开发数据驱动模型以准确估计脂肪酸乙烯的密度
Walid Abdelfattah1, Munthar Kadhim Abosaoda2,3, Hardik Doshi4
1Department of Mathematics, College of Science, Northern Border University, Arar, Saudi Arabia.
Scientific reports
|August 22, 2025
概括
这项研究开发了机器学习模型来预测脂肪酸乙烯的密度. 支持向量回归 (SVR) 证明是最准确的,为实验测量提供了可持续的替代方案.
科学领域:
- 绿色化学与可持续材料
- 计算化学和数据科学
背景情况:
- 脂肪酸乙烯 (FAEEs) 是用于生物燃料,制药和滑剂的多功能化合物.
- 它们的生物降解性和可再生性使它们成为环保的替代品,有助于环境可持续性.
研究的目的:
- 开发用于估计FAEE密度的先进预测算法.
- 探索各种机器学习方法,在各种条件下准确预测密度.
主要方法:
- 利用文献中的实验数据 (1307个数据点) 进行模型开发.
- 应用机器学习算法包括AdaBoost,决策树,KNN,随机森林,集体学习,CNN和SVR.
- 使用蒙特卡洛异常值检测算法确保数据可靠性.
主要成果:
- 这一数据集非常适合可靠的模型开发.
- 温度和压力被确定为影响FAEE密度的重要因素.
- 支持向量回归 (SVR) 在密度预测中表现出最高的准确性.
结论:
- 开发的SVR模型为FAEE提供了一个经济且节省时间的替代方案.
- 通过SHAP分析证实温度是影响FAEE密度的最主要因素.
- 该框架允许在各种条件下对FAEEs进行精确的密度估计.
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