Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in processing tomato paste shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds range 150–350, temperature 70–90 K, nanoparticle concentration 2–4% were selected as input variables, while corresponding considered target. has 3 inputs, 1 hidden layer 22 neurons an output layer. SOM was also used determine winner neurons. advanced optimal shows reasonable agreement predicting experimental data mean square errors 0.0023357 0.00011465 correlation coefficients 0.9994 0.9993 for set. obtained values eMAX are 0.1114, 0.02, respectively. Desirable results two factors coefficient error indicate successful prediction by topology 3-22-2.