Anal. Chem. 2003, 75, 2270-2275
Real-Time Monitoring of Distillations by
Near-Infrared Spectroscopy
Celio Pasquini* and Sérgio H. F. Scafi
Instituto de Quı́mica, Universidade Estadual de Campinas, Caixa Postal 6154,
CEP 13084-971, Campinas, Sao Paulo, Brazil
A simple device is described to couple a fast-scanning
acoustooptic tunable filter-based NIR spectrophotometer
to a distillation apparatus for monitoring the condensed
vapor in real time. The device consists of a small funnel
whose glass neck (2-mm diameter) is bent into an “U”
format to produce a flow cell of ∼150-µL inner volume.
A pair of optical fibers is used to deliver the monochromatic light and to collect the fraction passing through the
glass tube. The end of the condenser of the distillation
head touches the wall of the small funnel. The condensed
liquid flows uncoupled from pressure changes in the
interior of the distillation head. Absorbance spectra were
obtained, during the distillation, as averages of 50 scans
(4 s) every 5 s in the spectral range 950-1800 nm with
nominal resolution of 2.0 nm. In the first experiments,
the distillations were performed at constant power supplied to the sample (25 mL) in a microdistillation apparatus working without any type of reflux column. The
usefulness of the real-time monitoring of distillation is
demonstrated using some prepared binary mixtures and
by comparing the distillation behavior of adulterated and
regular gasoline samples. Data analysis and interpretation
are facilitated by employing principal component analysis.
The system accesses the composition of the condensate,
which can separate and concentrate one or more compounds present in the original sample.
Distillation is a separation technique routinely employed in
industry and laboratory activities to isolate a pure or a well-defined
fraction of constituents from a mixture on the basis of the distinct
boiling points of the substances. Particularly, distillation is
employed as a standard procedure for the quality control and
production of combustibles derived from petroleum.1,2 Standard
distillation protocols are employed with commercial automatic
distilling apparatus to access the distillation curve at a constant
flow of condensate. The distillation curve of gasoline, for instance,
provides the temperatures at which the distillation initiates and
the points when 10, 50, and 90% (v/v) of the sample is evaporated,
as well the final temperature of the distillation. Ranges of tolerance
* To whom correspondence should be addressed. E-mail: pasquini@
iqm.unicamp.br.
(1) American Society for Testing and Materials. Standard Test Method for
Distillation of Petroleum Products, ASTM D-86, 1995.
(2) Associação Brasileira de Normas Técnicas, NBR-9619, Produtos de PetróleoDeterminação das Propriedades de Destilação, 1998.
2270 Analytical Chemistry, Vol. 75, No. 10, May 15, 2003
for these temperature points, reflecting roughly the range of molar
mass and type of hydrocarbons present in the gasoline or diesel
fuel, are used to certify the quality of the combustible. Distillation
is also employed for the determination of the processing properties
of petroleum, whose real distillation curve serves as a guide for
the petroleum-refining industry.
On the other hand, near-infrared (NIR) spectroscopy is
intensively employed in the petroleum industry in view of its
capability to produce information on hydrocarbon content. More
specifically, it generates an absorption spectrum that results mainly
from the presence of the distinct C-H bonds present in the
enormous quantity of hydrocarbon compounds found in a fuel
obtained from petroleum. For instance, NIR spectroscopy has been
used for determination of bulk properties such as MON, RON,
and distillation parameters3-7 as well for determination of the
content of the individual components of fuels8-10 or even for raw
crude petroleum analysis.11,12 Recently, the use of NIR spectroscopy for quality monitoring of recovered organic solvents in a
distillation plant has been described.13
In terms of the overall composition of the three main types of
C-H bonds present in fuel hydrocarbons, NIR facilitates distinguishing among the linear, branched, and aromatic C-Hs as can
be observed in Figure 1, which shows three superimposed
experimental NIR absorbance spectra obtained for hexane, isooctane, and toluene. On the basis of the information presented in
Figure 1, it is possible, for instance, to trace the composition of a
condensate in terms of its relative content of the three principal
types of C-H by looking at the shift of the spectral features at
the first and second overtones of C-H, which occur in the spectral
regions from 1600 to 1900 and 1100-1300 nm, respectively. Of
course, if a distillation apparatus with high efficiency is employed
(3) Kelly, J. J.; Barlow, C.; Jinguji, T.; Callis, J. Anal. Chem. 1989, 61, 313320.
(4) Chung, H.; Lee, H.; Jun, Chi-Hyuck Bull. Korean Chem. Soc. 2001, 22, 3742.
(5) Valleur, M. Pet. Technol. 1999, 4, 81-85.
(6) Prüfer, H.; Mamma, D. Analusis 1995, 23, M14-M18.
(7) Rebouças, M. V.; Neto, B. D. J. Near Infrared Spectrosc. 2001, 9, 263-273.
(8) Faber, M. N.; Duewer, D. L.; Choquette, S. J.; Green, T. L.; Chesler, S. N.
Anal. Chem. 1998, 70, 2972-2982.
(9) Wong, J. L.; Jaselskis, B. Analyst 1982, 107, 1282-1285.
(10) Workman, J. J., Jr. Near Infrared Spectrosc. 1996, 4, 69-74.
(11) Blanco, M.; Maspoch, S.; Villarroya, I.; Peralta, X.; González, J. M.; Torres,
J. Analyst 2000, 125, 1823-1828.
(12) Hidajat, K.; Chong, S. M. J. Near Infrared Spectrosc. 2000, 8, 53-59.
(13) Boyd, D.; Maguire, B. In Proceedings of 9th International Conference on Near
Infrared Spectroscopy, Verona, Italy; NIR Publications: Chichester, U.K.; pp
357-363.
10.1021/ac034054d CCC: $25.00
© 2003 American Chemical Society
Published on Web 04/18/2003
Figure 1. Spectra for three typical types of hydrocarbons found in
fuel derived from petroleum. The spectra were obtained in a FT-NIR
spectrophotometer with spectral resolution of 2 cm-1.
and a pure compound can be isolated, its NIR spectrum and
auxiliary chemometrics, such as pattern recognition methods,
would allow a positive identification of the compound.
In fact, even some early work in the field of mid- and
far-infrared spectroscopy reported its use for evaluating the
composition of fractions of condensate generated by distillation
of combustibles or hydrocarbon mixtures.14,15 The procedures
worked in a discontinuous way; each fraction was collected and
taken to a spectrometer for spectrum acquisition. On the other
hand, modern instrumentation associated with NIR spectroscopy,
its easy use, and nonrestrictive optical components, allied to the
high storage capacity of personal microcomputers observed
nowadays certainly permit improvement in the potential use of
NIR spectroscopy for monitoring the distillation of fuels and
petroleum.
The most important instrumental improvement regarding
distillation monitoring in real time is the high scan speed of the
modern spectrometers. Instruments based on Fourier transform
(FT) and those based on the use of acoustooptical tunable filters
(AOTF) can provide a high scan speed. Specifically, the instruments based on AOTF can read the intensity of ∼6000 wavelengths/s. Besides its high speed, AOTF technology also provides
an inherent wavelength accuracy and repeatability that are relevant
factors in considering the process monitoring situation.
Another important aspect, regarding real-time monitoring of
distillation by NIR spectroscopy, is in the availability of modern
chemometrics techniques such as principal component analysis
(PCA).16 This statistical multivariate technique can help in
information extraction and interpretation from the enormous data
set by condensing the multivariate information into but a few new
variables. By applying this technique to a set of NIR spectra, the
similarities and dissimilarities among them can be easily observed
on a graph where the scores for each spectrum for the more
relevant principal components (PCs) are plotted. The scores’
values are the coordinates of the spectrum in the space defined
by PCs. Samples with similar scores in the first and second PCs
(14) Heigl, J. J.; Bell, M. F.; White, J. U. Anal. Chem. 1947, 19, 293-298.
(15) Spakowiski, A. E.; Evans, A.; Hibbard, R. R. Anal. Chem. 1950, 22, 14191422.
(16) Otto, M. Chemometrics; Wiley-VCH: New York, 1998.
Figure 2. Schematic diagram of the distillation apparatus and
detection flow cell used for real time monitoring of the condensate.
(A) Overview of the system: a, electrical heater; b, round-bottom flask
(50 mL); c, thermocouple; d, condenser; e, glass flow cell; g, optical
bundles; f, oprical switch for dropping count; h, collecting flask. (B)
Detailed view of the flow cell: a, lateral view of uncover cell; b,
direction of light incidence and collection; c, lateral view of the covered
cell showing the point where the optical bundle in coupled for spectral
data acquisition; d, back view of the mounted cell showing passage
and collection of light through optical bundles; e, optical bundles.
(which explain most of the variability among the spectra set)
present similar spectra, which is a consequence of their similar
composition (at least in relation to the substances present that
are capable of producing a NIR spectral feature).
These modern features of NIR instrumentation and chemometrics data treatment have not yet been exploited to follow the
composition of a condensate fraction from distillation and, in
particular, from the distillation of petroleum or petroleum products.
The coupling of NIR spectral information with the capability of
the distillation to separate and concentrate the species present in
a complex mixture can constitute a powerful tool to improve the
information about complex samples such as petroleum and
petroleum derivatives. The information gathered can open the way
to the development of new analytical methods to ensure, for
instance, better quality control of the combustibles and for
petroleum classification before processing.
This work aims at the use of NIR spectroscopy for real-time
monitoring of distillations. The high scan speed of an AOTF-based
NIR spectrometer has been combined with a simple flow cell
designed to collect the condensate whose absorbance spectrum
is remotely acquired in real time during the distillation, through
a pair of optical fiber bundles. The potential of the proposed
system has been demonstrated via its use in the distillation of
some laboratory-prepared binary mixtures of hydrocarbons,
hydrocarbon and ethanol, and regular and adulterated gasoline
samples.
EXPERIMENTAL SECTION
Figure 2A shows a schematic diagram of the distillation
apparatus employed for real-time monitoring of the condensate
by NIR spectroscopy and a detailed view (Figure 2B) of the flow
cell. The distillation apparatus is composed of a 50-mL roundbottom flask with a 15-cm-long neck at the top of which a type K
thermocouple was fixed to follow the temperature of the vapor
during the distillation. A lateral outlet, situated at 10 cm from the
end of the distillation flask, forces the vapor into a condenser
whose inner heat exchange tube is 10 cm long with 0.4 cm of
diameter. The condenser is cooled with water at ambient temperature (∼23 °C). The electrical heater is connected to a variablevoltage controller.
Analytical Chemistry, Vol. 75, No. 10, May 15, 2003
2271
Figure 2B shows in detail the flow cell employed by the
monitoring apparatus. The cell is made of boron silicate glass
tubing with 2 mm of inner diameter shaped as a funnel at one
end and bent to take a “U” format. The cell design ensures that
a small volume of the condensate will be always retained in the
bottom of the U, the place where a pair of optical fibers deliver
and collect the NIR radiation. The effective inner volume occupied
by a liquid inside this cell has been determined as 150 µL. The
condensate from the distillation head is allowed to flow freely to
the small funnel, and then through the cell, simply by touching
the outlet of the condenser onto the inner wall of the funnel.
The NIR absorption spectrum of the condensed vapor was
obtained during the distillation by using a Brimrose model
Luminar 2000 spectrometer in the range from 850 to 1800 nm.
This instrument is based on an AOTF monochromator and is
capable of scanning an entire spectrum containing 475 points,
equally spaced in wavelength (∆λ ) 2.0 nm), in the above spectral
region, in ∼0.08 s. A pair of low-OH content optical fiber bundles
(200-µm diameter), 1.0 m long, were employed to delivery the
monochromatic light selected by the AOTF to the cell and to
collect the nonabsorbed fraction of the radiation passing perpendicularly through the cell tube to return it to the instrument
detector. The optical path is roughly determined by the inner
diameter of the glass tube (∼2 mm). Absorbance spectra were
obtained by employing the empty cell as reference.
The distillations described in this work were performed at
constant power supplied to the electrical heater of the distillation
apparatus by fixing the voltage applied by the voltage controller.
The temperature of the vapor near the condenser inlet was
monitored, during distillation, by a type K thermocouple connected
to a multimeter set for the option of temperature reading. The
multimeter transmits the temperature value to the system microcomputer through a RS-232 standard serial interface. A software
written in VisualBasic 3.5 was employed for acquisition of the
vapor temperature.
Spectrum acquisition starts synchronously with power application to the heater. Therefore, a number of spectra are collected
while the cell is still empty. Each spectra is registered as an
average of 50 scans. The spectrometer is programmed to acquire
120 spectra, one spectrum each 5 s. This defines a time interval
between two successive spectra to be ∼9 s, resulting from the
5-s delay plus the 4 s necessary to scan the 50 spectra for
averaging.
During the distillation, the flow of the condensate through the
cell can be followed by an optoswitch17 that can detect the
dropping of the condensate from the end of the cell. The passage
of the drop through the optical path of the optical switch generates
a logical TTL level transition that is registered and further plotted
against time. The frequency of these transitions can be employed
to evaluate the flow rate of the condensate; long periods without
any transition reveal that the distillation has stopped, which occurs
during the distillation when the temperature rises to distill a
substance having a higher boiling point.
Distillations were carried out during a maximum time interval
of 18 min. In this time interval, depending on their composition,
some samples were completely distilled while some others ended
with a residue, usually of an even higher boiling point.
(17) Raimundo, I. M., Jr.; Pasquini, C. Lab. Microcomput. 1994, 13, 55-59.
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Analytical Chemistry, Vol. 75, No. 10, May 15, 2003
Data evaluation was made by PCA by using the chemometrics
software package UNSCRAMBLER (CAMO) version 7.5. The first
derivative of the original absorbance spectrum was always
employed, as it can correct for some baseline shifts probably due
to changes in the refractive index of the condensate. Internal
validation was performed through full cross-validation.
Substances with a degree of purity of at least 99% were
employed to prepare mixtures containing hexane-toluene and
ethanol-toluene. Regular and adulterated gasoline samples were
supplied by the Analytical Centre of the Chemistry Institute of
the State University of Campinas (Campinas, SP, Brazil). No prior
information of the type of adulteration was supplied by the
Analytical Centre.
RESULTS AND DISCUSSION
The first experiments were carried out to obtain the minimum
volume of condensate necessary to wash out the cell when it has
been previously filled with a pure substance. The cell was filled
with ethanol, and hexane was added from a micropipet until the
NIR spectra obtained for the cell contents corresponded to that
of pure hexane. It was found that a volume of 250 µL is necessary
to totally replace and wash out the initial content of the flow cell.
This is ∼1.0% of the total 25.0 mL normally distilled. As a
consequence, the cell can be easily washed out and a new fraction
of condensed vapor can be monitored for its composition through
its NIR spectrum. Of course, a larger volume would be necessary
to wash the condenser inner wall and to replace the vapor in the
neck of the distillation head.
The composition of the condensed vapor also depends on the
type of distillation head employed. An efficient column with a high
reflux ratio will produce a better separation among constituents
with close boiling points and can even deliver to the detection
cell a fraction containing a pure substance, if the column efficiency
can provide the necessary number of theoretical plates.
The distillation apparatus employed in the present studies was
selected in order to resemble that of commercial systems
employed for the standard distillation tests made for gasoline and
diesel fuel quality control. With the simple apparatus employed
in this first evaluation of the NIR distillation monitoring system,
a poor separation between substances with close boiling points,
present in gasoline samples, was obtained. On the other hand,
useful information can still be extracted from a distillation
monitored in real time, as can be observed by the following
examples.
Figure 3 shows how the data treatment, made by submitting
the spectra set to PCA, can be used to reduce the data and
facilitate its interpretation. To the spectra set shown in Figure
3A, obtained by distillation of a 50:50% (v/v) mixture of hexane
and toluene, were added five spectra: pure hexane, three mixtures
containing 70, 50, and 30% hexane and 30, 50, and 70% toluene,
respectively, and pure toluene. The new spectra set was submitted
to PCA, and the scores for each spectrum in the two first principal
components (which explain 100% of the data variability) are shown
in Figure 3B. Each score represents the new coordinate of the
spectra of the condensate in a given PC. Samples with similar
scores present a similar spectrum and probably have similar
chemical compositions.
The difference between the boiling points of the two pure
substances is ∼42 °C. However, as shown in Figure 3B, only at
Figure 4. Scores obtained by PCA of a distillation of a mixture of
toluene and ethanol (50:50%, v/v). The PCA was performed after five
spectra, of a mixture of toluene and ethanol similar to the azeotropic
composition and the those of the pure substances, obtained directly
in the flow cell, were incorporated into the distillation spectra set. The
boldface symbols represent the scores values for the five spectra
obtained the following: a, pure ethanol; b, azeotrope; c, pure toluene.
Figure 3. Example of multivariate data analysis of the spectra set
obtained in real-time monitoring of a distillation. (A) Collection of
spectra obtained during real-time monitoring of a distillation of a
mixture of toluene and hexane (50:50%, v/v). (B) Scores obtained
by PCA of the distillation data of the mixture in (A). The PCA was
carried out after five spectra of mixtures of toluene and hexane and
the pure substances, obtained directly in the flow cell, were incorporated into the distillation spectra set shown in (A). The boldface
symbols in (B) represent the scores values for the five spectra
obtained: a, pure hexane; b, binary mixture containing 70% (v/v)
hexane; c, a 50:50% mixture; d, mixture containing 70% (v/v) toluene;
e, pure toluene.
the end of the distillation did the condensate contain pure toluene,
due to the poor separation characteristics of the simple distillation
apparatus employed. During a long time interval, the vapor
temperature increases slowly and the NIR spectra of the condensed vapor show that both hydrocarbons are always present
in the condensate. Nevertheless, the experiment shows that the
vapor composition is, at the beginning of the distillation, richer
in the more volatile linear hydrocarbon, with predominance of
spectral features (for second C-H overtone) related to the C-H
bond characteristic of aliphatic compounds, as can be concluded
with the help of Figure 1.
Figure 4 shows the PCA results for the distillation of the
mixture containing 50:50% (v/v) ethanol and toluene. These
compounds form an azeotrope in the proportion of 71% ethanol
Figure 5. Scores for the first PC (98% of data variability explained)
and the temperature registered for each spectrum for the distillation
of a mixture containing 50:50% (v/v) of toluene and ethanol.
and 29% toluene (v/v) with a boiling point at 76.7 °C. The two
first PCs alone can explain 99% of data variability in the spectral
set to which additional spectra for the pure substances and one
containing the composition of the azeotrope were annexed before
the PCA. The azeotrope is distilled before the excess of toluene,
as can be observed in Figure 4, with the scores produced by these
spectra forming a group with small changes during the major part
of the distillation.
Figure 5 permits following the behavior of the scores for the
first PC and the temperature at the instant the spectrum of the
condensate was obtained. The slow and continuous change in the
scores for the first PC and an abrupt change when pure toluene
is being distilled shows a good correlation with the vapor
temperature of the condensate.
The proposed system for real-time monitoring of distillations
can, in view of the data presented above, help in the optimization
of distillation protocols and determine the parameters for a
successful separation or the properties of the distilled mixture
Analytical Chemistry, Vol. 75, No. 10, May 15, 2003
2273
Figure 6. Spectra set obtained for the distillation of a regular
gasoline.
as, for instance, the formation of azeotropes. The composition of
an azeotrope can be easily determined by further multivariate
calibration made with the use of reference mixtures prepared with
pure substances. For the ethanol-toluene mixture, for example,
the inclusion of five spectra (obtained within the flow cell used
during the distillation) of a mixture whose composition is that of
the azeotrope into the distillation data set produces PCA scores
values for the first and second principal components that are
similar to the scores obtained from most of the spectra of the
condensate at the known boiling point of the azeotrope. It confirms
the composition of the condensate as that of the azeotrope.
Interesting results were also obtained for the distillation of
gasoline. Twenty samples (10 with quality certified by all routine
tests and 10 adulterated) were distilled and the collection of
spectra submitted to PCA. Figure 6 shows a typical collection of
spectra obtained during the distillation of a regular gasoline. It is
important to remember that the gasoline commercialized in Brazil
contains ∼24% anhydrous ethanol. Many azeotropes are possible
between ethanol and the many hydrocarbons as well as among
the various hydrocarbons themselves. From the spectra collection,
it is clear which are the fractions of condensate containing
compounds with an O-H bond. The sudden change in the NIR
spectrum observed in Figure 6 coincides with the end of the
distillation of ethanol and the beginning of the distillation of higher
boiling point hydrocarbons. The absorbance in the region of 1400
and 1600 nm increases remarkably when the condensate fraction
contains a compound presenting an O-H bond. When hydrocarbons are being collected in the cell, it is possible to distinguish
between a branched C-H, a linear C-H, and an aromatic C-H.
Therefore, the overall composition of the condensate can be
analyzed by observing the behavior of the absorbances attributed
to the first and second overtones of the C-H vibration, which
occur in the ranges 1600-1900 and 1100-1300 nm. For instance,
it is possible to conclude that the fraction corresponding to the
temperature of 56 °C, obtained for the distillation of a standard
gasoline, is richer in linear hydrocarbons than the fraction
corresponding to the temperature of 141 °C, which presents a
higher absorbance for the characteristic aromatic C-H stretching,
as can be observed in Figure 7.
Figure 8 shows the distribution of scores for the first two PCs
for all 10 samples of regular gasoline and for 2 selected adulterated
samples. In this case, the scores in the first two PCs explain ∼91%
of data variability. The behavior of the scores for some regular
and adulterated samples and for the first two principal components
can be viewed in Figure 9. It is clear that the profiles of the
2274 Analytical Chemistry, Vol. 75, No. 10, May 15, 2003
Figure 7. Selected spectra from distillation of a regular gasoline
sample: a, spectrum obtained at the beginning of the distillation
(T ) 57 °C) showing a composition of the condensate rich in linear
hydrocarbons; b, spectrum at the middle of the distillation showing a
condensate rich in ethanol (T ) 73 °C); c, spectrum showing a
condensate rich in aromatic heavy hydrocarbons nearly at the end
of the distillation (T ) 141 °C).
Figure 8. Scores distribution for the two first PCs for 10 regular
samples of gasoline (b) and for two adulterated samples (O, 4). The
arrows indicate the direction of temperature/time increase during the
distillation of regular gasoline.
variation of the scores for the first and second PCs, throughout
the distillation, are very similar for the gasolines with certified
quality and different from those of the adulterated samples.
Furthermore, the behavior of the scores for each PC for the
adulterated samples demonstrates a quite different composition
among them. Quite probably, the proposed NIR monitoring
system could identify, if coupled to a distillation apparatus with
better performance, an adulterant, in cases where it can be
separated from the complex mixture of hydrocarbons present in
the gasoline.
CONCLUSIONS
It has been demonstrated that the proposed system for NIR
spectral monitoring of distillations in real time is capable of
improving the information about complex systems such as for fuels
from the huge quantity of data generated by the proposed system.
It is clear that the use of scores obtained by PCA contributes to
the identification of the composition of the condensate and can
form the basis for a classification protocol that, as demonstrated
here, can easily distinguish between a regular fuel composition
and that of an adulterated fuel.
Furthermore, the proposed system can find applications in the
classification of petroleum and help in its processing by the
refineries that will be able to have, besides the distillation curve,
a reasonable idea of the composition of each fraction of condensate.
Due its simplicity, the system is amenable to be hyphenated
to all commercial distillation apparatuses, presently employed for
the standard quality control testing of gasoline and diesel fuel1,2
and for petroleum fractionating. Therefore, the information on the
composition of these combustibles and raw materials, its classification, and quality can be achieved on the basis of a more
complete and effective picture.
The modern capabilities of NIR instrumentation also allow
thinking about a further miniaturization of the distillation system
with additional gains in terms of sample volume and increased
resolution regarding hydrocarbon separation from complex mixtures.
Figure 9. Scores in the first PC (A) and second PC (B) obtained
for distillations of adulterated gasoline (a-d) and regular gasoline
(e-g).
derived from petroleum. The use of the spectral multivariate
information with data compression techniques, as exemplified here
by PCA, can help the interpretation and extraction of information
ACKNOWLEDGMENT
The authors are grateful to Dr. Carol H. Collins for manuscript
revision and FINEP-CTPETRO (Proc. 65.00.0181.00) for financial
supporting. S.H.F.S. is grateful to CAPES for a Ph.D. fellowship.
Received for review January 20, 2003. Accepted March 27,
2003.
AC034054D
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