| publications-5211 |
Article |
2023 |
Ramos H.M.; Kuriqi A.; Coronado-HernΓ΅ndez O.E.; LΓ³pez-JimΓ©nez P.A.; PΓ©rez-SΓ΅nchez M. |
Are digital twins improving urban-water systems efficiency and sustainable development goals? |
Urban Water Journal |
10.1080/1573062X.2023.2180396 |
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The use of these new interaction tool implies the improvement of the awareness of the whole system and it lies in improving the sustainability and efficiency of the water systems with the integration of measurements. The research proposed a methodology, which enables improvement in the accuracy and reliability of data and it increases the performance of water systems. This study proposes a pressure-reduction strategy and the implementation of pumps as turbines (PATs), applicable in Sta Cruz, Madeira water system. The use of the developed digital twin model assures a decrease of 3.3 hm3 in water-demand volume, increasing renewable generation by micro-hydropower up to 1.2 GWh. These actions would result in savings above 1.5 M€, decreasing around 530 tons of CO2 emissions each year. The consideration of these values implies the improvement of different indicators, which allows the evaluation of different targets linked to sustainable development goals (SDGs). A digital twin is a tool, which enables a real-time simulation of the water systems and therefore, the water managers can make a decision in the management of the water system over time. The use of these new interaction tool implies the improvement of the awareness of the whole system and it lies in improving the sustainability and efficiency of the water systems with the integration of measurements. The research proposed a methodology to integrate GIS and water models, being the main goal the integration of social, economic, environmental and technical issues. This integration enables improvement in the accuracy and reliability of data and it increases the performance of water systems. This study proposes a pressure-reduction strategy and the implementation of pumps as turbines (PATs), applicable in Sta Cruz, Madeira water system. The use of the developed digital twin model assures a decrease of 3.3 hm3 in water-demand volume, increasing renewable generation by micro-hydropower up to 1.2 GWh. These actions would result in savings above 1.5 M€, decreasing around 530 tons of CO2 emissions each year. The consideration of these values implies the improvement of different indicators, which allows the evaluation of different targets linked to sustainable development goals (SDGs). © 2023 Informa UK Limited, trading as Taylor & Francis Group. |
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| publications-5212 |
Review |
2022 |
Mengucci C.; Ferranti P.; Romano A.; Masi P.; Picone G.; Capozzi F. |
Food structure, function and artificial intelligence |
Trends in Food Science and Technology |
10.1016/j.tifs.2022.03.015 |
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Background: The complexity of food structure is such as to hinder its inclusion in mathematical models predicting food properties and transformations, although a considerable impulse is being determined by using artificial intelligence. As a matter of fact, food definition currently neglects the structural description, even in those fields for which structure is demonstrated to have a decisive role, such as nutrition. Scope and approach: This review aims to analyse the current knowledge about the structure of foods and its potential use to numerically define the sensory and nutritional quality, as well as the stability properties. Starting from this information, a possible methodology is explored to build, even in an automated way, mathematical models for simulating and predicting the properties of food. A model pipeline has been proposed and applied to pasta, in particular exploiting the description of the structural changes occurring upon cooking. Key findings and conclusions: Foods may be designed in silico, based on automated pipelines for direct extraction of information on rheological and sensory properties as derived from structure images and from data on the dynamic state of the water. The ultimate goal of these approaches is to make more limited use of expensive and time-consuming experiments on physically prepared foods to get to use digital twins of foods designed in the laboratory. Β© 2022 Elsevier Ltd |
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| publications-5213 |
Article |
2023 |
Tegtmeier M.; Knierim L.; Schmidt A.; Strube J. |
Green Manufacturing for Herbal Remedies with Advanced Pharmaceutical Technology |
Pharmaceutics |
10.3390/pharmaceutics15010188 |
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Herbal remedies are in most cases still manufactured with traditional equipment installations and processes. Innovative chemical process engineering methods such as modeling and process intensification with green technology could contribute to the economic and ecologic future of those botanicals. The integration of modern unit operations such as water-based pressurized hot water extraction and inline measurement devices for process analytical technology approaches in traditional extraction processes is exemplified. The regulatory concept is based on the quality-by-design demand for autonomous feed-based recipe operation with the aid of digital twins within advanced process control. This may include real-time release testing to the automatic cleaning of validation issues. Digitalization and Industry 4.0 methods, including machine learning and artificial intelligence, are capable of keeping natural product extraction manufacturing and can contribute significantly to the future of human health. Β© 2023 by the authors. |
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| publications-5214 |
Article |
2022 |
Nieves J.; Bravo B.; Sierra D.-C. |
A Smart Digital Twin to Stabilize Return Sand Temperature without Using Coolers |
Metals |
10.3390/met12050730 |
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In order to ensure the optimal state of recovered molding sand inside a foundry, it is necessary to avoid temperature peaks and to ensure optimal humidity conditions prior to reusing the sand. Sand that is too hot or without optimal moisture can cause production delays due to a long mixing process, excessive consumption of raw materials, or poor agglutination. To ensure a stable and optimal sand temperature, many foundries choose to incorporate coolers into their process, however, it is a solution that is not always viable, either due to their high cost or a lack of space within the facility. Another solution is to incorporate water sprinklers into the cooling drum which contribute by reducing the temperature of the castings and the sand, but these systems do not prevent temperature peaks from occurring. Therefore, here, we present a control methodology, based on a digital architecture that, governed by an intelligent digital twin allows us to know the real situation and the current rate of production, providing suggestions for water addition. The obtained system reduces the average temperature and its variation, as well as eliminates temperature peaks, giving a more controlled manufacturing process. Β© 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
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| publications-5215 |
Article |
2022 |
Li M.; Feng X.; Han Y. |
Brillouin fiber optic sensors and mobile augmented reality-based digital twins for quantitative safety assessment of underground pipelines |
Automation in Construction |
10.1016/j.autcon.2022.104617 |
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The impossibility of visual inspection and the complexity of combined loads hamper the quantitative assessment, lifetime prediction and control of underground pipelines during their lifecycles. A methodology based on mobile augmented reality (MAR) and Brillouin fiber optic sensors (BFOSs) is presented to build a digital twin (DT) for underground pipelines. Field experiments were carried out to demonstrate that the proposed method can quantitatively assess and predict the structural safety of an underground pipeline from the DT. The results demonstrate that the distributed sensor networks can measure important but unpredictable deformations (i.e., longitudinal bending and axial thermal strain), the joint data-physics driven model can estimate the structural stress state more accurately than the common calculation model, and the MAR-based human–asset interaction interface enables more intuitive, efficient, automated operation and maintenance (O&M). In the future, in-line robotic systems and localized damage models should be further adopted for lifecycle O&M. © 2022 Elsevier B.V. |
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| publications-5216 |
Conference paper |
2022 |
Diakite A.A.; Ng L.; Barton J.; Rigby M.; Williams K.; Barr S.; Zlatanova S. |
Liveable City Digital Twin: A Pilot Project for the City of Liverpool (NSW, AUSTRALIA) |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
10.5194/isprs-annals-X-4-W2-2022-45-2022 |
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In recent years, the concept of Digital Twin (DT) for cities is increasingly at the core of most smart city initiatives, as it has been identified as a critical tool for tackling the challenges of this century. A robust city modelling framework is essential if local, state and national governments are to move towards sustainable built environments and work together across complex multi-sectoral problems to drive impacts that improve urban liveability and climate adaptability. Furthermore, the level of collaboration and interoperability required to address these cannot be achieved without proper standardisation of DT components. The aim of this project is to develop a demonstration DT that integrates existing data using a standardised 3D format based on CityGML and that embeds analytics, such as sun exposure and tree coverage, to assess liveability within a 3D city modelling framework. Common urban features such as buildings, roads, railways, vegetation and water bodies are also processed and incorporated. Additionally, IoT sensors are integrated into the model and all processes are performed using open-source tools to improve accessibility and repeatability. Details of the workflow, including the storage of the city features in a 3D City Database (3DCityDB), the 3D upgrading of urban features commonly available as 2D data as well as a few use cases are illustrated and discussed in this paper. Β© 2022 A. A. Diakite et al. |
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| publications-5217 |
Article |
2022 |
Ye Y.; Jiang Y.; Liang L.; Zhao H.; Gu J.; Dong J.; Cao Y.; Duan H. |
Digital twin watershed: new infrastructure and new paradigm of future watershed governance and management; [ζ•°ε—εη”_x009f_ζµε_x009f__x009f_: ζ_x009c_ζ_x009d_¥ζµε_x009f__x009f_ζ²»η†η®΅η†η_x009a_„ζ–°ε_x009f_Ίε»Ίζ–°θ_x008c_ƒεΌ_x008f_] |
Shuikexue Jinzhan/Advances in Water Science |
10.14042/j.cnki.32.1309.2022.05.001 |
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The digital twin watershed is an important part of the digital twin earth. Clarifying the theoretical definition and connotation of the digital twin watershed is the premise and foundation for the research and construction of the digital twin watershed, and it is of great significance for the intelligent management of the watershed. Based on the digital twin theory and technology, the following research has been carried out in this paper. β‘ The definition of a digital twin watershed is given, and it is considered that a digital twin watershed is a new infrastructure and new paradigm serving the entire life- cycle management of the watershed, which is the interactive mapping, co- intelligence evolution, and virtual reality integration between physical and virtual watersheds driven by the full amount of data and domain knowledge, and the differences between a digital twin watershed and traditional modeling and simulation are analyzed. β‘΅ The connotation of a digital twin watershed is to realize the full life- cycle control of physical watershed objects by loading the physical watershed into the virtual watershed, mapping the physical watershed with the virtual watershed, and then managing and controlling the physical watershed using the virtual watershed. Its characteristics include high fidelity, evolution autonomy, real- time synchronization, closed- loop interaction, and symbiotic evolution. β‘Ά The basic model of a digital twin watershed is composed of a physical watershed, a virtual watershed, the real- time connection and interaction, the digital enabling service, the twin watershed data, and the twin watershed knowledge. Its core capabilities include physical watershed perception and control, digital expression of all of the water- related elements, visual dynamic presentation of real scene, watershed data fusion supply, watershed knowledge fusion supply, watershed simulation and deduction, and self- learning and optimization of digital twin watershed. β‘£ This paper puts forward the key scientific problems and key technical systems to be solved in the digital twin watershed, and looks forward to the development direction of the digital twin watershed from the perspective of a perception network, data network, knowledge network, model network, and service network, and expounds the enabling field of the digital twin watershed. This paper aims to provide theoretical guidance for the application of digital twin watershed technology through the new research paradigm of digital twin watershed theory and to provide useful inspiration and reference for future smart watershed research and the application of digital technology in watershed governance and management. Β© 2022 China Water Power Press. All rights reserved. |
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| publications-5218 |
Article |
2023 |
Tariq R.; Ali M.; Sheikh N.A.; Shahzad M.W.; Xu B.B. |
Deep learning artificial intelligence framework for sustainable desiccant air conditioning system: Optimization towards reduction in water footprints |
International Communications in Heat and Mass Transfer |
10.1016/j.icheatmasstransfer.2022.106538 |
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Desiccant evaporative cooling systems pave the path towards energy and environmental sustainability in buildings especially; however, the direct evaporative coolers in such configurations result in high water consumption. The application of modern computational intelligence tools, including artificial intelligence and meta-heuristic optimization algorithms, can improve the operational comprehension of desiccant cooling systems while addressing the minimization of total water footprints with the maximization of the cooling capacity. The contribution/objective of this research is to address the gaps in understanding through the application of deep learning, genetic algorithm, and multicriteria decision analysis applied to a desiccant cooling system working under real transient experimental conditions of a building located in Austria. Within the methodology, calibrated, experimental, and validated data monitoring system displaying the real desiccant-enhanced cooling system is adapted to generate a set of input-output data sets. The set of data includes ambient temperature, ambient humidity, regeneration temperature, supply airflow rate, and return airflow rate yielding the cooling capacity and total water footprints of the system. The results of deep learning algorithm using an artificial neural network have suggested that the architectures 5-[6]-[6]-1 and 5-[12]-[12]-1 are the best to accurately predict the cooling capacity and total water footprints with a coefficient of determination of 0.98856 and 0.99246, respectively. Secondly, the β€_x009c_white-box modelβ€_x009d_ of the deep learning algorithm is used to develop a digital twin model which helps in the replication of the earlier experimental conditions. The optimization results have suggested that the optimized total water footprints are 45.17 kg/h with a system of 3.32 tons of refrigeration. These optimal values are found in the best combination of design variables in which the ambient temperature is 28 Β°C, ambient relative humidity is 52.0%, supply airflow rate is 2.13 kg/s, and regeneration flow rate is 2.35 kg/s, and the regeneration temperature is 70.0 Β°C. It is concluded that the application of data-driven models can extend the interpretation of desiccant cooling systems and can participate in its performance enhancement. Β© 2022 The Authors |
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| publications-5219 |
Article |
2022 |
Liu X.; Wang Y.; Koo R.C.H.; Kwan J.S.H. |
Development of a slope digital twin for predicting temporal variation of rainfall-induced slope instability using past slope performance records and monitoring data |
Engineering Geology |
10.1016/j.enggeo.2022.106825 |
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A slope digital twin is a virtual slope model that is able to continuously, even in real-time, learn from actual observations (e.g., monitoring data, slope performance records, and site investigation data) obtained from its physical counterpart to enhance the performance of the slope model. This study proposes a practical framework to develop a slope digital twin and describes its application to predict the temporal variation of rainfall-induced slope instability of a real slope in Hong Kong. When compared with a conventional slope model that remains unchanged, the proposed slope digital twin combines monitoring data (e.g., data on rainfall and pore water pressure in the slope) and slope survival records to probabilistically update the model. Specifically, the most suitable model settings are selected, and both the hydraulic and strength parameters of the soils are updated, thereby decreasing the associated uncertainties. The updated slope model can predict pore water pressure responses of a target rainfall consistent with the actual measurements. Furthermore, the model can be used to predict the temporal variation of slope stability (e.g., by using a factor of safety with quantified uncertainty or slope failure probability) during the target rainfall. Because the monitoring data and past slope survival records are incorporated in the model updating, the proposed slope digital twin enhances the prediction of soil hydraulic responses and slope stability. The predicted temporal variation of slope stability agrees well with the observed slope failure induced by an extreme rainstorm in June of 2008. Β© 2022 |
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| publications-5220 |
Article |
2022 |
Li N.; Li Z.-X. |
Digital twins for the underwater shake table array facility |
Earthquake Engineering and Resilience |
10.1002/eer2.26 |
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In this study, the three-dimensional six-degree-of-freedom underwater shaking table array facility for seismic simulation, which is built in Tianjin University, China, is taken as the research object. A digital twin of the shaking table is constructed. First,Β the components of the underwater shaking table array are introduced. And the simplified mechanical property of different components is illustrated. By using the system identification algorithm based on the measured dynamic responses, the basic model parameters, including the dynamics of the servo-valve, the horizontal and vertical effective mass, the effective stiffness and damping coefficient of the actuators, the accumulators, and the friction of each actuator in the system are identified. The suitability of various experimental data sets used to identify the digital twin's model parameters is described. The prediction of various shaking table testing instances, as well as the comparison of digital twin and real system performance, are then presented. The digital twins shaking table system's performance and accuracy are examined. The shaking table's performance for various reproduction signals is compared to the digital twins model, which validates the suggested model's feasibility. Finally, the digital twins model is adopted and tried to tune the facility offline meanwhile the shaking table facility is configured iteratively. These findings highlight the applicability of the digital twins technique, which is the next research direction. Β© 2022 Tianjin University and John Wiley & Sons Australia, Ltd. |
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