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基于机器学习的计算,估计了普通基中循环素二元混合物的密度
Ali Yarahmadi1, Ali Rashedi2, Amin Bemani3
1Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran.
Scientific reports
|March 12, 2025
概括
机器学习模型准确地预测了航空燃料混合密度. 决策树和随机森林模型显示出最佳性能,这对于燃料运输和燃烧研究至关重要.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 循环基是替代航空燃料混合物的重要组成部分.
- 准确预测燃料混合物特性对于运输和燃烧至关重要.
- 了解密度变化与运行条件和组成的变化至关重要.
研究的目的:
- 开发和比较机器学习模型,用于预测二进制环-n-混合物的密度.
- 为了确定操作参数 (温度,压力) 和成分对混合物密度的影响.
- 为航空燃料表征提供强大的工具.
主要方法:
- 利用学术出版物的实验室数据进行模型培训.
- 应用机器学习算法:随机森林 (RF),自适应提升,决策树 (DT),集体学习,K-最近邻居 (KNN),支持矢量机器 (SVM),多层感知器 (MLP) 和卷积神经网络 (CNN).
- 采用异常值检测和相关性因素分析来验证数据并评估参数的影响.
主要成果:
- 温度显著影响密度 (相关性 -0.9619),而压力影响最小 (相关性 0.041).
- 决策树 (DT) 和随机森林 (RF) 模型表现出高性能,R2值分别为0.9985和0.9982.
- 多层感知器 (MLP) 和自适应提升模型的性能较差 (R2值为0.9455和0.9477).
结论:
- 机器学习为预测航空燃料混合密度提供了有效的工具.
- 对于这种预测任务,DT和RF算法非常适合.
- 精确的密度预测有助于优化航空燃料的配方和性能.
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