一类面向多级过程系统的实时进化方法

A real-time evolution approach towards multilevel process systems

针对传统的实时优化执行前需要达到稳态、系统整体优化时间长、难以实时调整控制参数等问题提出一类面向多级过程系统的实时进化方法。把多级过程系统分成多个相互关联的子系统,当发生扰动时将系统等待稳态的过程分为若干个'拟稳态'。在每个拟稳态区间内,应用粒子群算法依次优化每一个子系统,其他各子系统设为静态,对子系统间的共享变量和内部变量进行协调,最终得到系统的最优解,把优化操作解送入控制器,连续改变系统设定值。给出了算法的详细步骤以两级串联反应器为对象进行了实例研究,验证了方法的有效性。

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, a particle 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.