Molecular-level inline gasoline blending optimization method and system, and electronic device

Abstract

A molecular-level online gasoline blending optimization method and system, the method comprising: acquiring in real time an inline near-infrared spectrum of each gasoline blending component, and inputting the inline near-infrared spectrum into a pre-constructed intelligent inversion model to obtain a first compound composition corresponding to each gasoline blending component among the gasoline blending components; on the basis of the first compound composition and a preset initial gasoline blending ratio, predicting a second compound composition of a finished gasoline; then, on the basis of the second compound composition and a preset molecular-level physical property model, calculating macroscopic physical properties corresponding to the finished gasoline; and on the basis of the macroscopic physical properties and a preset constraint index, iteratively updating the initial gasoline blending ratio by means of solving for a pre-constructed multi-objective optimization model having the lowest mass surplus and the lowest cost as objectives, so as to obtain the optimal gasoline blending ratio, thereby reducing the operation complexity and improving the efficiency and accuracy of the blending process.

Description

A molecular-level online gasoline blending optimization method, system, and electronic equipment Technical Field

[0001] This invention relates to the field of chemical production technology, and in particular to a molecular-level online gasoline blending optimization method, system, and electronic equipment. Background Technology

[0002] Gasoline blending, as the final stage in gasoline production for petrochemical and refining enterprises, directly determines the economic benefits of these companies. In the actual blending process, numerous gasoline components are involved, including catalytically cracked hydrogenated gasoline and isomerized gasoline. Many quality indicators and physical properties are also involved, such as octane number and distillation range. Currently, in gasoline blending, production personnel must first obtain the macroscopic properties of the blending components using time-consuming offline testing methods, then calculate the formula based on manual experience, and issue blending quantity production instructions for each component to the blending workshop. This process is not only time-consuming and lacks data timeliness, but the blending formula often fails to meet expectations.

[0003] With the development of instrumental analysis technology, in addition to traditional macroscopic property detection, production personnel have begun to utilize offline analysis methods and models to determine the compound composition and macroscopic properties of gasoline samples. Offline gas chromatography is a commonly used method. By analyzing samples using gas chromatography, information on the concentration of individual hydrocarbon components or group composition (i.e., PIONA composition) of gasoline components can be obtained. Then, the relative content of each component is calculated using the relative peak areas of each peak in the sample. On the other hand, numerous simulation methods for constructing gasoline molecules have emerged. Simulation methods based on molecular-level property calculation models can obtain the macroscopic properties of gasoline components. Furthermore, based on correlation models between near-infrared spectroscopy and macroscopic properties, multiple macroscopic properties, including octane number, can also be estimated, increasing the frequency of analytical detection for gasoline blending to a certain extent.

[0004] However, the traditional offline laboratory testing methods mentioned above have poor real-time performance, cumbersome testing procedures, and long processing times. They cannot meet the needs of the frequent changes in oil properties during the blending process. Furthermore, the collection and analysis of offline samples require a significant amount of time and manpower, resulting in poor real-time performance. Offline gas chromatography technology can only obtain sample molecular information offline and cannot detect real-time online changes in oil molecules. The near-infrared technology model for macroscopic properties has poor extension. As oil molecules change, the model cannot capture the changes in molecules from a macroscopic perspective. This poor extension can easily lead to inaccurate predictions of gasoline blending formulas based on near-infrared technology, failing to meet gasoline production requirements. Moreover, gasoline blending methods based on online near-infrared spectroscopy for predicting macroscopic properties cannot obtain molecular information feedback, and the effectiveness of the formula cannot be maintained in the long term as operating conditions change and adjustments are made.

[0005] Summary of the Invention

[0006] To address the aforementioned technical problems, this invention discloses a molecular-level online gasoline blending optimization method, system, and electronic device. This device is used to obtain the compound composition of component gasoline samples in real time and then calculate the compound composition of the finished gasoline, thereby improving the efficiency and real-time performance of gasoline blending, while reducing the complexity of operation and improving the efficiency and accuracy of the blending process.

[0007] To achieve the above objectives, in a first aspect, the present invention discloses a molecular-level online gasoline blending optimization method, comprising:

[0008] The online near-infrared spectra of each blending component gasoline in the blending pipeline are acquired in real time, and the online near-infrared spectra are input into a pre-constructed intelligent inversion model. The first compound composition corresponding to each blending component gasoline is obtained through the intelligent inversion model.

[0009] Based on the composition of the first compound and the preset initial gasoline blending ratio, the composition of the second compound in the finished gasoline is predicted by linear calculation.

[0010] The macroscopic properties of the finished gasoline are calculated based on the composition of the second compound and the preset molecular-level property model.

[0011] Based on the macroscopic physical properties and preset constraints, the initial gasoline blending ratio is iteratively updated by solving a pre-constructed multi-objective optimization model with the objectives of minimizing quality excess and minimizing cost, to obtain the optimal gasoline blending ratio.

[0012] This invention discloses a molecular-level online gasoline blending optimization method. First, it acquires the online near-infrared spectra of each gasoline component in the blending pipeline in real time, enabling online compositional analysis of the gasoline components. This solves the technical problems of low efficiency and complex operation caused by offline detection, achieving real-time optimization of gasoline blending and improving its efficiency and accuracy. Furthermore, this invention utilizes a pre-constructed intelligent inversion model to perform online analysis of the compound composition of the gasoline components, enabling real-time online monitoring of changes in the compound composition of the finished gasoline. This allows for the exploration of the positive or negative effects of compound composition on changes in the gasoline blending results, thereby improving the model's extensibility and ultimately enhancing the accuracy of gasoline blending.

[0013] Furthermore, after analyzing the compound composition of the component gasoline, the compound composition of the finished gasoline is first predicted by linear calculation using the set initial gasoline blending ratio. Then, the macroscopic properties of the finished gasoline are calculated using a molecular-level property model. Based on the macroscopic properties, the initial gasoline blending ratio is optimized by a pre-constructed multi-objective optimization model with the goals of minimizing excess quality and minimizing cost, to obtain the optimal blending ratio, achieve a better balance between performance and economy, and improve the quality and performance of gasoline.

[0014] As a preferred example, the real-time acquisition of the online near-infrared spectra of each blending component gasoline in the blending pipeline, and the input of the online near-infrared spectra into a pre-constructed intelligent inversion model, includes:

[0015] The initial online near-infrared spectra of each of the blending components of gasoline are acquired in real time using a preset online near-infrared spectrometer, and the initial online near-infrared spectra are processed to obtain the online near-infrared spectra.

[0016] The online near-infrared spectra of each of the blending components of gasoline are input into the intelligent inversion model. The intelligent inversion model analyzes and processes the online near-infrared spectra to obtain the first compound composition corresponding to each blending component of gasoline. The first compound composition includes the types of compounds and the concentration of each compound.

[0017] This invention utilizes an online near-infrared spectrometer and the aforementioned intelligent inversion model to obtain the compound composition of component gasoline in real time. No manual operation is required; the spectrum is analyzed in real time to obtain the gasoline compound composition. The operation is simple and easy to implement, reducing operational complexity and improving the efficiency of the blending process.

[0018] As a preferred example, the construction process of the intelligent inversion model includes:

[0019] The collected offline component gasoline samples were analyzed using a pre-set offline gas chromatograph to obtain the compound composition corresponding to each offline component gasoline sample.

[0020] The offline near-infrared spectra of each of the offline component gasoline samples were obtained using a preset offline near-infrared spectrometer.

[0021] An offline component gasoline sample database is constructed based on the compound composition and offline near-infrared spectrum of each offline component gasoline sample.

[0022] The intelligent inversion model is constructed based on the offline component gasoline sample database using a preset clustering algorithm.

[0023] This invention utilizes the composition of compounds obtained from offline gas chromatography and the offline near-infrared spectra obtained from offline near-infrared spectroscopy to construct an intelligent inversion model. This model enables the analysis of offline component gasoline samples at the molecular level, timely detection of changes in compound concentration, and further correlation between compound composition and the macroscopic properties of finished product samples, thereby improving the accuracy of gasoline tuning.

[0024] As a preferred example, the step of predicting the second compound composition of the finished gasoline by linear calculation based on the first compound composition and a preset initial gasoline blending ratio includes:

[0025] Based on the initial gasoline blending ratio, determine the proportion of each blending component gasoline;

[0026] Based on the stated ratio and the composition of the first compound, the composition of the second compound in the finished gasoline is calculated using a preset weighted summation method.

[0027] This invention utilizes the first compound composition of gasoline blending components to calculate the second compound composition of the finished gasoline product after mixing. Subsequently, the macroscopic properties of the finished gasoline product are predicted based on the second compound composition, which improves both analytical efficiency and the accuracy of sample analysis, thereby enhancing the accuracy of blending optimization.

[0028] As a preferred example, the calculation of the macroscopic properties of the finished gasoline based on the composition of the second compound and a preset molecular-level property model includes:

[0029] Based on the composition of the second compound, the macroscopic properties of the finished gasoline are calculated using the mixing rules set in the molecular-level property model; wherein, the macroscopic properties include research octane number, motor octane number, vapor pressure, density, benzene volume fraction, aromatics volume fraction, olefins volume fraction, oxygen mass fraction, sulfur mass fraction, and distillation range; wherein, the mixing rules corresponding to the research octane number and motor octane number are as follows:

[0030] ON ij =(ON) i +ON j ) / 2

[0031] Among them, v i The volume percentage of the i-th compound in the finished gasoline; The contribution value of the i-th compound in the finished gasoline to the octane number; ON i The octane number of the i-th compound in the finished gasoline is ON. j q is the octane number of the j-th compound in the finished gasoline.ij Let be the blending effect coefficient of the i-th compound and the j-th compound in the finished gasoline;

[0032] The mixing rule for the vapor pressure is as follows:

[0033] Wherein, the v i r is the volume percentage of the i-th compound in the finished gasoline; i Let be the activity coefficient of the i-th compound in the finished gasoline at the measurement temperature; Let be the saturated vapor pressure of the i-th compound in the finished gasoline at the measurement temperature V; PR is the Reid vapor pressure; k is the first model parameter; b is the second model parameter;

[0034] The mixing rules for density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction are as follows:

[0035] Wherein, fp represents the density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction of the mixture; and v represents... i The volume percentage of the i-th compound in the finished gasoline; the f i The density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction of the i-th compound in the finished gasoline;

[0036] The mixing rules for the distillation range are as follows:

[0037] The distribution ratio of different compounds with temperature in the vicinity of their boiling points is obtained by the Weber distribution; wherein, the expression of the Weber distribution is:

[0038] Normalizing the allocation ratio, we get: w = ω / ∑ω;

[0039] According to the content of each compound in the finished gasoline z i Calculate its temperature range [T] within the preset temperature range L ,T U The allocation within ] is z i ·w;

[0040] Wherein, k is a first distribution parameter, λ is a second distribution parameter, and the distribution ratio of different types of compounds with temperature is controlled by adjusting the first and second distribution parameters; T b,i The boiling point of the compound; the T LT represents the highest temperature threshold corresponding to each compound in the finished gasoline. U The lowest temperature threshold corresponding to each compound in the finished gasoline;

[0041] Based on the cumulative distribution amount and the temperature range, a true boiling point curve equation is constructed, and the true boiling point curve equation is converted into an Engler range; wherein, the expression for the Engler range is:

[0042] Wherein, T0, T10, T30, T50, T70, T90, and Tf are the temperature values ​​at which the distillation volume of the true boiling point curve is 0%, 10%, 30%, 50%, 70%, 90%, and 98%, respectively; and T... E This indicates the distillation temperature corresponding to different distillation volumes.

[0043] This invention calculates the macroscopic properties of finished gasoline online based on molecular-level property models and the compound composition of finished gasoline. Then, it uses the macroscopic properties and a pre-constructed multi-objective optimization model with the goals of minimizing excess mass and minimizing cost to solve for the optimal adjustment ratio. This allows for real-time detection of changes in compound concentration and macroscopic properties in finished gasoline through changes in the adjustment ratio, thereby improving the accuracy of gasoline blending.

[0044] As a preferred example, the process of iteratively updating the initial gasoline blending ratio by solving a pre-constructed multi-objective optimization model aimed at minimizing quality excess and minimizing cost to obtain the optimal gasoline blending ratio includes:

[0045] Based on the macroscopic physical properties and preset constraints, the multi-objective optimization model is solved using the interior point method. If the solution is successful, the initial gasoline blending ratio is determined as the optimal gasoline blending ratio; if the solution fails, the initial gasoline blending ratio is iteratively updated until the solution is successful, and the gasoline blending ratio corresponding to the successful solution is output as the optimal gasoline blending ratio. The constraints include at least one of the following: Research Octane Number (RON), Motor Octane Number (MAN), Red Vapor Pressure, Engler Range, Density, Benzene Volume Fraction, Aromatics Volume Fraction, Olefins Volume Fraction, Oxygen Mass Fraction, and Sulfur Mass Fraction. The Engler Range includes the initial boiling point, 10% distillation temperature, 50% distillation temperature, 90% distillation temperature, and final boiling point.

[0046] The expression for the multi-objective optimization model is:

[0047] s.th(x)=∑x-1=0

[0048] g i (x)=ψ1-φ i (x)≤0i=1,2,3…,M

[0049] χ i (x)=φ i (x)-ψ u ≤0i=1,2,3…,M

[0050] Wherein, f(x) represents the objective function; n represents the number of physical properties involved in the optimization; x represents the proportion of each blending component gasoline involved in the blending; and Φ i (x) represents a function of the i-th property involved in the harmonic process; Φi is calculated using the molecular-level property model; Ψ i The w represents the target value of the i-th property involved in the reconciliation; i The P represents the deviation weight of the i-th property involved in the reconciliation; i The excess equivalent cost of the i-th physical property of the blending component gasoline participating in the blending; K represents the unit price vector of the blending component gasoline participating in the blending; I represents the lower limit of the constraint index; u represents the upper limit of the constraint index; M represents the total number of constraint indexes.

[0051] This invention solves for the optimal adjustment ratio in the multi-objective optimization model, enabling real-time detection of changes in compound concentration and macroscopic properties in the finished gasoline through changes in the adjustment ratio, thereby improving the accuracy of gasoline blending.

[0052] Secondly, the present invention discloses a molecular-level online gasoline blending optimization system, including an online analysis module, a molecular prediction module, a property calculation module and a blending optimization module;

[0053] The online analysis module is used to acquire the online near-infrared spectra of each blending component gasoline in the blending pipeline in real time, and input the online near-infrared spectra into the pre-constructed intelligent inversion model, and obtain the first compound composition corresponding to each blending component gasoline in each blending component gasoline through the intelligent inversion model;

[0054] The molecular prediction module is used to predict the second compound composition of the finished gasoline by linear calculation based on the first compound composition and the preset initial gasoline blending ratio.

[0055] The property calculation module is used to calculate the macroscopic properties of the finished gasoline based on the composition of the second compound and the preset molecular-level property model.

[0056] The blending optimization module is used to iteratively update the initial gasoline blending ratio based on the macroscopic physical properties and preset constraint indicators by solving a pre-constructed multi-objective optimization model with the objectives of minimizing quality excess and minimizing cost, in order to obtain the optimal gasoline blending ratio.

[0057] This invention discloses a molecular-level online gasoline blending optimization system. First, it acquires the online near-infrared spectra of each gasoline component in the blending pipeline in real time, enabling online compositional analysis of the gasoline components. This solves the technical problems of low efficiency and complex operation caused by offline detection, achieving real-time optimization of gasoline blending and improving its efficiency and accuracy. Furthermore, this invention utilizes a pre-constructed intelligent inversion model to perform online analysis of the compound composition of the gasoline components, enabling real-time online monitoring of changes in the compound composition of the finished gasoline. This allows for the exploration of the positive or negative effects of compound composition on changes in the gasoline blending results, thereby improving the model's extensibility and ultimately enhancing the accuracy of gasoline blending.

[0058] Furthermore, after analyzing the compound composition of the component gasoline, the compound composition of the finished gasoline is first predicted by linear calculation using the set initial gasoline blending ratio. Then, the macroscopic properties of the finished gasoline are calculated using a molecular-level property model. Based on the macroscopic properties, the initial gasoline blending ratio is optimized by a pre-constructed multi-objective optimization model with the goals of minimizing excess quality and minimizing cost, to obtain the optimal blending ratio, achieve a better balance between performance and economy, and improve the quality and performance of gasoline.

[0059] As a preferred example, the online analysis module includes a spectral unit and an inversion unit;

[0060] The spectral unit is used to acquire the initial online near-infrared spectra of each of the blending components of gasoline in real time according to a preset online near-infrared spectrometer, and to perform spectral processing on the initial online near-infrared spectra to obtain the online near-infrared spectra.

[0061] The inversion unit is used to input the online near-infrared spectra of each of the blending component gasolines into the intelligent inversion model, and to analyze and process the online near-infrared spectra through the intelligent inversion model to obtain the first compound composition corresponding to each blending component gasoline; wherein, the first compound composition includes the types of compounds and the concentration of each compound.

[0062] This invention utilizes an online near-infrared spectrometer and the aforementioned intelligent inversion model to obtain the compound composition of component gasoline in real time. No manual operation is required; the spectrum is analyzed in real time to obtain the gasoline compound composition. The operation is simple and easy to implement, reducing operational complexity and improving the efficiency of the blending process.

[0063] As a preferred example, the molecular prediction module includes a scaling unit and a combination unit;

[0064] The proportioning unit is used to determine the proportion of each blending component gasoline according to the initial gasoline blending ratio;

[0065] The combination unit is used to calculate the second compound composition of the finished gasoline according to the ratio and the first compound composition by a preset weighted summation method.

[0066] This invention utilizes the first compound composition of gasoline blending components to calculate the second compound composition of the finished gasoline product after mixing. Subsequently, the macroscopic properties of the finished gasoline product are predicted based on the second compound composition, which improves both analytical efficiency and the accuracy of sample analysis, thereby enhancing the accuracy of blending optimization.

[0067] Thirdly, the present invention also discloses an electronic device, comprising: a memory having a computer program stored thereon; and a processor for executing the computer program in the memory to implement the steps of the method as described in the first aspect. Attached Figure Description

[0068] Figure 1: A schematic flowchart of a molecular-level online gasoline blending optimization method disclosed in an embodiment of the present invention;

[0069] Figure 2: A schematic diagram of the structure of a molecular-level online gasoline blending and optimization system disclosed in an embodiment of the present invention;

[0070] Figure 3: A schematic flowchart of a molecular-level online gasoline blending optimization method disclosed in another embodiment of the present invention;

[0071] Figure 4: A schematic diagram of the near-infrared spectrum of a hydrogenated gasoline disclosed in another embodiment of the present invention;

[0072] Figure 5: A schematic diagram of the near-infrared spectrum of reformed gasoline disclosed in another embodiment of the present invention;

[0073] Figure 6: A schematic diagram of the online near-infrared spectrum of gasoline to be inverted, disclosed in another embodiment of the present invention;

[0074] Among them, 201 is the online analysis module; 202 is the molecular prediction module; 203 is the property calculation module; and 204 is the harmonic optimization module. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0076] Example 1

[0077] This embodiment discloses a molecular-level online gasoline blending optimization method. The specific implementation flow of the optimization method is shown in Figure 1, and mainly includes steps 101 to 104. The steps are as follows:

[0078] Step 101: Real-time acquisition of the online near-infrared spectra of each blending component gasoline in the blending pipeline, and inputting the online near-infrared spectra into a pre-constructed intelligent inversion model, and obtaining the first compound composition corresponding to each blending component gasoline in each blending component gasoline through the intelligent inversion model.

[0079] In this embodiment, the step mainly includes: acquiring the initial online near-infrared spectra of each of the blending component gasolines in real time according to a preset online near-infrared spectrometer, and performing spectral processing on the initial online near-infrared spectra to obtain the online near-infrared spectra; inputting the online near-infrared spectra of each of the blending component gasolines into the intelligent inversion model, and analyzing and processing the online near-infrared spectra through the intelligent inversion model to obtain the first compound composition corresponding to each blending component gasoline; wherein, the first compound composition includes the types of compounds and the concentration of each compound.

[0080] Furthermore, the construction process of the intelligent inversion model includes: analyzing the collected offline component gasoline samples using a preset offline gas chromatograph to obtain the compound composition corresponding to each offline component gasoline sample; obtaining the offline near-infrared spectrum corresponding to each offline component gasoline sample using a preset offline near-infrared spectrometer; constructing an offline component gasoline sample database based on the compound composition and offline near-infrared spectrum corresponding to each offline component gasoline sample; and constructing the intelligent inversion model based on the offline component gasoline sample database using a preset clustering algorithm.

[0081] In this embodiment, this step utilizes an online near-infrared spectrometer and the aforementioned intelligent inversion model to obtain the compound composition of the component gasoline in real time. No manual operation is required; the spectrum is analyzed in real time to obtain the gasoline compound composition. This operation is simple and easy to implement, reducing operational complexity and improving the efficiency of the blending process. Furthermore, the intelligent inversion model is constructed by correlating the compound composition obtained from offline gas chromatography analysis with the offline near-infrared spectra obtained from the offline near-infrared spectrometer. This allows for the analysis of the offline component gasoline sample at the molecular level, timely detection of changes in compound concentration, and thus correlation between the compound composition and the macroscopic properties of the finished sample, improving the accuracy of gasoline optimization.

[0082] Step 102: Based on the composition of the first compound and the preset initial gasoline blending ratio, predict the composition of the second compound of the finished gasoline through linear calculation.

[0083] In this embodiment, the step mainly includes: determining the proportion of each blending component gasoline according to the initial gasoline blending ratio; and calculating the second compound composition of the finished gasoline according to the ratio and the first compound composition by a preset weighted summation method.

[0084] In this embodiment, this step uses the first compound composition of the gasoline blending components to calculate the second compound composition of the finished gasoline after mixing. Then, the macroscopic properties of the finished gasoline are predicted by the second compound composition, which improves both the analysis efficiency and the accuracy of sample analysis, thereby improving the accuracy of blending optimization.

[0085] Step 103: Calculate the macroscopic properties of the finished gasoline based on the composition of the second compound and the preset molecular-level property model.

[0086] In this embodiment, the step mainly includes: calculating the macroscopic properties of the finished gasoline according to the composition of the second compound and the mixing rules set in the molecular-level property model; wherein, the macroscopic properties include research octane number, motor octane number, vapor pressure, density, benzene volume fraction, aromatics volume fraction, olefins volume fraction, oxygen mass fraction, sulfur mass fraction, and distillation range; wherein, the mixing rules corresponding to the research octane number and the motor octane number are as follows:

[0087] ON ij =(ON) i +ON j ) / 2

[0088] Among them, v i The volume percentage of the i-th compound in the finished gasoline; The contribution value of the i-th compound in the finished gasoline to the octane number; ON i The octane number of the i-th compound in the finished gasoline is ON. j q is the octane number of the j-th compound in the finished gasoline. ij Let be the blending effect coefficient of the i-th compound and the j-th compound in the finished gasoline;

[0089] The mixing rule for the vapor pressure is as follows:

[0090] Wherein, the v i r is the volume percentage of the i-th compound in the finished gasoline; i Let be the activity coefficient of the i-th compound in the finished gasoline at the measurement temperature; Let be the saturated vapor pressure of the i-th compound in the finished gasoline at the measurement temperature V; PR is the Reid vapor pressure; k is the first model parameter; b is the second model parameter;

[0091] The mixing rules for density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction are as follows:

[0092] Wherein, fp represents the density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction of the mixture; and v represents... i The volume percentage of the i-th compound in the finished gasoline; the f i The density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction of the i-th compound in the finished gasoline;

[0093] The mixing rules for the distillation range are as follows:

[0094] The distribution ratio of different compounds with temperature in the vicinity of their boiling points is obtained by the Weber distribution; wherein, the expression of the Weber distribution is:

[0095] Normalizing the allocation ratio, we get: w = ω / Σω;

[0096] According to the content of each compound in the finished gasoline z i Calculate its temperature range [T] within the preset temperature range L ,T U The allocation within ] is z i ·w;

[0097] Wherein, k is a first distribution parameter, λ is a second distribution parameter, and the distribution ratio of different types of compounds with temperature is controlled by adjusting the first and second distribution parameters; T b,i The boiling point of the compound; the T L T represents the highest temperature threshold corresponding to each compound in the finished gasoline. U The lowest temperature threshold corresponding to each compound in the finished gasoline;

[0098] Based on the cumulative distribution amount and the temperature range, a true boiling point curve equation is constructed, and the true boiling point curve equation is converted into an Engler range; wherein, the expression for the Engler range is:

[0099] Wherein, T0, T10, T30, T50, T70, T90, and Tf are the temperature values ​​at which the distillation volume of the true boiling point curve is 0%, 10%, 30%, 50%, 70%, 90%, and 98%, respectively; and T... E This indicates the distillation temperature corresponding to different distillation volumes.

[0100] In this embodiment, this step calculates the macroscopic properties of the finished gasoline online based on the molecular-level property model and the compound composition of the finished gasoline. Then, the optimal adjustment ratio is solved by using the macroscopic properties and a pre-constructed multi-objective optimization model with the goals of minimizing excess mass and minimizing cost. This allows for real-time detection of changes in compound concentration and macroscopic properties in the finished gasoline through changes in the adjustment ratio, thereby improving the accuracy of gasoline blending.

[0101] Step 104: Based on the macroscopic physical properties and preset constraint indicators, solve the pre-constructed multi-objective optimization model with the objectives of minimizing quality excess and minimizing cost, and iteratively update the initial gasoline blending ratio to obtain the optimal gasoline blending ratio.

[0102] In this embodiment, the step mainly includes: solving the multi-objective optimization model using the interior point method based on the macroscopic properties and preset constraint indicators; if the solution is successful, the initial gasoline blending ratio is determined as the optimal gasoline blending ratio; if the solution fails, the initial gasoline blending ratio is iteratively updated until the solution is successful, and the gasoline blending ratio corresponding to the successful solution is output as the optimal gasoline blending ratio; wherein, the constraint indicators include at least one of the following: research octane number, motor octane number, Red vapor pressure, Engler range, density, benzene volume fraction, aromatics volume fraction, olefins volume fraction, oxygen mass fraction, and sulfur mass fraction; the Engler range includes the initial boiling point, 10% distillation temperature, 50% distillation temperature, 90% distillation temperature, and final boiling point;

[0103] The expression for the multi-objective optimization model is:

[0104] s.th(x)=Σx-1=0

[0105] g i (x)=ψ I -φ i (x)≤0i=1,2,3…,M

[0106] χ i (x)=φ i (x)-ψ u ≤0i=1,2,3…,M

[0107] Wherein, f(x) represents the objective function; n represents the number of physical properties involved in the optimization; x represents the proportion of each blending component gasoline involved in the blending; and Φ i (x) represents a function of the i-th property involved in the harmonic process; the Φ i The Ψ was obtained through calculations using the molecular-level property model; i The w represents the target value of the i-th property involved in the reconciliation; i The P represents the deviation weight of the i-th property involved in the reconciliation; i The excess equivalent cost of the i-th physical property of the blending component gasoline participating in the blending; K represents the unit price vector of the blending component gasoline participating in the blending; I represents the lower limit of the constraint index; u represents the upper limit of the constraint index; M represents the total number of constraint indexes.

[0108] In this embodiment, this step solves for the optimal adjustment ratio in the multi-objective optimization model, so that changes in the concentration of compounds and macroscopic properties in the finished gasoline can be detected in real time through changes in the adjustment ratio, thereby improving the accuracy of gasoline blending.

[0109] On the other hand, this embodiment also discloses a molecular-level online gasoline blending optimization system. The specific structural composition of the optimization system is shown in Figure 2, including an online analysis module 201, a molecular prediction module 202, a property calculation module 203, and a blending optimization module 204.

[0110] The online analysis module 201 is used to acquire the online near-infrared spectra of each blending component gasoline in the blending pipeline in real time, and input the online near-infrared spectra into a pre-constructed intelligent inversion model, and obtain the first compound composition corresponding to each blending component gasoline in each blending component gasoline through the intelligent inversion model.

[0111] The molecular prediction module 202 is used to predict the second compound composition of the finished gasoline by linear calculation based on the first compound composition and the preset initial gasoline blending ratio.

[0112] The property calculation module 203 is used to calculate the macroscopic properties of the finished gasoline based on the composition of the second compound and the preset molecular-level property model.

[0113] The blending optimization module 204 is used to iteratively update the initial gasoline blending ratio based on the macroscopic physical properties and preset constraint indicators by solving a pre-constructed multi-objective optimization model with the objectives of minimizing quality excess and minimizing cost, so as to obtain the optimal gasoline blending ratio.

[0114] In this embodiment, the online analysis module 201 includes a spectral unit and an inversion unit.

[0115] The spectral unit is used to acquire the initial online near-infrared spectra of each of the blending components of gasoline in real time according to a preset online near-infrared spectrometer, and to perform spectral processing on the initial online near-infrared spectra to obtain the online near-infrared spectra.

[0116] The inversion unit is used to input the online near-infrared spectra of each of the blending component gasolines into the intelligent inversion model, and to analyze and process the online near-infrared spectra through the intelligent inversion model to obtain the first compound composition corresponding to each blending component gasoline; wherein, the first compound composition includes the types of compounds and the concentration of each compound.

[0117] In this embodiment, the molecular prediction module 202 includes a scaling unit and a combination unit.

[0118] The proportioning unit is used to determine the proportion of each blending component gasoline based on the initial gasoline blending ratio.

[0119] The combination unit is used to calculate the second compound composition of the finished gasoline according to the ratio and the first compound composition by a preset weighted summation method.

[0120] In this embodiment, in addition to the above-described method and system, an electronic device is also disclosed. The electronic device includes a memory storing a computer program thereon; and a processor for executing the computer program in the memory to implement the steps of the gasoline blending optimization method based on spectral inversion provided in this embodiment.

[0121] Example 2

[0122] This embodiment further discloses a molecular-level online gasoline blending optimization method. The specific implementation flow of the optimization method can be referred to Figure 3, mainly including steps 301 to 305, which are as follows:

[0123] Step 301: Detect each offline component gasoline sample according to the preset offline gas chromatograph and offline near-infrared spectrometer, obtain the compound composition and offline near-infrared spectrum of each offline component gasoline sample, and construct an intelligent inversion model based on the compound composition and offline near-infrared spectrum.

[0124] In this embodiment, the main steps are as follows: constructing an offline component gasoline sample database based on the compound composition and offline near-infrared spectrum of each offline component gasoline sample, and then constructing the intelligent inversion model based on the offline component gasoline sample database using a preset clustering algorithm.

[0125] Specifically, the offline component gasoline samples are first analyzed using an offline gas chromatograph (GC) to obtain the compound composition of each sample. During the analysis, the gasoline sample is first retrieved from the sampling device and brought back to the laboratory. Then, the sample is injected into the GC using an appropriate method, such as injector injection or gas microextraction. After injection, a suitable column is selected to separate the target compounds in the gasoline sample. The column selection is typically based on the chemical properties, volatility, and molecular size of the target compounds. A suitable column temperature program and a suitable carrier gas are also set to achieve separation. The column temperature program usually includes parameters such as initial temperature, heating rate, and holding time. The carrier gas includes nitrogen, hydrogen, and helium, and its selection depends on the analytical purpose, column type, and characteristics of the target compounds.

[0126] Next, the detector is selected. The appropriate detector is chosen according to the needs. Common detectors include flame ionization detector (FID), electron capture detector (ECD), and mass spectrometry (MS). Then, the obtained gas chromatograms and peak data are analyzed to determine the type and concentration of target compounds in each offline gasoline sample. Offline gasoline sample samples can obtain gas chromatograms through offline gas chromatography. The composition of gasoline compounds can be determined by the peak data on the gas chromatograms.

[0127] During the simultaneous offline detection of the compound composition, an offline near-infrared spectrometer is used to acquire the offline near-infrared spectrum of the gasoline sample. During the acquisition of the offline near-infrared spectrum, an appropriate spectrometer configuration is selected, including parameters such as light source, spectral range, and resolution, to ensure that the offline near-infrared spectrometer is in a suitable working and calibrated state. Next, a series of reference samples with known components are prepared, the composition range of which should cover the components that may appear in the sample to be tested. The preparation of the reference samples should conform to standard methods. Then, the offline near-infrared spectrometer is used to acquire the spectra of the sample to be tested and the reference samples, ensuring that the instrument is stable and that there is good contact between the sample and the instrument during the acquisition process.

[0128] After obtaining the offline near-infrared spectra and compound compositions of the offline component gasoline samples, spectral processing is first performed. The spectral processing methods include, but are not limited to, mean centering, standardization, vector normalization, maximum-minimum normalization, standard normal variable transformation, multivariate scattering correction, smoothing, derivative method, detrending algorithm, and Rubberband, to obtain several sets of processed offline near-infrared spectra. Then, spectral interval selection is performed, including but not limited to custom interval selection and full interval selection. Finally, based on clustering algorithms such as K-means, hierarchical clustering, and distance weighting, several sets of offline near-infrared spectra and corresponding gasoline molecule data are matched to construct the intelligent inversion model. The intelligent inversion model takes the offline near-infrared spectrum as input and the compound composition as output; when a new offline near-infrared spectrum is input, a new compound composition can be obtained.

[0129] Step 302: Obtain the online near-infrared spectrum corresponding to each blending component gasoline in the blending pipeline in real time using a near-infrared spectrometer, and input the online near-infrared spectrum into the intelligent inversion model to obtain the first compound composition corresponding to each blending component gasoline.

[0130] In this embodiment, the main steps are as follows: acquiring the online near-infrared spectra of each of the blending components of gasoline in real time using a preset online near-infrared spectrometer, analyzing and processing the online near-infrared spectra using the intelligent inversion model, and obtaining the first compound composition corresponding to each blending component of gasoline, wherein the first compound composition includes the types of compounds and the concentration of each compound.

[0131] Specifically, the online near-infrared spectrum of the blending component gasoline in the blending pipeline is acquired in real time using an online near-infrared spectrometer. The near-infrared spectrometer is an instrument capable of measuring the absorption and scattering characteristics of substances in the near-infrared light band. By irradiating the blending component gasoline with a near-infrared light source and collecting the near-infrared spectral data of the reflected or transmitted light from the blending component gasoline, the near-infrared spectrum can also be processed in the same way as the spectrum is processed during the intelligent inversion model construction process during the acquisition of the online near-infrared spectral data.

[0132] The processed online near-infrared spectrum is then input into the established intelligent inversion model. The intelligent inversion model is used to invert the compound composition. The intelligent inversion model analyzes and processes the online near-infrared spectral data to predict the types of compounds in each blending component gasoline and the concentration of each type of compound.

[0133] Step 303: Based on the first compound composition and the preset initial gasoline blending ratio, obtain the second compound composition of the finished gasoline through linear calculation.

[0134] In this embodiment, the main steps are: determining the proportion of each blending component gasoline according to the initial gasoline blending ratio, and then calculating the second compound composition of the finished gasoline using a preset weighted summation method.

[0135] Specifically, the initial blending ratio of each blending component gasoline is input, and the first compound composition of each blending component gasoline is combined with the first compound composition of each blending component gasoline. The second compound composition of the finished gasoline is obtained through linear calculation. Specifically, the blending ratio is the proportion of each blending component gasoline, and the linear calculation refers to the weighted summation method, where the weight is the proportion of each blending component gasoline. The weight of each blending component gasoline is multiplied by its corresponding first compound composition, and the products of the weights of multiple blending component gasolines and the first compound composition are summed to obtain the second compound composition of the finished gasoline.

[0136] Step 304: Based on the composition of the second compound and a pre-constructed molecular-level property model, calculate the macroscopic properties of the finished gasoline.

[0137] In this embodiment, the step is as follows: based on the composition of the second compound, the macroscopic properties of the finished gasoline are calculated using the mixing rules set in the molecular-level property model; wherein, the macroscopic properties include research octane number, motor octane number, vapor pressure, density, benzene volume fraction, aromatics volume fraction, olefins volume fraction, oxygen mass fraction, sulfur mass fraction, and distillation range.

[0138] Specifically, the following mixing rule applies to the study octane number and motor octane number:

[0139] ON ij =(ON) i +ON j ) / 2

[0140] Among them, v i The volume percentage of the i-th compound in the finished gasoline; The contribution value of the i-th compound in the finished gasoline to the octane number; ON i The octane number of the i-th compound in the finished gasoline is ON. j q is the octane number of the j-th compound in the finished gasoline. ij Let be the blending effect coefficient of the i-th compound and the j-th compound in the finished gasoline;

[0141] The vapor pressure adopts the following mixing rules:

[0142] Wherein, the v i r is the volume percentage of the i-th compound in the finished gasoline; i Let be the activity coefficient of the i-th compound in the finished gasoline at the measurement temperature; Let be the saturated vapor pressure of the i-th compound in the finished gasoline at the measurement temperature V; PR is the Reid vapor pressure; k is the first model parameter; b is the second model parameter;

[0143] Density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction are determined using the following linear mixing rule:

[0144] Wherein, fp represents the density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction of the mixture; and v represents... i The volume percentage of the i-th compound in the finished gasoline; the f iThe density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction of the i-th compound in the finished gasoline are given.

[0145] Engler distillation range uses the following mixing rules:

[0146] The Weber distribution is used to describe the range [T] of different compounds near their boiling points. L ,T U The distribution ratio within the temperature range is as follows:

[0147] The normalized actual distribution ratio vector is: w = ω / Σω.

[0148] If the actual content of the compound is z i Then it is in the temperature range [T L ,T U The allocation within ] is z i ·w, where:

[0149] Wherein, k is a first distribution parameter, λ is a second distribution parameter, and the distribution ratio of different types of compounds with temperature is controlled by adjusting the first and second distribution parameters; T b,i The boiling point of the compound; the T L T represents the highest temperature threshold corresponding to each compound in the finished gasoline. U This refers to the lowest temperature threshold corresponding to each compound in the finished gasoline.

[0150] Based on the cumulative distribution and temperature range, the equation for the true boiling point curve is constructed, and the temperature values ​​at the points where the distillation volume of the true boiling point curve is 0%, 10%, 30%, 50%, 70%, 90%, and 98% are solved by interpolation and denoted as T0, T10, T30, T50, T70, T90, and Tf, respectively.

[0151] The true boiling point curve equation is converted to the Engler distillation range using the following formula:

[0152] Wherein, the T E This indicates the distillation temperature corresponding to different distillation volumes.

[0153] Step 305: Solve the pre-constructed multi-objective optimization model with the objectives of minimizing excess quality and minimizing cost based on the macroscopic physical properties, and iteratively update the initial gasoline blending ratio based on the solution results to obtain the optimal gasoline blending ratio.

[0154] In this embodiment, the specific step is as follows: Establish a multi-objective optimization model with the objective functions of minimizing excess quality and minimizing cost, wherein the expression of the multi-objective optimization model is:

[0155] s.th(x)=Σx-1=0

[0156] g i (x)=ψ1-φ i (x)≤0i=1,2,3…,M

[0157] χ i (x)=φ i (x)-ψ u ≤0i=1,2,3…,M

[0158] Wherein, f(x) represents the objective function; n represents the number of physical properties involved in the optimization; x represents the proportion of each blending component gasoline involved in the blending; and Φ i (x) represents a function of the i-th property involved in the harmonic process; Φi is calculated using the molecular-level property model; Ψ i The w represents the target value of the i-th property involved in the reconciliation; i The P represents the deviation weight of the i-th property involved in the reconciliation; i The excess equivalent cost of the i-th physical property of the blending component gasoline participating in the blending; K represents the unit price vector of the blending component gasoline participating in the blending; I represents the lower limit of the constraint index; u represents the upper limit of the constraint index; M represents the total number of constraint indexes.

[0159] The constraint indicators include at least one of the following: Research Octane Number (ROC), Motor Octane Number (MOC), Red Vapor Pressure, Engler Range, Density, Benzene Volume Fraction, Aromatics Volume Fraction, Olefin Volume Fraction, Oxygen Mass Fraction, and Sulfur Mass Fraction. The Engler Range includes the initial boiling point, the distillation temperature at 10% of the distillate volume, the distillation temperature at 50% of the distillate volume, the distillation temperature at 90% of the distillate volume, and the final boiling point.

[0160] When solving the multi-objective optimization model, the interior-point method is used to obtain the optimal blending ratio. Specifically, based on the set constraints, the multi-objective optimization model is solved using a sequential quadratic programming mathematical optimization method. If the solution is successful, the current initial gasoline blending ratio is determined as the optimal gasoline blending ratio, and the optimal blending ratio formula is issued to the production workshop. The blending control system controls the blending flow rate of the gasoline components. If the solution fails, the initial gasoline blending ratio is adjusted, and the molecular composition and macroscopic properties of the finished gasoline are recalculated until the solution is successful.

[0161] Optionally, 92-octane gasoline is blended according to the gasoline blending optimization method based on spectral inversion provided in this embodiment. Specifically:

[0162] (1) First, for a certain petrochemical refinery, establish a total molecular database covering the compound composition of all blended gasoline components. The pre-stored compound composition in the total molecular database includes hydrogenated gasoline, reformed gasoline, aromatic gasoline, alkylated gasoline, isomerized gasoline, blended C5 gasoline, blended aromatic gasoline, raffinate, naphtha, ethers (MTBE, TAME), ethanol, methanol, etc. The hydrocarbon molecule types contained in different compounds are analyzed by high-resolution capillary gas chromatography or GC-MS. The chromatographic analysis conditions are set as follows: PONA column can be used, vaporization chamber temperature is set to 250℃, and detector temperature is set to 300℃. In this way, a total molecular property database containing at least 305 gasoline molecules (i.e., 305 compounds) is established. The total molecular property database includes a summary of gasoline molecule (partial) types and properties, which can be referred to in Table 1:

[0163] Table 1 Summary of Gasoline Molecules (Partial) and Their Properties

[0164] Next, offline gas chromatography was used to detect and analyze the samples offline, and the gasoline molecular composition (i.e., compound composition) of k groups of samples was obtained respectively. The gasoline molecular composition can be referred to Table 2.

[0165] Table 2. Composition of gasoline molecules (partial) in a certain group of samples.

[0166] (2) Collect k sets of offline sample gasoline spectra using an offline near-infrared spectrometer. The offline sample gasoline spectra can be referred to Figures 4 and 5, where Figure 4 is the near-infrared spectrum of hydrogenated gasoline and Figure 5 is the near-infrared spectrum of reformed gasoline.

[0167] (3) Through steps (1) and (2), a near-infrared spectral database of gasoline sample molecular data is formed, with a total of k sets of data;

[0168] (4) Based on k sets of data, establish a near-infrared inversion molecular model. The methods include, but are not limited to, K-means clustering, hierarchical clustering, and spectral matching.

[0169] (5) The online near-infrared spectra of the gasoline blending components in the blending pipeline were collected in real time by an online near-infrared spectrometer. The spectrum of a gasoline component to be inverted is shown in Figure 6.

[0170] (6) Calculate the online near-infrared spectrum to be inverted based on the intelligent inversion model to obtain gasoline molecular composition data, wherein the gasoline molecular composition data can be referred to Table 3.

[0171] Table 3. Molecular data (partial) of gasoline samples obtained by infrared retrieval.

[0172] (7) Based on the inverted molecular data and the input ratio of blending components, the molecular composition of the finished gasoline is calculated linearly;

[0173] (8) Calculate the physical properties of finished gasoline based on the molecular composition and molecular-level physical property model of finished gasoline;

[0174] (9) Establish a multi-objective optimization model with the goals of minimizing quality overcapacity and minimizing cost, and take 92-octane gasoline (VIB) as an example to construct its corresponding constraint index, wherein the constraint index can be referred to Table 4.

[0175] Table 4. Constraints for 92-octane gasoline

[0176] (10) The interior-point method is used to solve the multi-objective optimization model to obtain the optimal harmonic ratio. The method for solving the multi-objective optimization model is as follows:

[0177] ① Set the blending ratio input x_1, and based on the blending ratio input, obtain the molecular composition data and molecular-level property model of the finished gasoline through inversion, and calculate the physical properties of each index of the finished gasoline;

[0178] ② Input the set blending ratio as the initial value for optimization, set the constraint index as shown in Table 4 above, and solve based on SQP. If the solution is successful, the optimized formula x_opt is obtained, and then proceed to step ③; if the solution fails, proceed to step ④.

[0179] ③ Based on the solved optimized formula x_opt, the range of all constraint indicators is calculated through the molecular-level physical property model. All of them are within the qualified indicator range, and the formula is issued to the harmonization control system.

[0180] ④ Reset the harmonic ratio input x_i (i = 2, 3, 4...n), and proceed to step ② to optimize and obtain the component ratio solution x_opt. The optimal ratio obtained by solving the multi-objective optimization model can be found in Table 5.

[0181] Table 5 Component optimization ratios obtained through multi-objective optimization

[0182] The physical property indices are calculated based on the optimized proportional solution x_opt, and these indices can be found in Table 6.

[0183] Table 6 Simulated and measured values ​​of each indicator after optimization ratio.

[0184] In this embodiment, a near-infrared online analysis system was used to rapidly detect gasoline components, and the molecular composition data of the gasoline components was calculated using an intelligent inversion model. This provided a data foundation for gasoline molecular-level blending. As shown in Table 6, the macroscopic property analysis results of the finished gasoline showed smaller errors and were basically consistent with the results measured by traditional testing methods. Therefore, based on molecular-level data, the accuracy of the property calculations of the blended finished gasoline was significantly improved.

[0185] This embodiment provides a molecular-level online gasoline blending optimization method. It employs an online near-infrared spectrometer to acquire real-time near-infrared spectral data of gasoline samples and rapidly retrieves the molecular composition and macroscopic properties of the gasoline using an intelligent inversion model. Compared to traditional offline analysis methods, the method provided in this embodiment offers better real-time performance, enabling the acquisition of analytical results in a short time, meeting the requirements for real-time online blending. Furthermore, the method significantly improves analytical efficiency by combining online near-infrared spectral data acquisition with an intelligent inversion model. This reduces sample preparation and data processing time, making the analytical process more efficient.

[0186] Furthermore, this embodiment employs an online near-infrared spectrometer for data acquisition, which is simple to operate and easy to implement. The establishment and application of the intelligent inversion model are also relatively straightforward, requiring minimal professional knowledge and skills. A near-infrared intelligent inversion method has been developed, which analyzes the sample molecular concentration from the near-infrared spectrum and calculates macroscopic property data based on a molecular-level property analysis model. This method analyzes the sample at the molecular level, and can promptly detect changes in molecular concentration and macroscopic properties even when sample properties fluctuate significantly. The model has stronger extrapolation and higher accuracy. Moreover, the method in this embodiment can not only acquire the molecular composition information of gasoline samples in real time, but also calculate the optimal blending ratio based on target requirements and optimization algorithms. Through optimized blending, a better balance between performance and economy can be achieved, improving the quality and performance of gasoline.

[0187] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A molecular-level online gasoline blending optimization method, characterized in that, include: The online near-infrared spectra of each blending component gasoline in the blending pipeline are acquired in real time, and the online near-infrared spectra are input into a pre-constructed intelligent inversion model. The first compound composition corresponding to each blending component gasoline is obtained through the intelligent inversion model. Based on the composition of the first compound and the preset initial gasoline blending ratio, the composition of the second compound in the finished gasoline is predicted by linear calculation. The macroscopic properties of the finished gasoline are calculated based on the composition of the second compound and the preset molecular-level property model. Based on the macroscopic physical properties and preset constraints, the initial gasoline blending ratio is iteratively updated by solving a pre-constructed multi-objective optimization model with the objectives of minimizing quality excess and minimizing cost, to obtain the optimal gasoline blending ratio.

2. The molecular-level online gasoline blending optimization method as described in claim 1, characterized in that, The real-time acquisition of online near-infrared spectra of each blending component gasoline in the blending pipeline, and inputting the online near-infrared spectra into a pre-constructed intelligent inversion model, includes: The initial online near-infrared spectra of each of the blending components of gasoline are acquired in real time using a preset online near-infrared spectrometer, and the initial online near-infrared spectra are processed to obtain the online near-infrared spectra. The online near-infrared spectra of each of the blending components of gasoline are input into the intelligent inversion model. The intelligent inversion model analyzes and processes the online near-infrared spectra to obtain the first compound composition corresponding to each blending component of gasoline. The first compound composition includes the types of compounds and the concentration of each compound.

3. The molecular-level online gasoline blending optimization method as described in claim 1, characterized in that, The construction process of the intelligent inversion model includes: The collected offline component gasoline samples were analyzed using a pre-set offline gas chromatograph to obtain the compound composition corresponding to each offline component gasoline sample. The offline near-infrared spectra of each of the offline component gasoline samples were obtained using a preset offline near-infrared spectrometer. An offline component gasoline sample database is constructed based on the compound composition and offline near-infrared spectrum of each offline component gasoline sample. The intelligent inversion model is constructed based on the offline component gasoline sample database using a preset clustering algorithm.

4. The molecular-level online gasoline blending optimization method as described in claim 1, characterized in that, The step of predicting the second compound composition of the finished gasoline by linear calculation based on the first compound composition and a preset initial gasoline blending ratio includes: Based on the initial gasoline blending ratio, determine the proportion of each blending component gasoline; Based on the stated ratio and the composition of the first compound, the composition of the second compound in the finished gasoline is calculated using a preset weighted summation method.

5. The molecular-level online gasoline blending optimization method as described in claim 1, characterized in that, The calculation of the macroscopic properties of the finished gasoline based on the composition of the second compound and a preset molecular-level property model includes: Based on the composition of the second compound, the macroscopic properties of the finished gasoline are calculated using the mixing rules set in the molecular-level property model; wherein, the macroscopic properties include research octane number, motor octane number, vapor pressure, density, benzene volume fraction, aromatics volume fraction, olefins volume fraction, oxygen mass fraction, sulfur mass fraction, and distillation range; wherein, the mixing rules corresponding to the research octane number and motor octane number are as follows: THERE IS ij =(ON i +ON j ) / 2 Among them, v i The volume percentage of the i-th compound in the finished gasoline; The contribution value of the i-th compound in the finished gasoline to the octane number; ON i The octane number of the i-th compound in the finished gasoline is ON. j q is the octane number of the j-th compound in the finished gasoline. ij Let be the blending effect coefficient of the i-th compound and the j-th compound in the finished gasoline; The mixing rule for the vapor pressure is as follows: Wherein, the v i r is the volume percentage of the i-th compound in the finished gasoline; i Let be the activity coefficient of the i-th compound in the finished gasoline at the measurement temperature; Let be the saturated vapor pressure of the i-th compound in the finished gasoline at the measurement temperature V; PR is the Reid vapor pressure; k is the first model parameter; b is the second model parameter; The mixing rules for density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction are as follows: Wherein, fp represents the density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction of the mixture; and v represents... i The volume percentage of the i-th compound in the finished gasoline; the f i The density, benzene volume fraction, aromatic hydrocarbon volume fraction, olefin volume fraction, oxygen mass fraction, or sulfur mass fraction of the i-th compound in the finished gasoline; The mixing rules for the distillation range are as follows: The distribution ratio of different compounds with temperature in the vicinity of their boiling points is obtained by using the Weber distribution; wherein, the expression of the Weber distribution is: Normalizing the allocation ratio, we get: w = ω / ∑ω; According to the content of each compound in the finished gasoline z i Calculate its temperature range [T] within the preset temperature range L ,T U The allocation within ] is z i ·w; Wherein, k is a first distribution parameter, λ is a second distribution parameter, and the distribution ratio of different types of compounds with temperature is controlled by adjusting the first and second distribution parameters; T b,i The boiling point of the compound; the T L T represents the highest temperature threshold corresponding to each compound in the finished gasoline. U The lowest temperature threshold corresponding to each compound in the finished gasoline; Based on the cumulative distribution amount and the temperature range, a true boiling point curve equation is constructed, and the true boiling point curve equation is converted into an Engler range; wherein, the expression for the Engler range is: Wherein, T0, T10, T30, T50, T70, T90, and Tf are the temperature values ​​at which the distillation volume of the true boiling point curve is 0%, 10%, 30%, 50%, 70%, 90%, and 98%, respectively; and T... E This indicates the distillation temperature corresponding to different distillation volumes.

6. The molecular-level online gasoline blending optimization method as described in claim 1, characterized in that, The process involves solving a pre-constructed multi-objective optimization model aimed at minimizing quality excess and cost, iteratively updating the initial gasoline blending ratio to obtain the optimal gasoline blending ratio, including: Based on the macroscopic physical properties and preset constraints, the multi-objective optimization model is solved using the interior point method. If the solution is successful, the initial gasoline blending ratio is determined as the optimal gasoline blending ratio; if the solution fails, the initial gasoline blending ratio is iteratively updated until the solution is successful, and the gasoline blending ratio corresponding to the successful solution is output as the optimal gasoline blending ratio. The constraints include at least one of the following: Research Octane Number (RON), Motor Octane Number (MAN), Red Vapor Pressure, Engler Range, Density, Benzene Volume Fraction, Aromatics Volume Fraction, Olefins Volume Fraction, Oxygen Mass Fraction, and Sulfur Mass Fraction. The Engler Range includes the initial boiling point, 10% distillation temperature, 50% distillation temperature, 90% distillation temperature, and final boiling point. The expression for the multi-objective optimization model is: s.th(x)=∑x-1=0 g i (x)=ψ I -f i (x)≤0i=1,2,3···,M x i (x)=φ i (x)-ψ u ≤0i=1,2,3···,M Wherein, f(x) represents the objective function; n represents the number of physical properties involved in the optimization; x represents the proportion of each blending component gasoline involved in the blending; and Φ i (x) represents a function of the i-th property involved in the harmonic process; the Φ i Calculated using the molecular-level property model; The Ψ i The w represents the target value of the i-th property involved in the reconciliation; i The P represents the deviation weight of the i-th property involved in the reconciliation; i The excess equivalent cost of the i-th physical property of the blending component gasoline participating in the blending; K represents the unit price vector of the blending component gasoline participating in the blending; I represents the lower limit of the constraint index; u represents the upper limit of the constraint index; M represents the total number of constraint indexes.

7. A molecular-level online gasoline blending optimization system, characterized in that, It includes an online analysis module, a molecular prediction module, a property calculation module, and a harmonic optimization module; The online analysis module is used to acquire the online near-infrared spectra of each blending component gasoline in the blending pipeline in real time, and input the online near-infrared spectra into the pre-constructed intelligent inversion model, and obtain the first compound composition corresponding to each blending component gasoline in each blending component gasoline through the intelligent inversion model; The molecular prediction module is used to predict the second compound composition of the finished gasoline by linear calculation based on the first compound composition and the preset initial gasoline blending ratio. The property calculation module is used to calculate the macroscopic properties of the finished gasoline based on the composition of the second compound and the preset molecular-level property model. The blending optimization module is used to iteratively update the initial gasoline blending ratio based on the macroscopic physical properties and preset constraint indicators by solving a pre-constructed multi-objective optimization model with the objectives of minimizing quality excess and minimizing cost, in order to obtain the optimal gasoline blending ratio.

8. The molecular-level online gasoline blending optimization system as described in claim 7, characterized in that, The online analysis module includes a spectral unit and an inversion unit; The spectral unit is used to acquire the initial online near-infrared spectra of each of the blending components of gasoline in real time according to a preset online near-infrared spectrometer, and to perform spectral processing on the initial online near-infrared spectra to obtain the online near-infrared spectra. The inversion unit is used to input the online near-infrared spectra of each of the blending component gasolines into the intelligent inversion model, and to analyze and process the online near-infrared spectra through the intelligent inversion model to obtain the first compound composition corresponding to each blending component gasoline; wherein, the first compound composition includes the types of compounds and the concentration of each compound.

9. The molecular-level online gasoline blending optimization system as described in claim 7, characterized in that, The molecular prediction module includes a scaling unit and a combination unit; The proportioning unit is used to determine the proportion of each blending component gasoline according to the initial gasoline blending ratio; The combination unit is used to calculate the second compound composition of the finished gasoline according to the ratio and the first compound composition by a preset weighted summation method.

10. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the molecular-level online gasoline blending optimization method according to any one of claims 1-6.

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