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IJCT Vol.13(2) [March 2006] >

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
Issue Date: Mar-2006
Publisher: CSIR
IPC CodeC09K5/00
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 architecture1. ANN model is trained using a water-water2 and water-40% glycerin3 system. The trained networks are then used for prediction of shell and tube side exit temperatures for water-20% glycerin3 system. It is observed that predicted values of water–20% glycerin system are in close agreement (98-99%) with the actual values.
Page(s): 173-176
ISSN: 0975-0991 (Online); 0971-457X (Print)
Source:IJCT Vol.13(2) [March 2006]

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