A real-time evolution approach towards multilevel process systems

Traditionally,real-time optimization needs to reach steady state before being implemented.While,regarding multivariable and high dimensional systems global optimization is always time-consuming and too long to adjust the control parameters in real time.To solve these problems,this paper proposes a real-time evolution approach towards multilevel process system,which divides a multilevel process system into several interconnected subsystems.When the system is disturbed,the process waiting for steady state is divided into several pseudo-steady states.In each steady state,aparticle swarm optimization algorithm is adapted to optimize each sub-system in turn,while the rest of sub-systems are regarded as the static system.Then coordinate the shared and internal variables between the sub-systems,and put the finally optimal solution into the controller to change the system set-point continuously.This paper gives the detailed algorithm steps and the two stage series reactor is carried out to demonstrate the benefits of the proposed methodology.