A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network

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...

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Autores principales:	Ahmed Benyekhlef, Brahim Mohammedi, Salah Hanini, Mouloud Boumahdi, Ahmed Rezrazi, Maamar Laidi
Formato:	article
Lenguaje:	EN HR
Publicado:	Croatian Society of Chemical Engineers 2021
Materias:	heat exchanger-condenser fouling modelling artificial neural network graphical user interface inverse artificial neural network Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/8121aba11fc54025bf176661f36ca459
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spelling	oai:doaj.org-article:8121aba11fc54025bf176661f36ca4592021-11-03T23:19:13ZA Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network10.15255/KUI.2020.0760022-98301334-9090https://doaj.org/article/8121aba11fc54025bf176661f36ca4592021-11-01T00:00:00Zhttp://silverstripe.fkit.hr/kui/assets/Uploads/2-639-650-KUI-11-12-2021.pdfhttps://doaj.org/toc/0022-9830https://doaj.org/toc/1334-9090The 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.Ahmed BenyekhlefBrahim MohammediSalah HaniniMouloud BoumahdiAhmed RezraziMaamar LaidiCroatian Society of Chemical Engineersarticleheat exchanger-condenserfoulingmodellingartificial neural networkgraphical user interfaceinverse artificial neural networkChemistryQD1-999ENHRKemija u Industriji, Vol 70, Iss 11-12, Pp 639-650 (2021)
institution	DOAJ
collection	DOAJ
language	EN HR
topic	heat exchanger-condenser fouling modelling artificial neural network graphical user interface inverse artificial neural network Chemistry QD1-999
spellingShingle	heat exchanger-condenser fouling modelling artificial neural network graphical user interface inverse artificial neural network Chemistry QD1-999 Ahmed Benyekhlef Brahim Mohammedi Salah Hanini Mouloud Boumahdi Ahmed Rezrazi Maamar Laidi A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network
description	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.
format	article
author	Ahmed Benyekhlef Brahim Mohammedi Salah Hanini Mouloud Boumahdi Ahmed Rezrazi Maamar Laidi
author_facet	Ahmed Benyekhlef Brahim Mohammedi Salah Hanini Mouloud Boumahdi Ahmed Rezrazi Maamar Laidi
author_sort	Ahmed Benyekhlef
title	A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network
title_short	A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network
title_full	A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network
title_fullStr	A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network
title_full_unstemmed	A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network
title_sort	contribution to the modelling of fouling resistance in heat exchanger-condenser by direct and inverse artificial neural network
publisher	Croatian Society of Chemical Engineers
publishDate	2021
url	https://doaj.org/article/8121aba11fc54025bf176661f36ca459
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A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network

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