原油密度预测通过多块分析改善 里埃变换离子旋转子共振质谱,里埃变换红外和近红外光谱数据
Elise L Gjelsvik1, Martin Fossen2, Anders Brunsvik2
1Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway.
Applied spectroscopy
|August 1, 2023
概括
里埃变换离子循环子共振质谱和红外光谱分析复杂的原油. 将数据源与顺序和正交的PLSR相结合,提高了原油密度预测的准确性.
科学领域:
- 石油地质化学 石油地质化学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 原油是复杂的有机混合物,难以完全描述.
- 传统的分析技术往往缺乏必要的解决能力.
- 像质谱学和红外光谱学这样的先进方法提供了增强的表征能力.
研究的目的:
- 为了比较不同化学测量方法对预测原油密度的有效性.
- 评估结合来自多种分析技术的数据的好处.
- 确定影响原油密度预测的关键变量.
主要方法:
- 里叶变换离子循环子共振质谱 (FT-ICR MS) 用于高质量精度分析.
- 福里埃变换红外 (FT-IR) 和近红外光谱学用于化学指纹.
- 部分最小平方回归 (PLSR) 模型:单块,多块,顺序和正交的PLSR (SO-PLSR).
- 变量在投影中的重要性 (VIP) 对于特征选择.
主要成果:
- 结合来自不同分析方法的数据,提高了预测准确度.
- 多块和SO-PLSR模型的性能优于单块PLSR.
- 使用缩小数据集的SO-PLSR产生了最准确的原油密度预测.
- 变量重要性分析确定了关键的化学指标.
结论:
- FT-ICR MS和IR光谱的数据融合提高了原油的特征.
- 像SO-PLSR这样的先进化学测量技术对于预测原油特性是有效的.
- 变量选择对于优化预测模型和理解化学驱动因素至关重要.
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