通过矩阵完成来预测温度依赖的亨利定律常数
Nicolas Hayer1, Hans Hasse1, Fabian Jirasek1
1Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, Kaiserslautern 67663, Germany.
The journal of physical chemistry. B
|January 9, 2025
概括
本研究介绍了先进的机器学习方法,用于预测温度依赖的亨利法则.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 物理化学 物理化学
- 计算化学计算化学
背景情况:
- 预测亨利定律常数 (H) 对化学工程应用至关重要.
- 现有的方法往往侧重于同热条件,限制了它们在温度范围内的适用性.
研究的目的:
- 开发和评估用于预测温度依赖亨利定律常数的机器学习方法.
- 将数据驱动的方法与物理模型相结合,以提高准确性.
主要方法:
- 开发了两种矩阵完成方法 (MCM):数据驱动的MCM和混合MCM,其中包括预测Soave-Redlich-Kwong (PSRK) 状态方程 (EoS).
- 来自多特蒙德数据库 (122个溶解物,399个溶剂,173.15573.15 K) 的亨利定律常数实验数据用于培训.
- 留下一个缺席的分析被用来评估预测的准确性.
主要成果:
- 与独立的PSRK-EoS相比,提出的MCM显示出更高的预测准确性.
- 混合MCM有效地将物理知识与机器学习相结合,以提高性能.
结论:
- 机器学习,特别是MCM与物理方程相结合,为预测温度依赖的亨利定律常数提供了一种强大的方法.
- 这些先进的方法可以显著改善化学工程中的可溶性预测.
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