Deep hydrodesulfurization of atmospheric gas oil - Effects of operating conditions and modelling by artificial neural network techniques

Artificial neural networks (ANN) are currently being explored in various engineering fields as valuable tools for automatic model-building and knowledge acquisition. This technique was applied to model hydrodesulfurization of atmospheric gas oil in a mini-pilot trickle-bed reactor. Sulfur removal was measured as a function of temperature, pressure and liquid hourly space velocity (LHSV) for three sulfur feed concentrations. The potential of a two-stage process was also tested. A set of experimental data was used to teach a three-layer neural network. The capability of the artificial neural network to predict the performance was tested with a different set of data. The agreement between predicted and experimental values was good. Temperature, LHSV and staging of the process were determined to be important parameters, while pressure had a little effect over the range tested in this study. Copyright (C) 1996 Elsevier Science Ltd.