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-4781 Article 2024 Dai Y.; He Y.; Zhao X.; Xu K. Testing method of autonomous navigation systems for ships based on virtual-reality integration scenarios Ocean Engineering 10.1016/j.oceaneng.2024.118597 Autonomous navigation system is a pivotal component of intelligent ships, with related research advancing rapidly and yielding significant results in recent years. Testing technology has emerged as a fundamental requirement for validating the practicality of these research outcomes. To address the issues of authenticity, safety, and cost-effectiveness in traditional testing methods, this paper first combines the real sailing environment with digital simulation technology, innovatively constructing a virtual-reality fusion testing scene. Subsequently, based on the key scientific and technological issues in the research of autonomous navigation of ships, the testing method, content, and indicator system of the autonomous navigation system for ships are proposed. Furthermore, a testing program has been developed using the C++ programming language and Qt5 GUI program, which connects the test system and the autonomous navigation system. Finally, the North Channel of the Yangtze River Estuary and the autonomous navigation system in such waters are selected as the specific test scenarios and objects, and the test experiments are carried out aiming at the proposed test indicators. The results show that the testing method can fulfill the requirements for establishing test standards and systems, and is expected to promote the development of testing technology for intelligent ships and the practical application of autonomous navigation research outcomes. Β© 2024 Elsevier Ltd
publications-4782 Article 2024 Kim M.; Zaman M.; Jang E.; Nakhla G.; Ward M.; Gutierrez O.; Willis J.; Walton J.; Santoro D. Experimental investigation on hydrogen sulfide production, wastewater characteristics and microbial ecology profiles in anaerobic sewer lines using a sewer physical twin Journal of Environmental Chemical Engineering 10.1016/j.jece.2024.111965 Over the past decade, considerable emphasis has been placed on the development of digital twins for water and wastewater applications. However, physical twins – if properly designed - offer a higher quality comparison with the real system that cannot be achieved by a digital twin. This paper focuses on experimental investigations of hydrogen sulfide formation in anaerobic sewer lines through biochemical pathways using a sewer physical twin (SPT). The SPT was conceived to independently control sewage residence time, horizontal pipe velocity, and other important sewer parameters (scouring velocity, well shear, etc.) able to affect hydrogen sulfide production, wastewater characteristics and microbial ecology distribution both in the bulk flow and in the biofilm. The SPT, constructed with three identical parallel lines to allow robust data collection and benchmarking of in-sewer treatment strategies for odor control, was operated for over 200 days using real sewage. Longitudinal wastewater fractionation studies and microbial ecology profiles confirmed the presence of active hydrolysis and fermentation processes, with a mature biofilm. The activity of sulfate-reducing bacteria (SRB) decreased as the biofilm thickness decreased, while the concentration of hydrogen sulfide followed the opposite trend. The addition of ferrous ions effectively controlled the concentration of dissolved sulfide, resulting in the formation of ferrous sulfide. Distinct bacterial populations were identified for the bulk wastewater and the biofilm, with SRB and methanogens being predominant in the biofilm and sulfur-oxidizing and facultative bacteria more abundant in the bulk wastewater. In conclusion, this study successfully demonstrated that the SPT accurately mimics the observed trends in full scale associated with sulfide emissions thus providing data with high reproducibility, satisfactory accuracy, and novel insights into system performance. It is anticipated that the SPT will play a crucial role also in laboratory and pilot studies associated in adjacent areas including sewer epidemiology and GHG emissions to allow high quality data collection under a variety of operating conditions and sewer process dynamics. © 2024 The Authors
publications-4783 Article 2024 Testasecca T.; Maniscalco M.P.; Brunaccini G.; AirΓ² Farulla G.; Ciulla G.; Beccali M.; Ferraro M. Toward a Digital Twin of a Solid Oxide Fuel Cell Microcogenerator: Data-Driven Modelling Energies 10.3390/en17164140 Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R2 score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions. Β© 2024 by the authors.
publications-4784 Article 2024 Rizwana S.; Hazarika M.K. Study of the Soaking Process of a ready-to-eat rice of Assam (Komal Chaul): A Mechanistic and a Machine Learning Based Approach for spectra-based Estimation of Endpoint Food Biophysics 10.1007/s11483-024-09852-8 This research article focuses on two approaches to study the hydration behavior of a low amylose rice of Assam for the manufacture of a no-cooking rice known as Komal Chaul. Fick’s second law was used to study the diffusion of water during the soaking of brown Chokuwa rice. A machine learning (ML) approach to calibrate NIR spectral data with moisture values. ML models like PCR, and PLS were used for regression, and classification models like Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Classification and Regression Tree, Naïve Bayes, Support Vector Machines, and Random Forest Classifiers were used. The concentration-dependent diffusion coefficients as estimated by applying Fick’s model were found to lie within the range of 2.83 ×10-11 m2/s - 7.92 ×10-11 m2/s. The ML regression models didn’t work well however, the spectral data endpoint classification on a target moisture value of 30% during soaking showed that the Random Forest (RF) classifier predicted the best with classification accuracy close to 0.90. Mechanistic models help us understand the physical phenomenon and the advancement of numerical tools and concepts of digital twins for process operations have led to the use of a sensor-based approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
publications-4785 Article 2024 Rivas A.; Delipei G.K.; Davis I.; Bhongale S.; Hou J. A system diagnostic and prognostic framework based on deep learning for advanced reactors Progress in Nuclear Energy 10.1016/j.pnucene.2024.105114 To meet the projected energy demand in the next 30 years, advanced reactor designers are looking to maximize system capacity factor to increase economic competitiveness. To maximize capacity factor, operators must minimize the system downtime due to forced shutdowns from transients. To accomplish this, the objective of this work is to develop a System level Diagnostic/Prognostic (SDP) framework based on state-of-the-art Machine Learning Models (MLM) to support operators by detecting and diagnosing anomalous behaviors and predicting the onset of exceeding safety limits. This Accident Management Support Tool (AMST) consists of a Long Short Term Memory Autoencoder (LSTM-AE) model to identify if an anomaly is present, a Convolutional Neural Network (CNN) diagnostic model to characterize that anomaly, and a Long Short Term Memory Dense layered (LSTM-D) model to provide Remaining Useful Life (RUL) predictions. These models were trained on data from various system wide transients that occur at different power levels and at different rates using a digital twin of the Xe-100 Pebble-Bed High Temperature Gas Reactor (PB-HTGR) developed in SimuPACT. This framework's capability is showcased with a water ingress constant reactivity insertion event that caused the reactor outlet temperature to exceed its safety threshold. This study showed that as the transient progresses, the LSTM-AE detects an anomaly within 20 s of event initiation, the CNN characterization stays steady throughout the transient with a 60 s delay, and the LSTM-D is able to accurately predict the time to threshold as the reactor outlet temperature approaches its safety threshold 720 s after fault initiation. Β© 2024 Elsevier Ltd
publications-4786 Article 2024 Daneshgar S.; Borzooei S.; Debliek L.; Van Den Broeck E.; Cornelissen R.; De Langhe P.; Piacezzi C.; Daza M.; Duchi S.; Rehman U.; Nopens I.; Torfs E. A dynamic compartmental model of a sequencing batch reactor (SBR) for biological phosphorus removal Water Science and Technology 10.2166/wst.2024.231 Bioreactors are usually modelled as continuous stirred tank reactors (CSTRs) or CSTRs connected in series (Tanks-In-Series configuration). In large systems with non-ideal mixing, such approaches do not sufficiently capture the complex hydrodynamics, leading to model inaccuracies due to the lumping of spatial gradients. Highly detailed computational fluid dynamics (CFD) models provide insight into complex hydrodynamics but are computationally too expensive for flow-sheet models and digital twin applications. A compartmental model (CM) can be a middle-ground by providing a more realistic representation of the hydrodynamics and still being computationally affordable. However, the hydrodynamics of a plant can be very different under varying flow conditions. Dynamic CMs can capture these changes in an elegant way. So far, the application of CMs has been limited mostly to continuous flow systems. In this study, a dynamic CM of a sequencing batch reactor (SBR) is developed for a bio-P removal process. The SBR comes with challenges for CM development due to its distinct operational stages. The dynamic CM shows significant improvements over the CSTR model (using the same biokinetic parameters) for dissolved oxygen and phosphate predictions reducing the need for model recalibration that can lead to over-fitting and limited extrapolation capability of the model. Β© 2024 The Authors.
publications-4787 Article 2024 Sanfilippo A.; Kafi A.; Jovanovic R.; Shannak S.; Ahmad N.; Wanik Z. Sustainable energy management for indoor farming in hot desert climates Sustainable Energy Technologies and Assessments 10.1016/j.seta.2024.103958 Achieving food self-sufficiency in hot desert climates requires year-round farming, which is challenging due to extreme weather, water scarcity, and limited arable land. Indoor soil-less farming can mitigate these issues by reducing land and water use but increases operational complexity and electricity needs for cooling, impacting economic sustainability. This paper presents a resource management system using Artificial Intelligence of Things (AIoT) to simplify operations and optimize resources, alongside techno-economic analysis for economic viability. A case study on hydroponic tomato farming in hot deserts demonstrates that beyond a crop yield threshold (24.022 kg/m2), significantly more energy is required for marginal yield increases (e.g., 18% more electricity for a 0.35% yield increase). Despite higher energy use, the techno-economic analysis shows a net present value increase even with unsubsidized electricity. Thus, optimizing energy alongside water and nutrients is crucial for economic sustainability in indoor farming. Β© 2024 Elsevier Ltd
publications-4788 Article 2024 Karnik N.; Abdo M.G.; Estrada-Perez C.E.; Yoo J.S.; Cogliati J.J.; Skifton R.S.; Calderoni P.; Brunton S.L.; Manohar K. Constrained Optimization of Sensor Placement for Nuclear Digital Twins IEEE Sensors Journal 10.1109/JSEN.2024.3368875 The deployment of extensive sensor arrays in nuclear reactors is infeasible due to challenging operating conditions and inherent spatial limitations. Strategically placing sensors within defined spatial constraints is essential for the reconstruction of reactor flow fields and the creation of nuclear digital twins. We develop a data-driven technique that incorporates constraints into an optimization framework for sensor placement, with the primary objective of minimizing reconstruction errors under noisy sensor measurements. The proposed greedy algorithm optimizes sensor locations over high-dimensional grids, adhering to user-specified constraints. We demonstrate the efficacy of optimized sensors by exhaustively computing all feasible configurations for a low-dimensional dynamical system. To validate our methodology, we apply the algorithm to the Out-of-Pile Testing and Instrumentation Transient Water Irradiation System (OPTI-TWIST) prototype capsule. This capsule is electrically heated to emulate the neutronic effect of the nuclear fuel. The TWIST prototype that will eventually be inserted in the Transient Reactor Test Facility (TREAT) at the Idaho National Laboratory (INL), serves as a practical demonstration. The resulting sensor-based temperature reconstruction within OPTI-TWIST demonstrates minimized error, provides probabilistic bounds for noise-induced uncertainty, and establishes a foundation for communication between the digital twin and the experimental facility. Β© 2001-2012 IEEE.
publications-4789 Article 2024 Espejo F.; Molina J.-L.; Zazo S.; MuΓ±oz-SΓ΅nchez R.; Patino-Alonso C. Low-cost β€_x009c_bufferβ€_x009d_ structural measure for flooding risk reduction in irrigation reservoirs Journal of Hydrology 10.1016/j.jhydrol.2024.131017 Irrigation reservoirs (IRs) largely located off-stream are the great forgotten hydraulic infrastructures in comparison with the deeply studied dams. This research is mainly aimed at reducing the risk associated with IR-Breach floods in irrigation ponds through a novel and low-cost structural measure, named β€_x009c_bufferβ€_x009d_, which is in the risk element. This guarantees a maximum flood lamination at a breaching event as well as the minimization hydrograph peak. Therefore, a reduction of the potential hazard associated to these undesired events is produced. Methodology starts with the identification of flood hazard generating causes; then, a detailed/comprehensive numerical hydraulic simulation framework is performed through synthetic models of IRs with an analysis based on breaching events and slope β€_x009c_bufferβ€_x009d_ thresholds. Finally, the decision-making stage is developed. This crucial one involves the simulation, adaptation, and optimization of both the IR and the β€_x009c_bufferβ€_x009d_ to the lowest risk location. This is inspired/supported by the implementation of an IR-breach Digital Twin approach that involves the most likely breaching events modelling with/without β€_x009c_bufferβ€_x009d_ solution. In this way, the optimal design of the β€_x009c_bufferβ€_x009d_ as structural solution completely adapted to the IR site is achieved. Thus, it is possible to design an effective and economical measure for the identified hazard. This approach has been exemplified in two large Spanish IRs (320,000 and 201,000 m3 roughly). The general efficiency of β€_x009c_bufferβ€_x009d_ solution is found to be at 20 % of generating flooding risk reduction, with a maximum longitudinal slope of only 0.50 % (flat terrain), and without the need to implement additional structural measures. This approach may be taken as a soft and adaptive engineering solution for the optimal and safe design of IRs. Β© 2024 The Author(s)
publications-4790 Conference paper 2024 Liu J.; Zhang M.T. Beyond Limit: A Service Design Intervention to Enhance Sustainable Awareness in Sanyang Wetland Community Frontiers in Artificial Intelligence and Applications 10.3233/FAIA240059 The Sanyang Wetland, once a vital agricultural resource, faces severe ecological challenges due to persistent reclamation efforts and local activities. Water quality has deteriorated, leading to eutrophication and a loss of wetland attributes. This paper proposes an integrated approach to address these issues, combining ecological restoration with sustainable education. We introduce two key components: an artificial wetland in a greenhouse and education for sustainability. The wetland family park setup, including artificial floating islands, serves as a practical example within the digital twin application of this approach. It engages the public, particularly teenagers, in wetland conservation through an immersive educational experience. This study addresses ecological and educational dimensions simultaneously. It emphasizes sustained research and collaboration for impactful results, offering insights applicable in various contexts. This work presents a promising path to enhance wetland ecosystems, raise awareness, and empower communities to contribute to their preservation. Β© 2024 The Authors.