ARTIFICIAL NEURAL NETWORK ASSISTED MULTI-OBJECTIVE OPTIMIZATION OF A METHANE-FED DIR-SOFC SYSTEM WITH WASTE HEAT RECOVERY       
Yazarlar (4)
Dr. Öğr. Üyesi Ünsal AYBEK Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Lütfü Namlı
Ondokuz Mayıs Üniversitesi, Türkiye
Mustafa Özbey
Ondokuz Mayıs Üniversitesi, Türkiye
Dr. Öğr. Üyesi Bekir DOĞAN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Makale Türü Açık Erişim Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale
Dergi Adı Thermal Science
Dergi ISSN 0354-9836 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q4
Makale Dili İngilizce
Basım Tarihi 09-2023
Cilt No 27
Sayı 4
Sayfalar 3413 / 3422
DOI Numarası 10.2298/TSCI2304413A
Makale Linki https://doiserbia.nb.rs/img/doi/0354-9836/2023/0354-98362304413A.pdf
Özet
The main purpose of this study is to enhance the performance of solid oxide fuel cell systems. For this purpose, a mathematical model of a direct internal reforming (DIR) methane-fed solid oxide fuel cell system with waste heat recovery was designed in the engineering equation solver program. We optimised the performance of the solid oxide fuel cell using a genetic algorithm and TOPSIS technique considering exergy, power, and environmental analyzes. An ANN working with the Levenberg-Marquardt training function was designed in the MATLprogram to create the decision matrix to which the TOPSIS method will be applied. According to the power optimization, 786 kW net power was obtained from the system. In exergetic optimization, the exergy efficiency was found to be 57.6%. In environmental optimization, the environmental impact was determined as 330.6 kgCO2/MWh. According to the multi-objective optimization results, the exergy efficiency, the net power of the solid oxide fuel cell system, and the environmental impact were 504.1 kW, 40.08%, and 475.4 kgCO2/MWh.
Anahtar Kelimeler
ANN | clean energy | Levenberg-Marquardt | multi-objective optimization | solid oxide fuel cell