Method and system for raman spectroscopy of fuel

Abstract

The application relates to the technical field of fuel detection, and discloses a fuel Raman spectrum determination method and system. The method is applied to a fuel Raman spectrum determination system, the system comprises a Raman spectrum determination module, a reference determination module and a dynamic automatic modeling software module; the method comprises the following steps: collecting Raman spectrum data of fuel through the Raman spectrum determination module; performing calculation analysis on the Raman spectrum data of the fuel through the dynamic automatic modeling software module, obtaining an evaluation result, and determining whether the evaluation result meets the standard; if the evaluation result does not meet the standard, corresponding fuel is transported to the reference determination module to determine on-site measured data of the fuel; if the evaluation result meets the standard, the corresponding fuel is updated into standard fuel in a standard fuel library. Through the method, intelligent testing of fuel Raman spectrum is realized.

Description

Technical Field

[0001] This invention relates to the field of fuel detection technology, and in particular to a method and system for measuring fuel Raman spectroscopy. Background Technology

[0002] Raman spectroscopy has strong analytical and characterization capabilities for various aromatic compounds, substituent groups, and highly branched components in petroleum products. It can reflect the fingerprint information of different organic groups and obtain characteristic information such as their molecular structure and physicochemical properties. Based on this, qualitative and quantitative analysis of petroleum products can be performed.

[0003] Significant progress has been made in the fundamental research and application of Raman spectroscopy in oil analysis. In the petroleum industry, Raman spectroscopy is primarily used to analyze the aromatic and olefin composition of hydrocarbon mixtures. Currently, the application of Raman fingerprinting technology in fuel oil is experiencing rapid development. The combination of Raman detection and fingerprinting technology will bring tremendous application value in areas such as species identification, index prediction, online detection, and fuel quality monitoring and management.

[0004] Currently, Raman spectroscopy is applied to the identification of fuel types and grades, as well as the analysis of oil quality. The main methods used are characteristic peak identification and chemometrics-based methods. Furthermore, online detection has become a hot application area for Raman spectroscopy, for example, in real-time monitoring of material changes during production.

[0005] Raman spectroscopy is a technique that combines sophisticated instruments and data analysis. It demands strict adherence to operational, instrument, and parameter selection requirements. Otherwise, even subtle differences in the spectra can affect subsequent data analysis. Currently, quantitative analysis methods based on chemometrics are all "secondary" analytical methods. The basic process involves establishing a multivariate calibration model between the spectral characteristics of the sample and data measured using national or industry standard methods to rapidly predict and analyze unknown samples. The core work lies in the establishment and maintenance of the multivariate calibration model, which is typically performed by specialized companies or technical personnel and requires a large number of standard samples with reliable indicators. For users, this is akin to a "black box," as they cannot know or intervene in the analytical model, requiring significant human and material resources. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system for measuring fuel Raman spectroscopy.

[0007] This invention provides the following technical solution:

[0008] In a first aspect, this disclosure provides a method for determining fuel oil Raman spectroscopy, applied to a fuel oil Raman spectroscopy system, the system including a Raman spectroscopy module, a reference determination module, and a dynamic automatic modeling software module; the method includes:

[0009] Raman spectral data of fuel oil were acquired using the Raman spectroscopy measurement module.

[0010] The dynamic automatic modeling software module is used to calculate and analyze the Raman spectral data of the fuel to obtain evaluation results and determine whether the evaluation results meet the standards.

[0011] If the standard is not met, the corresponding fuel will be sent to the reference measurement module to measure the on-site measured data of the fuel.

[0012] If the standard is met, the corresponding fuel will be updated to the standard fuel in the standard fuel tank.

[0013] Furthermore, the evaluation results obtained by calculating and analyzing the Raman spectral data of the fuel through the dynamic automatic modeling software module include:

[0014] A standard sample set was selected by analyzing fingerprint similarity.

[0015] Based on the standard sample set, a dynamically optimized multivariate calibration model was established using a chemometrics software system;

[0016] The fuel is input into the dynamically optimized multivariate correction model to obtain the quantitative analysis results of the fuel.

[0017] The confidence level of the quantitative analysis results of the fuel was evaluated to obtain the corresponding evaluation results.

[0018] Furthermore, the step of selecting a standard sample set through fingerprint similarity analysis includes:

[0019] Set a similarity threshold and calculate the similarity between the fuel and all standard fuels in the standard fuel database;

[0020] A standard sample set is formed by combining all standard oil samples with a similarity greater than or equal to the aforementioned similarity threshold.

[0021] Further, calculating the similarity between the fuel and all standard fuels in the standard fuel database includes:

[0022] Spectral characteristic curves were established based on the Raman spectral data of all fuels, and the spectral characteristic curves were then subjected to linear transformation.

[0023] Based on the transformed spectral characteristic curves, the similarity of the spectral characteristics of all fuels is compared to obtain the similarity between the fuel and all standard fuels in the standard fuel database.

[0024] Furthermore, the step of establishing a dynamically optimized multivariate calibration model based on the standard sample set using a chemometrics software system includes:

[0025] Based on the aforementioned standard sample set, a quantitative prediction multivariate correction model is established;

[0026] The effectiveness of the quantitative prediction multivariate correction model is evaluated using an interactive verification method, and a dynamically optimized multivariate correction model is established.

[0027] Furthermore, the evaluation of the effectiveness of the quantitative prediction multivariate correction model using an interactive verification method, and the establishment of a dynamically optimized multivariate correction model, includes:

[0028] The effective component chemical information in the Raman spectral data of the fuel was extracted using partial least squares method, and the functional relationship between the Raman spectral data of the fuel and the Raman spectral data of the standard fuel was obtained, and a dynamically optimized multivariate correction model was established.

[0029] Furthermore, the field-measured data includes distillation range, density, closed-cup flash point, and cetane index.

[0030] Secondly, this disclosure provides a fuel Raman spectroscopy measurement system, the system including a Raman spectroscopy measurement module, a reference measurement module, and a dynamic automatic modeling software module;

[0031] The reference measurement module is used to measure the field-tested data of fuel.

[0032] The Raman spectroscopy module is used to collect the Raman spectral data of the fuel.

[0033] The dynamic automatic modeling software module is used to calculate and analyze the Raman spectral data of the fuel, obtain evaluation results, and determine whether the evaluation results meet the standards.

[0034] The dynamic automatic modeling software module is also used to transport substandard fuel to the reference measurement module for reference measurement, and update the compliant fuel to the standard fuel in the standard fuel database.

[0035] Furthermore, the reference measurement module includes a micro-distillation range measurement submodule and a density measurement submodule;

[0036] The micro-distillation range determination submodule is used to determine the distillation characteristics of the fuel oil and obtain the distillation range of the fuel oil.

[0037] The density measurement submodule is used to measure the density of the fuel and obtain the density of the fuel.

[0038] Furthermore, the reference determination module also includes a closed-cup flash point calculation submodule and a cetane index calculation submodule;

[0039] The closed-cup flash point calculation submodule is used to calculate the closed-cup flash point of the fuel based on the measurement data from the micro-distillation range determination submodule.

[0040] The cetane index calculation submodule is used to calculate the cetane index of the fuel based on the measurement data from the micro-distillation range determination submodule and the density determination submodule.

[0041] The embodiments of this application have the following advantages:

[0042] The fuel Raman spectroscopy determination method provided in this application is applied to a fuel Raman spectroscopy determination system. The system includes a Raman spectroscopy determination module, a reference determination module, and a dynamic automatic modeling software module. The method includes: acquiring Raman spectral data of the fuel through the Raman spectroscopy determination module; calculating and analyzing the Raman spectral data of the fuel through the dynamic automatic modeling software module to obtain an evaluation result, and determining whether the evaluation result meets the standard; if it does not meet the standard, the corresponding fuel is sent to the reference determination module to measure the on-site measured data of the fuel; if it meets the standard, the corresponding fuel is updated to standard fuel in the standard fuel database. This method achieves intelligent testing of fuel Raman spectra.

[0043] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the various drawings, similar components are numbered similarly.

[0045] Figure 1 A flowchart of a fuel Raman spectroscopy method provided in an embodiment of this application is shown;

[0046] Figure 2 A flowchart of another fuel Raman spectroscopy method provided in this application embodiment is shown;

[0047] Figure 3 A flowchart of a similarity algorithm provided in an embodiment of this application is shown;

[0048] Figure 4 A schematic diagram of a fuel Raman spectroscopy measurement system provided in an embodiment of this application is shown.

[0049] Explanation of key component symbols:

[0050] 10-Reference determination module; 11-Micro distillation range determination submodule; 12-Density determination submodule; 13-Closed-cup flash point calculation submodule; 14-Cetane index calculation submodule; 20-Raman spectroscopy determination module; 30-Dynamic automatic modeling software module. Detailed Implementation

[0051] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0052] It should be noted that when an element is said to be "fixed" to another element, it can be directly on the other element or there may be an intervening element. When an element is said to be "connected" to another element, it can be directly connected to the other element or there may be an intervening element. Conversely, when an element is said to be "directly" on another element, there is no intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0053] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0054] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the template description is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0056] Example 1

[0057] like Figure 1 The diagram shown is a flowchart of a fuel Raman spectroscopy method according to an embodiment of this application. The fuel Raman spectroscopy method provided in this embodiment includes the following steps:

[0058] Step S110: The Raman spectral data of the fuel are acquired by the Raman spectroscopy measurement module.

[0059] In the embodiments of this application, the analysis object is fuel products among petroleum products, such as automotive gasoline, automotive diesel, military diesel, jet fuel, etc., which are essentially a mixture of organic substances.

[0060] Specifically, a near-infrared laser is selected as the Raman excitation source, that is, a semiconductor laser with a wavelength of 785nm is used, which is in the near-infrared wavelength range and can suppress the fluorescence effect of fuel to a certain extent. Then, the Raman spectroscopy measurement module is used to collect the Raman spectral data of fuel by combining software Raman fluorescence interference removal methods such as curve fitting.

[0061] The specific Raman spectroscopy data acquisition method is as follows: First, each fuel sample is numbered. 0.75 mL of fuel is drawn using a syringe and stored in a 2 mL glass bottle, which is then immediately sealed. Subsequently, Raman spectroscopy data is acquired sequentially from each 2 mL bottle. Before each acquisition, the bottle is shaken and then placed in a dark chamber for 30 seconds to allow the internal liquid flow to stabilize before starting the Raman spectroscopy data acquisition. Data is acquired three times consecutively for each sample. After one acquisition of the same sample, it is removed from the dark chamber, the bottle is shaken, and then placed back in the dark chamber for 30 seconds before the next data acquisition.

[0062] It should be noted that the purpose of shaking the glass bottle and then placing it in the dark chamber is to ensure that the sample is mixed evenly and to avoid separation of the sample due to prolonged standing. Since there may be residue interference if the measurement is performed immediately after shaking, it is necessary to let it stand for 30 seconds to allow any obvious residue to settle, so as not to affect the measurement of the sample's Raman spectrum data.

[0063] By measuring the Raman spectral data of fuel, and then calculating and analyzing the results, evaluation results are obtained, enabling users to perform dynamic automatic modeling without interference.

[0064] Step S120: The Raman spectral data of the fuel are calculated and analyzed by the dynamic automatic modeling software module to obtain the evaluation result and determine whether the evaluation result meets the standard.

[0065] Furthermore, a similarity analysis is performed on the Raman spectral data of the fuel to select a standard sample set, and then a dynamically optimized multivariate calibration model is established. The quantitative analysis results of the fuel to be tested are achieved through the dynamically optimized multivariate calibration model. These results determine whether the fuel to be tested needs to undergo reference testing in the reference determination module, and the standard oil sample library is dynamically and automatically updated accordingly.

[0066] In one alternative implementation, such as Figure 2 As shown, step S120 further includes:

[0067] Step S121: Select a standard sample set through fingerprint similarity analysis;

[0068] Step S122: Based on the standard sample set, a dynamically optimized multivariate calibration model is established using a chemometrics software system;

[0069] Step S123: Input the fuel into the dynamically optimized multivariate correction model to obtain the quantitative analysis results of the fuel;

[0070] Step S124: Analyze the confidence level of the quantitative analysis results of the fuel to obtain the corresponding evaluation results.

[0071] First, a certain similarity threshold is set, such as 0.95, 0.97, or 0.98; the comparison in this application is not limited to this. A spectral characteristic curve is established based on the Raman spectral data of the fuel. Generally, the fingerprint region of the Raman spectrum is collected, i.e., 600 cm⁻¹. -1 ~1600cm -1 The spectral feature curves are then subjected to linear transformations such as rotation, translation, and scaling. Based on the linearly transformed spectral feature curves, the similarity of the spectral features of all fuels is compared to obtain the similarity between the fuel and all standard fuels in the standard fuel database. Standard fuels with a similarity not less than a set similarity threshold are selected to form a preferred standard sample set.

[0072] Secondly, a quantitative prediction multivariate correction model was established based on the standard sample set, and partial least squares (PLS) was used to extract the effective component chemical information from the fuel Raman spectral data, thus obtaining the functional relationship between the fuel Raman spectral data and the standard fuel Raman spectral data. It is understood that the Raman spectral data contains both the effective component chemical information to be analyzed and information on interfering components such as instrument or environmental factors. Partial least squares, a method combining principal component analysis and multiple linear regression, can effectively extract the effective component chemical information and eliminate interfering component information.

[0073] like Figure 3 As shown, the least squares method not only orthogonally decomposes the matrix X of the Raman spectrum of the test fuel, but also orthogonally decomposes the matrix Y of the Raman spectrum of the standard fuel at the same time as decomposing X. In this way, the principal components of the matrix Y of the Raman spectrum of the standard fuel are also separated.

[0074] X = USV T =U * S * V T* +E X =T * V T* +E X (1)

[0075] Y = PGQ T =P * G * Q T* +E Y =R * Q T* +E Y (2)

[0076] T * The Raman spectrum matrix X of the fuel to be tested was obtained by decomposition.

[0077] R * The Raman spectral matrix Y of standard fuel was obtained through decomposition.

[0078] U: The matrix that performs singular decomposition on matrix X, whose column vectors form a set of orthogonal basis vectors for input to matrix X;

[0079] S: Diagonal matrix, i.e., the singular values ​​of the X matrix;

[0080] V: The matrix into which the singular decomposition of matrix X is performed, and whose column vectors form a set of orthogonal basis vectors for the input of matrix X;

[0081] Ex: represents the spectral data matrix X, the residual matrix after principal component decomposition;

[0082] P: The matrix from which the singular decomposition of the Y matrix is ​​performed, and whose column vectors form a set of orthogonal basis vectors for the input of the Y matrix;

[0083] G: Diagonal matrix, i.e., the singular values ​​of the Y matrix;

[0084] Q: The column vectors of the matrix Y, which are the result of singular decomposition of the matrix Y, form a set of orthogonal basis vectors for the input of the matrix Y.

[0085] E Y : Represents the residual matrix after principal component decomposition of the spectral data matrix Y;

[0086] In equations (1) and (2) above, the matrix T is obtained by decomposing the Raman spectrum matrix X of the fuel to be tested. * And matrix R obtained by decomposing the Raman spectrum matrix Y of standard fuel * This represents information about the response and target indicators after removing instrument or environmental influences. Furthermore, matrix T is considered during the simultaneous decomposition. * sum matrix R * The linear relationship applied between them means that when decomposing matrix X, factors of matrix Y are considered, and when decomposing matrix Y, factors of matrix X are considered. This interactive verification and mutual influence, through iterative exchange of iteration vectors, merges the two decomposition processes into one. Therefore, through the regression analysis using the partial least squares method described above, the predictive analysis objective is achieved, and a dynamically optimized multivariate correction model is established.

[0087] Furthermore, the fuel to be tested is input into the dynamically optimized multivariate calibration model to obtain the quantitative analysis results of the fuel. The confidence level of the quantitative analysis results of the fuel sample is evaluated, and based on the evaluation results, it is determined whether the fuel to be tested should be subjected to reference determination analysis by the reference determination module, and the standard fuel sample library is dynamically and automatically updated accordingly.

[0088] In step S130, if the standard is not met, the corresponding fuel is sent to the reference measurement module to measure the on-site measured data of the fuel.

[0089] When the evaluation result fails to meet the standard, the corresponding fuel is sent to the reference measurement module to measure the on-site measured data of the fuel.

[0090] Specifically, the field-measured data of the fuel oil include distillation range, density, closed-cup flash point, and cetane index, etc. Correspondingly, the reference measurement module includes a micro-distillation range measurement submodule, a density measurement submodule, a closed-cup flash point calculation submodule, and a cetane index operator module. The distillation range of the fuel oil is determined by the micro-distillation range measurement submodule, the density of the fuel oil is determined by the density measurement submodule, the closed-cup flash point of the fuel oil is determined by the closed-cup flash point calculation submodule, and the cetane index of the fuel oil is determined by the cetane index operator module.

[0091] Step S140: If the target is met, update the corresponding fuel to the standard fuel in the standard fuel tank.

[0092] When the evaluation result meets the standard, the corresponding fuel is updated to the standard fuel in the standard fuel library. It can be understood that the standard fuel can be used for similarity analysis of the fuel to be tested, thereby establishing a dynamically optimized multivariate correction model to obtain the quantitative analysis result of the fuel.

[0093] The fuel Raman spectroscopy determination method provided in this application is applied to a fuel Raman spectroscopy determination system. The system includes a Raman spectroscopy determination module, a reference determination module, and a dynamic automatic modeling software module. The method includes: measuring on-site measured data of the fuel through the reference determination module and acquiring Raman spectral data of the fuel through the Raman spectroscopy determination module; calculating and analyzing the Raman spectral data of the fuel through the dynamic automatic modeling software module to obtain an evaluation result and determine whether the evaluation result meets the standard; if it does not meet the standard, the corresponding fuel is sent to the reference determination module for measurement; if it meets the standard, the corresponding fuel is updated to standard fuel in the standard fuel database. This method achieves intelligent testing of fuel Raman spectra.

[0094] Example 2

[0095] like Figure 4 The diagram shown is a structural schematic of a fuel Raman spectroscopy measurement system according to an embodiment of this application. The system includes a Raman spectroscopy measurement module 20, a reference measurement module 10, and a dynamic automatic modeling software module 30.

[0096] The Raman spectroscopy measurement module 20 is used to collect the Raman spectral data of the fuel.

[0097] Specifically, a near-infrared laser is selected as the Raman excitation source, that is, a semiconductor laser with a wavelength of 785nm is used, which is in the near-infrared wavelength range and can suppress the fluorescence effect of fuel to a certain extent. Then, the Raman spectroscopy measurement module 20 is used to collect the Raman spectral data of fuel by combining software Raman fluorescence interference removal methods such as curve fitting.

[0098] The dynamic automatic modeling software module 30 is used to calculate and analyze the Raman spectral data of the fuel, obtain evaluation results, and determine whether the evaluation results meet the standards.

[0099] The dynamic automatic modeling software module 30 is also used to transport substandard fuel to the reference measurement module 10 for reference measurement, and update the compliant fuel to the standard fuel in the standard fuel database.

[0100] Furthermore, the Raman spectral data of the fuel are analyzed for similarity using the dynamic automatic modeling software module 30 to select a standard sample set and establish a dynamically optimized multivariate calibration model. The quantitative analysis results of the fuel under test are obtained through the dynamically optimized multivariate calibration model. The confidence level of the quantitative analysis results of the fuel sample is evaluated, and whether the results meet the standards is assessed. When the evaluation result does not meet the standards, the corresponding fuel is sent to the reference measurement module 10 to measure the on-site measured data of the fuel. When the evaluation result meets the standards, the corresponding fuel is updated to standard fuel in the standard fuel library. The standard fuel can be used for similarity analysis of the fuel under test, thereby establishing a dynamically optimized multivariate calibration model and obtaining the quantitative analysis results of the fuel.

[0101] The reference measurement module 10 is used to measure the field measured data of fuel oil; the reference measurement module includes a micro-distillation range measurement submodule 11, a density measurement submodule 12, a closed-cup flash point calculation submodule 13, and a cetane index calculation submodule 14;

[0102] The micro-distillation range determination submodule 11 is used to determine the distillation characteristics of the fuel oil to obtain its distillation range; the density determination submodule 12 is used to determine the density of the fuel oil to obtain its density; the closed-cup flash point calculation submodule 13 is used to calculate the closed-cup flash point of the fuel oil based on the measurement data from the micro-distillation range determination submodule; and the cetane index calculation submodule 14 is used to calculate the cetane index of the fuel oil based on the measurement data from the micro-distillation range determination submodule and the density determination submodule.

[0103] The fuel Raman spectroscopy measurement system provided in this application includes a Raman spectroscopy measurement module, a reference measurement module, and a dynamic automatic modeling software module. The reference measurement module is used to measure on-site measured data of the fuel. The Raman spectroscopy measurement module is used to collect the Raman spectral data of the fuel. The dynamic automatic modeling software module is used to calculate and analyze the Raman spectral data of the fuel, obtain evaluation results, and determine whether the evaluation results meet the standards. The dynamic automatic modeling software module is also used to send substandard fuel to the reference measurement module for reference measurement and update compliant fuel with standard fuel from the standard fuel database. Through this system, automatic dynamic modeling can be achieved without the need for professional personnel to update and maintain the model, realizing intelligent testing of fuel Raman spectra.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0105] In addition, the functional modules or sub-modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0106] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes: USB flash drive, external hard drive, and read-only memory.

[0107] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for Raman spectroscopy determination of fuel oil, characterized in that, An application is made in a fuel Raman spectroscopy determination system, the system comprising a Raman spectroscopy determination module, a reference determination module, and a dynamic automatic modeling software module; the method includes: Raman spectral data of fuel oil were acquired using the Raman spectroscopy measurement module. The dynamic automatic modeling software module is used to calculate and analyze the Raman spectral data of the fuel to obtain evaluation results and determine whether the evaluation results meet the standards. If the standard is not met, the corresponding fuel will be sent to the reference measurement module to measure the on-site measured data of the fuel. If the standard is met, the corresponding fuel will be updated to the standard fuel in the standard fuel tank.

2. The method for determining fuel oil using Raman spectroscopy according to claim 1, characterized in that, The evaluation results obtained by calculating and analyzing the Raman spectral data of the fuel through the dynamic automatic modeling software module include: A standard sample set was selected by analyzing fingerprint similarity. Based on the aforementioned standard sample set, a dynamically optimized multivariate calibration model is established using a chemometrics software system. The fuel is input into the dynamically optimized multivariate correction model to obtain the quantitative analysis results of the fuel. The confidence level of the quantitative analysis results of the fuel was evaluated to obtain the corresponding evaluation results.

3. The method for determining fuel oil using Raman spectroscopy according to claim 2, characterized in that, The standard sample set is selected through fingerprint similarity analysis, including: Set a similarity threshold and calculate the similarity between the fuel and all standard fuels in the standard fuel database; A standard sample set is formed by combining all standard oil samples with a similarity greater than or equal to the aforementioned similarity threshold.

4. The method for determining fuel oil using Raman spectroscopy according to claim 3, characterized in that, The calculation of the similarity between the fuel and all standard fuels in the standard fuel database includes: Spectral characteristic curves were established based on the Raman spectral data of all fuels, and the spectral characteristic curves were then subjected to linear transformation. Based on the transformed spectral characteristic curves, the similarity of the spectral characteristics of all fuels is compared to obtain the similarity between the fuel and all standard fuels in the standard fuel database.

5. The method for determining fuel oil Raman spectroscopy according to claim 2, characterized in that, The step of establishing a dynamically optimized multivariate calibration model based on the standard sample set using chemometrics software includes: Based on the aforementioned standard sample set, a quantitative prediction multivariate correction model is established; The effectiveness of the quantitative prediction multivariate correction model is evaluated using an interactive verification method, and a dynamically optimized multivariate correction model is established.

6. The method for determining fuel oil Raman spectroscopy according to claim 5, characterized in that, The method of evaluating the effectiveness of the quantitative prediction multivariate correction model using interactive verification to establish a dynamically optimized multivariate correction model includes: The effective component chemical information in the fuel Raman spectral data is extracted using partial least squares method to obtain the functional relationship between the fuel Raman spectral data and the standard fuel Raman spectral data, and a dynamically optimized multivariate correction model is established.

7. The method for determining fuel oil using Raman spectroscopy according to claim 1, characterized in that, The field-measured data include distillation range, density, closed-cup flash point, and cetane index.

8. A fuel Raman spectroscopy system, characterized in that, The system includes a Raman spectroscopy measurement module, a reference measurement module, and a dynamic automatic modeling software module; The reference measurement module is used to measure the field-tested data of fuel. The Raman spectroscopy module is used to collect the Raman spectral data of the fuel. The dynamic automatic modeling software module is used to calculate and analyze the Raman spectral data of the fuel, obtain evaluation results, and determine whether the evaluation results meet the standards. The dynamic automatic modeling software module is also used to transport substandard fuel to the reference measurement module for reference measurement, and update the compliant fuel to the standard fuel in the standard fuel database.

9. The fuel Raman spectroscopy system according to claim 8, characterized in that, The reference measurement module includes a micro-distillation range measurement submodule and a density measurement submodule; The micro-distillation range determination submodule is used to determine the distillation characteristics of the fuel oil and obtain the distillation range of the fuel oil. The density measurement submodule is used to measure the density of the fuel and obtain the density of the fuel.

10. The fuel Raman spectroscopy system according to claim 9, characterized in that, The reference determination module also includes a closed-cup flash point calculation submodule and a hexadecane index calculation submodule; The closed-cup flash point calculation submodule is used to calculate the closed-cup flash point of the fuel based on the measurement data from the micro-distillation range determination submodule. The cetane index calculation submodule is used to calculate the cetane index of the fuel based on the measurement data from the micro-distillation range determination submodule and the density determination submodule.

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