Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/122
Title: Different methods for modeling absorption heat transformer powered by solar pond
Authors: Sencan, Arzu 
Kızılkan, Önder 
Bezir, Nalan Cicek 
Kalogirou, Soteris A. 
Sencan, Arzu 
K?z?lkan, ?nder 
Bezir, Nalan Cicek 
Kalogirou, Soteris A. 
Keywords: Solar ponds
Absorption heat transformers
Pace regression
SMO
M5 model tree
Decision table
Back propagation neural network
Issue Date: 2007
Publisher: Elsevier B. V.
Source: Energy Conversion and Management, Vol. 48, no. 3, 2007, pp. 724-735
Abstract: Solar ponds are a type of solar collector used for storing solar energy at temperature below 90°C. Absorption heat transformers (AHTs) are devices used to increase the temperature of moderately warm fluid to a more useful temperature level. In this study, a theoretical modelling of an absorption heat transformer for the temperature range obtained from an experimental solar pond with dimensions 3.5 × 3.5 × 2 m is presented. The working fluid pair in the absorption heat transformer is aqueous ternary hydroxide fluid consisting of sodium, potassium and caesium hydroxides in the proportions 40:36:24 (NaOH:KOH:CsOH). Different methods such as linear regression (LR), pace regression (PR), sequential minimal optimization (SMO), M5 model tree, M5′ rules, decision table and back propagation neural network (BPNN) are used for modelling the absorption heat transformer. The best results were obtained by the back propagation neural network model. A new formulation based on the BPNN is presented to determine the flow ratio (FR) and the coefficient of performance (COP) of the absorption heat transformer. The BPNN procedure is more accurate and requires significantly less computation time than the other methods.
URI: http://ktisis.cut.ac.cy/handle/10488/122
ISSN: 0196-8904
DOI: 10.1016/j.enconman.2006.09.013
Rights: Copyright © 2006 Elsevier Ltd All rights reserved.
Appears in Collections:Άρθρα/Articles

Show full item record

SCOPUSTM   
Citations 10

30
checked on May 7, 2017

WEB OF SCIENCETM
Citations 10

27
checked on Jun 24, 2017

Page view(s)

13
Last Week
0
Last month
4
checked on Jun 28, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.