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Indian Journal of Chemical Technology (IJCT) >
IJCT Vol.11 [2004] >
IJCT Vol.11(6) [November 2004] >
| Title: | Optimising ANN architecture for shell and tube heat exchanger modelling |
| Authors: | Pandharipande, S L
Siddiqui, M A
Dubey, A
Mandavgane, S A |
| Keywords: | Artificial neural network
ANN
heat exchanger
modelling |
| Issue Date: | Nov-2004 |
| Publisher: | CSIR |
| IPC Code: | C09 K 5/00 |
| Abstract: | Heat exchangers have
a special place in chemical process industries. The shell and tube heat
exchanger is commonly used for heating or cooling of process fluids. The
various parameters to be taken into account for developing a model are inlet
and outlet temperatures of shell and tube side fluids and their flow rates.
Artificial Neural Networks (ANN) are effective in modeling of non-linear multi
variable relationships and also referred to as the black box models. In the
present work, various ANN models have been developed with single, two and three
hidden layers for estimation of exit temperature of both the fluids as a
function of inlet temperature conditions and also flow rates. The data used for
training of ANN is generated on a small shell and tube heat exchanger,
fabricated for this purpose. The ANN models thus developed are validated for
test data that was not used for training of these models. The comparisons
between models have been carried out. It is observed that ANN model with three
hidden layers (15-15-15 neurons) has good level of accuracy (95-98%) for
predicted values of training and test data set. |
| Page(s): | 804-810 |
| ISSN: | 0975-0991 (Online); 0971-457X (Print) |
| Source: | IJCT Vol.11(6) [November 2004]
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