Optimizing control of adsorption separation processes based on the improved moving asymptotes algorithm

Abstract

We adopt the orthogonal collocation finite element method to solve an equilibrium diffusion model of a simulated moving bed chromatography separation process. We propose an optimization strategy based on the improved moving asymptotes algorithm to improve system performance indexes. By the triangle theory, we verify the feasibility of the improved moving asymptotes method. The simulation results illustrate that the improved moving asymptotes method converges quickly and results in uniformly distributed optimal solutions. This work aims to show that the simulated moving bed process can be dynamically controlled and optimized using an optimizing controller based on the method of improved moving asymptotes, facilitating the design and operation of a simulated moving bed.

Applicant hierarchical fuzzy controller for concentration control of simulated moving bed

“…An optimization strategy based on the improved moving asymptote algorithm is proposed by Yonghui Yang etc. 14 . The research shows that the optimized controller based on the improved moving asymptote method can dynamically control and optimize the process of the simulated moving bed, which is conducive to the design and operation of the simulated moving bed.…”

An Automatic Advanced Fuzzy Controller for Simulated Moving Bed

“…By adjusting the flow rate and switching period, the spatial position of adsorption and desorption waves can be tuned up, and then the purity and yield of raffinate and extraction port can be adjusted. In reference [11], Yan et al applied apply the model predictive control method to SMB Chromatographic Separation process. The researchers established a state space model to model the production.…”

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].…”