Development of inverse soft sensor-based feed forward control.

Abstract

Model predictive control is widely used as a process control method for a complicated multivariable process. However, optimization of control parameters is complicated and a data set for system identification cannot always be obtained in a real process. In order to solve these problems and perform more effective control, we propose a new process control method using soft sensor models. We refer to this method as inverse soft sensor-based feed forward (ISFF) control. Soft sensor models are constructed between a controlled variable (y) as an objective variable, and manipulated variables (U) and other process variables (X) as explanatory variables. The optimal control strategy of U which optimizes the objective function including y is determined with inverse analysis on the soft sensor models while considering X variables. The proposed method was applied to the change of a set point of a simulated CSTR system and the optimization of y of a simulated fed-batch fermentation process, and the validity of ISFF was confirmed.