Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/7004
Title: Application of ANN for modeling of heat exchanger with concentration as variable
Authors: Mandavgane, S A
Pandharipande, S L
Keywords: Artificial neural networks (ANN)
Shell and tube heat exchanger
Modeling
Issue Date: Mar-2006
Publisher: CSIR
Series/Report no.: C09K5/00
Abstract: Artificial Neural Networks (ANN) are effective in modeling of non-linear multi variable relationships and also referred to as black box models. Generally, for modeling of heat exchangers the various parameters to be taken into account are inlet and outlet temperatures of shell, tube side fluids, and their flow rates. In the present paper, the concentration of flowing fluids is also considered as one of the variable parameters for heat exchanger modeling. For the study three different fluids are used, (i) water, (ii) 20% glycerin and (iii) 40% glycerin. Heat exchanger model is developed using optimized ANN architecture<sup>1</sup>. ANN model is trained using a water-water<sup>2</sup> and water-40% glycerin<sup>3</sup> system. The trained networks are then used for prediction of shell and tube side exit temperatures for water-20% glycerin<sup>3</sup> system. It is observed that predicted values of water–20% glycerin system are in close agreement (98-99%) with the actual values.
Description: 173-176
URI: http://hdl.handle.net/123456789/7004
ISSN: 0975-0991 (Online); 0971-457X (Print)
Appears in Collections:IJCT Vol.13(2) [March 2006]

Files in This Item:
File Description SizeFormat 
IJCT 13(2) 173-176.pdf88.58 kBAdobe PDFView/Open


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