Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms. (August 2019)

• Record Type: Journal Article
• Authors: Nassef, Ahmed M.; Fathy, Ahmed; Sayed, Enas Taha; Abdelkareem, Mohammad Ali; Rezk, Hegazy; Tanveer, Waqas Hassan; Olabi, A.G.
• Is Part Of: Renewable energy. Volume 138(2019)
• Page Start: 458
• Page End: 464
• Publication Date: 2019-08
• Subjects: SOFC -- Parameter identification -- Radial movement optimizer -- Energy efficiency; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.01.072
• Languages: English
• ISSNs: 0960-1481

Abstract

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/cm 2, 2.25 W/cm 2 and 2.72 W/cm 2 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. Highlights: SOFC is modeled using Artificial Neural Networks (ANN) based on experimental data sets. Radial movement optimizer (RMO) is used for determining the optimal operating parameters of the SOFC. The optimized parameters are ASL thickness and porosity, electrolyte thickness, and cathode interlayer thickness. A 33.8% and 17.28% increase in power density attained compared to experimental and genetic optimization.