Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/138
Title: Thermodynamic analysis of absorption systems using artificial neural network
Authors: Sencan, Arzu 
Yakut, Kemal A. 
Kalogirou, Soteris A. 
Sencan, Arzu 
Yakut, Kemal A. 
Kalogirou, Soteris A. 
Keywords: Artificial Neural Networks (ANN)
Absorption heat pump
Lithium bromide–water
Lithium chloride–water
Thermodynamic properties
Issue Date: 2007
Publisher: Elsevier B. V.
Source: Renewable Energy, Vol. 31, no. 1, 2007, Pages 29-43
Abstract: Thermodynamic analysis of absorption systems is a very complex process, mainly because of the limited experimental data and analytical functions required for calculating the thermodynamic properties of fluid pairs, which usually involves the solution of complex differential equations. In order to simplify this complex process, Artificial Neural Networks (ANNs) are used. In this study, ANNs are used as a new approach for the determination of the thermodynamic properties of LiBr–water and LiCl–water solutions which have been the most widely used in the absorption heat pump systems. Instead of complex differential equations and limited experimental data, faster and simpler solutions were obtained by using equations derived from the ANN model. It was found that the coefficient of multiple determination (R2-value) between the actual and ANN predicted data is equal to about 0.999 for the enthalpy of both LiBr–water and LiCl–water solutions. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable limits. In addition, the coefficient of performance (COP) of absorption systems operating under different conditions with LiBr–water and LiCl–water solutions is calculated. The use of the derived equations, which can be employed with any programming language or spreadsheet program for the estimation of the enthalpy of the solutions, as described in this paper, may make the use of dedicated ANN software unnecessary.
URI: http://ktisis.cut.ac.cy/handle/10488/138
ISSN: 0960-1481
DOI: 10.1016/j.renene.2005.03.011
Rights: © 2005 Elsevier Ltd All rights reserved.
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