Araştırma Makalesi
BibTex RIS Kaynak Göster

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

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1
https://doi.org/10.2339/politeknik.1296119

Öz

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.

Kaynakça

  • [1] Gebrail Bekdaş, Sinan Melih Nigdeli, Melda Yücel, Aylin Ece Kayabekir Yapay Zeka Optimizasyon Algoritmaları ve Mühendislik Uygulamaları", Seçkin Yayıncılık (2021),.
  • [2] Nurhan Karaboğa), "Optimizasyon Yöntemleri ve Matlab Uygulamaları" , Nobel Akademik Yayıncılık (2023).
  • [3] U.M. Damo , M.L. Ferrari, A. Turan , A.F. Massardo ," Solid oxide fuel cell hybrid system: A detailed review of an environmentally clean and efficient source of energy", Energy (2019).
  • [4] S. Ahmad Hajimolana , M. Azlan Hussain , W.M. Ashri Wan Daud , M. Soroush , A. Shamiri , "Mathematical modeling of solid oxide fuel cells: A review ", Renewable and Sustainable Energy Reviews (2011).
  • [5] S.C Singhal, "Solid oxide fuel cells for stationary, mobile, and military applications", Solid State Ionics (2002).
  • [6] Noriko Hikosaka Behling, "Fuel cells: Current technology challenges and future research needs", Elsevier (2005).
  • [7] Raj, A., Sasmito, A.P., & Shamim, T. Influence of operating parameters on the performance of planar type solid oxide fuel cell and parasitic load: A numerical study. Energy Conversion and Management, 90, 138-145 (2015).
  • [8] Wang, D., Dahan, F., Chaturvedi, R., Almojil, S. F., Almohana, A. I., Alali, A. F., Almoalimi, K. T., & Alyousuf, F. Q. A. Thermodynamic performance optimization and environmental analysis of a solid oxide fuel cell powered with biomass energy and excess hydrogen injection. International Journal of Hydrogen Energy. Advance online publication. doi: https://doi.org/10.1016/j.ijhydene.2023.01.038 (2023).
  • [9] Cheng, S.-J. ve Lin, J.-K., Performance Prediction Model of Solid Oxide Fuel Cell System Based on Neural Network Autoregressive with External Input Method, MDPI Processes (2020).
  • [10] Zhang, J., Wu, W., & Mobayen, S. System identification of solid oxide fuel cell models using improved version of cat and mouse optimizer. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(1) (2023).
  • [11] Jian Wang , Yi-Peng Xu, Chen She, Ping Xu, Hamid Asadi Bagal, "Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm", Elsevier, Energy (2022).
  • [12] Khani, L., Saberi Mehr, A., Yari, M., & Mahmoudi, S.M.s. Multi-objective optimization of an indirectly integrated solid oxide fuel cell-gas turbine cogeneration system. International Journal of Hydrogen Energy (2016).
  • [13] Fei, Y., Rong, G., Wang, B., & Wang, W. Parallel L-BFGS-B algorithm on GPU. Computers & Graphics, 40, 1-9 (2014).
  • [14] Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190-1208 (1995).
  • [15] Najafabadi, M. M., Khoshgoftaar, T. M., Villanustre, F., & Holt, J. Large-scale distributed L-BFGS. Journal of Big Data (2017).
  • [16] Li, X. A limited memory BFGS subspace algorithm for bound constrained nonsmooth problems. Journal of Inequalities and Applications (2020).
  • [17] Yunbo Gao, Guorong Wang, Cuiran Li, Maoqing Li, Xuan Cheng,” Energy-efficient power allocation algorithm based on the BFGS algorithm and the armijo criterion” Elsevier, Sustainable Energy Technologies and Assessments (2023).
  • [18] Dhiman, G., & Kumar, V. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20-50 (2018).
  • [19] Khalid, O.W., Mat Isa, N.A., & Mat Sakim, H.A. Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms. Alexandria Engineering Journal, 63, 487-526 (2023).
  • [20] Zhikai Xing,” An improved emperor penguin optimization based multilevel thresholding for color image segmentation” Elsevier, Knowledge-Based Systems (2020).
  • [21] Sameh, M. A., Marei, M. I., Badr, M. A., & Attia, M. A. An optimized PV control system based on the emperor penguin optimizer. Energies, 14(3), 751 (2021).
  • [22] Kumar, B. S., & Rao, P. T. An Optimal Emperor Penguin Optimization Based Enhanced Flower Pollination Algorithm in WSN for Fault Diagnosis and Prolong Network Lifespan. Wireless Personal Communications, 127(3), 2003-2020 (2022).
  • [23] Detlef Stolten & Bernd Emonts, “Fuel Cells: Basics and Applications”, Wiley-VCH (2010).
  • [24] Li, G., Gou, Y., Qiao, J., Sun, W., Wang, Z., & Sun, K. Recent progress of tubular solid oxide fuel cell: From materials to applications. Journal of Power Sources, 477, 228693 (2020).
  • [25] Bard, A. J., & Faulkner, L. R. Electrochemical methods: fundamentals and applications (2nd ed.). New York: Wiley (2001).
  • [26] Zhang, Y., Shi, J., Zeng, L. et al. Analysis of the Nernst equation for SOFCs with different fuel types. J Solid State Electrochem 16, 951–960 (2012).
  • [27] Nocedal, J., & Wright, S. J. Numerical optimization. Springer Science & Business Media (2006).
  • [28] Nocedal, J., & Wright, S. Numerical optimization (Vol. 2). Springer Science & Business Media (2006).
  • [29] El-Baz, A. M., Gouda, I. S., & El-Metwally, S. T. Comparative study between optimization algorithms for the solution of groundwater management problems. Water resources management, 29(5), 1485-1499 (2015).
  • [30] Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190-1208 (1995).

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

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1
https://doi.org/10.2339/politeknik.1296119

Öz

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.

Kaynakça

  • [1] Gebrail Bekdaş, Sinan Melih Nigdeli, Melda Yücel, Aylin Ece Kayabekir Yapay Zeka Optimizasyon Algoritmaları ve Mühendislik Uygulamaları", Seçkin Yayıncılık (2021),.
  • [2] Nurhan Karaboğa), "Optimizasyon Yöntemleri ve Matlab Uygulamaları" , Nobel Akademik Yayıncılık (2023).
  • [3] U.M. Damo , M.L. Ferrari, A. Turan , A.F. Massardo ," Solid oxide fuel cell hybrid system: A detailed review of an environmentally clean and efficient source of energy", Energy (2019).
  • [4] S. Ahmad Hajimolana , M. Azlan Hussain , W.M. Ashri Wan Daud , M. Soroush , A. Shamiri , "Mathematical modeling of solid oxide fuel cells: A review ", Renewable and Sustainable Energy Reviews (2011).
  • [5] S.C Singhal, "Solid oxide fuel cells for stationary, mobile, and military applications", Solid State Ionics (2002).
  • [6] Noriko Hikosaka Behling, "Fuel cells: Current technology challenges and future research needs", Elsevier (2005).
  • [7] Raj, A., Sasmito, A.P., & Shamim, T. Influence of operating parameters on the performance of planar type solid oxide fuel cell and parasitic load: A numerical study. Energy Conversion and Management, 90, 138-145 (2015).
  • [8] Wang, D., Dahan, F., Chaturvedi, R., Almojil, S. F., Almohana, A. I., Alali, A. F., Almoalimi, K. T., & Alyousuf, F. Q. A. Thermodynamic performance optimization and environmental analysis of a solid oxide fuel cell powered with biomass energy and excess hydrogen injection. International Journal of Hydrogen Energy. Advance online publication. doi: https://doi.org/10.1016/j.ijhydene.2023.01.038 (2023).
  • [9] Cheng, S.-J. ve Lin, J.-K., Performance Prediction Model of Solid Oxide Fuel Cell System Based on Neural Network Autoregressive with External Input Method, MDPI Processes (2020).
  • [10] Zhang, J., Wu, W., & Mobayen, S. System identification of solid oxide fuel cell models using improved version of cat and mouse optimizer. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(1) (2023).
  • [11] Jian Wang , Yi-Peng Xu, Chen She, Ping Xu, Hamid Asadi Bagal, "Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm", Elsevier, Energy (2022).
  • [12] Khani, L., Saberi Mehr, A., Yari, M., & Mahmoudi, S.M.s. Multi-objective optimization of an indirectly integrated solid oxide fuel cell-gas turbine cogeneration system. International Journal of Hydrogen Energy (2016).
  • [13] Fei, Y., Rong, G., Wang, B., & Wang, W. Parallel L-BFGS-B algorithm on GPU. Computers & Graphics, 40, 1-9 (2014).
  • [14] Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190-1208 (1995).
  • [15] Najafabadi, M. M., Khoshgoftaar, T. M., Villanustre, F., & Holt, J. Large-scale distributed L-BFGS. Journal of Big Data (2017).
  • [16] Li, X. A limited memory BFGS subspace algorithm for bound constrained nonsmooth problems. Journal of Inequalities and Applications (2020).
  • [17] Yunbo Gao, Guorong Wang, Cuiran Li, Maoqing Li, Xuan Cheng,” Energy-efficient power allocation algorithm based on the BFGS algorithm and the armijo criterion” Elsevier, Sustainable Energy Technologies and Assessments (2023).
  • [18] Dhiman, G., & Kumar, V. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20-50 (2018).
  • [19] Khalid, O.W., Mat Isa, N.A., & Mat Sakim, H.A. Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms. Alexandria Engineering Journal, 63, 487-526 (2023).
  • [20] Zhikai Xing,” An improved emperor penguin optimization based multilevel thresholding for color image segmentation” Elsevier, Knowledge-Based Systems (2020).
  • [21] Sameh, M. A., Marei, M. I., Badr, M. A., & Attia, M. A. An optimized PV control system based on the emperor penguin optimizer. Energies, 14(3), 751 (2021).
  • [22] Kumar, B. S., & Rao, P. T. An Optimal Emperor Penguin Optimization Based Enhanced Flower Pollination Algorithm in WSN for Fault Diagnosis and Prolong Network Lifespan. Wireless Personal Communications, 127(3), 2003-2020 (2022).
  • [23] Detlef Stolten & Bernd Emonts, “Fuel Cells: Basics and Applications”, Wiley-VCH (2010).
  • [24] Li, G., Gou, Y., Qiao, J., Sun, W., Wang, Z., & Sun, K. Recent progress of tubular solid oxide fuel cell: From materials to applications. Journal of Power Sources, 477, 228693 (2020).
  • [25] Bard, A. J., & Faulkner, L. R. Electrochemical methods: fundamentals and applications (2nd ed.). New York: Wiley (2001).
  • [26] Zhang, Y., Shi, J., Zeng, L. et al. Analysis of the Nernst equation for SOFCs with different fuel types. J Solid State Electrochem 16, 951–960 (2012).
  • [27] Nocedal, J., & Wright, S. J. Numerical optimization. Springer Science & Business Media (2006).
  • [28] Nocedal, J., & Wright, S. Numerical optimization (Vol. 2). Springer Science & Business Media (2006).
  • [29] El-Baz, A. M., Gouda, I. S., & El-Metwally, S. T. Comparative study between optimization algorithms for the solution of groundwater management problems. Water resources management, 29(5), 1485-1499 (2015).
  • [30] Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190-1208 (1995).
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Ramiz İlker Tuna 0009-0002-0042-7605

Faruk Ayata 0000-0003-2403-3192

Ebubekir Seyyarer 0000-0002-8981-0266

Erken Görünüm Tarihi 16 Temmuz 2024
Yayımlanma Tarihi
Gönderilme Tarihi 12 Mayıs 2023
Yayımlandığı Sayı Yıl 2024 ERKEN GÖRÜNÜM

Kaynak Göster

APA Tuna, R. İ., Ayata, F., & Seyyarer, E. (2024). Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno ve İmparator Penguen Algoritmasının Kullanılması. Politeknik Dergisi1-1. https://doi.org/10.2339/politeknik.1296119
AMA Tuna Rİ, Ayata F, Seyyarer E. Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno ve İmparator Penguen Algoritmasının Kullanılması. Politeknik Dergisi. Published online 01 Temmuz 2024:1-1. doi:10.2339/politeknik.1296119
Chicago Tuna, Ramiz İlker, Faruk Ayata, ve Ebubekir Seyyarer. “Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Ve İmparator Penguen Algoritmasının Kullanılması”. Politeknik Dergisi, Temmuz (Temmuz 2024), 1-1. https://doi.org/10.2339/politeknik.1296119.
EndNote Tuna Rİ, Ayata F, Seyyarer E (01 Temmuz 2024) Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno ve İmparator Penguen Algoritmasının Kullanılması. Politeknik Dergisi 1–1.
IEEE R. İ. Tuna, F. Ayata, ve E. Seyyarer, “Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno ve İmparator Penguen Algoritmasının Kullanılması”, Politeknik Dergisi, ss. 1–1, Temmuz 2024, doi: 10.2339/politeknik.1296119.
ISNAD Tuna, Ramiz İlker vd. “Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Ve İmparator Penguen Algoritmasının Kullanılması”. Politeknik Dergisi. Temmuz 2024. 1-1. https://doi.org/10.2339/politeknik.1296119.
JAMA Tuna Rİ, Ayata F, Seyyarer E. Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno ve İmparator Penguen Algoritmasının Kullanılması. Politeknik Dergisi. 2024;:1–1.
MLA Tuna, Ramiz İlker vd. “Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Ve İmparator Penguen Algoritmasının Kullanılması”. Politeknik Dergisi, 2024, ss. 1-1, doi:10.2339/politeknik.1296119.
Vancouver Tuna Rİ, Ayata F, Seyyarer E. Katı Oksit Yakıt Pillerinin Hücre Gerilimini Minimize Etmek İçin Limited-Memory Broyden-Fletcher-Goldfarb-Shanno ve İmparator Penguen Algoritmasının Kullanılması. Politeknik Dergisi. 2024:1-.
 
TARANDIĞIMIZ DİZİNLER (ABSTRACTING / INDEXING)
181341319013191 13189 13187 13188 18016 

download Bu eser Creative Commons Atıf-AynıLisanslaPaylaş 4.0 Uluslararası ile lisanslanmıştır.