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International Journal of Chemical Reactor Engineering

Ed. by de Lasa, Hugo / Xu, Charles Chunbao

4 Issues per year

IMPACT FACTOR 2013: 0.584

SCImago Journal Rank (SJR): 0.303
Source Normalized Impact per Paper (SNIP): 0.426

Mathematical Modeling of Gas Oil HDS and Optimization of Operational Conditions in Trickle Bed Reactor by Genetic Algorithm

Reza Abbasi1 / Shohreh Fatemi2

1University of Tehran,

2University of Tehran,

Citation Information: International Journal of Chemical Reactor Engineering. Volume 7, Issue 1, ISSN (Online) 1542-6580, DOI: 10.2202/1542-6580.1864, April 2009

Publication History

Published Online:

The present work aims to employ genetic algorithm (GA) to optimize a HDS process, which is difficult to optimize by conventional methods. The considered chemical process is the three phase catalytic trickle-bed reactor in which hydrodesulphurization reaction occurs. Non-linear kinetics coupled with the transitional mathematical model of the gas, liquid and solid phases are used to describe the dynamic behavior of the multivariable process. The model, based on a two-film theory, was tested with regards to hydrodesulphurization of vacuum gas oil in a high-pressure pilot plant operated under isothermal conditions. Due to the high dimensionality and non-linearity of the model, a rigorous one, the solution of the optimization problem through conventional algorithms does not always lead to the convergence. This fact justifies the use of an evolutionary method, based on the GAs, to deal with this process. In this way, in order to optimize the process, the GA code is coupled with the rigorous model of the reactor. The aim of the optimization through GAs was to search for the optimal conditions that minimize the gas make and sulfur content of the outlet oil. Many simulations are conducted in order to find the maximization of the objective function without violating the constraints. The results show that the GA is used successfully in the process optimization.

Keywords: mathematical modeling; trickle bed reactor; dynamic simulation; non-linear dynamics; optimization; genetic algorithm

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