Effect of precipitation chamber geometry on the production of microparticles by antisolvent process
Effect of precipitation chamber geometry on the production of microparticles by antisolvent process
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DOI：
10.1016/j.supflu.2017.09.015
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发表时间：
2018-03-01
影响因子：
3.9
通讯作者：
Cardozo-Filho, L.
中科院分区：
文献类型：
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作者：
Cardoso, F. A. R.;Rezende, R. V. P.;Cardozo-Filho, L.
超临界反溶剂工艺（SAS 和 SEDS）用于在超临界条件下沉淀各种药物和食品化合物的微粒/纳米颗粒。由于超临界条件下颗粒生产涉及大量操作参数，这种生产微粒和纳米颗粒的有效方法的可扩展性仍然存在技术挑战。与群体平衡方程 (PBE) 耦合的计算流体动力学 (CFD) 模型已被证明是一种有效的方法，可以更好地了解通过超临界反溶剂方法生产微粒和纳米颗粒。在本研究中，考虑到 CO2 在超临界条件下的非理想行为，采用基于 CFD 的建模与 PBE 相结合，并使用不同几何形状的沉淀室获得的文献实验数据，评估了沉淀室体积对沉淀 PHBV 粒径的影响。物理性质（密度、导热率、粘度和质量扩散率）分别通过Peng-Robinson状态方程（EOS）和Van der Walls、Chung以及Riazi和Whitson方法的平方混合规则计算。模拟在 85 bar 的压力和 313 K 的温度下进行。该模型能够预测平均 PHBV 纳米颗粒直径，误差为 7%。由于射流相互作用促进的流动模式，具有较大轴向长度和较小室直径的几何形状导致较大颗粒沉淀。
Supercritical antisolvent processes (SAS and SEDS) are employed for the precipitation of the microparticles/nanoparticles of a wide variety of pharmaceutical and food compounds under supercritical conditions. The scalability of this effective method for the production of microparticles and nanoparticles still represents technological challenge due to the large number of operational parameters involved in the production of particles under supercritical conditions. A computational fluid dynamics (CFD) model coupled to a population balance equation (PBE) has been shown to be an efficient approach to gaining a better understanding of the production of microparticles and nanoparticles through the supercritical antisolvent method. In this study, taking into account the non-ideal behavior of CO2 under supercritical conditions, the influence of the precipitation chamber volume on the precipitated PHBV particle size was evaluated applying CFD-based modeling coupled with PBE and using experimental data from the literature obtained with chambers with different geometries. The physical properties (density, thermal conductivity, viscosity and mass diffusivity) were calculated through Peng-Robinson's equation of state (EOS) and the squared mixing rule of the Van der Walls, Chung and Riazi and Whitson methods, respectively. Simulations carried out at a pressure of 85 bar and temperature of 313 K. The model was able to predict the mean PHBV nanoparticle diameter with an error of 7%. A geometry with a larger axial length and smaller chamber diameter led to larger particles being precipitated due to the flow pattern promoted by the jet interaction.