基于红外光谱的燃油组分检测技术研究进展  

Research Progress on Fuel Detection Using Fourier Transform Infrared Spectroscopy Technology

作　　者：崔家学 李政涛 徐邦联 张大伟[2] 廖信清[1] 金宇辉 薛一川 Cui Jiaxue;Li Zhengtao;Xu Banglian;Zhang Dawei;Liao Xinqing;Jin Yuhui;Xue Yichuan(School of Publishing,Printing and Art Design,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Optoelectronic Information and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Zhengfei Electronic Technology Co.,Ltd.,Shanghai 200436,China)

机构地区：[1]上海理工大学出版印刷与艺术设计学院,上海200093 [2]上海理工大学光电信息与计算机工程学院,上海200093 [3]上海政飞电子科技有限公司,上海200436

出　　处：《激光与光电子学进展》2025年第1期76-88,共13页Laser & Optoelectronics Progress

基　　金：上海市产业协同创新项目(HCXBCY-2022-006)。

摘　　要：本文对红外光谱技术在汽油、柴油和喷气燃料组分检测中的研究进展进行了详细的介绍。首先介绍两种常用红外光谱仪器的基本结构和检测原理,进而阐述了适用于燃油组分分析的数据建模和预测分析方法,探讨了红外光谱技术在汽油辛烷值、甲醇、乙醇含量以及柴油十六烷值、甲醇、乙醇含量等检测中的应用,同时对喷气燃料的闪点、馏程、密度、初馏点等关键物理性质的红外光谱测定方法进行了介绍,并评估了各种建模方法和优化算法的效果,展示了红外光谱在提升燃油质量和环保性能方面的潜力。利用红外光谱技术可以实现燃油多组分的快速检测,未来通过与人工智能技术的结合,有望不断提升模型的预测精度和扩展预测范围,显示出十分显著的发展前景。This article comprehensively reviews the advancements in infrared spectroscopy technology for detecting components in gasoline,diesel,and jet fuels.First,the basic structure and detection principles of two commonly used infrared spectroscopy instruments are outlined.Next,the article elaborates on the data modeling and predictive analysis methods relevant to fuel component analysis.The application of infrared spectroscopy in determining gasoline octane number and methanol and ethanol content,as well as the cetane number of diesel,is discussed.The infrared spectroscopy measurement methods for key physical properties of jet fuel,such as flash point,distillation range,density,and initial boiling point,are also introduced.Additionally,the effectiveness of various modeling methods and optimization algorithms is evaluated,highlighting the potential of infrared spectroscopy to enhance fuel quality and environmental performance.Infrared spectroscopy enables rapid detection of multiple fuel components.In the future,the integration of infrared spectroscopy with artificial intelligence is anticipated to improve prediction accuracy and broaden the predictive capabilities of the method,indicating significant development prospects.

关 键 词：红外光谱 燃油检测 光谱仪器 数据模型 

分 类 号：O657.33[理学—分析化学]