| publications-4821 |
Article |
2024 |
Menapace A.; Zanfei A.; Herrera M.; Brentan B. |
Graph Neural Networks for Sensor Placement: A Proof of Concept towards a Digital Twin of Water Distribution Systems |
Water (Switzerland) |
10.3390/w16131835 |
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Urban water management faces new challenges due to the rise of digital solutions and abundant data, leading to the development of data-centric tools for decision-making in global water utilities, with AI technologies poised to become a key trend in the sector. This paper proposes a novel methodology for optimal sensor placement aimed at supporting the creation of a digital twin for water infrastructure. A significant innovation in this study is the creation of a metamodel to estimate pressure at consumption nodes in a water supply system. This metamodel guides the optimal sensor configuration by minimizing the difference between estimated and observed pressures. Our methodology was tested on a synthetic case study, showing accurate results. The estimated pressures at each network node exhibited low error and high accuracy across all sensor configurations tested, highlighting the potential for future development of a digital twin for water distribution systems. Β© 2024 by the authors. |
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| publications-4822 |
Article |
2024 |
Serrao M.; Jauzein V.; Juran I.; Tassin B.; Vanrolleghem P. |
Hybrid modelling of nitrogen removal by biofiltration using high-frequent operational data |
Water Science and Technology |
10.2166/wst.2024.293 |
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In this research, a parallel hybrid model is presented for the simulation of nitrogen removal by submerged biofiltration of a very large-size wastewater treatment plant. This hybrid model combines a mechanistic and a machine learning model to produce accurate predictions of water quality variables. The models are calibrated and validated using detailed and quality-controlled operational data collected over a period of 3.5 months in 2020. The mechanistic model is a modified activated sludge model that describes the biological, physical and chemical processes taking place in a biofilm reactor based on the domain knowledge of these processes. A three-layer feed-forward artificial neural network with a rectified linear activation function that aims to reduce the mechanistic model’s residual error and then correct its output. The results show how the hybrid model outperforms and significantly reduces the size of the mechanistic model’s prediction errors of the effluent nitrate concentration from a relative mean error of 12% (mechanistic model) to 2% (hybrid model) during training. The error on nitrate simulations increases to 8% during hybrid model testing, still significantly lower than the error of the mechanistic model. These results support future operational applications of hybrid biofilm models, such as in digital twins. © 2024 The Authors. |
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| publications-4823 |
Article |
2024 |
Xu W.; Zhong W.; Zhou G.; Chen X.; Liu X.; Shi J. |
Optimization of air distribution and coal blending in pulverized coal boilers for high-temperature corrosion prevention based on POD reduced-order modeling |
Applied Thermal Engineering |
10.1016/j.applthermaleng.2024.123705 |
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High-temperature corrosion in coal-fired boilers poses a significant threat to safe operation. However, there is currently a lack of effective online monitoring and optimization methods for high-temperature corrosion. Therefore, this study proposes a novel approach to rapidly predict the distribution of chemical species and evaluate high-temperature corrosion degrees, along with optimizing operating conditions to prevent high-temperature corrosion. Firstly, a total of 564 Computational Fluid Dynamics (CFD) simulations are performed on a 330 MW tangentially fired boiler, covering various operating conditions, including coal blending, air distribution, boiler load, etc., to obtain a database of O2, CO, and H2S distributions within the boiler. Then a method based on Proper Orthogonal Decomposition (POD) and Support Vector Regression (SVR) is used to process the database to realize real-time prediction of boiler chemical species distribution, which is next utilized as inputs of a high-temperature corrosion evaluation to obtain in-situ corrosion degree distribution. Finally, Particle Swarm Optimization (PSO) is used to optimize coal blending and air distribution schemes, effectively reducing severe corrosion ratio from 36.34 % to 10.04 % in a typical case by improving the atmosphere near the water walls. This study thus provides a new perspective for online boiler diagnostics and digital twin construction, particularly by achieving online monitoring of high-temperature corrosion and optimizing operating conditions to prevent corrosion. Β© 2024 Elsevier Ltd |
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| publications-4824 |
Article |
2024 |
Barnes A.M.; Afroz M.M.; Shin Y.K.; van Duin A.C.T.; Li-Oakey K.D. |
Mapping TpPa-1 covalent organic framework (COF) molecular interactions in mixed solvents via atomistic modeling and experimental study |
Journal of Membrane Science |
10.1016/j.memsci.2024.122613 |
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Complex solvent environments continue to limit the widespread adoption of organic solvent nanofiltration (OSN) in many chemical industry applications. In this paper we employ a commercially available covalent organic framework (COF), TpPa-1, and force field models to molecularly map separation performance of TpPa-1 membrane in mixed solvents. To minimize time and length scale mismatch between atomistic modeling and experiments, solvent permeance was normalized with water in modeling and experimental results to enable direct comparison. Model outputs, such as organic solvent permeance and solute rejection rate, matched well with filtration results. Since the atomistic models assume that all mass transfer is via through-pore transport, the discrepancies between modeling and experimental results provide insights on the effect of linear polymer defects, adsorption and interstitial mass transfer on polycrystalline COF membrane performance. In sum, force field models can serve as digital twins of COF membranes to simulate separation processes while capturing the effects of COF structure, chemistry, and crystallinity on membrane performance in complex organic solvent environments. This approach will provide insight into future COF design and synthesis for persisting separation challenges. Β© 2024 Elsevier B.V. |
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| publications-4825 |
Article |
2024 |
Mohan A.; Franciosa P.; Dai D.; Ceglarek D. |
A novel approach to control thermal induced buckling during laser welding of battery housing through a unilateral N-2-1 fixturing principle |
Journal of Advanced Joining Processes |
10.1016/j.jajp.2024.100256 |
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Battery housing (BH) in modern electric vehicles must meet demanding functional requirements. The design and geometry of the BH become intricate to prevent damage during collisions and to ensure absolute impermeability to gases and water during operation. Moreover, in the pursuit of a lightweight BH, manufacturers rely on high-strength 6xxx aluminium alloys, posing significant challenges for the welding processes. It is estimated that up to 30 m of weld length is required during the construction of battery housings including joining the lid and under-shield to the main structural frame and joining the ribs to the frame for standard vehicles. Due to the increasing use of thin sheets for lightweighting the structure, thermal-induced buckling may occur and generate critical dimensional unconformities going beyond design tolerances. This underpins the need to optimise fixturing design to control thermal-induced buckling. This paper goes beyond the state-of-the-art β€_x009c_N-2-1β€³ approaches for fixturing thin and deformable parts and proposes the new principle of β€_x009c_unilateral N-2-1 fixturingβ€_x009d_. The driving idea is adding unilateral restraints to the direction of thermal contraction, which ultimately causes buckling; and, keeping the direction where the thermal expansion occurs in a free state. The methodology is based on three main steps: (1) physics-based modelling of parts and fixtures using a thermo-mechanical FEA simulation; (2) calibration of the weld heat source using metallographic data; (3) validation using optical scanning technology. The methodology was demonstrated during the laser beam welding of a high-strength aluminium 6xxx thin deformable lid to a rigid high-strength 6xxx aluminium extrusion frame. Results indicated that the thermal induced buckling deformation was reduced from 15 mm, when using the state-of-the-art fixturing approach, to approximately 2 mm with the proposed methodology. Β© 2024 The Author(s) |
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| publications-4826 |
Conference paper |
2024 |
Ren S.; Yang B.; Wang J.; Wang Y.; Narynbaeva B.; Imazov M. |
Features of training specialists in the field of environmental safety in water resources management |
BIO Web of Conferences |
10.1051/bioconf/202410705001 |
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The article substantiates the necessity of transforming the educational process to train specialists in the field of environmental safety in water resources management. The main peculiarity of labor functions performed by such specialists is in the randomness and secrecy of natural processes and the appearance of consequences from any type of activity over a long period of time. It is necessary to understand the cause-and-effect relationships between processes and the individual parameters that affect the speed of their realization in order to solve such problems. The result of the research was the development of the training concept, which allows the presentation of a task as a set of elementary actions that require particular resource support for their fulfillment. Potentially possible problems in their fulfillment are determined. It is recommended that models of "digital twins" of systems capable of imitating the reaction to certain influences be used to improve the efficiency of the educational process and perform highly specialized actions. Β© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/). |
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| publications-4827 |
Article |
2024 |
Tavares M.; PΓ©rez-SΓ΅nchez M.; Carravetta A.; Coronado-HernΓ΅ndez O.E.; LΓ³pez-JimΓ©nez P.A.; Ramos H.M. |
Smart feasibility optimization of hybrid renewable water supply systems by digital twin technologies: A multicriteria approach applied to isolated cities |
Sustainable Cities and Society |
10.1016/j.scs.2024.105834 |
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This research presents a multicriteria approach for the best hybrid water supply solution of a multipurpose Pumped-Storage Hydropower (PSH) system, using the Generalized Reduced Gradient (GRG) method in Solver, with the optimization process considering key factors, such as Net Present Value (NPV), the number of energy conversion devices, renewable energy production, source availability, reservoir capacities, topographic constraints, and energy tariffs. The methodology combines a literature review, methodological development, and machine learning applications for hybrid water-energy systems. Results indicate that solar-only solutions are insufficient in high hydropower potential scenarios while integrating wind turbines significantly enhances energy production and profitability by generating surplus energy for grid sales. The timing of energy sales and the incorporation of battery storage also impact NPV, which can exceed 180 million euros. Wind energy contributes to continuous profitability and optimized system performance, particularly in isolated regions. The PSH system can manage 130,000 cubic meters of water daily, storing 25 MWh of energy, and reducing CO2 emissions by over 18,000 tonnes per year. These findings highlight the importance of renewables, such as wind energy, and effective operational management. It enhances the economic viability and environmental sustainability of hybrid water-energy systems. Β© 2024 The Author(s) |
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| publications-4828 |
Article |
2024 |
Li Y.; Zhang J.; Fan Z.; Li Q.; Pan J.; Li M.; Zhang J.; Zhang J.; Liang H. |
Key technologies for digital intelligence of long-distance CO2 pipeline and their innovative practices in the Qilu Petrochemical–Shengli Oilfield CO2 pipeline; [ι½ι²η_x009f_³ε_x008c_–β€”θƒ_x009c_ε©ζ²Ήη”° CO2 ι•ΏθΎ“η®΅ι“ζ•°ζ™Ίε_x008c_–ε…³ι”®ζ_x008a_€ζ_x009c_―δΈ_x008e_ε›ζ–°ε®_x009e_θ·µ] |
Natural Gas Industry |
10.3787/j.issn.1000-0976.2024.09.001 |
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CO2 pipeline transportation is one of the key links in carbon capture, utilization and storage (CCUS). As China's first demonstration project of CO2 pipeline with supercritical pressure and a length of one hundred kilometers and a capacity of one million tons, the long-distance CO2 pipeline from Qilu Petrochemical to Shengli Oilfield takes digital intelligence as the important technical means to ensure safe pipeline operation maintenance, which has achieved important theoretical innovation and new construction progress. Taking the Qilu Petrochemical–Shengli Oilfield CO2 pipeline as an example, this paper analyzes the challenges, systematically reviews the application of the key technologies and digital intelligence technologies for safe operation maintenance of long-distance pipeline such as low-level mechanism model building and multi-system data integration from the aspects of flow assurance, corrosion prevention & control and leakage monitoring, and finally predicts the development of long-distance CO2 pipeline technologies. The following results are obtained. First, the thermal hydraulic flow model based on CO2 pipeline, the pipeline corrosion rate model and the gas–solid two-phase transfer model in the soil outside the pipeline are established in the mode of "100% digital delivery of engineering construction + multi-element mechanism data driven", and the station–pipeline integrated multi-mechanism fusion digital twin pipeline is constructed. Second, the key technology system for flow assurance of supercritical CO2 pipeline is formed. Third, the corrosion mechanisms inside the supercritical CO2 pipeline are revealed, and the pipeline corrosion prediction model is established. Fourth, the small-hole leakage diffusion model of dense phase CO2 pipeline under supercritical pressure is established, and the digital intelligence warning model of CO2 pipeline leakage is constructed. β… n conclusion, the Qilu Petrochemical–Shengli Oilfield CO2 pipeline has achieved theoretical innovation from aspects of flow assurance, corrosion prevention & control and leakage monitoring under the orientation of digital intelligence construction, which is of important guidance and practical significance for the development of the digital intelligence technologies for CO2 pipelines under the goal of "carbon neutrality". Β© 2024 Natural Gas Industry Journal Agency. All rights reserved. |
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| publications-4829 |
Article |
2024 |
Hallik J.; Arumägi E.; Pikas E.; Kalamees T. |
Comparative assessment of simple and detailed energy performance models for urban energy modelling based on digital twin and statistical typology database for the renovation of existing building stock |
Energy and Buildings |
10.1016/j.enbuild.2024.114775 |
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The renovation wave calls for an integrated, participatory, and neighbourhood to neighbourhood approach tailored to the local environments. This can be hindered by the lack of geometric and performance input data about the existing building stock. A parametric digital urban building energy modelling (UBEM) tool was developed for local municipalities to automate calculations for comprehensive renovation strategies utilizing public databases specifically at the neighbourhood level. This article compares the accuracy of two energy performance calculation models (seasonal heat balance method and hourly resistance–capacitance method) used within the tool to detailed energy simulation software and measured energy consumption data of existing group of residential buildings in a same neighbourhood. The accuracy for estimating the total primary energy demand was roughly below 10 % for both simplified models. The seasonal calculation method showed consistent overestimation of space heating, depending on building characteristics. Solar and internal heat gains significantly affect the accuracy of simplified heat balance calculations, particularly in well-insulated buildings, while the more detailed 5R1C lumped capacitance hourly method provides improved accuracy for space heating demand. The comparison of uniform (proportional) and project based window area distribution used in the calculation models showed only marginal difference. The study validated simple seasonal and 5R1C hourly calculation methods using data from an apartment building neighbourhood with 22 apartment buildings. Despite larger deviations in the case of individual buildings, the average deviation from measured heating demand was only around 3 %, the seasonal method slightly overestimating and the 5R1C hourly method slightly underestimating the measured values. The primary energy demand including typical values of domestic hot water and household electricity was overestimated by both simplified methods due to lower electricity and water consumption in reality although the difference was below 7 % for the entire district. This concludes that pooling the building envelope heat loss calculation on a district level improves accuracy on average allowing better assessment of comparative renovation strategies. © 2024 Elsevier B.V. |
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| publications-4830 |
Review |
2024 |
Di Sarno L.; Forgione R. |
Innovative steel modular housing system for multiple natural hazard mitigation |
International Journal of Disaster Risk Reduction |
10.1016/j.ijdrr.2024.104734 |
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Floods are among the most destructive natural disasters threatening lives, communities, and economies, with annual damage reaching billions of pounds worldwide. Human activities and recent climate emergencies are exacerbating the frequency and severity of these catastrophic events, exposing large communities to the risk of such natural hazards. Various mitigation techniques at both community and property levels can be adopted to provide responsive solutions for natural threats. In this study, a transformative steel modular housing system capable of rising above the ground in the event of a flood is presented. The robustness and efficiency of such an innovative system were tested on a full-scale prototype at the state-of-the-art HR Wallingford testing facility for flood resilience in the UK. To extend the applicability and assess the reliability of the innovative flood-resilient system, a comprehensive numerical investigation was carried out to check whether the modular system can reliably withstand multiple natural hazards, such as flooding, strong winds, and seismic ground motions. A refined numerical model was first calibrated on the basis of experimental outcomes to create a digital twin of the tested building. Such a model was then used to demonstrate the effectiveness of the proposed modular steel building under different flooding scenarios, i.e., in configurations with increasing height above the ground, namely 300 mm, 600 mm, and 900 mm. The results of experimental tests and the comprehensive parametric numerical analyses demonstrate that the proposed newly developed steel modular housing system ensures structural integrity, adequate performance, and resilience even for extreme flood scenarios characterised by rapid water velocities and severe wind conditions. The innovative and resilient modular housing system presented has also been demonstrated to be reliable for areas with moderate seismicity, i.e., with peak ground accelerations lower than 0.25 g. The proposed resilient and sustainable adaptation technology can thus be employed efficiently in regions worldwide that are exposed to multiple natural hazards, e.g., floods, high winds, and earthquakes. Β© 2024 Elsevier Ltd |
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