CN111650324A - Online detection method for hydrocarbon content - Google Patents
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- CN111650324A CN111650324A CN201910196994.1A CN201910196994A CN111650324A CN 111650324 A CN111650324 A CN 111650324A CN 201910196994 A CN201910196994 A CN 201910196994A CN 111650324 A CN111650324 A CN 111650324A
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- alkyne
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- olefin
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- 238000001514 detection method Methods 0.000 title claims abstract description 35
- 150000002430 hydrocarbons Chemical class 0.000 title claims description 14
- 229930195733 hydrocarbon Natural products 0.000 title claims description 13
- 239000004215 Carbon black (E152) Substances 0.000 title claims description 12
- 150000001336 alkenes Chemical class 0.000 claims abstract description 29
- 230000003595 spectral effect Effects 0.000 claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 25
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 20
- JRZJOMJEPLMPRA-UHFFFAOYSA-N olefin Natural products CCCCCCCC=C JRZJOMJEPLMPRA-UHFFFAOYSA-N 0.000 claims abstract description 19
- 238000012937 correction Methods 0.000 claims abstract description 17
- 150000001335 aliphatic alkanes Chemical class 0.000 claims abstract description 16
- 150000001345 alkine derivatives Chemical class 0.000 claims abstract description 16
- 238000001228 spectrum Methods 0.000 claims abstract description 13
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 11
- 238000003786 synthesis reaction Methods 0.000 claims abstract description 11
- 238000004817 gas chromatography Methods 0.000 claims abstract description 10
- 238000012360 testing method Methods 0.000 claims abstract description 9
- 239000000523 sample Substances 0.000 claims description 57
- 238000005259 measurement Methods 0.000 claims description 10
- 239000000463 material Substances 0.000 claims description 8
- 230000001105 regulatory effect Effects 0.000 claims description 6
- 239000013307 optical fiber Substances 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 238000012569 chemometric method Methods 0.000 claims description 2
- 239000003153 chemical reaction reagent Substances 0.000 abstract description 2
- 230000007613 environmental effect Effects 0.000 abstract description 2
- LIKMAJRDDDTEIG-UHFFFAOYSA-N 1-hexene Chemical compound CCCCC=C LIKMAJRDDDTEIG-UHFFFAOYSA-N 0.000 description 21
- VLKZOEOYAKHREP-UHFFFAOYSA-N n-Hexane Chemical compound CCCCCC VLKZOEOYAKHREP-UHFFFAOYSA-N 0.000 description 16
- 239000003921 oil Substances 0.000 description 10
- 238000004587 chromatography analysis Methods 0.000 description 6
- 230000000875 corresponding effect Effects 0.000 description 6
- 238000004476 mid-IR spectroscopy Methods 0.000 description 5
- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 description 3
- 239000000835 fiber Substances 0.000 description 3
- GHUURDQYRGVEHX-UHFFFAOYSA-N prop-1-ynylbenzene Chemical compound CC#CC1=CC=CC=C1 GHUURDQYRGVEHX-UHFFFAOYSA-N 0.000 description 3
- 238000004566 IR spectroscopy Methods 0.000 description 2
- 150000004945 aromatic hydrocarbons Chemical class 0.000 description 2
- 238000005336 cracking Methods 0.000 description 2
- WYQUDTBKJDQONL-UHFFFAOYSA-N prop-2-ynylcyclopropane Chemical compound C#CCC1CC1 WYQUDTBKJDQONL-UHFFFAOYSA-N 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- FDTLHFSXVJXWSS-UHFFFAOYSA-N 1-chloro-3-prop-1-ynylbenzene Chemical compound CC#CC1=CC=CC(Cl)=C1 FDTLHFSXVJXWSS-UHFFFAOYSA-N 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 150000001298 alcohols Chemical class 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 239000011203 carbon fibre reinforced carbon Substances 0.000 description 1
- 150000001728 carbonyl compounds Chemical class 0.000 description 1
- 239000013064 chemical raw material Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000010687 lubricating oil Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- TVMXDCGIABBOFY-UHFFFAOYSA-N octane Chemical compound CCCCCCCC TVMXDCGIABBOFY-UHFFFAOYSA-N 0.000 description 1
- 239000012188 paraffin wax Substances 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 239000004014 plasticizer Substances 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000011112 process operation Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 235000013599 spices Nutrition 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000004094 surface-active agent Substances 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 239000004711 α-olefin Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/62—Detectors specially adapted therefor
- G01N30/74—Optical detectors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/60—Construction of the column
- G01N30/6034—Construction of the column joining multiple columns
- G01N30/6043—Construction of the column joining multiple columns in parallel
Abstract
The invention relates to an on-line detection method of olefin content, which specifically comprises the following steps: (1) establishing a correction model, selecting various Fischer-Tropsch synthesis distillate oil samples, and determining the contents of olefin, alkane and alkyne in the samples by adopting a gas chromatography; and then, performing mid-infrared spectrum scanning on each sample through a sample cell, and selecting C-C bond at 3100-3010 cm‑1Is a characteristic spectral region; the C-C bond is 2975-2800 cm‑1Is a characteristic spectral region; the C ≡ C bond is 3300-2150 cm‑1Relating the response value of the characteristic spectrum region with the contents of alkene, alkane and alkyne of a sample determined by adopting a gas chromatography, and respectively establishing a correction model by adopting a least square method of a stoichiometric method; (2) the content determination of the unknown sample is carried out on the mid-infrared spectrum scanning of the unknown sample under the same test condition as the establishment of the correction model, and the scanning range is 3100-3010 cm‑1、2975～2800cm‑1、3300～2150cm‑1Response of spectral regionAnd substituting the values into respective corresponding correction models to obtain the contents of olefin, alkane and alkyne in the unknown sample. The method is simple to operate, has wide requirements on environmental conditions, has no damage to samples during detection, and does not need to add other auxiliary reagents.
Description
Technical Field
The invention relates to an on-line detection method of hydrocarbon content based on mid-infrared spectrum, which is particularly suitable for the on-line detection method of alkane, alkene and alkyne content in Fischer-Tropsch synthetic distillate oil.
Background
As is well known, an olefin is a hydrocarbon compound containing a C ═ C bond (carbon-carbon double bond) (olefinic bond), which is an important chemical raw material and an organic synthetic intermediate, and has been widely used in the chemical field. It can be used to synthesize some high molecular materials, and can be used as synthetic intermediate of surfactant, plasticizer and additive for synthesizing hydrocarbon lubricating oil and oil product. In particular, the alpha-olefin has important application in the industries of spice, paper, daily chemical and the like.
Olefins in the current market are mainly derived from cracking of petroleum, from elimination of alcohols to produce olefins, from carbonyl compounds by a series of reactions, paraffin cracking, fischer-tropsch synthesis, and the like. In the process of production, the precision of process control has a great influence on the quality of products, so that the online real-time detection of the content of olefin is very important.
Patent CN 103760131A discloses a gasoline oil attribute real-time prediction method based on near infrared spectrum detection. The method requires scanning for gasoline component C4～C12The octane number can be calculated by establishing a model, and the contents of olefin, alkane and alkyne can not be directly measured.
Patent CN 104122235a discloses a device and a method for detecting olefin gas. The method can only detect whether various olefins leak in the device area, but the gas detection of the olefins needs gaseous olefins or detects the gasified olefins, the detection process is complicated, the pretreatment is complex, and quantitative detection cannot be formed.
The Lioshi (in-line infrared spectroscopy for detecting olefin and aromatic hydrocarbon (benzene) in gasoline, fine petrochemical engineering, No. 4, 7 months in 2001) provides a method for detecting olefin and aromatic hydrocarbon in gasoline by using in-line infrared spectroscopy. The method adopts near infrared spectrum to perform online detection on gasoline in a pipeline, the application range of a detection sample is small, and the repeatability and reproducibility deviation of olefin detection are large.
Disclosure of Invention
In order to solve the technical problem, the invention provides an online detection method for hydrocarbon content, which comprises the following steps:
(1) calibration model establishment
Selecting various Fischer-Tropsch synthesis distillate oil samples, and determining the contents of olefin, alkane and alkyne in the Fischer-Tropsch synthesis distillate oil samples by adopting a gas chromatography; and then, performing mid-infrared spectrum scanning on each sample through a sample cell, and selecting C-C bond at 3100-3010 cm-1Is a characteristic spectral region; the C-C bond is 2975-2800 cm-1Is a characteristic spectral region; the C ≡ C bond is 3300-2150 cm-1Relating the response value of the characteristic spectrum region with the contents of alkene, alkane and alkyne of a sample determined by adopting a gas chromatography, and respectively establishing a correction model by adopting a least square method of a stoichiometric method;
(2) determination of unknown sample content
Performing mid-infrared spectrum scanning on an unknown sample under the same test condition as that of the calibration model, wherein the mid-infrared spectrum scanning is respectively 3100-3010 cm-1、2975～2800cm-1、3300～2150cm-1And substituting the response values of the spectral regions into the corresponding correction models to obtain the contents of the olefin, the alkane and the alkyne in the unknown sample.
Preferably, in the step (1), an inlet pipe and an outlet pipe which enter the test sample cell are respectively led out before and after the regulating valve with the pressure difference in the material pipeline;
preferably, in the step (1), the pressure in the sample cell is regulated to be below 69bar through a valve, and the temperature of materials entering the sample cell is not higher than 200 ℃;
preferably, in the step (1), a branch flow sample cell is adopted, and the infrared spectrometer adopts a fiber probe type measurement.
Preferably, the mid-infrared spectrum has a type Mettler Reactr 15, a spectral range of 4000--1Resolution of 4cm-1。
Preferably, the spectral scanning is performed by a fiber optic probe, and the spectral peak height at the characteristic wavelength is obtained by processing the spectral scanning by an instrument software workstation.
Preferably, the step of establishing the correction model by using the least square method of the chemometric method comprises the following steps:
(1) assuming that the spectral peak height y of the olefin (or the alkane or the alkyne) at the characteristic wavelength and the concentration value x of the olefin (or the alkane or the alkyne) have the following relationship, wherein y is ax + b, a is a coefficient, and b is an intercept;
(2) corresponding relation exists between each group of data (xi, yi);
(3) error e ═ yi- (axi + b);
(4) when in use
The minimum degree of fitting is the highest, i.e.
Minimum;
(5) respectively solving first-order partial derivatives:
(6) let the above two formulae equal 0, respectively, have
(7) Finally, the following can be obtained:
(8) substituting the values x and y into each sample to obtain the values a and b.
Compared with the prior art, the method has the following advantages:
(1) the mid-infrared spectrum has strong anti-interference capability and high measurement precision.
(2) The branch sample cell combines the measurement of fiber probe, has reduced sample measurement pretreatment process, reduces the loaded down with trivial details process of detection, realizes on-line measuring.
(3) The detection method is easy to realize, has wide requirements on environmental conditions, and is suitable for detecting the olefin, the alkane and the alkyne in the hydrocarbons which are liquid at the temperature of between 80 ℃ below zero and 200 ℃ and under the pressure of 69 bar.
(4) The detection has no damage to the sample, other auxiliary reagents are not required to be added, the detection difficulty is reduced, the detection frequency is improved, and timely data guidance is provided for process operation.
(5) The detection carbon number is C4-C40, and the range is wider.
Drawings
FIG. 1 is a schematic view of the detection of a sample according to the present invention
FIG. 2 is a graph showing the linear relationship between the predicted value of the 1-hexene model and the measured value by gas chromatography
FIG. 3 is a linear relationship diagram of a predicted value of n-hexane model and a measured value of gas chromatography
FIG. 4 is a graph showing the linear relationship between the predicted value of the 3-chlorophenyl-1-propyne model and the value measured by gas chromatography
Detailed Description
The detection method of the present invention is further described below with reference to the accompanying drawings.
Example 1
An on-line detection method for the content of 1-hexene in Fischer-Tropsch synthesis distillate oil comprises the following steps:
(1) calibration model establishment
Selecting 80 Fischer-Tropsch synthesis distillate oil samples, determining the content of 1-hexene in the Fischer-Tropsch synthesis distillate oil samples by adopting a gas chromatography, and selecting representative 20 samples to form a correction set;
then, performing mid-infrared spectrum scanning on the sample with the concentrated correction through a sample cell, and selecting C-C bond at 3100-3010 cm-1Is a characteristic spectral region; comparing the response value of the characteristic spectrum region with that of gas chromatographyThe 1-hexene content measured by the method is correlated, and a least square method of a stoichiometric method is adopted to respectively establish a correction model;
the sample enters a sample cell from an inlet pipe after passing through a material pipeline, the inlet pipe and an outlet pipe which enter a test sample cell are respectively led out from the front and the back of a regulating valve with pressure difference in the material pipeline, the pressure in the sample cell is regulated to be below 69bar through a valve, the temperature of the material entering the sample cell is not higher than 200 ℃, the sample cell is a branch flow sample cell, and an infrared spectrometer adopts an optical fiber probe type measurement.
The gas chromatograph is Agilent 7820 gas chromatograph, PONA chromatographic column, split/non-split sample inlet, and PONA chromatographic column.
The mid-infrared spectrometer is a Mettler Reactr 15 with a spectrum range of 4000-650cm-1Resolution of 4cm-1。
And performing spectral scanning through the optical fiber probe, and processing through an instrument software workstation to obtain the spectral peak height under the characteristic wavelength.
The linear regression by the least square method comprises the following steps:
1. assuming that the spectral peak height y of 1-hexene under the characteristic wavelength is in the following relation with the concentration value x of 1-hexene, wherein y is ax + b, a is a coefficient and b is an intercept;
2. corresponding relation exists between each group of data (xi, yi);
3. error e ═ yi- (axi + b);
4. when in use
For the least time fit to the highest, i.e.
Minimum;
5. respectively solving first-order partial derivatives:
6. let the above two formulae equal 0, respectively, have
7. Finally, the following can be obtained:
8. the values of x and y of each sample are substituted to obtain a-0.01458 and b-0.01822, and then the linear relation equation y-0.01458 x-0.01822 is obtained (see fig. 2).
(2) Determination of sample content
Selecting 10 1-hexene samples to perform mid-infrared spectrum scanning under the same test condition as that of the calibration model, wherein the mid-infrared spectrum scanning is 3100-3010 cm-1Substituting the response value (peak height of spectrum peak) of the spectrum region into the corresponding correction model to obtain the 1-hexene content of the sample, and comparing the 1-hexene content with the chromatographic analysis concentration and the actual concentration of the sample, wherein the specific conditions are shown in table 1:
TABLE 1.1 comparison of mid-IR spectral measurement of hexene content with chromatographic analysis and actual sample concentration
As can be seen from Table 1, the error of on-line detection of 1-hexene content by mid-infrared spectroscopy is at most 2%, which is within the acceptable range.
Example 2
An online detection method for n-hexane content in Fischer-Tropsch synthesis distillate oil comprises the following steps:
(1) calibration model establishment
The specific steps are basically the same as those in the embodiment 1, except that the characteristic spectrum region of the n-hexane is selected to be 2975-2800 cm-1。
Finally, obtaining: the linear relationship equation y is 0.02826x-0.01109 (see fig. 3).
(2) Determination of sample content
Selecting 10 n-hexane samples to perform mid-infrared spectrum scanning under the same test condition as that of the calibration model, wherein the mid-infrared spectrum scanning is 2975-2800 cm-1Substituting the response value (peak height of spectrum peak) of the spectrum region into the corresponding correction model to obtain the n-hexane content of the sample, and comparing the n-hexane content with the chromatographic analysis concentration and the actual concentration of the sample, wherein the specific conditions are shown in table 2:
TABLE 2 comparison of mid-IR spectrum measurement of n-hexane content with chromatographic analysis and measurement and actual concentration of sample
As can be seen from Table 2, the error of online detection of n-hexane content by using mid-infrared spectroscopy is 3%, which is within the acceptable range.
Example 3
An on-line detection method for the content of 3-cyclic phenyl-1-propyne in Fischer-Tropsch synthesis distillate oil comprises the following steps:
(1) calibration model establishment
The specific steps are basically the same as those of the example 1, except that the characteristic spectrum region of the 3-cyclic phenyl-1-propyne is selected to be 3300-2150 cm-1。
Finally, obtaining: the linear relationship equation y is 0.02096x +0.05166 (see fig. 4).
(2) Determination of sample content
Selecting 10 3-cyclic phenyl-1-propyne samples to carry out mid-red treatment under the same test conditions as those for establishing a correction modelScanning external spectrum, wherein the distance is 3300-2150 cm-1Substituting the response value (peak height of spectral peak) of the spectral region into the corresponding correction model to obtain the content of the 3-cyclopropyl-1-propyne in the sample, and comparing the content with chromatographic analysis concentration and actual sample concentration, wherein the specific conditions are shown in Table 2:
TABLE 3 comparison of mid-IR spectral measurement of the content of 3-Cyclophenyl-1-propyne with chromatographic analysis and actual concentration of the sample
As can be seen from Table 3, the error of the on-line detection of the content of 3-cyclopropyl-1-propyne by using the mid-infrared spectrum is 1%, and is within the acceptable range.
Although the present invention has been described in further detail with reference to the above embodiments, it should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and various obvious changes, rearrangements and substitutions can be made by those skilled in the art without departing from the scope of the invention.
Claims (7)
1. An online detection method for hydrocarbon content comprises the following steps:
(1) calibration model establishment
Selecting various Fischer-Tropsch synthesis distillate oil samples, and determining the contents of olefin, alkane and alkyne in the Fischer-Tropsch synthesis distillate oil samples by adopting a gas chromatography; and then, performing mid-infrared spectrum scanning on each sample through a sample cell, and selecting C-C bond at 3100-3010 cm-1Is a characteristic spectral region; the C-C bond is 2975-2800 cm-1Is a characteristic spectral region; the C ≡ C bond is 3300-2150 cm-1Relating the response value of the characteristic spectrum region with the contents of alkene, alkane and alkyne of a sample determined by adopting a gas chromatography, and respectively establishing a correction model by adopting a least square method of a stoichiometric method;
(2) determination of unknown sample content
Performing mid-infrared spectrum scanning on an unknown sample under the same test condition as that of the calibration model, wherein the mid-infrared spectrum scanning is respectively 3100-3010 cm-1、2975～2800cm-1、3300～2150cm-1And substituting the response values of the spectral regions into the corresponding correction models to obtain the contents of the olefin, the alkane and the alkyne in the unknown sample.
2. The on-line detection method of hydrocarbon content as claimed in claim 1, characterized in that: in the step (1), an inlet pipe and an outlet pipe which enter the test sample cell are respectively led out from the material pipeline before and after the regulating valve with the pressure difference.
3. The on-line detection method of hydrocarbon content as claimed in claim 2, characterized in that: in the step (1), the pressure in the sample cell is regulated to be lower than 69bar through a valve, and the temperature of materials entering the sample cell is not higher than 200 ℃.
4. The on-line detection method of hydrocarbon content as claimed in claim 1, characterized in that: in the step (1), a branch flow sample cell is adopted, and an infrared spectrometer adopts an optical fiber probe type measurement.
5. The on-line detection method of hydrocarbon content as claimed in claim 1, characterized in that: the mid-infrared spectrum model is Mettler Reactr 15, the spectral range is 4000--1Resolution of 4cm-1。
6. The on-line detection method of hydrocarbon content as claimed in claim 4, characterized in that: and performing spectral scanning through the optical fiber probe, and processing through an instrument software workstation to obtain the spectral peak height under the characteristic wavelength.
7. The on-line detection method of hydrocarbon content as claimed in claim 6, characterized in that: the method for establishing the correction model by adopting the least square method of the chemometric method comprises the following steps:
(1) assuming that the spectral peak height y of the olefin or the alkane or the alkyne at the characteristic wavelength and the concentration value x of the olefin or the alkane or the alkyne have the following relationship, wherein y is ax + b, a is a coefficient, and b is an intercept;
(2) corresponding relation exists between each group of data (xi, yi);
(3) error e ═ yi- (axi + b);
(4) when in use
The minimum degree of fitting is the highest, i.e.
Minimum;
(5) respectively solving first-order partial derivatives:
(6) let the above two formulae equal 0, respectively, have
(7) Finally, the following can be obtained:
(8) substituting the values x and y into each sample to obtain the values a and b.
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Application publication date: 20200911 |