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akaturk Akademik ölçüm

Makale detayı · 2024

Evaluation of exergy destructions of different refrigerants in a vaccine cooling system with artificial intelligence

International Journal of Exergy

YÖKSİS OpenAlex SJR Q3 JCR Q4 Atıf 0 Yüzdelik 10.6% FWCI 0.0
Yıl
2024
ISSN
1742-8297
Tür
article

Veri kaynağı ayrımı

  • YÖKSİS YÖKSİS makale kaydı
  • OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

İngilizce (OpenAlex)

Nowadays, low-temperature storage and distribution of many vaccines are as important as their production. In this study, the performance of a storage device operating in a vapour compression refrigeration cycle designed to provide low-temperature cooling between 201 K and 275 K using R134a, R1234yf, R502, and R717 fluids is evaluated by both thermodynamic and artificial neural network (ANN) methods. Levenberg-Marquardt, Bayesian regularisation, and scaled conjugate gradient algorithms are compared with thermodynamical calculations to predict the energy efficiency and exergy destruction of the cooling system. All the considered artificial intelligence algorithms are found to accurately predict the expected outputs with R2 values greater than 0.9.

Konular

  • Refrigeration and Air Conditioning Technologies
  • Food Supply Chain Traceability

Birincil konu Refrigeration and Air Conditioning Technologies

Yazarlar

  1. ELİF ALTINTAŞ KAHRİMAN BARTIN ÜNİVERSİTESİ
  2. ALİŞAN GÖNÜL SİİRT ÜNİVERSİTESİ
  3. ALİ KÖSE İSTANBUL GEDİK ÜNİVERSİTESİ
  4. CEM İSMAİL PARMAKSIZOĞLU