changes that happen to the ship (e.g., foulding affecting maneuverability). In addition to this, mostly due to computational resources required, in real world deployments models are simplified, thus reducing their overall prediction accuracy. In our work, we implement AI-enabled coupled abstractions of the asset-twin system, which rely on machine learning methods for constant learning of the evolving over time behavior of a vessel based on historical trip data and information related to vessel's structure and loading capacity. The evaluation results indicate that the inclusion of vessel and journey specific information is beneficial for the predictions. Β© 2022 The authors and IOS Press. All rights reserved.
publications-5182	Article	2022	Brooks S.; Roy R.	Design and complexity evaluation of a self-cleaning heat exchanger	International Journal of Heat and Mass Transfer	10.1016/j.ijheatmasstransfer.2022.122725	Self-engineering (SE) systems have valuable abilities to register and respond to lost function and return it. A self-cleaning (SC) system was designed for effective automated cleaning of a heat exchanger (HX) fouled by brewing wort. The system uses temperature outputs in a Digital Twin (DT) simulation and a controller to identify when fouling occurs and trigger a cleaning response. This paper utilises the SE complexity framework and investigates the effectiveness of different complexity designs. Three levels are created for each factor of the framework (repeatability, redundancy and self-control). For repeatability, the number of cleaning cycles was changed, while for redundancy, the flow rate was changed. For self-control, the cleaning mechanism was changed; pulses and foam balls were both used as the cleaning mechanisms. Balls were used to block pipes and redirect flow. An orthogonal matrix is used to reduce the number of experiments. SC effectiveness was measured for each cleaning cycle, and the results were evaluated. Cleaning with the max flow rate (0.21 kg sβ’1) and using balls and pulses together provided the most effective cleaning, while the worst was with a low flow rate (0.09 kg sβ’1) and just pulses. Further experiments verified these results and showed that better cleaning settings could lower water use in cleaning. A longer simulation demonstrated when the SC system would be stopped. Β© 2022
publications-5183	Article	2022	Wei Y.; Law A.W.-K.; Yang C.	Real-Time Data-Processing Framework with Model Updating for Digital Twins of Water Treatment Facilities	Water (Switzerland)	10.3390/w14223591	Machine learning (ML) models are now widely used in digital twins of water treatment facilities. These models are commonly trained based on historical datasets, and their predictions serve various important objectives, such as anomaly detection and optimization. While predictions from the trained models are being made continuously for the digital twin, model updating using newly available real-time data is also necessary so that the twin can mimic the changes in the physical system dynamically. Thus, a synchronicity framework needs to be established in the digital twin, which has not been addressed in the literature so far. In this study, a novel framework with new coverage-based algorithms is proposed to determine the necessity and timing for model updating during real-time data transfers to improve the ML performance over time. The framework is tested in a prototype water treatment facility called the secure water treatment (SWaT) system. The results show that the framework performs well in general to synchronize the model updates and predictions, with a significant reduction in errors of up to 97%. The good performance can be attributed particularly to the coverage-based updating algorithms which control the size of training datasets to accelerate the ML model updating during synchronization. Β© 2022 by the authors.
publications-5184	Article	2022	Zhou H.; Li K.; An Y.; Lou C.	Research progress on monitoring three-dimensional temperature distributions in coal-fired boilers and industrial furnaces; [η‡ƒη…¤η”µη«™ι”…η‚‰ε_x008f__x008a_ε·¥δΈ_x009a_η‘η‚‰δΈ‰η»΄η‡ƒηƒ§ζΈ©εΊ¦ε†εΈƒη›‘ζµ‹η ”η©¶θΏ›ε±•]	Clean Coal Technology	10.13226/j.issn.1006-6772.HK22081001	Under the background of carbon neutrality, the deep peak shaving and flexible operation of coal-fired generating units put forward urgent requirements for real-time monitoring of the three-dimensional combustion conditions in the furnace. This paper summarized the research progress of three-dimensional combustion temperature distribution monitoring of coal-fired power plant boilers and industrial furnaces. As for the radiation imaging model of combustion flame, the DRESOR method for directional radiation intensity calculation based on Monte Carlo method and the recent optimization of DRESOR method were mainly introduced, which laid a good foundation for improving the inversion accuracy of combustion temperature and inversion of the distribution of radiation characteristic parameters of combustion medium. The basic method to solve the simultaneous inversion problem of three-dimensional temperature field and radiation parameters is to reconstruct the temperature distribution in the furnace from the monochromatic radiative intensity images by Tikhonov regularization method. Then the radiative properties of the particle medium are updated with optimization method and solved iteratively. Recently, there have been new developments in the inversion algorithm. The new algorithm can be divided into three stages. Firstly, assuming uniform distribution of absorption coefficient, scattering coefficient and reflectivity of the furnace wall, the optimal radiation parameters and temperature distribution inside the furnace are obtained by the optimization solution. Secondly, on the basis of stage 1, the absorption and scattering coefficients in the furnace are set as second-order polynomial fitting distributions in spatial coordinates, and the walls are still set with uniform reflectivity to further optimize the iterative calculation. Finally, on the basis of the convergence of the calculation in stage 2, the second-order polynomial distribution of the reflectivity of the furnace wall in wall coordinates is further assumed, and then the calculation is optimized iteratively. The latest development of the inversion algorithm has obtained the reconstruction result of the combustion temperature with reconstruction error within 1%, and realizes the reconstruction of the relative distribution of pulverized coal concentration in the furnace based on the radiative properties. The monitoring systems of three-dimensional temperature field in the furnace has been industrially applied in the combustion monitoring of 200, 300 and 600 MW coal-fired power plant boilers and further expanded to oil-fired or gas-fired industrial kilns such as walking furnace in rolling mill, tubular furnace in petrochemical plant, single burner combustion furnace and cracking furnace in chemical plant, showing a good application prospect. In the future, machine learning and artificial intelligence theory need to be adopted to further improve the efficiency of solution of the coupled reconstruction problem, combined with three-dimensional real-time and dynamic modeling of furnace conditions and thermal system, to realize real-time monitoring and diagnosis of the parameters of the three-dimensional furnace conditions distribution (furnace atmosphere, particulate matter, pollutants, furnace heat load, furnace wall heat load distribution, etc.) and modeling and prediction of the parameters of the distribution of hydrodynamic and thermal systems in the boiler water wall, to further build a multi-timescale big data-driven digital twin system for coal-fired generating units, contributing to the development of the smart boiler and furnace optimization control system. Β© 2022 China International Book Trading Corp. (Guoji Shudian). All rights reserved.
publications-5185	Article	2023	Domingo J.; Iranzo A.; Arnanz D.; Srivastava A.K.; Groombridge M.; Hansen J.	CFD Analysis of Mixing Process of Detergents in Rotational and Displacement Vessels	Processes	10.3390/pr11010029	As part of the European Commission research project DIY4U focused on the development of machinery to be installed in supermarket allowing customers to define their customized detergent according to their needs. These machines will mix the detergent components (surfactant, fatty acid, water, perfume, etc.) already in the detergent canister as sold to consumers. To avoid long waiting times for customers, and to obtain a product with good quality and consistency, mixing must be very efficient. A mixing process with rotation and displacement by means of rotating the canister around an axis below the canister bottom has been checked by means of Computational Fluid Dynamics (CFD) tools after validation of one case with lab results. This is a new approach for liquid detergents, as commonly is a powder detergent production process. The mixing process has been simulated for 39 different combinations of components mass fraction percentages and the mixing quality observed during the mixing period. A response surface obtained from these simulations has been developed to be included in a Digital Twin, this being a task within this DIY4U project. The results show that this system is very efficient, taking a few seconds to develop a complete mixing. Also, the mixing time differences are quite small, requiring all customers to wait just few seconds independently of their detergent formulation. Β© 2022 by the authors.
publications-5186	Conference paper	2023	Kasat P.; Kulkarni M.; Gundeti K.; Kangale K.; Deshmukh B.B.; Mistry R.D.	Developing a digital twin of centrifugal pump for performance evaluation	Materials Today: Proceedings	10.1016/j.matpr.2022.09.574	Performance Evaluation of a physical asset can be made effortless with a headway to its virtual replica β€_x009c_A Digital Twinβ€_x009d_. This article focuses on a thorough review of a digital twin of a centrifugal pump for performance evaluation. An exception to conventional performance evaluation method of a centrifugal pump can be made by its digital twin. This also proves beneficial for higher capacity pumps with appropriate scaling of the twin as the variant changes. A digital twin system may be useful to eliminate the setup and installation time, and the capital cost to develop product specific test bench. A twin which can be simulated and evaluated using MATLAB can be developed. The process that was used for creating a digital twin of centrifugal pump involves steps like selection of parameters to be measured, selection of microcontroller for data acquisition, IOT cloud interfacing using ThingSpeak, simulation of MATLAB model of centrifugal pump in ideal environment for flow rate measurement using Simscape. For obtaining the real-time data for flow rate of the centrifugal pump, a water flow sensor was attached to the pump and the sensor data was obtained and uploaded to ThingSpeak using ESP32 WiFi module. The ideal data for the flow rate of the centrifugal pump was obtained by creating a SimScape model of the centrifugal pump and flow sensor arrangement and simulating it in ideal environment. This will include, receiving sensor data to and during the operation of the pump and monitored on ThingSpeak IOT platform. The result of this entire process will be a statistical twin of a centrifugal pump for its performance evaluation based on the performance parameter- flow rate. Β© 2023
publications-5187	Article	2022	Matheri A.N.; Mohamed B.; Ntuli F.; Nabadda E.; Ngila J.C.	Sustainable circularity and intelligent data-driven operations and control of the wastewater treatment plant	Physics and Chemistry of the Earth	10.1016/j.pce.2022.103152	Rapid urbanization, population increase, emerging contaminants and increasing water scarcity have put a major constraint on the wastewater treatment system. Scarcity of water is steering current way of water recycle, and the drive focus towards resource recovery. Zero waste pathway in circular bioeconomy can bring transformation of wastewater commercialization by adding value with resource recovery. The complex biological reactions, unforeseen microbial behaviours, lack of reliable on-line instrumentation, complex modelling, lack of visualize techniques, low-quality industrial measurements and highly time-varying intensive data-driven operations call for the intelligence techniques and operations. The study is a review of sustainable circularity and intelligent data-driven operations and control of the wastewater treatment plant. Water surveillance and monitoring, circular economy and sustainability, automation pyramid, digital transformation, artificial intelligence, data pipeline, digital twin, data mining, and data-driven visualization, cyber-physical systems and water-energy-health management were reviewed. The deployment of the digital systems has evidently proven to bridges the gap between the data-driven soft sensor, operation and control systems in WWTP. Accurate prediction of the WWTP variables can support process design and control, reduce operation cost, improve system reliability, predictive maintenance and troubleshooting, increase water quality, increase stakeholder's engagement and endorse optimization of the plant performance. This procures the best compliance with international standards and diversification. The inclusion of life cycle environmental or cost management technologies in optimization models is an interesting pathway towards sustainable water treatment in-line with sustainable development goals, circular bioeconomy and industry 4.0. Β© 2022 Elsevier Ltd
publications-5188	Article	2022	Patriarca R.; Simone F.; Di Gravio G.	Modelling cyber resilience in a water treatment and distribution system	Reliability Engineering and System Safety	10.1016/j.ress.2022.108653	Considering the increasing introduction of cyber-physical systems in modern industrial plants, the analysis of systemsβ€™ performance pushes for developing a cyber resilience perspective to complement a traditional physical resilience assessment. This point of view becomes central for critical infrastructures, considering the potential societal and economic consequences a disruption may have. This work provides a cyber-resilience simulation-based assessment for a seawater desalination plant and its connected distribution system. For this purpose, a digital twin has been developed. It integrates a MATLAB/Simulink model of the reverse osmosis treatment plant with a georeferenced water distribution network designed in EPANET. Four stochastic cyber resilience metrics have been proposed and computed to assess the impact of a successful replay cyber attack. The results exemplify the benefits of cyber-physical simulations to understand the behavior of modern water treatment plants, to identify system's criticalities, and eventually to support decision making by identifying hotspots and prioritizing mitigating actions. Β© 2022 Elsevier Ltd
publications-5189	Article	2022	Li D.; Lyu B.; Wu W.; Li H.	Status and prospect of offshore platform on-site monitoring technology; [ζµ·ζ΄‹εΉ³ε_x008f_°η_x008e_°ε_x009c_Ίη›‘ζµ‹ζ_x008a_€ζ_x009c_―η_x008e_°η_x008a_¶ε_x008f__x008a_ε±•ζ_x009c_›]	China Offshore Oil and Gas	10.11935/j.issn.1673-1506.2022.02.019	On-site monitoring is an important means for offshore platform safety assurance and design verification. With the increase of the number and types of offshore structures, offshore platform on-site monitoring has attracted more and more attention. Firstly, the research status of ocean environmental loads monitoring and platform structure response monitoring are systematically described, the principle and applicability of each monitoring technology are summarized, and the limitations of marine environment and platform structure response monitoring technologies at the present stage and the key research directions in the future are pointed out in this paper. Secondly, the collection, transmission and data analysis methods of various monitoring data are summarized, and it is pointed out that in the future, the on-site monitoring data analysis of offshore platforms will directly conduct real-time evaluation of monitoring data on site through a large number of applications of new theories and algorithms to guide offshore safety operations, thus gradually reduce the dependence on land researchers. Finally, it is pointed out that with the progress of on-site monitoring hardware, software and analysis methods, as well as the application of emerging concepts such as big data, Internet of things, digital twins and Metaverse, the offshore platform monitoring technology will gradually develop towards standardization, deep-water and intelligentization. Β© 2022 Editorial Board of China Offshore Oil and Gas.
publications-5190	Conference paper	2023	Shkodyrev V.P.; Khokhlovskiy V.; Oleinikov V.	Building a Digital Twin for Local Heating Housing Services	Lecture Notes in Networks and Systems	10.1007/978-3-031-20875-1_11	The paper presents a digital twin of a local heating station with aΒ heat consumption loop of a building. It contains a model of the object of control in a PLC which is utilized to stabilize the temperature inside the building. The work discusses the development of the application for process control based on theΒ model loaded into a PLC. The distinguishing feature of the approach is based on code generation from a mathematical model of the control object created in MATLAB Simulink. An additional goal for the control system of the heating station deals with environmental conditions, namely, outdoor temperature. The model of a water-jet unit is built, simulated data are presented, and discussed, the control system is investigated, and the analysis of the data is carried out. The model is checked based on data for the city of St. Petersburg, Russia as an example. Implementation of the approach leads to overcoming risks and disadvantages of reactive organization of heating housing services, theΒ main of which are downtime of the heating supply process and life quality reduction for energy end-users. Β© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.