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 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. This data points contains 6 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 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 (R-2). The obtained model was found with the performance of {MSE = 6.5377 x 10(-4), MAPE = 2.40% and R-2 = 0.99756} for the training stage, {MSE = 3.9629 x 10(-4), MAPE = 1.8922% and R-2 = 0.99835} for the test stage and {MSE = 5.8303 x 10(-4), MAPE = 2.57% and R-2 = 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.

Automated summary

An artificial neural network model was developed to predict fouling resistance for heat exchangers. The MLP network with 20 hidden neurons trained using the LM algorithm showed the best performance, with high accuracy in all training, testing, and validation stages. Inputs variables were found to have a strong effect on estimating fouling resistance after conducting a sensitivity analysis.