Simulation of distillation unit based on artificial immune network multi-agent algorithm

In petrochemical field,the process simulation for distillation is an important task.The key parameter in the distillation process simulation is the tray efficiency,which can not be obtained easily.Thus the determination of appropriate tray efficiency is an important issue.In this study,artificial immune network multi-agent optimization strategy(Maopt-aiNet),which combines immune mechanics and multi-agent technology,is used to determine the Murphree efficiency.The main search operators of Maopt-aiNet include neighborhood clonal selection,neighborhood competition,self-confidence motivation,self-confidence neighborhood learning,and neighborhood collaborative operators.Based on the process and analysis data of the distillation unit,Maopt-aiNet is applied to determine the Murphree efficiency for each stage and to minimize the square summation of model’s analog relative error of the stage temperature.The experimental results show that with the tray efficiency determined by Maopt-aiNet the model fits the actual distillation unit fairly well.The method can be used to guide the operation of the distillation process.