Thermodynamic analyses of refrigerant mixtures using artificial neural networks

Erol ArcaklIoglu, Abdullah Çavusoglu and Ali Erisen

Applied Energy, 2004, vol. 78, issue 2, 219-230

Abstract: 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 R2 values of 0.9999 and maximum errors for training and test data were found to be 2 and 3%, respectively.

Keywords: Artificial; neural-networks; Refrigerant; mixture; Coefficient; of; performance; Irreversibility; REFPROP (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306-2619(03)00165-X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:78:y:2004:i:2:p:219-230

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().