整合实验数据和机器学习模型,用于在超临界二氧化碳中预测黄色23的可溶性
Seyed Ali Sajadian1, Amir Hossein Sheikhshoaei2, Nadia Esfandiari3
1Department of Chemical Engineering, Faculty of Engineering, University of Kashan Kashan Iran seyedali.sajadian@gmail.com.
RSC advances
|December 18, 2025
概括
这项研究详细介绍了黄23在超临界二氧化碳中的溶解度,为其工业应用提供了关键数据. K-J模型和MLP机器学习模型准确地预测了可溶性,MLP显示出卓越的性能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
- 热力学是一种热力学.
背景情况:
- 超临界二氧化碳 (scCO2) 是一种具有可调节性质的环保溶剂.
- 了解黄色23等染料在scCO2中的溶解度对于染料和提取过程中的应用至关重要.
- 在各种条件下,关于黄23在scCO2中的溶解度的数据有限.
研究的目的:
- 为了研究黄23在超临界二氧化碳中的可溶性.
- 评估半实证和机器学习模型在预测黄23溶解度方面的性能.
- 为了确定最准确的模型来表示黄色23的溶解度数据.
主要方法:
- 在压力范围为12-30 MPa和温度范围为313-343 K的范围内,实验确定黄23在scCO2中的溶解度.
- 应用和比较四种半经验性溶解性模型 (MST,Chrastil,Bartle等,K-J).
- 实施和评估三种机器学习模型:多层感知器 (MLP),高斯过程回归 (GPR) 和随机森林 (RF).
主要成果:
- 在所研究的范围中量化了黄色23在scCO2中的实验摩尔溶解度.
- 该K-J半经验模型证明最适合实验数据,平均绝对相对偏差为6.39%.
- 所有机器学习模型都显示出强大的预测能力,MLP实现了最高的R2 (99.7) 和最低的MSE (0.001).
结论:
- 黄色23在scCO2中的可溶性随温度增加而随压力降低.
- K-J模型提供了一种可靠的方法来估计黄23在scCO2中的溶解度.
- 在预测黄23溶解度方面,MLP模型非常有效,为传统模型提供了强大的替代方案.
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