10784 research outputs found
Sort by
CHESTER: Experimental prototype of a compressed heat energy storage and management system for energy from renewable sources
Get PDFPublisher Copyright: © 2024 The AuthorsThe increasing share of renewable energies in the electricity grid requires storage technologies to balance energy supply and demand. Thermally integrated pumped thermal energy storage systems are considered a promising technology for medium to large-scale storage applications. Among these, compressed thermal energy storage in particular has been identified in numerous theoretical studies as a promising candidate. Despite these studies, the feasibility of the thus far theoretical concept has not yet been proven experimentally. To overcome this gap this publication presents for the first time the entire setup and experimental results of the world's first CHESTER (Compressed Heat Energy Storage for Energy from Renewable Sources) laboratory prototype at a representative scale consisting of a high-temperature heat pump and an organic Rankine cycle coupled by a combination of a sensible and a novel dual-tube latent heat storage as a high-temperature thermal energy storage system. The stable operation of a fully integrated CHEST system on a 10 kW scale was demonstrated and the stable function of the latent heat storage unit as both a condenser and an evaporator was confirmed. With the current prototype, which combines three first of its kind subsystems, efficiencies of up to 37 % have been achieved. The presented results confirm the practical feasibility of the thus far theoretical concept and provide guidance for further optimization of the components and more importantly the interaction between the individual subsystems.Peer reviewe
A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends
No full textPublisher Copyright: © 2014 Chinese Association of Automation.When data privacy is imposed as a necessity, Federated learning (FL) emerges as a relevant artificial intelligence field for developing machine learning (ML) models in a distributed and decentralized environment. FL allows ML models to be trained on local devices without any need for centralized data transfer, thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties. This paradigm has gained momentum in the last few years, spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources. By virtue of FL, models can be learned from all such distributed data sources while preserving data privacy. The aim of this paper is to provide a practical tutorial on FL, including a short methodology and a systematic analysis of existing software frameworks. Furthermore, our tutorial provides exemplary cases of study from three complementary perspectives: i) Foundations of FL, describing the main components of FL, from key elements to FL categories; ii) Implementation guidelines and exemplary cases of study, by systematically examining the functionalities provided by existing software frameworks for FL deployment, devising a methodology to design a FL scenario, and providing exemplary cases of study with source code for different ML approaches; and iii) Trends, shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape. The ultimate purpose of this work is to establish itself as a referential work for researchers, developers, and data scientists willing to explore the capabilities of FL in practical applications.Peer reviewe
A New Indicator for Measuring Efficiency in Urban Freight Transportation: Defining and Implementing the OEEM (Overall Equipment Effectiveness for Mobility)
Get PDFPublisher Copyright: © 2024 by the authors.Featured Application: This work defines a KPI for the measurement of efficiency in urban freight transportation. It also develops a methodology that explains how to obtain data in real-time and the development of the programming language for the calculation of the previously defined KPI to, finally, visualize it in a control panel. This work serves as a basis for several companies to apply this methodology to the measurement of efficiency in their urban freight transportation activities. Urban freight transportation is the activity that has the greatest impact on urban areas in terms of sustainability and livability, and it is, therefore, necessary to reduce its impact. Currently, there is a lack of methodologies to validate the methods proposed by companies to reduce their impacts. The proposed methodology presents the implementation of a KPI (Key Performance Indicator) based on the triple bottom line approach: economic, social and environmental, since a company with good results on the “triple bottom line” will experience an increase in its economic profitability and its environmental commitment while reducing the impacts that generate negative perceptions of it. This KPI is the OEEM (Overall Equipment Effectiveness for Mobility), a redesign of the well-known OEE (Overall Equipment Effectiveness), but adapted to the needs of urban freight transportation since this indicator provides a quick overview of the efficiency or performance of the activity according to five components: quality of deliveries, vehicle utilization, availability of the vehicle–driver tandem and efficiency (result of traffic and efficiency of delivery stops). The methodology developed will be implemented in a case study where the KPI will be calculated on the basis of real-time data and visualized on a control panel; thanks to this KPI, the company will be able to validate whether the measures taken have a positive or negative impact.Peer reviewe
Simultaneous design optimisation methodology for floating offshore wind turbine substructure and feedback-based control strategy
Get PDFPublisher Copyright: © 2024 The Author(s)This research article explores the application of control co-design methodologies for optimising floating offshore wind turbine systems concurrently. The primary objective is to offer insights into concurrent design approaches employing an advanced genetic optimisation algorithm. To achieve this, a reduced-order dynamic model is employed to minimise computational time requirements, complemented by a modified version of the levelized cost of energy equation serving as the cost function. Furthermore, various optimisation scenarios are investigated under diverse wind and wave conditions to assess the advantages and drawbacks of increasing the complexity of dynamic cases used in evaluating the cost function. The optimised system designs are then compared against baseline floating system designs to underscore the advantages of employing this approach to floating wind turbine design.Peer reviewe
Influence of Copper Addition on the Mechanical Properties and Corrosion Resistance of Self-Hardening Secondary Aluminium Alloy AlZn10Si8Mg
Get PDFPublisher Copyright: © 2024 by the authors.Aluminium alloys have a wide range of applications, mainly due to their advantageous strength-to-weight ratio, denoted as specific strength and corrosion resistance. In recent decades, there has been a notable surge in the usage of recycled alloys, attributed to their reduced production costs and emissions. One of the conditions for secondary production is the optimal sorting of used scrap. Once the aluminium scrap has been melted, it is tough to reduce the content of the various additives. Copper is the primary alloying element in some aluminium alloys, which leads to an increased amount of copper in the aluminium scrap. Therefore, it is important to investigate its effect on the properties of aluminium alloys in which it is not commonly present. For this reason, this paper is concerned with the influence of copper on the microstructure and properties of the secondary aluminium alloy AlZn10Si8Mg. Specifically, it compares two melts of self-hardening AlZn10Si8Mg alloys differing in copper content (0.019% and 1.72%). A complex quantitative and metallographic analysis by optical and electron microscopy has been performed. Mechanical properties were investigated by tensile test, Brinell hardness, and Vickers microhardness measurements. The corrosion resistance of the individual melts was verified by the Audi test.Peer reviewe
Advances in the understanding of alkaline waste materials as potential eco-pozzolans: Characterisation, reactivity and behaviour
Get PDFPublisher Copyright: © 2024 The AuthorsThis paper explores the potential of three industrial alkaline waste materials – concrete construction and demolition waste (CDW-C), white ladle furnace slag (LFS) and biomass ash (BA) – for use as secondary raw materials in the manufacture of future eco-cements with a reduced carbon footprint. Circularity is one of the key strategies behind the circular economy, the cement industry roadmap and the climate neutrality targets set for 2050. The three materials were characterised using various instrumental techniques (XRF, laser, BET, XRD–Rietveld, SEM/EDX, FTIR, TG/DTA and NMR) and their chemical reactivity, the changes in their mineralogical phases and the thermodynamic stability within the pozzolan/lime system were determined. Finally, their physical and mechanical behaviour in binary cement pastes at replacement proportions of 7 % and 20 % over 90 days of curing were analysed. The results obtained show that these alkaline waste materials are different in nature and composition to standard pozzolans, with the LFS containing fluorine and the BA containing sulphates, potash and chlorides. The standard combined water test showed different levels of chemical reactivity (BA > LFS > CDW-C). Analysis of the materials’ composition, mineralogical phases and thermodynamic stability over 90 days of reaction in the pozzolan/Ca(OH)2 system revealed that hydrogarnet was the stable phase in the LFS cement paste, while in the BA and CDW-C pastes ettringite and CSH and C-(A)-SH gels, among others, were the stable phases. All the blended cement pastes with 7 % and 20 % replacement content met the physical requirements and maintained the strength category of the starting cement.Peer reviewe
Effect of enzyme lignin oxidation by laccase on the enzymatic-mechanical production process of lignocellulose nanofibrils from mechanical pulp
No full textPublisher Copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2024.The use of endoglucanase enzymes as pretreatment of high-yield pulps to produce lignocellulose nanofibrils (LCNFs) has garnered increasing interest at both industrial and scientific levels. However, the lignin present in the lignocellulosic fibers hinders the enzymatic treatment reducing the efficiency of the further fibrillation process. This work postulates that modifying the structure of the residual lignin in the pulp can help to improve LCNF production. Laccase-mediator system (LMS) was evaluated to promote lignin oxidation of pressurized groundwood pulp from Pinus radiata previous to a treatment with endoglucanases and mechanical refining to produce LCNFs. As a result, it was observed that the LMS treatment improved the accessibility of the endoglucanase enzyme in the fibers, increasing their efficiency. Furthermore, it was observed a reduction in residual lignin and an increment in acidic groups in the LMS treated pulps facilitated the mechanical fibrillation process, enabling the production of LCNFs with a high aspect ratio. It was also observed that the pulps treated with a laccase-endoglucanase combination allowed to production of LCNF suspensions with zeta potential values sufficient for the nanofibrils not to form aggregates and to be considered stable (< − 25 mV).Peer reviewe
Monitoring Mooring Lines of Floating Offshore Wind Turbines: Autoregressive Coefficients and Stacked Auto-Associative-Deep Neural Networks
No full textPublisher Copyright: © 2024 20th International Conference on Condition Monitoring and Asset Management, CM 2024. All rights reserved.This study introduces a pioneering monitoring system designed to mitigate operational costs and enhance the sustainability of Floating Offshore Wind Turbines (FOWT). The proposed framework combines Autoregressive models with a Stacked Auto-Associative-based Deep Neural Network (AANN-DNN) to detect and classify damages in mooring systems of FOWTs. By extracting damage-sensitive features (DSFs) using the AR models from time-series data and employing unsupervised learning in the auto-associative neural network, followed by supervised training with DNN, the approach demonstrates exceptional accuracy in damage identification and classification. Numerical simulations conducted using NREL’s OpenFAST software under diverse metocean conditions validate the method’s efficacy, offering a promising solution for efficient FOWT mooring line monitoring.Peer reviewe
Development of Coated PLA Films Containing a Commercial Olive Leaf Extract for the Food Packaging Sector
Get PDFPublisher Copyright: © 2024 by the authors.A commercial olive leaf extract (OL), effective against Salmonella enterica, Escherichia coli, Listeria monocytogenes, and Staphylococcus aureus, was added to three different coating formulations (methylcellulose, MC; chitosan, CT; and alginate, ALG) to produce active polylactic acid (PLA) coated films. Evaluation of these coated PLA films revealed significant inhibition of S. aureus growth, particularly with the MC and CT formulations exhibiting the highest inhibition rates (99.7%). The coated films were then tested for food contact compatibility with three food simulants (A: 10% ethanol; B: 3% acetic acid; D2: olive oil), selected to assess their suitability for pre-cut hams and ready-to-eat vegetables in relation to overall migration. However, coated films with active functions exhibited migration values in simulants A and B above legal limits, while promising results were obtained for simulant D2, highlighting the need to deeply investigate these coatings’ impact on a real food system. Untargeted metabolomics revealed that the type of coating influenced the selective release of certain phenolic classes based on the food simulant tested. The Oxitest analysis of simulant D2 demonstrated that the MC and ALG-coated PLA films slightly slowed down the oxidation of this food simulant, which is an edible vegetable oil.Peer reviewe
Polyurethane/acrylic coatings for wood made hydrophobic by melamine addition
No full textPublisher Copyright: © 2023 Elsevier B.V.Hydrophobic polyurethane/acrylic hybrid dispersions were obtained for exterior wood coatings. Thus, polyurethane was synthesised using as solvent methyl methacrylate, and after phase inversion provoked by water addition, the acrylic monomer radically polymerised giving rise to the polyurethane/acrylic hybrid dispersion. Melamine was added as a hydrophobic and fire retardant additive by two different approaches: in the phase inversion (A) and the beginning of the synthesis (B). FTIR-ATR, 1H NMR, and molar mass measurements (AF4/MALS/RI) showed that in the B method, the melamine reacted with isocyanate. However, the addition of melamine in the phase inversion provoked a reduction of the molecular weight of the acrylic part because of the interaction with the radical initiator. According to TGA, Pyrolysis Combustion Flow Calorimetry (PCFC), and water permeation experiments, melamine induced the formation of a carbonaceous char when heating and reduced the water vapour transmission rate of the hybrid films. Moreover, wood application tests showed that the incorporation of melamine improved the transparency, adhesion, and water resistance of the coatings but the employed amount was not enough to produce significant changes in the fire behaviour of the coatings measured in the cone calorimetry.The University of the Basque Country (UPV/EHU) predoctoral grant of M. Puyadena and the funding by the Basque Government through grant IT1667-22 and PIBA20/16 are gratefully acknowledged. Technical and human support provided by SGIker is also sincerely acknowledged (UPV/EHU/ERDF, EU). The University of the Basque Country (UPV/EHU) predoctoral grant of M. Puyadena and the funding by the Basque Government through grant IT1667-22 and PIBA20/16 are gratefully acknowledged. Technical and human support provided by SGIker is also sincerely acknowledged (UPV/EHU/ERDF, EU).Peer reviewe