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New Paradigms in Flow Battery Modelling
This book provides a comprehensive review of the latest modelling developments in flow batteries, as well as some new results and insights. Flow batteries have long been considered the most flexible answer to grid scale energy storage, and modelling is a key component in their development. Recent modelling has moved beyond macroscopic methods, towards mesoscopic and smaller scales to select materials and design components. This is important for both fundamental understanding and the design of new electrode, catalyst and electrolyte materials. There has also been a recent explosion in interest in machine learning for electrochemical energy technologies. The scope of the book includes these la...
Energy Storage Technologies in Grid Modernization
ENERGY STORAGE TECHNOLOGIES IN GRID MODERNIZATION Written and edited by a team of experts, this exciting new volume discusses the various types of energy storage technologies, the applications of energy storage systems, their role in the real-time operation of power markets, and the operational issues of modern power systems, including renewable-based generating sources. The worldwide energy sector, specifically power generation, has undergone a huge transformation in recent years, and the focus is to make it sustainable, environmentally friendly, reliable, and highly efficient. As a result, a significant share of highly intermittent but clean renewable sources is being integrated into the p...
Polymer Electrolyte-Based Electrochemical Devices
Polymer Electrolyte–Based Electrochemical Devices: Advances, provides a complete overview of the theoretical and applied aspects of energy-related polymer electrolyte-based technologies. The book presents detailed thermodynamic and other basic requirements for smart materials like fuel cells, electrolyzers, batteries, sensors and devices for the abatement of pollutants. Delving into the physical-chemical, electrochemical, and mechanical properties of smart materials, it covers fundamental analysis and modeling to optimize the application of smart materials in terms of conductivity, chemical stability and kinetic properties. Detailed protocols for operation are suggested and discussed, incl...
Electrode Materials for Energy Storage and Conversion
This book provides a comprehensive overview of the latest developments and materials used in electrochemical energy storage and conversion devices, including lithium-ion batteries, sodium-ion batteries, zinc-ion batteries, supercapacitors and conversion materials for solar and fuel cells. Chapters introduce the technologies behind each material, in addition to the fundamental principles of the devices, and their wider impact and contribution to the field. This book will be an ideal reference for researchers and individuals working in industries based on energy storage and conversion technologies across physics, chemistry and engineering. FEATURES Edited by established authorities, with chapt...
Renewable and Scalable Energy Storage Materials Derived from Quinones in Biomass
Currently there is an urgent need to reduce the use of fossil fuels, and efficient sustainable energy harvesters from sun and wind have been developed and are widely used for electricity generation. Storage of electrical energy is accordingly necessary to accommodate the time varying supply of wind and solar electricity. Quinones (Q) are attractive as energy storage materials due to their high theoretical charge density and the renewable and abundant source – biomass. Plant-based biomass materials – such as lignin and humic acids – contain redox active Q-groups that potentially could be used for electricity storage instead of simply burning the biomass, which releases CO2, CH4, NOx, an...
Le tour du monde
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Deep Learning for Hyperspectral Image Analysis and Classification
This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
