CN111426680B - Method for rapidly measuring coal caking index and colloidal layer index - Google Patents

Method for rapidly measuring coal caking index and colloidal layer index Download PDF

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CN111426680B
CN111426680B CN202010207211.8A CN202010207211A CN111426680B CN 111426680 B CN111426680 B CN 111426680B CN 202010207211 A CN202010207211 A CN 202010207211A CN 111426680 B CN111426680 B CN 111426680B
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王哲
侯宗余
顾炜伦
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Abstract

A method for rapidly measuring a coal caking index and a colloidal layer index comprises the steps of firstly, acquiring spectral data of a calibration sample by using a laser-induced breakdown spectroscopy system, and then selecting spectral lines by two modes: firstly, selecting related elements and spectral lines according to the physical background of coal properties; secondly, a regression equation between the binding index and the colloidal layer index and the spectral line strength is established by using a partial least square method, and a part of spectral lines with the lowest relative standard deviation of the regression coefficients are selected. And (3) taking the union of the spectral lines selected by the two methods as input to establish a partial least squares scaling model. And for the sample to be detected, acquiring spectral data by using a laser-induced breakdown spectroscopy system, and substituting the spectral data into the calibration model to obtain the bonding index and the colloidal layer index of the sample. The method improves the measurement accuracy by a novel spectral line selection means, and the measurement speed is obviously higher than that of a national standard method, so that online and in-situ measurement can be realized.

Description

Method for rapidly measuring coal caking index and colloidal layer index
Technical Field
The invention relates to a method for quickly measuring a coal caking index and a colloidal layer index, which is a measuring method based on Laser Induced Breakdown Spectroscopy (LIBS), and belongs to the technical field of atomic emission spectroscopy measurement.
Background
At present, off-line sampling and laboratory analysis methods are commonly adopted to detect the caking index and the colloidal layer index of coal in coal-using units such as coal washery, coking plant and the like. The method has the disadvantages of complicated process, low analysis speed, incapability of timely acquiring the properties of the coal and difficulty in meeting the production requirements. In particular, the standard detection method of the coal caking index is from GB/T5547-2014, the test steps comprise sampling, weighing, stirring, compacting, heating, cooling and drum test, and the measurement time of a single sample is about 30 minutes; the standard test for coal colloidal layer index is from GB/T479-2016, which requires heating a coal sample to 730 ℃ at a specified rate, with the measurement time for a single sample exceeding 3 hours. Therefore, the method for measuring the caking index and the colloidal layer index with high precision and high speed is developed to measure the properties of coal in real time, and has important significance for improving the production efficiency and reducing the pollution emission of coal units such as coal washing plants, coking plants and the like.
LIBS is a spectral analysis technology with wide application, has the advantages of high sensitivity, no need of sample pretreatment, simultaneous measurement of multiple elements and the like, and has great application potential in measurement of coal caking index and colloidal layer index. However, the LIBS technique has a significant matrix effect and low repeatability, requires algorithm optimization for specific application scenarios, and is difficult to directly measure the bond index and the colloidal layer index.
Disclosure of Invention
Aiming at the defects that the traditional coal caking index and colloidal layer index measuring method is slow in speed and only can carry out off-line measurement, the invention provides a method for improving the measuring speed by utilizing an LIBS technology; aiming at the problem of insufficient measurement precision of the LIBS technology, a novel spectral line screening method is provided, and quantitative measurement is carried out by combining a Partial Least Squares (PLS) method, so that the measurement precision is improved.
The technical scheme of the invention is as follows:
1. a method for rapidly measuring a coal caking index and a colloidal layer index is characterized by comprising the following steps:
1) firstly, a group of coal samples with known caking index and colloidal layer index are used as calibration samples, and a laser-induced breakdown spectroscopy system is used for collecting spectral data of the calibration samples to obtain spectral line intensity;
2) screening spectral lines, and establishing a calibration model by using a partial least square method:
the "target property" is used below to refer to the bond index and the colloidal layer index, where the colloidal layer index includes the colloidal layer maximum thickness value and the final shrinkage, each modeling being for only one target property;
a) searching an element associated with the target characteristic according to the physical background of the target characteristic, and selecting corresponding spectral lines to obtain a spectral line set;
b) randomly rejecting g samples in n calibration samples, wherein n/10 is more than or equal to g and is less than or equal to n/8, and establishing a regression equation of the target characteristics and all spectral line intensities by using a partial least square method:
Figure GDA0002796051280000011
wherein,
Figure GDA0002796051280000021
the 1 st calculated dependent variable matrix is composed of target characteristics of all calibration samples and is a vector of n-g rows and 1 column;
Figure GDA0002796051280000022
is the residual of the regression equation calculated at the 1 st time;
Figure GDA0002796051280000023
is the independent variable matrix calculated at the 1 st time, and the structure is as follows:
Figure GDA0002796051280000024
Figure GDA0002796051280000025
indicating the ith calibration sample at wavelength λjThe corresponding line intensity, i is 1,2, …, n-g, j is 1,2, …, m, m is the number of lines;
A(1)for the regression coefficient matrix calculated at the 1 st time, a set of regression coefficients for all spectral lines is given, and the structure is as follows:
Figure GDA0002796051280000026
Figure GDA0002796051280000027
1 st regression coefficient representing j-th spectral line, j being 1,2, …, m;
c) repeating the step b) e times, wherein e is more than 30, and obtaining e groups of regression coefficients A of all spectral lines(1),…,A(e)Numbers in upper parentheses indicate the number of calculations; for each spectral line regression coefficient, the relative standard deviation was calculated from the following formula:
Figure GDA0002796051280000028
wherein,
Figure GDA0002796051280000029
the t regression coefficient of the j spectral line,
Figure GDA00027960512800000210
mean regression coefficient, RSD, for the j-th linejThe relative standard deviation of the regression coefficient of the jth spectral line is shown;
according to the results, selecting 100 spectral lines with the lowest regression coefficients relative to the standard deviation to obtain a spectral line set II;
d) taking a union set of the spectral line set I and the spectral line set II to obtain r spectral lines; taking the r spectral lines as model input, and establishing a calibration model of the target characteristic by using a partial least square method:
F0＝E0A+Fh
wherein, F0A dependent variable matrix of n rows and 1 column, E0Is an independent variable matrix with n rows and r columns, A is a regression coefficient matrix with r rows and 1 column, FhResidual error of the scaled model for the target property;
3) carrying out quantitative measurement on a sample to be measured with unknown bonding index and unknown colloidal layer index: and (3) acquiring the spectral data of the sample to be detected by using a laser-induced breakdown spectroscopy system to obtain spectral line intensity, and substituting the spectral line intensity into the calibration model in the step d) of 2) to obtain the caking index and the colloidal layer index of the coal sample to be detected.
2. The method for rapidly measuring the coal caking index and the colloidal layer index according to claim 1, which is characterized in that: the related elements of the caking index and the colloidal layer index are one or more elements of carbon, nitrogen, silicon, oxygen and aluminum, and the corresponding spectral lines comprise atomic lines and ion lines of each element, CN and C2And (4) a molecular line.
The invention has the following advantages and prominent technical effects: the LIBS technology can complete single measurement within several minutes, and the experiment time is far shorter than that of the existing standard method; the LIBS technology is adopted to realize on-line and in-situ measurement, and the measurement process can be fully automated; the LIBS technology has obvious matrix effect and low repeatability, the novel spectral line screening method is provided, the physical background and the statistical performance are considered, and the measurement precision is effectively improved; the LIBS technology can obtain the spectral information of a large number of elements, and can obtain the industrial analysis and element analysis results of coal while measuring the caking index and the colloidal layer index.
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FIG. 1 is a schematic diagram of a laser induced breakdown spectroscopy system.
Fig. 2 is a flow chart of the present invention.
Fig. 3 is a graph of the measurement results of the adhesion index (G value).
Fig. 4 is a graph showing the measurement results of the final shrinkage (X value) of the colloidal layer.
Fig. 5 is a graph showing the measurement result of the maximum thickness (Y value) of the colloidal layer.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides a method for rapidly measuring a coal caking index and a colloidal layer index, which specifically comprises the following steps:
1) firstly, a group of coal samples with known caking index and colloidal layer index are used as calibration samples, and a LIBS system is used for collecting spectral data of the calibration samples to obtain spectral line intensity: in fig. 1, pulse laser is emitted from a laser 1, the laser enters a focusing lens 2 along a light path, plasma is generated on the surface of a coal sample 3 after focusing, the plasma emits radiation outwards in the cooling process, part of the radiation enters an optical fiber 5 through a collecting lens 4 so as to reach a spectrometer 6, a readable electric signal is obtained through steps of light splitting, conversion and the like in the spectrometer, and the electric signal is collected by a computer 7 so as to obtain the spectrum data of the sample; in the spectrum, identifying the peak position and range of each spectral line, deducting the base line, and calculating the area under the spectral line to obtain the spectral line intensity;
2) screening spectral lines, and establishing a calibration model by using a PLS method:
the "target property" is used below to refer to the bond index and the colloidal layer index, where the colloidal layer index includes the colloidal layer maximum thickness value and the final shrinkage, each modeling being for only one target property;
a) searching elements related to the target characteristic according to the physical background of the target characteristic, and searching corresponding spectral lines of the elements in a spectral database to obtain a spectral line set; wherein, for any targetThe related elements comprise carbon, nitrogen, silicon, oxygen and aluminum, and the corresponding spectral lines comprise atomic lines and ion lines of the elements, CN and C2A molecular line;
b) randomly eliminating g samples from n calibration samples (n/10 is more than or equal to g and less than or equal to n/8), and establishing a regression equation of the target characteristics and all spectral line intensities by using a PLS method:
Figure GDA0002796051280000031
wherein,
Figure GDA0002796051280000032
the 1 st calculated dependent variable matrix is composed of target characteristics of all calibration samples and is a vector of n-g rows and 1 column;
Figure GDA0002796051280000033
is the residual of the regression equation calculated at the 1 st time;
Figure GDA0002796051280000034
is the independent variable matrix calculated at the 1 st time, and the structure is as follows:
Figure GDA0002796051280000041
Figure GDA0002796051280000042
indicating the ith calibration sample at wavelength λjThe corresponding line intensity, i is 1,2, …, n-g, j is 1,2, …, m, m is the number of lines;
A(1)for the regression coefficient matrix calculated at the 1 st time, a set of regression coefficients for all spectral lines is given, and the structure is as follows:
Figure GDA0002796051280000043
Figure GDA0002796051280000044
1 st regression coefficient representing j-th spectral line, j being 1,2, …, m;
c) repeating the step b) e times (e is more than 30), and obtaining e groups of regression coefficients A of all spectral lines(1),…,A(e)Numbers in upper parentheses indicate the number of calculations; for the regression coefficient of each spectral line, the Relative Standard Deviation (RSD) was calculated from the following formula:
Figure GDA0002796051280000045
wherein,
Figure GDA0002796051280000046
the t regression coefficient of the j spectral line,
Figure GDA0002796051280000047
mean regression coefficient, RSD, for the j-th linejRSD of the jth spectral line regression coefficient;
according to the results, selecting 100 spectral lines with the lowest regression coefficient RSD to obtain a spectral line set II;
d) taking a union set of the spectral line set I and the spectral line set II to obtain r spectral lines; using the r spectral lines as model input, establishing a calibration model of the target characteristics by using a PLS method:
F0＝E0A+Fh (5)
wherein, F0A dependent variable matrix of n rows and 1 column, E0Is an independent variable matrix with n rows and r columns, A is a regression coefficient matrix with r rows and 1 column, FhResidual error of the scaled model for the target property;
3) carrying out quantitative measurement on a sample to be measured with unknown bonding index and unknown colloidal layer index: collecting spectral data of a sample to be detected by using a LIBS system to obtain spectral line intensity; establishing an independent variable matrix E according to step d) of 2)0Substituting into the calibration model (5) to obtain the bonding index andcolloidal layer index.
Implementation example: and (3) measuring the caking index and the colloidal layer index of 40 coal samples:
1) in the embodiment, 40 parts of coal samples are used in total, 35 parts of samples are selected as calibration samples, and the rest 5 parts of samples are used as samples to be detected; firstly, measuring the caking index and the colloidal layer index of 40 coal samples by using the existing standard method as reference values, wherein the reference values are shown in a table 1; because the data is more, part of the sample data is omitted;
TABLE 1 calibration sample composition
Figure GDA0002796051280000048
Figure GDA0002796051280000051
2) Collecting spectral data of 35 calibration samples by using a LIBS system to obtain spectral line intensity;
3) screening spectral lines, and establishing a calibration model by using a PLS method:
a) selecting atomic line and ion line of carbon, nitrogen, silicon, oxygen and aluminum, and CN and C according to physical background2Molecular line, and the obtained spectral line set is integrated;
b) randomly removing 5 of the 35 calibration samples, and establishing a regression equation for the bonding index and the colloidal layer index by using a PLS method respectively;
c) repeating the step b) for 30 times, and calculating the RSD of the regression coefficient of each spectral line; selecting 100 spectral lines with smaller RSD to obtain a spectral line set II;
d) taking a union set of the spectral line set I and the spectral line set II as model input, and establishing a calibration model of the bonding index and the colloidal layer index by using a PLS method;
4) in order to verify the performance of the model, the spectral line intensities of 35 parts of calibration sample and 5 parts of sample to be tested are respectively substituted into the calibration model in the step d) of 3), and the predicted values of the caking index and the colloidal layer index are obtained, as shown in fig. 3, 4 and 5. In the figure, the abscissa is a reference value obtained by a standard method, and the ordinate is a predicted value; the black data point is a calibration sample, the red data point is a sample to be measured, and the closer the data point is to the black straight line, the better the measurement effect is. As can be seen from the figure, the method achieves a good detection effect, and the precision of the method meets the production requirement. And after the calibration model is established, the bonding index and the colloidal layer index of the sample to be measured can be obtained within one minute, and the speed is higher than that of the existing standard method.

Claims (2)

1. A method for rapidly measuring a coal caking index and a colloidal layer index is characterized by comprising the following steps:
1) firstly, a group of coal samples with known caking index and colloidal layer index are used as calibration samples, and a laser-induced breakdown spectroscopy system is used for collecting spectral data of the calibration samples to obtain spectral line intensity;
2) screening spectral lines, and establishing a calibration model by using a partial least square method:
the "target property" is used below to refer to the bond index and the colloidal layer index, where the colloidal layer index includes the colloidal layer maximum thickness value and the final shrinkage, each modeling being for only one target property;
a) searching an element associated with the target characteristic according to the physical background of the target characteristic, and selecting corresponding spectral lines to obtain a spectral line set;
b) randomly rejecting g samples in n calibration samples, wherein n/10 is more than or equal to g and is less than or equal to n/8, and establishing a regression equation of the target characteristics and all spectral line intensities by using a partial least square method:
Figure FDA0002796051270000011
wherein,
Figure FDA0002796051270000012
the 1 st calculated dependent variable matrix is composed of target characteristics of all calibration samples and is a vector of n-g rows and 1 column;
Figure FDA0002796051270000013
is the residual of the regression equation calculated at the 1 st time;
Figure FDA0002796051270000014
is the independent variable matrix calculated at the 1 st time, and the structure is as follows:
Figure FDA0002796051270000015
Figure FDA0002796051270000016
indicating the ith calibration sample at wavelength λjThe corresponding line intensity, i is 1,2, …, n-g, j is 1,2, …, m, m is the number of lines;
A(1)for the regression coefficient matrix calculated at the 1 st time, a set of regression coefficients for all spectral lines is given, and the structure is as follows:
Figure FDA0002796051270000017
Figure FDA0002796051270000018
1 st regression coefficient representing j-th spectral line, j being 1,2, …, m;
c) repeating the step b) e times, wherein e is more than 30, and obtaining e groups of regression coefficients A of all spectral lines(1)，…，A(e)Numbers in upper parentheses indicate the number of calculations; for each spectral line regression coefficient, the relative standard deviation was calculated from the following formula:
Figure FDA0002796051270000019
wherein,
Figure FDA00027960512700000110
the t regression coefficient of the j spectral line,
Figure FDA00027960512700000111
mean regression coefficient, RSD, for the j-th linejThe relative standard deviation of the regression coefficient of the jth spectral line is shown;
according to the results, selecting 100 spectral lines with the lowest regression coefficients relative to the standard deviation to obtain a spectral line set II;
d) taking a union set of the spectral line set I and the spectral line set II to obtain r spectral lines; taking the r spectral lines as model input, and establishing a calibration model of the target characteristic by using a partial least square method:
F0＝E0A+Fh
wherein, F0A dependent variable matrix of n rows and 1 column, E0Is an independent variable matrix with n rows and r columns, A is a regression coefficient matrix with r rows and 1 column, FhResidual error of the scaled model for the target property;
3) carrying out quantitative measurement on a sample to be measured with unknown bonding index and unknown colloidal layer index: and (3) acquiring the spectral data of the sample to be detected by using a laser-induced breakdown spectroscopy system to obtain spectral line intensity, and substituting the spectral line intensity into the calibration model in the step d) of 2) to obtain the caking index and the colloidal layer index of the coal sample to be detected.
2. The method for rapidly measuring the coal caking index and the colloidal layer index according to claim 1, which is characterized in that: the related elements of the caking index and the colloidal layer index are one or more elements of carbon, nitrogen, silicon, oxygen and aluminum, and the corresponding spectral lines comprise atomic lines and ion lines of each element, CN and C2And (4) a molecular line.
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