AI Insight
This study develops an optimal design model for solid oxide fuel cells (SOFCs) using the Puma Optimization Algorithm (POA) to extract unknown parameters from SOFC stack performance data. The researchers tested the model under four operating conditions at temperatures ranging from 923-1073 K and 3 bar pressure, finding that POA outperformed several established optimization algorithms including Marine Predator Algorithm, Moth Flame Algorithm, Sine Cosine Algorithm, and Grey Wolf Optimizer. The computed polarization curves closely matched experimental measurements, with statistical analysis confirming the superior convergence rates and parameter extraction accuracy of the proposed method.
Why it matters
Improving SOFC modeling and parameter optimization can accelerate the development of more efficient fuel cells for clean energy applications in both stationary power generation and mobile equipment. Better predictive models enable faster design iteration and optimization without extensive experimental testing, potentially reducing development costs and time for this promising energy conversion technology.
Understand the Science
by Amlak Abaza, Ragab A. El-Sehiemy, Rania M. Ghoniem, Mahana M. Elbana, Ahmed Bayoumi
One promising technology for a clean and effective energy conversion option is the solid oxide fuel cell (SOFC) being developed for a broad, widespread role in mobile equipment power supply, and stationary power generation. In this endeavor, an optimal design model based on extracted unknown parameters of the SOFC stack, a dimensional nonlinear optimization problem, is developed using the Puma optimization algorithm (POA). The idea of predator-prey relationships in the natural world forms the basis of POA. By implementing innovative and powerful techniques at every stage of exploration and exploitation, this algorithm has enhanced its performance against a broad variety of optimization tasks. Additionally, a new class of intelligent mechanisms, which is a type of phase change hyper-heuristic, is proposed. There are four operating circumstances in which the stack model is tested: four temperatures in the range 923–1073 K and 3 bar, with two conditions for validation and the others for testing the model. The proposed POA is compared with several well-known algorithms. The findings of the simulation are contrasted with those from published works using the Marine Predator Algorithm (MPA), Moth Flame Algorithm (MFA), Sine Cosine Algorithm (SCA), and Grey Wolf Optimizer (GWO), demonstrating the superior performance of POA in comparison to these competitive algorithms. Under different operating conditions, the computed polarization curves, V-I and P-I, closely resemble the measured datasets. Statistical indices and the ANOVA test confirm that there are differences in the mean values among the optimizer groups, demonstrating the viability and robustness of the proposed optimizer in comparison to other recent complex optimizers. Finally, the proposed POA yields significantly improved parameters with good convergence rates across various SOFC operating conditions.