Abstract
Adaptive Fuzzy Controller Design for Simulated Moving Bed System
Highlights
Chromatography is a technique used for the separation of a mixture
In the classical linear model predictive controller (MPC), an improved prediction mechanism based on the strategy of switching the system was proposed by Nogueira et al[6] The results showed that this method can control the process in both servo and regulator cases
Lee and Seidel-Morgenstern proposed a method based on the results of a simulation study, in which the controller can estimate the current process state and find the best operation conditions under the competitive Langmuir isotherm.[8]. Yang et al proposed an optimization strategy based on the improved moving asymptote algorithm, demonstrating that a controller based on the improved moving asymptote method can dynamically control simulated moving bed process (SMB).[9]. A model predictive control method applied to SMB that is based on the established state-space model was presented by Martins et al[10]
Summary
Chromatography is a technique used for the separation of a mixture. The simulated moving bed process (SMB) has had increasing application in large-quantity supercritical fluid extraction owing to its advantages such as a low production cost, a low solvent consumption, and its ability to carry out a clean, continuous operation.[1,2] The basic principle of SMB is to use multiple columns containing the solid adsorbent and move these columns in the opposite direction to the fluid to achieve a countercurrent flow, the term “moving bed”. An adaptive nonlinear model predictive control method was proposed by Andrade Neto et al[5] for the separation of enantiomers of praziquantel in a simulated moving bed. In the classical linear model predictive controller (MPC), an improved prediction mechanism based on the strategy of switching the system was proposed by Nogueira et al[6] The results showed that this method can control the process in both servo and regulator cases. Lee and Seidel-Morgenstern proposed a method based on the results of a simulation study, in which the controller can estimate the current process state and find the best operation conditions under the competitive Langmuir isotherm.[8] Yang et al proposed an optimization strategy based on the improved moving asymptote algorithm, demonstrating that a controller based on the improved moving asymptote method can dynamically control SMB.[9] A model predictive control method applied to SMB that is based on the established state-space model was presented by Martins et al[10]
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