| publications-4771 |
Article |
2024 |
Romay-Gainza A.; Elduayen-Echave B.; HernΓ΅ndez B.; Larraona G.S.; Arnau R.; Climent J.; Ayesa E. |
A CFD-based compartmental modelling approach for long-term dynamic simulation of water resource recovery facilities |
Water Science and Technology |
10.2166/wst.2024.230 |
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This article presents a methodology for compartmental model (CM) creation for long-term simulation of water resource recovery facilities (WRRFs). CMs are often focused on reproducing with a lower computational cost, previously simulated scenarios. In contrast, the methodology presented here can represent variable hydraulic conditions, based on the interpolation of data gathered from a set of computational fluid dynamics (CFD) simulations that reproduce representative hydraulic scenarios. This is achieved by modelling with bidirectional flows the exchange flows between fixed compartments, which are defined based on the geometry of the reactors. The resultant hydraulic surrogate model can be implemented in commercial water treatment software to solve biochemical kinetics. The methodology was applied to simulate in WESTΒ®-DHI a WRRF in Vila-Real, Spain. In this contribution, the CM was validated with real plant data. The developed CM provided a quick response simulation with a high level of hydraulic and biochemical detail. This allowed observation of a spatial distribution of component concentration, which could help with sensor location or plant optimisation. The methodology presented here could also be a useful enabler of digital twins to be implemented in WRRF. Β© 2024 The Authors. |
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| publications-4772 |
Review |
2024 |
Ahmed Murtaza A.; Saher A.; Hamza Zafar M.; Kumayl Raza Moosavi S.; Faisal Aftab M.; Sanfilippo F. |
Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study |
Results in Engineering |
10.1016/j.rineng.2024.102935 |
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This paper examines the integration of Industry 5.0 principles with advanced predictive maintenance (PdM) and condition monitoring (CM) practices, based on Industry 4.0's enabling technologies. It provides a comprehensive review of the roles of Machine Learning (ML), Digital Twins (DT), the Internet of Things (IoT), and Big Data (BD) in transforming PdM and CM. The study proposes a six-layered framework designed to enhance sustainability, human-centricity, and resilience in industrial systems. This framework includes layers for data acquisition, processing, human-machine interfaces, maintenance execution, feedback, and resilience. A case study on a boiler feed-water pump is also presented which demonstrates the framework's potential benefits, such as reduced downtime, extended lifespan, real-time equipment monitoring and improved efficiency. The findings of this study emphasises the importance of integrating human intelligence with advanced technologies for a collaborative and adaptive industrial environment, and suggest areas for future research. Β© 2024 The Author(s) |
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| publications-4773 |
Article |
2024 |
Guo Y.; Tang Q.; Darkwa J.; Wang H.; Su W.; Tang D.; Mu J. |
Multi-objective integrated optimization of geothermal heating system with energy storage using digital twin technology |
Applied Thermal Engineering |
10.1016/j.applthermaleng.2024.123685 |
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Heat energy storage technology plays a significant role in energy systems, and the various technological solutions brought about by digitalization are especially valuable in the field of energy storage. This article proposes an innovative model based on digital twin technology to solve the supply–demand mismatch problem in geothermal heating systems. This model achieves multi-objective optimization of comprehensive cost, geothermal energy utilization rate, and carbon emission by constructing a heat storage geothermal heating system. Digital twin technology integrates data and information models of public buildings and facilitates their sharing and transmission throughout the entire lifecycle of the geothermal heating system. Initially, the proposed method employs a machine learning-based approach to accurately predict heating demand. Subsequently, the operation of the heat storage water tank and heat pump units is optimized to resolve difficulties in matching energy supply and demand. Finally, the method takes full advantage of time-of-use electricity pricing policies to reduce costs. The data utilized were collected from an office building in China over a period of six months. Experimental results demonstrate that: (1) In terms of predicting heating demand, the improved neural network proposed in this study achieved a prediction accuracy of 98%, which is a 10% improvement over comparative algorithms. Additionally, the experimental comparison of four types of errors showed that the machine learning method proposed had smaller errors across the board. (2) The method realized collaborative multi-objective optimization, and in five scenarios, the comprehensive performance index increased by up to 38.03% compared to the benchmark system. This indicates that intelligent technology is an effective means of enhancing the energy sustainability of geothermal heating systems, and the use of geothermal energy as a clean energy source effectively addresses issues related to the storage, utilization, management, and energy conservation of buildings. © 2024 Elsevier Ltd |
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| publications-4774 |
Article |
2024 |
Assani N.; Matić P.; Kezić D.; Pleić N. |
Modeling Fluid Flow in Ship Systems for Controller Tuning Using an Artificial Neural Network |
Journal of Marine Science and Engineering |
10.3390/jmse12081318 |
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Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be re-adjusted for the optimal control of the process. To avoid experimenting on operational real systems, models are convenient alternatives. When real-time information is needed, digital twin (DT) concepts become highly valuable. The aim of this paper is to analyze and determine the optimal NARX model architecture in order to achieve a higher-accuracy model of a ship’s flow process. An artificial neural network (ANN) was used to model the process in MATLAB. The experiments were performed using a multi-start approach to prevent overtraining. To prove the thesis, statistical analysis of the experimental results was performed. Models were evaluated for generalization using mean squared error (MSE), best fit, and goodness of fit (GoF) measures on two independent datasets. The results indicate the correlation between the number of input delays and the performance of the model. A permuted k-fold cross-validation analysis was used to determine the optimal number of voltage and flow delays, thus defining the number of model inputs. Permutations of training, test, and validation datasets were applied to examine bias due to the data arrangement during training. © 2024 by the authors. |
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| publications-4775 |
Article |
2024 |
Lumley D.J.; Polesel F.; Refstrup SΓΈrensen H.; Gustafsson L.-G. |
Connecting digital twins to control collections systems and water resource recovery facilities: From siloed to integrated urban (waste)water management |
Water Practice and Technology |
10.2166/wpt.2024.128 |
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The use of digital twins is a rapidly emerging field for improved real-time control (RTC) and decision support for the operation of collection systems and water resource recovery facilities (WRRFs). Digital twins for collection systems can help minimize the impacts of flow variation due to extreme weather events, attenuate flows to the WRRF, and reduce sewer overflows and the associated effects. Similarly, digital twins for WRRFs can help improve process, energy, and cost efficiency, fully utilise plant volumes, reduce carbon footprint, and support operator training. The current study provides an overview of two digital twin applications for collection systems (Future City Flow) and WRRFs (TwinPlant) and presents a first example of digital twin integration for proactive collection system-WRRF operation under wet-weather conditions. Current applications of the integrated digital twin are described, including (i) proactive implementation of wet-weather operation mode in WRRF based on inflow forecast and (ii) evaluation of the impacts of RTC in collection systems on WRRF performance. Other potential application examples are described together with the challenges related to the use of this solution. Overall, this new approach has a wide potential to support the cooperation within water utilities towards the adoption of integrated wastewater management. Β© 2024 The Authors. |
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| publications-4776 |
Article |
2024 |
Baniasadi E.; Rezk A.; Tola Y.B.; Alaswad A.; Imran M.; Humphries P. |
Renewable-driven hybrid refrigeration system for enhancing food preservation: Digital twin design and performance assessment |
Energy Conversion and Management |
10.1016/j.enconman.2024.119165 |
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This study presents a new method for sustainable cooling systems using a hybrid refrigeration system powered by hybrid renewable energy sources. The system comprises a modular unit of vertical wind turbines integrated with bio-photovoltaic films to provide sustainable energy. The hybrid refrigeration system combines evaporative and solar thermal-driven adsorption cooling systems. In addition, a finite volume of soil is proposed for thermal energy storage. Experimental data inform the development of a digital twin for an integrated system, soil thermophysical characteristics, wind turbine performance, and technical specifications for other system components. This sustainable cooling package is cost-effective and space-efficient, particularly in remote or off-grid locations. Notably, the evaporative cooler and chilled water coil contribute to a cooling effect of 20.4 kW, and solar power generation reaches 12.38 kW at an intensity of 1053 W/m2. The annual electrical output averages 1.7 kW at a wind speed of 3.5 m/s. Under best conditions, wind power can surge to 7.99 kW at 9.88 m/s. The ratio of power generated by wind to solar energy ranges from 1.1 to 1.3. The system effectively meets a peak thermal energy demand of approximately 74 GJ/month, facilitated by solar collectors, underground thermal storage, and a renewable energy-fed auxiliary heater. This study paves the way for future techno-economic optimisation and advancements in sustainable energy solutions for remote cold storage facilities. Β© 2024 The Author(s) |
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| publications-4777 |
Conference paper |
2024 |
Kaewpoonsuk P.; Subsomboon K. |
Methodology of 3D Underground Object Models and Reality 3D Models for Urban Information Modeling (UIM) |
AIP Conference Proceedings |
10.1063/5.0235930 |
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This research introduces a method for developing urban information models (UIM) in the initial phase by extending the concept of building information modeling (BIM) in conjunction with photogrammetry. Utilizing data collected from aerial surveys using unmanned aerial vehicles (UAVs), with Naresuan University as a case study, the development is divided into two parts: 1) Development of BIM for underground structures, including water pipes, sewerage pipes, wastewater treatment systems, water storage tanks, and electrical and communication conduit systems; and 2) Development of realistic three-dimensional models for above-ground structures, especially focusing on old buildings lacking sufficient plans for BIM development. This involves aerial photography using UAVs, followed by computer software processing based on photogrammetry concepts and collaborative integration into a UIM. The research findings indicate that this approach can develop urban information models contingent upon the level of development (LOD) and level of information (LOI) specified in the model, as well as the definition of flight patterns and photography formats, while also considering certain challenges such as lighting and shadows. Β© 2024 American Institute of Physics Inc.. All rights reserved. |
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| publications-4778 |
Article |
2024 |
Ranjbar R.; Segovia P.; Duviella E.; Etienne L.; Maestre J.M.; Camacho E.F. |
Digital Twin of Calais Canal with Model Predictive Controller: A Simulation on a Real Database |
Journal of Water Resources Planning and Management |
10.1061/JWRMD5.WRENG-6266 |
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This paper presents the design of a model predictive control (MPC) for the Calais canal, located in the north of France for satisfactory management of the system. To estimate the unknown inputs/outputs arising from the uncontrolled pumps, a digital twin (DT) in the framework of a Matlab-SIC2 is used to reproduce the dynamics of the canal, and the real database corresponding to a period of three days is employed to evaluate the control strategy. The canal is characterized by two operating modes due to high and low tides. As a consequence of this, time-varying constraints on the use of gates must be considered, which leads to the design of two multiobjective control problems, one for the high tide and another for the low tide. Furthermore, a moving horizon estimation (MHE) strategy is used to provide the MPC with unmeasured states. The simulation results show that the different objectives are met satisfactorily. Β© 2024 American Society of Civil Engineers. |
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| publications-4779 |
Article |
2025 |
Kim W.; Youn B.D. |
Physics-based digital twin updating and twin-based explainable crack identification of mechanical lap joint |
Reliability Engineering and System Safety |
10.1016/j.ress.2024.110515 |
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The mechanical joints, including the lap joint, weld, bolt, and pin, are vulnerable to fatigue failure because of stress concentration and internal flaws. Digital twin (DTw) strategies were proposed to prevent catastrophic system failure by fatigue damage in mechanical joints. In previous studies, the data-driven approach, such as deep learning and machine learning were utilized to estimate severity of the damage. However, it needs to improve its prediction accuracy because of insufficient data and physical interpretability. In this study, the physics-based digital twin model updating and twin-based crack identification of fatigue damage in riveted lap joints were proposed using lamb waves with consideration of uncertain crack growth path. The proposed approach is based on three techniques; (i) Data pre-processing, including filtering and optimization-based signal synchronization, (ii) Lamb-wave propagation analysis with sensor dynamics model and uncertain crack path, and (iii) Optimization based physics-based model updating and inference. In data pre-processing, the excitation frequency magnitude and truncation time are estimated using the observed actuator signal in the Lamb-wave test. The sensor dynamic model and model parameters are updated using the Bayesian optimization method to minimize both the errors in the predicted (y^t) and observed (yt) wave signal and the errors in the inferred (l*) and observed (l) crack length. The crack growth path is sampled based on angular and spline schemes to consider uncertain crack propagation paths. The validity of the proposed method is demonstrated using an open data set (2019 PHM society data challenge). The results conclude that the proposed digital twin approach can improve estimation accuracy considering both the crack growth path and sensor dynamics model. Β© 2024 Elsevier Ltd |
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| publications-4780 |
Article |
2024 |
Wu S.; Yin A.; Zhang B. |
Digital twin system for TEG dehydration of natural gas device; [天 然 ζ°” δΈ‰ η” ι†‡ θ„± ζ°΄ θ£… η½® ζ•° ε— ε η”_x009f_ η³» η»_x009f_] |
Chongqing Daxue Xuebao/Journal of Chongqing University |
10.11835/j.issn.1000-582X.2023.104 |
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The digital twin concept completes the mapping and interaction between physical space and digital space, showing great potential for development in the industrial field. With considering the low detection efficiency of natural gas dehydration performance parameters and the inability to optimize gas station process parameters online, this paper applies the digital twin concept in the chemical industry to establish an overall framework of the digital twin system for triethylene glycol(TEG) dehydration. On one hand, the geometric model of the twin system is constructed by integrating physical devices. On the other hand, the flow model dehydration system technology is established based on the real-time driving of physical data. Finally, the twin model of dehydration is established by designing virtual-real mapping model, completing the mapping of physical space and digital space, which enables the parallel operation of the physical device and the virtual device. Through the proposed digital twin system, real-time prediction of natural gas water dew point and other dehydration performance parameters can be achieved. To achieve the goal of low power consumption, the optimization of dehydration process parameters is realized by combining optimization algorithms with the twin model, thereby improving economic efficiency. Β© 2024 Chongqing University. All rights reserved. |
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