Artificial neural network modeling techniques applied to the hydrodesulfurization process

Reduction of harmful emissions in the combustion of fossil fuels imposes tighter specifications limiting the sulfur content of fuels. Hydrodesulfurization (HDS) is a key process in most petroleum refineries in which the sulfur is mostly eliminated. The modeling and simulation of the HDS process are necessary for a better understanding of the process operation; it is also a requirement to optimize process operation. The objective of this work is to explore the use of different artificial neural network (ANN) architectures in creating various models of the HDS process for the prediction of sulfur removal from naphtha. A database was build using daily records of the HDS process from a Mexican refinery. Accuracy of the predictions was quantified by the root of the mean squared difference between the measured and the predicted sulfur content in the desulfurized naphtha, along with the coefficient of correlation as a measure of the goodness of fit. Results show that the ANN models can be used as practical tools for predictive purposes. One particular example is the ability to anticipate such situations, in the process, that could increase alertness because some variables are deviating from acceptable limits.