CN114414524A - Method for rapidly detecting properties of aviation kerosene - Google Patents
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Abstract
The invention provides a method for rapidly detecting the properties of aviation kerosene, and belongs to the technical field of aviation kerosene property detection. The method of the invention comprises the following steps: acquiring physical and chemical property data of a plurality of aviation kerosene samples; collecting near infrared spectrum data of a plurality of aviation kerosene samples, and carrying out optimization processing on the near infrared spectrum data at least twice; establishing an analysis model of correlation between aviation kerosene physicochemical property data and near infrared spectrum data; and collecting near infrared spectrum data of a sample to be detected, optimizing the data at least twice, and substituting the optimized data into the analysis model to obtain the physicochemical property of the sample to be detected. The method is based on a composite detection technology combining a near infrared spectrum analysis technology and a proper modeling method, can realize the rapid detection of the physicochemical properties of the aviation kerosene, and has the advantages of high spectrum information acquisition speed, high sensitivity, low price, higher accuracy and stability.
Description
Technical Field
The invention belongs to the technical field of aviation kerosene property detection, and particularly relates to a method for rapidly detecting aviation kerosene properties.
Background
With the rapid development of aviation industry, strict requirements are provided for the safe supply of aviation kerosene, and the physicochemical properties of aviation kerosene determine the storage mode, transportation process, production process, application range and the like of the aviation kerosene, so that the acquisition of the physicochemical properties of aviation kerosene is of great importance. The traditional aviation kerosene property detection method can provide more accurate aviation kerosene property data, but the operation is complex, the experiment consumes long time, and the traditional aviation kerosene property detection method is limited by professional laboratory space, and the requirement of real-time monitoring on the aviation kerosene property data in the daily transportation, storage and delivery processes of aviation kerosene is difficult to meet. Based on the method, the rapid detection of the physicochemical property of the aviation kerosene is realized, the significance for guiding the storage of the aviation kerosene and the daily production of enterprises is great, and meanwhile, the application prospect of the aviation kerosene can be further expanded.
Therefore, there is a need to provide a new method for rapidly detecting the properties of aviation kerosene.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art and provides a method for rapidly detecting the properties of aviation kerosene.
The invention provides a method for rapidly detecting the properties of aviation kerosene, which comprises the following steps:
acquiring physical and chemical property data of a plurality of aviation kerosene samples;
collecting near infrared spectrum data of the plurality of aviation kerosene samples, and carrying out optimization processing on the near infrared spectrum data at least twice;
establishing an analysis model of the aviation kerosene physicochemical property data and the near infrared spectrum data;
and collecting near infrared spectrum data of a sample to be detected, optimizing the data at least twice, and substituting the optimized data into the analysis model to obtain the physicochemical property of the sample to be detected.
Optionally, the physicochemical properties include at least one of density, freezing point, flash point, initial boiling point, 50% recovery temperature, and end point.
Optionally, collecting near infrared spectrum data of the aviation kerosene sample by using a transmission type near infrared spectrometer; wherein the content of the first and second substances,
the collection range is 4000cm-1~12000cm-1The scanning frequency range is 5-120 times, and the resolution range is 7cm-1~9cm-1The measurement temperature range is 17-25 ℃.
Optionally, the performing at least two times of optimization processing on the near infrared spectrum data includes:
performing first optimization processing on the near infrared spectrum data by using a second derivative and a data smoothing method;
and performing second optimization treatment by using a PCA-MD method to remove abnormal aviation kerosene samples.
Optionally, performing a first optimization process on the near infrared spectrum data by using a data smoothing method, including:
and carrying out Savitzky-Golay first derivative processing with the window width range of 5-10 on the near infrared spectrum data of all aviation kerosene by using the data smoothing method.
Optionally, performing a second optimization treatment by using a PCA-MD method to remove abnormal aviation kerosene samples, including:
calculating the March distance value and the March distance standard variance of the aviation kerosene sample after the first optimization treatment by adopting a PCA-MD method;
obtaining a Mahalanobis distance threshold of the abnormal aviation kerosene sample according to the Mahalanobis distance value and the Mahalanobis distance standard variance;
and comparing the Mahalanobis distance value of each aviation kerosene sample subjected to the first optimization processing with the Mahalanobis distance threshold value of the abnormal aviation kerosene sample, and rejecting the corresponding sample when the Mahalanobis distance value is greater than the Mahalanobis distance threshold value.
Optionally, the mahalanobis distance threshold is calculated according to the following formula:
in the formula (I), the compound is shown in the specification,W MD the mahalanobis distance threshold for the abnormal jet fuel sample,mean(MD) Is a function of the mean value of the MD,std(MD) The standard variance function of MD is adopted, and k is a threshold value adjustment weight coefficient; and the number of the first and second groups,
the formula for calculating the mahalanobis distance value is as follows:
in the formula (I), the compound is shown in the specification,
as an aviation kerosene sampleiThe value of the mahalanobis distance of (c),t i as an aviation kerosene sampleiThe score vector of (a) is calculated,
the average of the score vectors for all samples;Ca covariance matrix which is a scoring matrix of all aviation kerosene samples,Tis a scoring matrix for all aviation kerosene samples.
Optionally, the establishing an analysis model of the aviation kerosene physicochemical property data and the near infrared spectrum data, includes:
dividing the near infrared spectrum data into n spectral intervals, respectively establishing local prediction models for the n spectral intervals, and selecting a preset spectral interval by taking a root mean square error as an evaluation standard.
Optionally, the establishing an analysis model of the aviation kerosene physicochemical property data and the near infrared spectrum data, includes:
establishing the analysis model by using PLS based on the preset spectral interval, and performing matrix decomposition on the near infrared spectrum data matrix X and the physicochemical property data matrix Y of the preset spectral interval, wherein the matrix decomposition specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,Tis composed ofXThe score matrix of the matrix is a function of,Pis composed ofXThe load matrix of the matrix is,Eis composed ofXA residual matrix of the matrix;
in the formula (I), the compound is shown in the specification,Uis composed ofYThe score matrix of the matrix is a function of,Qis composed ofYThe load matrix of the matrix is,Fis composed ofYA residual matrix of the matrix;
performing linear regression fitting on the scoring matrixes T and U, wherein the specific relation is as follows:
in the formula (I), the compound is shown in the specification,Bis a regression coefficient matrix.
Optionally, near infrared spectrum data of the sample to be measured is collected, and the optimized data is substituted into the analysis model to obtain the physicochemical properties of the sample to be measured, wherein the specific relation is as follows:
in the formula (I), the compound is shown in the specification,Ynis the physical and chemical properties of the sample to be measured,Tnand (4) obtaining a scoring matrix of the near infrared spectrum data of the sample to be detected.
The invention provides a method for rapidly detecting the properties of aviation kerosene, which comprises the following steps: acquiring physical and chemical property data of a plurality of aviation kerosene samples; collecting near infrared spectrum data of the plurality of aviation kerosene samples, and carrying out optimization processing on the near infrared spectrum data at least twice; establishing an analysis model of the aviation kerosene physicochemical property data and the near infrared spectrum data; and collecting near infrared spectrum data of a sample to be detected, optimizing the data at least twice, and substituting the optimized data into the analysis model to obtain the physicochemical property of the sample to be detected. The method is based on a composite detection technology combining a near infrared spectrum analysis technology and a proper modeling method, can realize the rapid detection of the physicochemical properties of the aviation kerosene, and has the advantages of high spectrum information acquisition speed, high sensitivity, low price, higher accuracy and stability.
Drawings
FIG. 1 is a block flow diagram of a method for rapidly testing the properties of aviation kerosene according to an embodiment of the present invention;
FIG. 2 is a spectrum of an original jet fuel sample of the present invention;
FIG. 3 is a spectrum of an aviation kerosene sample after optimization treatment according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
As shown in FIG. 1, the invention provides a method S100 for rapidly detecting the properties of aviation kerosene, which comprises the following steps S110-S140:
s110, acquiring physical and chemical property data of a plurality of aviation kerosene samples.
Specifically, in the embodiment, aviation kerosene samples having regions, crude oil producing areas and typical refining processes all over the country are collected, the total sample amount is not less than 300, the physicochemical properties of each sample are measured by adopting the method required in the national standard GB6537-2018 of jet fuel No. 3, and a physicochemical property database is established.
Further, the physicochemical properties of the present embodiment include at least one of density, freezing point, flash point, initial boiling point, 50% recovery temperature, and end point.
It should be noted that, at present, most aviation kerosene is analyzed for the concentration of free water and micro impurities, or the concentration of sulfur in aviation kerosene is analyzed and detected, or the concentration of aviation kerosene is detected, and the detection of physicochemical properties of aviation kerosene is not involved, especially the detection of density, freezing point, flash point, initial boiling point, 50% recovery temperature and final boiling point, and the physicochemical properties are important for storage, transportation and specific production application. Therefore, the present embodiment proposes a rapid detection method for the above physicochemical properties of aviation kerosene.
And S120, collecting near infrared spectrum data of the plurality of aviation kerosene samples, and performing optimization processing on the near infrared spectrum data at least twice.
It should be noted that the near infrared spectroscopy is one of the most widely used rapid analysis techniques in the industrial production field. The technology has the advantages of high spectrum information acquisition speed, high sensitivity, low price and the like. Based on this, the embodiment utilizes a composite technology combining a near infrared spectrum analysis technology and a suitable modeling method to realize the rapid detection of the physicochemical properties of the aviation kerosene.
In the embodiment, the near infrared spectrum data of the aviation kerosene sample is acquired by using the low-cost transmission type near infrared spectrometer. Specifically, each sample adopts a transmission type near-infrared spectrometer to perform spectrum scanning on all samples, and the oil sample is ensured to be uniform all the time in the measurement process, wherein the collection range is 4000cm-1~12000cm-1The scanning frequency range is 5-120 times, and the resolution range is 7cm-1~9cm-1(e.g., resolution 8 cm)-1) The measurement temperature ranges from 17 ℃ to 25 ℃ (e.g., 20 ℃).
Further, the near infrared spectrum data is optimized at least twice, and the optimization comprises the following steps: and performing first optimization processing on the near infrared spectrum data of each sample by using a second derivative and data smoothing method to eliminate influence factors such as baseline inclination, background interference, noise of a foreign peak and the like and improve the resolution of a spectrogram. And after the first optimization processing, performing second optimization processing by using a principal component Analysis-Mahalanobis distance method (PCA-MD) to remove the abnormal aviation kerosene sample.
Specifically, the first optimization processing of the near infrared spectrum data by using a data smoothing method comprises the following steps: and carrying out Savitzky-Golay first derivative processing with the window width range of 5-10 on the near infrared spectrum data of all aviation kerosene by using the data smoothing method.
Further, a second optimization treatment is carried out by utilizing a PCA-MD method to eliminate abnormal aviation kerosene samples, and the second optimization treatment comprises the following steps: calculating the March distance value and the March distance standard variance of the aviation kerosene sample after the first optimization treatment by adopting a PCA-MD method; obtaining a Mahalanobis distance threshold of the abnormal aviation kerosene sample according to the Mahalanobis distance value and the Mahalanobis distance standard variance; and comparing the Mahalanobis distance value of each aviation kerosene sample subjected to the first optimization processing with the Mahalanobis distance threshold value of the abnormal aviation kerosene sample, and rejecting the corresponding sample when the Mahalanobis distance value is larger than the Mahalanobis distance threshold value. That is, the mahalanobis distance value for each optimized sample is compared to the mahalanobis distance threshold for the abnormal sample to reject samples that are greater than the mahalanobis distance threshold.
The formula for calculating the mahalanobis distance threshold is as follows:
in the formula (I), the compound is shown in the specification,W MD the mahalanobis distance threshold for the abnormal jet fuel sample,mean(MD) Is a function of the mean value of the MD,std(MD) The standard variance function of MD is adopted, and k is a threshold value adjustment weight coefficient; and, the formula for calculating the mahalanobis distance value is as follows:
in the formula (I), the compound is shown in the specification,
as an aviation kerosene sampleiThe value of the mahalanobis distance of (c),t i as an aviation kerosene sampleiThe score vector of (a) is calculated,
the average of the score vectors for all samples;Ca covariance matrix which is a scoring matrix of all aviation kerosene samples,Tis a scoring matrix for all aviation kerosene samples.
It should be noted that, this embodiment may also perform optimization processing on the near infrared spectrum data again, or perform optimization processing on the near infrared spectrum data by using a combination optimization method combining multiple preprocessing methods, which is not particularly limited.
It should be further noted that, in this embodiment, an aviation kerosene spectrum database is established from the optimized near infrared spectrum data.
S130, establishing an analysis model of correlation between the aviation kerosene physicochemical property data and the near infrared spectrum data.
It should be noted that, based on the aviation kerosene spectral database, the ratio of 5: 1, randomly selecting training set samples and verification set samples according to the proportion, namely collecting the screened spectral data, randomly extracting 83% of samples to form a training set, taking the rest 17% of samples as verification sets, establishing an analysis model by using the training set, and verifying the analysis model by using the verification sets.
Specifically, near infrared spectrum data is divided into n spectral intervals, local prediction models are respectively established for the n spectral intervals, Root Mean Square Error (RMSE) is used as an evaluation standard, spectral intervals participating in modeling are increased from an interval with the minimum root mean square error through cross validation, 2-5 spectral intervals with the minimum root mean square error are selected as preset spectral intervals for establishing analysis models, and the analysis models are established by a Partial least square regression (PLS) method based on the preset spectral intervals.
Wherein, the root mean square error RMSE calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
as an aviation kerosene sampleiThe predicted value of (a) is determined,
as an aviation kerosene sampleiThe true value of (c) is given,nis the number of samples.
Further, an analysis model is established by using PLS based on the preset spectral interval, which includes: performing matrix decomposition on a near infrared spectrum data matrix X and a physicochemical property data matrix Y in a preset spectrum interval, which specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,Tis composed ofXThe score matrix of the matrix is a function of,Pis composed ofXThe load matrix of the matrix is,Eis composed ofXA residual matrix of the matrix;
in the formula (I), the compound is shown in the specification,Uis composed ofYThe score matrix of the matrix is a function of,Qis composed ofYThe load matrix of the matrix is,Fis composed ofYA residual matrix of the matrix;
performing linear regression fitting on the scoring matrixes T and U, wherein the linear regression fitting method specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,Bis a regression coefficient matrix.
S140, collecting near infrared spectrum data of the sample to be detected, optimizing the data at least twice, and substituting the optimized data into an analysis model to obtain the physicochemical property of the sample to be detected.
Specifically, near infrared spectrum data of the aviation kerosene sample to be measured are collected, the same optimization processing process is carried out on the near infrared spectrum data, the processed data are substituted into the relational expression, so that corresponding physicochemical properties are obtained, and the specific relational expression is as follows:
in the formula (I), the compound is shown in the specification,Tnis a scoring matrix of the near infrared spectrum data of the sample to be detected,Ynis the physicochemical property of the sample to be measured.
It should be noted that, in the embodiment, a plurality of physicochemical properties of the sample to be detected can be obtained through one operation, the prediction efficiency is high, the detection time is effectively shortened, and the detection accuracy is high.
The method for rapidly detecting the properties of the aviation kerosene is described by the following specific examples:
the present example is described by taking the predicted density as an example, and the specific steps include:
step 1: establishing a library containing 300 aviation kerosene samples, and measuring the density data of all aviation kerosene samples by using a standard method;
step 2: the sample temperature is controlled at 20 ℃, a near infrared spectrometer is selected to collect near infrared spectrum data of the aviation kerosene sample, and the original spectrum of the near infrared spectrum data is shown in figure 2. Wherein the collected wavelength range is 4000cm-1~12000cm-1The scanning times are 100 times, and the resolution is 8cm-1；
And step 3: performing first optimization processing on the aviation kerosene near infrared spectrum data acquired in the step 2 by using a second derivative and data smoothing method, wherein the optimized spectrum is shown in fig. 3, and establishing an aviation kerosene spectrum database based on the optimized spectrum data;
and 4, step 4: performing PCA-MD principal component analysis-Mahalanobis distance analysis on the spectral data subjected to the first optimization to judge abnormal samples, and eliminating samples with abnormal Mahalanobis distance values;
and 5: establishing an aviation kerosene spectral database, and performing a step of 5: 1, randomly selecting training set samples and verification set samples according to the proportion;
step 6: dividing the scanning wavelength into 5 sections, respectively establishing PLS local prediction models, and determining the optimal modeling interval to be 4200 cm by comparing the predicted root mean square error-1-4700 cm-1、6240 cm-1-6950 cm-1And combining the characteristic modeling intervals to participate in the final modeling so as to obtain an analysis model.
And 7: and acquiring near infrared spectrum data of the aviation kerosene sample to be detected, performing optimization processing twice, and substituting the processed data into the analysis model to obtain density data of the aviation kerosene sample to be detected.
It should be noted that the density of the aviation kerosene sample to be detected can be rapidly detected based on the analysis model. Of course, it will be understood that the same method can be used to build corresponding analytical models for detecting other different physicochemical properties, such as: freezing point, flash point, initial boiling point, 50% recovery temperature and end point. Of course, any one of the physicochemical properties can be detected by using the analysis model, and a plurality of physicochemical properties can also be detected simultaneously, so as to improve the detection efficiency.
The invention provides a method for rapidly detecting the properties of aviation kerosene, which has the following beneficial effects compared with the prior art: the method disclosed by the invention adopts a composite technology combining a near infrared spectrum technology and modeling, can realize rapid and nondestructive detection on the aviation kerosene sample, and has the advantages of high detection efficiency, high detection accuracy, good stability and greatly shortened detection time. In addition, the method of the invention does not need any disposable consumables, has lower cost, simple and convenient operation and low requirement on the technical threshold of operators, and is suitable for large-scale popularization.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various changes and modifications may be made without departing from the spirit of the invention, and these changes and modifications are also considered to be within the scope of the invention.
Claims (10)
1. A method for rapidly detecting the properties of aviation kerosene is characterized by comprising the following steps:
acquiring physical and chemical property data of a plurality of aviation kerosene samples;
collecting near infrared spectrum data of the plurality of aviation kerosene samples, and carrying out optimization processing on the near infrared spectrum data at least twice;
establishing an analysis model of the aviation kerosene physicochemical property data and the near infrared spectrum data;
and collecting near infrared spectrum data of a sample to be detected, optimizing the data at least twice, and substituting the optimized data into the analysis model to obtain the physicochemical property of the sample to be detected.
2. The method of claim 1, wherein the physicochemical properties include at least one of density, freezing point, flash point, initial boiling point, 50% recovery temperature, and end point.
3. The method of claim 1, wherein the near infrared spectroscopy data of the aviation kerosene sample is collected using a transmission near infrared spectrometer; wherein the content of the first and second substances,
the collection range is 4000cm-1~12000cm-1The scanning frequency range is 5-120 times, and the resolution range is 7cm-1~9cm-1The measurement temperature range is 17-25 ℃.
4. The method of claim 1, wherein said performing at least two optimizations on the near infrared spectral data comprises:
performing first optimization processing on the near infrared spectrum data by using a data smoothing method;
and performing second optimization treatment by using a PCA-MD method to remove abnormal aviation kerosene samples.
5. The method of claim 4, wherein the first optimization of the near infrared spectral data using data smoothing comprises:
and carrying out Savitzky-Golay first derivative processing with the window width range of 5-10 on the near infrared spectrum data of all aviation kerosene by using the data smoothing method.
6. The method as claimed in claim 4, wherein the second optimization process using the PCA-MD method to eliminate abnormal aviation kerosene samples comprises:
calculating the March distance value and the March distance standard variance of the aviation kerosene sample after the first optimization treatment by adopting a PCA-MD method;
obtaining a Mahalanobis distance threshold of the abnormal aviation kerosene sample according to the Mahalanobis distance value and the Mahalanobis distance standard variance;
and comparing the Mahalanobis distance value of each aviation kerosene sample subjected to the first optimization processing with the Mahalanobis distance threshold value of the abnormal aviation kerosene sample, and rejecting the corresponding sample when the Mahalanobis distance value is greater than the Mahalanobis distance threshold value.
7. The method of claim 6, wherein the mahalanobis distance threshold is calculated as follows:
in the formula (I), the compound is shown in the specification,W MD the mahalanobis distance threshold for the abnormal jet fuel sample,mean(MD) Is a function of the mean value of the MD,std(MD) The standard variance function of MD is adopted, and k is a threshold value adjustment weight coefficient; and the number of the first and second groups,
the formula for calculating the mahalanobis distance value is as follows:
in the formula (I), the compound is shown in the specification,
as an aviation kerosene sampleiThe value of the mahalanobis distance of (c),t i as an aviation kerosene sampleiThe score vector of (a) is calculated,
the average of the score vectors for all samples;Ca covariance matrix which is a scoring matrix of all aviation kerosene samples,Tis a scoring matrix for all aviation kerosene samples.
8. The method of claim 1, wherein prior to establishing the analytical model relating the aviation kerosene physicochemical property data to the near infrared spectral data, comprising:
dividing the near infrared spectrum data into n spectral intervals, respectively establishing local prediction models for the n spectral intervals, and selecting a preset spectral interval by taking a root mean square error as an evaluation standard.
9. The method of claim 8, wherein the establishing an analytical model of the aviation kerosene physicochemical property data associated with the near infrared spectral data comprises:
establishing the analysis model by using PLS based on the preset spectral interval, and performing matrix decomposition on the near infrared spectrum data matrix X and the physicochemical property data matrix Y of the preset spectral interval, wherein the matrix decomposition specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,Tis composed ofXThe score matrix of the matrix is a function of,Pis composed ofXThe load matrix of the matrix is,Eis composed ofXA residual matrix of the matrix;
in the formula (I), the compound is shown in the specification,Uis composed ofYThe score matrix of the matrix is a function of,Qis composed ofYThe load matrix of the matrix is,Fis composed ofYA residual matrix of the matrix;
performing linear regression fitting on the scoring matrixes T and U, wherein the specific relation is as follows:
in the formula (I), the compound is shown in the specification,Bis a regression coefficient matrix.
10. The method according to claim 9, characterized in that near infrared spectral data of a sample to be tested are collected, and the optimized data are substituted into the analysis model to obtain physicochemical properties of the sample to be tested, and the specific relation is as follows:
in the formula (I), the compound is shown in the specification,Ynis the physical and chemical properties of the sample to be measured,Tnand (4) obtaining a scoring matrix of the near infrared spectrum data of the sample to be detected.
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