Purity Control Based on a Type-II Fuzzy Controller for a Simulated Moving Bed

Abstract

The control of a simulated moving bed (SMB) is always a challenging chemical control topic due to its complexity and nonlinearity. Its mathematical model must undergo an affine transformation and digitization before it can be controlled. Basically, there are three aspects that need to be considered in the nonlinear control of an SMB. First, the nonlinear characteristics are more complicated due to the switching time parameters of discrete events. Second, the control objective is not to minimize the control output error, but to make the separated concentrations between the components of the substance reach a certain ratio. Finally, the control variables are highly coupled. So far, the vast majority of the industry still uses relatively simple PLC controls; a few use specific controllers based on materials to be separated such as model predictive controls and PID controllers. Therefore, there is no unified intelligent processing mode. In this paper, a type-II fuzzy controller is presented and used as an SMB control. The interference of the related parameters was tested to observe the stability and robustness of the controller. The type-II fuzzy control was based on type-II fuzzy sets, which resulted in the type-II fuzzy controller having more flexible attribution function values. The results showed that the type-II fuzzy controller was not only more accurate in the control, but also better for robustness and adaptability than an ordinary fuzzy controller and PID controller.

Discrete Dynamic System Modeling for Simulated Moving Bed Processes

“…Hoon et al employed a data-driven Deep Q Network, a model-free reinforcement learning method, to train a control strategy for SMB processes which approaches optimality [18]. More relevant studies can be found in the references [19][20][21][22][23][24][25]. Although using machine learning and deep learning approaches to treat the SMB system as a black box can help bypass the complexities of the underlying mechanism, this approach may become highly susceptible to disturbances or To control the simulated moving bed process of binaphthol enantiomers separations, Nogueira et al proposed a nominally stable MPC controller, also known as infinite horizon model predictive control (IHMPC) [11].…”