CN116413238A - Oil product group composition determination method, system, equipment and storage medium - Google Patents
Oil product group composition determination method, system, equipment and storage medium Download PDFInfo
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
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Abstract
The invention discloses a method, a system, equipment and a storage medium for determining oil group composition, wherein the method comprises the following steps: acquiring spectrum data of an oil product to be tested; inputting the spectrum data of the oil to be measured into a physical property prediction model to predict physical property data corresponding to the spectrum data of the oil to be measured; and determining the group composition of the oil to be measured according to the physical property data based on a known corresponding relation model between the physical property data and the group composition. The invention can realize rapid detection of the group composition of the oil product by detecting the spectrum data of the oil product, and has no damage, no introduction of additional chemical reagent and no pollution.
Description
Technical Field
The invention relates to the technical field of petroleum processing, in particular to a method, a system, equipment and a storage medium for determining oil product group composition.
Background
With the wide application of petroleum refining molecular management technology, for example, a molecular composition analysis method, a petroleum molecular composition model construction technology based on molecular composition property prediction and oriented to molecular management, a computer-aided reaction network construction and solving method, a petroleum composition and blending model based on molecular management, a reaction conversion process model development based on molecular management technology, application of a molecular management technology in real-time optimization of refineries, and the like, the molecular management of petroleum products is increasingly important.
The group composition of oil products is important basic data for molecular management, and how to rapidly detect the group composition of oil products is an important research topic.
In the prior art, through the molecular composition that adds the oil dropwise into the detector to the oil detects the analysis to confirm the group and constitute, during the detection analysis, there is certain loss and the detector is clean not thoroughly to introducing extra chemical reagent to the oil, leads to the testing result inaccurate, in addition, detects the back, need with the oil clearance that adds in the detector, can cause certain pollution to the environment. Moreover, the oil product includes several tens, even hundreds, of molecular compositions, and thus, even after the molecular composition of the oil product is detected by the detector, analysis of the detected molecular composition has problems such as slow speed and complicated process.
Disclosure of Invention
In order to solve the problems existing in the prior art, one of the purposes of the present invention is: a method for determining the composition of oil product family is provided.
In order to achieve the above object, the present invention provides the following technical solutions:
a method of determining oil family composition, the method comprising:
acquiring spectrum data of an oil product to be tested;
inputting the spectrum data of the oil to be measured into a physical property prediction model to predict physical property data corresponding to the spectrum data of the oil to be measured;
and determining the group composition of the oil to be measured according to the physical property data based on a known corresponding relation model between the physical property data and the group composition.
Preferably, the spectrum data is the absorption intensity of the light with the specified wavelength by the oil to be measured after the light with the specified wavelength acts on the oil to be measured.
Preferably, the spectral data is measured by a near infrared spectrometer or a mid infrared spectrometer.
Preferably, the group composition of the oil comprises at least one of linear alkanes, isoparaffins, alkenes, cycloalkanes, and arenes.
Preferably, the physical property prediction model is obtained by training the following steps:
establishing an initial physical property prediction model;
collecting spectrum data of a known oil sample and measuring physical property data of group composition of the known oil sample;
and training the initial physical property prediction model by utilizing the spectrum data of the known oil sample and the physical property data of the group composition of the known oil sample to obtain a trained physical property prediction model.
Preferably, the physical property prediction model is one of a multiple linear regression model, a principal component regression model, a partial least squares model, an artificial neural network model, a deep learning model and a topological method model.
Preferably, the determining the group composition of the oil to be measured according to the physical property data based on the correspondence model between the known physical property data and the group composition includes:
inputting predicted physical property data into a corresponding relation model between known physical property data and group composition, and searching for the group composition corresponding to the predicted physical property data;
outputting the family composition when the predicted physical property data is consistent with the physical property data of the family composition;
when the predicted physical property data does not match any of the physical property data of the group composition, the group composition is adjusted based on the predicted physical property data until the predicted physical property data matches the physical property data of the group composition, and the adjusted group composition is output.
Preferably, the physical property data is any one of boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, congeal point, cloud point, pour point, aniline point, freeze point, viscosity index, viscosity, API gravity and wax content.
In order to solve the problems in the prior art, the second object of the present invention is to: an oil family composition determination system is provided.
In order to achieve the above object, the present invention provides the following technical solutions:
an oil family composition determination system comprising:
the acquisition unit is used for acquiring spectrum data of the oil to be detected;
the prediction unit is used for inputting the spectrum data of the oil to be detected into the physical property prediction model and predicting the physical property data corresponding to the spectrum data of the oil to be detected;
and the determining unit is used for determining the group composition of the oil to be detected according to the physical property data based on a corresponding relation model between the known physical property data and the group composition.
Preferably, in the prediction unit, the physical property prediction model is obtained by training:
establishing an initial physical property prediction model;
collecting spectrum data of a known oil sample and measuring physical property data of group composition of the known oil sample;
and training the initial physical property prediction model by utilizing the spectrum data of the known oil sample and the physical property data of the group composition of the known oil sample to obtain a trained physical property prediction model.
Preferably, the determining unit is specifically configured to:
inputting predicted physical property data into a corresponding relation model between known physical property data and group composition, and searching for the group composition corresponding to the predicted physical property data;
outputting the family composition when the predicted physical property data is consistent with the physical property data of the family composition;
when the predicted physical property data does not match any of the physical property data of the group composition, the group composition is adjusted based on the predicted physical property data until the predicted physical property data matches the physical property data of the group composition, and the adjusted group composition is output.
Preferably, in the determining unit, the group composition of the oil product includes at least one of a linear alkane, an isoparaffin, an alkene, a cycloalkane, and an arene.
In order to solve the problems in the prior art, the third object of the present invention is to: an oil family composition determination device is provided.
In order to achieve the above object, the present invention provides the following technical solutions:
the equipment for determining the oil product group composition comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing the steps of the oil product group composition determining method when executing the program stored in the memory.
In order to solve the problems existing in the prior art, the fourth object of the present invention is: a method for determining the composition of oil product family is provided.
In order to achieve the above object, the present invention provides the following technical solutions:
a computer readable storage medium storing one or more programs executable by one or more processors to implement the steps of the above oil family composition determination method.
The invention has the beneficial effects that:
according to the invention, the spectrum data of the oil to be measured is obtained; inputting the spectrum data of the oil to be measured into a physical property prediction model to predict physical property data corresponding to the spectrum data of the oil; based on the known correspondence between the physical property data and the group composition, the group composition of the oil to be detected is determined according to the physical property data, and the invention can realize rapid detection of the group composition of the oil by detecting the spectrum data of the oil without damage and introducing additional chemical reagent, thereby having no pollution.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for determining the composition of an oil product family according to embodiment 1 of the present invention;
FIG. 2 is a flow chart of the physical property prediction model training procedure provided in example 2 of the present invention;
FIG. 3 is a schematic flow chart of the step of determining the group composition of the oil based on the physical property data provided in example 3 of the present invention;
FIG. 4 is a schematic diagram of a system for determining the composition of an oil family according to embodiment 4 of the present invention;
fig. 5 is a schematic structural diagram of an oil group composition determining apparatus according to embodiment 5 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, the embodiment of the invention provides a method for determining the composition of an oil product group. Referring to FIG. 1, the oil family composition determination method comprises the following steps:
s11, acquiring spectrum data of an oil product to be tested;
in this embodiment, the spectrum data is the absorption intensity of the light with the specified wavelength by the oil to be measured after the light with the specified wavelength passes through the oil to be measured. The spectrum data is a relation between a wavelength of light and an absorption intensity of the light by an oil product, and in practical application, the spectrum data may be a spectrum curve with a wavelength on an abscissa and an absorption intensity on an ordinate, where in the spectrum curve, the absorption intensity corresponding to the wavelength can be queried under the condition that the wavelength is known. The spectral data may be measured by a near infrared spectrometer or a mid infrared spectrometer.
S12, inputting the spectrum data of the oil to be detected into a physical property prediction model, and predicting physical property data corresponding to the spectrum data of the oil to be detected;
in this embodiment, the physical property data is any one of boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, condensation point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API gravity, and wax content.
S13, determining the group composition of the oil to be measured according to the physical property data based on a known corresponding relation model between the physical property data and the group composition.
In this embodiment, the group composition of the oil comprises at least one of linear alkanes, isoparaffins, olefins, naphthenes, and aromatics (PIONA). Assuming that the group composition of the oil includes five kinds of straight-chain alkanes, isoparaffins, olefins, naphthenes and aromatics, the sum of the contents of these five kinds of group compositions is 1.
Example 2
In this embodiment, as shown in fig. 2, in step S12, the physical property prediction model is obtained by training the following steps:
s21, establishing an initial physical property prediction model, wherein the initial physical property prediction model is one of multiple linear regression, principal component regression, partial least square, artificial neural network, deep learning and topological method models.
S22, collecting spectrum data of a known oil sample and measuring physical property data of group composition of the known oil sample;
s23, training the initial physical property prediction model by utilizing the spectrum data of the known oil sample and the physical property data of the group composition of the known oil sample to obtain a physical property prediction model after training.
When the spectrum data of the known oil sample is spectrum data collected from 780nm-2500nm by a near infrared spectrometer at a step length of 4nm, the physical property data of the group composition of the known oil sample comprises physical property data of molecules of P-straight chain alkane, I-isoparaffin, N-cycloparaffin, O-olefin and A-arene, the physical property data of the oil sample is any one of boiling point, density, octane number, arene, olefin, benzene, flash point, refractive index, condensation point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API (application program interface) degree and wax content, and the spectrum data is a spectrum curve with an abscissa being wavelength and an ordinate being absorption intensity, training the physical property prediction model by utilizing the spectrum data of the known oil sample and the physical property data of the group composition of the known oil sample, wherein the spectrum data comprises the following steps:
for each of a plurality of known oil samples comprising a PIONA group composition, boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, congeal point, cloud point, pour point, aniline point, freezing point, viscosity index, viscosity, API degree and wax content, collecting absorption intensity data at different wavelengths of a spectrum, and establishing a predictive model of the spectrum data and the physical properties by regression, wherein the type of the sample is more than 80; the family composition data is the result of a joint calculation calibration of multiple sets of correlation models.
For each known oil sample containing the P-linear alkane, taking the absorbance of the known oil sample at the wavelength corresponding to the P-linear alkane as input, taking physical property data corresponding to the P-linear alkane as output, and training the initial physical property prediction model, so that the physical property prediction model after training can predict the physical property data corresponding to the P-linear alkane according to the absorbance of the sample at the wavelength corresponding to the P-linear alkane;
for each known oil sample containing I-isoparaffin in a plurality of known oil samples, collecting the absorbance of the known oil samples at the wavelength corresponding to the I-isoparaffin and the physical property data corresponding to the I-isoparaffin, wherein the variety of the sample is more than 80;
for each known oil sample containing I-isoparaffin, taking the absorbance of the known oil sample at the wavelength corresponding to the I-isoparaffin as input, taking physical property data corresponding to the I-isoparaffin as output, and training the initial physical property prediction model, so that the physical property prediction model after training can predict the physical property data corresponding to the I-isoparaffin according to the absorbance of the sample at the wavelength corresponding to the I-isoparaffin;
collecting, for each of a plurality of known oil samples, the absorbance at a wavelength corresponding to N-cycloalkane and physical property data corresponding to N-cycloalkane, wherein the type of the sample is 80 or more;
for each known oil sample containing N-cycloparaffin, taking the absorbance of the known oil sample at the wavelength corresponding to the N-cycloparaffin as input and the physical property data corresponding to the N-cycloparaffin as output, training the initial physical property prediction model so that the physical property prediction model after training can predict the physical property data corresponding to the N-cycloparaffin according to the absorbance of the sample at the wavelength corresponding to the N-cycloparaffin;
for each known oil sample containing O-olefin in a plurality of known oil samples, collecting the absorbance of the known oil samples at the wavelength corresponding to the O-olefin and the physical property data corresponding to the O-olefin, wherein the types of the samples are more than 80;
for each known oil sample containing O-olefin, taking the absorbance of the known oil sample at the wavelength corresponding to the O-olefin as input, taking physical property data corresponding to the O-olefin as output, and training the initial physical property prediction model, so that the physical property prediction model after training can predict the physical property data corresponding to the O-olefin according to the absorbance of the sample at the wavelength corresponding to the O-olefin;
collecting the absorbance of each known oil sample containing the P-linear alkane at the wavelength corresponding to the P-linear alkane and the physical property data corresponding to the P-linear alkane, wherein the variety of the sample is more than 80;
for each known oil sample containing A-arene, taking the absorbance of the known oil sample at the wavelength corresponding to the A-arene as input, taking physical property data corresponding to the A-arene as output, and training the initial physical property prediction model, so that the physical property prediction model after training can predict the physical property data corresponding to the A-arene according to the absorbance of the sample at the wavelength corresponding to the A-arene.
Example 3
In this embodiment, as shown in fig. 3, in step S13, the determining, based on the known correspondence model between the physical property data and the group composition, the group composition of the oil to be measured according to the physical property data includes:
s31, inputting predicted physical property data into a corresponding relation model between known physical property data and group composition, and searching for the group composition corresponding to the predicted physical property data;
s32, judging whether the predicted physical property data is consistent with the physical property data of the group composition:
if yes, that is, if the predicted physical property data is identical to the physical property data of the group composition, executing step S33;
if not, that is, if the predicted physical property data does not match any of the physical property data of the group composition, step S34 is executed;
s33, outputting the group composition;
and S34, adjusting the group composition according to the predicted physical property data until the predicted physical property data is consistent with the physical property data of the group composition, and outputting the adjusted group composition.
In practical application, searching the group composition corresponding to each predicted physical property data comprises the following steps:
extracting known physical property data from a corresponding relation model between the known physical property data and the group composition;
comparing the predicted physical property data with each of the known physical property data;
when there is a match with the known physical property data in the predicted physical property data, the group composition corresponding to the known physical property data is obtained from the correspondence between the known physical property data and the group composition, and the group composition corresponding to the predicted physical property data matching the known physical property data is obtained as the group composition.
The searching for a group composition corresponding to each predicted physical property data when the known correspondence between physical property data and group composition includes a correspondence between P-linear alkane and physical property data thereof, a correspondence between I-isoparaffin and physical property data thereof, a correspondence between N-cycloparaffin and physical property data thereof, a correspondence between O-olefin and physical property data thereof, and a correspondence between a-isomerised (or branched) molecule and physical property data thereof, includes:
comparing the predicted physical property data with physical property data corresponding to the P-straight-chain alkane, physical property data corresponding to the I-isoparaffin, physical property data corresponding to the N-cycloparaffin, physical property data corresponding to the O-olefin and physical property data corresponding to the A-arene;
when any one of the physical property data corresponding to the P-linear alkane, the physical property data corresponding to the I-isoparaffin, the physical property data corresponding to the N-cycloparaffin, the physical property data corresponding to the O-olefin, and the physical property data corresponding to the a-aromatic hydrocarbon is present in the predicted physical property data, the molecule corresponding to the physical property data corresponding to the predicted physical property data is a group composition corresponding to each of the predicted physical property data.
According to the oil product group composition determination method, the physical property data predicted according to the spectrum data of the oil product is compared with the physical property data corresponding to the group composition, the group composition in the oil product is rapidly judged according to the comparison result, and the molecular composition of the oil product can be rapidly grouped by determining the group composition of the oil product according to the spectrum data, so that the molecular composition of the oil product can be conveniently further analyzed.
Example 4
Based on the same inventive concept as embodiments 1-3, as shown in fig. 4, embodiment 4 of the present invention provides an oil family composition determining system including an obtaining unit 11, a predicting unit 12, and a determining unit 13.
In this embodiment, the obtaining unit 11 is configured to obtain spectral data of an oil to be measured.
In this embodiment, the prediction unit 12 is configured to input the spectrum data of the oil to be measured into a physical property prediction model, and predict physical property data corresponding to the spectrum data of the oil to be measured.
In the present embodiment, the determining unit 13 is configured to determine the group composition of the oil to be measured according to the physical property data based on a known correspondence model between the physical property data and the group composition.
In some embodiments, in the obtaining unit 11, the spectrum data is the absorption intensity of the light with the specified wavelength by the oil to be measured after the light with the specified wavelength acts on the oil to be measured. In some embodiments, the spectral data is measured in the acquisition unit 11 by a near infrared spectrometer or a mid infrared spectrometer.
In some embodiments, in the determining unit 13, the group composition of the oil product includes at least one of an alkane, an isoparaffin, an alkene, a cycloalkane, and an arene.
In some embodiments, the physical property prediction model is trained in the prediction unit 12 by:
establishing an initial physical property prediction model;
collecting spectrum data of a known oil sample and measuring physical property data of group composition of the known oil sample;
and training the initial physical property prediction model by utilizing the spectrum data of the known oil sample and the physical property data of the group composition of the known oil sample to obtain a trained physical property prediction model.
In some embodiments, the physical property prediction model in the prediction unit 12 includes a multiple linear regression model, a principal component regression model, a partial least squares model, an artificial neural network model, a deep learning model, and a topology method model.
In some embodiments, in the determining unit 13, the determining the group composition of the oil to be measured according to the physical property data based on the known correspondence model between the physical property data and the group composition includes:
inputting predicted physical property data into a corresponding relation model between known physical property data and group composition, and searching for the group composition corresponding to the predicted physical property data;
outputting the family composition when the predicted physical property data is consistent with the physical property data of the family composition;
when the predicted physical property data does not match any of the physical property data of the group composition, the group composition is adjusted based on the predicted physical property data until the predicted physical property data matches the physical property data of the group composition, and the adjusted group composition is output.
In some embodiments, the physical property data in prediction unit 12 is any one of boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, congeal point, cloud point, pour point, aniline point, freeze point, viscosity index, viscosity, API gravity, and wax content.
The implementation process of the functions and roles of each unit in the above system is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
For system embodiments, reference is made to the description of method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Example 5
Based on the same inventive concept as embodiments 1-3, as shown in fig. 5, embodiment 5 of the present invention provides an oil product group composition determining apparatus, which includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 complete communication with each other through the communication bus 1140;
a memory 1130 for storing a computer program;
acquiring spectrum data of an oil product to be tested;
inputting the spectrum data of the oil to be measured into a physical property prediction model to predict physical property data corresponding to the spectrum data of the oil to be measured;
and determining the group composition of the oil to be measured according to the physical property data based on a known corresponding relation model between the physical property data and the group composition.
The communication bus 1140 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface 1120 is used for communication between the electronic device and other devices described above.
The memory 1130 may include random access memory (Random Access Memory, simply RAM) or may include non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. Optionally, the memory 1130 may also be at least one storage device located remotely from the processor 1110.
The processor 1110 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Example 6
Based on the same inventive concept as embodiments 1-3, embodiments of the present invention provide a computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps of the oil family composition determination method in any of the possible implementations described above.
Alternatively, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
Based on the same inventive concept as embodiments 1-3, embodiments of the present invention also provide a computer program product comprising a computer program which when executed by a processor realizes the steps of the oil family composition determination method in any of the possible implementations described above.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.) means from one website, computer, server, or data center. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc., that contain an integration of one or more available media. Usable media may be magnetic media, (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid State Disks (SSDs)), among others.
Although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (14)
1. A method for determining the composition of an oil product family, the method comprising:
acquiring spectrum data of an oil product to be tested;
inputting the spectrum data of the oil to be measured into a physical property prediction model to predict physical property data corresponding to the spectrum data of the oil to be measured;
and determining the group composition of the oil to be measured according to the physical property data based on a known corresponding relation model between the physical property data and the group composition.
2. The method according to claim 1, wherein the spectral data is the absorption intensity of the light with the specified wavelength by the oil to be measured after the light with the specified wavelength acts on the oil to be measured.
3. The method of claim 2, wherein the spectral data is measured by a near infrared spectrometer or a mid infrared spectrometer.
4. The method of claim 1, wherein the group composition of the oil comprises at least one of linear alkanes, isoparaffins, alkenes, cycloalkanes, and aromatics.
5. The method for determining the composition of an oil product family according to claim 1, wherein the physical property prediction model is obtained by training the following steps:
establishing an initial physical property prediction model;
collecting spectrum data of a known oil sample and measuring physical property data of group composition of the known oil sample;
and training the initial physical property prediction model by utilizing the spectrum data of the known oil sample and the physical property data of the group composition of the known oil sample to obtain a trained physical property prediction model.
6. The method according to claim 5, wherein the physical property prediction model is one of a multiple linear regression model, a principal component regression model, a partial least squares model, an artificial neural network model, a deep learning model, and a topology method model.
7. The method for determining the composition of an oil product family according to claim 1, wherein the determining the composition of the family of the oil product to be measured based on the known correspondence model between the physical property data and the composition of the family comprises:
inputting predicted physical property data into a corresponding relation model between known physical property data and group composition, and searching for the group composition corresponding to the predicted physical property data;
outputting the family composition when the predicted physical property data is consistent with the physical property data of the family composition;
when the predicted physical property data does not match any of the physical property data of the group composition, the group composition is adjusted based on the predicted physical property data until the predicted physical property data matches the physical property data of the group composition, and the adjusted group composition is output.
8. The method of claim 1, wherein the physical property data is any one of boiling point, density, octane number, aromatic hydrocarbon, olefin, benzene, flash point, refractive index, congeal point, cloud point, pour point, aniline point, freeze point, viscosity index, viscosity, API gravity, and wax content.
9. An oil family composition determination system, comprising:
the acquisition unit is used for acquiring spectrum data of the oil to be detected;
the prediction unit is used for inputting the spectrum data of the oil to be detected into the physical property prediction model and predicting the physical property data corresponding to the spectrum data of the oil to be detected;
and the determining unit is used for determining the group composition of the oil to be detected according to the physical property data based on a corresponding relation model between the known physical property data and the group composition.
10. The oil family composition determination system of claim 9, wherein in the prediction unit, the physical property prediction model is trained by:
establishing an initial physical property prediction model;
collecting spectrum data of a known oil sample and measuring physical property data of group composition of the known oil sample;
and training the initial physical property prediction model by utilizing the spectrum data of the known oil sample and the physical property data of the group composition of the known oil sample to obtain a trained physical property prediction model.
11. The oil family composition determination system of claim 9, wherein the determination unit is specifically configured to:
inputting predicted physical property data into a corresponding relation model between known physical property data and group composition, and searching for the group composition corresponding to the predicted physical property data;
outputting the family composition when the predicted physical property data is consistent with the physical property data of the family composition;
when the predicted physical property data does not match any of the physical property data of the group composition, the group composition is adjusted based on the predicted physical property data until the predicted physical property data matches the physical property data of the group composition, and the adjusted group composition is output.
12. The oil family composition determination system of claim 9, wherein in the determination unit, the family composition of the oil comprises at least one of a linear alkane, isoparaffin, alkene, cycloalkane, and arene.
13. The equipment for determining the oil product group composition is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
a memory for storing a computer program;
a processor for implementing the steps of the method for determining oil family composition of any one of claims 1-8 when executing a program stored on a memory.
14. A computer readable storage medium storing one or more programs executable by one or more processors to perform the steps of the method for determining oil family composition of any one of claims 1-8.
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