Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

In this work, an artificial neural network (ANN) model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. These data points contain six inputs, including...

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Autores principales:	Ahmed Benyekhlef, Brahim Mohammedi, Djamel Hassani, Salah Hanini
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	artificial neural networks fouling graphical user interface heat exchanger modeling nanofluid Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/b9e8fccc2c064480b41687bd2dcf7b51
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spelling	oai:doaj.org-article:b9e8fccc2c064480b41687bd2dcf7b512021-11-06T11:16:22ZApplication of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids0273-12231996-973210.2166/wst.2021.253https://doaj.org/article/b9e8fccc2c064480b41687bd2dcf7b512021-08-01T00:00:00Zhttp://wst.iwaponline.com/content/84/3/538https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732In this work, an artificial neural network (ANN) model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. These data points contain six inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation of the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10−4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10−4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10−4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance. HIGHLIGHTS Reduction of fouling resistance in heat exchangers.; A high determination coefficient has been reached (R2 = 0.99770).; Developing a program and an interface to facilitate the calculation for users.;Ahmed BenyekhlefBrahim MohammediDjamel HassaniSalah HaniniIWA Publishingarticleartificial neural networksfoulinggraphical user interfaceheat exchangermodelingnanofluidEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 84, Iss 3, Pp 538-551 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural networks fouling graphical user interface heat exchanger modeling nanofluid Environmental technology. Sanitary engineering TD1-1066
spellingShingle	artificial neural networks fouling graphical user interface heat exchanger modeling nanofluid Environmental technology. Sanitary engineering TD1-1066 Ahmed Benyekhlef Brahim Mohammedi Djamel Hassani Salah Hanini Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids
description	In this work, an artificial neural network (ANN) model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. These data points contain six inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation of the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10−4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10−4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10−4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance. HIGHLIGHTS Reduction of fouling resistance in heat exchangers.; A high determination coefficient has been reached (R2 = 0.99770).; Developing a program and an interface to facilitate the calculation for users.;
format	article
author	Ahmed Benyekhlef Brahim Mohammedi Djamel Hassani Salah Hanini
author_facet	Ahmed Benyekhlef Brahim Mohammedi Djamel Hassani Salah Hanini
author_sort	Ahmed Benyekhlef
title	Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids
title_short	Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids
title_full	Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids
title_fullStr	Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids
title_full_unstemmed	Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids
title_sort	application of artificial neural network (ann-mlp) for the prediction of fouling resistance in heat exchanger to mgo-water and cuo-water nanofluids
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/b9e8fccc2c064480b41687bd2dcf7b51
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Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

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