Method and device for predicting octane number of blended gasoline
By constructing a weighted average prediction model, considering the linear and nonlinear effects of each component oil, the problem of low prediction accuracy of gasoline octane in the prior art is solved, especially in the case of MTBE component oil, and more accurate is achieved octane number prediction and production guidance.
Patent Information
- Application Number
- CN202310822441.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-05
- Publication Date
- 2025-01-07
AI Technical Summary
The existing gasoline octane prediction model has low prediction accuracy during the blending process and cannot accurately reflect the nonlinear law of gasoline octane number, especially the nonlinear effect of MTBE component oil has not been fully considered.
By obtaining the volume fraction, octane number, MTBE sensitive factor, olefin and aromatic hydrocarbon volume fraction of each component oil, and using least squares regression to calculate the constant coefficient, a prediction model is constructed to calculate the octane number of blended gasoline with weighted average, considering that Linear and nonlinear effects of oils in each component.
Improve the prediction accuracy of gasoline octane number, especially when the MTBE component oil is included, provides a more accurate blending production reference and enhances the reliability of daily production scheduling.
Smart Images
Figure CN119274682A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of petrochemical engineering, and in particular to a method and device for predicting the octane number of blended gasoline. Background Art
[0002] Gasoline is blended from multiple component oils. Using the existing component oils, how to adjust the proportion of each component oil, accurately predict and estimate the blending octane number, and improve the success rate of one-time blending has a strong guiding significance for actual blending production.
[0003] However, in the actual blending process, the blending octane number will show a certain nonlinear law. The gasoline octane number is not only related to the octane number of each gasoline component itself, but also affected by other properties of the blending components themselves. Although the current domestic and foreign public data contain some prediction models for predicting the octane number of blended gasoline, its prediction accuracy is still limited by the factors considered by the model. It is still a difficult problem to improve the gasoline octane number prediction model and accurately predict the octane number of blended gasoline. Summary of the invention
[0004] The purpose of the embodiments of the present application is to provide a method and device for predicting the octane number of blended gasoline, which can improve the prediction accuracy of the gasoline octane number.
[0005] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for predicting the octane number of a blended gasoline, wherein the blended gasoline is blended by a plurality of component oils, wherein the plurality of component oils include a MTBE (methyl tertiary butyl ether) component oil, and the method comprises:
[0006] Obtaining the volume fraction of each component oil of the blended gasoline;
[0007] Obtaining the octane number of each component oil of the blended gasoline;
[0008] Obtaining the MTBE sensitivity factor, olefin and aromatic volume fractions of each component oil of the blended gasoline; and
[0009] The volume fraction, octane number, MTBE sensitivity factor, olefin and aromatic volume fraction of each component oil of the blended gasoline are input into the following prediction model to obtain the octane number of the blended gasoline:
[0010]
[0011] in,
[0012] RON is the octane number of the blended gasoline,
[0013] is the volume weighted average of the octane numbers of the component oils, which is obtained according to the volume fraction and octane number of the component oils;
[0014] is the volume weighted average of the olefin volume fractions of each component oil, is the volume weighted average of the squares of the olefin volume fractions of the component oils, and is obtained based on the volume fractions of the component oils and the olefin volume fractions;
[0015] is the volume weighted average of the aromatic volume fractions of each component oil, is the volume weighted average of the squares of the aromatic volume fractions of the component oils, and is obtained based on the volume fractions of the component oils and the aromatic volume fractions;
[0016] is the volume weighted average of the MTBE sensitivity factors of the component oils, It is the volume weighted average of the squares of the MTBE sensitivity factors of the component oils, and is obtained based on the volume fractions of the component oils and the MTBE sensitivity factors. The MTBE sensitivity factor is an artificially constructed factor (or coefficient) that reflects the impact of the nonlinear effect of the blending of the component oils and MTBE on the octane number of the finished oil. Different component oils have different impacts.
[0017] M is the volume fraction of the MTBE component oil;
[0018] a i is a constant coefficient, the i That is a1~a3 in the above prediction model.
[0019] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, R i is the octane number of the i-th component oil.
[0020] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, O i is the olefin volume fraction of the i-th component oil.
[0021] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, A i is the volume fraction of aromatics in the i-th component oil.
[0022] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, K i is the MTBE sensitivity factor of the i-th component oil.
[0023] In the examples of the present application, the MTBE sensitivity factors of the component oils are as follows: wax oil catalytic gasoline: 82; heavy oil catalytic gasoline: 78; S Zorb oil: 82; reformed gasoline: 0; naphtha or raffinate: 100; MTBE: 0; and / or alkylate or isooctane oil: 85.
[0024] In the embodiment of the present application, the constant coefficient a i Obtained through least squares regression of historical blended data.
[0025] In the embodiment of the present application, A=[a1,a2,a3] T , that is, XA = Y, using the least squares method to perform coefficient regression, through A = (X T X) -1 X T Y, get the constant coefficient a i .
[0026] In the embodiment of the present application, the method further includes: using daily production data related to blended gasoline to correct the constant coefficient a i .
[0027] In the embodiments of the present application, the octane number may be a research octane number or an octane number characterized by other methods.
[0028] A second aspect of the present application provides a device for predicting the octane number of blended gasoline, the device comprising: a memory; and a processor, the processor being configured to execute the above-mentioned method for predicting the octane number of blended gasoline.
[0029] A third aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configure the processor to execute the above-mentioned method for predicting the octane number of blended gasoline.
[0030] A fourth aspect of the present application provides a computer program product, comprising a computer program, which implements the above-mentioned method for predicting the octane number of blended gasoline when executed by a processor.
[0031] Through the above technical solution, the linear influence of the octane number of each component oil on the octane number of blended gasoline is considered, and the nonlinear influence of the properties of each component oil on the blended gasoline is also considered, especially the nonlinear influence of the MTBE component oil, which can ultimately improve the prediction accuracy. In the daily gasoline blending production process of petroleum refining enterprises, this prediction model is used to predict the octane number of blended gasoline to provide a reference for daily production scheduling.
[0032] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:
[0034] Figure 1 The flowchart of the method for predicting the octane number of blended gasoline according to an embodiment of the present application is schematically shown;
[0035] Figure 2 The following is a schematic diagram showing a flow chart of a method for predicting the octane number of blended gasoline according to another embodiment of the present application;
[0036] Figure 3 This is a diagram showing the effect of predicting the octane number of blended gasoline according to the method for predicting the octane number of the present invention;
[0037] Figure 4 A schematic diagram schematically shows a flow chart of a method for predicting the octane number of blended gasoline according to another embodiment of the present application; and
[0038] Figure 5 The internal structure of the device for predicting the octane number of blended gasoline according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION
[0039] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0040] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.
[0041] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.
[0042] Figure 1 FIG. 1 is a flow chart of a method for predicting the octane number of blended gasoline in one embodiment. Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.
[0043] The gasoline research octane number is an indicator of gasoline's anti-knock ability, and is numerically equal to the volume percentage of isooctane contained in the standard fuel with the same anti-knock performance as the sample under specified conditions. The standard fuel is composed of a mixture of isooctane and n-heptane. Isooctane is used as a standard for excellent anti-knock performance, and its octane number is set to 100. N-heptane is used as a standard for poor anti-knock performance, and its octane number is 0. By mixing these two hydrocarbons in different volume ratios, standard fuels with octane numbers ranging from 0 to 100 can be prepared. By mixing them in different volume ratios, standard fuels with octane numbers ranging from 0 to 100 can be prepared.
[0044] like Figure 1 As shown, the present application provides a method for predicting the octane number of blended gasoline (for example, research octane number), wherein the blended gasoline is blended by a plurality of component oils, wherein the plurality of component oils include MTBE component oil, and the method comprises:
[0045] Step 102, obtaining the volume fraction of each component oil of the blended gasoline;
[0046] Step 104, obtaining the octane number (e.g., research octane number) of each component oil of the blended gasoline;
[0047] Step 106, obtaining the MTBE sensitivity factor, olefin and aromatic volume fraction of each component oil of the blended gasoline; and
[0048] Step 108, input the volume fraction of each component oil of the blended gasoline, the octane number (for example, the research octane number), the MTBE sensitivity factor, and the volume fraction of olefins and aromatics into the following prediction model to obtain the octane number of the blended gasoline:
[0049]
[0050] in,
[0051] RON is the octane number of the blended gasoline,
[0052] is the volume weighted average of the octane numbers of the component oils, which is obtained according to the volume fraction and octane number of the component oils;
[0053] is the volume weighted average of the olefin volume fractions of each component oil, is the volume weighted average of the squares of the olefin volume fractions of the component oils, and is obtained based on the volume fractions of the component oils and the olefin volume fractions;
[0054] is the volume weighted average of the aromatic volume fractions of each component oil, is the volume weighted average of the squares of the aromatic volume fractions of the component oils, and is obtained based on the volume fractions of the component oils and the aromatic volume fractions;
[0055] is the volume weighted average of the MTBE sensitivity factors of the component oils, It is the volume weighted average of the squares of the MTBE sensitivity factors of the component oils, and is obtained based on the volume fractions of the component oils and the MTBE sensitivity factors. The MTBE sensitivity factor is an artificially constructed factor (or coefficient) that reflects the impact of the nonlinear effect of the blending of the component oils and MTBE on the octane number of the finished oil. Different component oils have different impacts.
[0056] M is the volume fraction of the MTBE component oil;
[0057] a i is a constant coefficient, the i That is a1~a3 in the above prediction model.
[0058] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, Ri is the octane number of the i-th component oil.
[0059] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, O i is the olefin volume fraction of the i-th component oil.
[0060] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, A i is the volume fraction of aromatics in the i-th component oil.
[0061] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, K i is the MTBE sensitivity factor of the i-th component oil.
[0062] In the embodiment of the present application, the MTBE sensitivity factor of each component oil is as follows: wax oil catalytic gasoline: 82; heavy oil catalytic gasoline: 78; S Zorb oil: 82; reformed gasoline: 0; naphtha or raffinate oil: 100; MTBE: 0; and / or alkylate oil or isooctane oil: 85. Of course, the specific numbers given here are only exemplary, and more component oils may be included and the MTBE sensitivity factor of each component oil may be slightly adjusted according to actual conditions.
[0063] It should be noted that although various specific formulas are given above, it is possible for technical personnel in this field to make slight modifications to the above formulas based on the main idea of this case. These modifications should be regarded as equivalent implementation methods of the above formulas in this case and are within the scope of protection of this case.
[0064] Through the above technical solution, the linear influence of the octane number of each component oil on the octane number of the blended gasoline is taken into account, and the nonlinear influence of the properties of each component oil on the blended gasoline is also taken into account, especially the nonlinear influence of the MTBE component oil, which can ultimately improve the prediction accuracy. The high prediction accuracy of this case will be confirmed from the data by comparing the embodiment with the comparative example.
[0065] The above prediction model can be established in the following ways. Collect historical blending formulas, use the analytical test data of each historical blending formula (which may include the accurately measured research octane number of each component oil, MTBE sensitivity factor, volume fraction of aromatics, olefins, etc., and octane number data of the blended finished oil) to form a complete formula data sample. Accumulate a large amount of complete historical formula data as a formula sample library, and use the least squares method to calculate the constant coefficients in the prediction model. In addition, in production practice, the existing formula database can be continuously updated, the constant coefficients can be verified and corrected, and the prediction accuracy of the model can be improved.
[0066] Two specific embodiments and a comparative example of this case are given below.
[0067] Embodiment 1
[0068] Example 1 Process Figure 2 As shown in the figure, the complete process of establishing a prediction model and using the prediction model to predict octane number.
[0069] Specifically, the process of building a prediction model includes the following operations:
[0070] 1) Collect and organize gasoline blending historical formula data, using analytical test data of each historical blending formula, including: volume fraction of each component oil (or mass fraction and corresponding density, which also determines the volume fraction), research octane number of each component oil, MTBE sensitivity factor, olefins, and aromatics volume fraction of each component oil. The sample data of the embodiment are shown in Tables 1 to 6.
[0071] 2) Clean the data, remove missing information, or data that obviously does not correspond to the properties of the corresponding component oil, to ensure the accuracy and rationality of the data, such as Sample ID 12 in Table 2.
[0072] 3) Substitute the sorted and cleaned data into the following prediction model,
[0073]
[0074] in,
[0075] where w i is the volume fraction of the i-th component oil, R i is the research octane number of the i-th component oil;
[0076] where w i is the volume fraction of the i-th component oil, O i is the olefin volume fraction of the i-th component oil;
[0077] where w i is the volume fraction of the i-th component oil, A i is the volume fraction of aromatic hydrocarbons in the i-th component oil;
[0078] In the embodiments of the present application, Among them, w i is the volume fraction of the i-th component oil, K i is the MTBE sensitivity factor of the i-th component oil.
[0079] M is the volume fraction of MTBE component oil;
[0080] a i is a constant coefficient,
[0081] make That is, XA = Y, and the coefficients are regressed using the least squares method, through A = (X T X) -1 X T Y, get the constant coefficient a i .
[0082] 4) After calculating the constant coefficient a i After that, the prediction model is established and can be used for daily production prediction.
[0083] In this embodiment, the model coefficients a1 = -0.0025, a2 = 0.0018, and a3 = -0.0040. The deviation between the model prediction value and the measured value is as follows: Figure 3 As shown, after calculation, the mean of the deviation is 0.337 and the standard deviation is 0.413.
[0084] Comparative Example 1
[0085] Collect and organize gasoline blending historical formula data, and use analytical test data of each historical blending formula, including: volume fraction (or mass fraction and corresponding density) of each component oil, and research octane number of each component oil. Example sample data are shown in Tables 1-6.
[0086] Clean the data and remove missing information or data that obviously does not correspond to the properties of the corresponding component oil to ensure the accuracy and rationality of the data, such as Sample ID 12 in Table 2.
[0087] Bring the cleaned data into the linear prediction model: RON is the research octane number of the blended oil, w i is the volume fraction of the i-th component oil, R i is the research octane number of the ith component oil.
[0088] In this comparative example, after calculation, the predicted value of the linear prediction model deviates from the measured value, and the mean of the deviation is 0.556 and the standard deviation is 0.638. It can be seen that the mean and standard deviation of the deviation are much higher than those of the above-mentioned embodiment 1 of this case, which can reflect the prediction accuracy of the prediction model of the embodiment of this case.
[0089] Embodiment 2
[0090] Example 2 Process as Figure 4 As shown, the difference from the first embodiment is that the second embodiment contains the use of daily production data to calibrate the normal coefficient a i Through this correction step, the constant coefficient a can be further adjusted i , further improving the prediction accuracy of the prediction model.
[0091] Specifically, the process of building a prediction model includes the following operations:
[0092] 1) Collect and organize historical gasoline blending formula data, and use the analytical data of each historical blending formula, including: the volume fraction of each component oil (or the mass fraction and the corresponding density, which determines the volume fraction), the research octane number of each component oil, and the MTBE sensitivity factor, olefins, and aromatics volume fraction of each component oil.
[0093] 2) Clean the data, remove missing information, or data that obviously does not correspond to the properties of the corresponding component oil, to ensure the accuracy and rationality of the data, such as Sample ID 12 in Table 2.
[0094] 3) The cleaned data is brought into the prediction model, and the coefficient regression is performed using the least squares method to obtain the constant coefficient a i .
[0095] 4) Calculate the constant coefficient a i The forecasting model is used for daily production forecasting.
[0096] 5) Feedback daily production data and analysis test structure to sample data to correct the constant coefficient a i .
[0097] Table 1 Oil quality of each component
[0098]
[0099] Table 2 Raffinate oil properties
[0100]
[0101] Table 3S Zorb Oil Properties
[0102]
[0103] Table 4 Properties of reforming oil
[0104]
[0105] Table 5 Properties of isooctane
[0106]
[0107] Table 6 Properties of blended gasoline
[0108]
[0109] In one embodiment, Figure 5 As shown, a device for predicting the octane number of blended gasoline is provided, including a processor and a memory, wherein the processor executes a program stored in the memory to implement corresponding functions.
[0110] The processor includes a kernel, and the kernel calls the corresponding program unit from the memory. One or more kernels can be set, and the method for predicting gasoline octane number is realized by adjusting kernel parameters.
[0111] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.
[0112] Specifically, the above-mentioned device for predicting gasoline octane number includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 is used to provide computing and control capabilities. The memory includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, the above-mentioned method for predicting gasoline octane number is implemented. The display screen A04 can be a liquid crystal display screen or an electronic ink display screen, and the input device A05 can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.
[0113] An embodiment of the present application provides a storage medium on which a program is stored. When the program is executed by a processor, the method for predicting the octane number of blended gasoline is implemented.
[0114] An embodiment of the present application provides a processor, which is used to run a program, wherein the program executes the above-mentioned method for predicting the octane number of blended gasoline when running.
[0115] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.
[0116] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. 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 processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device 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.
[0117] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising 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.
[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0119] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0120] The memory may include non-permanent memory 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.
[0121] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. 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, magnetic tape magnetic 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 computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0122] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0123] The above are only embodiments of the present application and are 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 within the scope of the claims of the present application.
Claims
1. A method for predicting the octane number of a blended gasoline, wherein the blended gasoline is blended by a plurality of component oils, wherein the plurality of component oils include MTBE component oil, characterized in that: The method comprises: Obtaining the volume fraction of each component oil of the blended gasoline; Obtaining the octane number of each component oil of the blended gasoline; Obtaining the MTBE sensitivity factor, olefin and aromatic volume fractions of each component oil of the blended gasoline; and The volume fraction of each component oil of the blended gasoline, MTBE sensitivity factor, octane number, olefin and aromatic volume fractions are input into the following prediction model to obtain the octane number of the blended gasoline: in, RON is the octane number of the blended gasoline, is the volume weighted average of the octane numbers of the component oils, which is obtained according to the volume fraction and octane number of the component oils; is the volume weighted average of the olefin volume fractions of each component oil, is the volume weighted average of the squares of the olefin volume fractions of the component oils, and is obtained based on the volume fractions of the component oils and the olefin volume fractions; is the volume weighted average of the aromatic volume fractions of each component oil, is the volume weighted average of the squares of the aromatic volume fractions of the component oils, and is obtained based on the volume fractions of the component oils and the aromatic volume fractions; is the volume weighted average of the MTBE sensitivity factors of the component oils, It is the volume weighted average of the squares of the MTBE sensitivity factors of the component oils, and is obtained according to the volume fractions of the component oils and the MTBE sensitivity factors; M is the volume fraction of the MTBE component oil; a i is a constant coefficient.
2. The method according to claim 1, characterized in that Among them, w i is the volume fraction of the i-th component oil, R i is the octane number of the i-th component oil.
3. The method according to claim 1, characterized in that: Among them, w i is the volume fraction of the i-th component oil, O i is the olefin volume fraction of the i-th component oil.
4. The method according to claim 1, characterized in that: Among them, w i is the volume fraction of the i-th component oil, A i is the volume fraction of aromatics in the i-th component oil.
5. The method according to claim 1, characterized in that: Among them, w i is the volume fraction of the i-th component oil, K i is the MTBE sensitivity factor of the i-th component oil.
6. The method according to claim 1, characterized in that The MTBE sensitivity factors of each component oil are as follows: Wax oil catalytic gasoline: 82; Heavy oil catalytic gasoline: 78; S Zorb Oil: 82; Reformed gasoline: 0; Naphtha or raffinate: 100; MTBE: 0; and / or Alkylate or isooctane oil:
85.
7. The method according to claim 1, characterized in that The constant coefficient a i Obtained through least squares regression of historical blended data.
8. The method according to claim 7, characterized in that make A=[a1,a2,a3] T , that is, XA = Y, using the least squares method to perform coefficient regression, through A = (X T X) -1 X T Y, get the constant coefficient a i .
9. The method according to claim 1, characterized in that: The method further includes: Using daily production data related to blended gasoline, the constant coefficient a is corrected i .
10. The method according to any one of claims 1 to 9, characterized in that The octane number stated is a research octane number.
11. A device for predicting the octane number of blended gasoline, the device comprising: Memory; and A processor, the processor being configured to execute the method for predicting the octane number of blended gasoline according to any one of claims 1 to 10.
12. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the method for predicting the octane number of blended gasoline according to any one of claims 1 to 10.