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In relation to this article, we declare that there is no conflict of interest.
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Received September 9, 2009
Accepted November 4, 2009
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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4-Lump kinetic model for vacuum gas oil hydrocracker involving hydrogen consumption

Faculty of Chemical and Natural Resources Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia 1Catalysis Research Center, Research Institute of Petroleum Industry, P.O. Box 14665-137, Tehran, Iran
Korean Journal of Chemical Engineering, July 2010, 27(4), 1099-1108(10), 10.1007/s11814-010-0172-0
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Abstract

A 4-lump kinetic model including hydrogen consumption for hydrocracking of vacuum gas oil in a pilot scale reactor is proposed. The advantage of this work over the previous ones is consideration of hydrogen consumption, imposed by converting vacuum gas oil to light products, which is implemented in the kinetic model by a quadratic expression as similar as response surface modeling. This approach considers vacuum gas oil (VGO) and unconverted oil as one lump whilst others are distillate, naphtha and gas. The pilot reactor bed is divided into hydrotreating and hydrocracking sections which are loaded with different types of catalysts. The aim of this paper is modeling the hydrocracking section, but the effect of hydrotreating is considered on the boundary condition of the hydrocracking part. The hydrocracking bed is considered as a plug flow reactor and it is modeled by the cellular network approach. Initially,_x000D_ a kinetic network with twelve coefficients and six paths is considered. But following evaluation using measured data and order of magnitude analysis, the three route passes and one activation energy coefficient are omitted; thus the number of coefficients is reduced to five. This approach improves the average absolute deviation of prediction from 7.2% to 5.92%. Furthermore, the model can predict the hydrogen consumption for hydrocracking with average absolute deviation about 8.59% in comparison to those calculated from experimental data.

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