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- Title
A COMPERATIVE STUDY ON ARTIFICIAL NEURAL NETWORK-BASED MULTI-OBJECTIVE OPTIMIZATION FOR PROTON EXCHANGE MEMBRANE FUEL CELL.
- Authors
Ghosh, Sankhadeep; Routh, Avijit; Mondal, Saikat; Rahaman, Mehabub; Ghosh, Avijit
- Abstract
Proton exchange membrane fuel cells (PEMFCs) offer significant potential for conversion from chemical energy to electrical energy due to their significant low-temperature range, high power density, mobility, and increasing start-up. PEMFC design and applications have been simplified through the utilization of artificial neural network (ANN) based multi-objective optimization (ANN-MOO). This approach can be easily adjusted to consider multiple factors simultaneously, even when dealing with customized objective functions or new case scenarios. This study thoroughly examines the existing body of literature on the application of Artificial Neural Network-Multi-Objective Optimization in the Proton Exchange Membrane Fuel Cell industry. The original elucidation of ANN-MOO provided a full explanation of its definition, classifications, and structure. The study evaluates objectives, intelligence algorithms, and tradeoff methods in PEMFC, focusing on ANN-MOO's application in components, kinetics, control, performance, and hybrid systems. The relevant issues, including the techniques, variables, objectives, and outcomes of optimization, were enumerated and discussed. The present review aimed to examine the existing limitations in the field of study and put up suggestions for future research.
- Subjects
PROTON exchange membrane fuel cells; ARTIFICIAL neural networks; CHEMICAL energy conversion; FUEL cell industry; FUEL cells; ELECTRICAL energy
- Publication
Rasayan Journal of Chemistry, 2024, Vol 17, Issue 2, p576
- ISSN
0974-1496
- Publication type
Article
- DOI
10.31788/RJC.2024.1728807