Active direct methanol fuel cell operating parameters optimization based on Propagation of Error and Response Surface Methodology.
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| Title: | Active direct methanol fuel cell operating parameters optimization based on Propagation of Error and Response Surface Methodology. |
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| Authors: | Zhao, Zhengang1,2 (AUTHOR), Yang, Xinyi1 (AUTHOR), Zhang, Dacheng1,3 (AUTHOR), Yang, Bo1 (AUTHOR) bo.yang@kust.edu.cn |
| Source: | International Journal of Green Energy. 2025, Vol. 22 Issue 13, p2889-2898. 10p. |
| Subject Terms: | *Direct methanol fuel cells, *Response surfaces (Statistics), *Sensitivity analysis, *Empirical research, *Monte Carlo method, *Mathematical optimization, *Equilibrium, *Power density |
| Abstract: | This paper proposes a robust design methodology combining Response Surface Methodology (RSM) and Propagation of Error (POE) to optimize the key operating parameters of Direct Methanol Fuel Cells (DMFC). The primary objective is to enhance power density while minimizing fluctuations in the target response, thereby ensuring stable and efficient performance. Three key parameters – methanol concentration, methanol flow rate, and temperature are optimized using experimental data, which are modeled with a quadratic regression model. The model is validated through Analysis of Variance (ANOVA), and multi-response optimization is performed by introducing POE. Monte Carlo Simulation (MCS) is employed to validate the robustness of the optimized conditions, demonstrating reduced sensitivity to parameter variations and ensuring stable performance. The results show that by incorporating POE, a slight trade-off in maximum power density can lead to significantly improved stability, making this approach promising for the practical application of DMFCs. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | This paper proposes a robust design methodology combining Response Surface Methodology (RSM) and Propagation of Error (POE) to optimize the key operating parameters of Direct Methanol Fuel Cells (DMFC). The primary objective is to enhance power density while minimizing fluctuations in the target response, thereby ensuring stable and efficient performance. Three key parameters – methanol concentration, methanol flow rate, and temperature are optimized using experimental data, which are modeled with a quadratic regression model. The model is validated through Analysis of Variance (ANOVA), and multi-response optimization is performed by introducing POE. Monte Carlo Simulation (MCS) is employed to validate the robustness of the optimized conditions, demonstrating reduced sensitivity to parameter variations and ensuring stable performance. The results show that by incorporating POE, a slight trade-off in maximum power density can lead to significantly improved stability, making this approach promising for the practical application of DMFCs. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15435075 |
| DOI: | 10.1080/15435075.2025.2475397 |
