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A light gas oil hydrodesulfurization process via catalytic distillation is developed and compared to a conventional process.
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A novel approach based on integration of data rectification techniques and support vector regression (SVR) is proposed to predict the sulfur content of treated product in gas oil hydrodesulfurization (HDS) process.
Within the frame of second generation bio-fuels development, the present work reports for the first time the impact of co-processing guaiacol as an oxygenated molecule representative of pyrolitic bio oils with a straight run gas oil (SRGO) in the framework of a hydrodesulfurization process (HDS).
The results show that the light gas oil hydrodesulfurization via catalytic distillation is as or more efficient than the conventional process.
However, the octane number loss of gasoline is an inevitable problem for the hydrodesulfurization process.
Fortunately, DSOs are highly active in the catalytic reaction with hydrogen and they can be easily removed and converted to alkanes and hydrogen sulfide in the hydrodesulfurization process.
The aliphatic acyclic sulfides (thioethers) and cyclic sulfides (thiolanes) are easy to remove during a hydrodesulfurization process or by thermal treatment.
The performance of gas oil hydrodesulfurization catalysts was successfully improved through the modification of oxidic precursors with thioglycolic acid.
In this study, we have implemented a multi-objective optimization method to obtain the design and optimization of three distillation reactor-side in the hydrodesulfurization process.
In general, the optimal design of hydrodesulfurization process is a highly non-linear and multivariable multiobjective optimization problem, with the presence of both continuous and discontinuous design variables.
The results obtained in the Pareto Fronts indicate competition between total annual cost, CO2 emissions and the amount of sulfur compounds of the hydrodesulfurization process.
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