Department of Chemical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran.
Eur J Pharm Sci. 2022 Oct 1;177:106273. doi: 10.1016/j.ejps.2022.106273. Epub 2022 Aug 5.
In the present study, the solubility of paracetamol in supercritical CO is measured at temperatures between 311 and 358 K and pressures between 95 and 265 bar. It was shown that the solubility of paracetamol through a static solubility measurement method was between 0.3055 × 10 to 16.3582 × 10 based on mole fraction. The obtained experimental solubility data revealed the direct effect of pressure on the paracetamol experimental data, while the temperature has a dual effect of both increasing and decreasing effect considering the shifting point known as crossover pressure which was measured to be around 110 bar for paracetamol. Besides, two theoretical approaches were applied to predict the paracetamol experimental results. The experimental data were modeled with two various equations of state (EoSs) i.e. Peng-Robinson EoS and the simplified perturbed chain statistical associating fluid theory (sPC-SAFT) EoS, which their binary interaction parameters were optimized by using genetic algorithm. The obtained results demonstrated the sPC-SAFT EoS predicted the solubility of paracetamol with more accuracy (the average percent deviation was equal to 2.5215), especially at higher pressures. Furthermore, four well-known semi-empirical density-based correlations namely Mendez-Santiago and Teja (MST), Kumar-Johnston (KJ), Bartle et al., and Garlapati and Madras models were applied for modeling the solubility of paracetamol experimental data. According to self-consistency test, KJ model presented more accurate correlative capability with average percent deviation about 4.09%. As a final point, the rapid expansion of supercritical solution process was performed to reduce particle size of paracetamol and mean particle size of paracetamol was calculated based on EoSs and mathematical modeling.
在本研究中，在 311 至 358 K 的温度和 95 至 265 bar 的压力下测量了扑热息痛在超临界 CO2 中的溶解度。结果表明，通过静态溶解度测量方法，扑热息痛的溶解度基于摩尔分数在 0.3055×10 到 16.3582×10 之间。获得的实验溶解度数据表明压力对扑热息痛实验数据有直接影响，而温度的影响是双重的，既增加又减少，考虑到交叉压力点，该点的测量值约为 110 bar 左右。此外，还应用了两种理论方法来预测扑热息痛的实验结果。实验数据用两种不同的状态方程（EOS）进行建模，即 Peng-Robinson EOS 和简化的扰动链统计关联流体理论（sPC-SAFT）EOS，其二元相互作用参数通过遗传算法进行了优化。结果表明，sPC-SAFT EOS 更准确地预测了扑热息痛的溶解度（平均百分比偏差等于 2.5215），特别是在较高的压力下。此外，还应用了四种著名的基于密度的半经验关联式，即 Mendez-Santiago 和 Teja（MST）、Kumar-Johnston（KJ）、Bartle 等人和 Garlapati 和 Madras 模型，用于扑热息痛实验数据的建模。根据自洽性检验，KJ 模型表现出更准确的相关能力，平均百分比偏差约为 4.09%。最后，进行了超临界溶液快速膨胀过程，以减小扑热息痛的颗粒尺寸，并根据 EOS 和数学模型计算扑热息痛的平均颗粒尺寸。
