Multi-objective optimization for gasoline catalytic distillation of alkylation desulfurization

Catalytic distillation was used to promote target reaction of oil alkylation desulfurization, and the process was modeled and analyzed with a multi-objectives optimization method. An equilibrium stage model for catalytic distillation of alkylation desulfurization was first established and solved using Aspen Plus. A multi-objective optimization model for increasing the alkylation reaction selectivity, decreasing the total stages and operation cost was then established, which was solved using non-dominated sorting genetic algorithm II (NSGA-II). Finally, the multi-objective optimized Pareto front and Pareto solution set of the alkylation catalytic distillation desulfurization process were obtained. The results show that there is a linear relationship between feed location and the number of rectifying stages by analyzing the results in the Pareto set. The decrease of reaction stages and the increase in selectivity come at the cost of increased operating costs, and the number of stripping stages shows little effect on reaction selectivity and operation cost. Comparing with the optimized result of sensitivity analysis, the conversion and selectivity of the alkylation reaction are improved, and sulfur contents in distillate and operation cost are reduced by the multi-objective optimization method. These results prove the effectiveness of using multi-objective optimization in catalytic distillation alkylation desulfurization processes. © 2020, Editorial Board of "Journal of Chemical Engineering of Chinese Universities". All right reserved.