Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms

Ahmed M. Nassef, Ahmed Fathy, Enas Taha Sayed, Mohammad Ali Abdelkareem, Hegazy Rezk, Waqas Hassan Tanveer and A.G. Olabi

Renewable Energy, 2019, vol. 138, issue C, 458-464

Abstract: A modern optimization algorithm is used for maximizing the performance of solid oxide fuel cell. At first, the cell is modeled using Artificial Neural Networks based on the experimental data sets. Then, a robust, simple, and quick optimization algorithm named radial movement optimizer is used for determining the optimal operating parameters of the cell. The cell parameters used in the optimization process are anode support layer thickness, anode porosity, electrolyte thickness, and cathode interlayer thickness. The optimization obtained results are compared with the previous optimized experimental results and those obtained using genetic algorithm. Two sets of the parameters' constraints are considered during the optimization process. In the first set, the resulting optimal cell parameters are 0.5 mm, 76%, 20 μm, and 62.26 μm for anode thickness, anode porosity, electrolyte thickness, and cathode thickness respectively. Under this condition, the cell maximum power density is 1.8 W/cm2, 2.25 W/cm2 and 2.72 W/cm2 for experimentally, genetic algorithm and the proposed strategy, respectively. This implies that using the proposed method increases the power density by 33.8% and 17.28% over the experimental and genetic, respectively. In the second set, the proposed optimizer increases the maximum power by 28.85% compared with genetic optimizer.

Keywords: SOFC; Parameter identification; Radial movement optimizer; Energy efficiency (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148119300874
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:138:y:2019:i:c:p:458-464

DOI: 10.1016/j.renene.2019.01.072

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().