Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno ve İmparator Penguen Algoritmasının Kullanılması

Year 2024, EARLY VIEW, 1 - 1

Ramiz İlker Tuna , Faruk Ayata , Ebubekir Seyyarer

https://doi.org/10.2339/politeknik.1296119

Abstract

Optimizasyon yöntemleri çeşitli endüstriyel, bilimsel ve mühendislik uygulamalarında, en verimli planlama stratejisi belirlemek, bir finansal portföyün en iyi dağılımını belirlemek, bir lojistik ağın en verimli şekilde tasarlanması veya bir yapay zekâ modelinin en iyi performansını elde etmek için yaygın olarak kullanılmaktadır. Bu çalışmada ise katı oksit yakıt pillerinin hücre gerilimini minimuma indirerek pillerin performansını arttırmak ve enerji verimliliğini iyileştirmek amaçlanmaktadır. Bu kapsamda L-BFGS-B algoritması ve İmparator Penguen algoritması ile yapılan optimizasyon çalışmalarında Faraday sabiti, Gaz sabiti, Aktivasyon polarizasyonu katsayısı, Ters akım yoğunluğu, Elektrot kalınlığı girdi değerler sabitlenerek sıcaklık (T), oksijen basıncı (p(O2)), hidrojen basıncı (p(H2)) ve su buharı basıncı (p(H2O))’nın minimum gerilim için değerleri hesaplanmaktadır. İki optimizasyon yöntemi için de optimum sıcaklık değeri 1000 K, optimum oksijen basıncı değeri 1.0, optimum hidrojen basıncı değeri 0.000001 ve optimum su buharı basıncı değeri de 0.000001 olarak hesaplanmaktadır. İki optimizasyon yönteminde de minimum hücre gerilimi 0.6486 olarak hesaplanmış ancak L-BFGS-B algoritması sonuca 6 iterasyon ve 0.0046 saniye de ulaşmış; İmparator Penguen algoritması ise 44 iterasyon ve 0,01 saniye de ulaşmıştır. Analiz sonuçlarına göre iki yöntemin de hücre gerilim değerleri aynı olmasına rağmen iterasyon ve süre bakımından L-BFGS-B algoritmasının daha başarılı olduğu görülmektedir.

Keywords

Katı oksit yakıt pilleri, Hücre gerilimi, L-BFGS-B, İmparator penguen

Using Limited-Memory Broyden-Fletcher-Goldfarb-Shanno and Emperor Penguin Algorithm to Minimize the Cell Voltage of Solid Oxide Fuel Cells

Abstract

Optimization methods are widely used in various industrial, scientific, and engineering applications to determine the most efficient planning strategy, determine the best distribution of a financial portfolio, design a logistics network in the most efficient way possible, or achieve the best performance of an artificial intelligence model. In this study, the aim is to minimize the cell voltage of solid oxide fuel cells to improve their performance and energy efficiency. In the optimization studies carried out with the L-BFGS-B algorithm and Emperor Penguin algorithm, the values of temperature (T), oxygen pressure (p(O2)), hydrogen pressure (p(H2)), and water vapor pressure (p(H2O)) are calculated for minimum voltage while the input values of Faraday constant, Gas constant, Activation polarization coefficient, Reverse current density, and Electrode thickness are fixed. For both optimization methods, the optimum temperature value is calculated as 1000 K, the optimum oxygen pressure value as 1.0, the optimum hydrogen pressure value as 0.000001, and the optimum water vapor pressure value as 0.000001. The minimum cell voltage was calculated as 0.6486 for both optimization methods, but the L-BFGS-B algorithm reached the result in 6 iterations and 0.0046 seconds, while the Emperor Penguin algorithm reached it in 44 iterations and 0.01 seconds. According to the analysis results, although the cell voltage values of the two methods are the same, it can be seen that the L-BFGS-B algorithm is more successful in terms of iteration and time.

Keywords

solid oxide fuel cells, cell voltage, L-BFGS-B, Emperor Penguin

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