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

Authors: Elif Altıntaş Kahriman; Alişan Gönül; Ali Köse; İsmail Cem Parmaksızoğlu

Addresses: Department of Software Engineering, Haliç University, Istanbul, Turkey ' Department of Mechanical Engineering, Siirt University, Siirt, Turkey ' Energy Institute, Istanbul Technical University, Istanbul, Turkey ' Department of Mechanical Engineering, Istanbul Technical University, Istanbul, Turkey

Abstract: 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.

Keywords: low temperature cooling; artificial intelligence; exergy analysis; vaccine storage unit; artificial neural network; ANN.

DOI: 10.1504/IJEX.2024.140174

International Journal of Exergy, 2024 Vol.44 No.3/4, pp.244 - 260

Received: 13 Dec 2023
Accepted: 12 Mar 2024
Published online: 27 Jul 2024 *

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