数据驱动的工业过程运行优化控制

现代工业过程机理复杂使得很难对生产过程以及运行指标与被控变量之间关系精确建模.如何基于工业运行过程数据信息,不依赖模型参数给出设定值设计方案,优化运行指标是一挑战性难题.本文针对在稳态附近可以线性化的一类工业过程,考虑运行控制环和底层控制环不同时间尺度,提出一种基于Q--学习方法的次优设定值学习算法.此算法完全利用数据,学习得到次优设定值,实现运行指标以次优的方式跟踪理想值.浮选过程仿真结果表明本文所提方法的有效性.

Data-driven operational optimization control of industrial processes

It is dif?cult to accurately model productive processes and describe relationship between operational indices and controlled variables for modern industrial processes. How to design the setpoints by using only data generated by op- erational processes for optimizing operational indices, without requiring the knowledge of model parameters of operational processes, poses a challenge on operational optimization control. This paper focuses on a class of industrial processes that can be linearized near the steady states and take different time scales adopted in the operational control loop and process control loop into account. In this context, a Q--learning based suboptimal setpoint learning algorithm is proposed to learn suboptimal setpoints by utilizing only data, such that the operational indices can track the desired values in an suboptimal manner. A simulation experiment in ?otation process is implemented to show the effectiveness of the proposed method.