Original scientific paper
https://doi.org/10.15255/KUI.2020.076
A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network
Ahmed Benyekhlef
orcid.org/0000-0001-6021-915X
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria
Brahim Mohammedi
orcid.org/0000-0001-7512-3978
; Nuclear Research Center of Birine, Djelfa,17 000, Algeria
Salah Hanini
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria
Mouloud Boumahdi
orcid.org/0000-0002-4145-5824
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria
Ahmed Rezrazi
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria
Maamar Laidi
orcid.org/0000-0002-8977-9895
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria
Abstract
The aim of this study was to predict the fouling resistance (FR) using the artificial neural networks (ANN) approach. An experimental database collected from the literature regarding the fouling of condenser tubes cooling seawater of a nuclear power plant was used to build the ANN model. All models contained 7 inputs: dimensionless condenser cooling seawater temperature, dimensionless inside overall heat transfer coefficient, dimensionless outside overall heat transfer coefficient, dimensionless condenser temperature, dimensionless condenser pressure, dimensionless output power, and dimensionless overall thermal efficiency. Dimensionless fouling resistance was the output. The accuracy of the model was confirmed by comparing the predicted and experimental data. The results showed that ANN with a configuration of 7 input neurons, 7 hidden neurons, and 1 output neuron presented an excellent agreement, with the root mean squared error RMSE = 3.6588 ∙ 10–7, average absolute percentage error MAPE = 0.1295 %, and high determination coefficient of R2 = 0.99996. After conducting the sensitivity analysis (all input variables had strong effect on the estimation of the fouling resistance), in order to control the fouling, an inverse artificial neural network (ANNi) model was established, and showed good agreement in the case of different values of dimensionless condenser cooling seawater temperature.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Keywords
heat exchanger-condenser; fouling; modelling; artificial neural network; graphical user interface; inverse artificial neural network
Hrčak ID:
264636
URI
Publication date:
2.11.2021.
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