Research On Optimization Of Simulated Moving Bed

In recent years, the simulated moving bed chromatography separation technology is widely used in petrochemical industry, sugar industry and the pharmaceutical industry. In a complex simulated moving bed system, there are many factors that can affect the separation performance of simulated moving bed, included the flow in each zone, switching time, the concentration of the feed and the size of the chromatographic column, etc. Therefore, research on optimization of the simulated moving bed not only has important theoretical value, but also great practical application value in the separation performance.Using the optimization algorithm is one of the most common methods in the simulated moving bed separation process. In this paper, the moving asymptotes method apply to the purity of extract, the purity of raffinate, productivity and solvent consumption in the simulated moving bed separation process. Feasibility of the moving asymptotes is verified by the triangle theory. Compared with the results of the particle swarm optimization, the moving asymptotes method not only is fast in convergence rate, and distribution of the optimal solution is uniform. The research of this paper worked information as follows:(1) Basic separation principle of simulated moving bed is introduced. The development of research on the application and optimization and mathematical model is given.(2) Based on Equilibrium Dispersive Model, applying the orthogonal configuration on the finite element method is used to solve the mathematical model. And the application of MATLAB solve simulation of process.(3) In order to improve the separation performance of simulated moving bed, this paper uses the moving asymptote method and particle swarm optimization respectively in the maximum purity of extract and raffinate purity and maximize productivity and minimize solvent consumption in the simulated moving bed. Feasibility of two methods is verified by the triangle theory. And results of the simulation is compared.