首页|New Artificial Intelligence Study Findings Have Been Reported by Researchers at British University of Egypt (A Comprehensive Survey of Artificial Intelligence-b ased Techniques for Performance Enhancement of Solid Oxide Fuel Cells: Test Case s ...)
New Artificial Intelligence Study Findings Have Been Reported by Researchers at British University of Egypt (A Comprehensive Survey of Artificial Intelligence-b ased Techniques for Performance Enhancement of Solid Oxide Fuel Cells: Test Case s ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on Artificial Intelligence are discussed in a new report. According to news reporting originating in Cairo, Egy pt, by NewsRx journalists, research stated, "Since installing solid oxide fuel c ells (SOFCs)-based systems suffers from high expenses, accurate and reliable mod eling is heavily demanded to detect any design issue prior to the system establi shment. However, such mathematical models comprise certain unknowns that should be properly estimated to effectively describe the actual operation of SOFCs." Financial support for this research came from British University in Egypt (BUE). The news reporters obtained a quote from the research from the British Universit y of Egypt, "Accordingly, due to their recent promising achievements, a tremendo us number of metaheuristic optimizers (MHOs) have been utilized to handle this t ask. Hence, this effort targets providing a novel thorough review of the most re cent MHOs applied to define the ungiven parameters of SOFCs stacks. Specifically, among over 300 attempts, only 175 articles are reported, where thirty up-to-da te MHOs from the last five years are comprehensively illustrated. Particularly, the discussed MHOs are classified according to their behavior into; evolutionary -based, physics-based, swarm-based, and nature-based algorithms. Each is touched with a brief of their inspiration, features, merits, and demerits, along with t heir results in SOFC parameters determination. Furthermore, an overall platform is constructed where the reader can easily investigate each algorithm individual ly in terms of its governing factors, besides, the simulation circumstances rela ted to the studied SOFC test cases. Over and above, numerical simulations are al so introduced for commercial SOFCs' stacks to evaluate the proposed MHOs-based m ethodology. Moreover, the mathematical formulation of various assessment criteri a is systematically presented."
CairoEgyptAfricaArtificial Intelli genceEmerging TechnologiesMachine LearningMathematicsBritish University of Egypt.
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