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 inmost petroleumrefineries inwhich the sulfur ismostly eliminated. Themodeling 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 themeasured and the predicted sulfur content in the desulfurized naphtha, along with the coefficient of correlation as ameasure of the goodness of fit. Results show that the ANNmodels 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. © 2008 Elsevier Ltd. All rights reserved.