Application of Monte Carlo techniques to LCO gas oil hydrotreating: Molecular reconstruction and kinetic modelling

• We model both feedstock composition and process reactions at a molecular level.
• The feedstock is represented by a mixture of molecules having the same properties.
• The reactions are simulated by a kinetic Monte Carlo method on the set of molecules.
• The methodology was applied to the hydrotreating of various LCO gas oils.
• Both the feed and the effluents were favorably compared to the experimental data.

The increasing environmental constraints on oil products leads to the need for developing accurate models in order to predict the detailed performances of refining processes. In the current study, a stochastic two-step procedure using Monte Carlo techniques is applied to and validated on the hydrotreating of Light Cycle Oil (LCO) gas oils. In the first step, a mixture of molecules representative of the LCO gas oils is generated using a molecular reconstruction method termed SR-REM. Subsequently, the Stochastic Simulation Algorithm (SSA) is applied to simulate the evolution of the mixture composition during hydrotreating. The results show that an accurate representation of eleven different LCO gas oils was obtained by the application of the molecular reconstruction method. The hydrotreating simulations of three LCO gas oils at different operating conditions showed a good agreement with the experimental data obtained at laboratory scale. The current stochastic procedure is demonstrated to be a valid tool for the reconstruction of the composition of LCO gas oils and the simulation of the hydrotreating process.

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