Method and device for rapidly analyzing naphtha molecular components

Abstract

The invention relates to the technical field of oil product analysis, and provides a method and a device for rapidly analyzing naphtha molecular components. The method comprises the following steps: receiving scanning spectrum data of target naphtha; correcting the scanning spectrum data by using the correction spectrogram data set; the correction spectrogram data set is obtained by scanning a plurality of naphtha samples by using a plurality of spectrometers under a preset detection condition, and the scanning spectrogram data is obtained by scanning target naphtha by using a first scanner in the plurality of spectrometers; from a preset naphtha molecular composition library, matching molecular compositions corresponding to the corrected scanning spectrum data, and obtaining predicted molecular properties of the target naphtha according to the molecular compositions, so as to determine macroscopic properties of the target naphtha; the naphtha molecular composition library is obtained by using a comprehensive two-dimensional gas chromatography technology. According to the embodiment of the invention, the analysis of fraction molecular components of the stone brain distillate oil and the characterization of the macroscopic physical property of the petroleum fraction can be accurately and efficiently realized.

Description

Technical Field

[0001] The present invention relates to the technical field of oil product analysis, and in particular to a method and device for rapid analysis of naphtha molecular components. Background Art

[0002] Petroleum fraction refers to a complex mixture of various types of hydrocarbons and non-hydrocarbon compounds that make up crude oil. Generally, the light fraction between the temperature at which crude oil begins to distill (initial distillation point) and 200°C (or 180°C) during atmospheric distillation is called gasoline fraction (also called light oil, naphtha, light distillate oil or naphtha fraction). Existing naphtha composition analysis mainly uses spectral technology to characterize the macroscopic physical properties of petroleum fractions, and uses one-dimensional gas chromatography technology to analyze the molecular composition of light distillate oil. However, due to the limitations of the operating environment, sample processing and separation time of one-dimensional gas chromatography analysis, it cannot be widely used in production sites, and there are many types of spectral instruments and one-dimensional gas chromatography analysis instruments without unified standards. When one-dimensional gas chromatography is used to analyze naphtha, it is impossible to analyze the components above C8 in detail, and the components above C8 can only be expressed in a lumped form, which has the problem of low analysis accuracy. Therefore, how to accurately and efficiently realize the analysis of the molecular components of the naphtha fraction and the characterization of the macroscopic physical properties of the petroleum fraction is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In view of the current operating environment, sample processing and separation time limitations of one-dimensional gas chromatography analysis, it cannot be widely used in production sites. In addition, there are many types of spectral instruments and one-dimensional gas chromatography analysis instruments, and there is no unified standard. When using one-dimensional gas chromatography to analyze naphtha fractions, it is impossible to analyze components above C8 in detail, and components above C8 can only be expressed in a lumped form, resulting in low analysis accuracy and efficiency. This scheme is proposed to overcome the above problems or at least partially solve the above problems.

[0004] On the one hand, the purpose of some embodiments of this specification is to provide a method for rapid analysis of naphtha molecular components, the method comprising:

[0005] receiving scanning spectrum data of target naphtha;

[0006] The scanned spectrum data is corrected using a correction spectrum data set; wherein the correction spectrum data set is obtained by scanning a plurality of naphtha samples under preset detection conditions using a plurality of spectrometers, and the scanned spectrum data is obtained by scanning a target naphtha using a first scanner among the plurality of spectrometers;

[0007] From a preset naphtha molecular composition library, the molecular composition corresponding to the corrected scanning spectral data is matched, and the predicted molecular properties of the target naphtha are obtained based on the molecular composition, thereby determining the macroscopic properties of the target naphtha; wherein the naphtha molecular composition library utilizes comprehensive two-dimensional gas chromatography technology to determine the monomer hydrocarbon components of a variety of naphtha samples, and is established based on the monomer hydrocarbon components of a variety of naphtha samples.

[0008] Furthermore, the correction spectrum data set includes at least first correction spectrum data and second correction spectrum data.

[0009] Furthermore, the calibration spectrum data set is constructed using the following steps:

[0010] Scanning at least one naphtha sample using a first spectrometer to obtain first calibration spectrum data;

[0011] Scanning at least one naphtha sample using a second spectrometer to obtain second calibration spectrum data;

[0012] A corresponding correction spectrum data set is constructed according to the first correction spectrum data and the second correction spectrum data.

[0013] Furthermore, the calibration spectrum data set is constructed using the following steps:

[0014] The calibration spectrum data set is constructed using the following steps, including:

[0015] Scanning at least one naphtha sample using a first spectrometer, sharpening the scan result, and obtaining first calibrated spectrum data;

[0016] Scanning at least one naphtha sample using a second spectrometer, sharpening the scan result, and obtaining second correction spectrum data;

[0017] A corresponding correction spectrum data set is constructed according to the first correction spectrum data and the second correction spectrum data.

[0018] Further, the first spectrometer and the second spectrometer are Fourier transform near infrared spectrometers;

[0019] The first spectrometer and the second spectrometer scan multiple naphtha samples under the following preset detection conditions:

[0020] Using transmission measurement method;

[0021] Scanning resolution no less than 8cm -1 ;

[0022] The scanning wave number range is 4000cm -1 ~10000cm-1 ;

[0023] The cumulative number of scans is at least 64.

[0024] Further, the use of the correction spectrum data set to correct the scanned spectrum data includes:

[0025] Selecting the spectrum data to be fitted corresponding to the scanned spectrum data from the first corrected spectrum data;

[0026] Determining fitting parameters according to the spectrum data to be fitted and the scanned spectrum data;

[0027] The scanned spectrum data is corrected according to the fitting parameters and the second corrected spectrum data.

[0028] Further, determining fitting parameters according to the spectrum data to be fitted and the scanned spectrum data includes:

[0029] According to the spectrum data to be fitted and the scanned spectrum data, the fitting parameters are determined using the following formula:

[0030]

[0031] Among them, X1 is the scanned spectrum data, n is the amount of spectrum data to be fitted, i is the ordinal number of the spectrum data to be fitted, ω 1i is the i-th fitting parameter, X 1i is the ith spectrum data to be fitted.

[0032] Furthermore, determining fitting parameters according to the spectrum data to be fitted and the scanned spectrum data further includes:

[0033] Establishing an objective function according to the spectrum data to be fitted and the second corrected spectrum data;

[0034] Iterating the objective function using a preset interval of iterative data volume;

[0035] The data amount of the spectrogram data to be fitted is determined according to the iterative data amount corresponding to the minimum iterative output result.

[0036] Furthermore, the objective function is established using the following formula:

[0037]

[0038] Among them, c m is the output value of the objective function, m is the iterative data amount of the spectrum data to be fitted, i is the ordinal number of the spectrum data to be fitted, is the i-th fitting parameter, is the i-th spectrum data corresponding to the i-th spectrum data to be fitted in the second correction spectrum data, X b It is the real spectrum data obtained by scanning the target naphtha using the second spectrometer.

[0039] Furthermore, the step of correcting the scanned spectrum data according to the fitting parameters and the second corrected spectrum data comprises:

[0040] According to the fitting parameters and the second correction spectrum data, the scanning spectrum data is corrected using the following formula:

[0041]

[0042] in, is the corrected scanning spectrum data, n is the data volume of the fitting parameter, i is the ordinal number of the fitting parameter, ω 1i is the i-th fitting parameter, X 2i is the i-th spectrum data corresponding to the i-th spectrum data to be fitted in the second correction spectrum data.

[0043] Further, the molecular composition corresponding to the calibrated scan spectrum data is matched from a preset naphtha molecular composition library, including:

[0044] Selecting the to-be-matched spectrum data corresponding to the scanned spectrum data from the second corrected spectrum data;

[0045] Determining matching parameters according to the spectrum data to be matched and the corrected scanning spectrum data;

[0046] Searching for a molecular composition to be matched corresponding to the spectrum data to be matched from a preset naphtha molecular composition library;

[0047] The molecular composition corresponding to the corrected scanning spectrum data is determined according to the matching parameters and the molecular composition to be matched.

[0048] Furthermore, matching parameters are determined according to the spectrum data to be matched and the corrected scan spectrum data, including:

[0049] According to the spectrum data to be matched and the corrected scan spectrum data, the matching parameters are determined using the following formula:

[0050]

[0051] in, is the corrected scanning spectrum data, l is the data volume of the matching parameter, j is the ordinal number of the matching parameter, ω 2j is the jth matching parameter, X 2jis the j-th spectrum data in the second correction spectrum data corresponding to the j-th spectrum data to be matched.

[0052] Furthermore, the determining of matching parameters according to the to-be-matched spectrum data and the corrected scan spectrum data further comprises:

[0053] Establishing an optimization function according to the to-be-matched spectrum data and the second corrected spectrum data;

[0054] Iterating the optimization function using a preset interval of iterative data volume;

[0055] The matching parameter is determined according to the amount of iterative data corresponding to the minimum iterative output result.

[0056] Furthermore, the optimization function is established using the following formula:

[0057]

[0058] Among them, p k is the output value of the optimization function, k is the iterative data amount of the spectrum data to be matched, i is the ordinal number of the spectrum data to be matched, is the i-th matching parameter, z i is the molecular composition of the i-th naphtha sample in the naphtha molecular composition library corresponding to the i-th spectrum data to be matched, z b The real molecular composition of the target naphtha was obtained by using comprehensive two-dimensional gas chromatography technology.

[0059] Further, according to the matching parameters and the molecular composition to be matched, determining the molecular composition corresponding to the corrected scanning spectrum data includes:

[0060] According to the matching parameters and the molecular composition to be matched, the molecular composition corresponding to the corrected scan spectrum data is determined using the following formula:

[0061]

[0062] Where z is the molecular composition of the target naphtha, l is the data volume of the matching parameter, and i is the ordinal number of the matching parameter. is the i-th matching parameter, z i is the molecular composition of the i-th naphtha sample corresponding to the i-th spectrum data to be matched in the naphtha molecular composition library.

[0063] Furthermore, the preset naphtha molecular composition library is established according to the following steps:

[0064] Using comprehensive two-dimensional gas chromatography technology, various naphtha samples were tested to obtain the monomer hydrocarbon components of various naphtha samples;

[0065] The naphtha molecular composition library is established according to monomer hydrocarbon components of a plurality of naphtha samples.

[0066] Further, the predicted molecular properties of the target naphtha are obtained according to the molecular composition, thereby determining the macroscopic properties of the target naphtha, including:

[0067] Determine the molecular structure group information corresponding to the molecular composition; the molecular structure group information at least includes the basic group and the combined group of the molecular structure;

[0068] Inputting the molecular structure group information into a preset molecular property prediction model to obtain predicted molecular properties of the target naphtha;

[0069] The predicted molecular properties are input into a preset molecular property mixing model to obtain the macroscopic properties of the target naphtha.

[0070] Furthermore, the preset molecular property prediction model is trained in the following manner:

[0071] Get historical naphtha analysis data;

[0072] The molecular structure group information in the historical naphtha analysis data is used as input, and the molecular property labels in the historical naphtha analysis data are used as output to construct training samples, and a supervised learning model is constructed based on the input and output;

[0073] The supervised learning model is trained using the training samples, and the trained supervised learning model is used as a molecular property prediction model.

[0074] Furthermore, in some embodiments, using the training samples to train the supervised learning model includes:

[0075] Dividing the training samples into a training set and a validation set;

[0076] Training the supervised learning model using the training set;

[0077] Input the molecular structure group information in the validation set into the trained supervised learning model;

[0078] Calculate the prediction accuracy based on the output of the supervised learning model and the molecular property labels in the validation set;

[0079] If the prediction accuracy is less than a set threshold, the supervised learning model is retrained until the prediction accuracy is not less than the set threshold.

[0080] Furthermore, the molecular property mixing model is constructed using the following formula:

[0081]

[0082]

[0083]

[0084]

[0085] Among them, ON Linear is a linear mixed submodel, ON NonLinear is the nonlinear correction submodel, h and g are the numbers of various compounds in different naphtha samples, and v h is the volume fraction of the hth compound, v g is the volume fraction of the g-th compound, ON h is the target property of the hth compound, ΔON hg is the target property of the hth compound after the hth compound is corrected based on the interaction between the hth compound and the gth compound, are the interaction parameters between the hth compound and the gth compound, respectively.

[0086] On the other hand, some embodiments of the present specification also provide a device for rapid analysis of naphtha molecular components, the device comprising:

[0087] A receiving module, used for receiving scanning spectrum data of target naphtha;

[0088] A correction module, used to correct the scanned spectrum data using a correction spectrum data set; wherein the correction spectrum data set is obtained by scanning a plurality of naphtha samples using a plurality of spectrometers under preset detection conditions, and the scanned spectrum data is obtained by scanning a target naphtha using a first scanner among the plurality of spectrometers;

[0089] A determination module is used to match the molecular composition corresponding to the corrected scanning spectral data from a preset naphtha molecular composition library, and obtain the predicted molecular properties of the target naphtha based on the molecular composition, thereby determining the macroscopic properties of the target naphtha; wherein the naphtha molecular composition library uses full two-dimensional gas chromatography technology to determine the monomer hydrocarbon components of a variety of naphtha samples, and is established based on the monomer hydrocarbon components of a variety of naphtha samples.

[0090] On the other hand, some embodiments of the present specification further provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, the instructions of the above method are executed.

[0091] On the other hand, some embodiments of the present specification further provide a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor of a computer device, the instructions of the above method are executed.

[0092] On the other hand, some embodiments of the present specification further provide a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor of a computer device, the instructions of the above method are executed.

[0093] One or more technical solutions provided by some embodiments of this specification have at least the following technical effects:

[0094] The embodiments of the present specification utilize a first scanner to scan the target naphtha to obtain scanning spectral data, and utilize multiple spectrometers to scan multiple naphtha samples under preset detection conditions to obtain a correction spectrum data set, and correct the scanning spectral data to improve the accuracy of the corrected scanning spectral data. On the basis of the corrected scanning spectral data, the monomer hydrocarbon components of multiple naphtha samples are determined by full two-dimensional gas chromatography technology, and a naphtha molecular composition library is established based on the monomer hydrocarbon components of multiple naphtha samples to determine the molecular composition corresponding to the corrected scanning spectral data. This can accurately and conveniently analyze the molecular components of light distillate oil fractions and characterize the macroscopic physical properties of petroleum fractions.

[0095] The above description is only an overview of the technical solutions of some embodiments of this specification. In order to more clearly understand the technical means of some embodiments of this specification, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of some embodiments of this specification more obvious and easy to understand, the specific implementation methods of some embodiments of this specification are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate some embodiments of this specification or technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or prior art descriptions. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0097] Figure 1 A schematic diagram of an implementation system of a method for rapid analysis of naphtha molecular components in some embodiments of this specification is shown;

[0098] Figure 2 A flow chart showing a method for rapid analysis of naphtha molecular components in some embodiments of this specification;

[0099] Figure 3 A schematic diagram of the steps of constructing a calibration spectrum data set in some embodiments of this specification;

[0100] Figure 4 A schematic diagram of the steps of processing first calibration spectrum data in some embodiments of this specification;

[0101] Figure 5 A schematic diagram of the steps of processing the second calibration spectrum data in some embodiments of this specification;

[0102] Figure 6 A schematic diagram of the steps of correcting scanning spectrum data in some embodiments of this specification;

[0103] Figure 7 A schematic diagram of the steps for determining fitting parameters in some embodiments of this specification;

[0104] Figure 8 A schematic diagram of the steps of matching the molecular composition corresponding to the scanned spectrum data after correction in some embodiments of this specification;

[0105] Fig. 9 A schematic diagram of the steps of determining matching parameters in some embodiments of this specification;

[0106] Fig.10 Schematic diagram of the steps of establishing a naphtha molecular composition library in some embodiments of this specification;

[0107] Fig.11 A schematic diagram of the steps for determining the macroscopic properties of target naphtha in some embodiments of this specification;

[0108] Fig.12 Schematic diagram of the steps of training a molecular property prediction model in some embodiments of this specification;

[0109] Fig.13 A schematic diagram of the steps of optimizing the molecular property prediction model in some embodiments of this specification;

[0110] Fig.14 This is a schematic diagram of the structure of a device for rapid analysis of naphtha molecular components in some embodiments of this specification;

[0111] Fig.15 This is a schematic diagram of the computer device structure provided in some embodiments of this specification.

[0112] [Description of Reference Numerals]

[0113] 101. Terminal;

[0114] 102. Server;

[0115] 1401, receiving module;

[0116] 1402, correction module;

[0117] 1403. Determine module;

[0118] 1502. Computer equipment;

[0119] 1504. Processor;

[0120] 1506. Memory;

[0121] 1508, driving mechanism;

[0122] 1510, input / output interface;

[0123] 1512. Input devices;

[0124] 1514. Output device;

[0125] 1516. Presentation equipment;

[0126] 1518. Graphical user interface;

[0127] 1520, network interface;

[0128] 1522. Communication link;

[0129] 1524. Communication bus. DETAILED DESCRIPTION

[0130] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in some embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on some embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0131] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of this article described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

[0132] like Figure 1 The schematic diagram of the implementation system of a method for rapid analysis of naphtha molecular components in an embodiment of the present invention is shown, which may include: a terminal 101 and a server 102, wherein the terminal 101 and the server 102 communicate with each other through a network, and the network may include a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof, and is connected to a website, a user device (such as a computing device), and a back-end system. A staff member may send a request for rapid analysis of naphtha molecular components to the server 102 through the terminal 101, and after receiving the request for rapid analysis of naphtha molecular components, the server 102 calls the data in the database for calculation and processing, obtains the analysis result, and sends the analysis result to the terminal 101, so that the staff member processes the business according to the analysis result.

[0133] In the embodiments of this specification, the server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms.

[0134] In an optional embodiment, the terminal 101 may include but is not limited to electronic devices such as self-service terminal devices, desktop computers, tablet computers, laptop computers, smart wearable devices, etc. Optionally, the operating system running on the electronic device may include but is not limited to Android system, IOS system, Linux, Windows, etc. Of course, the terminal 101 is not limited to the above-mentioned electronic devices with a certain entity, and it can also be software running in the above-mentioned electronic devices.

[0135] In addition, it should be noted that Figure 1 What is shown is only an application environment provided by the present disclosure. In actual application, multiple terminals 101 may be included, and this specification does not limit this.

[0136] Figure 2 This is a flow chart of a method for rapid analysis of naphtha molecular components provided by an embodiment of the present invention. This specification provides method operation steps as described in the embodiment or flow chart, but may include more or fewer operation steps based on conventional or non-creative work. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or device product is executed, it can be executed in the order or in parallel according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 2 As shown, applied to the above-mentioned server side, the method may include:

[0137] S201: receiving scanning spectrum data of target naphtha;

[0138] S202: Correcting the scanned spectrum data using a correction spectrum data set; wherein the correction spectrum data set is obtained by scanning a plurality of naphtha samples using a plurality of spectrometers under preset detection conditions, and the scanned spectrum data is obtained by scanning a target naphtha using a first scanner among the plurality of spectrometers;

[0139] S203: Match the molecular composition corresponding to the calibrated scanned spectral data from a preset naphtha molecular composition library, and obtain the predicted molecular properties of the target naphtha based on the molecular composition, thereby determining the macroscopic properties of the target naphtha; wherein the naphtha molecular composition library utilizes full two-dimensional gas chromatography technology to determine the monomer hydrocarbon components of a variety of naphtha samples, and is established based on the monomer hydrocarbon components of a variety of naphtha samples.

[0140] The embodiments of the present specification utilize a first scanner to scan the target naphtha to obtain scanning spectral data, and utilize multiple spectrometers to scan multiple naphtha samples under preset detection conditions to obtain a correction spectrum data set, and correct the scanning spectral data to improve the accuracy of the corrected scanning spectral data. On the basis of the corrected scanning spectral data, the monomer hydrocarbon components of multiple naphtha samples are determined by full two-dimensional gas chromatography technology, and a naphtha molecular composition library is established based on the monomer hydrocarbon components of multiple naphtha samples to determine the molecular composition corresponding to the corrected scanning spectral data. This can accurately and conveniently analyze the molecular components of light distillate oil fractions and characterize the macroscopic physical properties of petroleum fractions.

[0141] It can be understood that, in some embodiments, different spectrograms may be obtained by scanning the same naphtha sample using different spectrometers, and thus the obtained spectral data are also different. In order to achieve rapid analysis of the molecular components of the naphtha sample, the original spectral data of the target naphtha can be corrected using the established correction spectral data set to obtain the corrected spectral data of the target naphtha, thereby improving the accuracy of the scanned spectral data. In the embodiments provided by the present application, the molecular composition of the target naphtha is obtained from the established naphtha molecular composition library based on the corrected scanning spectral data of the target naphtha, and the molecular composition of the naphtha sample is directly calculated from the scanning spectrum of the naphtha sample, without the need to install a gas chromatograph at the production site, thereby saving a large amount of human resource costs and capital costs for purchasing equipment for the refinery, and having certain guiding significance for the formulation of subsequent naphtha processing plans.

[0142] Further, in some embodiments, the correction spectrum data set includes at least first correction spectrum data and second correction spectrum data.

[0143] See attached Figure 3 In some embodiments, the calibration spectrum data set is constructed using the following steps:

[0144] S301: Scanning at least one naphtha sample using a first spectrometer to obtain first calibration spectrum data;

[0145] S302: Scanning at least one naphtha sample using a second spectrometer to obtain second calibration spectrum data;

[0146] S303: Constructing a corresponding calibration spectrum data set according to the first calibration spectrum data and the second calibration spectrum data.

[0147] It can be understood that, in some embodiments, the first correction spectrum data is the spectrum data of multiple naphtha samples obtained by using the first spectrometer, and the second correction spectrum data is the spectrum data of multiple naphtha samples obtained by using the second spectrometer. In the specific implementation process, the first spectrometer and the second spectrometer can be near-infrared spectrometers or other spectrometers, which are not limited by the description of this specification. Naphtha samples can be collected from refineries and various types of naphtha produced by different raw materials and processes. Using the first correction spectrum data and the second correction spectrum data to construct a corresponding correction spectrum data set can reduce the error in the spectrum data, so as to improve the scanning accuracy of the target naphtha.

[0148] See attached Figure 4 In some embodiments, the calibration spectrum data set is constructed using the following steps:

[0149] S401: Scanning at least one naphtha sample using a first spectrometer, sharpening the scan result, and obtaining first calibrated spectrum data;

[0150] S402: Scanning at least one naphtha sample using a second spectrometer, sharpening the scan result, and obtaining second correction spectrum data;

[0151] S403: Constructing a corresponding calibration spectrum data set according to the first calibration spectrum data and the second calibration spectrum data.

[0152] It can be understood that, in some embodiments, the sharpening processing performed on the first corrected scanning spectrum data and / or the second corrected scanning spectrum data can be performed for the first-order differential of the spectrum, which means that on the spectrum graph, the first-order differential value at each spectral wavelength is obtained by using the first-order derivative method. By calculating the first-order differential value, the rate of change of the spectrum at each wavelength can be obtained, thereby better revealing the characteristics of the spectrum.

[0153] Further, in some embodiments, the first spectrometer and the second spectrometer are Fourier transform near infrared spectrometers;

[0154] See attached Figure 5 In some embodiments, the first spectrometer and the second spectrometer scan multiple naphtha samples under the following preset detection conditions:

[0155] S501: adopts transmission measurement method;

[0156] S502: Scanning resolution is not less than 8cm -1 ;

[0157] S503: Scanning wave number range is 4000cm -1 ~10000cm -1 ;

[0158] S504: The cumulative number of scans is at least 64 times.

[0159] It can be understood that, in some embodiments, the first spectrometer and the second spectrometer can be Fourier transform near-infrared spectrometers, and the above-mentioned preset detection conditions can ensure the accuracy of the scanning results and the validity of the data. Specifically, the first Fourier transform near-infrared spectrometer is used to detect a variety of naphtha samples according to the preset detection conditions to obtain a first spectrum of each naphtha sample, and the second Fourier transform near-infrared spectrometer is used to detect a variety of naphtha samples according to the above-mentioned preset detection conditions to obtain a second spectrum of each naphtha sample. Scanning a variety of naphtha samples can improve the richness and completeness of the correction spectrum data set. In some embodiments, a near-infrared spectrometer can also be added to construct a correction spectrum data set. This document does not limit this. It should be noted that the attached Figure 5 The serial numbers S501-S504 described in the above text are only used to distinguish different detection conditions, and do not mean that the detection conditions of the spectrometer need to be set in the order of the serial numbers. In commonly used embodiments, the detection conditions corresponding to S501-S504 are indispensable to ensure effective and accurate scanning results.

[0160] See attached Figure 6 In some embodiments, the use of the correction spectrum data set to correct the scanned spectrum data may include:

[0161] S601: Filtering the spectrum data to be fitted corresponding to the scanned spectrum data from the first corrected spectrum data;

[0162] S602: Determine fitting parameters according to the spectrum data to be fitted and the scanned spectrum data;

[0163] S603: Correcting the scanned spectrum data according to the fitting parameters and the second corrected spectrum data.

[0164] It can be understood that, in some embodiments, from the first correction spectrum data, according to the similarity between the scanning spectrum data and the first correction spectrum data, the spectrum data to be fitted corresponding to the scanning spectrum data is screened. In the specific implementation process, the common method can be used to calculate the similarity between the scanning spectrum data and each spectrum data in the first correction spectrum data, which is not limited by the description of this specification. In the specific implementation process, a variety of methods can be used to screen out a variety of spectrum data to be fitted from the first correction spectrum data according to the similarity calculated above. For example, all the calculated similarities can be sorted, and the spectrum data corresponding to the first n similarities can be used as the spectrum data to be fitted. For another example, the spectrum data corresponding to the similarity greater than the preset threshold can be used as the spectrum data to be fitted, so as to ensure the calibration accuracy of the correction of the scanning spectrum data.

[0165] Furthermore, in some embodiments, the fitting parameters are determined according to the spectrum data to be fitted and the scanned spectrum data, including

[0166] According to the spectrum data to be fitted and the scanned spectrum data, the fitting parameters are determined using the following formula:

[0167]

[0168] Among them, X1 is the scanned spectrum data, n is the amount of spectrum data to be fitted, i is the ordinal number of the spectrum data to be fitted, ω 1i is the i-th fitting parameter, X 1i is the ith spectrum data to be fitted.

[0169] In the specific implementation process, since the amount of the spectrum data to be fitted involved in the above calculation will affect the calculation accuracy of the fitting parameters, it is necessary to determine an appropriate amount of the spectrum data to be fitted involved in the calculation.

[0170] See attached Figure 7 In some embodiments, determining the fitting parameters according to the spectrum data to be fitted and the scanned spectrum data may further include:

[0171] S701: establishing an objective function according to the spectrum data to be fitted and the second corrected spectrum data;

[0172] S701: Iterating the objective function using a preset interval of iterative data volume;

[0173] S702: Determine the data volume of the spectrum data to be fitted according to the iterative data volume corresponding to the minimum iterative output result.

[0174] It can be understood that, in some embodiments, the iterative data volume is a parameter, denoted as m, and m is located in a preset iterative data volume interval. According to different values ​​of the iterative data volume m of multiple spectrogram data to be fitted participating in the calculation, the objective function is iteratively solved to obtain multiple iterative output results, and the minimum value among the multiple iterative output results is obtained, and the iterative data volume m corresponding to the minimum value is used as the data volume of the spectrogram data to be fitted. Specifically, in some embodiments, the objective function can be iteratively solved for different values ​​of the iterative data volume m in the objective function: 2, 3, 4, ..., 10, wherein when the iterative data volume m is 5, the value of the objective function obtained is the smallest, then it can be determined that when the data volume of the spectrogram data to be fitted is 5, the accuracy of the calculation result of the fitting parameter obtained is the highest, and subsequently 5 types of spectrogram data to be fitted can be screened out from the first correction spectrogram data to participate in the calculation of obtaining the fitting parameters.

[0175] Furthermore, in some embodiments, the objective function is established using the following formula:

[0176]

[0177] Among them, c m is the output value of the objective function, m is the iterative data amount of the spectrum data to be fitted, i is the ordinal number of the spectrum data to be fitted, is the i-th fitting parameter, is the i-th spectrum data corresponding to the i-th spectrum data to be fitted in the second correction spectrum data, X b It is the real spectrum data obtained by scanning the target naphtha using the second spectrometer.

[0178] It can be understood that, in some embodiments, the objective function determines whether the data amount of the current spectrum data to be fitted can ensure the accuracy of the fitting parameters by measuring the data distance between the i-th spectrum data corresponding to the i-th spectrum data to be fitted in the second correction spectrum data and the real spectrum data obtained by scanning the target naphtha using the second spectrometer. The output value of the objective function is the data distance. The smaller the data distance is, the closer the spectrum data corresponding to the target naphtha matched from the second correction spectrum data (which can be regarded as a database) is to the real spectrum data, thereby ensuring the calculation accuracy of the subsequent fitting parameters.

[0179] Furthermore, in some embodiments, the step of correcting the scanned spectrum data according to the fitting parameters and the second corrected spectrum data comprises:

[0180] According to the fitting parameters and the second correction spectrum data, the scanning spectrum data is corrected using the following standard spectrum calculation formula:

[0181]

[0182] in, is the corrected scanning spectrum data, n is the data volume of the fitting parameter, i is the ordinal number of the fitting parameter, ω 1i is the i-th fitting parameter, X 2i is the i-th spectrum data corresponding to the i-th spectrum data to be fitted in the second correction spectrum data.

[0183] It can be understood that in some embodiments, a standard spectrum calculation formula can be constructed using the database topology principle to calculate the corrected scanning spectrum data of the target naphtha. Furthermore, the original spectrum data obtained by scanning the target naphtha using any spectrometer can be corrected to obtain the corrected scanning spectrum data of the target naphtha, so that the corrected scanning spectrum data can be used to obtain the molecular composition of the target naphtha from the naphtha molecular composition library.

[0184] See attached Figure 8 In some embodiments, matching the molecular composition corresponding to the corrected scan spectrum data from a preset naphtha molecular composition library may include:

[0185] S801: Filtering the spectrum data to be matched corresponding to the scanned spectrum data from the second corrected spectrum data;

[0186] S802: determining matching parameters according to the spectrum data to be matched and the corrected scan spectrum data;

[0187] S803: searching for a molecular composition to be matched corresponding to the spectrum data to be matched from a preset naphtha molecular composition library;

[0188] S804: Determine the molecular composition corresponding to the corrected scan spectrum data according to the matching parameters and the molecular composition to be matched.

[0189] It can be understood that, in some embodiments, since the corrected scanning spectrum data of the target naphtha to be tested is obtained by fitting, it is not one-to-one corresponding to the spectrum data in the second corrected spectrum database. In order to obtain a more accurate molecular composition of the target naphtha to be tested from the established naphtha molecular composition library, the matching parameters can be obtained in the following way, and then the molecular composition of the target naphtha to be tested can be obtained according to the matching parameters and the molecular composition of the naphtha sample corresponding to the spectrum data to be matched in the naphtha molecular composition library. It should be noted that the naphtha molecular composition library belongs to a database, which contains multiple spectrum data and molecular composition data and the correspondence between the two.

[0190] Furthermore, in some embodiments, determining matching parameters according to the to-be-matched spectrum data and the corrected scan spectrum data includes:

[0191] According to the spectrum data to be matched and the corrected scan spectrum data, the matching parameters are determined using the following standard spectrum matching formula:

[0192]

[0193] in, is the corrected scanning spectrum data, l is the data volume of the matching parameter, j is the ordinal number of the matching parameter, ω 2j is the jth matching parameter, X 2j is the j-th spectrum data in the second correction spectrum data corresponding to the j-th spectrum data to be matched.

[0194] It can be understood that, in some embodiments, multiple spectral data to be matched and the corrected scanning spectral data of the target naphtha to be measured are substituted into the standard spectral matching formula shown above, and the matching parameters obtained can ensure that the corrected scanning spectral data accurately corresponds to the spectral data in the second corrected spectral data.

[0195] See attached Fig. 9 In some embodiments, determining the matching parameters according to the to-be-matched spectrum data and the corrected scan spectrum data may further include:

[0196] S901: establishing an optimization function according to the spectrum data to be matched and the second correction spectrum data;

[0197] S902: Iterating the optimization function using a preset interval of iterative data volume;

[0198] S903: Determine the matching parameter according to the iterative data volume corresponding to the minimum iterative output result.

[0199] It can be understood that, in some embodiments, determining appropriate matching parameters can ensure the molecular composition analysis effect of the target naphtha. Therefore, in order to accurately determine the matching parameters, an optimization function is established according to the distance between the spectrum data to be matched and the second correction spectrum data, and the iterative data volume is incremented or decremented according to a preset iterative data volume interval to obtain different optimization function output values. The matching parameters determined by the iterative data volume corresponding to the minimum optimization function output value can ensure the accuracy of the subsequent determination of the molecular composition.

[0200] Furthermore, in some embodiments, the optimization function is established using the following formula:

[0201]

[0202] Among them, p kis the output value of the optimization function, k is the iterative data amount of the spectrum data to be matched, i is the ordinal number of the spectrum data to be matched, is the i-th matching parameter, z i is the molecular composition of the i-th naphtha sample in the naphtha molecular composition library corresponding to the i-th spectrum data to be matched, z b The real molecular composition of the target naphtha was obtained by using comprehensive two-dimensional gas chromatography technology.

[0203] It can be understood that, in some embodiments, when establishing an optimization function based on the distance between the to-be-matched spectrum data and the second corrected spectrum data, the optimization function can be established based on the deviation value between the molecular composition information corresponding to the to-be-matched spectrum data and the real molecular composition information measured by the instrument related to the comprehensive two-dimensional gas chromatography technology. Specifically, z b and z i In terms of data form, they can all be represented as vectors. In terms of data processing, the optimization function calculates the difference in distance between the corresponding vector of the molecular composition in the spectral data to be matched and the corresponding vector of the actually measured molecular composition. The smaller the data difference reflected by the optimization function, the more accurate the spectral data to be matched and the matching parameters are.

[0204] Further, in some embodiments, determining the molecular composition corresponding to the corrected scan spectrum data according to the matching parameters and the molecular composition to be matched includes:

[0205] According to the matching parameters and the molecular composition to be matched, the molecular composition corresponding to the corrected scan spectrum data is determined using the following formula:

[0206]

[0207] Where z is the molecular composition of the target naphtha, l is the data volume of the matching parameter, and i is the ordinal number of the matching parameter. is the i-th matching parameter, z i is the molecular composition of the i-th naphtha sample corresponding to the i-th spectrum data to be matched in the naphtha molecular composition library.

[0208] It can be understood that in some embodiments, after determining the matching parameters and the molecular composition to be matched, the molecular composition corresponding to the scanning spectral data can be determined efficiently and accurately using linear regression fitting, so that the corresponding macroscopic properties can be determined subsequently using the molecular composition corresponding to the corrected scanning spectral data.

[0209] See attached Fig.10 In some embodiments, the preset naphtha molecular composition library is established according to the following steps:

[0210] S1001: Using comprehensive two-dimensional gas chromatography technology, various naphtha samples are tested to obtain monomer hydrocarbon components of various naphtha samples;

[0211] S1002: Establishing the naphtha molecular composition library according to the monomer hydrocarbon components of a plurality of naphtha samples.

[0212] It can be understood that, in some embodiments, the target naphtha to be tested is a light oil product obtained by distilling crude oil or cutting the corresponding fraction from secondary processing of petroleum. The naphtha molecular composition library includes monomer hydrocarbon components of multiple naphtha samples. By using comprehensive two-dimensional gas chromatography technology, multiple naphtha samples are detected to obtain monomer hydrocarbon components of multiple naphtha samples. In the specific implementation process, the monomer hydrocarbon composition of the naphtha sample can be detected by comprehensive two-dimensional gas chromatography according to the optimal conditions for comprehensive two-dimensional gas chromatography equipment analysis. The conditions for equipment analysis may include but are not limited to the heating program of the chromatographic column, the carrier gas flow rate and pressure, etc. Since different types of naphtha samples contain different numbers of molecules, the naphtha molecular composition library may include all molecular component data of the naphtha samples that can be detected by comprehensive two-dimensional chromatography. In some embodiments, after the molecular composition library is established, the database can be continuously expanded. Furthermore, the use of comprehensive two-dimensional gas chromatography to detect the monomer hydrocarbon composition of naphtha samples can effectively solve the disadvantage that one-dimensional chromatography cannot analyze components above C8+.

[0213] See attached Fig.11 In some embodiments, the predicted molecular properties of the target naphtha are obtained according to the molecular composition, thereby determining the macroscopic properties of the target naphtha, including:

[0214] S1101: Determine the molecular structure group information corresponding to the molecular composition; the molecular structure group information at least includes the basic group and the combined group of the molecular structure;

[0215] S1102: inputting the molecular structure group information into a preset molecular property prediction model to obtain predicted molecular properties of the target naphtha;

[0216] S1103: Input the predicted molecular properties into a preset molecular property mixing model to obtain macroscopic properties of the target naphtha.

[0217] It can be understood that, in some embodiments, the macroscopic properties of the target naphtha to be tested include at least: octane number, vapor pressure, and D86 distillation curve, etc., and the group information of the molecular structure of various molecules contained in the target naphtha to be tested is determined according to the molecular composition of the target naphtha to be tested, wherein the group information of the molecular structure includes at least the basic group and the combined group of the molecular structure. The basic group may include but is not limited to: methyl, methylene, etc., and the combined group is a group obtained by combining multiple basic groups. The isomers and complex structure molecules of the naphtha sample can be distinguished and identified through the combined group. In the specific implementation process, the group information of the molecular structure of various molecules contained in the target naphtha to be tested can be input into the molecular property prediction sub-model to obtain the predicted properties of various molecules in the target naphtha to be tested (for example, octane number, vapor pressure, boiling point, critical properties, etc.), and the predicted molecular properties are input into the preset molecular property mixing model to quickly obtain the macroscopic properties of the target naphtha. It should be noted that the molecular property prediction model is obtained by supervised learning training using historical naphtha analysis data, and the molecular property mixing model can output the macroscopic properties corresponding to various molecular compositions with linear or nonlinear relationships in the target naphtha.

[0218] See attached Fig.12 In some embodiments, the preset molecular property prediction model is trained in the following manner:

[0219] S1201: Obtain historical naphtha analysis data;

[0220] S1202: Using the molecular structure group information in the historical naphtha analysis data as input, using the molecular property labels in the historical naphtha analysis data as output, constructing training samples, and constructing a supervised learning model based on the input and output;

[0221] S1203: Using the training samples to train the supervised learning model, and using the trained supervised learning model as a molecular property prediction model.

[0222] It can be understood that in some embodiments, a molecular property prediction sub-model can be constructed based on a variety of supervised machine learning algorithms, such as: support vector machine, random forest or artificial neural network, etc., using the molecular structure group information in the historical naphtha analysis data as input, and the molecular property labels in the historical naphtha analysis data as output, constructing training samples and iterating the training multiple times, which can ensure that the trained molecular property prediction model can output accurate predicted molecular properties based on the molecular structure group information of the target naphtha.

[0223] See attached Fig.13 In some embodiments, training the supervised learning model using the training samples may include:

[0224] S1301: Divide the training samples into a training set and a validation set;

[0225] S1302: Training the supervised learning model using the training set;

[0226] S1303: inputting the molecular structure group information in the validation set into the trained supervised learning model;

[0227] S1304: Calculating the prediction accuracy based on the output results of the supervised learning model and the molecular property labels in the validation set;

[0228] S1305: If the prediction accuracy is less than a set threshold, retrain the supervised learning model until the prediction accuracy is not less than the set threshold.

[0229] It can be understood that, in some embodiments, the training samples are divided into a training set and a validation set. Considering the time cost of manually producing molecular property labels and the ratio setting commonly used in deep learning, the ratio of the training set to the validation set in the embodiments of this specification is 10:1. After training the supervised learning model with the training set, the molecular structure group information in the validation set is respectively input into the trained supervised learning model. According to the model output results and the molecular property labels in the validation set, the accuracy is calculated and the training process is iterated, which can improve the prediction accuracy of the molecular property prediction model.

[0230] Furthermore, in some embodiments, considering the interactions between various compounds in naphtha and their respective target properties, the linear relationship and nonlinear relationship when the compounds are mixed are taken into account. Specifically, for molecular target properties with linear relationships, such as boiling point, the following formula can be used to determine different linear macroscopic properties of the target naphtha:

[0231]

[0232] Among them, P b is the target linear macroscopic property of the target naphtha, P i is the target property of the i-th molecule in the target naphtha, x i is the percentage of the i-th molecule in the target naphtha, and n is the number of various molecules in the target naphtha. The target property of the molecule corresponds to the target macroscopic property of the naphtha to be tested that needs to be predicted. For example, if the target macroscopic property of the naphtha to be tested is the boiling point of the naphtha to be tested, then the target property of the molecule is the boiling point of the molecule.

[0233] Further, in some embodiments, there is a nonlinear relationship between the target properties of various molecules in the naphtha to be measured (e.g., octane number, etc.), and a molecular property mixed model for determining the macroscopic physical properties of naphtha with linear and nonlinear relationships can be constructed using the following formula to ensure the accuracy of the obtained molecular properties;

[0234]

[0235]

[0236]

[0237]

[0238] Among them, ON Linear is a linear mixed submodel, ON NonLinear is the nonlinear correction submodel, h and g are the numbers of various compounds in different naphtha samples, and v h is the volume fraction of the hth compound, v g is the volume fraction of the g-th compound, ON h is the target property of the hth compound, ΔON hg is the target property of the hth compound after the hth compound is corrected based on the interaction between the hth compound and the gth compound, are the interaction parameters between the hth compound and the gth compound, respectively.

[0239] It should be noted that, although the operations of the method of the present invention are described in a specific order in the above embodiments and the accompanying drawings, this does not require or imply that the operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0240] Corresponding to the above-mentioned method for rapid analysis of naphtha molecular components, some embodiments of this specification also provide a device for rapid analysis of naphtha molecular components, referring to Fig.14 As shown, in some embodiments, the apparatus may include:

[0241] The receiving module 1401 is used to receive the scanning spectrum data of the target naphtha;

[0242] The correction module 1402 is used to correct the scanned spectrum data using a correction spectrum data set; wherein the correction spectrum data set is obtained by scanning a plurality of naphtha samples under preset detection conditions using a plurality of spectrometers, and the scanned spectrum data is obtained by scanning the target naphtha using a first scanner among the plurality of spectrometers;

[0243] The determination module 1403 is used to match the molecular composition corresponding to the corrected scanning spectrum data from a preset naphtha molecular composition library, and obtain the predicted molecular properties of the target naphtha based on the molecular composition, thereby determining the macroscopic properties of the target naphtha; wherein the naphtha molecular composition library uses a comprehensive two-dimensional gas chromatography technology to determine the monomer hydrocarbon components of a variety of naphtha samples, and is established based on the monomer hydrocarbon components of a variety of naphtha samples.

[0244] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0245] It should be noted that in the embodiments of this specification, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user and fully authorized by all parties.

[0246] The embodiments of this specification also provide a computer device. Fig.15As shown, in some embodiments of the present specification, the computer device 1502 may include one or more processors 1504, such as one or more central processing units (CPUs) or graphics processing units (GPUs), and each processing unit may implement one or more hardware threads. The computer device 1502 may also include any memory 1506, which is used to store any kind of information such as code, settings, data, etc. In a specific embodiment, the computer program on the memory 1506 and can be run on the processor 1504, when the computer program is run by the processor 1504, the instructions of the method described in any of the above embodiments may be executed. Non-limitingly, for example, the memory 1506 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 1502. In one embodiment, when the processor 1504 executes the associated instructions stored in any memory or combination of memories, the computer device 1502 can perform any operation of the associated instructions. The computer device 1502 also includes one or more drive mechanisms 1508 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0247] The computer device 1502 may also include an input / output interface 1510 (I / O) for receiving various inputs (via input devices 1512) and for providing various outputs (via output devices 1514). A specific output mechanism may include a presentation device 1516 and an associated graphical user interface 1518 (GUI). In other embodiments, the input / output interface 1510 (I / O), input device 1512, and output device 1514 may not be included, and the computer device 1502 may be used as a computer device in a network. The computer device 1502 may also include one or more network interfaces 1520 for exchanging data with other devices via one or more communication links 1522. One or more communication buses 1524 couple the components described above together.

[0248] The communication link 1522 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 1522 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0249] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), computer-readable storage media, and computer program products of some embodiments of the present specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processor to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processor generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0250] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processor to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0251] These computer program instructions can also be loaded onto a computer or other programmable data processor so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0252] In a typical configuration, a computer device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0253] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0254] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computer device. As defined in this specification, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0255] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may take the form of complete hardware embodiments, complete software embodiments or embodiments combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0256] The present specification embodiments may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present specification embodiments may also be practiced in distributed computing environments where tasks are performed by remote processors connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0257] It should also be understood that in the embodiments of this specification, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0258] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0259] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of this specification. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0260] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for rapid analysis of naphtha molecular components, characterized in that: The method comprises: receiving scanning spectrum data of target naphtha; The scanned spectrum data is corrected using a correction spectrum data set; wherein the correction spectrum data set is obtained by scanning a plurality of naphtha samples under preset detection conditions using a plurality of spectrometers, and the scanned spectrum data is obtained by scanning a target naphtha using a first scanner among the plurality of spectrometers; From a preset naphtha molecular composition library, the molecular composition corresponding to the corrected scanning spectral data is matched, and the predicted molecular properties of the target naphtha are obtained based on the molecular composition, thereby determining the macroscopic properties of the target naphtha; wherein the naphtha molecular composition library utilizes comprehensive two-dimensional gas chromatography technology to determine the monomer hydrocarbon components of a variety of naphtha samples, and is established based on the monomer hydrocarbon components of a variety of naphtha samples.

2. The method according to claim 1, characterized in that: The calibration spectrum data set includes at least first calibration spectrum data and second calibration spectrum data.

3. The method according to claim 1, characterized in that The calibration spectrum data set is constructed using the following steps: Scanning at least one naphtha sample using a first spectrometer to obtain first calibration spectrum data; Scanning at least one naphtha sample using a second spectrometer to obtain second calibration spectrum data; A corresponding correction spectrum data set is constructed according to the first correction spectrum data and the second correction spectrum data.

4. The method according to claim 1, characterized in that: The calibration spectrum data set is constructed using the following steps, including: Scanning at least one naphtha sample using a first spectrometer, sharpening the scan result, and obtaining first calibrated spectrum data; Scanning at least one naphtha sample using a second spectrometer, sharpening the scan result, and obtaining second correction spectrum data; A corresponding correction spectrum data set is constructed according to the first correction spectrum data and the second correction spectrum data.

5. The method according to claim 3 or 4, characterized in that: The first spectrometer and the second spectrometer are Fourier transform near infrared spectrometers; The first spectrometer and the second spectrometer scan multiple naphtha samples under the following preset detection conditions: Using transmission measurement method; Scanning resolution no less than 8cm -1 ; The scanning wave number range is 4000cm -1 ~10000cm -1 ; The cumulative number of scans is at least 64.

6. The method according to claim 2, characterized in that The method of correcting the scanned spectrum data by using the correction spectrum data set includes: Selecting the spectrum data to be fitted corresponding to the scanned spectrum data from the first corrected spectrum data; Determining fitting parameters according to the spectrum data to be fitted and the scanned spectrum data; The scanned spectrum data is corrected according to the fitting parameters and the second corrected spectrum data.

7. The method according to claim 6, characterized in that The step of determining fitting parameters according to the spectrum data to be fitted and the scanned spectrum data comprises: According to the spectrum data to be fitted and the scanned spectrum data, the fitting parameters are determined using the following formula: Among them, X1 is the scanned spectrum data, n is the amount of spectrum data to be fitted, i is the ordinal number of the spectrum data to be fitted, ω 1i is the i-th fitting parameter, X 1i is the ith spectrum data to be fitted.

8. The method according to claim 7, characterized in that The step of determining fitting parameters according to the spectrum data to be fitted and the scanned spectrum data further comprises: Establishing an objective function according to the spectrum data to be fitted and the second corrected spectrum data; Iterating the objective function using a preset interval of iterative data volume; The data amount of the spectrogram data to be fitted is determined according to the iterative data amount corresponding to the minimum iterative output result.

9. The method according to claim 8, characterized in that The objective function is established using the following formula: Among them, c m is the output value of the objective function, m is the iterative data amount of the spectrum data to be fitted, i is the ordinal number of the spectrum data to be fitted, is the i-th fitting parameter, is the i-th spectrum data corresponding to the i-th spectrum data to be fitted in the second correction spectrum data, X b It is the real spectrum data obtained by scanning the target naphtha using the second spectrometer.

10. The method according to claim 6, characterized in that The step of correcting the scanned spectrum data according to the fitting parameters and the second corrected spectrum data comprises: According to the fitting parameters and the second correction spectrum data, the scanning spectrum data is corrected using the following formula: in, is the corrected scanning spectrum data, n is the data volume of the fitting parameter, i is the ordinal number of the fitting parameter, ω 1i is the i-th fitting parameter, X 2i is the i-th spectrum data corresponding to the i-th spectrum data to be fitted in the second correction spectrum data.

11. The method according to claim 2, characterized in that From the preset naphtha molecular composition library, match the molecular composition corresponding to the calibrated scan spectrum data, including: Selecting the to-be-matched spectrum data corresponding to the scanned spectrum data from the second corrected spectrum data; Determining matching parameters according to the spectrum data to be matched and the corrected scanning spectrum data; Searching for a molecular composition to be matched corresponding to the spectrum data to be matched from a preset naphtha molecular composition library; The molecular composition corresponding to the corrected scanning spectrum data is determined according to the matching parameters and the molecular composition to be matched.

12. The method according to claim 11, characterized in that According to the spectrum data to be matched and the corrected scan spectrum data, matching parameters are determined, including: According to the spectrum data to be matched and the corrected scan spectrum data, the matching parameters are determined using the following formula: in, is the corrected scanning spectrum data, l is the data volume of the matching parameter, j is the ordinal number of the matching parameter, ω 2j is the jth matching parameter, X 2j is the j-th spectrum data in the second correction spectrum data corresponding to the j-th spectrum data to be matched.

13. The method according to claim 12, characterized in that The step of determining matching parameters according to the to-be-matched spectrum data and the corrected scan spectrum data further comprises: Establishing an optimization function according to the to-be-matched spectrum data and the second corrected spectrum data; Iterating the optimization function using a preset interval of iterative data volume; The matching parameter is determined according to the amount of iterative data corresponding to the minimum iterative output result.

14. The method according to claim 13, characterized in that The optimization function is established using the following formula: Among them, p k is the output value of the optimization function, k is the iterative data amount of the spectrum data to be matched, i is the ordinal number of the spectrum data to be matched, is the i-th matching parameter, z i is the molecular composition of the i-th naphtha sample in the naphtha molecular composition library corresponding to the i-th spectrum data to be matched, z b The real molecular composition of the target naphtha was obtained by using comprehensive two-dimensional gas chromatography technology.

15. The method according to claim 11, characterized in that Determining the molecular composition corresponding to the corrected scan spectrum data according to the matching parameters and the molecular composition to be matched includes: According to the matching parameters and the molecular composition to be matched, the molecular composition corresponding to the corrected scan spectrum data is determined using the following formula: Where z is the molecular composition of the target naphtha, l is the data volume of the matching parameter, and i is the ordinal number of the matching parameter. is the i-th matching parameter, z i is the molecular composition of the i-th naphtha sample corresponding to the i-th spectrum data to be matched in the naphtha molecular composition library.

16. The method according to claim 1, characterized in that The preset naphtha molecular composition library is established according to the following steps: Using comprehensive two-dimensional gas chromatography technology, various naphtha samples were tested to obtain the monomer hydrocarbon components of various naphtha samples; The naphtha molecular composition library is established according to monomer hydrocarbon components of a plurality of naphtha samples.

17. The method according to claim 1, characterized in that The predicted molecular properties of the target naphtha are obtained according to the molecular composition, thereby determining the macroscopic properties of the target naphtha, including: Determine the molecular structure group information corresponding to the molecular composition; the molecular structure group information at least includes the basic group and the combined group of the molecular structure; Inputting the molecular structure group information into a preset molecular property prediction model to obtain predicted molecular properties of the target naphtha; The predicted molecular properties are input into a preset molecular property mixing model to obtain the macroscopic properties of the target naphtha.

18. The method according to claim 17, characterized in that The preset molecular property prediction model is trained in the following manner: Get historical naphtha analysis data; The molecular structure group information in the historical naphtha analysis data is used as input, and the molecular property labels in the historical naphtha analysis data are used as output to construct training samples, and a supervised learning model is constructed based on the input and output; The supervised learning model is trained using the training samples, and the trained supervised learning model is used as a molecular property prediction model.

19. The method according to claim 18, characterized in that Training the supervised learning model using the training samples includes: Dividing the training samples into a training set and a validation set; Training the supervised learning model using the training set; Input the molecular structure group information in the validation set into the trained supervised learning model; Calculate the prediction accuracy based on the output of the supervised learning model and the molecular property labels in the validation set; If the prediction accuracy is less than a set threshold, the supervised learning model is retrained until the prediction accuracy is not less than the set threshold.

20. The method according to claim 17, characterized in that The molecular property mixing model is constructed using the following formula: Among them, ON Linear is a linear mixed submodel, ON NonLinear is the nonlinear correction submodel, h and g are the numbers of various compounds in different naphtha samples, and v h is the volume fraction of the hth compound, v g is the volume fraction of the g-th compound, ON h is the target property of the hth compound, ΔON hg is the target property of the hth compound after the hth compound is corrected based on the interaction between the hth compound and the gth compound, are the interaction parameters between the hth compound and the gth compound, respectively.

21. A device for rapid analysis of naphtha molecular components, characterized in that: The device comprises: A receiving module, used for receiving scanning spectrum data of target naphtha; A correction module, used to correct the scanned spectrum data using a correction spectrum data set; wherein the correction spectrum data set is obtained by scanning a plurality of naphtha samples using a plurality of spectrometers under preset detection conditions, and the scanned spectrum data is obtained by scanning a target naphtha using a first scanner among the plurality of spectrometers; A determination module is used to match the molecular composition corresponding to the corrected scanning spectral data from a preset naphtha molecular composition library, and obtain the predicted molecular properties of the target naphtha based on the molecular composition, thereby determining the macroscopic properties of the target naphtha; wherein the naphtha molecular composition library uses full two-dimensional gas chromatography technology to determine the monomer hydrocarbon components of a variety of naphtha samples, and is established based on the monomer hydrocarbon components of a variety of naphtha samples.

22. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the computer program is executed by the processor, the computer program executes the instructions of the method according to any one of claims 1 to 20.

23. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor of a computer device, the computer program executes instructions of the method according to any one of claims 1 to 20.

24. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the instructions of the method according to any one of claims 1 to 20 are executed.

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