Artificial neural network modeling of hydrogen sulphide gas coolers ensuring extrapolation capability

Abstract

Hydrogen sulphide coolers are jacketed shell-and-tube heat exchangers designed to cool down the gas from 416.15 K to 310.15 K, as well as to remove sulphur carryovers. It is difficult to accurately compute their performance by traditional methods, since thermal analysis is based on several simplifications and empirical correlations. To overcome this limitation, the aim of present research was to propose an artificial neural network model for prediction of coolers outputs, using the mean absolute percentage error, correlation coefficient and extrapolation capability as selection criteria. Structure optimization was carried out through a network growing strategy, using 120 experimental data points for networks training, validation and testing. Model generalization was verified by comparing responses against the predictions of a validated phenomenological model, based on the ε-NTU method, for one set of 20 unseen data points. Best performance was obtained with the 6-5-4-3 multilayer perceptron, using the Levenberg-Marquardt learning algorithm. 99.47 % overall correlation and 0.33 % mean absolute percentage error were achieved when computing the hydrogen sulphide and water streams outlet temperatures. Despite the high prediction performance, a few model responses were found deprived of physical sense