CN102590132A - Method for measuring methanol content in methanol gasoline - Google Patents
Method for measuring methanol content in methanol gasoline Download PDFInfo
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- CN102590132A CN102590132A CN2012100324868A CN201210032486A CN102590132A CN 102590132 A CN102590132 A CN 102590132A CN 2012100324868 A CN2012100324868 A CN 2012100324868A CN 201210032486 A CN201210032486 A CN 201210032486A CN 102590132 A CN102590132 A CN 102590132A
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- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 title claims abstract description 243
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000001228 spectrum Methods 0.000 claims abstract description 37
- 238000012360 testing method Methods 0.000 claims abstract description 26
- 238000010606 normalization Methods 0.000 claims abstract description 6
- 239000011159 matrix material Substances 0.000 claims description 27
- 230000003595 spectral effect Effects 0.000 claims description 21
- 238000002329 infrared spectrum Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 6
- 238000002835 absorbance Methods 0.000 claims description 5
- 229910052736 halogen Inorganic materials 0.000 claims description 5
- 229910052721 tungsten Inorganic materials 0.000 claims description 5
- 239000010937 tungsten Substances 0.000 claims description 5
- -1 tungsten halogen Chemical class 0.000 claims description 5
- 101150073669 NCAN gene Proteins 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 229910052739 hydrogen Inorganic materials 0.000 claims description 3
- 239000001257 hydrogen Substances 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 11
- 239000002199 base oil Substances 0.000 abstract description 4
- 238000004611 spectroscopical analysis Methods 0.000 abstract description 3
- 229930195735 unsaturated hydrocarbon Natural products 0.000 abstract 1
- 239000004215 Carbon black (E152) Substances 0.000 description 4
- 229930195733 hydrocarbon Natural products 0.000 description 4
- 150000002430 hydrocarbons Chemical class 0.000 description 4
- 239000003921 oil Substances 0.000 description 4
- 239000013307 optical fiber Substances 0.000 description 3
- 239000003208 petroleum Substances 0.000 description 3
- 101710179738 6,7-dimethyl-8-ribityllumazine synthase 1 Proteins 0.000 description 2
- 101710186608 Lipoyl synthase 1 Proteins 0.000 description 2
- 101710137584 Lipoyl synthase 1, chloroplastic Proteins 0.000 description 2
- 101710090391 Lipoyl synthase 1, mitochondrial Proteins 0.000 description 2
- 238000004497 NIR spectroscopy Methods 0.000 description 2
- 238000000862 absorption spectrum Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 2
- 238000005260 corrosion Methods 0.000 description 2
- 230000007797 corrosion Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000004817 gas chromatography Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- TVMXDCGIABBOFY-UHFFFAOYSA-N octane Chemical compound CCCCCCCC TVMXDCGIABBOFY-UHFFFAOYSA-N 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000007789 sealing Methods 0.000 description 2
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000013064 chemical raw material Substances 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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Abstract
The invention discloses a method for measuring the methanol content in methanol gasoline, which comprises the following steps: firstly, a near-infrared measuring system is utilized to obtain the near-infrared spectroscopic data of a standard sample, then integer interpolation is carried out on a spectrum, and the baseline of each spectrum is found out and removed; secondly, the characteristic peak of unsaturated hydrocarbon in the gasoline component is utilized for normalization, so the influence which is generated by different base oil in the methanol gasoline to the spectrum is overcome; and finally, the normalized spectrum is applied, and the characteristic peak of the methanol is combined to built a model. In the test period, after the spectroscopic data of an unknown test sample is obtained, the spectrum pretreatment is also carried out according to the method, and finally, the built model is utilized to pretest on the basis of the standardized spectrum. The method not only can quickly and accurately measure the methanol content in the methanol gasoline, but also can effectively reduce the measurement error which is caused by gasoline component variation, so the measurement stability is improved.
Description
Technical Field
The invention relates to an oil product detection technology, in particular to a method for rapidly detecting the content of methanol in methanol gasoline by applying near infrared spectroscopy.
Background
Because of the increasing shortage of petroleum resources, the contradiction between supply and demand of petroleum is continuously increased, the price of petroleum is also rapidly increased, the development of novel alternative energy is accelerated in all countries in the world, and methanol and a certain proportion of gasoline are mixed to be widely applied as an alternative energy for vehicles. The methanol is mixed, so that the octane value of the gasoline can be effectively improved, the gasoline can be completely combusted, the combustion efficiency is increased, the economy is improved, and the atmospheric pollution is reduced. The methanol has low heat value, and after a large amount of methanol is mixed into gasoline, gas resistance is easily generated to influence oil supply; the methanol is easy to generate adverse effects such as corrosion, abrasion and the like on sealing systems of engines, particularly piston rings, cylinder walls and the like; the rubber and the plastic are easy to generate adverse effects such as swelling, corrosion and the like, and the sealing system of equipment such as an oil pump and the like is also influenced.
With the continuous progress of the technology, the advantages of methanol as the vehicle energy source are more obvious, and the defects are controlled. The methanol is a basic organic chemical raw material, and has important significance for accurately and quickly analyzing the components and the content of the synthetic methanol gasoline. Since 2003, methanol gasoline is popularized and used in Shaanxi, Shanxi, Heilongjiang and other places in China, the technical obstacles restricting the development of methanol gasoline are also overcome technically, and the technical indexes can be compared with the international advanced level.
In order to accelerate the pace of using methanol gasoline, relieve the production and operation dilemma faced by methanol enterprises in China at present and regulate the production of methanol gasoline, the national standardization regulatory committee issued national standard (GB/T23799) for methanol gasoline for vehicles (M85) in 7 months in 2009 (2009). In addition, relevant methanol gasoline standards are also provided in various places, such as local standards in shanxi province, namely M15 methanol gasoline for vehicles (DB61/T352-2004) and M25 methanol gasoline for vehicles (DB61/T353-2004), local standards in heilongjiang province, namely M15 methanol gasoline for vehicles (DB 23/T988-), "M15 methanol gasoline for vehicles (DB13/T1303-2010) in north river, and the like, which strictly regulate the content of methanol in methanol gasoline.
At present, the main methods for detecting the content of methanol in the methanol gasoline are gas chromatography, spectrometry and the like. The gas chromatography has short measurement time and high precision, but the method needs complicated experimental instruments and has high requirements on experimental environment, and meanwhile, the method can generate measurement waste liquid to pollute the environment, so that the method is not suitable for the requirements of industrial field on-line analysis and field environment measurement. The near infrared spectroscopy is a mature indirect measurement method, is widely applied to rapid detection of fuel quality such as gasoline octane number and the like at present, and has the advantages of high detection speed, good repeatability, easiness in operation and maintenance, low measurement cost and the like.
Disclosure of Invention
The invention aims to provide a method for quickly measuring the content of methanol in methanol gasoline so as to realize quick, accurate and stable measurement of the content of methanol.
The purpose of the invention is realized by the following technical scheme: a method for detecting the content of methanol in methanol gasoline comprises the following steps:
(1) obtaining a plurality of methanol gasoline standard samples with known methanol content, wherein the methanol content of the standard samples can cover the required detection range as much as possible;
(2) the method comprises the steps of utilizing a tungsten halogen lamp as a light source, adopting a long-wave near-infrared spectrometer to measure the near-infrared spectrum of a standard sample and carrying out integer interpolation, and recording a spectrum data matrix after interpolation asWhereinpIs the number of samples to be tested,qcounting the number of spectral data points;
(3) respectively searching minimum points in two no-signal areas of the near infrared spectrum of each standard sample, and fitting a straight line with the two minimum pointsL i ,L i Namely the base line, obtaining a matrix of the base line;
(4) According to the spectrum data matrix after interpolation obtained in the step 2AAnd the spectrum base line matrix obtained in step 3LCalculating the spectrum matrix after baseline correctionC = A-L;
(5) Calculating the average absorbance of a plurality of points near the characteristic peak of the unsaturated carbon-hydrogen bondBy usingFor the product obtained in step 4CNormalization is carried out to obtain a normalized spectral data matrix;
(6) To spectral data matrixGSelecting wave bands to obtain a spectrum data matrix after the wave bands are selectedWhereinzThe number of spectral data points selected for a band,Ncan reflect the content information of the methanol;
(7) utilizing the spectrum matrix obtained in the step 6 after the wave band selectionNAnd the methanol content in the standard sample,Selecting a partial least square model for modeling;
(8) a testing stage, in which the near infrared spectrum of the unknown test sample is obtained in the step 2x test Sequentially utilizing the spectrum of the test sample in the steps 3-6x test Processing to obtain analysis model inputn test ;
(9) Using the model established in step 7 and the model obtained in step 8n test The methanol content of the test sample was calculated.
The invention has the beneficial effects that: the method of the invention not only can rapidly and accurately measure the content of the methanol in the methanol gasoline, but also can effectively reduce the error brought to the measurement due to the large change of the gasoline components, and improve the measurement robustness. The method can be applied to daily measurement in a laboratory and can be embedded into an analytical instrument, which has important significance for rapidly detecting the methanol content of the gasoline in industrial application.
Drawings
FIG. 1 is a flow chart of a method for rapidly detecting the methanol content in methanol gasoline;
FIG. 2 is a schematic diagram of the operation of obtaining a near infrared spectrum of a sample;
FIG. 3 is a spectrum of a near infrared absorbance spectrum of 21 samples;
FIG. 4 is a graph of the near infrared spectra of 21 samples obtained by baseline removal and normalization according to the present invention;
FIG. 5 is a graph showing the predicted results of example 1;
FIG. 6 is a graph showing the predicted results of example 2;
FIG. 7 is a graph showing the predicted results of example 3.
Detailed Description
The method for detecting the content of the methanol in the methanol gasoline comprises the following steps:
1. obtaining a plurality of methanol gasoline standard samples with known methanol content, wherein the methanol content of the standard samples can cover the required detection range as much as possible;
2. the method comprises the steps of utilizing a tungsten halogen lamp as a light source, adopting a long-wave near-infrared spectrometer to measure the near-infrared spectrum of a standard sample and carrying out integer interpolation, and recording a spectrum data matrix after interpolation asWhereinpIs the number of samples to be tested,qcounting the number of spectral data points;
3. respectively searching minimum points in two no-signal areas of the near infrared spectrum of each standard sample, and fitting a straight line with the two minimum pointsL i ,L i Namely the base line, obtaining a matrix of the base line;
4. According to the spectrum data matrix after interpolation obtained in the step 2AAnd the spectrum base line matrix obtained in step 3LCalculating the spectrum matrix after baseline correctionC = A-L;
5. Calculating the average absorbance of a plurality of points near the characteristic peak of the unsaturated carbon-hydrogen bondBy usingFor the product obtained in step 4CNormalization is carried out to obtain a normalized spectral data matrix;
6. To spectral data matrixGSelecting wave bands to obtain a spectrum data matrix after the wave bands are selectedWhereinzThe number of spectral data points selected for a band,Ncan reflect the content information of the methanol;
7. utilizing the spectrum matrix obtained in the step 6 after the wave band selectionNAnd the methanol content in the standard sample,Selecting a partial least square model for modeling;
8. a testing stage, in which the near infrared spectrum of the unknown test sample is obtained in the step 2x test Sequentially utilizing the spectrum of the test sample in the steps 3-6x test Processing to obtain analysis model inputn test ;
9. Using the model established in step 7 and the model obtained in step 8n test The methanol content of the test sample was calculated.
In step 2 of the invention, the method for measuring the near infrared spectrum of the sample comprises the following steps: the method is characterized in that a tungsten halogen light source emits stable and continuous Near Infrared (NIR), the NIR is transmitted through an optical fiber, light penetrates through a transparent container with a sample and is partially absorbed and then enters an NIR spectrometer, the NIR spectrometer converts an NIR optical signal carrying sample information into an electric signal, the electric signal is converted into a digital quantity form through an A/D converter and then is output to the NIR spectrometerAnd finally, outputting corresponding information by the analysis system to obtain a required spectrogram of the sample. Here, dark spectral data when the light source is turned off is first measureddThen, reference spectrum data when the light source works normally and the sample container is air is measuredrAnd then measuring the spectral data of the sample successivelySFinally, the absorbance spectrum is calculated using the formula (1)A。
; (1)
Wherein,Sis the spectral data of the sample and is,rin order to refer to the spectral data,din order to obtain dark spectral data,0<i<p, 0<j<q, pis the number of samples to be tested,qthe number of data points of the original spectrum is the same as that of the data points of the original spectrum, and the data points correspond to the nano points one by one.
The method of the present invention is further illustrated below with reference to the figures and examples.
FIG. 1 is a flow chart of a method for rapidly detecting methanol content. The method comprises the following steps:
the sample was taken from two base oil samples of known brand from a refinery, the brands being 90# and 93#, respectively. Each base oil is respectively provided with 10 methanol gasolines, the methanol contents of the base oils are respectively M0, M10, M20, M30, M40, M50, M60, M70, M80 and M90, and a pure methanol is added to make 21 samples in total.
1. The NIR light source is a tungsten halogen light source with the model number LS-1 of American Ocean Optics Inc. (Ocean Optics Inc.), the wavelength range of emitted light is 360-2500 nm, and an SMA905 joint is configured. The spectrometer is an NIRQuest model spectrometer of the company, the wavelength range of the spectrometer is 883nm to 1700nm, and the number of data points is 512. A light source LS-1 is connected into an oil sample bottle containing a sample through an optical fiber, light is transmitted to an NIR spectrometer through the optical fiber after penetrating through the sample, the NIR spectrometer converts an NIR optical signal carrying sample information into an electric signal, the electric signal is converted into a digital form through an A/D (analog/digital) and output to an analysis system, and a spectrum of the required sample can be obtained after the signal is processed by the analysis system, as shown in figure 2;
2. turning off the light source, first measuring the dark spectrumd 1×512 Then turn on the light source to measure the air spectrumr 1×512 Then respectively measuring the near infrared spectrum of 21 methanol gasoline samplesS 21×512 Finally, calculating the absorbance spectrogram of 21 methanol gasoline samples according to the formula (1)A 21×512 As in fig. 3;
3. to spectral dataA 21×512 Performing nm integral point interpolation, selecting a wavelength range of 1101 nm-1650 nm, removing data points before 1101nm and after 1650, and obtaining 550 data points of each spectrogram corresponding to the data pointsB 21×550 ;
4. For interpolated spectral dataB 21×550 Finding the lowest point in two non-information areas 1101 nm-1131 nm and 1261 nm-1321 nm, and fitting a straight line with the two pointsL 21×550 ,By usingC 21×550 = B 21×550 - L 21×550 Obtaining a baseline corrected spectrumC 21×550 ;
5. For the spectrumC 21×550 Normalization treatment is carried out, each spectrum is divided by the average value of the absorbance with the wavelength range of 1135nm to 1145nm to obtainG 21×550 Selecting spectral data with the wavelength range of 1301nm to 1650nm for modeling to obtain the spectral data as shown in FIG. 4N 21×350 ;
6. A suitable model is selected, which is discussed here by way of example as a partial least squares model PLS.
To verify the validity of the method of the invention, the following are verified:
here, the standard error of prediction is adopted (Standard Error of Prediction,SEP) And corresponding complex phaseCoefficient of correlation (R 2 ) To measure the accuracy of the model:
; (2)
here, ,n p in order to predict the number of samples,0<k<n p ,yfor the actual value of the property of the sample,to average the actual values of all sample attributes of the prediction set,y p is a sample property prediction value.
Example 1: here, 11 samples were randomly selected from the 21 samples as standard samples and the remaining 10 samples were selected as predicted samples, the number of PLS major factors was 6, and the results obtained in one experiment are shown in FIG. 5.
Example 2: a model is built by using 10 methanol gasoline samples and pure methanol samples matched with 90# hydrocarbon gasoline, total 11 samples are used as standard samples, then 10 methanol gasoline samples matched with 93# hydrocarbon gasoline are used as prediction samples, the number of PLS main factors is 6, and the obtained result is shown in FIG. 6.
Example 3: a model is built by using 10 methanol gasoline samples and pure methanol samples matched with 93# hydrocarbon gasoline, total 11 samples are used as standard samples, 10 alcohol gasoline samples matched with 90# hydrocarbon gasoline are used as prediction samples, the number of PLS main factors is 6, and the result is shown in FIG. 7.
Table 1 shows the results of inventive examples 1-3, which demonstrate the high prediction accuracy and robustness of the inventive method.
TABLE 1 Rapid measurement of methanol content in full Range
Inspection method | R2 | SEP |
Example 1: 11 random samples were used as standard samples | 0.9990 | 0.6560 |
Example 2: the 90# prepared 10 sample plus pure methanol sample is taken as a standard sample | 0.9990 | 0.5867 |
Example 3: 93# 10 sample + pure methanol sample as standard sample | 0.9994 | 0.6749 |
Claims (1)
1. A method for detecting the content of methanol in methanol gasoline is characterized by comprising the following steps:
(1) obtaining a plurality of methanol gasoline standard samples with known methanol content, wherein the methanol content of the standard samples can cover the required detection range as much as possible;
(2) the method comprises the steps of utilizing a tungsten halogen lamp as a light source, adopting a long-wave near-infrared spectrometer to measure the near-infrared spectrum of a standard sample and carrying out integer interpolation, and recording a spectrum data matrix after interpolation as WhereinpIs the number of samples to be tested,qcounting the number of spectral data points;
(3) respectively searching minimum points in two no-signal areas of the near infrared spectrum of each standard sample, and fitting a straight line with the two minimum pointsL i ,L i Namely the base line, obtaining a matrix of the base line;
(4) According to the spectrum data matrix after interpolation obtained in the step 2AAnd the spectrum base line matrix obtained in step 3LCalculating the spectrum matrix after baseline correctionC = A-L;
(5) Calculating the average absorbance of a plurality of points near the characteristic peak of the unsaturated carbon-hydrogen bondBy usingFor the product obtained in step 4CNormalization is carried out to obtain a normalized spectral data matrix;
(6) To spectral data matrixGSelecting wave bands to obtain a spectrum data matrix after the wave bands are selectedWhereinzThe number of spectral data points selected for a band,Ncan reflect the content information of the methanol;
(7) utilizing the spectrum matrix obtained in the step 6 after the wave band selectionNAnd the methanol content in the standard sample,SelectingModeling by a partial least square model;
(8) a testing stage, in which the near infrared spectrum of the unknown test sample is obtained in the step 2x test Sequentially utilizing the spectrum of the test sample in the steps 3-6x test Processing to obtain analysis model inputn test ;
(9) Using the model established in step 7 and the model obtained in step 8n test The methanol content of the test sample was calculated.
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Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
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CN102890067A (en) * | 2012-09-17 | 2013-01-23 | 江苏惠通集团有限责任公司 | Methanol gasoline quick detector based on near infrared rays |
CN103257117A (en) * | 2013-02-25 | 2013-08-21 | 中国科学院安徽光学精密机械研究所 | Rapid gasoline component measurement system |
CN103760131A (en) * | 2014-01-17 | 2014-04-30 | 华东理工大学 | Real-time gasoline product attribute prediction method based on near infrared spectrum detection |
CN107037000A (en) * | 2016-11-23 | 2017-08-11 | 华东交通大学 | A kind of detection method of environmental-protective alcohol diesel oil |
CN109374565A (en) * | 2018-09-30 | 2019-02-22 | 华东交通大学 | A kind of methanol gasoline ethanol petrol differentiates and content assaying method |
CN109632700A (en) * | 2019-01-03 | 2019-04-16 | 北京化工大学 | Methanol content rapid detection method and device in a kind of biodiesel synthesis process based on near-infrared |
CN111198165A (en) * | 2020-01-14 | 2020-05-26 | 重庆理工大学 | Method for measuring water quality parameters based on spectral data standardization |
CN111309958A (en) * | 2020-03-30 | 2020-06-19 | 四川长虹电器股份有限公司 | Spectrum reconstruction method based on interpolation operation |
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Cited By (8)
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CN102890067A (en) * | 2012-09-17 | 2013-01-23 | 江苏惠通集团有限责任公司 | Methanol gasoline quick detector based on near infrared rays |
CN103257117A (en) * | 2013-02-25 | 2013-08-21 | 中国科学院安徽光学精密机械研究所 | Rapid gasoline component measurement system |
CN103760131A (en) * | 2014-01-17 | 2014-04-30 | 华东理工大学 | Real-time gasoline product attribute prediction method based on near infrared spectrum detection |
CN107037000A (en) * | 2016-11-23 | 2017-08-11 | 华东交通大学 | A kind of detection method of environmental-protective alcohol diesel oil |
CN109374565A (en) * | 2018-09-30 | 2019-02-22 | 华东交通大学 | A kind of methanol gasoline ethanol petrol differentiates and content assaying method |
CN109632700A (en) * | 2019-01-03 | 2019-04-16 | 北京化工大学 | Methanol content rapid detection method and device in a kind of biodiesel synthesis process based on near-infrared |
CN111198165A (en) * | 2020-01-14 | 2020-05-26 | 重庆理工大学 | Method for measuring water quality parameters based on spectral data standardization |
CN111309958A (en) * | 2020-03-30 | 2020-06-19 | 四川长虹电器股份有限公司 | Spectrum reconstruction method based on interpolation operation |
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