Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1510
Title: | A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples | Authors: | Sencan, Arzu Kalogirou, Soteris A. |
Major Field of Science: | Engineering and Technology | Field Category: | Environmental Engineering | Keywords: | Artificial Neural Networks (ANN);Vapor pressure;Lithium chloride;Lithium bromide;Lithium iodide;Lithium nitrate | Issue Date: | Sep-2005 | Source: | Energy Conversion and Management, 2005, Vol. 46, no. 15-16, pp. 2405-2418 | Volume: | 46 | Issue: | 15-16 | Start page: | 2405 | End page: | 2418 | Journal: | Energy Conversion and Management | Abstract: | This paper presents a new approach using artificial neural networks (ANN) to determine the thermodynamic properties of two alternative refrigerant/absorbent couples (LiCl–H2O and LiBr + LiNO3 + LiI + LiCl–H2O). These pairs can be used in absorption heat pump systems, and their main advantage is that they do not cause ozone depletion. In order to train the network, limited experimental measurements were used as training and test data. Two feedforward ANNs were trained, one for each pair, using the Levenberg–Marquardt algorithm. The training and validation were performed with good accuracy. The correlation coefficient obtained when unknown data were applied to the networks was 0.9997 and 0.9987 for the two pairs, respectively, which is very satisfactory. The present methodology proved to be much better than linear multiple regression analysis. Using the weights obtained from the trained network, a new formulation is presented for determination of the vapor pressures of the two refrigerant/absorbent couples. The use of this new formulation, which can be employed with any programming language or spreadsheet program for estimation of the vapor pressures of fluid couples, as described in this paper, may make the use of dedicated ANN software unnecessary. | URI: | https://hdl.handle.net/20.500.14279/1510 | ISSN: | 01968904 | DOI: | 10.1016/j.enconman.2004.11.007 | Rights: | © Elsevier 2004 | Type: | Article | Affiliation : | Süleyman Demirel University Higher Technical Institute Cyprus |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
CORE Recommender
SCOPUSTM
Citations
72
checked on Nov 9, 2023
WEB OF SCIENCETM
Citations
64
Last Week
0
0
Last month
0
0
checked on Oct 29, 2023
Page view(s) 20
477
Last Week
0
0
Last month
8
8
checked on Aug 28, 2024
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.