435 research outputs found

    Numerical Analysis of Lithium-ion Battery Thermal Management System Towards Fire Safety Improvement

    Full text link
    The development of alternative energy sources aims to tackle the energy crisis and climate change. Due to the intermittent nature of renewable energy, energy storage systems find antidotes to the current flaws for ensuring a stable and consistent power supply and reducing our reliance on fossil fuels. Lithium-ion batteries are the most used energy storage unit and have been applied in many fields, such as portable devices, building infrastructure, automotive industries, etc. Nevertheless, there remain significant safety concerns and fire risks. Thus, this has created much interest particularly in developing a comprehensive numerical tool to effectively assess the thermal behaviour and safety performance of battery thermal management systems (BTMs). In this thesis, a modelling framework was built by integrating the artificial neural network model with the computational fluid dynamics analysis. This includes (i) a comparison of natural ventilation and forced air cooling under various ambient pressures; (ii) an analysis of thermal behaviour and cooling performance with different ambient temperatures and ventilation velocities; and (iii) optimisation of battery pack layout for enhancing the cooling efficiency and reducing the risks of thermal runaway and fire outbreak. The optimal battery design achieved a 1.9% decrease in maximum temperature and a 4.5% drop in temperature difference. Moreover, this thesis delivered an overall review of BTMs employing machine learning (ML) techniques and the application of various ML models in battery fire diagnosis and early warning, which brings new insights into BTMs design and anticipates further smart battery systems. In addition, the battery thermal propagation effect under various abnormal heat generation locations was demonstrated to investigate several stipulating thermal propagation scenarios for enhancing battery thermal performances. The results indicated that various abnormal heat locations disperse heat to the surrounding coolant and other cells, affecting the cooling performance of the battery pack. The feasibility of compiling all pertinent information, including battery parameters and operation conditions, was studied in this thesis since ML models can build non-related factors relationships. The integrated numerical model offers a promising and efficient tool for simultaneously optimising multiple factors in battery design and facilitates a constructive understanding of battery performance and potential risks

    Abstracts of the 1st GeoDays, 14th–17th March 2023, Helsinki, Finland

    Get PDF
    Non peer reviewe

    A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

    Get PDF
    The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

    Quantification of the emission, impact and control of odour derived from urban wastewater treatment

    Get PDF
    En la sociedad actual, especialmente en los países más desarrollados, no se concibe una urbe donde no se realice un adecuado tratamiento de las aguas residuales, previamente canalizadas hasta una estación depuradora (EDAR). Sin embargo, el impacto odorífero de este tipo de instalaciones es fuente de frecuentes quejas y protestas en las áreas residenciales cercanas a las mismas, debido a que la contaminación por olores puede causar importantes efectos negativos sobre la salud humana y el medio ambiente. Dicha contaminación suele derivar de la presencia de compuestos orgánicos volátiles (COV) y otros nitrogenados o sulfurados en las emisiones gaseosas de las EDAR, algunos de los cuales presentan umbrales olfativos muy bajos (ppb o ppt). En el marco de la economía circular, abordar las prioridades sociales con un enfoque múltiple es una de las directrices marcadas por todas las instituciones gubernamentales. Siguiendo esa dinámica, este trabajo de investigación se ha centrado en dos retos sociales marcados por la Unión Europea: “Acción por el clima, eficiencia de recursos y materias primas” y “Energía segura, limpia y eficiente”. El tratamiento integral del agua residual en las EDAR, y todos los factores asociados a él, forman parte del primer reto, lo que incluye, por tanto, la correcta gestión de las emisiones odoríferas contaminantes. En este sentido, en la presente Tesis Doctoral se ha abordado el origen, cuantificación y modelado de la dispersión de dichas emisiones, así como su control a través de dos tecnologías de desodorización: la adsorción mediante carbón activo granular (CAG) y la biofiltración. En el primer bloque de este trabajo, se ha realizado una comparación entre dos EDAR urbanas de pequeño-mediano tamaño con diferentes tecnologías biológicas y ampliamente implantadas en el tratamiento secundario de las aguas residuales (fangos activos de aireación prolongada y biodiscos), demostrando que el modo de operación y el consecuente contenido bacteriano de los lodos generados tienen gran influencia en el impacto odorífero resultante. Por otra parte, también se ha estudiado la emisión odorífera de una EDAR urbana de gran tamaño (950.000 habitantes equivalentes) que dispone de desodorización mediante CAG. Así, se ha cuantificado la emisión de sus principales puntos críticos de olor, con el añadido de estimar el impacto odorífero que en su conjunto produce la EDAR en zonas colindantes. Dicho objetivo se ha alcanzado a través del desarrollo de un modelo de dispersión Euleriano, demostrándose de forma satisfactoria cómo varía de forma cuantitativa dicho impacto en función de las diferentes estaciones del año. En un segundo bloque, gracias al estudio del CAG contaminado (procedente de distintos emplazamientos en la desodorización de la EDAR urbana mencionada con anterioridad), desde diferentes perspectivas como la olfatométrica, fisicoquímica y textural, así como al análisis cuantitativo de los compuestos volátiles retenidos, se ha profundizado en la comprensión del proceso de eliminación de olores en EDAR mediante la tecnología de adsorción. Sin embargo, una vez cubierta su función, el citado material adsorbente es catalogado como residuo peligroso y generalmente termina siendo depositado en vertedero sin tratamiento ni valorización alguna. Entre las alternativas de valorización, la regeneración térmica del CAG en atmósfera inerte es la más utilizada a escala industrial, pero las complejas condiciones en las que es necesario realizarla hacen que actualmente su depósito en vertedero siga resultando la opción más económica, puesto que, aunque el producto resultante sea de valor añadido, el coste de implantación de un proceso supone la inversión en la nueva instalación, sumado a los costes de operación para mantener las condiciones inertes y el aporte energético, dado que se requieren altas temperaturas de operación. Todo ello, hacen poco atractiva la inversión en este sistema de valorización. Además, transportar el CAG contaminado a otras localizaciones geográficas alejadas incrementa aún más el coste de regeneración haciendo poco escogida esta opción. En este contexto, este trabajo ha permitido demostrar que la regeneración térmica oxidativa del CAG procedente de la desodorización, a bajas temperaturas comprendidas entre 250 y 350ºC, constituye una alternativa más simple y económica que la citada anteriormente, al objeto de obtener carbones regenerados cuyas propiedades texturales y estructurales hacen que sean susceptibles de ser reutilizados como rellenos adsorbentes de olores en EDAR. Desde una perspectiva también circular, y dando respuesta al reto “Energía segura, limpia y eficiente”, en este segundo bloque se ha enlazado el problema de la generación de residuos en forma de carbón activado contaminado de las EDAR con la necesidad de materiales carbonosos para la nueva generación de baterías de litio-azufre (Li-S), las cuales han ido adquiriendo gran importancia, hasta el punto de convertirse en un sistema de almacenamiento de energía muy efectivo. Con esta investigación, se ha conseguido demostrar el notable rendimiento electroquímico obtenido en baterías Li-S usando electrodos preparados a partir de carbones procedentes de la regeneración oxidativa de carbones activados procedentes del control de emisiones olorosas de EDAR. De esta manera, se ha demostrado que, tras un proceso sencillo y económico de regeneración en atmósfera oxidativa (aire), también es posible obtener carbones con excelentes propiedades para permitir una segunda aplicación de los mismos en el desarrollo de baterías Li-S sostenibles. Finalmente, en el tercer y último bloque, se ha abordado la eliminación mediante biofiltración de dos compuestos gaseosos presentes de forma habitual en las emisiones odoríferas de EDAR: ácido butírico y D-limoneno. Para ello, se han realizado experimentos de biofiltración a escala piloto con diferentes rellenos (virutas de madera de forma exclusiva o mezcladas con compost estabilizado de lodos de EDAR), estudiando la influencia que tienen tanto el material de relleno como la naturaleza del compuesto a eliminar mediante biofiltración en las eficacias de eliminación de olor de los diferentes biofiltros seleccionados. Todo ello acompañado de análisis microbiológicos que han permitido cuantificar los microorganismos aerobios que sobreviven durante la experimentación, así como la identificación taxonómica de las comunidades bacterianas presentes en los rellenos de los biofiltros, con el objetivo de evaluar la evolución de estas comunidades microbianas cuando se exponen a corrientes gaseosas independientes de ácido butírico y D-limoneno. Gracias a la presente Tesis Doctoral, se aporta nueva y relevante información, así como conocimiento científico que puede servir de apoyo para una correcta implantación y gestión de EDAR, sobre todo en la línea de olor, línea menos estudiada frente a las líneas de aguas y lodos. Además, este trabajo tiene una repercusión muy favorable sobre el medio ambiente, en tanto que contribuye a profundizar en el conocimiento sobre las emisiones odoríferas generadas en las EDAR y su minimización, así como en la búsqueda de alternativas de valorización de los residuos que se generan en el tratamiento de tales emisiones contaminantes.Wastewater treatment is essential for the development of cities in today's society, especially in the most developed countries. Nevertheless, the odour impact of wastewater treatment plants (WWTPs) is the source of many complaints and protests in nearby residential areas, since odour pollution can cause significant negative effects on human health and the environment. This is due to the large number of volatile organic compounds (VOCs) and other nitrogenous or sulphur compounds contained in the gaseous emissions derived from such facilities, some of which have very low odour threshold values in terms of ppbv or pptv. In the context of the circular economy, addressing social priorities with a multiple pproach is one of the guidelines set by all government institutions. In this regard, this research work focuses on two societal challenges set by the European Union: "Climate action, environment, resource efficiency and raw materials" and "Secure, clean and efficient energy". The integral wastewater treatment carried out in WWTPs, and all the factors associated with it, are part of the first challenge, which therefore includes the adequate management of odour pollution. In this sense, this Doctoral Thesis has addressed the origin, quantification and modelling of the dispersion of odour emissions, as well as their control through two deodorisation technologies: adsorption by means of granular activated carbon (GAC) and biofiltration. In the first section of this research study, two small-medium sized municipal WWTPs, based on activated sludge process (extended aeration) and rotating biological contactors as biological treatments, were comparatively evaluated, demonstrating that the biological wastewater treatment technology and the consequent bacterial content in the sludge generated have a marked influence on the odour impact of such facilities. On the other hand, the odour emission from a large urban WWTP (950,000 equivalent inhabitants), with deodorisation thorough GAC, has also been studied. Thus, the emission from the most critical odour sources has been quantified, also estimating the odour impact that this WWTP has in neighbouring areas. This objective has been achieved through the development of an Eulerian dispersion model, successfully demonstrating how such impact varies quantitatively depending on the different seasons of the year. In the second section, physicochemical, olfactometric and textural characterizations of the GAC used by the above mentioned facility as odour treatment system at four different stages, as well as the chromatographic quantification of the retained odoriferous compounds, have been carried out in order to better understand the odour removal process by GAC adsorption. However, when the lifespan of GAC used in deodorisation is completed, it becomes hazardous industrial waste, which is mostly discarded in landfills. Among the recovery technologies of such waste, thermal regeneration in inert atmosphere is the most widely used one at industrial scale, but the complex conditions in which it is necessary to carry it out entail that landfilling continues to be the most economical alternative nowadays, although the resulting product has added value. This is due to the fact that implementing a process involves investing in the new installation, added to the operational costs to maintain the inert conditions and the energy input, since high operating temperatures are required. For those reasons, such recovery technology is not an attractive alternative for facilities that use CAG routinely. In this context, this study has proven that the oxidative thermal regeneration of GAC derived from deodorisation, at low temperatures between 250 and 350 ºC, constitutes a simpler and cheaper alternative than its counterpart in an inert atmosphere to obtain regenerated carbons whose textural and structural properties make them susceptible to being reused as odour adsorbents in WWTPs. Furthermore, from a circular perspective as well and responding to the challenge “Secure, clean and efficient energy”, the problem of the generation of waste in terms of contaminated activated carbon from WWTPs has been linked to the need for carbonaceous materials for the sustainable development of emerging energy storage systems, such as lithium-sulphur (Li-S) batteries. In this sense, this PhD Thesis demonstrates the remarkable electrochemical performance of Li-S batteries using electrodes prepared from carbon from the oxidative thermal regeneration of activated carbons used in WWTP deodorisation. In this way, it has been shown that, after a simple and economical process of regeneration in air atmosphere, it is also possible to obtain carbon with excellent properties for contributing to the development of sustainable Li-S batteries. Finally, in the third and last section, the removal through biofiltration of two gaseous compounds commonly present in odour emissions derived from WWTPs, butyric acid and D-limonene, has been evaluated. For this purpose, several biofiltration experiments have been carried out on a pilot scale with different packed beds (wood chips exclusively or mixed with sewage sludge compost), studying the influence of the biofiltered compound as well as the filter bed on the odour removal performance. The study has been successfully complemented with microbiological monitoring to quantify the aerobic microorganisms that survived during the experimentation, as well as the taxonomic identification of the bacterial communities present in the above mentioned packing materials, with the aim of evaluating the evolution of such communities when they are subjected to separate gaseous streams of butyric acid and D-limonene. As a result of this Doctoral Thesis, new and relevant results are provided, as well as scientific knowledge that might serve as support for an adequate implementation and management of WWTPs, especially in the odour line, which is a less studied field compared to the wastewater line and sludge line. In addition, this research study has a significant favourable impact on the environment, since it contributes to deepen the knowledge on odour emissions derived from WWTPs and their minimization, as well as on the search for alternatives to recover waste generated in the treatment of such polluting emissions

    Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

    Full text link
    Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

    日射量予測を考慮した太陽光発電コミュニティにおけるエネルギーシェアリングに関する研究

    Get PDF
    The power sector plays an important role in energy conservation and emission reduction. Renewable energy, especially solar PV, has been growing steadily in recent years. The development of solar energy can not only reduce the use of fossil energy, but also increase the energy self-sufficiency rate. After the implementation of the FiT system in 2011, there has been an explosive growth in the import of solar PV. However, solar power generation exhibits unstable output characteristics as it is affected by weather conditions. Large-scale introduction can affect the stability of the grid. Therefore, this study considers the unstable weather conditions (mainly, solar radiation) and proposes the concept of energy sharing to increase the chances of local energy self-consumption and renewable energy penetration in the future. At the same time, we aim to explore the interactions between smart grids, smart buildings, and distributed energy storage to achieve better energy management practices.北九州市立大

    Fundamentals

    Get PDF
    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
    corecore