A Multi-criteria Assessment of Biomass Conversion Technologies with Pythagorean Fuzzy Axiomatic Design Approach

İLBAHAR E., ÇEBİ S., Kahraman C.

JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, cilt.37, sa.3-4, ss.317-334, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 3-4
  • Basım Tarihi: 2021
  • Dergi Adı: JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, zbMATH
  • Sayfa Sayıları: ss.317-334
  • Anahtar Kelimeler: Biomass to energy, Conversion technologies, Axiomatic design, Pythagorean fuzzy sets, RENEWABLE ENERGY TECHNOLOGIES, LIFE-CYCLE ASSESSMENT, DECISION-MAKING, SELECTION, MODEL, PLANT, SUSTAINABILITY, ALTERNATIVES, PROJECT, VIKOR
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Renewable energy is a substantial component of sustainable development all over the world. In order to ensure the optimal exploitation of renewable energy sources, selection of appropriate renewable energy technologies with a thorough assessment approach is required. There are several conversion technologies for biomass, which constitutes an important part of renewable energy sources. The evaluation of biomass conversion technologies is a complex problem since it contains many criteria, some of which are contradictory and must be taken into account simultaneously. Axiomatic design outperforms other design techniques by considering both what decision makers want to achieve and what the system can provide as Pythagorean fuzzy sets provide a greater domain to experts while assigning membership and non-membership values in a decision making process. Therefore, in this study, Pythagorean fuzzy sets are combined with axiomatic design approach to evaluate biomass conversion technologies. It is revealed that combustion is the best biomass conversion technology for the Central Anatolia Region of Turkey. The sensitivity and comparative analyses are conducted to show robustness and reliability of the results.