Abstract: Optimizing the operation parameters of atmospheric and vacuum distillation units can effectively improve the economic benefits of refineries，but it would be time-consuming if the optimization process was iteratively conducted with their rigorous mechanism model. In order to reduce the computational cost，Kriging surrogate-based intelligent method is proposed to optimize the operation parameters of an atmospheric distillation system in this paper. In detail，Kriging meta-modeling technology is adopted to construct the relationship model between some key operation parameters and output variables for the distillation process，and then the relationship model is used to represent the MESH equations of the complex distillation system. Moreover，employing particle swarm optimization (PSO) algorithm in the design space to conduct global intelligent search of operation parameters. The proposed method can not only ensure the global optimization of the operation parameters，but also greatly reduce the computing time when solving the problems. Simulation results, using Aspen HYSYS, shows the effectiveness and superiority of our method and confirms its obvious engineering practicability.

Key words: complex distillation system, atmospheric distillation system, Kriging surrogate model, particle swarm optimization