Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process. (15th September 2020)

• Record Type: Journal Article
• Authors: Fathy, Ahmed; Rezk, Hegazy; Mohamed Ramadan, Haitham Saad
• Is Part Of: Energy. Volume 207(2020)
• Publication Date: 2020-09-15
• Subjects: Solid oxide fuel cell -- Parameter extraction -- Moth-flame optimizer -- Energy efficiency; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118326
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes a recent approach-based moth-flame optimizer (MFO) to enhance the output power of solid oxide fuel cell (SOFC) via identifying the optimal parameters of its model. The cell is modeled via artificial neural network (ANN) trained by experimental dataset. Six inputs are fed to ANN to get the SOFC terminal voltage. Levenberg-Marquardt is used in training process with minimizing the mean squared error (MSE). The SOFC model polarization curves are compared to experimental data under variable operating conditions. The proposed MFO is employed to estimate the optimal values of SOFC model, anode support layer (ASL) thickness; ASL porosity; thickness of electrolyte and cathode functional layer (CFL) thickness to enhance the SOFC extracted power. Furthermore, a quantitative and qualitative comparative study with ANN-based SOFC optimized via Genetic Algorithm (GA), Social Spider Optimizer (SSO), Radial Movement Optimizer (RMO) and the experimental data is presented under different operating conditions. Sensitivity analysis is performed by changing the upper and lower thresholds of the estimated variables. The proposed ANN-MFO approach enhanced the SOFC power by 18.92% and 5.56% in comparison with ANN-GA and ANN-RMO respectively. The obtained results confirmed the significance of the proposed MFO in enhancing of the SOFC output power. Highlights: SOFC is modeled via artificial neural network (ANN) trained by experimental dataset. MFO is used to identify the optimal parameters of SOFC to enhance the output power. Comparison to ANN-based SOFC optimized via GA, SSO and RMO is presented. Sensitivity analysis is performed by changing the upper and lower thresholds of the estimated variables. The output power of SOFC based MFO is increased by 18.92% and 5.56% compared to GA and RMO.