Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks

Hüseyin Kurt and Muhammet Kayfeci

Applied Energy, 2009, vol. 86, issue 10, 2244-2248

Abstract: The objective of this study is to develop an artificial neural network (ANN) model to predict the thermal conductivity of ethylene glycol-water solutions based on experimentally measured variables. The thermal conductivity of solutions at different concentrations and various temperatures was measured using the cylindrical cell method that physical properties of the solution are being determined fills the annular space between two concentric cylinders. During the experiment, heat flows in the radial direction outwards through the test liquid filled in the annual gap to cooling water. In the steady state, conduction inside the cell was described by the Fourier equation in cylindrical coordinates, with boundary conditions corresponding to heat transfer between the solution and cooling water. The performance of ANN was evaluated by a regression analysis between the predicted and the experimental values. The ANN predictions yield R2 in the range of 0.9999 and MAPE in the range of 0.7984% for the test data set. The regression analysis indicated that the ANN model can successfully be used for the prediction of the thermal conductivity of ethylene glycol-water solutions with a high degree of accuracy.

Keywords: Thermal; conductivity; Ethylene; glycol-water; solutions; Artificial; neural; network (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306-2619(08)00333-4
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:86:y:2009:i:10:p:2244-2248

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 ().