Method for optimizing chromatograph operating parameters of simulated mobile bed

Abstract

The invention discloses a method for optimizing chromatograph operating parameters of a simulated mobile bed. According to the method, the target preference requirement of a chromatograph production process of the simulated mobile bed is introduced for guiding the optimization direction to optimize the purity and production rate of a simulated mobile bed product. Compared with an experimental optimizing method, the method saves a large amount of operating time and economic cost; compared with a common single-target optimizing method, the method more accords with the fact that the weight relationship between a plurality of production targets or targets in the practical production process of the simulated mobile bed; and compared with a common multi-target optimizing method, the method adds the production target preference of the simulated mobile bed according to real process, so that the obtained optimizing results are positioned within a target preference range, the pertinence of the optimizing results is improved and better working points are acquired.

Description

technical field [0001] The invention relates to the technical field of simulated moving bed chromatography, in particular to a method for optimizing operating parameters of simulated moving bed chromatography applied to high-demand separation occasions such as fine chemical industry and chiral drug resolution. Background technique [0002] Simulated Moving Bed (SMB) technology, as the main representative of continuous chromatography, has the advantages of high production efficiency, less consumption of organic solvents, large mass transfer driving force, and convenient automatic continuous production. It is a new generation of green chemical separation in the world. Technology is also an important research direction for the greening and automation of the process industry. Its application range is also expanding from traditional petrochemical and sugar separation to biopharmaceuticals and fine chemicals. [0003] In the practical application of SMB technology, people's main ...

AI Technical Summary

Problems solved by technology

The experimental method has strong practical applicability, but the time cost and economic cost are relatively large, and the applicability of the method is poor, and it is difficult to generalize; the triangle theory has important guiding significance for the SMB optimization process, which makes the optimization process be simplified

However, this theory is based on the assumption of ideal conditions, ignoring the mass transfer resistance between the two terms, and there is a large gap with the actual separation process, so it cannot be used for quantitative research

The single-objective optimization method requires artificial weight setting for each production target. This weight setting method has a lot of subjective factors and is not conducive to the actual process, and the global search ability of this method is poor; multi-objective optimization Although the method can deal with multi-objective problems better, it optimizes the entire space of the target space when optimizing, so that the final Pareto solution is evenly distributed in the target space, but the separation target that really meets the actual production requirements The number of operating points in the area is small, which brings great limitations to the selection of actual working conditions

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