Feed Variability Effect on Performance of a Commercial Residue Hydrocracker

Le résumé fourni par la source

Feed quality has been found to be related to both reactivity and sediment formation propensity in the residue hydrocracking process defining the conversion level. In this research, unlike other investigations, which examine hydrocrackability of individual vacuum residues, 529 mixtures of 33 vacuum residues were investigated for their hydrocrackability in a commercial H-Oil ebullated bed reactor unit. Intercriteria and regression analyses, together with singular value decomposition (SVD) and deep learning neural network techniques were employed to analyze data and model the vacuum residue conversion in the H-Oil unit. It was found that SVD model provided the best fit of H-Oil conversion training data (standard error of 0.95 wt.%). However, due to overfitting, the SVD model failed to predict H-Oil conversion on unseen data (standard error of 5.1 wt.%). The deep learning neural network exhibited standard error for all data (training, validation and testing) of 1.99 wt.%, while for the test data it was 2.35 wt.%. The linear regression model showed a standard error of 3.9 wt.% for the training data and 7.5 wt.% for the test data. Eleven properties of the vacuum residue (density, microcarbon residue, sulfur, nitrogen, saturate, aromatic, resin, C5-asphaltene, C7-asphaltene, Na, and Ni+V content) seem to be sufficiently informative for the purposes of modeling and predicting H-Oil conversion, thus enabling the assessment of the suitability of a given vacuum residue to be used as a feedstock for the H-Oil process. The best predicting model was found to be the deep learning neural network, which can be used for the purpose of the crude selection process.

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