| Anti-solvent crystallization is a common method of purification and separation,which is widely used in industry and has the advantages of low operating temperature,simple equipment,low energy consumption and high efficiency.With the development of chemical industry and pharmaceutical industry,the control of the crystal shape,purity,polymorphism and particle size distribution of crystal have a lager requirement.It is an effective way to improve the crystal quality that model the crystallization and find the relationship between the operating parameters and the quality.This paper focuses on the product quality control of anti-solvent crystallization by combining experimental techniques and computer simulation.First,a modified numerical scheme is developed to solve the process model of anti-solvent batch crystallization.The proposed numerical scheme can handle growth,nucleation,agglomeration and breakage kinetics by means of the Method of Characteristics(MOC),which provides an accurate and efficient result of the crystal size distribution(CSD).The result of case studies indicates that the calculated results of the MOC modified process model were in excellent agreement with the experimental results,Then Non-linear Model Predictive Control(NMPC)and non-linear Moving Horizon Estimation(MHE)are presented to provide a temperature profile for optimizing the CSD.The NMPC-MHE optimized temperature control strategy was tested using the verification experiment of anti-solvent crystallization of ?-Artemether,the validated result indicated that the method developed had high feasibility and accuracy.The calculated time shows the high efficiency of this method,which can achieve the requirement of on-line feedback control of the crystallization process.The process model and control model program is saved in M file in Matlab and packaged in the form of Matlab toolbox,then was linked to the crystallization process monitoring system developed for parameter optimization of crystallization process. |
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