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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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
IWA Publishing
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/b9e8fccc2c064480b41687bd2dcf7b51 |
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| Résumé: | 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.; |
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