基于伊米达的离子液体及其混合物的粘度预测与机器学习模型的新洞察力
Amir Hossein Sheikhshoaei1, Ali Sanati2
1Petroleum and Petrochemical Engineering School, Hakim Sabzevari University, Sabzevar, Iran.
Scientific reports
|July 2, 2025
概括
机器学习模型准确地预测了基于伊米达的离子液体 (IL) 和它们的混合物的粘度. 这项研究突出了随机森林和CatBoost模型对IL粘度预测的有效性.
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
- 物理化学 物理化学
背景情况:
- 离子液体 (ILs) 被公认为具有广泛应用的环保溶剂,特别是在石油行业.
- 基于伊米达的IL被广泛研究,但实验粘度数据有限,需要准确的预测模型.
- 粘度预测对于ILs在工业过程中的实际应用至关重要.
研究的目的:
- 预测基于伊米达的离子液体及其混合物的粘度.
- 为了评估机器学习模型的性能与分子和离子型模型对抗粘度预测.
- 分析关键性质,温度和压力对IL粘度的影响.
主要方法:
- 开发和实施机器学习 (ML) 模型,包括随机森林 (RF) 和CatBoost.
- 将ML模型与已建立的模型比较,例如ePC-SAFT-FVT (ePC-FVT-MB).
- 应用灵敏度分析和异常值检测 (杆方法) 来验证模型性能和数据完整性.
主要成果:
- 射频模型证明了纯 ILs 的最低预测误差.
- CatBoost模型在预测IL混合物的粘度方面取得了最佳表现.
- 灵敏度分析证实,粘度随着温度的增加而下降,随着压力的增加而增加.
- 异常值分析证实了超过94%的纯和混合IL粘度数据的统计有效性.
结论:
- 机器学习模型,特别是RF和CatBoost,提供了非常准确和可靠的方法来预测基于伊米达的IL及其混合物的粘度.
- 开发的模型为优化ILs在各种工业应用中的使用提供了有价值的工具,特别是在石油行业.
- 这些发现支持ILs的实际实施,通过在不同的操作条件下提供准确的粘度预测.
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