Method and device for predicting octane number of blended gasoline
By constructing a prediction model based on least squares regression and combining historical data to calculate the normal coefficient, the problem of low prediction accuracy of gasoline octane is solved, and accurate prediction of gasoline octane is achieved, especially in the oil containing MTBE component In case.
Patent Information
- Application Number
- CN202310822545.X
- 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 limited prediction accuracy during the blending process, and it is difficult to accurately reflect the nonlinear law of gasoline octane, especially the influence of MTBE component oil.
By obtaining the volume fraction, octane number, alkane, olefin and aromatic hydrocarbon volume fraction of each component oil, and using the least squares method to obtain constant coefficients, construct a prediction model, and combining historical blending data correction coefficients to improve prediction accuracy.
The prediction accuracy of gasoline octane number is significantly improved, and the linear and nonlinear effects of each component oil on blended gasoline octane number can be more accurately reflected, improving the reliability of blended production.
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Figure CN119274683A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of petrochemical industry, 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 existing component oils, adjusting the ratio of each component oil allows for accurate prediction and estimation of the blended octane number, improving the success rate of a single blend and providing valuable guidance for actual blending production.
[0003] However, in the actual blending process, the blending octane number exhibits certain nonlinear behaviors. Gasoline octane number is not only related to the octane number of each gasoline component itself, but is also influenced by other properties of the blending components. Although there are currently some publicly available models for predicting the octane number of blended gasoline, their accuracy is still limited by the factors considered by the models. Improving gasoline octane number prediction models and accurately predicting the octane number of blended gasoline remains a challenge. Summary of the Invention
[0004] The purpose of the embodiments of the present application is to provide a method and apparatus for predicting the octane number of blended gasoline, which can improve the prediction accuracy of the gasoline octane number.
[0005] To achieve the above objectives, the present application provides, in a first aspect, a method for predicting the octane number of a blended gasoline, wherein the blended gasoline is blended by combining multiple component oils, wherein the multiple component oils include MTBE (methyl tertiary butyl ether) component oil. 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 volume fractions of paraffins, olefins, and aromatics of each component oil of the blended gasoline; and
[0009] The volume fraction, octane number, paraffin, olefin and aromatic volume fractions 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 based on the volume fraction and octane number of the component oils;
[0014] is the volume-weighted average of the olefin volume fractions of the component oils, 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 the various component oils, 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 paraffin volume fractions of the component oils, which is obtained based on the volume fractions of the component oils and the paraffin volume fraction; M is the volume fraction of the MTBE component oil;
[0017] a i is a constant coefficient, the a i These are a1 to a5 in the above prediction model.
[0018] In the embodiments of this 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.
[0019] In the embodiments of this 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.
[0020] In the embodiments of this application, Among them, 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.
[0021] In the embodiments of this application, Among them, w i is the volume fraction of the i-th component oil, P i is the volume fraction of paraffins in the i-th component oil.
[0022] In the embodiment of the present application, the constant coefficient a i Obtained through least squares regression of historical blended data.
[0023] In the embodiment of the present application, A=[a1,a2,a3,a4,a5]T , that is, XA=Y, and the coefficient regression is performed using the least square method, through A=(X T X) -1 X T Y, get the constant coefficient a i .
[0024] 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 .
[0025] In the embodiments of the present application, the octane number may be a research octane number or an octane number characterized by other methods.
[0026] In a second aspect, the present application provides a device for predicting the octane number of blended gasoline. The device comprises: a memory; and a processor, wherein the processor is configured to execute the above-mentioned method for predicting the octane number of blended gasoline.
[0027] A third aspect of the present application provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned method for predicting the octane number of blended gasoline.
[0028] 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.
[0029] This technical solution considers both the linear impact of each component oil's octane number on the blended gasoline octane number and the nonlinear impact of each component oil's properties on the blended gasoline octane number, particularly the nonlinear impact of MTBE components. This ultimately improves prediction accuracy. This prediction model is used to predict the octane number of blended gasoline during routine gasoline blending production at petroleum refining companies, providing a reference for daily production scheduling.
[0030] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] 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 detailed description, 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:
[0032] Figure 1 The following schematically shows a flow chart of a method for predicting the octane number of blended gasoline according to an embodiment of the present application;
[0033] Figure 2The following schematically shows a flow chart of a method for predicting the octane number of blended gasoline according to another embodiment of the present application;
[0034] Figure 3 This is a diagram showing the effect of octane number prediction according to the method for predicting the octane number of blended gasoline of the present invention;
[0035] Figure 4 A schematic diagram schematically illustrates a flow chart of a method for predicting the octane number of blended gasoline according to yet another embodiment of the present application; and
[0036] 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
[0037] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions 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.
[0038] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.
[0039] 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 for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.
[0040] Figure 1 FIG. 1 is a flow diagram of a method for predicting the octane number of blended gasoline in one embodiment. Figure 1The 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. In addition, 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.
[0041] The gasoline research octane number (OCN) reflects gasoline's anti-knock performance. It's numerically equal to the volume percentage of isooctane in a standard fuel that, under specified conditions, exhibits the same anti-knock performance as the test sample. The standard fuel consists of a mixture of isooctane and n-heptane. Isooctane serves as a standard for excellent anti-knock performance, with an octane number of 100, while n-heptane serves as a standard for poor anti-knock performance, with an octane number of 0. By mixing these two hydrocarbons in varying volume ratios, standard fuels with octane numbers ranging from 0 to 100 can be prepared.
[0042] like Figure 1 As shown, the present application provides a method for predicting the octane number (e.g., research octane number) of blended gasoline, 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:
[0043] Step 102, obtaining the volume fraction of each component oil of the blended gasoline;
[0044] Step 104, obtaining the octane number (e.g., research octane number) of each component oil of the blended gasoline;
[0045] Step 106, obtaining the volume fractions of paraffins, olefins, and aromatics of each component oil of the blended gasoline; and
[0046] Step 108: Input the volume fractions of the component oils, octane numbers (e.g., research octane numbers), paraffins, olefins, and aromatics of the blended gasoline into the following prediction model to obtain the octane number of the blended gasoline:
[0047]
[0048] in,
[0049] RON is the octane number of the blended gasoline,
[0050] is the volume-weighted average of the octane numbers of the component oils, which is obtained based on the volume fraction and octane number of the component oils;
[0051] is the volume-weighted average of the olefin volume fractions of the component oils, 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;
[0052] is the volume-weighted average of the aromatic volume fractions of the various component oils, 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;
[0053] P is the volume-weighted average of the paraffin volume fractions of the component oils, which is obtained based on the volume fractions of the component oils and the paraffin volume fraction;
[0054] M is the volume fraction of the MTBE component oil;
[0055] a i is a constant coefficient, the a i These are a1 to a5 in the above prediction model.
[0056] In the embodiments of this 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.
[0057] In the embodiments of this 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.
[0058] In the embodiments of this application, Among them, 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.
[0059] In the embodiments of this application, Among them, w i is the volume fraction of the i-th component oil, P i is the volume fraction of paraffins in the i-th component oil.
[0060] It should be noted that although various specific formulas are given above, it is possible for those skilled in the art 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.
[0061] The above technical solution considers the linear influence of each component oil's octane number on the blended gasoline octane number, as well as the nonlinear influence of each component oil's properties on the blended gasoline, particularly the nonlinear influence of the MTBE component oil. This ultimately improves prediction accuracy. The following comparison between the Examples and Comparative Examples demonstrates the high prediction accuracy of this solution.
[0062] The above-mentioned prediction model can be established in the following manner. Historical blending recipes are collected and analytical data for each historical blending recipe is used (this may include accurately measured research octane numbers of each component oil, volume fractions of paraffins, aromatics, olefins, etc., and octane data for the final blended oil) to construct a complete recipe data sample. A large amount of complete historical recipe data is accumulated as a recipe sample library, and the constant coefficients in the prediction model are calculated using the least squares method. Furthermore, the existing recipe database can be continuously updated during production practice to verify and correct the constant coefficients to improve the model's prediction accuracy.
[0063] Two specific embodiments and a comparative example of this case are given below.
[0064] Example 1
[0065] Example 1 process as follows Figure 2 As shown in FIG, the figure shows the complete process of establishing a prediction model and using the prediction model to predict octane number.
[0066] Specifically, the process of building a prediction model includes the following operations:
[0067] 1) Collect and organize historical gasoline blending recipe data, using analytical data from each historical blending recipe, including: the volume fraction (or mass fraction and corresponding density, which determines the volume fraction) of each component oil, the research octane number of each component oil, and the volume fractions of paraffins, olefins, and aromatics in each component oil. Sample data for the examples are shown in Tables 1-6.
[0068] 2) Clean the data by removing missing information or data that is obviously inconsistent with the properties of the corresponding component oil to ensure the accuracy and rationality of the data, such as Sample ID 12 in Table 2.
[0069] 3) Substitute the cleaned data into the following prediction model,
[0070]
[0071] in,
[0072] 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;
[0073] 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;
[0074] 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;
[0075] Among them, w i is the volume fraction of the i-th component oil, P i is the volume fraction of paraffins in the i-th component oil.
[0076] M is the volume fraction of MTBE component oil;
[0077] a i is a constant coefficient,
[0078] make A=[a1,a2,a3,a4,a5] T , that is, XA=Y, and the coefficient regression is performed using the least square method, through A=(X T X) -1 X T Y, get the constant coefficient a i .
[0079] 4) After calculating the constant coefficient a i After that, the prediction model is established and can be used for daily production prediction.
[0080] In this embodiment, the model coefficients a1 = -0.0001, a2 = 0.0017, a3 = 0.4450, a4 = -1.2830, and a5 = 0.6304. The deviation between the model prediction value and the measured value is as follows: Figure 3 As shown, the calculated mean of the deviation is 0.336 and the standard deviation is 0.388.
[0081] Comparative Example 1
[0082] Collect and organize historical gasoline blending recipe data, using analytical data from each historical blending recipe, including: the volume fraction (or mass fraction and corresponding density) of each component oil, and the research octane number of each component oil. Example sample data are shown in Tables 1-6.
[0083] Clean the data and remove missing information or data that is obviously inconsistent with the properties of the corresponding component oil to ensure the accuracy and rationality of the data, such as Sample ID 12 in Table 2.
[0084] 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 i-th component oil.
[0085] In this comparative example, the deviation between the predicted value and the measured value of the linear prediction model was calculated, with a mean of 0.556 and a standard deviation of 0.638. This indicates that both the mean and standard deviation are much higher than those of the first embodiment of this case, reflecting the accuracy of the prediction model of this embodiment.
[0086] Example 2
[0087] Example 2 process as follows 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.
[0088] Specifically, the process of building a prediction model includes the following operations:
[0089] 1) Collect and organize historical gasoline blending recipe data, using analytical and laboratory data from each historical blending recipe, including: the volume fraction (or mass fraction and corresponding density, which determines the volume fraction) of each component oil, the research octane number of each component oil, and the volume fractions of paraffins, olefins, and aromatics of each component oil.
[0090] 2) Clean the data by removing missing information or data that is obviously inconsistent with the properties of the corresponding component oil to ensure the accuracy and rationality of the data, such as Sample ID 12 in Table 2.
[0091] 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 .
[0092] 4) Calculate the constant coefficient a i The forecast model is used for daily production forecasting.
[0093] 5) Feedback daily production data and analysis test structure into sample data to calibrate the constant coefficient a i .
[0094] Table 1 Oil quality of each component
[0095]
[0096] Table 2 Raffinate oil properties
[0097]
[0098] Table 3S Zorb Oil Properties
[0099]
[0100] Table 4 Properties of reformed oil
[0101]
[0102] Table 5 Properties of isooctane
[0103]
[0104] Table 6 Properties of blended gasoline
[0105]
[0106] In one embodiment, Figure 5 As shown, a device for predicting the octane number of blended gasoline is provided, which includes a processor and a memory, and the processor executes a program stored in the memory to implement corresponding functions.
[0107] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be provided, and the method for predicting gasoline octane number can be implemented by adjusting kernel parameters.
[0108] 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.
[0109] 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 via a system bus. 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 via 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 or an electronic ink display screen, and the input device A05 can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.
[0110] An embodiment of the present application provides a storage medium having a program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting the octane number of blended gasoline.
[0111] An embodiment of the present application provides a processor, which is configured to run a program, wherein the program executes the above-mentioned method for predicting the octane number of blended gasoline when running.
[0112] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can 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 can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
[0113] 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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.
[0114] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.
[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device 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.
[0116] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.
[0117] 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.
[0118] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. 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 disc read-only memory (CD-ROM), digital versatile disc (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 transitory computer-readable media such as modulated data signals and carrier waves.
[0119] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.
[0120] The above are merely 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 modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all 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 volume fractions of paraffin, olefin and aromatic hydrocarbons of each component oil of the blended gasoline; and The volume fraction, octane number, paraffin, 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: 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 paraffin volume fractions of the component oils, and is obtained based on the volume fractions of the component oils and the paraffin volume fraction; 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, P i is the volume fraction of paraffins in the i-th component oil.
6. The method according to claim 1, characterized in that The constant coefficient a i Obtained through least squares regression of historical blended data.
7. The method according to claim 6, characterized in that make A=[a1,a2,a3,a4,a5] 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 .
8. 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 .
9. The method according to any one of claims 1 to 8, characterized in that The octane number stated is a research octane number.
10. 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 9.
11. 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 9.