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Modeling the outlet temperature in heat exchangers: Case study

ABSTRACT
This article presents the results of the study of the heat transfer in a heat exchanger where the working fluid is the crude oil prepared for desalination, and the thermic agent is the recirculating heavy gasoline fraction. Firstly, the Reynolds numbers have been computed using the temperatures and flow rates of the fluids as input variables. Then, General Regression Neural Network and Multilayer Perceptron (MLP) were used for the outlet temperatures estimation using the inlet temperatures and the Reynolds numbers as input variables. The best models on the training dataset were obtained utilizing a MLP with one hidden layer, while the best performance on the validation dataset was obtained using a MLP network with two hidden layers
KEYWORDS
PAPER SUBMITTED: 2019-09-13
PAPER REVISED: 2019-11-05
PAPER ACCEPTED: 2019-11-07
PUBLISHED ONLINE: 2019-12-22
DOI REFERENCE: https://doi.org/10.2298/TSCI190913449B
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