CCD近红外光谱仪在柴油生产控制分析中的应用

介绍了电荷耦合器件（ Ｃ Ｃ Ｄ）近红外光谱仪在柴油生产控制分析中的应用,通过对直馏柴油、加氢精制柴油及成品柴油十六烷值、密度、凝点、闪点及馏程等质量指标的测定及与标准测定方法的对比,验证了近红外光谱测定柴油性质的可靠性。试验证明,近红外方法具有分析速度快、重现性好、分析成本低等特点。     

近红外光谱技术快速测定柴油物理性质

分别在近红外短波和长波范围内考察了采用ＣＣＤ近红外光谱仪和傅立叶变换近红外光谱仪测定柴油物理性质的可行性。通过正确的光谱预处理方法,光谱区域的选择和偏最小二乘回归确定定量校正模型,建立了近红外光谱快速测柴油性质的方法。结果表明,在近红外短波和长波范围内,该方法测定均能达到标准方法的误差要求。     

柴油近红外光谱的独立分量分析方法

 将新兴的多变量分析工具—独立分量分析(ICA)应用到石化行业中，分析柴油的十六烷值和密度、组分中芳烃含量与其近红外光谱之间的关系。ICA方法用于提取柴油近红外光谱数据矩阵的独立成分和相应的混合矩阵，再用BP神经网络对混合矩阵和十六烷值、芳烃含量以及密度进行回归分析，提出了新的柴油组分含量测定和特性分析的基于独立分量分析-神经网络回归(ICA-NNR)的近红外光谱分析方法。通过分析独立分量数和网络中间隐层的神经元数对模型性能的影响，分别建立柴油组分含量测定模型和密度特性的关联模型。结果表明，该方法用于实测的柴油样品近红外光谱数据的处理，测试样品集的标准方法测定值与所建模型预测值的相关性及相对误差均优于现行常用的PLS、PCR等方法。基于ICA-NNR的近红外光谱分析方法对石化行业中油品的组分及物理特性分析具有很好的可行性。     

近红外光谱测定柴油十六烷值

采用傅立叶变换近红外光谱技术与偏最小二乘方法结合测定柴油的十六烷值。讨论了训练集体本光谱范围的选择,光谱预处理方法最佳主因子数的选择。近红外光谱技术测定的与标准方法测定的结果有良好的相关性。采用不同的校正模型测定了催化裂化柴油馏分,成品柴油等不同类型样品的十六烷值。     

近红外光谱法测定柴油中的芳烃含量

研究了采用近红外光谱测定柴油中芳烃含量的方法。通过高效液相色谱分离、移动丝氢火焰检测器检测获得柴油中芳烃的基础数据。在５０００～１００００ｃｍ－１波长范围内,应用傅立叶变换近红外光谱仪,交互检验偏最小二乘方法对使用的光谱范围进行优化。所得校正模型对训练集样品预测的平均偏差为０２３％,对未知样品将近红外预测结果与ＨＰＬＣ的测定结果比较,平均偏差为０２７％。作为一种快速分析技术,近红外光谱测定柴油中的芳烃含量是简便可靠的方法。     

拉曼和红外光谱快速评价原油性质的可行性比较

分子光谱技术如中红外和近红外光谱已应用于原油性质的快速分析,而拉曼光谱在这方面的应用鲜有报道。为探讨拉曼光谱进行原油性质快速分析的可行性,采用拉曼、中红外、近红外3种分子光谱方法结合化学计量学来预测原油的密度、残炭和蜡含量,对3种方法采集原油光谱的重复性和预测原油性质的准确性进行了比较。结果表明,拉曼光谱采集原油信号的重复性差,分析原油性质的准确性较低,在进行原油性质快速分析方面存在较大的局限性。     

LTAG加氢单元原料和产品组成的近红外快速分析及应用

利用383个催化裂化柴油和加氢柴油样品的近红外光谱及标准族组成数据,结合偏最小二乘(PLS)方法建立近红外混合分析模型,针对LTAG加氢单元中LCO原料和产品的详细族组成进行快速分析,并将该近红外快速分析方法应用于某炼油厂LTAG工艺过程中。模型验证结果显示预测值与样品族组成实测值非常接近,模型准确性与标准方法相当,重复性测试结果显示模型重复性优于标准方法;对炼油厂LTAG加氢装置原料和产品某些组分含量连续15天的近红外监控结果也证明该快速分析方法可满足实际应用需求。     

汽柴油、蜡油及润滑油基础油快速分析技术

基于汽柴油的近红外光谱,中国石化石油化工科学研究院开发的汽柴油、蜡油及润滑油基础油快速分析技术,可对炼油厂多种汽油加工装置馏出口的汽油(FCC汽油、S Zorb汽油以及烷基化汽油)的辛烷值(预测标准偏差SEP为0.5)及PONA值(SEP为1.0％)进行快速分析;可对炼油厂多种柴油加工装置馏出口的柴油(加氢精制柴油、加...     

近红外原油快速评价技术二次开发与工业应用

在现有技术基础上,对中国石化石油化工科学研究院近红外原油快速评价系统进行二次开发,建立了针对炼化企业的近红外原油光谱数据库,以库光谱拟合及识别相结合的方式建立快速评价分析模型。将二次开发后的该套快速评价系统应用于A、B两家炼油厂,对其加工原油进行种类识别及性质预测。结果表明,在数据库完善的情况下,炼油厂加工的原油可以被完全识别,两个炼油厂加工原油的密度、酸值、硫含量和残炭性质的预测标准偏差（RMSEP）较小,分析重复性与标准方法相当。     

近红外光谱技术与仪器的发展和推广应用

近红外光谱分析技术与近红外光谱仪器近年来有很大发展,在不少领域获得了成功的应用;但是无论是近红外光谱仪器还是近红外光谱分析技术的推广应用,都是全球亟待解决的前沿课题。     

A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum

The implementation of near infrared spectroscopy in monitoring diesel fuel properties is highly dependent on the capability of its predictive model. This study investigates the feasibility of a single layer artificial neural networks among various predictive models in predicting the diesel fuel properties using near infrared spectrum. Results were compared and discussed with that reported in previous studies that used the same data in predicting the diesel fuel properties. Findings show that the proposed single layer outperforms popular models, and is comparable with a recent advanced models in predicting the diesel fuel properties using near Infrared spectrum without data reduction.     

Determination of Asphalt Cement Properties by Near Infrared Spectroscopy and Chemometrics

In the present study, near infrared (NIR) spectroscopy and chemometrics techniques have been combined to predict some properties used to characterize and to classify asphalt cement grade. Principal Components Regression (PCR) and Partial Least Squares Regression (PLSR) were employed to develop linear models to predict penetration value, absolute viscosity at 60°C, kinematic viscosity at 135°C and flash point of asphalt cements. The good correlation between NIR pretreated spectra and the properties investigated, measured by values of correlation coefficients and root mean squared errors of both calibration and prediction are here discussed. It was concluded that the combination of both techniques can be used to predict properties of asphalt cement to be delivered after on-line blending of hard and soft binders stocked in refineries.     

Determination of Asphalt Cement Properties by Near Infrared Spectroscopy and Chemometrics

Abstract

In the present study, near infrared (NIR) spectroscopy and chemometrics techniques have been combined to predict some properties used to characterize and to classify asphalt cement grade. Principal Components Regression (PCR) and Partial Least Squares Regression (PLSR) were employed to develop linear models to predict penetration value, absolute viscosity at 60°C, kinematic viscosity at 135°C and flash point of asphalt cements. The good correlation between NIR pretreated spectra and the properties investigated, measured by values of correlation coefficients and root mean squared errors of both calibration and prediction are here discussed. It was concluded that the combination of both techniques can be used to predict properties of asphalt cement to be delivered after on-line blending of hard and soft binders stocked in refineries.     

生物喷气燃料的组成与基本性质研究

通过对不同原料来源的生物喷气燃料的烃类组成分析,分析原料对产品的影响,同时与石油基喷气燃料比较,研究生物喷气燃料组成与石油基喷气燃料的差别,进一步与石油基喷气燃料调合,分析烃类组成和理化性能,比较生物喷气燃料与石油基喷气燃料性能。数据表明,生物喷气燃料的性能完全符合ASTM D7566附录2的要求,调合后的喷气燃料完全符和GB6537对3号喷气燃料的要求。     

Near Infrared Spectroscopic Determination of Diesel Fuel Parameters Using Genetic Multivariate Calibration

The use of full spectral region from near infrared spectroscopic analysis does not always end up with a good multivariate calibration model as many of the wavelengths do not contain necessary information. Due to the complexity of the spectra, some of the wavelengths or regions may, in fact, disturb the model-building step. Genetic algorithms are one of the useful tools for solving wavelength selection problems and may improve the predictive ability of conventional multivariate calibration methods. This study demonstrates application of genetic algorithm-based multivariate calibration to near infrared spectroscopic determination of several diesel fuel parameters. The parameters studied are cetane number, boiling and freezing point, total aromatic content, viscosity, and density. Multivariate calibration models were generated using genetic inverse least squares (GILS) method and used to predict the diesel fuel parameters based on their near infrared spectra. For each property, a different data set was used and in all cases the number of samples was around 250. Overall, percent standard error of prediction (%SEP) values ranged between 2.48 and 4.84% for boiling point, total aromatics, viscosity, and density. However, %SEP results for cetane number and freezing point were 11.00% and 14.86%, respectively.     

近红外光谱法在线分析重烷基苯

采用近红外光谱技术建立一种能够同时测量重烷基苯的馏程、烷基苯含量和相对分子质量等性质的分析方法。实验结果表明,5%馏程、95%馏程、烷基苯含量及相对分子质量的近红外校正模型的交互验证标准差分别为1.5℃,1.8℃,0.82%,1.7;外部验证标准差分别为2.4℃,3.3℃,1.10%,3.0;馏程、烷基苯含量及相对分子质量的分析精密度分别为±0 4℃、±0.4%和±1。近红外光谱法测定的结果明显优于常规分析方法,表明利用近红外光谱法分析重烷基苯的馏程、烷基苯含量和相对分子质量是可行的。该方法具有快速、多通道的优点,为实现重烷基苯生产过程的质量在线监测提供了手段。     

Near Infrared Spectroscopic Determination of Diesel Fuel Parameters Using Genetic Multivariate Calibration

Abstract

The use of full spectral region from near infrared spectroscopic analysis does not always end up with a good multivariate calibration model as many of the wavelengths do not contain necessary information. Due to the complexity of the spectra, some of the wavelengths or regions may, in fact, disturb the model-building step. Genetic algorithms are one of the useful tools for solving wavelength selection problems and may improve the predictive ability of conventional multivariate calibration methods. This study demonstrates application of genetic algorithm-based multivariate calibration to near infrared spectroscopic determination of several diesel fuel parameters. The parameters studied are cetane number, boiling and freezing point, total aromatic content, viscosity, and density. Multivariate calibration models were generated using genetic inverse least squares (GILS) method and used to predict the diesel fuel parameters based on their near infrared spectra. For each property, a different data set was used and in all cases the number of samples was around 250. Overall, percent standard error of prediction (%SEP) values ranged between 2.48 and 4.84% for boiling point, total aromatics, viscosity, and density. However, %SEP results for cetane number and freezing point were 11.00% and 14.86%, respectively.     

Improvements of heat resistance and adhesive property of condensed poly-nuclear aromatic resin via epoxy resin modification

A bisphenol epoxy resin was used as modifier to increase the heat resistance of condensed poly-nuclear aromatic （COPNA） resin. The basic properties of COPNA resin and modified resin were characterized by Fourier transform infrared spectroscopy （FT-IR）, nuclear magnetic resonance spectroscopy （1H-NMR）, vapor pressure osmometry （VPO） and elemental analysis （EA）. Average structural parameters of resins were calculated by the improved Brown-Ladner method, and heat resistance of resins was tested by thermogravimetric analysis （TGA）. The chemical structure, mechanical properties and heat resistivity of the resin/graphite composites prepared with different resins were compared. The results show that the adhesive property and heat resistance of COPNA resin can be remarkably improved by addition of 5 wt.% epoxy resin. The reason is that the reactions between epoxy groups of epoxy resin and hydroxyl groups of COPNA resin improve the heat resistance and adhesive property of COPNA resin. Electric motor brushes with good mechanical properties and low electrical resistivity were successfully prepared by using the modified resin as binder.     

近红外光谱在清洁汽油生产控制分析中的应用

介绍近红外光谱分析在清洁汽油生产控制中的应用。在标准方法的基础上,采用偏最小二乘方法建立了适合催化裂化、催化重整、90号清洁汽油和93号清洁汽油测定RON、MON、烯烃含量、芳烃含量、苯含量和氧含量的分析模型,通过大量试样对所建分析模型的可靠性进行了考察。结果表明,近红外光谱的测定结果与标准方法的测定结果有很好的一致性。近红外光谱技术的应用可以提高分析效率,降低成本,对装置的平稳运行和汽油的优化调合起到积极作用。