.png){kind=icon}
Return
Antisolvent crystallization: Model identification, experimental validation and dynamic simulation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ces.2008.08.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper is concerned with the development, simulation and experimental validation of a detailed antisolvent crystallization model. A population balance approach is adopted to describe the dynamic change of particle size in crystallization processes under the effect of antisolvent addition. Maximum likelihood method is used to identify the nucleation and growth kinetic models using data derived from controlled experiments. The model is then validated experimentally under a new solvent feedrate profile and showed to be in good agreement. The resulting model is directly exploited to understand antisolvent crystallization behavior under varying antisolvent feeding profiles. More significantly, the model is proposed for the subsequent step of model-based optimization to readily develop optimal antisolvent feeding recipes attractive for pharmaceutical and chemicals crystallization operations. (c) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Antisolvent crystallization
Population balance
Kinetics
Parameter identification
Particle size
Pharmaceuticals
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.3
Papers:
2.3W
Citations:
5.5W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSave
View more