用机器学习方法在超临界CO2中确定吗啡硫酸止痛药的可溶性
Gholamhossein Sodeifian1, Chieh-Ming Hsieh2, Farnoush Masihpour3
1Department of Chemical Engineering, Faculty of Engineering, Laboratory of Supercritical Fluids and Nanotechnology, and Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran. sodeifian@kashanu.ac.ir.
Scientific reports
|September 27, 2024
概括
这项研究使用UV-Vis光谱测定了在超临界二氧化碳 (ScCO2) 中的吗啡硫酸盐溶解度. 与传统理论驱动模型相比,随机森林模型显示出更高的预测准确性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 热力学是一种热力学.
- 材料科学 材料科学 材料科学
背景情况:
- 在超临界二氧化碳 (ScCO2) 中准确的溶液溶解度数据对于优化材料加工条件至关重要.
- 存在理论驱动和数据驱动的建模方法,但需要实验数据来提高它们的性能.
研究的目的:
- 首次确定和建模吗啡硫酸盐在ScCO2中的可溶性.
- 为了比较13个半实证 (理论驱动) 和随机森林 (数据驱动) 模型的预测性能.
主要方法:
- 用紫外线可见光 (UV-Vis) 谱法在308-338 K的温度和12-27 MPa的压力下获得了吗啡硫酸盐的实验性溶解性数据.
- 总共收集了48个数据点.
- 13个半经验模型和一个随机森林模型被用于溶解度预测.
主要成果:
- 吗啡硫酸盐的溶解度从0.806 × 10−5到5.902 × 10−5摩尔分数不等.
- 随机森林模型实现了1.29%的优异平均绝对相对偏差 (AARD%),明显优于半经验模型 (AARD%9.33-19.76%).
- 溶解物分子重量被确定为随机森林模型预测中最有影响的因素.
结论:
- 与传统方法相比,随机森林模型提供了在ScCO2中的吗啡硫酸盐溶解度的优越预测能力.
- 该研究提供了基本的热力学数据,包括溶解的总和蒸发度 (分别为35.12和59.04kJ/mol).
- 这项研究为ScCO2应用提供了有价值的实验数据和建模见解.
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