油品烷基化催化精馏脱硫过程的多目标优化

Multi-objective optimization for gasoline catalytic distillation of alkylation desulfurization

针对促进油品烷基化脱硫目标反应的催化精馏过程,采用多目标优化方法对该过程进行建模和分析。建立烷基化脱硫催化蒸馏的平衡级模型,使用Aspen Plus进行求解。建立基于提高烷基化反应选择性、降低全塔总板数和操作成本的多目标优化模型,使用带精英保留策略的非支配排序遗传算法(NSGA-II)对该模型进行求解,得到烷基化催化精馏脱硫过程的多目标优化的Pareto前沿和Pareto解集。通过分析Pareto解集,发现进料位置和精馏段塔板数存在线性关系,反应段塔板数的降低及反应选择性的提高都以提升操作费用为代价,提馏段塔板数对反应选择性及操作成本影响不大。与灵敏度分析的优化结果相比,采用多目标优化方法提高了烷基化反应的转化率和选择性,降低了馏分中的硫含量,降低了操作费用。这些结果证明在烷基化催化精馏脱硫过程中使用多目标优化的有效性。

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.