MODELING THE HYDROCRACKING PROCESS USING ARTIFICIAL NEURAL NETWORKS

ABSTRACT Feed-forward neural networks that models the hydrocracking process of Arabian light vacuum gas oil are presented. The input-output data to the neural networks was obtained from actual local refineries. Several network architectures were tried and the networks that best simulate the hydrocracking process were retained. The networks are able to predict yields and properties of products of the hydrocracking unit (e.g. iC4, nC4, light and heavy naphtha, light and heavy ATK, Diesel, etc.). The predictions of yields and properties of various desired and undesired products at different conditions are required by refineries for process optimization, control, design, catalyst selection, and planning. The predictions of the prepared neural networks have been cross validated against data not originally used in the training process. The networks compared well against this new set of data with an average percent error always less than 8.71 for the different products of the hydrocracking unit.