WO2007084406A2 - Fourier transform infrared (ftir) chemometric method to determine cetane number of diesel fuels containing fatty acid alkyl ester additives - Google Patents

Fourier transform infrared (ftir) chemometric method to determine cetane number of diesel fuels containing fatty acid alkyl ester additives Download PDF

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Publication number
WO2007084406A2
WO2007084406A2 PCT/US2007/000914 US2007000914W WO2007084406A2 WO 2007084406 A2 WO2007084406 A2 WO 2007084406A2 US 2007000914 W US2007000914 W US 2007000914W WO 2007084406 A2 WO2007084406 A2 WO 2007084406A2
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cetane number
ftir
fatty acid
acid alkyl
alkyl ester
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PCT/US2007/000914
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WO2007084406A3 (en
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Luis Ramirez-Arizmendi
Heather D. Hamje
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ExxonMobil Technology and Engineering Co
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ExxonMobil Research and Engineering Co
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Priority to CA2635930A priority Critical patent/CA2635930C/en
Priority to EP07709790.5A priority patent/EP1989279A4/en
Publication of WO2007084406A2 publication Critical patent/WO2007084406A2/en
Publication of WO2007084406A3 publication Critical patent/WO2007084406A3/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/26—Oils; Viscous liquids; Paints; Inks
    • G01N33/28—Oils, i.e. hydrocarbon liquids
    • G01N33/2829—Mixtures of fuels
    • C—CHEMISTRY; METALLURGY
    • C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
    • C10L—FUELS NOT OTHERWISE PROVIDED FOR; NATURAL GAS; SYNTHETIC NATURAL GAS OBTAINED BY PROCESSES NOT COVERED BY SUBCLASSES C10G OR C10K; LIQUIFIED PETROLEUM GAS; USE OF ADDITIVES TO FUELS OR FIRES; FIRE-LIGHTERS
    • C10L1/00—Liquid carbonaceous fuels
    • C10L1/10—Liquid carbonaceous fuels containing additives
    • C10L1/14—Organic compounds
    • C10L1/18—Organic compounds containing oxygen
    • C10L1/19—Esters ester radical containing compounds; ester ethers; carbonic acid esters
    • C—CHEMISTRY; METALLURGY
    • C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
    • C10L—FUELS NOT OTHERWISE PROVIDED FOR; NATURAL GAS; SYNTHETIC NATURAL GAS OBTAINED BY PROCESSES NOT COVERED BY SUBCLASSES C10G OR C10K; LIQUIFIED PETROLEUM GAS; USE OF ADDITIVES TO FUELS OR FIRES; FIRE-LIGHTERS
    • C10L10/00—Use of additives to fuels or fires for particular purposes
    • C10L10/02—Use of additives to fuels or fires for particular purposes for reducing smoke development
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR

Definitions

  • the present invention is a method to determine the cetane number of diesel fuels containing a fatty acid alkyl ester using multivariate fourier transform infrared models.
  • the method determines the cetane number of diesel fuels containing rapeseed methyl ester.
  • FTIR Fourier Transform Infrared
  • an engine is used to measure cetane number of diesel fuels.
  • the operation of the engine is carried out in a laboratory by a skilled operator.
  • An FTIR analyzer can be used as an alternative method to provide a direct measurement of cetane number online or in the laboratory.
  • the FTIR analysis is fast and repeatable.
  • the determination of cetane number of diesel fuels containing RSME by using chemometric data from Mid-FTIR spectroscopic analyzers has not been demonstrated.
  • a method is needed to expand the use of FTIR spectroscopy to measure cetane number of diesel fuels with blended RSME.
  • the present invention is a method to determine the cetane number of diesel fuels containing a fatty acid alkyl ester additive (i.e., rapeseed methyl ester (RSME)).
  • the method involves the utilization of an FTIR-based multivariate/chernometric model in a preferred embodiment to correlate a measurec infrared spectrum of a biodiesel sample to its cetane number.
  • the two regions of the infrared spectrum employed in the determination of the cetane number include the frequency ranges 4900-3500 cm "1 and 2200-1624 cm “1 .
  • the infrared spectrum used in the analysis includes three frequency ranges: 4900-3500 cm “1 , 2200-1800 cm “1 and 1700-1624 cm “1 . Three regions are used in the analysis only when the absorption band associated with the ester functional group ( ⁇ 1746 cm “1 ) exceeds the linear range of the infrared detector (i.e. at —1.5 absorbance units).
  • Figure 1 shows a calibration parity plot for the model of Example I .
  • Figure 2 shows a calibration parity plot for the model of Example 2.
  • rapeseed methyl ester As a diesel fuel blending component to reduce exhaust emission by diesel engines is increasing.
  • Current Mid FTIR-based chemometric models cannot be used in quality or process control of RSME-containing fuels (i.e., biodiesel) because the models do not recognize these fuels.
  • the present invention can be employed to determine the cetane number of fuels encompassing rapeseed methyl ester.
  • the invention involves the use of two oi three infrared regions in the development of the FTIR-based multivariate model. The number of regions used depend on whether or not the frequency of the characteristic ester functional group of the blend component ( ⁇ 1746 cm-1) is included in the model.
  • a total of 23 diesel samples doped with RSME (0.5, 1.0, 1.5, 2.0, 2.5, 3.0, and 5 vol%) were added into a calibration set used to develop several FTIR cetane number models.
  • the models utilize known chemometric software to correct for baseline ; variations and water vapor interferences (see U.S. 5,121,337).
  • the resulting FTIR models are now capable of recognizing RSME-containing fuels, and thus, able to determine cetane number of diesel fuels containing this component.
  • Model A An FTIR-based model (Model A) was developed earlier to be used for cetane number prediction of diesel fuels.
  • the model is based on data collected on base and cetane-improved fuels from several fuel refineries in Europe.
  • samples containing RSME rapeseed methyl ester
  • RSME-containing fuels are not'recognized by the model.
  • Model B recognizes all of the RSME-containing samples, and, contrary to Model A, samples representing an extrapolation of the model (i.e., spectral outliers) were not detected in the calibration set.
  • Model C A different approach was used to develop Model C.
  • the method consists of excluding the spectral region of the ester group of RSME ( ⁇ 1746 cm “1 ) when constructing the model.
  • the frequency ranges used are: 1) 4900-3500 cm “1 , 2) 2200-1800 cm “1 , and 3) 1700-1624 cm “1 .
  • This approach is attractive for use in those applications for which the absorbance of the ester group of RSME exceeds the linear range of the spectrometer.
  • the SEC for Model C was calculated to be 1.5.
  • Figure 2 shows the calibration parity plot associated with the three spectral regions approach. None of the RSME-conta ⁇ ning samples appear a spectral outliers.
  • Table 2 shows cetane number predictions on randomly selected samples taken from the diesel dataset and analyzed using Model A, and the approaches described herein (i.e., two or three spectral regions).
  • Model A is not capable of predicting cetane number of diesel fuels containing the RSME component accurately since these samples appear as outliers in the analysis.
  • Models B and C two and three spectral regions, respectively
  • cetane number predictions for samples without RSME are not affected by the use of the new approaches.

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  • Chemical & Material Sciences (AREA)
  • Oil, Petroleum & Natural Gas (AREA)
  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biochemistry (AREA)
  • Pathology (AREA)
  • General Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Immunology (AREA)
  • Analytical Chemistry (AREA)
  • Organic Chemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Emergency Medicine (AREA)
  • Combustion & Propulsion (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The present invention is a method to determine the cetane number of a diesel fuel containing a fatty acid alkyl ester including determining the infrared spectrum of the fuel and correlating the spectrum to the cetane number using a multivariate-based Mid-FTIR model. The fatty acid alkyl ester may be rapeseed methyl ester and the infrared spectrum includes the frequency ranges 4900-3500 cm-1 and 2200-1624 cm-1 or the frequency ranges 4900-3500 cm-1, 2200-1800 cm-1 arid 1700-1624 cm-1.

Description

FOURIER TRANSFORM INFRARED (FTIR) CHEMOMETRIC METHOD
TO DETERMINE CETANE NUMBER OF DIESEL FUELS CONTAINING
FATTY ACID ALKYL ESTER ADDITIVES
BACKGROUND OF THE INVENTION
[0001] The present invention is a method to determine the cetane number of diesel fuels containing a fatty acid alkyl ester using multivariate fourier transform infrared models. In particular, the method determines the cetane number of diesel fuels containing rapeseed methyl ester.
[0002] The European Union is encouraging the use of rapeseed methyl ester (RSME) as a blending component of diesel fuel at levels up to 5% (from hereon known as biodiesel). The cetane number of such diesel fuels is measured in the laboratory by using an engine test method (ASTM D613). This method is not capable of measuring cetane number online, it is time consuming and requires a highly trained operator. As a result, a faster, repeatable method is needed to measure cetane number of biodiesel that could be used for process control and/or to certify the quality of products real-time, as they are being produced (i.e., online certification). Fourier Transform Infrared (FTIR) chemometric modeling can be employed to estimate the cetane number of diesel fuels online or in a laboratory. However, current FTIR methods involve chemometric models incapable of recognizing RSME-containing fuels, and, thus cannot be used for control of biodiesel production or certification.
[0003] Currently, an engine is used to measure cetane number of diesel fuels. The operation of the engine is carried out in a laboratory by a skilled operator. The engine analysis is time consuming, uses up a lot of fuel and it is not very repeatable (r = 0.9 cetane number). An FTIR analyzer can be used as an alternative method to provide a direct measurement of cetane number online or in the laboratory. The FTIR analysis is fast and repeatable. However, the determination of cetane number of diesel fuels containing RSME by using chemometric data from Mid-FTIR spectroscopic analyzers has not been demonstrated. As a result, a method is needed to expand the use of FTIR spectroscopy to measure cetane number of diesel fuels with blended RSME.
SUMMARY OF THE INVENTION
[0004] The present invention is a method to determine the cetane number of diesel fuels containing a fatty acid alkyl ester additive (i.e., rapeseed methyl ester (RSME)). The method involves the utilization of an FTIR-based multivariate/chernometric model in a preferred embodiment to correlate a measurec infrared spectrum of a biodiesel sample to its cetane number. The two regions of the infrared spectrum employed in the determination of the cetane number include the frequency ranges 4900-3500 cm"1 and 2200-1624 cm"1. In another preferred embodiment, the infrared spectrum used in the analysis includes three frequency ranges: 4900-3500 cm"1, 2200-1800 cm"1 and 1700-1624 cm"1. Three regions are used in the analysis only when the absorption band associated with the ester functional group (~1746 cm"1) exceeds the linear range of the infrared detector (i.e. at —1.5 absorbance units).
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] Figure 1 shows a calibration parity plot for the model of Example I .
[0006] Figure 2 shows a calibration parity plot for the model of Example 2.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0007] The use of rapeseed methyl ester (RSME) as a diesel fuel blending component to reduce exhaust emission by diesel engines is increasing. Current Mid FTIR-based chemometric models cannot be used in quality or process control of RSME-containing fuels (i.e., biodiesel) because the models do not recognize these fuels. The present invention can be employed to determine the cetane number of fuels encompassing rapeseed methyl ester. The invention involves the use of two oi three infrared regions in the development of the FTIR-based multivariate model. The number of regions used depend on whether or not the frequency of the characteristic ester functional group of the blend component (~1746 cm-1) is included in the model. A total of 23 diesel samples doped with RSME (0.5, 1.0, 1.5, 2.0, 2.5, 3.0, and 5 vol%) were added into a calibration set used to develop several FTIR cetane number models. The models utilize known chemometric software to correct for baseline; variations and water vapor interferences (see U.S. 5,121,337). The resulting FTIR models are now capable of recognizing RSME-containing fuels, and thus, able to determine cetane number of diesel fuels containing this component. These methods to FTIR model development for subsequent diesel fuel analysis can be used in process control applications and/or release of RSME-containing diesel products.
[0008] An FTIR-based model (Model A) was developed earlier to be used for cetane number prediction of diesel fuels. The model is based on data collected on base and cetane-improved fuels from several fuel refineries in Europe. However, samples containing RSME (rapeseed methyl ester), which will be increasingly used as a blending component in European diesel over the next few years, were not included in the calibration set used to build the model. As a result, RSME-containing fuels are not'recognized by the model.
[0009] Experiments were carried out to develop an FTIR-based method that could be used for measuring cetane number of RSME-containing fuels. The methoc involves two chemometric approaches that depend on whether two or three mid-infrared (MIR) spectral regions are used for model development.
Results and Discussion
[0010] A total of 23 samples containing various concentrations of RSME (0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 5.0 vol%) were prepared. After collection of FTIR spectra of these fuels, they were added to a diesel fuel dataset, which was then divided into a calibration and pre- validation set by using a genetic algorithm. Model Development
[0011] The samples used as a training set for the development of FTIR models total 241 (14 containing RSME), with 1205 engine measurements.
Example 1 - Two Spectral Regions
[0012] Two regions from the IR spectrum were used to develop the FTIR linear Model B. The frequency ranges included are, 1) 4900-3500 cm"1, and 2) 2200-1624 cm"1. The latter spectral range includes the frequency of the characteristic ester functional group of RSME (~1746 cm"1). The calibration results are shown in the parity plot in Figure 1. As can be seen in Figure 1, all cetane predictions by FTIR fall within the Reproducibility (R) of the engine. The Standard Error of Calibration (SEC), used as a measure of the difference between cetane values estimated by the model and the values measured by the engine for the samples in the calibration set, was calculated to be 1.5 (Table 1). This value compares well with that of Model A (SEC = 1.5). Most importantly, Model B recognizes all of the RSME-containing samples, and, contrary to Model A, samples representing an extrapolation of the model (i.e., spectral outliers) were not detected in the calibration set.
Table 1 — Calibration for Cetane Number Model B
Figure imgf000006_0001
Example 2 - Three Spectral Regions
[0013] A different approach was used to develop Model C. The method consists of excluding the spectral region of the ester group of RSME (~1746 cm"1) when constructing the model. The frequency ranges used are: 1) 4900-3500 cm"1, 2) 2200-1800 cm"1, and 3) 1700-1624 cm"1. This approach is attractive for use in those applications for which the absorbance of the ester group of RSME exceeds the linear range of the spectrometer. As for the previous models, the SEC for Model C was calculated to be 1.5. Figure 2 shows the calibration parity plot associated with the three spectral regions approach. None of the RSME-contaϊning samples appear a spectral outliers.
Comparison with Current Approach
[0014] Table 2 shows cetane number predictions on randomly selected samples taken from the diesel dataset and analyzed using Model A, and the approaches described herein (i.e., two or three spectral regions). As can be seen, Model A is not capable of predicting cetane number of diesel fuels containing the RSME component accurately since these samples appear as outliers in the analysis. However, Models B and C (two and three spectral regions, respectively) clearly recognize these fuels. In addition, the cetane number predictions for samples without RSME are not affected by the use of the new approaches.
Table 2 — Comparison of Cetane Number Predictions on Selected Samples
Figure imgf000007_0001
(1) Model outlier due to the presence of RSMB

Claims

CLAIMS:
1. A method to determine the cetane number of a diesel fuel containing a fatty acid alkyl ester comprising determining the infrared spectrum of said fuel and correlating said spectrum to said cetane number using FTIR.
2. The method of claim 1 wherein said fatty acid alkyl ester is rapeseed methyl ester.
3. The method of claim 1 wherein said infrared spectrum includes the frequency ranges 4900-3500 cm"1 and 2200-1624 cm"1.
4. The method of claim 1 wherein said infrared spectrum includes the frequency ranges 4900-3500 cm"1, 2200-1800 cm"1 and 1700-1624 cm'1.
5. The method of claim 1 wherein the IR spectrum includes frequencies that are characteristic of fatty alkyl esters.
6. The method of claim 1 wherein the IR spectrum includes frequencies that are characteristic of rapeseed methyl ester.
7. The method of claim 1 wherein said method is corrected for baseline variations.
8. The method of claim 1 wherein said method is corrected for water vapor interferences.
9. The method of claim 1 wherein said method is corrected for baseline variations and water vapor interferences.
10. The method of claim 1 wherein said correlation is determined by a model using data that is divided between a calibration set and pre-validation set by a genetic algorithm.
PCT/US2007/000914 2006-01-17 2007-01-12 Fourier transform infrared (ftir) chemometric method to determine cetane number of diesel fuels containing fatty acid alkyl ester additives Ceased WO2007084406A2 (en)

Priority Applications (2)

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CA2635930A CA2635930C (en) 2006-01-17 2007-01-12 Fourier transform infrared (ftir) chemometric method to determine cetane number of diesel fuels containing fatty acid alkyl ester additives
EP07709790.5A EP1989279A4 (en) 2006-01-17 2007-01-12 FOURIER TRANSFORMED INFRARED CHEMOMETRIC PROCESS (FTIR) FOR DETERMINING THE DIESEL FUEL CÉTANE INDEX CONTAINING FATTY ACID ALKYL ESTER ADDITIVES

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US75941006P 2006-01-17 2006-01-17
US60/759,410 2006-01-17
US11/651,184 US7420170B2 (en) 2006-01-17 2007-01-09 Fourier transform infrared (FTIR) chemometric method to determine cetane number of diesel fuels containing fatty acid alkyl ester additives
US11/651,184 2007-01-09

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Cited By (2)

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CN101936893A (en) * 2010-07-30 2011-01-05 华中农业大学 A method for detecting protein and amino acid from rapeseed
DE102014224719A1 (en) 2014-12-03 2016-06-09 Robert Bosch Gmbh Acoustic monitoring device for the fuel quality

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JP4483922B2 (en) 2007-09-26 2010-06-16 トヨタ自動車株式会社 Fuel deterioration detection device for internal combustion engine
US10677718B2 (en) * 2011-02-22 2020-06-09 Saudi Arabian Oil Company Characterization of crude oil by near infrared spectroscopy
US8911512B2 (en) * 2012-09-20 2014-12-16 Kior, Inc. Use of NIR spectra for property prediction of bio-oils and fractions thereof

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101936893A (en) * 2010-07-30 2011-01-05 华中农业大学 A method for detecting protein and amino acid from rapeseed
DE102014224719A1 (en) 2014-12-03 2016-06-09 Robert Bosch Gmbh Acoustic monitoring device for the fuel quality
WO2016087129A1 (en) 2014-12-03 2016-06-09 Robert Bosch Gmbh Acoustic monitoring device for fuel quality
US10466203B2 (en) 2014-12-03 2019-11-05 Robert Bosch Gmbh Acoustic monitoring device for fuel quality

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US7420170B2 (en) 2008-09-02
EP1989279A4 (en) 2016-10-19
US20070163168A1 (en) 2007-07-19
WO2007084406A3 (en) 2007-11-29
EP1989279A2 (en) 2008-11-12
CA2635930C (en) 2012-08-28

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