CN105891141A - Method for rapidly measuring gasoline property data - Google Patents
Method for rapidly measuring gasoline property data Download PDFInfo
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- CN105891141A CN105891141A CN201610192727.3A CN201610192727A CN105891141A CN 105891141 A CN105891141 A CN 105891141A CN 201610192727 A CN201610192727 A CN 201610192727A CN 105891141 A CN105891141 A CN 105891141A
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- gasoline
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- wavelet transform
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 239000013598 vector Substances 0.000 claims abstract description 18
- 230000009466 transformation Effects 0.000 claims abstract description 12
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 9
- 238000001228 spectrum Methods 0.000 claims abstract description 9
- 238000003556 assay Methods 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 238000004611 spectroscopical analysis Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 3
- TVMXDCGIABBOFY-UHFFFAOYSA-N octane Chemical compound CCCCCCCC TVMXDCGIABBOFY-UHFFFAOYSA-N 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 4
- 238000012545 processing Methods 0.000 abstract description 4
- 230000003595 spectral effect Effects 0.000 abstract description 4
- 238000012360 testing method Methods 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000012628 principal component regression Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
Classifications
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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
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- 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
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention provides a method for rapidly measuring gasoline property data. After a near infrared spectrum of a gasoline sample is conventionally preprocessed, approximate wavelet transformation coefficient row vectors A and detail wavelet transformation coefficient row vectors D of spectral data are obtained through wavelet transformation, and the accumulative ratio of wavelet transformation coefficients is calculated. When the accumulative ratio of the n approximate coefficients reaches 70% or above, first n pieces of coefficient reconstruction information data in the approximate wavelet transformation coefficient row vectors A are selected, then a partial least squares model is established, and the to-be-measured sample is predicted. According to the method, feature information of the gasoline spectrum is effectively extracted, and under the condition that the overall prediction precision of a system is not sacrificed basically, the data processing capacity in the modeling process is reduced, calculated loads are reduced, and the real-time performance of gasoline online detection is improved.
Description
Technical field
The present invention relates to the oil property detection of petrochemical industry, a kind of near infrared spectrum utilizing gasoline sample to be measured
The characteristic parameter prediction gasoline property of figure.
Background technology
During the gasoline concoction of Petrochemical Enterprises, for obtaining more preferable economic benefit and social benefit, enterprise is P to measuring
The requirement of matter is more and more higher.
At present, near-infrared spectral analytical method is widely used in gasoline property analysis, with traditional P quality inspection of laboratory
Survey method is compared, and the method has analysis, and speed is fast, precision is high and expends the advantages such as few, makes the most complicated loaded down with trivial details analysis process
Become simple efficient, i.e. by analyzing the near-infrared spectrogram of gasoline sample, a large amount of character of gasoline can be obtained within a short period of time
Data.
In current actual application, commonly used near-infrared spectrum technique combines principal component regression, offset minimum binary (PLS) returns
The method founding mathematical models such as return, then based on this model realization quick mensuration to gasoline property.Early stage is it is proposed that cross " a kind of
Octane number detection method based on similar differentiation " (the patent of invention number of accepting: 201510355734.6), with common prediction method phase
Ratio, the precision of prediction of the method increases.But find in Ying Yong, along with the increase of sample size, its calculating process exists meter
Calculation amount increases, the longest problem, brings challenge to the real-time of practical engineering application.
Summary of the invention
In order to reduce the calculated load in engineer applied, improving the prediction real-time of system further, this patent proposes a kind of vapour
The rapid assay methods of oil nature data.The method is modeled as basis with common PLS, in known Near-infrared spectrum database
Search the spectrum identical with gasoline sample type to be measured, then process and take by wavelet transformation (Wavelet Transform) closely
Like the part coefficient reconfiguration information data of wavelet conversion coefficient, then set up PLS model by these data, finally use build up little
Sample to be tested is predicted by wave conversion-offset minimum binary (Wavelet-PLS) model, has steps of:
(1) near infrared spectrum of gasoline sample to be measured is obtained；
(2) spectrum of gasoline sample in gasoline sample to be measured and library of spectra is carried out conventional pretreatment；
(3) pretreated spectroscopic data is carried out wavelet transform process, obtain the Approximate Wavelet Transform line of coefficients of spectroscopic data to
Amount A and detail wavelet conversion coefficient row vector D；
(4) take Approximate Wavelet Transform line of coefficients vector A, and calculate the accumulative accounting rate of Approximate Wavelet Transform coefficient, when n system
The accumulative accounting rate of number is when reaching more than 70%, writes down number n of Approximate Wavelet Transform coefficient now；
(5) value of the n determined based on step (4), takes the Approximate Wavelet Transform line of coefficients vector A after step (3) processes
Front n coefficient reconstruct obtain 2n Information Number strong point, then setting up partial least square model by these data；
(6) sample to be tested is predicted by the model built up by step (5).
In this programme, take front n the coefficient of Approximate Wavelet Transform line of coefficients vector A when being reconstructed information data, variable n
Determination in: when the accumulative accounting rate of n Approximate Wavelet Transform coefficient reaches more than 70%, write down n now.
Preferably, the preprocess method described in step (2) uses vector normalizing and baseline correction.
Preferably, wavelet transformation uses Haar small echo.
Preferably, the rapid assay methods of described gasoline property is used for detecting research octane number (RON).
Choosing accumulative accounting rate is 70% and n above Approximate Wavelet Transform coefficient reconfiguration information data, this is because typically use
The data of front 2n approximate information have contained 70% and above spectral effective characteristic information, so effective characteristic information extraction
While also eliminate the impact that numerous co-existence information is overlapped, reduce the redundancy rate of data.
Beneficial effect:
The present invention proposes the rapid assay methods of a kind of character data of gasoline, according to the near infrared spectrum data of gasoline sample, logical
Cross wavelet transform process and take the part coefficient reconfiguration information data of Approximate Wavelet Transform coefficient, setting up PLS model and treat test sample
Originally it is predicted.This method is the most effectively extracted the characteristic information of gasoline spectrum, and the most pre-in the most not sacrificial system
Under conditions of surveying precision, decrease the data processing amount in modeling process, reduce calculated load, improve the real-time of detection
Property.
Accompanying drawing explanation
The implementing procedure figure of the rapid assay methods of Fig. 1 character data of gasoline
Detailed description of the invention
The present invention is further illustrated with case study on implementation below in conjunction with the accompanying drawings.
The present invention, as a example by certain 95# product oil, introduces the rapid assay methods of character data of gasoline based on wavelet transformation, wherein
Character data of gasoline is as a example by RON.Table 1 is numbering and the RON of correspondence thereof of certain all sample of 95# product oil.
Table 1 certain 95# product oil sample number and RON of correspondence
In Table 1, numbering 95#-1～71 sample be modeling sample, numbering 95#-72～81 sample be calibration samples.Right
After gasoline sample near infrared spectrum data carries out conventional pretreatment, obtain Approximate Wavelet Transform line of coefficients vector by wavelet transformation
A, and calculate the accumulative accounting rate of front n Approximate Wavelet Transform coefficient, as shown in table 2.In the present embodiment, wavelet transformation is adopted
With Haar Wavelet transformation, preprocess method uses vector normalizing and baseline correction.
The Approximate Wavelet Transform coefficient of certain 95# product oil sample spectrum data of table 2 adds up accounting rate
As shown in Table 2, the accumulative accounting rate of 45 Approximate Wavelet Transform coefficients i.e. can reach more than 70%, i.e. approximate information
The accumulation contribution rate at front 90 Information Number strong points is more than 70%；The accumulative accounting rate of 60 Approximate Wavelet Transform coefficients is close
90%, i.e. the accumulation contribution rate at front 120 Information Number strong points of approximate information is about 90%.
In case study on implementation, the present invention has done two kinds of Wavelet-PLS model tests, wherein, tries at Wavelet-PLS model 1
In testing, front 45 coefficients reconstruct taken in Approximate Wavelet Transform line of coefficients vector A obtains 90 Information Number strong points to set up mould
Type；In Wavelet-PLS model 2 is tested, front 60 coefficients reconstruct taken in Approximate Wavelet Transform line of coefficients vector A obtains
Model is set up at 120 Information Number strong points.For com-parison and analysis, continue to use the near infrared light of 71 identical modeling samples
Spectrum, sets up common PLS model after conventional pretreatment.The detailed forecasts result of three tests is as shown in table 3.The most exhausted
Deviation is referred to the absolute value of the actual value of RON and the difference of predictive value.
Table 3 PLS model compares with predicting the outcome of Wavelet-PLS model
It is computed, common PLS model, the standard deviation in population difference of Wavelet-PLS model 1 and Wavelet-PLS model 2
It is 0.293,0.240,0.285.Data in analytical table 3, compare the examination of Wavelet-PLS model 1 and common PLS model
Test result, it is known that in 10 samples to be tested, the absolute deviation of 5 samples has increased, and remaining 5 sample is the most inclined
Subtractive is little or keeps constant, and the standard deviation in population of Wavelet-PLS model 1 relatively reduces 0.53, illustrates the method
Macro-forecast precision increase.
According to the near infrared spectrum of known gasoline sample, calculate the wavelet conversion coefficient of spectroscopic data, and take Approximate Wavelet Transform system
Front 45 coefficients reconstruct in several rows vector A obtains 90 Information Number strong points, and comprises 208 numbers in original spectral data
Strong point, after part wavelet conversion coefficient reconstruction processing, can reduce the data processing amount of more than half.In same test conditions
Under, use same computer to carry out test emulation, find for same sample to be tested, use the prediction of common PLS model
Time is 4.1 seconds, and using the predicted time of Wavelet-PLS model is 3.8 seconds, it is seen then that the method fall that the present invention proposes
Low calculated load, this real-time contributing to promoting gasoline on-line blending.
By comparing Wavelet-PLS model 1, Wavelet-PLS model 2 and the result of the test of common PLS model, it is known that relatively
For common PLS model, the standard deviation in population of Wavelet-PLS model is smaller, also illustrate that Wavelet-PLS simultaneously
Model, under conditions of not sacrificing macro-forecast precision, improves the real-time of prediction, most important to engineering operation.
Claims (4)
1. the rapid assay methods of a character data of gasoline, it is characterised in that have steps of:
(1) near infrared spectrum of gasoline sample to be measured is obtained；
(2) spectrum of gasoline sample in gasoline sample to be measured and library of spectra is carried out conventional pretreatment；
(3) pretreated spectroscopic data is carried out wavelet transform process, obtain the Approximate Wavelet Transform line of coefficients vector of spectroscopic data
A and detail wavelet conversion coefficient row vector D；
(4) take Approximate Wavelet Transform line of coefficients vector A, and calculate the accumulative accounting rate of Approximate Wavelet Transform coefficient, when n coefficient
Accumulative accounting rate when reaching more than 70%, write down number n of Approximate Wavelet Transform coefficient now；
(5) value of the n determined based on step (4), takes the Approximate Wavelet Transform line of coefficients vector A's after step (3) processes
Front n coefficient reconstruct obtains 2n Information Number strong point, then setting up partial least square model by these data；
(6) sample to be tested is predicted by the model built up by step (5).
The rapid assay methods of a kind of character data of gasoline the most according to claim 1, it is characterised in that step (2) is described
Preprocess method use vector normalizing and baseline correction.
The rapid assay methods of a kind of character data of gasoline the most according to claim 1, it is characterised in that wavelet transformation uses
Haar small echo.
The rapid assay methods of a kind of character data of gasoline the most according to claim 1, it is characterised in that described gasoline property
Rapid assay methods be used for detecting research octane number (RON).
Priority Applications (1)
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| CN201610192727.3A CN105891141A (en) | 2016-03-30 | 2016-03-30 | Method for rapidly measuring gasoline property data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106407648A (en) * | 2016-08-26 | 2017-02-15 | 南京富岛信息工程有限公司 | Rapid batch forecast method for key property of gasoline |
| CN106770015A (en) * | 2017-01-10 | 2017-05-31 | 南京富岛信息工程有限公司 | A kind of oil property detection method based on the similar differentiation of principal component analysis |
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