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CN-116008216-B - Method and apparatus for detecting oil blending

CN116008216BCN 116008216 BCN116008216 BCN 116008216BCN-116008216-B

Abstract

The application provides a method and equipment for detecting oil blending. The method comprises the steps of obtaining near infrared spectrums of oil samples to be detected, obtaining absorbance of a characteristic spectrum area from the near infrared spectrums of the oil samples to be detected, and determining the category of the oil samples to be detected according to the absorbance of the characteristic spectrum area and a pre-established discriminant analysis model, wherein the discriminant analysis model is generated according to the near infrared spectrums of a plurality of samples of first oil and second oil, the mixing proportion of the near infrared spectrums and the absorbance of the characteristic spectrum area. According to the scheme, a discriminant analysis model can be constructed based on the mixed spectrum synthesized by the near infrared spectrums of the samples of the known first oil and the second oil, so that the second oil spectrum mixed with the first oil spectrum in any proportion can be synthesized by utilizing the spectrum of the known samples under the typical and sufficient conditions, and the second oil spectrum mixed with different first oil proportions is simulated. Compared with a method for directly collecting a sample of blended oil liquid to build a model, the method is more reliable and simple.

Inventors

  • WANG HAIPENG
  • CHU XIAOLI
  • LI JINGYAN
  • CHEN PU
  • LIU DAN
  • XU YUPENG

Assignees

  • 中国石油化工股份有限公司
  • 中国石油化工股份有限公司石油化工科学研究院

Dates

Publication Date
20260505
Application Date
20211022

Claims (9)

  1. 1. A method for detecting oil blending, the method comprising: Acquiring a near infrared spectrum of an oil sample to be tested; obtaining the absorbance of a characteristic spectrum area from the near infrared spectrum of the oil sample to be detected and Determining the category of the oil sample to be detected according to the absorbance of the characteristic spectrum area and a pre-established discriminant analysis model, wherein the discriminant analysis model is generated according to the near infrared spectrums of a plurality of samples of the first oil and the second oil, the mixing proportion and the absorbance of the characteristic spectrum area, Calculating a class label value of the oil sample to be detected according to the following formula, and comparing the class label value with a threshold value to determine the class of the oil sample to be detected: y un ＝b PLS x un , Wherein x un is the absorbance of the characteristic spectrum area of the oil sample to be detected, b PLS ＝w f T (p f w f T ) -1 q f b PLS is the regression coefficient of Partial Least Squares (PLS) algorithm, f is the optimal principal factor number of partial least squares determined by an interactive verification method, w f is the weight vector of the absorbance matrix of the sample spectrum used by the established discriminant analysis model under f principal components, p f is the load of the absorbance matrix of the sample spectrum used by the established discriminant analysis model under f principal components, q f is the load of the class label matrix corresponding to the sample spectrum used by the established discriminant analysis model under f principal components, Wherein, the magnitude relation of y un and the threshold value 0 of the class label value is sequentially compared, if y un is less than 0, the sample to be detected is judged as the aviation kerosene sample mixed with diesel oil, if y un is more than 0, the sample to be detected is judged as the pure aviation kerosene sample, The characteristic spectrum region is 4462-4752 cm -1 .
  2. 2. The method of claim 1, wherein the discriminant analysis model is generated in accordance with: obtaining a plurality of samples of the first oil and the second oil; a first near infrared spectrum is obtained according to ①, Wherein m represents a first near infrared spectrum synthesized by a spectrum of the first oil and a spectrum of the second oil, d is the near infrared spectrum of the first oil, j is the near infrared spectrum of the second oil, x is the blending proportion of the near infrared spectrum of the first oil, Generating a library sample spectrum by using the first near infrared spectrum, setting a corresponding class label value according to the near infrared spectrum mixing proportion of the first oil liquid, and And (3) obtaining the absorbance of the characteristic spectrum region of each library sample spectrum, and constructing a partial least square discriminant analysis model by combining the absorbance of the characteristic spectrum region of each library sample spectrum and the corresponding class label value.
  3. 3. The method of claim 1, wherein the oil sample to be tested is an actual oil sample or an oil sample configured by ②, In the formula ②, m represents a sample obtained after mixing the first oil and the second oil, d is a sample of the first oil, j is a sample of the second oil, c is a volume ratio of the sample of the first oil, wherein the value range of c is And c takes 0, m represents a pure sample of the second oil, wherein Is the maximum blending proportion of the first oil liquid.
  4. 4. The method according to claim 1, characterized in that the method further comprises: Performing first-order or second-order differential treatment on the near infrared spectrum of the oil sample to be detected to obtain a differential spectrum of the oil sample to be detected; obtaining differential absorbance of a characteristic spectrum region in the differential spectrum, and And determining the category of the oil sample to be detected according to the differential absorbance and a pre-established discriminant analysis model, wherein the discriminant analysis model is generated according to the near infrared spectrum and the mixing proportion of a plurality of samples of the first oil and the second oil, and the differential spectrum of the near infrared spectrum and the differential absorbance of the characteristic spectrum region thereof.
  5. 5. The method of claim 4, wherein the discriminant analysis model is generated in accordance with: obtaining a plurality of samples of the first oil and the second oil; a first near infrared spectrum is obtained according to ①, Wherein m represents a first near infrared spectrum synthesized by a spectrum of the first oil and a spectrum of the second oil, d is the near infrared spectrum of the first oil, j is the near infrared spectrum of the second oil, x is the blending proportion of the near infrared spectrum of the first oil, Performing first-order or second-order differential processing on each first near infrared spectrum to obtain first differential spectrums; acquiring first differential absorbance of a characteristic spectrum region in each first differential spectrum; and correlating all the first differential absorbance with the class label value corresponding to all the first differential absorbance to construct a partial least square discriminant analysis model.
  6. 6. The method according to claim 4 or 5, wherein the window width of the first order differential process is 19 and the window width of the second order differential process is 25.
  7. 7. The method of claim 1, wherein the first oil is diesel and the second oil is aviation kerosene.
  8. 8. An apparatus for detecting oil blending, the apparatus comprising: memory, and A processor configured to perform the method of detecting oil blending of any of claims 1-7.
  9. 9. A machine-readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to be configured to perform the method of detecting oil blending of any of claims 1 to 7.

Description

Method and apparatus for detecting oil blending Technical Field The application relates to the field of detection, in particular to a method and equipment for detecting oil blending. Background Aviation kerosene is a special fuel for jet aircraft. Because of the special nature of the working environment, the requirements on the performance of the fuel are very strict, such as good low-temperature fluidity, larger net heat value and density, faster combustion speed, complete combustion, good stability and the like. These properties are largely dependent on the chemical composition of the aviation kerosene, which may also vary from feedstock to feedstock and from production process to production process, thereby affecting certain properties of the aviation kerosene. At present, aviation kerosene is mainly obtained by a conventional petroleum refining process, and the yield of aviation kerosene accounts for more than 80% of the total yield. The aviation kerosene refining technology is to utilize atmospheric and vacuum distillation technology to distill, condense and collect compounds with different boiling points to separate substances and obtain fractions such as liquefied gas, naphtha, gasoline, kerosene, diesel oil, lubricating oil, fuel oil, residual oil and the like. However, because the boiling ranges of kerosene (180-310 ℃) and diesel (180-370 ℃) are coincident, when the aviation kerosene fraction required by cutting is obtained, the diesel fraction is easily cut into the aviation kerosene fraction, so that the difficulty of the refining process of the subsequent aviation kerosene is increased, and the cost is increased. In order to facilitate the smooth proceeding of the subsequent process of aviation kerosene and ensure the quality of finished aviation kerosene to the greatest extent, the aviation kerosene cutting process needs to be monitored. Monitoring of this process first requires a method to be sought that can distinguish between a pure aviation kerosene fraction and an aviation kerosene cut into a diesel fraction. Disclosure of Invention The embodiment of the application aims to provide a method and equipment for detecting oil blending. In order to achieve the above object, a first aspect of the present application provides a method for detecting oil blending, the method comprising obtaining a near infrared spectrum of an oil sample to be detected, obtaining a characteristic spectrum area absorbance from the near infrared spectrum of the oil sample to be detected, and determining a category of the oil sample to be detected according to the characteristic spectrum area absorbance and a pre-established discriminant analysis model, wherein the discriminant analysis model is generated according to the near infrared spectrums of a plurality of samples of a first oil and a second oil, blending proportion thereof, and characteristic spectrum area absorbance. In an embodiment of the application, the discriminant analysis model is generated according to the following operations: obtaining a plurality of samples of the first oil and the second oil; a first near infrared spectrum is obtained according to ①, m=xd+(1-x)j ① Wherein m represents a first near infrared spectrum synthesized by a spectrum of the first oil and a spectrum of the second oil, d is the near infrared spectrum of the first oil, j is the near infrared spectrum of the second oil, and x is a blending ratio of the near infrared spectrum of the first oil, for example, x is more than or equal to 0 and less than or equal to 1. Generating a library sample spectrum using the first near infrared spectrum, and setting a corresponding class label value according to a near infrared spectrum blending ratio of the first oil (for example, a class label of the first near infrared spectrum synthesized at 0< x <1 may be set to-1, and a class label of the first near infrared spectrum at x=0 may be set to 1), and And (3) obtaining the absorbance of the characteristic spectrum region of each library sample spectrum, and constructing a partial least square discriminant analysis model by combining the absorbance of the characteristic spectrum region of each library sample spectrum and the corresponding class label value. In the embodiment of the application, the characteristic spectrum area is 4462-4752cm -1. In the embodiment of the application, the oil sample to be detected is an actual oil sample or an oil sample configured by ②, m=cd+(1-c)j ② In the formula ②, m represents a sample obtained after mixing the first oil and the second oil, d is a sample of the first oil, j is a sample of the second oil, c is a volume ratio of the sample of the first oil, wherein the value range of c is 0≤c≤x max <1, and when c is 0, m represents a pure sample of the second oil, wherein x max is a maximum mixing proportion of the first oil. In the embodiment of the application, the class label value of the oil sample to be detected is calculated according to the following form