Thermodynamic analyses of refrigerant mixtures using artificial neural networks
Yükleniyor...
Tarih
2004
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The aim of this study is to make a contribution towards the efforts of reducing the use of CFCs by finding a drop-in replacement for pure refrigerants used in domestic and industrial appliances. The suggested solution is the use of HFC and HC based refrigerant mixtures. In this study, we investigate different possible ratios of these mixtures and their corresponding performances by using Artificial Neural-Networks (ANNs). We believe this dramatically reduces the times and efforts required to achieve these targets. Coefficients of Performances (COPs) and Total Irreversibilities (TIs) of refrigerants and their mixtures have been calculated for a vapor-compression refrigeration system with a liquid/suction line heat-exchanger. The constant cooling-load method is taken as a reference. The thermodynamic properties of refrigerants have been taken from REFPROP 6.01. To train the network, based on Scaled Conjugate Gradient (SCG), Pola-Ribiere Conjugate Gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function, we have used various ratios of 7 refrigerant mixtures of HFCs and HCs along with three CFCs (R12, R22, and R502). They were used as inputs while the COP and TI values, calculated,as above, were the outputs. The network has yielded R-2 values of 0.9999 and maximum errors for training and test data were found to be 2 and 3%, respectively. (C) 2003 Elsevier Ltd. All rights reserved.
Açıklama
ARCAKLIOGLU, Erol/0000-0001-8073-5207
Anahtar Kelimeler
artificial neural-networks, refrigerant mixture, coefficient of performance, irreversibility, REFPROP
Kaynak
Applied Energy
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
78
Sayı
2
Künye
closedAccess
