KR20170043771A - Detection of illicit diesel by multivariate model application to spectrum data - Google Patents
Detection of illicit diesel by multivariate model application to spectrum data Download PDFInfo
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- KR20170043771A KR20170043771A KR1020150143256A KR20150143256A KR20170043771A KR 20170043771 A KR20170043771 A KR 20170043771A KR 1020150143256 A KR1020150143256 A KR 1020150143256A KR 20150143256 A KR20150143256 A KR 20150143256A KR 20170043771 A KR20170043771 A KR 20170043771A
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- 238000001228 spectrum Methods 0.000 title claims abstract description 55
- 238000001514 detection method Methods 0.000 title description 2
- 239000003350 kerosene Substances 0.000 claims abstract description 63
- 238000002835 absorbance Methods 0.000 claims description 22
- 238000000034 method Methods 0.000 claims description 16
- 239000002904 solvent Substances 0.000 claims description 14
- VLKZOEOYAKHREP-UHFFFAOYSA-N n-Hexane Chemical compound CCCCCC VLKZOEOYAKHREP-UHFFFAOYSA-N 0.000 claims description 12
- 230000008033 biological extinction Effects 0.000 claims description 9
- HEDRZPFGACZZDS-UHFFFAOYSA-N Chloroform Chemical compound ClC(Cl)Cl HEDRZPFGACZZDS-UHFFFAOYSA-N 0.000 claims description 8
- IMNFDUFMRHMDMM-UHFFFAOYSA-N N-Heptane Chemical compound CCCCCCC IMNFDUFMRHMDMM-UHFFFAOYSA-N 0.000 claims description 8
- 238000007865 diluting Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 239000000446 fuel Substances 0.000 claims description 5
- 238000000611 regression analysis Methods 0.000 claims description 3
- 238000002156 mixing Methods 0.000 abstract description 14
- 238000012850 discrimination method Methods 0.000 abstract description 13
- 239000003795 chemical substances by application Substances 0.000 abstract description 9
- 239000002283 diesel fuel Substances 0.000 abstract description 6
- 239000000523 sample Substances 0.000 description 19
- 230000006870 function Effects 0.000 description 14
- 230000009977 dual effect Effects 0.000 description 8
- 239000000203 mixture Substances 0.000 description 8
- 239000011159 matrix material Substances 0.000 description 6
- 230000004888 barrier function Effects 0.000 description 4
- 238000004817 gas chromatography Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 239000003208 petroleum Substances 0.000 description 2
- 239000003209 petroleum derivative Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000010183 spectrum analysis Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 235000013405 beer Nutrition 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 239000012470 diluted sample Substances 0.000 description 1
- 238000002290 gas chromatography-mass spectrometry Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000000491 multivariate analysis Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000001179 sorption measurement Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000002211 ultraviolet spectrum Methods 0.000 description 1
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/38—Diluting, dispersing or mixing samples
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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
-
- 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
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- Engineering & Computer Science (AREA)
- Pathology (AREA)
- Immunology (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Oil, Petroleum & Natural Gas (AREA)
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- Food Science & Technology (AREA)
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- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The present invention relates to a counterfeit light oil discrimination method, comprising the steps of measuring a spectrum of a sample taken with a spectrophotometer, calculating the concentration of light oil and kerosene from the measured spectrum using a multivariate model, It is possible to measure and analyze only the spectrum of the diesel oil without adding the identifying agent so that the presence or absence of the kerosene and the mixing ratio of the kerosene / diesel can be provided and provided.
Description
The present invention relates to a counterfeit light oil discrimination method, and more particularly, to a method for discriminating a counterfeit light oil by calculating a concentration of light oil and kerosene by applying a spectrum to a multivariate model.
The fake diesel is a mixture of other petroleum products, such as kerosene, in light oil. It is classified as a fake petroleum product in accordance with Article 2 (10) of the Petroleum and Petroleum Alternative Fuel Business Act. In order to discriminate fake passageways that can be distributed on the market, the currently used methods are as follows. First, a statutory identification agent is added to kerosene, and second, the gas diesel that is distributed in the market is measured and added to kerosene The presence of the presence of the false passages are determined. Such a conventional method is a good method for discriminating a kerosene mixed with kerosene when a statutory identification agent added to kerosene is discovered in the light oil without the identification agent. However, when a statutory identification agent added to kerosene is removed by adsorption or the like, there is virtually no way to identify a fake kerosene added with kerosene. Although it is possible to identify fake diesel with kerosene added by analysis methods such as gas chromatography and gas chromatography-mass spectrometry, it takes much time to analyze, And can not be used in the case of a fake railway control site requiring analysis results in a short time.
A problem to be solved by the present invention is to provide a method for judging a forgery diesel by calculating the concentration of light oil and kerosene by applying a spectrum to a multivariate model.
system; Calculating a concentration of light oil and kerosene from the measured spectrum using a multivariate model; And a step of judging whether or not the fuel is falsified according to the concentration of the light oil and the kerosene.
According to the embodiment of the present invention, it may be a counterfeit light oil discrimination method further comprising a step of diluting the collected sample with a solvent.
According to an embodiment of the present invention, the solvent is a solvent comprising at least one of hexane, heptane or chloroform, and the step of diluting the sampled sample in a solvent comprises dissolving the sample in 10 ml of a solvent And then diluting the diluted solution.
According to the embodiment of the present invention, the step of measuring the spectrum of the sample may be a counterfeit light oil discrimination method in which the spectrum is repeatedly measured a predetermined number of times.
According to the embodiment of the present invention, the step of measuring the spectrum of the sample may be a counterfeit light oil discrimination method wherein the range of the measurement spectrum is 200 to 350 nm.
According to an embodiment of the present invention, the step of calculating the concentration of light oil and kerosene may include calculating an absorbance from the spectrum; And calculating a extinction coefficient and a concentration of each of the light oil and the kerosene from the calculated absorbance using the multivariate model.
According to an embodiment of the present invention, the multivariate model minimizes residuals of an objective function using nonlinear programming under an inequality constraint composed of physical constraints, and uses the absorbance of the sample to determine extinction of light oil and kerosene And the coefficient and the concentration are calculated.
According to an embodiment of the present invention, the multivariate model is a model for minimizing the residual of an objective function using regression analysis under an inequality constraint composed of physical constraints. have.
According to the present invention, it is possible to measure and analyze only the spectrum of light oil without addition of the identifying agent, thereby providing the presence or absence of kerosene and the mixing ratio of kerosene / light oil, thereby providing the disadvantages Improvement. Since it is not necessary to add an identification agent to kerosene, it can be used at low cost. Above all, when removing the identification agent added to the kerosene, there is no way to distinguish the fake diesel oil by the conventional method, but the present invention can be used for distinguishing diesel fuel diesel because the fake diesel oil can be discriminated.
1 is a flowchart of a counterfeit light oil discrimination method according to an embodiment of the present invention.
2 is a flowchart of a counterfeit light oil discrimination method according to an embodiment of the present invention.
FIG. 3 shows a spectrum of a result obtained by the counterfeit light oil discrimination method according to the embodiment of the present invention.
FIG. 4 is a graph comparing the results obtained by the counterfeit light oil discrimination method according to the embodiment of the present invention with the actually measured spectra.
Prior to the description of the concrete contents of the present invention, for the sake of understanding, the outline of the solution of the problem to be solved by the present invention or the core of the technical idea is first given.
The method for discriminating falsified light oil according to an embodiment of the present invention includes the steps of measuring a spectrum of a sample collected by a spectrophotometer, calculating the concentration of light oil and kerosene from the measured spectrum using a multivariate model, And judging whether or not the fuel is falsified according to the concentration of the kerosene.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. It will be apparent to those skilled in the art, however, that these examples are provided to further illustrate the present invention, and the scope of the present invention is not limited thereto.
BRIEF DESCRIPTION OF THE DRAWINGS The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings, in which: It is to be noted that components are denoted by the same reference numerals even though they are shown in different drawings, and components of different drawings can be cited when necessary in describing the drawings. In the following detailed description of the principles of operation of the preferred embodiments of the present invention, it is to be understood that the present invention is not limited to the details of the known functions and configurations, and other matters may be unnecessarily obscured, A detailed description thereof will be omitted.
1 is a flowchart of a counterfeit light oil discrimination method according to an embodiment of the present invention.
According to one embodiment of the present invention, the falsified light oil discrimination method can quickly and accurately determine whether or not falsification passes by analyzing the spectrum of the sample using the multivariate model without the identification agent, by calculating the concentrations of the light oil and the kerosene mixed therewith.
More specifically, the spectrum of the sample collected to determine whether or not the light is forged is measured by a spectrophotometer. A spectrophotometer is a device that detects the amount of light absorbed or reflected by a specimen through a specimen through a spectroscopic monochromatic light. It is a device that detects by UV light, infrared (IR), gas chromatography (GC) A spectrophotometer can be used.
The collected sample can be diluted in a solvent and the spectrum of the diluted sample can be measured. The solvent used for diluting the sample may include at least one of hexane, heptane, and chloroform.
10 μl of the sample is taken and dissolved in 10 ml of a solvent such as hexane, heptane, or chloroform to dilute it.
In measuring the spectrum of the sample, the spectrum is measured repeatedly for a predetermined number of times. In applying the multivariate model, multiple spectra are measured over time to take advantage of the spectra over time. Preferably from 10 to 12, or less. When the UV spectrum is used, the range of the measurement spectrum can be set to 200 to 350 nm.
More specifically, the spectra measured in
In
To analyze multiple spectra over time, we derive the desired results using a multivariate model with time and absorbance as variables. The multivariate model used in the spectrum analysis minimizes the residual of the objective function by using nonlinear programming under the inequality constraint composed of physical constraints. The extinction coefficient and concentration of light oil and kerosene are calculated using the absorbance of the sample. do. The multivariate model may be a model that minimizes the residual of an objective function using regression analysis under an inequality constraint consisting of physical constraints.
One embodiment of the multivariate model used for spectral analysis will be described in detail as follows. The following multivariate model is called the multivariate model Eureka. It is natural that other multivariate models other than Eureka can be used.
When two substances of light oil and kerosene are mixed, the absorbance of the mixture measured by the spectrophotometer is the sum of the absorbances of light oil and kerosene according to Beer's Law, and is expressed by Equation (1).
Where A mix is the absorbance of the mixture, A d and A k are the absorbances of light oil and kerosene respectively, ε d and ε k are the extinction coefficients of light oil and kerosene respectively, b is the path length of cell, , And C d and C k are concentrations of kerosene and light oil, respectively. Since the path length is usually 1 cm and is known, when the measured data is large,
A is the nxm absorbance matrix of the mixture measured m times at n wavelengths, E is the extinction coefficient nx2 matrix at n wavelengths of light oil and kerosene, and C is a 2xm matrix of diesel and kerosene concentrations. The matrix A obtained by measuring the absorbance of the mixture with a spectrophotometer is applied to the multivariable model to estimate the matrices E and C. The spectra of the light oil and the kerosene and the mixing ratios thereof are determined and the false ratios are determined at the estimated mixing ratios.
The multivariate model according to an embodiment of the present invention can be classified into inequality constraints created by physical constraints and nonlinear programming based on an objective function, ), That is, a model minimizing A-EC in Equation (2), and estimates E and C as output data of the model.
First, the Fundamental Natural Physical Constraints (FNPCs) required to estimate E and C are as follows
1. The measured data shall be reproduced within the error range by the model.
2. The estimated E value should be a positive value.
3. The C value estimated from the measured data should also be a positive value.
4. The sum of the E values calculated for each chemical should be equal to or less than 1.
5. The sum of all EC values calculated from any single measurement shall be within the error range from the total absorbance measured.
A method for estimating the C value from a given E value and a single measured absorbance a value is described below and is based on a Primal-Dual Interior Point Nonlinear Programming. The following nonlinear program for a single measurement can be easily extended to nonlinear programs for multiple measurements. The objective function of this nonlinear program problem should be such that the difference (L2 Norm) between the measured value reproduced by the C value estimated from the given E value and the actually measured concentration C value is minimum. The objective function is subject to the inequality constraints of FNPCs 3 and 5. Therefore, the objective function starts by constructing the first nonlinear program. The problem to solve is as follows.
The primal problem is as follows.
Equation (3) is governed by Equations (4) and (5) below.
Nonlinear program Primal Problem 3 can be expressed by Equation 6 and is the same as Equation 3.
Equation (6) is governed by equations (7) and (8).
Where P = E'E, q '= a'E, a is the
Such a quadratic programming problem can be solved with a primal dual internal point path nonlinear program.
As the name implies, the primal dual interior point method begins by looking for a dual expression of primal expression 6 - 8. The dual objective function is obtained by constructing a Lagrangian with constraints of the objective function and the primal problem, and solving for the primal variables minimizing Lagrangian. This leads to the following dual problem.
Equation (9) is governed by equations (10) and (11).
In the constrained optimization problem, a well-known logarithmic barrier function technique is now introduced into the prime equation (6). If the logarithmic barrier function term "μ ln c" is added to the prime equation 6 - 8, then the prime expression is transformed as follows.
Equation (12) is governed by Equations (13) and (14).
Where μ> 0 is the barrier penalty parameter. The solution of the new primal equations 12-14 is constrained by the logarithmic barrier function term "[mu] lnc" in equation (12). As the iteration continues, μ gradually decreases to a small value and becomes zero, and the result of Eqs. 12 - 14 converges to the original 6 - 8 solution of the primal equation.
The primal expression minimizes the objective function, and the dual expression maximizes the objective function to provide the lower bound of the primal expression. The difference between the solutions of the primal equation and the dual equation is called the duality gap. This duality value becomes zero with respect to the optimization problem under Karush-Kuhn-Tucker (KKT) conditions. (c, y, z) is a solution of both primal and dual equations. The necessary and sufficient conditions are characterized by the following KKT optimization conditions.
Where L is the Lagrangian function of Equation 12. Lagrangian, solving for the gradient of L and setting it to zero, the above equations 15-17 are transformed into the following equations.
Where Z and C are diagonal matrices of z and c, respectively, and e is a p-vector formed by all ones. The system of equations (18) - (20) constructs a first Taylor series approximation for the point (c, y.z) to obtain the solution. Next, the Newtonian search direction ΔN = (Δc, Δy, Δz) is obtained by solving the linear equation obtained by the result. The solution becomes a system of linear form 21 - 23 as follows so that the answer can be easily obtained for ΔN.
The calculation of ΔN is repeated until convergence.
The concentration of light oil and kerosene is calculated using the above multivariate model.
In
More specifically, depending on the concentration of light oil and kerosene, it is possible to know whether or not kerosene is contained in light oil as a sample, and the degree of mixing can be known. If it is judged that there is kerosene below the threshold value or the kerosene above the threshold value which can be judged by the forgery by the forgery, the sample is judged to be forgery.
FIG. 3 shows a spectrum of a result obtained by the counterfeit light oil discrimination method according to the embodiment of the present invention.
The spectra measured by the UV spectrophotometer are input to the multivariable model and the results are shown in FIG. 3 as a multivariate model.
The results of analyzing the spectrum with the multivariate model and comparing the results of the estimated diesel oil and kerosene spectrum with the actual spectrum measured directly with light oil and kerosene are shown in FIG. As can be seen from FIG. 4, it can be seen that the spectrum of the light oil and kerosene estimated as a multivariable model closely matches the spectrum directly measured. If the estimated spectrum does not agree with the measured spectrum, the result estimated by the multivariate model is unreliable, but as shown in FIG. 4, the estimated result is reliable.
Since the spectrum of the diesel oil and the kerosene and the mixture ratio can be estimated only by the spectrum of the sample, the estimated spectrum and the mixture ratio are connected to each other in a pair. Table 1 shows the comparison of the mixing ratios of light oil and kerosene, which are estimated as a multivariate model, and the actual mixing ratios of kerosene and kerosene.
As shown in Table 1, it can be seen that the actual mixing ratio of the light oil and kerosene of Example 1 and the estimated mixing ratio agree well within the error range.
The apparatus for discriminating falsified light according to an embodiment of the present invention calculates the concentration of light oil and kerosene from the measured spectrum using a spectrophotometer and a multivariate model for measuring the spectrum of the collected sample, The control unit may further include a storage unit for storing the processing result of the processing unit or a display unit for indicating the concentration of the calculated light oil and the calculated kerosene and whether or not the falsified light oil is judged. The detailed description of the counterfeit light oil discrimination apparatus according to one embodiment of the present invention corresponds to the detailed description of the counterfeit light oil discrimination method of FIGS. 1 to 4, and a duplicate description will be omitted.
Embodiments of the present invention may be implemented in the form of program instructions that can be executed on various computer means and recorded on a computer readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.
As described above, the present invention has been described with reference to particular embodiments, such as specific constituent elements, and limited embodiments and drawings. However, it should be understood that the present invention is not limited to the above- And various modifications and changes may be made thereto by those skilled in the art to which the present invention pertains.
Accordingly, the spirit of the present invention should not be construed as being limited to the embodiments described, and all of the equivalents or equivalents of the claims, as well as the following claims, belong to the scope of the present invention .
Claims (9)
Calculating a concentration of light oil and kerosene from the measured spectrum using a multivariate model; And
And determining whether or not the fuel is falsified according to the concentration of the light oil and the kerosene.
Further comprising the step of diluting the collected sample with a solvent.
The solvent may be,
Wherein the solvent is at least one solvent selected from the group consisting of hexane, heptane, and chloroform.
The step of diluting the collected sample with a solvent includes:
Wherein the sample is diluted by dissolving in 10 ml of a solvent per 10 占 퐇 of the sample.
The step of measuring the spectrum of the sample comprises:
And the spectrum is measured repeatedly by a predetermined number of times.
The step of measuring the spectrum of the sample comprises:
Wherein the range of the measurement spectrum is 200 to 350 nm.
The step of calculating the concentration of light oil and kerosene may include:
Calculating an absorbance from the spectrum; And
And calculating a extinction coefficient and a concentration of each of light oil and kerosene from the calculated absorbance using the multivariate model.
In the multivariate model,
A model that minimizes the residual of the objective function using nonlinear programming under inequality constraints consisting of physical constraints,
Wherein the extinction coefficient and the concentration of each of the light oil and the kerosene are calculated using the absorbance of the sample.
Characterized in that a multivariate model is used that minimizes the residual of the objective function using regression analysis or nonlinear programming under inequality constraints consisting of physical constraints.
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