Simulated moving bed (SMB) is a kind of continuous process which can increase the efficiency of adsorbents in the adsorbent bed. It contains several sectors of flow rate, the switching time of valves and many other possible influencing variables, moreover, these parameters are highly sensitive, so it is very difficult to achieve precise prediction and control. Model predictive control and PID controller are often used in industrial system. Model predictive control needs a lot of accurate industry experience data, and PID controller depends on the selection of control parameters. Therefore, SMB needs an intelligent controller to bypass those complex mechanisms and parameter adjustment processes. This paper we propose the hierarchical fuzzy controller fuzzy controller which is applied to the SMB system to observe the final concentration. Compared with the PID and MPC controller, it is found that the hierarchical fuzzy controller can control good without knowing the system parameters too accurately.

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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.

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