CN107748146A - A kind of crude oil attribute method for quick predicting based near infrared spectrum detection - Google Patents

A kind of crude oil attribute method for quick predicting based near infrared spectrum detection Download PDF

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CN107748146A
CN107748146A CN201710984193.2A CN201710984193A CN107748146A CN 107748146 A CN107748146 A CN 107748146A CN 201710984193 A CN201710984193 A CN 201710984193A CN 107748146 A CN107748146 A CN 107748146A
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钱锋
隆建
杨明磊
杜文莉
钟伟民
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East China University of Science and Technology
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    • 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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    • 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
    • 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
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    • 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
    • G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
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    • G01N2201/1293—Using chemometrical methods resolving multicomponent spectra

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Abstract

The present invention relates to structure for the method for the near-infrared model of crude oil attribute forecast and the crude oil attribute method for quick predicting detected based near infrared spectrum.The inventive method includes：Detect the attribute of different types of crude oil sample；Determine the atlas of near infrared spectra of the crude oil sample；To 12500~4000cm in the atlas of near infrared spectra that is obtained‑1The absorbance for composing area carries out first differential processing and multiplicative scatter correction；Principal component analysis is carried out to pretreated spectrum, calculates the T2 values of each crude oil sample, the sample more than threshold value is rejected, establishes initial training collection；The principal component scores concentrated using the initial training of acquisition select sample, it is determined that final training set as characteristic variable using the Euclidean distance between sample；One or more wave number sections are selected using moving window PLS；And the near-infrared model established using homing method between crude oil attribute and near infrared spectrum.The present invention is especially suitable for the online fast prediction analysis of crude oil.

Description

A kind of crude oil attribute method for quick predicting based near infrared spectrum detection
Technical field
The present invention relates to a kind of crude oil attribute real-time predicting method based near infrared spectrum detection.
Background technology
With the rapid development of modern industry, oil played as national strategy goods and materials in national economy it is most important Effect.The petroleum resources in China are deficienter, according to《BP world energy sourceses statistical yearbook 2016》Report, 2015, Chinese stone Oily net importation amount increases by 770,000 barrel per days, and China turns into the maximum oil importers in the whole world again, and the species of imported crude oil is very More, wherein it is much so-called " chance oil " to have, either proportion is very big or very high containing acid for they, or impurity is a lot.Have When, oil tanker, which is arrived at after harbour, just knows it is what oil.This just needs related personnel quickly to be made an appraisal to these crude oil, Er Qieyuan Depending on oil price wants the result that based on crude is evaluated, if evaluation is time-consuming long, it cannot fix price according to quality in time, it is possible to cause Economic loss, therefore crude oil Fast Evaluation increasingly shows its importance.
On the other hand, the crude oil change of the refinery of input at present is frequent, will usually become once within 3 two days.When extreme, daily The crude oil for entering factory is all different.Frequently change can cause many difficulties, especially destilling tower to following process.If crude oil can be made The speed of evaluation is accelerated, such as is once evaluated for every 10 minutes, it is possible to using the method for automatic governing, as much as possible not Congener crude oil is suitably concocted, and great fluctuation process is crossed to reduce into the oil property of destilling tower.
In addition, with the continuous heaviness of world wide crude oil, the mink cell focus that refinery of China processes even super mink cell focus Ratio more and more higher, the requirement to processing of heavy oil are also increasingly finer and efficient.Therefore can not using traditional evaluation method Meets the needs of practical application.The computer technology continued to develop at present, enable to analyze mass data reality in very short time It is existing, the rapid development of Modern Analytical Instrument technology is promoted, these have all established solid foundation for crude oil Fast Evaluation.NIR Analytical technology is that most have one of prospect and most widely used rapid analysis method at present, and On-line near infrared analyzer technology is also to develop most One of rapid process analysis technique.Because near-infrared analyzer is secondary meter, i.e., near-infrared analyzer can not be straight Measurement of species attribute is connect, and the mathematical modeling that must first establish between determinand qualitative attribution and near infrared spectrum is then according to mould Type carrys out measurement of species attribute.Therefore, it is that can near infrared technology effectively be applied to establish the near-infrared model that precision is high, robustness is good Key.
The content of the invention
In view of the above problems, the present invention proposes a kind of crude oil attribute fast prediction side based near infrared spectrum detection Method.On the basis of this method is using offline/on-line nir system collection crude oil atlas of near infrared spectra, using first differential and The method of multiplicative scatter correction pre-processes to the crude oil sample atlas of near infrared spectra collected, eliminates interference；By it is main into Divide analytical technology and T2 screening sample methods, select the suitable wave-number range of crude oil spectra figure；Finally, according to offset minimum binary Method, the mathematical modeling established between crude oil property value and its near infrared spectrum data, can be according to real-time by using the model Spectrum samples (simultaneously) prediction crude oil multiple property values.By offline/on-line nir system, meet to be adapted to crude oil The quick analysis of matter, particularly online real-time forecast analysis.
First aspect present invention provides a kind of method for building the near-infrared model for crude oil attribute forecast, methods described Including：
(1) attribute of different types of crude oil sample is detected；
(2) atlas of near infrared spectra of the crude oil sample is determined；
(3) 12500~4000cm in the atlas of near infrared spectra obtained to step (2)-1The absorbance for composing area carries out single order Differential process and multiplicative scatter correction；
(4) principal component analysis is carried out to the spectrum that step (3) obtains, preserves the principal component scores of each sample, and count The T2 values of each crude oil sample are calculated, and are contrasted with threshold value, the sample more than threshold value is rejected, establishes initial training collection；
(5) principal component scores of the initial training concentration obtained using step (4) utilize the Europe between sample as characteristic variable Formula distance selects sample, it is determined that final training set；
(6) one or more wave number sections are selected using moving window PLS, for establishing model；With
(7) near-infrared model established using homing method between crude oil attribute and near infrared spectrum.
In one or more embodiments, the attribute is selected from：Density, sulfur content, nitrogen content, acid number, carbon residue and reality One or more of boiling point distillation data.
In one or more embodiments, the quantity of step (1) Crude Oil sample is 100~600 parts.
In one or more embodiments, the near-infrared of crude oil sample is gathered using offline or on-line nir system Spectrum.
In one or more embodiments, in the measure described in step (2), spectral scanning range 4000- 12500cm-1, resolution ratio 2-32cm-1, multiple scanning 10-400 times, take average near infrared light spectrum.
In one or more embodiments, the first differential processing described in step (3) includes with multiplicative scatter correction：
(a) averaged spectrum of sample is calculatedWith
(b) to a spectrum x withCarry out linear regression,B is asked for least square method0And b；
Wherein, the spectrum after processing is xMSC=(x-b0)/b。
In one or more embodiments, in step (4), T2 values are calculated as below：
In above formula, t is variables of the original spectrum matrix X after principal component analysis dimensionality reduction, and σ is t standard deviation, and Iter is The principal component number of extraction, iter refer to i-th ter principal component, and j refers to j-th of sample.
In one or more embodiments, the threshold value is calculated as below in step (4)：Calculate all spectrum in Sample Storehouse The T2 values of sample, and using 99% confidential interval as upper threshold, the threshold value is calculated by following formula according to F distribution tables, its In, n is sample number, and F0.001 refers to F inspections, confidence level 0.01：
In one or more embodiments, step (5) includes：Become the principal component scores of each sample as feature Amount, the Euclidean distance of all samples between any two is calculated, two farthest samples of chosen distance enter training set, then calculate residue The distance between sample and the two selected samples, choose its minimum value and establish a data set, then from the data set Choose the sample corresponding to maximum to be added in training set, repeat said process, until choosing z training sample, establish most Whole training set.
In one or more embodiments, step (5) includes：
E) the Euclidean distance d of all samples between any two is calculated as followsij, chosen distance farthest two samples x1 and x2 Into training set：
In formula, dij：Euclidean distance between i-th of sample and j-th of sample；
m：For number of principal components；
xi：For i-th of sample；With
xj：For j-th of sample；
F) above formula is pressed, calculates the distance between remaining n-2 sample and selected two samples x1 and x2, and respectively take most Small value min (dI, x1, dI, x2), then choose wherein maximum max (min (dI, x1, dI, x2)) corresponding to sample x3 enter instruction Practice collection；
G) remaining n-3 sample and selected before three samples x1, the distance between x2 and x3 are calculated, and are respectively taken most Small value min (dI, x1, dI, x2, dI, x3), then choose wherein maximum max (min (dI, x1, dI, x2, dI, x3)) corresponding to a sample This x4 enters training set；With
H) said process is repeated, until choosing z training sample.
In one or more embodiments, step (6) includes, first wave number point by spectral window from whole spectrum Start to be moved rearwards until last, the wave number point fallen in window arrives (i+w-1) for i, and wherein i is the starting wavenumber point of window, w For window width, (l-w+1) individual wavelet number area for including w wave number point is thus selected successively from whole spectrum；For every One Ge Zi wave numbers area, sub- PLS regression models are established respectively, and the son for including different PLS number of principal components is calculated using interaction effect The cross validation root-mean-square error RMSECV of PLS regression models；Then the starting of resulting RMSECV values with corresponding window Figure is done in position, one or several wavelet number areas containing useful information is selected from figure, to establish final model.
In one or more embodiments, for density, the wave-number range of selection is 4586-5025cm-1；For residual Charcoal, the wave-number range of selection is 4247-6107cm-1；For acid number, the light wave-number range of selection is 4597-5461cm-1With 6094-7513cm-1；For sulfur content, the wave-number range of selection is 4484-6493cm-1；For nitrogen content, the wave number model of selection Enclose for 4500-6600cm-1；For wax content, the wave-number range of selection is 4500-6600cm-1；For asphalt content, selection Wave-number range be 4500-6600cm-1；It is 4599-7500cm with the wave-number range for true boiling point distillation, selection-1。
In one or more embodiments, prediction error RMSECV calculation formula is as follows：
In formula：Z is training set sample number；yiIt is the actual value that i-th of sample measures；It is the predicted value of i-th of sample.
In one or more embodiments, step (7) using PLS establish crude oil attribute with it is near red Near-infrared model between external spectrum.
In one or more embodiments, the near-infrared model that step (7) is established is：
Y=a0+a1x1+a2x2+…+anxn
Wherein：Y is to predict attribute, aiFor model parameter, xiFor the absorbance of i-th of wave number point of spectrum.
In one or more embodiments, step (7) includes：
(a) first pair of composition of two set of variables is extracted respectively, and is allowed to correlation up to maximum；
It is proposed that first pair of composition is t respectively from two groups of variables1And u1, t1It is independent variable collection X=(x1..., xm)TLinear group Close：u1It is dependent variable collection Y=(y1..., yp)TLinear combination：Wherein, it is desirable to t1And u1Each change of set of variables where extraction as much as possible Different information；And t1And u1Degree of correlation reach maximum；
By the standardization observation data matrix E of two groups of variables sets0And F0The score vector of first pair of composition is calculated, is designated asWith
Wherein, E0Remember for spectrum matrix, F0Remember for concentration matrix, it is as follows：
Use the score vector of first pair of compositionInner product calculate first couple of composition t1And u1Covariance Cov (t1, u1), so two above requirement can turn to constrained extremal problem mathematically, it is shown below：
Using Lagrange Multiplier Methods, problem, which turns to, seeks unit vector w1And v1, makeIt is maximum； By calculating m m matrixCharacteristic value and characteristic vector, and M eigenvalue of maximum isIt is corresponding single Position characteristic vector is exactly required solution w1, and v1By w1It is calculated
(b) y is established1,…,ypTo t1Recurrence and x1,…,xmTo t1Recurrence；
If regression model is：
Wherein α1=(α11... α1m)T, β1=(β11... β1m)TIt is the parameter vector in many-to-one regression model respectively, E1And F1It is residual error battle array, regression coefficient vector α1, β1Least-squares estimation be
Claim α1, β1For model effect load；
(c) residual error battle array E is used1And F1Instead of E0And F0Repeat above step；
NoteThen residual error battle arrayIf residual error battle array F1 The absolute value of middle element is approximately 0, then it is assumed that the regression equation precision established with first composition has met needs, can stop taking out Composition is taken, otherwise with residual error battle array E1And F1Instead of E0And F0Above step is repeated to produce：
w2=(W21,…w2m)T；v2=(v21... v2p)T
AndFor the score vector of second pair of composition；
The load of respectively Y, X second pair of composition, at this moment
(d) z × m data matrixes E is set0Order be r≤min (z-1, m), then r composition t be present1, t2..., trSo that
Substitute into Y=t1β1+…+trβr, p are produced because becoming The PLS equation of amount：
yj=aj1x1+…+ajmxm, (j=1,2 ..., p)
HereMeet
In one or more embodiments, step (7) also includes cross validation test：I.e. for extraction needed for modeling Composition number l, determined by cross validation test：Cast out i-th of observation (i=1,2 ..., n) every time, to remaining N-1 observation is modeled with partial least-square regression method, and considers to extract the regression equation being fitted after h composition, then house I-th of the observation station gone substitutes into be fitted regression equation, obtains yj(j=1,2 ..., p) it is pre- in i-th of observation station Measured value y(i)j(h)；The checking of the above is repeated to i=1,2 ..., n, produces j-th of dependent variable y when extracting h compositionj(j=1, 2 ..., p) Prediction sum squares, be
Y=(y1..., yp)TPrediction sum squares be
In addition, using all sample points again, the regression equation containing h composition is fitted；At this moment, i-th sample point is remembered Predicted value isBy yjError sum of squares be defined as
Define Y error sum of squares be
When PRESS (h) reaches minimum value, corresponding h is required composition number；
Defining Cross gain modulation isCalculate and terminate in each step of modeling Before, cross validation test is carried out, if walked in hThen model reaches precision and wanted Ask, stop extract component；IfRepresent the t of h step extractionshThe contributrion margin of composition is notable, should continue h+1 Step calculates.
Second aspect of the present invention provides a kind of crude oil attribute forecast method based near infrared spectrum detection, methods described bag Include：
(A) atlas of near infrared spectra of crude oil to be detected is determined；
(B) 12500~4000cm in the atlas of near infrared spectra obtained to step (A)-1The absorbance for composing area carries out single order Differential process and multiplicative scatter correction；
(C) near-infrared model for crude oil attribute forecast that structure obtains herein is utilized to carry out the crude oil attribute pre- Survey.
In one or more embodiments, the attribute is selected from：Density, sulfur content, nitrogen content, acid number, carbon residue and reality One or more of boiling point distillation data.
It is to be detected using the collection of offline or on-line nir system in step (A) in one or more embodiments The near infrared spectrum of crude oil.
In one or more embodiments, in the measure described in step (A), spectral scanning range 4000- 12500cm-1, resolution ratio 2-32cm-1, multiple scanning 10-400 times, take average near infrared light spectrum.
In one or more embodiments, the first differential processing described in step (B) includes with multiplicative scatter correction：
(a) averaged spectrum of each sample is calculated
(b) to a spectrum x withCarry out linear regression,B is asked for least square method0And b；With
(c) spectrum after handling is xMSC=(x-b0)/b。
In one or more embodiments, in step (C), in 12500~4000cm-1Selection wave number area in wavenumber range Between, the crude oil attribute is predicted using the near-infrared model for crude oil attribute forecast that structure obtains herein.
Beneficial effects of the present invention are as follows：Comprehensive modeling method proposed by the present invention, i.e., gathered based on near-infrared analyzer Crude oil spectra figure, the crude oil sample atlas of near infrared spectra collected is carried out using the method for first differential and multiplicative scatter correction Pretreatment, eliminate interference；By principal component analysis technology and T2 screening sample methods, the suitable wave number model of crude oil spectra figure is selected Enclose；Finally, according to PLS, the mathematical modeling established between crude oil property value and its near infrared spectrum data, realize The fast prediction analysis of unknown crude oil property value.This method is adapted to offline, on-line nir system.Using entering infrared point online Analyzer, the near infrared spectrum of crude oil sample is obtained in real time, can be realized based on the present invention pre- real-time to the progress of crude oil attribute Survey.For the quick analysis demand of crude oil, crude oil near infrared spectrum comprehensive modeling method of the present invention, have pretreated near red The advantages that external spectrum signal to noise ratio is high, analyze speed is fast, modeling accuracy is high, the present invention can detect the close of the oil product of crude oil simultaneously Degree, carbon residue, nitrogen content, sulfur content, acid number, true boiling point distillation yield etc.；Solve the time-consuming, laborious etc. of conventional method of analysis to ask Topic, improves analysis efficiency, is a kind of effective method.
Brief description of the drawings
Fig. 1：The flow chart of crude oil attribute method for quick predicting based near infrared spectrum detection.
Fig. 2：Crude oil near-infrared primary light spectrogram.
Fig. 3：Pretreated crude oil near-infrared spectrogram.
Fig. 4：The error line that moving window offset minimum binary obtains.
Fig. 5：Near-infrared oil density value regression model.
Embodiment
The flow chart of crude oil attribute method for quick predicting of Fig. 1 displays present invention based near infrared spectrum detection, while The near-infrared model building process of crude oil attribute fast prediction of the present invention is shown, specifically includes following steps：
(1) attribute of different types of crude oil sample is detected；
(2) atlas of near infrared spectra of the crude oil sample is determined；
(3) 12500~4000cm in the atlas of near infrared spectra obtained to step (2)-1The absorbance for composing area carries out single order Differential process and multiplicative scatter correction；
(4) principal component analysis is carried out to the spectrum that step (3) obtains, preserves the principal component scores of each sample, and count The T2 values of each crude oil sample are calculated, and are contrasted with threshold value, the sample more than threshold value is rejected, establishes initial training collection；
(5) principal component scores of the initial training concentration obtained using step (4) utilize the Europe between sample as characteristic variable Formula distance selects sample, it is determined that final training set；
(6) one or more wave number sections are selected using moving window PLS, for establishing model；With
(7) near-infrared model established using homing method between crude oil attribute and near infrared spectrum.
(1) attribute of different types of crude oil sample is detected
Different types of crude oil sample is collected, generally covers paraffinic base crude oil, intermediate base crude and naphthene base crude etc.. Generally, collected crude oil sample quantity is no less than 50.It is preferred that the density (20 DEG C) of collected crude oil sample, sulphur Content and acid number index control the model in 0.7~1.1g/cm3,0.03%~5.50% and 0.01~12.00mgKOH/g respectively Within enclosing.Then multiple attributes of collected crude oil are measured using traditional standard method, as density, carbon residue, nitrogen content, Sulfur content, acid number, salt content, wax content, asphalitine and true boiling point distillation yield etc., and record data.Crude oil species can be 50 More than individual, preferably at least more than 100.
(2) atlas of near infrared spectra of the crude oil sample is determined
The offline or On-line NIR instrument that suitable types can be chosen is equipped with the Transflective near-infrared fibre-optical of specific model Probe is scanned, and fibre-optical probe light path can select in 0.5mm~20mm scopes, straight using Transflective near-infrared fibre-optical is popped one's head in The metering system patched into crude oil sample (crude oil sample temperature maintains 30 DEG C) obtains the near infrared spectrum of every part of sample.Every The spectral scan time is 10-400 times, is averaged.Spectral scanning range is 4000-12500cm-1, resolution ratio 2-32cm-1.It is former Oily pre-processed spectrum is shown in Fig. 2.
(3) 12500~4000cm in the atlas of near infrared spectra obtained to step (2)-1The absorbance for composing area carries out single order Differential process and multiplicative scatter correction
The atlas of near infrared spectra of the crude oil sample of collection is pre-processed, preprocess method is to 12500-4000cm-1 Spectrum area carry out first differential processing, eliminate the interference of baseline and other backgrounds, resolution ratio and sensitivity improved, afterwards to spectrum Carry out multiplicative scatter correction.Specific method is as follows：
A) averaged spectrum of each sample is calculated(i.e. " preferable spectrum ")；With
B) to a spectrum x (1 × m) withCarry out linear regression,B is asked for least square method0And b；
Wherein, pretreated spectrum is xMSC=(x-b0)/b。
The interference of baseline and other backgrounds can be effectively eliminated using first differential method, weight peak is differentiated, improves and differentiate Rate and sensitivity；And multiplicative scatter correction can then eliminate that distribution of particles is uneven and granular size caused by scattering influence, enter And establish initial training collection.
(4) principal component analysis is carried out to the spectrum that step (3) obtains, preserves the principal component scores of each sample, and count The T2 values of each crude oil sample are calculated, and are contrasted with threshold value, the sample more than threshold value is rejected, establishes initial training collection
Step (4) is that the selection of sample is corrected to the spectrum after pretreated in step (3), is selected from Sample Storehouse Representative strong Sample Establishing model, the storage area established speed, reduce model library of model so can be not only improved, more For it is important that it is easy to the renewal and maintenance of model.
Before training sample set is selected, rejecting abnormalities sample, these exceptional samples it should may contain abnormal chemical first Component or concentration of component are more extreme, significant difference be present with other samples., can shadow if these sample points participate in the foundation of model Ring the accuracy and robustness of calibration model.Rejecting abnormalities sample can be counted to detect exceptional value using T2, and T2 is united Larger sample is measured therefrom to reject.
The description formula of T2 statistics is as follows：
In above formula, t is variables of the original spectrum matrix X after PCA dimensionality reductions, and σ is t standard deviation, and Iter is extraction Principal component number, iter refer to i-th ter principal component, and j refers to j-th of sample.Because the T2 values of exceptional sample can be far longer than normally Sample, so the T2 values of the spectrum samples in all Sample Storehouses are calculated, and using 99% confidential interval as upper threshold, under Formula, and look into F distribution tables threshold value can be calculated,
By the T2 values of all samples in Sample Storehouse compared with threshold value, the sample more than threshold value is rejected, establishes initial instruction Practice collection.After obtaining initial training collection, using the Euclidean distance between variable, sample is chosen in feature space.
(5) principal component scores of the initial training concentration obtained using step (4) utilize the Europe between sample as characteristic variable Formula distance selects sample, it is determined that final training set
After carrying out principal component (PCA) to sample spectrum, characteristic variable is used as by the use of principal component scores, between recycling sample Euclidean distance select sample, it is determined that final training set.Comprise the following steps that：
If sharing n sample, z sample is therefrom selected；
A) the Euclidean distance d of all samples between any two is calculated as followsij, chosen distance farthest two samples x1 and x2 Into training set：
In formula, dij：Euclidean distance between i-th of sample and j-th of sample；
m：For number of principal components；
xi：For i-th of sample；
xj：For j-th of sample；
B) above formula is pressed, calculates the distance between remaining n-2 sample and selected two samples x1 and x2, and respectively take most Small value min (dI, x1, dI, x2), then choose wherein maximum max (min (dI, x1, dI, x2)) corresponding to sample x3 enter instruction Practice collection；
C) remaining n-3 sample and selected before three samples x1, the distance between x2 and x3 are calculated, and are respectively taken most Small value min (dI, x1, dI, x2, dI, x3), then choose wherein maximum max (min (dI, x1, dI, x2, dI, x3)) corresponding to a sample This x4 enters training set；
D) said process is repeated, until choosing z training sample.
(6) one or more wave number sections are selected using moving window PLS, for establishing model
The step (6) of the present invention is to carry out wave number selection to the spectrum samples in training set, and traditional viewpoint thinks polynary Bearing calibration has stronger antijamming capability, can all-wave number participate in model foundation, but with to offset minimum binary etc. side The further investigation of method, characteristic waves are screened by ad hoc approach or section is possible to obtain preferably quantitative model.Pass through wave number Selection can be with simplified model, and can reject incoherent variable by wave number selection, and it is stronger to obtain predictive ability, robustness More preferable model.
The present invention uses moving window PLS, and the basic thought of this method is the spectrum by a width for w Window is moved rearwards since first wave number point of whole spectrum up to last, and it is i to (i+w- to fall the wave number point in window 1), wherein i is the starting wavenumber point of window, and w is window width.So, we select can successively from whole spectrum (l-w+1) the individual wavelet number area comprising w wave number point establishes sub- PLS regression models for each wavelet number area respectively, and The intersection that the sub- PLS models comprising different PLS number of principal components (having a maximum PLS principal components limitation) are calculated using interaction effect is tested Demonstrate,prove root-mean-square error RMSECV.Then resulting RMSECV values can be made Fig. 4 by we with the original position of corresponding window It is shown.We can easily select one or several wavelet number areas containing useful information from figure.Recycle what is selected Wavelet number area establishes final model.
Predict that error RMSECV calculation formula is as follows：
In formula：Z is training set sample number；yiIt is the actual value that i-th of sample measures；It is the predicted value of i-th of sample.
(7) near-infrared model established using homing method between crude oil attribute and near infrared spectrum
The step (7) of the present invention is to utilize the near-infrared by the crude oil sample in the training set of pretreatment and wave number selection Spectrogram establishes model with property value, and the property value for unknown sample is predicted.Built in the present invention using PLS Formwork erection type, the main thought of this method are as follows：
PLS not only only accounts for spectrum matrix, while have also contemplated that concentration compared with principal component regression The influence of matrix, for convenience's sake, spectrum matrix are designated as E0, concentration matrix is designated as F0, it is as follows：
Comprise the following steps that：
(1) first pair of composition of two set of variables is extracted respectively, and is allowed to correlation up to maximum.
Assuming that propose that first pair of composition is t respectively from two groups of variables1And u1, t1It is independent variable collection X=(x1..., xm)TLine Property combination：u1It is dependent variable collection Y=(y1..., yp)TLinear combination：For the needs of regression analysis, it is desirable to：
1)t1And u1The variation information of set of variables where each extracting as much as possible；
2)t1And u1Degree of correlation reach maximum.
By the standardization observation data matrix E of two groups of variables sets0And F0, the score vector of first pair of composition can be calculated, is designated asWith
First couple of composition t1And u1Covariance Cov (t1,u1) score vector of first pair of composition can be usedInner product come Calculate.So two above requirement can turn to constrained extremal problem mathematically：
Using Lagrange Multiplier Methods, problem, which turns to, seeks unit vector w1And v1, makeIt is maximum. The solution of problem need only be by calculating m m matrixCharacteristic value and characteristic vector, and M maximum feature It is worth and isCorresponding unit character vector is exactly required solution w1, and v1Can be by w1It is calculated
(2) y is established1,…,ypTo t1Recurrence and x1,…,xmTo t1Recurrence.
It is assumed that regression model is：
Wherein α1=(α11... α1m)T, β1=(β11... β1m)TIt is the parameter vector in many-to-one regression model respectively, E1And F1It is residual error battle array.Regression coefficient vector α1, β1Least-squares estimation be
Claim α1, β1For model effect load.
(3) residual error battle array E is used1And F1Instead of E0And F0Repeat above step.
NoteThen residual error battle arrayIf residual error battle array F1 The absolute value of middle element is approximately 0, then it is assumed that the regression equation precision established with first composition has met needs, can stop Extract composition.Otherwise residual error battle array E is used1And F1Instead of E0And F0Above step is repeated to produce：
w2=(w21... w2m)T；v2=(v21... v2p)T
AndFor the score vector of second pair of composition.
The load of respectively Y, X second pair of composition.At this moment have
(4) z × m data matrixes E is set0Order be r≤min (z-1, m), then r composition t be present1, t2..., trSo that
Substitute into Y=t1β1+…+trβr, produce p because The PLS equation of variable：
yj=aj1x1+…+ajmxm, (j=1,2 ..., p)
HereMeet
(5) cross validation test.
Generally, PLS and existing r composition t need not be selected1, t2..., trReturned to establish Formula, and as principal component analysis, only select preceding l composition (r >=l), you can obtain the preferable regression model of predictive ability.It is right In the composition number l extracted needed for modeling, can be determined by cross validation test.
Cast out i-th of observation (i=1,2 ..., n) every time, to remaining n-1 observation PLS side Method models, and considers to extract the regression equation being fitted after h composition, then i-th of the observation station cast out is substituted into time being fitted Return equation, obtain yjThe predicted value y of (j=1,2 ..., p) in i-th of observation station(i)j(h).To i=1,2 ..., n repeat with On checking, produce extract h composition when j-th of dependent variable yjThe Prediction sum squares of (j=1,2 ..., p) are
Y=(y1..., yp)TPrediction sum squares be
In addition, using all sample points again, the regression equation containing h composition is fitted.At this moment, i-th sample point is remembered Predicted value isY can then be definedjError sum of squares be
Define Y error sum of squares be
When PRESS (h) reaches minimum value, corresponding h is required composition number.Generally, always there is PRESS (h) big In SS (h), and SS (h) is then less than SS (h-1).Therefore, in extract component, always wish that ratio PRESS (h)/SS (h) is smaller more It is good；Limits value can typically be set as 0.05, that is, worked as
When, increase composition thBe advantageous to the raising of model accuracy.Or conversely speaking, when
When, it is considered as increasing new composition th, to reducing the prediction error of equation without obvious improvement result.
Therefore, definition Cross gain modulation isSo, in each step of modeling Before calculating terminates, cross validation test is carried out, if had in h steps Then model reaches To required precision, extract component can be stopped；IfRepresent the t of h step extractionshThe contributrion margin of composition is notable, H+1 steps should be continued to calculate.
The present invention determines the atlas of near infrared spectra of crude oil sample to be measured first when predicting the property value of sample to be tested, and And using the method described in step (3), the atlas of near infrared spectra of crude oil sample to be measured is pre-processed, afterwards according to step (6) the wave-number range selection variable of selection, utilizes the multiple attributes for the model prediction crude oil sample to be measured established in step (7) Value.
The inventive method can predict original by establishing the model between the atlas of near infrared spectra of crude oil sample and property value A variety of physical datas of oil, effective, reliable foundation is provided for industrial process, (quick to divide especially suitable for on-line prediction simultaneously Analysis) crude oil multiple property values such as, density, carbon residue, nitrogen content, sulfur content, acid number, true boiling point distillation yield etc..
The present invention is specifically described below by embodiment, this example is not limited to this by taking oil density as an example. It is necessarily pointed out that following examples are served only for, the invention will be further described, it is impossible to is interpreted as protecting the present invention Protect the limitation of scope, some nonessential improvement and tune that professional and technical personnel in the field makes according to present disclosure It is whole, still fall within protection scope of the present invention.
Embodiment 1
The instrument that crude oil near infrared spectrum is gathered in embodiment is Brooker Matrix-F Fourier Transform Near Infrareds Instrument, spectral region scanning range are 4000-12500cm-1, resolution ratio 32cm-1, add up scanning times 32 times, transflector measurement side Formula.
Step 1：Different types of crude oil sample 150 is gathered, the near infrared spectrum of crude oil sample is determined, such as Fig. 2 institutes Show.And the density of crude oil sample is measured according to laboratory method.
Step 2：Choose 8000-4000cm-1The absorbance of Spectral range, first differential+multiplicative scatter correction is carried out to it Pretreatment, establishes crude oil sample near infrared light spectrum matrix.Fig. 2 is the original atlas of near infrared spectra of crude oil sample of measure, spectrum Baseline drift is serious, and peak overlap is serious；Fig. 3 is the spectrogram after pretreatment.
Step 3：To pretreated crude oil sample spectrum matrix carry out principal component analysis, preserve each sample it is main into Get point, and calculate the T2 values of each crude oil sample, and contrasted with threshold value 7.06, reject the sample more than threshold value This, establishes initial training collection.
Step 4：Using the principal component scores of each sample recorded before as characteristic variable, all samples are calculated first Euclidean distance between any two, two farthest samples of chosen distance enter training set, then calculate remaining sample and selected The distance between the two samples, and choose its minimum value and establish a data set, then choose maximum from the data set Corresponding sample is added in training set, said process is repeated, until choosing 80 training samples.
Step 5：Wave number selection is carried out to it using moving window PLS to the sample in training set, wherein Window size is 21, and the selection of the wavelet number of gained indulges seat as shown in figure 4, abscissa is the initial point position of moving window in figure The cross validation prediction error RMSECV for the PLS models that corresponding moving window includes the foundation of wavelet number area is designated as, is therefrom chosen Wavenumber range is 4586-5025cm-1。
Step 6：Establish the regression model of density value and near infrared spectrum with PLS, prediction attribute with it is near Relation such as following formula between infrared spectrum
Y=aO+a1x1+a2x2++anxn
Wherein：Y is to predict attribute, aiFor model parameter, xiFor the absorbance of i-th of wave number point of spectrum.
The regression model built is as shown in Figure 5.
In addition, also verified using checking the set pair analysis model.Specifically, to crude oil sample to be measured first according to step Method in two, to 8000-4000cm-1First differential+multiplicative scatter correction pretreatment is carried out in spectrum area to it, is selected afterwards 4586-5025cm-1Wavenumber range, finally it is predicted using the model in step 6.The coefficient of determination up to 0.9593, Validation-cross mean square error is 0.000571.
Based on the above method, the comparative result of oil density predicted value and actual value based near infrared spectroscopic method is such as Shown in table 1, the model prediction is accurate, efficient.
Table 1：Predicted value and actual value result

Claims (10)

  1. A kind of 1. method for building the near-infrared model for crude oil attribute forecast, it is characterised in that methods described includes：
    (1) attribute of different types of crude oil sample is detected；
    (2) atlas of near infrared spectra of the crude oil sample is determined；
    (3) 12500~4000cm in the atlas of near infrared spectra obtained to step (2)-1The absorbance for composing area carries out first differential Processing and multiplicative scatter correction；
    (4) principal component analysis is carried out to the spectrum that step (3) obtains, preserves the principal component scores of each sample, and calculated every The T2 values of one crude oil sample, and contrasted with threshold value, the sample more than threshold value is rejected, establishes initial training collection；
    (5) principal component scores concentrated using the initial training that step (4) obtains as characteristic variable, using between sample it is European away from From sample is selected, it is determined that final training set；
    (6) one or more wave number sections are selected using moving window PLS, for establishing model；With
    (7) near-infrared model established using homing method between crude oil attribute and near infrared spectrum.
  2. 2. the method as described in claim 1, it is characterised in that
    The attribute is selected from：One or more of density, sulfur content, nitrogen content, acid number, carbon residue and true boiling point distillation data；
    The quantity of step (1) Crude Oil sample is 100~600 parts；Using offline or on-line nir system collection crude oil sample The near infrared spectrum data of product.
  3. 3. method as claimed in claim 1 or 2, it is characterised in that in the measure described in step (2), spectral scanning range is 4000-12500cm-1, resolution ratio 2-32cm-1, multiple scanning 10-400 times, take average near infrared light spectrum.
  4. 4. such as the method any one of claim 1-3, it is characterised in that the processing of first differential described in step (3) with Multiplicative scatter correction includes：
    (a) averaged spectrum of each sample is calculatedWith
    (b) to a spectrum x withCarry out linear regression,B is asked for least square method0And b；
    Wherein, the spectrum after processing is xMSC=(x-b0)/b。
  5. 5. such as the method any one of claim 1-3, it is characterised in that
    In step (4), T2 values are calculated as below：
    <mrow> <msubsup> <mi>T</mi> <mi>j</mi> <mn>2</mn> </msubsup> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>I</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> </mrow> </munderover> <mfrac> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> </mrow> <mn>2</mn> </msubsup> <msubsup> <mi>&amp;sigma;</mi> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> </mrow> </msub> <mn>2</mn> </msubsup> </mfrac> </mrow>
    In above formula, t is variables of the original spectrum matrix X after principal component analysis dimensionality reduction, and σ is t standard deviation, and Iter is extraction Principal component number, iter refers to i-th ter principal component, and j refers to j-th of sample；
    In step (4), the threshold value is calculated as below：The T2 values of all spectrum samples in Sample Storehouse are calculated, and are put with 99% Letter section is upper threshold, and the threshold value is calculated by following formula according to F distribution tables, wherein, n is sample number, and F0.001 refers to F inspections Test, confidence level 0.01：
    <mrow> <msup> <mi>T</mi> <mn>2</mn> </msup> <mi>c</mi> <mi>r</mi> <mi>i</mi> <mi>t</mi> <mo>=</mo> <mfrac> <mrow> <mi>I</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mi>n</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> <mrow> <mi>n</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>-</mo> <mi>I</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <msub> <mi>F</mi> <mn>0.01</mn> </msub> <mrow> <mo>(</mo> <mi>I</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mi>I</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> <mo>)</mo> </mrow> <mo>.</mo> </mrow>
  6. 6. such as the method any one of claim 1-5, it is characterised in that step (5) includes：By the master of each sample Component score calculates the Euclidean distance of all samples between any two, two farthest samples of chosen distance enter as characteristic variable Enter training set, then calculate the distance between remaining sample and the two selected samples, choose its minimum value and establish a number According to collection, then from the data set choose maximum corresponding to sample be added in training set, repeat said process, until choosing Z training sample, establish final training set；
    Preferably, step (5) includes：
    A) the Euclidean distance d of all samples between any two is calculated as followsij, chosen distance farthest two samples x1 and x2 enter Training set：
    <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <msqrt> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mi>k</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>x</mi> <mrow> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow>
    In formula, dij：Euclidean distance between i-th of sample and j-th of sample；
    m：For number of principal components；
    xi：For i-th of sample；With
    xj：For j-th of sample；
    B) above formula is pressed, calculates the distance between remaining n-2 sample and selected two samples x1 and x2, and respectively take minimum value min(dI, x1, dI, x2), then choose wherein maximum max (min (dI, x1, dI, x2)) corresponding to sample x3 enter training Collection；
    C) remaining n-3 sample and selected before three samples x1, the distance between x2 and x3 are calculated, and respectively take minimum value min(dI, x1, dI, x2, dI, x3), then choose wherein maximum max (min (dI, x1, dI, x2, dI, x3)) corresponding to a sample x4 Into training set；With
    D) said process is repeated, until choosing z training sample.
  7. 7. such as the method any one of claim 1-6, it is characterised in that step (6) includes, by spectral window from whole First wave number point of individual spectrum starts to be moved rearwards until last, and it is i to (i+w-1), wherein i to fall the wave number point in window For the starting wavenumber point of window, w is window width, thus selects that (l-w+1) is individual to include w wave number successively from whole spectrum The wavelet number area of point；For each wavelet number area, sub- PLS regression models are established respectively, and calculate and include using interaction effect The cross validation root-mean-square error RMSECV of the sub- PLS regression models of different PLS number of principal components；Then resulting RMSECV Value does figure with the original position of corresponding window, one or several wavelet number areas containing useful information is selected from figure, to build Vertical final model；
    Preferably, step (6) is included in 4000-12500cm-1Wave-number range in the one or more wave number sections of selection, preferably exist 4000-8000cm-1Wave-number range in；
    Preferably, predict that error RMSECV calculation formula is as follows：
    <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> <mi>C</mi> <mi>V</mi> <mo>=</mo> <msqrt> <mrow> <mfrac> <mn>1</mn> <mrow> <mi>z</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>z</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>^</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mrow>
    In formula：Z is training set sample number；yiIt is the actual value that i-th of sample measures；It is the predicted value of i-th of sample.
  8. 8. such as the method any one of claim 1-7, it is characterised in that step (7) is using PLS The near-infrared model established between crude oil attribute and near infrared spectrum；Preferably, the near-infrared model that step (7) is established is：
    Y=a0+a1x1+a2x2+…+anxn
    Wherein：Y is to predict attribute, aiFor model parameter, xiFor the absorbance of i-th of wave number point of spectrum.
  9. 9. method as claimed in claim 8, it is characterised in that step (7) includes：
    (a) first pair of composition of two set of variables is extracted respectively, and is allowed to correlation up to maximum；
    It is proposed that first pair of composition is t respectively from two groups of variables1And u1, t1It is independent variable collection X=(x1..., xm)TLinear combination：u1It is dependent variable collection Y=(y1..., yp)TLinear combination：Wherein, it is desirable to t1And u1Each change of set of variables where extraction as much as possible Different information；And t1And u1Degree of correlation reach maximum；
    By the standardization observation data matrix E of two groups of variables sets0And F0The score vector of first pair of composition is calculated, is designated asWith
    Wherein, E0Remember for spectrum matrix, F0Remember for concentration matrix, it is as follows：
    Use the score vector of first pair of compositionInner product calculate first couple of composition t1And u1Covariance Cov (t1,u1), So two above requirement can turn to constrained extremal problem mathematically, it is shown below：
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mo>&lt;</mo> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <mo>,</mo> <mover> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <mo>&gt;</mo> <mo>=</mo> <mo>&lt;</mo> <msub> <mi>E</mi> <mn>0</mn> </msub> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>F</mi> <mn>0</mn> </msub> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>&gt;</mo> <mo>=</mo> <msubsup> <mi>w</mi> <mn>1</mn> <mi>T</mi> </msubsup> <msubsup> <mi>E</mi> <mn>0</mn> <mi>T</mi> </msubsup> <msub> <mi>F</mi> <mn>0</mn> </msub> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>&amp;DoubleRightArrow;</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>w</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mi>w</mi> <mo>=</mo> <mo>|</mo> <mo>|</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msubsup> <mi>v</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mi>v</mi> <mo>=</mo> <mo>|</mo> <mo>|</mo> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> <mo>=</mo> <mn>1</mn> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Using Lagrange Multiplier Methods, problem, which turns to, seeks unit vector w1And v1, makeIt is maximum；Pass through Calculate m m matrixCharacteristic value and characteristic vector, and M eigenvalue of maximum isCorresponding unit is special Sign vector is exactly required solution w1, and v1By w1It is calculated
    (b) y is established1,…,ypTo t1Recurrence and x1,…,xmTo t1Recurrence；
    If regression model is：
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>E</mi> <mn>0</mn> </msub> <mo>=</mo> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;alpha;</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>E</mi> <mn>1</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>F</mi> <mn>0</mn> </msub> <mo>=</mo> <mover> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;beta;</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>F</mi> <mn>1</mn> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Wherein α1=(α11... α1m)T, β1=(β11... β1m)TIt is the parameter vector in many-to-one regression model respectively, E1And F1 It is residual error battle array, regression coefficient vector α1, β1Least-squares estimation be
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&amp;alpha;</mi> <mn>1</mn> </msub> <mo>=</mo> <msubsup> <mi>E</mi> <mn>0</mn> <mi>T</mi> </msubsup> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <mo>/</mo> <mo>|</mo> <mo>|</mo> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;beta;</mi> <mn>1</mn> </msub> <mo>=</mo> <msubsup> <mi>F</mi> <mn>0</mn> <mi>T</mi> </msubsup> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <mo>/</mo> <mo>|</mo> <mo>|</mo> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Claim α1, β1For model effect load；
    (c) residual error battle array E is used1And F1Instead of E0And F0Repeat above step；
    NoteThen residual error battle arrayIf residual error battle array F1Middle member The absolute value of element is approximately 0, then it is assumed that the regression equation precision established with first composition has met needs, can stop being taken into Point, otherwise with residual error battle array E1And F1Instead of E0And F0Above step is repeated to produce：
    w2=(w21... w2m)T；v2=(v21... v2p)T
    AndFor the score vector of second pair of composition；
    <mrow> <msub> <mi>&amp;alpha;</mi> <mn>2</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msubsup> <mi>E</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mover> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>^</mo> </mover> </mrow> <mrow> <mo>|</mo> <mo>|</mo> <mover> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>^</mo> </mover> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> </mfrac> <mo>,</mo> <msub> <mi>&amp;beta;</mi> <mn>2</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msubsup> <mi>F</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mover> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>^</mo> </mover> </mrow> <mrow> <mo>|</mo> <mo>|</mo> <mover> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>^</mo> </mover> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> </mfrac> </mrow>
    The load of respectively Y, X second pair of composition, at this moment
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>E</mi> <mn>0</mn> </msub> <mo>=</mo> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;alpha;</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mo>+</mo> <mover> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;alpha;</mi> <mn>2</mn> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>E</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>F</mi> <mn>0</mn> </msub> <mo>=</mo> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;beta;</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mo>+</mo> <mover> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;beta;</mi> <mn>2</mn> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>F</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>
    (d) z × m data matrixes E is set0Order be r≤min (z-1, m), then r composition t be present1, t2..., trSo that
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>E</mi> <mn>0</mn> </msub> <mo>=</mo> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;alpha;</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mo>+</mo> <mo>...</mo> <mo>+</mo> <mover> <msub> <mi>t</mi> <mi>r</mi> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;alpha;</mi> <mi>r</mi> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>E</mi> <mi>r</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>F</mi> <mn>0</mn> </msub> <mo>=</mo> <mover> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;beta;</mi> <mn>1</mn> <mi>T</mi> </msubsup> <mo>+</mo> <mo>...</mo> <mo>+</mo> <mover> <msub> <mi>t</mi> <mi>r</mi> </msub> <mo>^</mo> </mover> <msubsup> <mi>&amp;beta;</mi> <mi>r</mi> <mi>T</mi> </msubsup> <mo>+</mo> <msub> <mi>F</mi> <mi>r</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Substitute into Y=t1β1+…+trβ x, produce p dependent variable PLS equation：
    yj=aj1x1+…+ajmxm, (j=1,2 ..., p)
    HereMeet
    Optionally, step (7) also includes cross validation test：I.e. for the composition number l extracted needed for modeling, pass through intersection Validity check determines：Cast out i-th of observation (i=1,2 ..., n) every time, use remaining n-1 observation an inclined most young waiter in a wineshop or an inn Multiply homing method modeling, and consider to extract the regression equation being fitted after h composition, i-th of the observation station cast out then is substituted into institute The regression equation of fitting, obtains yjThe predicted value y of (j=1,2 ..., p) in i-th of observation station(i)j(h)；To i=1, 2 ..., n repeat the checking of the above, produce j-th of dependent variable y when extracting h compositionjThe prediction error of (j=1,2 ..., p) is put down Fang He, it is
    <mrow> <msub> <mi>PRESS</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mi>j</mi> </mrow> </msub> <mo>(</mo> <mi>h</mi> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>p</mi> <mo>)</mo> </mrow> </mrow>
    Y=(y1..., yp)TPrediction sum squares be
    <mrow> <mi>P</mi> <mi>R</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> <mrow> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>p</mi> </munderover> <msub> <mi>PRESS</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> </mrow>
    In addition, using all sample points again, the regression equation containing h composition is fitted；At this moment, the prediction of i-th of sample point is remembered It is worth and isBy yjError sum of squares be defined as
    <mrow> <msub> <mi>SS</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mover> <mi>y</mi> <mo>^</mo> </mover> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mi>j</mi> </mrow> </msub> <mo>(</mo> <mi>h</mi> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow>
    Define Y error sum of squares be
    <mrow> <mi>S</mi> <mi>S</mi> <mrow> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>p</mi> </munderover> <msub> <mi>SS</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>h</mi> <mo>)</mo> </mrow> </mrow>
    When PRESS (h) reaches minimum value, corresponding h is required composition number；
    Defining Cross gain modulation isBefore each step calculating of modeling terminates, Cross validation test is carried out, if walked in hThen model reaches required precision, stops Extract component；IfRepresent the t of h step extractionshThe contributrion margin of composition is notable, should continue h+1 steps and calculate.
  10. A kind of 10. crude oil attribute forecast method based near infrared spectrum detection, it is characterised in that methods described includes：
    (A) atlas of near infrared spectra of crude oil to be detected is determined；
    (B) 12500~4000cm in the atlas of near infrared spectra obtained to step (A)-1The absorbance for composing area carries out first differential Processing and multiplicative scatter correction；
    (C) the obtained near-infrared mould for crude oil attribute forecast is built using the method any one of claim 1-9 Type is predicted to the crude oil attribute.
CN201710984193.2A 2017-10-20 2017-10-20 A kind of crude oil attribute method for quick predicting based near infrared spectrum detection Pending CN107748146A (en)

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