Crude oil and fuel oil identification method based on non-measurement multi-dimensional scale transformation analysis
Through infrared absorption spectroscopy technology based on NMDS, a fingerprint database of crude oil and fuel oil is constructed, which solves the problem of difficulty in distinguishing crude oil and fuel oil in the existing technology, and achieves rapid and effective identification and supervision of oil product attributes.
Patent Information
- Application Number
- CN202510031254.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-09
- Publication Date
- 2025-04-18
AI Technical Summary
The prior art is difficult to quickly and effectively distinguish between crude oil and fuel oil, resulting in insufficiency of regulatory capabilities and customs clearance efficiency of imported refining raw materials.
The method based on non-metric multi-dimensional scale transformation analysis (NMDS) was used and combined with infrared absorption spectroscopy technology to construct an infrared fingerprint database of crude oil and fuel oil, and the oil products to be detected were qualitatively judged through the NMDS analysis method.
It has achieved rapid screening and attribute identification of crude oil and fuel oil, improved regulatory efficiency and customs clearance efficiency, and can effectively distinguish crude oil and fuel oil.
Smart Images
Figure CN119845893A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of oil product detection, and in particular, relates to a method for identifying crude oil and fuel oil based on non-metric multidimensional scaling transformation analysis. Background Art
[0002] The distinction between crude oil and fuel oil is mainly focused on the source and use. It is difficult to directly identify the commodity classification attributes based on the test results, which restricts the supervision capacity and customs clearance time of imported refining raw materials. Not only can it not effectively distinguish falsely declared goods, but it also affects the customs clearance efficiency of normal trade. Since refining raw materials usually have large customs clearance batches and high transportation costs, customs clearance efficiency has had an adverse impact on the production of related companies and has attracted concerns from many parties. Therefore, it is urgent to establish a rapid screening method for the identification of the attributes of such commodities to achieve effective and rapid supervision of related products.
[0003] At present, different forms of crude oil fingerprint databases have been established internationally and in my country, but most of them are for the purpose of oil spill pollution monitoring. Since the oil spill monitoring sample is an oil-water mixture, which is different from the properties of crude oil, the crude oil fingerprint database for the purpose of oil spill pollution monitoring cannot be directly used for the identification of crude oil sources for the purpose of supervision. At present, in my country, only Sun Jian et al. proposed to use the chromatographic fingerprint feature method to identify the source of crude oil for the purpose of import supervision. However, in practical applications, people have also found that chromatography has many problems. Chromatographic detection usually takes a long time, and the heavy components in crude oil are also easy to clog the capillary chromatographic column, which increases the cost of consumables. In addition, the fingerprint information usually extracted by chromatography does not reflect its main components and cannot be used to infer other physical and chemical properties of crude oil.
[0004] Although fingerprint data can be certain compounds or classes of compounds in complex organic mixtures, they are more derived from the special physical and chemical characterization of the mixture. Common physical and chemical characterization detection methods include spectroscopy, chromatography, and mass spectrometry. Chromatography is accurate in characterization, has a low detection limit, and has good reproducibility. It is a major source of fingerprint data for complex organic mixtures. The combination of mass spectrometry and chromatography can more accurately characterize the chemical composition of organic mixtures, and is also an important source for studying fingerprints of organic mixtures.
[0005] In the field of oil testing, the method of predicting the quality of finished oil using mid-infrared absorption spectroscopy has gradually matured, especially in predicting the physical and chemical properties of gasoline, which has been commercially promoted and applied. In the direction of crude oil source identification, there are also many application examples of chemical fingerprints, but no research and application of chemical fingerprints in crude oil and fuel oil identification has been seen. Summary of the invention
[0006] The object of the present invention is to provide a method for identifying crude oil and fuel oil based on non-metric multidimensional scaling analysis (NMDS) to make up for the shortcomings of the prior art.
[0007] To achieve the above object, the present invention is achieved through the following technical solutions:
[0008] A method for identifying crude oil and fuel oil based on non-metric multidimensional scaling analysis comprises the following steps:
[0009] (1) Select crude oil and fuel oil samples from various known sources and collect infrared absorption spectrum data;
[0010] (2) Based on the infrared spectrum data collected, 19 candidate characteristic absorption peaks were determined as candidate characteristic values in the mid-infrared wavelength range, as shown in Table 3; based on the difference in background absorption intensity of coal tar and fuel oil in different wavenumber ranges, the average background absorption intensity of three wavenumber ranges was selected as the candidate characteristic value, as shown in Table 4;
[0011] (3) Constructing infrared fingerprint database of crude oil and fuel oil samples based on alternative feature values;
[0012] (4) Using the NMDS analysis method, the fingerprint data of different oil products are compressed into two-dimensional data and plotted;
[0013] (5) Perform infrared spectroscopy on the unknown oil samples to be tested to obtain infrared absorption spectra, and then make qualitative judgments on the unknown oil samples based on the extraction method of the established infrared fingerprint database of crude oil and fuel oil and the NMDS two-dimensional projection method.
[0014] Furthermore, in step (1), a Fourier transform infrared spectrometer (Necolet 380) is used to detect the infrared absorption spectrum of the sample. In the instrument parameter setting, the resolution is 2 cm-1 and the wave number measurement range is 4000-650 cm -1 The scanning number is 16 times. The instrument detection wavelength is calibrated by standard wavelength polystyrene film. The sample preparation method is attenuated total reflection (ATR) method, the refractive crystal is ZnSe, and the refractive angle is 45°.
[0015] Furthermore, in step (2), Table 3 and Table 4 are as follows:
[0016] Table 3 Absorption peaks as alternative characteristic values
[0017]
[0018]
[0019] Table 4 Background ranges for alternative feature values
[0020]
[0021] Furthermore, in the step (4), an infrared fingerprint database of crude oil and fuel oil samples is constructed based on the extracted feature quantities, and based on the Bray-Curtis similarity matrix, the fingerprint data feature values of different oil products are compressed into two-dimensional data according to the similarity ordinal number, and the NMDS projection diagram of the sample on the two-dimensional plane is drawn to intuitively reflect the similarity degree of different oil products.
[0022] Compared with the prior art, the present invention has the following beneficial effects:
[0023] The present invention adopts infrared spectroscopy technology and establishes a fast and effective crude oil and fuel oil attribute identification screening method based on the crude oil and fuel oil fingerprint database. The present invention uses Fourier transform infrared spectroscopy and attenuated total reflection as data acquisition means, extracts 19 infrared absorption spectrum feature quantities and 3 background spectrum data on the basis of statistical theory of feature quantities and the relationship between chemical bonds and infrared absorption features, and constructs a crude oil and fuel oil infrared fingerprint database with infrared absorption spectrum feature quantities as fingerprint features; and identifies crude oil and fuel oil commodity attributes based on NMDS analysis.
[0024] The verification results show that this method can effectively identify crude oil and normal fuel oil, and can be used for rapid screening of crude oil and fuel oil commodity attribute identification. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 This is the infrared absorption spectrum of crude oil (Hungo).
[0026] Figure 2 These are the infrared absorption spectra of three typical fuel oils.
[0027] Figure 3 It is the data processing flow of the chemometric method for infrared fingerprint data.
[0028] Figure 4 This is the NMDS analysis result diagram of the infrared fingerprint database of crude oil and fuel oil.
[0029] Figure 5 This is the NMDS analysis result diagram of the unknown sample (Note: ○ is a known type of crude oil; ● is an unknown type of crude oil; □ is a known type of fuel oil; ■ is an unknown type of fuel oil). DETAILED DESCRIPTION
[0030] The technical solution of the present invention is further described below in conjunction with embodiments.
[0031] Example 1
[0032] A method for identifying crude oil and fuel oil based on non-metric multidimensional scaling analysis, the basic process is as follows Figure 3 As shown, the specific steps include:
[0033] 1. Use Fourier transform infrared spectrometer (Necolet 380) to detect the infrared absorption spectrum of the sample. In the instrument parameter setting, the resolution is 2cm-1 and the wave number measurement range is 4000~650cm -1 , the number of scans was 16 times. The instrument detection wavelength was calibrated by a standard wavelength polystyrene film. The sample preparation method was the attenuated total reflection (ATR) method, the refractive crystal was ZnSe, and the refractive angle was 45°. Before each test, the background was collected and the background value was subtracted. Figure 1 This is the infrared absorption spectrum of crude oil (Hungo) collected. Figure 2 These are three typical infrared absorption spectra of fuel oil collected.
[0034] 2. Selection of fingerprint database samples
[0035] The crude oil fingerprint database samples are derived from the normal import of crude oil, and a total of 48 crude oil varieties are collected, including 27 countries from Africa, the Middle East, America, Europe, Southeast Asia and Oceania. The crude oil varieties and origins are shown in Table 1.
[0036] Table 1 Crude oil varieties and origins in the infrared spectral fingerprint database
[0037]
[0038]
[0039] The samples of the fuel oil fingerprint database come from fuel oil that is normally imported and sold in the domestic market. A total of 13 types of fuel oil are collected, and their sources include China, Russia, Australia, etc. The sources and identification of the samples in the fingerprint database are shown in Table 2.
[0040] Table 2 Fuel oil sources in the infrared spectral fingerprint database
[0041]
[0042] 3. Alternative feature value and feature quantity selection method
[0043] Each sample is between 4000 and 650 cm -1There are 3481 data points within the wavenumber measurement range. If these data points are not selected, it will not only increase the difficulty of data processing, but also cover up the differences between the infrared absorption characteristic spectra of different crude oils due to accidental measurement errors. Therefore, based on the absorption characteristics of crude oil and fuel oil in the mid-infrared wavelength range, 19 alternative characteristic absorption peaks were determined as alternative characteristic values (see Table 3). In addition, since the background absorption intensity of different oil products in different wavenumber ranges is different, in order to reduce the source of error between samples, the average background absorption intensity of 3 wavenumber ranges was selected as the alternative characteristic value (see Table 4) according to the difference in background absorption intensity of coal tar and fuel oil in different wavenumber ranges.
[0044] Table 3 Absorption peaks as alternative characteristic values
[0045]
[0046]
[0047] Table 4 Background ranges for alternative feature values
[0048]
[0049] 4. Establishment of infrared fingerprint database and chemometric method
[0050] Based on the extracted features, an infrared fingerprint database of crude oil and fuel oil samples was constructed. Based on the Bray-Curtis similarity matrix, the fingerprint data feature values of different oil products were compressed into two-dimensional data according to the similarity ordinal number, and the NMDS projection diagram of the samples on the two-dimensional plane was drawn (see Figure 5 ) in order to intuitively reflect the similarity of different oil products.
[0051] 4.1 Establishing infrared fingerprint database
[0052] The infrared fingerprint data of each crude oil and fuel oil sample are extracted using the feature values selected in Table 3 and the background range feature values in Table 4 to construct a database (Table 5 and Table 6). The optimal two-dimensional projection coordinates are calculated according to the NMDS method (Table 7).
[0053]
[0054]
[0055]
[0056]
[0057]
[0058] Table 7 NMDS optimal two-dimensional projection data
[0059]
[0060]
[0061] Result analysis:
[0062] The characteristic quantities of infrared spectrum data of crude oil and fuel oil in the database are extracted, and the non-parametric multivariate statistical method NMDS is used to obtain the optimal two-dimensional coordinate values of non-scalar quantity after parameter dimension compression (see Table 7), and the distribution diagram is drawn (with rotation, see Figure 4 ). The pressure value of the NMDS scalar-free optimal two-dimensional distribution map is only 0.09, indicating that the NMDS scalar-free optimal two-dimensional distribution map can represent more than 90% of the information of all characteristic peaks, that is, the information loss is less than 10%. Analysis of the distribution of different types of oil products in the NMDS two-dimensional distribution map shows that there is obvious aggregation between fuel oils, and most of the samples are close in two-dimensional space. Only the fuel oil numbered 1710 is far away from other fuel oils, but it is farther away from crude oil and will not be confused with crude oil. Most crude oils have a high degree of aggregation, but there are three types of crude oils that are far away from other crude oils. Among them, the Sakhalin Vityaz Blend crude oil originating in the Sakhalin region of Russia and the Basrahlight nigerian crude oil in Iraq are far away from other crude oils and fuel oil in the NMDS two-dimensional distribution map because their density is much lower than other crude oils, so they are far away from other crude oils and even farther away from fuel oil, so they will not be confused with crude oil. Dar crude oil originating from the Republic of Sudan and Mar crude oil originating from Malaysia are special types of heavy crude oil with high density, high viscosity and poor fluidity. These two types of crude oil are close to conventional fuel oil, but it can be seen from the NMDS two-dimensional distribution map that their expansion direction is inconsistent with conventional fuel oil and they are easy to distinguish from fuel oil after training. The above results show that it is feasible to use NMDS analysis to screen the commodity attributes of oil products.
[0063] Embodiment 2:
[0064] This embodiment uses the extraction method of the infrared fingerprint database of crude oil and fuel oil and the NMDS two-dimensional projection method established in Embodiment 1 to perform qualitative identification on unknown oil samples and verify the applicability of the infrared fingerprint database and the measurement method.
[0065] Four samples of unknown crude oil and four samples of fuel oil of unknown origin were selected as blind samples (see Table 8). Based on the infrared fingerprint database established in this study and the proposed measurement method, qualitative discriminant analysis was performed on the blind samples to verify the applicability of the infrared fingerprint database and measurement method. Figure 4 , Figure 5 .
[0066] Table 8 Blind sample sources
[0067]
[0068] Depend on Figure 5 It can be seen that the four unknown crude oils and the crude oil samples in the database are all located in area A and intersect with each other; the four unknown fuel oils and the fuel oil samples in the database are all located in area B. This shows that the NMDS analysis of infrared spectral data can identify the above eight different types of crude oil and fuel oil from different sources.
[0069] The above analysis shows that in the result verification, all unknown crude oil and fuel oil samples of unknown origin can be correctly distinguished, and NMDS analysis can more intuitively determine the commodity attributes of unknown samples.
[0070] Finally, although this specification is described according to implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.
Claims
1. A method for identifying crude oil and fuel oil based on non-metric multidimensional scaling analysis, characterized in that: The steps include: (1) Select crude oil and fuel oil samples from various known sources and collect infrared absorption spectrum data; (2) According to the infrared spectrum data obtained, the absorption characteristics in the mid-infrared wavelength range are used to determine the candidate characteristic absorption peaks as the candidate characteristic values, see Table 3; according to the difference in the background absorption intensity of coal tar and fuel oil in different wave number ranges, the candidate characteristic values are selected, see Table 4; (3) Constructing infrared fingerprint database of crude oil and fuel oil samples based on alternative feature values; (4) Using the NMDS analysis method, the fingerprint data of different oil products are compressed into two-dimensional data and plotted; (5) Perform infrared spectroscopy on the unknown oil samples to be tested to obtain infrared absorption spectra, and then make qualitative judgments on the unknown oil samples based on the extraction method of the established infrared fingerprint database of crude oil and fuel oil and the NMDS two-dimensional projection method.
2. The method for identifying crude oil and fuel oil according to claim 1, characterized in that: In the step (1), a Fourier transform infrared spectrometer is used to detect the infrared absorption spectrum of the sample. In the instrument parameter setting, the resolution is 2 cm -1 , wave number measurement range is 4000~650cm -1 The instrument detection wavelength is calibrated by standard wavelength polystyrene film, the sample preparation method is attenuated total reflection method, the refractive crystal is ZnSe, and the refractive angle is 45°.
3. The method for identifying crude oil and fuel oil according to claim 1, characterized in that: Table 3 in step (2) is as follows: Table 3 Absorption peaks as alternative characteristic values 4. The method for identifying crude oil and fuel oil according to claim 1, characterized in that: Table 4 in step (2) is as follows: Table 4 Background ranges for alternative feature values 5. The method for identifying crude oil and fuel oil according to claim 1, characterized in that: In the step (4), an infrared fingerprint database of crude oil and fuel oil samples is constructed based on the extracted feature quantities, and based on the Bray-Curtis similarity matrix, the fingerprint data feature values of different oil products are compressed into two-dimensional data according to the similarity ordinal numbers, and the NMDS projection diagram of the samples on the two-dimensional plane is drawn.