Massimo Santarelli – författare

Solar Hydrogen Production: Processes, Systems and Technologies presents the most recent developments in solar-driven hydrogen generation methods. The book covers different hydrogen production routes, from renewable sources, to solar harvesting technologies. Sections focus on solar energy, presenting the main thermal and electrical technologies suitable for possible integration into solar-based hydrogen production systems and present a thorough examination of solar hydrogen technologies, ranging from solar-driven water electrolysis and solar thermal methods, to photo-catalytic and biological processes. All hydrogen-based technologies are covered, including data regarding the state-of-the art of each process in terms of costs, efficiency, measured parameters, experimental analyses, and demonstration projects.

In the last part of the book, the role of hydrogen in the integration of renewable sources in electric grids, transportation sector, and end-user applications is assessed, considering their current status and future perspectives. The book includes performance data, tables, models and references to available standards. It is thus a key-resource for engineering researchers and scientists, in both academic and industrial contexts, involved in designing, planning and developing solar hydrogen systems.

Fuel cells are widely regarded as the future of the power and transportation industries. Intensive research in this area now requires new methods of fuel cell operation modeling and cell design. Typical mathematical models are based on the physical process description of fuel cells and require a detailed knowledge of the microscopic properties that govern both chemical and electrochemical reactions. Advanced Methods of Solid Oxide Fuel Cell Modeling proposes the alternative methodology of generalized artificial neural networks (ANN) solid oxide fuel cell (SOFC) modeling.

Advanced Methods of Solid Oxide Fuel Cell Modeling provides a comprehensive description of modern fuel cell theory and a guide to the mathematical modeling of SOFCs, with particular emphasis on the use of ANNs. Up to now,  most of the equations involved in SOFC models have required the addition of numerous factors that are difficult to determine. The artificial neural network (ANN) can be applied to simulate an object’s behavior without an algorithmic solution, merely by utilizing available experimental data.

The ANN methodology discussed in Advanced Methods of Solid Oxide Fuel Cell Modeling can be used by both researchers and professionals to optimize SOFC design. Readers will have access to detailed material on universal fuel cell modeling and design process optimization, and will also be able to discover comprehensive information on fuel cells and artificial intelligence theory.