Estimated outlet temperatures in shell-and-tube heat exchanger using artificial neural network approach based on practical data

Abstract EN

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries.

Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery.

The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case.

Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air.

Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

150 sets of data were generated in different days by the reference heat exchanger model to training the network.

Regression between desired target and prediction ANN output for training, validation, testing and all samples show reasonably values are equal to one (R = 1) .

50 sets of data were generated to test the network and compare between desired and predicated exit temperature (water temp.

and air temp.) show a good agreement (± 0.3 %).