In pursuit of the best artificial neural network configuration for the prediction of output parameters of corrugated plate heat exchanger

Artificial Neural Network (ANN) is very effective tool for modeling non-linear multivariable relationships. In this study, the aim is to develop ANN models to predict the overall heat transfer coefficient, effectiveness factor, friction factor of cold and hot fluids: output parameters of the plate heat exchanger. The experimental data of two non-Newtonian fluids: Carboxymethyl cellulose (CMC) and Xanthan gum (XG) as cold fluid in different concentrations varying from 0.1 to 0.6% w/w and water as hot fluid is taken for the analysis of ANN. Four-input parameters namely cold fluid Reynolds number and Prandtl number, hot fluid Reynolds number and Prandtl number and five-input parameters namely concentration of the cold fluid, cold fluid Reynolds number and Prandtl number, hot fluid Reynolds number and Prandtl number are employed to develop two different models for each of the fluids. It is observed that the configuration 4-5-5-5-4 and 5-5-5-5-4 is best for XG with maximum absolute relative error as 6.792 and 4.03, respectively. Similarly, configuration 4-6-4 and 5-5-4 is best for CMC with a maximum absolute relative error of 5.85 and 5.392, respectively.