机器学习方法可以预测 perfluoropolyether 油的粘度
Amir Hossein Sheikhshoaei1, Reza Zabihi2
1Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Scientific reports
|October 9, 2025
概括
预测完聚乙烯 (PFPE) 粘度对于高性能工业至关重要. 高斯过程回归 (GPR) 提供了一种高度准确和可靠的方法,性能优于传统模型.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学的计算化学
背景情况:
- 多聚乙烯 (PFPE) 具有出色的化学稳定性和耐热性.
- 在航空航天和半导体等要求高的行业中,PFPE是重要的滑剂.
- 对PFPE进行实验性粘度测定是昂贵且耗时的.
研究的目的:
- 开发PFPE粘度的准确预测模型.
- 将各种智能模型的性能与传统的相关性进行比较.
- 建立一个可靠的计算方法,用于PFPE滑剂的特征.
主要方法:
- 使用多层感知器 (MLP),支向量回归 (SVR),高斯过程回归 (GPR) 和自适应提升支向量回归 (AdaBoost-SVR).
- 输入参数包括温度,密度和平均聚合物链长度.
- 使用统计错误分析和杆技术评估模型性能.
主要成果:
- 高斯过程回归 (GPR) 显示出卓越的精度,RMSE为0.4535和R2为0.999.
- 这种GPR模型的表现优于传统的沃特顿和Vogel-Fulcher-Thamman (VFT) 相对关系.
- 杆分析证实了GPR模型的稳定性,其中98.33%的数据点处于有效范围内.
结论:
- GPR提供了一个非常准确和可靠的方法来预测PFPE粘度.
- 开发的GPR模型捕捉了基本的物理化学趋势,提供了超出简单预测的洞察力.
- 这种计算方法显著减少了昂贵的实验粘度测量的需要.
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