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<p>The mathematical modeling for aspirin crystallization via the gas antisolvent process was investigated. In addition, aspirin was precipitated experimentally from an ethanol solution using CO<sub>2</sub> as an antisolvent in the supercritical condition. Breakage, agglomeration, nucleation, and growth kinetics are essential for modeling the process and controlling the particle size later. The current study deployed equations of state, population balance, and material balance equations. Since such models are intricate, a robust numerical algorithm was adopted. Furthermore, the kinetic parameters were identified by applying the maximum likelihood method with the aid of the genetic algorithm using the experimental data. Comparison between the experimental and modeling data revealed that the model could closely predict the particle size distribution. The results indicated that increasing the antisolvent addition rate and the operating temperature would decrease the mean particle size. Moreover, a bimodal particle size distribution was obtained at a lower antisolvent addition rate and temperature.</p>

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Model identification of gas antisolvent crystallization of aspirin using nucleation, growth, and agglomeration kinetics

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Mohammad Khalili Gorjimahalleh,
  • Dariush Jafari,
  • Seyed Mostafa Nowee,
  • Mostafa Khedrinia

摘要

The mathematical modeling for aspirin crystallization via the gas antisolvent process was investigated. In addition, aspirin was precipitated experimentally from an ethanol solution using CO2 as an antisolvent in the supercritical condition. Breakage, agglomeration, nucleation, and growth kinetics are essential for modeling the process and controlling the particle size later. The current study deployed equations of state, population balance, and material balance equations. Since such models are intricate, a robust numerical algorithm was adopted. Furthermore, the kinetic parameters were identified by applying the maximum likelihood method with the aid of the genetic algorithm using the experimental data. Comparison between the experimental and modeling data revealed that the model could closely predict the particle size distribution. The results indicated that increasing the antisolvent addition rate and the operating temperature would decrease the mean particle size. Moreover, a bimodal particle size distribution was obtained at a lower antisolvent addition rate and temperature.