U.S. patent application number 11/879629 was filed with the patent office on 2008-02-21 for method and apparatus on-line measurement of polymer properties.
Invention is credited to Oscar K. III Broussard, Robert L. Long, Stephen K. Morgan, Carl J. Thomas.
Application Number | 20080042064 11/879629 |
Document ID | / |
Family ID | 32907577 |
Filed Date | 2008-02-21 |
United States Patent
Application |
20080042064 |
Kind Code |
A1 |
Long; Robert L. ; et
al. |
February 21, 2008 |
Method and apparatus on-line measurement of polymer properties
Abstract
The invention is a method and apparatus for on-line measurement
of polymer properties such as moisture content, ethylene content,
Epoxidized Soy Bean Oil (ESBO) content, calcium stearate content
and ethylidene norbornene (ENB) content.
Inventors: |
Long; Robert L.; (Houston,
TX) ; Morgan; Stephen K.; (Denham Springs, LA)
; Thomas; Carl J.; (Baton Rouge, LA) ; Broussard;
Oscar K. III; (Baton Rouge, LA) |
Correspondence
Address: |
ExxonMobil Chemical Company;Law Technology
P.O. Box 2149
Baytown
TX
77522-2149
US
|
Family ID: |
32907577 |
Appl. No.: |
11/879629 |
Filed: |
July 18, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10499794 |
Jun 22, 2004 |
7307257 |
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PCT/US02/40768 |
Dec 20, 2002 |
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11879629 |
Jul 18, 2007 |
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60346095 |
Jan 1, 2002 |
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Current U.S.
Class: |
250/339.09 |
Current CPC
Class: |
G01N 21/3563 20130101;
G01N 21/274 20130101; G01N 2021/3595 20130101; G01N 21/359
20130101 |
Class at
Publication: |
250/339.09 |
International
Class: |
G01N 21/00 20060101
G01N021/00 |
Claims
1-8. (canceled)
9. An apparatus for analyzing at least one property of a sample
comprising: (a) a conveying device for conveying said sample along
a predetermined path of travel through an inspection zone, (b) a
light source associated with said inspection zone for emitting
light onto said sample, wherein said light source is not in contact
with said sample, (c) light collection optics associated with said
inspection zone for detecting light reflected off of said sample,
(d) at least one fiber optic cable for transmitting said reflected
light from said light collection optics to a spectrometer, (e) a
spectrometer located remotely from said conveying device for
generating a reflectance spectrum, and (f) a computer adapted to:
(i) derive a predictive model relating said reflectance spectrum
and said property of said sample, and (ii) predict a value for said
property of said sample from said predictive model and said
reflectance spectrum.
10. The apparatus of claim 9, wherein said at least one property is
selected from moisture content, ethylene content, Epoxidized Soy
Bean Oil (ESBO) content, ethylidene norbornene (ENB) content,
calcium stearate content and combinations thereof.
11. The apparatus of claim 9, wherein said sample is a polymer.
12. The apparatus of claim 9, wherein said sample is a rubber.
13. The apparatus of claim 9, wherein said spectrometer is a
Fourier Transform Near Infrared (FTNIR) spectrometer
14-26. (canceled)
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional application of Ser. No.
10/499,794, filed Jun. 22, 2004 which is a National Stage of
International Application No. PCT/U502/40768, filed Dec. 20, 2002,
which claims the benefit of U.S. Provisional Application No.
60/346,095, filed Jan. 1, 2002, the disclosures of which are herein
incorporated by reference in their entireties.
FIELD OF THE INVENTION
[0002] The invention relates to a method and apparatus for on-line
measurement of polymer properties. More particularly, the polymer
properties measured include, but are not limited to, moisture
content, ethylene content, Epoxidized Soy Bean Oil (ESBO) content,
calcium stearate content and ethylidene norbornene (ENB)
content.
BACKGROUND
[0003] Measurement of polymer moisture content and other polymer
properties during the finishing process of polymer production is
important to ensure that the product meets particular
specifications. The measurement is particularly important for
detecting grade switches, wherein production of one polymer having
specific properties ends and production of another polymer having
different properties begins. It is important to accurately detect
the transition from the old grade to the new grade in order to
properly label the product and reset the finishing plant controls
for the new grade in a timely manner.
[0004] One of the previous methods of measuring polymer properties
involves taking a sample from the process line and running tests in
the laboratory. The major disadvantage of this approach is the long
delay between taking the sample and receiving the results of the
analysis. This delay can be at least one to two hours. The time
delay leads to off-specification product, which, in turn, leads to
less profit. The time delay also makes it difficult to optimize
control of the process due to the delay in resetting the
controls.
[0005] Another method of measuring polymer properties is the use of
Fourier Transform Near-Infrared spectrometer (FTNIR) photometers.
These devices permit measurement of moisture content while the
polymer is on a conveyor line, but they do not allow for the
measurement of other polymer properties. Photometer-based
technology is also limited by its ability to provide a moisture
value that only trends actual moisture variation in the polymer,
rather than the highly accurate polymer moisture value resulting
from the method of the present invention.
[0006] Yet another method of measuring polymer properties is the
use of multi-wavelength dispersive-type spectrometers. These
devices have been used on conveyor lines in a non-contact
configuration, allowing for on-line measurements, but they provide
inferior measurement accuracy and calibration stability as compared
to the FTNIR spectrometer.
[0007] Prior methods of measuring polymer properties have either
been inefficient due to time delays or have resulted in
measurements that merely trend, rather than accurately measure, the
value of the property. A need therefore exists for obtaining
stable, accurate, real-time measurements of moisture content and
other properties of polymers while the polymer is on the conveyor
line.
SUMMARY OF THE INVENTION
[0008] One embodiment of the invention relates to a method for
measuring a property of a test sample comprising (a) providing a
corrected set of measured spectra for a set of calibration samples
by (i) obtaining a set of measured spectra for a set of calibration
samples, and (ii) performing constrained principal spectral
analysis on said calibration samples to provide a corrected set of
measured spectra, (b) providing said test sample having an unknown
value of said property, (c) applying light to said test sample
resulting in reflected light, (d) detecting said reflected light,
(e) transmitting said reflected light to a spectrometer, (f)
measuring a spectrum for said test sample, (g) performing
constrained principal spectral analysis on said test sample
spectrum to provide a corrected test sample spectrum, (h)
performing locally weighted regression analysis by selecting a
subset of said corrected calibration sample spectra and building a
regression model based upon said selected subset, and (i)
predicting a value for said unknown property of said test sample
using said corrected test sample spectrum and said regression
model. Properties that can be measured include, but are not limited
to moisture content, ethylene content, Epoxidized Soy Bean Oil
(ESBO) content, calcium stearate content, and ethylidene norbornene
(ENB) content.
[0009] In another embodiment, the invention is an apparatus for
analyzing at least one property of a sample comprising, (a) a
conveying device for conveying the sample along a predetermined
path of travel through an inspection zone, (b) a light source
associated with the inspection zone for emitting light onto said
sample, wherein said light source is not in contact with said
sample, (c) light collection optics associated with the inspection
zone for detecting light reflected off of the sample, (d) at least
one fiber optic cable for transmitting the reflected light from the
light collection optics to a spectrometer, (e) a spectrometer
located remotely from the conveying device for generating a
reflectance spectrum, and (f) a computer adapted to: (i) derive a
predictive model relating the reflectance spectrum and the property
of said sample, and (ii) predict a value for said property of said
sample from said predictive model and said reflectance
spectrum.
[0010] In yet another embodiment, the invention relates to a method
for online control of a process to produce a product with a
property P and having a desired value D comprising (a) obtaining a
set of measured spectra for a set of calibration samples, (b)
performing constrained principal spectral analysis on the set of
measured spectra for the set of calibration samples to produce
corrected spectra for the set of calibration samples, (c) providing
a test sample having an unknown value of said property P, (d)
applying light to said test sample resulting in reflected light,
(e) detecting said reflected light, (f) transmitting the reflected
light to a spectrometer, (g) measuring a spectrum for the test
sample, (h) performing constrained principal spectral analysis on
the test sample spectrum to produce a corrected test sample
spectrum, (i) performing locally weighted regression analysis by
selecting a subset of the corrected calibration sample spectra and
building a regression model based upon the selected subset, (j)
predicting a value for said property P of said test sample using
the corrected test sample spectrum and the regression model, (k)
adjusting the process parameters based upon the difference in the
predicted value of said property P and the desired value D.
[0011] In yet another embodiment, the invention is a method for
online detection of a change from a first polymer grade to a second
polymer grade in a polymer finishing process comprising (a)
obtaining a set of measured spectra for a set of calibration
samples, (b) performing constrained principal spectral analysis on
said set of measured spectra to produce corrected spectra for said
set of calibration samples, (c) assigning a grade type to each said
corrected spectrum of said set of calibration samples, (d) applying
light to a polymer sample in said polymer finishing process
resulting in reflected light, (e) detecting said reflected light,
(f) transmitting said reflected light to a spectrometer, (g)
measuring a spectrum for said polymer sample, (h) performing
constrained principal spectral analysis on said polymer sample
spectrum to provide a corrected polymer sample spectrum, (i)
performing K-nearest neighbor analysis on said corrected polymer
sample spectrum comprising selecting a subset of said corrected
calibration spectra based upon the similarity of said subset
spectra to said corrected polymer sample spectrum, and counting the
assigned grade type of each said corrected calibration spectrum in
said subset, (j) assigning a grade type to said polymer sample
based upon said count, and (k) comparing said assigned grade type
to said polymer sample with said first polymer grade.
[0012] In yet another embodiment, the invention is a method for
online control of a polymer finishing process resulting from a
change from a first polymer grade to a second polymer grade
comprising (a) obtaining a set of measured spectra for a set of
calibration samples, (b) performing constrained principal spectral
analysis on said set of measured spectra to produce corrected
spectra for said set of calibration samples, (c) assigning a grade
type to each said corrected spectrum of said set of calibration
samples, (d) providing a test sample having an unknown grade type,
(e) applying light to said test sample resulting in reflected
light, (f) detecting said reflected light, (g) transmitting said
reflected light to a spectrometer, (h) measuring a spectrum for
said test sample, (i) performing K-nearest neighbor analysis on
said test sample spectrum comprising selecting a subset of
corrected calibration spectra from said set of corrected
calibration spectra based upon the similarity of said subset
spectra to said test sample spectrum, counting the assigned grade
type of each said corrected spectrum in said subset, and assigning
a grade type to said test sample based upon the comparison to said
subset, and (j) if necessary, altering the conditions of said
finishing process for said second polymer grade.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 presents a schematic representation of one embodiment
of the invention.
DETAILED DESCRIPTION
[0014] The method and apparatus described herein involve the
measurement of polymer properties through spectroscopic analysis.
Spectroscopic analysis generally involves the identification of
elements and elucidation of atomic and molecular structure by
illuminating or irradiating the substance under examination and
then measuring the radiant energy absorbed or emitted by the
substance. The energy absorbed or emitted may be in any of the
wavelengths of the electromagnetic spectrum. By comparing and/or
correlating the measured wavelengths absorbed or emitted by the
sample with wavelengths absorbed or emitted from known elements or
molecules, information about a sample may be determined.
Apparatus
[0015] In one embodiment, an apparatus for the on-line analysis of
polymers is provided. A particular embodiment includes an apparatus
for ascertaining the moisture content, ethylene content, epoxidized
soybean oil (ESBO) content, calcium stearate content, and
ethylidene norbornene (ENB) content of crumb rubber on a conveyor.
Use of the term rubber herein is descriptive of all elastomeric
polymers and plastics, and includes ethylene-propylene-diene
monomer rubber (EPDM), ethylene propylene rubber (EPR), butyl
rubber, halobutyl rubber, styrene-isoprene-styrene (SIS),
styrene-butadiene copolymers (SBC), poly-isoprene rubber,
poly-isobutylene rubber (PIB), styrene-butadiene-styrene (SBS),
styrene-butadiene rubber (SBR), poly-butadiene rubber (BR), blends
of said elastomeric polymers as well as blends of these rubbers
with thermoplastics.
[0016] Referring now to FIG. 1, the apparatus comprises a conveyor
10, a light source 12, light collection optics 14, at least one
fiber optic cable 16, a spectrometer 18, and a computer 20. The
conveyor 10 includes any device that is suitable for conveying
material, particularly solid material, along a predetermined path
through an inspection zone. Examples of conveyors include, but are
not limited to a conveyor belt, a belt puller, and rollers. In one
embodiment, the conveyor is a vibrating conveyor belt suitable for
transporting crumb rubber. An example of a suitable vibrating
conveyor belt is a type IBHR Fluid Bed conveyor (Isolated Balanced
Heavy-duty with Rocker arms and shock absorbers) available from
Carrier Vibrating Equipment, Inc. of Louisville, Ky.
[0017] The term "inspection zone" generally refers to an area
through which a sample passes wherein light is emitted and
reflected off of a sample. Associated with the inspection zone is
the light source 12 and light collection optics 14, collectively
referred to as the "sensor equipment," which are mounted above the
conveyor 10 in a position so that they are not in contact with the
material on the conveyor 10, and so that light emitted by the light
source 12 may be reflected off of the material on the conveyor 10
and detected by the light collection optics 14. Preferably, the
distance from the material on the conveyor to the sensor equipment
is from about 10 to about 16 inches.
[0018] The light source 12 and light collection optics 14 may be
contained in a separate housing units or single housing unit. The
housing unit may be made of any suitable material, such as metal.
An example of a suitable configuration of a light source and light
collection optics in a housing unit is the ReflectIR module
available from Orbital Sciences of Pomona, Calif.
[0019] In one embodiment, the sensor equipment is mounted to an
instrument stand-pipe. The mount allows the sensor equipment to be
displaced for access to the conveyor, for example for cleaning and
plug removal. Suitable mounts include, but are not limited to a
u-joint connection and swing arm. The u-joint connection also
allows the sensor equipment to be repeatedly returned to its
original location if moved.
[0020] In an embodiment of the invention, the light source 12
comprises from 1 to 3 infrared-emitting light bulbs. The light
source 12 and light collection optics 14 are preferably located in
the finishing unit of a rubber processing plant at a point after
the crumb exits the extruder and before the crumb enters the
bailer.
[0021] At least one fiber optic cable 16 is used to connect the
light source 12 and light collection optics 14 to a spectrometer
18. The use of at least one fiber optic cable 16 allows the
spectrometer 18 to be located in an area that is remote from the
light source 12 and light collection optics 14. It is desirable to
locate the spectrometer 18 remotely from the light source 12 and
light collection optics 14 because the spectrometer contains an
internal laser that is susceptible to vibration, which may be
present on the production floor where the light source 12 and light
collection optics 14 are located.
[0022] In one embodiment, the spectrometer 18 is a Fourier
Transform Infrared (FTNIR) spectrometer. In another embodiment, the
spectrometer 18 is a PCM1000 FTNIR Analyzer available from Orbital
Sciences of Pomona, Calif.
[0023] In another embodiment, the apparatus comprises multiple
conveyors, each conveyor having a light source and light collection
optics in a location as described above for a single conveyor. At
least one fiber optic cable is used to connect the light source and
light collection optics of the multiple conveyors to a single
spectrometer.
[0024] The spectrum generated by the spectrometer 18 is processed
by a computer 20, which is discussed further below. One of ordinary
skill in the art would understand the light source 12, light
collection optics 14, fiber optic cable 16, and spectrometer 18, as
they are commonly known in the market.
Data Analysis
[0025] The method and apparatus described in this specification
involve an analytical measurement technique based on near-infrared
(NIR) spectroscopy, which uses electromagnetic radiation in the
near-infrared region. NIR refers to wavelengths in the region of
from approximately 500 to 2500 nm.
[0026] In the NIR spectroscopy discussed herein, the radiation from
a light source is directed at a sample on a conveying means.
Because the samples described herein are opaque, the light is
reflected off of rather than absorbed by the sample. Next, the
reflected light is detected by the light collection optics and
transmitted through at least one fiber optic cable to a
spectrometer. The spectrometer converts the reflected light into a
reflectance spectrum in which the amount of radiation reflected is
plotted as a function of wavelength. The reflectance spectrum is
then transmitted to a data analysis device, for example a computer,
for further processing, as discussed below. The method of
transmitting the spectra to the data analysis device is not crucial
to the invention. For example, data can be transferred by cable, by
diskette or any other appropriate means.
[0027] It is well recognized that chemical and physical information
of samples can be obtained through spectral reflectance data.
Because each composition in the sample will have a characteristic
reflectance spectrum, the NIR spectra reflect the chemical
composition of the compound(s) measured.
[0028] The NIR reflectance spectrum of a typical elastomer contains
extensively overlapping bands, which represent the reflectance
features that are associated with each of the components of
interest. The overlapping bands preclude the use of simple
univariate calibration methods for quantitation of the sample
components. This problem is overcome by applying multivariate
mathematical calibration techniques to the analysis of the spectral
data. These well-known multivariate mathematical techniques use
complex mathematics such as matrix vector algebra and statistics to
extract quantitative information, for example concentrations, from
highly convoluted or statistically confounded data. Non-limiting
examples of multivariate mathematical techniques are partial least
squares (PLS), locally weighted regression (LWR), and combinations
thereof.
[0029] Multivariate mathematical techniques are typically performed
in general purpose computers suitable for running commercially
available software programs. Numerous software packages are
currently available. Examples of the available software packages
include, but are not limited to "AnaGrams," available from Orbital
Sciences of Pomona, Calif.; MATLAB.TM., available from The Math
Works, Inc., of Natick, Mass.; Pirouette.TM., available from
Infometrix, Inc., of Woodinville, Wash.; and Spectral ID.TM.,
available from Thermo Galactic, of Salem, N.H. Preferably, the
computer 20 is configured with a processor having capabilities that
are at least commensurate with that of the 100 MHz Intel Pentium
processor, and having at least 8 Megabytes of memory. In general,
more rather than less memory is preferred, and higher rather than
lower speed processors are preferred to enable analysis of large
amounts of wavelength data in real time during a process being
monitored.
[0030] In practice, quantitative NIR analysis using these
mathematical techniques requires fairly extensive calibration. This
calibration is achieved by analyzing a set of calibration samples
with known values for all of the properties to be measured by NIR.
The results for the calibration sample set are used to build a
calibration model using the multivariate calibration procedure.
Once a suitable model is built, it is used to calculate the
property or properties of interest from the NIR reflectance spectra
of samples with unknown properties.
[0031] In one embodiment, the data analysis further includes using
the apparatus of the current invention to correct spectral data for
errors that result from the spectral measurement process itself.
U.S. Pat. No. 5,121,337, hereby incorporated by reference,
discloses a process, hereinafter referred to as Constrained
Principal Spectral Analysis or "CPSA," for correcting spectral data
for data due to the spectral measurement process and estimating
unknown property and/or composition data of a sample using such
method.
[0032] In a particular embodiment, the invention involves
measurement of a single property in the presence of a wide
variation of unmeasured properties. In this embodiment, the CPSA
correction is performed followed by the LWR multivariate
mathematical technique. As is known to those of ordinary skill in
the art, LWR is a variant of the least squares regression method.
However, unlike linear regression methods which develop regression
coefficients from the entire calibration sample set, the LWR
technique follows preset rules to select a subset of samples (from
the calibration set) that are similar to the unknown sample in
order to produce a local (subset) regression just for the unknown
property. Typical selection rules for the subset is by
classification or cluster analysis, which is commonly practiced in
statistical data analysis, for example Principal Components
Analysis. The sample selection and regression calculation are
repeated for every unknown sample to generate a set of regression
coefficients specific for every new unknown. Accordingly, the
combination of performing CPSA followed by LWR builds models "on
the fly," that is, by providing a different and constantly changing
set of calibration samples for each prediction of an unknown
spectra, as opposed to those regression models using a static
calibration set with fixed regression coefficients for the unknown
predictions. This LWR technique is especially useful for the
measurement of a property such as moisture, ethylene, ENB, ESBO,
and calcium stearate content in an elastomeric or other polymer
that has a widely varying underlying chemical composition or
structure that is not directly associated with the measured
property.
[0033] The initial model can be further improved with off-line
spectral data for various elastomer grades, or can be upgraded
after actual use with data generated on different elastomer grades
on the conveyor. As more on-line data becomes available, the
on-line data can be used to replace the original off-line data in
the mathematical model. Eventual replacement of the off-line data
with on-line data, including data on various elastomer grades,
results in a highly accurate property analysis. In one embodiment,
the measurements resulting from the process of the present
invention are used to detect grade switches of polymer and reset
the finishing process controls for the new grade. In another
embodiment, the measurements are used to determine whether the
product is off-specification. In a preferred embodiment, product
grade change and/or the presence of off-specification product is
determined by performing CPSA analysis followed by K-nearest
neighbor (KNN) analysis. As is known to those of ordinary skill in
the art, KNN is a similarity-based classification method that
categorizes groups of samples according to their similarity in the
measurement space. After the CPSA is performed, the unknown sample
is classified, or grade-predicted, by comparing the sample to a
number of similar training samples having a predetermined
classification or grade type based on distance (or similarity) of
the unknown to the calibration set. The use of the CPSA technique
as a pre-processing step prior to the KNN analysis provides for
much improved classification analysis due to the removal of
unwanted instrumental and spectral variation not associated with
the class of the polymer. This method allows for a more robust
automated detection of grade change, which allows for precise and
timely resetting of finishing unit equipment for the new grade of
polymer.
[0034] Persons of ordinary skill in the art will recognize that
many modifications may be made to the present invention without
departing from the spirit and scope of the present invention. The
embodiments described herein are meant to be illustrative only and
should not be taken as limiting the invention, which is defined by
the following claims.
* * * * *