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![Figure 3. Schematic Diagram of Molten Carbonate Fuel Cell [6]](publication/341162885/figure/fig2/AS:887880067063809@1588698512104/Schematic-Diagram-of-Molten-Carbonate-Fuel-Cell-6_Q320.jpg)
This study aims to review the issues affecting the long term performance and the life span of the fuel cell in accordance with the various surveys of the currently available. According to current research, parameters such as temperature, pressure along with other issues such as fuel and oxidant starvation (stoichiometric effect), corrosion, poor wa...
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... is also called as Polymer electrolyte membrane as in figure 2, these fuel cell provides high power density and the advantages such as low weight and volume as compared to other types of fuel cells. These fuel cells also employ porous carbon electrodes containing a platinum catalyst and a solid polymer as the electrolyte. ...
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... Polarization curves representing active, ohmic, and activation regions[66]. ...
In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine-electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information , which gives an unobtrusive, rapid yet restricted examination, and advanced characterization , which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.
