使用GC/MS和化学测量方法确定石油衍生品和生物柴油的物理化学性质,并估计不确定性
Werickson Fortunato de Carvalho Rocha1, David A Sheen2
1National Institute of Metrology, Quality, and Technology-INMETRO, Division of Chemical Metrology, 25250-020 Duque de Caxias, RJ, Brazil.
概括
部分最小平方 (PLS) 和支向量机 (SVM) 等化学模型可以从GC-MS数据中预测燃料特性. 总体而言,SVM表现更好,为各种应用提供了强大的,不确定性量化预测.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 材料科学 材料科学 材料科学
背景情况:
- 燃料的物理化学性质对于性能和安全至关重要.
- 标准的ASTM方法来确定属性是昂贵和耗时的.
- 化学测量建模提供了一个潜在的更快,更经济的替代方案.
研究的目的:
- 为了比较部分最小平方 (PLS) 回归和支向量机 (SVM) 模型的准确性和稳定性.
- 使用GC-MS数据预测碳化合物混合物的物理化学性质.
- 为了将不确定性估计纳入可靠的模型应用.
主要方法:
- 收集和分析碳化合物混合物 (原油,石油,汽油,生物燃料/生物柴油).
- 气色谱-质谱 (GC-MS) 数据的采集.
- 开发了PLS和SVM预测模型,用于不确定性估计.
主要成果:
- 无论是PLS还是SVM模型都成功地从GC-MS数据中预测了物理化学性质.
- 与PLS模型相比,SVM模型通常表现出更高的预测准确度.
- 不确定性估计提高了化学模型的可靠性和一般适用性.
结论:
- 化学模型,特别是SVM,为预测燃料特性提供了传统ASTM方法的可行和强大的替代方案.
- 不确定性量化对于确保化学测量预测的可靠性至关重要.
- 这些模型支持燃料行业的监管,经济和安全评估.
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