CN114724644B - Method and equipment for predicting gasoline octane number based on intermolecular interaction - Google Patents

Abstract

The application provides a gasoline octane number prediction method and equipment based on intermolecular interactions, and relates to the field of gasoline octane number prediction. The method includes: based on the interaction between six groups of gasoline molecules, constructing any two groups of interaction equations; based on any two groups of interaction equations, constructing a nonlinear molecular interaction function between gasoline molecules ; Based on the molecular interaction function, a mixture equation for predicting gasoline octane number is obtained. The method of this application realizes the model construction of the gasoline mixture from the molecular level, thereby avoiding ignoring the interaction relationship between different types of gasoline molecules during the simulation process, making the simulation process closer to the real situation, and the data obtained after the simulation have small errors, and the results It is more reliable, and it is beneficial to theoretically guide the blending process of different grades of commercial gasoline, and avoid the waste of petroleum raw materials in the blending process.

Description

Gasoline Octane Number Prediction Method and Equipment Based on Intermolecular Interaction

technical field

The present application relates to gasoline octane number prediction, in particular to a gasoline octane number prediction method and equipment based on intermolecular interactions.

Background technique

Gasoline is one of the most important petrochemical products. Refined oil is blended from various component oils, including catalytic cracking, hydrogenation, alkylation, reforming, and straight-run gasoline. The composition and properties of different types of gasoline streams vary widely. Optimization of refinery blending recipes requires accurate prediction of the macroscopic properties of each "blending component" and refined oil. Of all the macroscopic properties involved, octane prediction is by far the most difficult. Accurately predicting octane ratings requires addressing two issues. On the one hand, because the octane number is highly sensitive to the molecular structure, the change of the methyl position will also lead to a significant difference in the octane number, and the molecular burning speed depends on the thermal stability of the molecule, which in turn is highly related to the molecular structure , Therefore, the octane number is closely related to the molecular composition of gasoline. To accurately predict the octane number, it is necessary to establish a quantitative relationship between the molecular structure and the octane number, so as to understand the contribution of different structures to the octane number. On the other hand, the octane number will show a strong nonlinearity in the mixing process, which has both synergistic and antagonistic effects. Due to the high degree of nonlinearity of the octane number, it is necessary to explore the interaction between different types of gasoline components relationship and determine its mixing law, which can help us make full use of the synergistic effect of each component to improve the quality of gasoline.

Researchers have developed a variety of octane prediction methods for gasoline mixtures, which are mainly divided into methods based on macroscopic property mixing, spectral techniques, and gasoline molecular lumping. Since the octane number is determined by comparing the behavior of the test fuel to that of a mixture of n-heptane and isooctane defined by its liquid volume fraction. The researchers used a combination of several compounds (n-heptane, isooctane, and toluene) to substitute and simulate actual gasoline fuel, while describing their mixing behavior in detail.

However, the molecular composition and mixing effect of actual gasoline are extremely complex, and the octane number prediction model developed from gasoline substitute mixtures will have large errors when applied to gasoline blending. Therefore, the above methods have common shortcomings: they ignore the information at the molecular level of gasoline, and do not explore the interaction relationship between different types of gasoline molecules.

Contents of the invention

This application provides a gasoline octane number prediction method and equipment based on intermolecular interactions to solve the problem that the octane number prediction model developed for gasoline substitute mixtures ignores information at the molecular level of gasoline and does not explore the interactions between different types of gasoline molecules relationship problem.

In the first aspect, the application provides a method for predicting gasoline octane number based on intermolecular interactions, including:

Based on the interactions of the six major groups of gasoline molecules, the interaction equations of any two groups are constructed, wherein the interactions of the six major groups include paraffins and alkenes, paraffins and aromatics, alkenes and cycloalkanes, Interactions between naphthenes and aromatics, olefins and aromatics, and oxygenates and hydrocarbons;

Based on the interaction equations of any two families, a nonlinear molecular interaction function between gasoline molecules is constructed;

Based on the molecular interaction function, a mixing equation for predicting gasoline octane number is obtained.

In a possible design, based on the interactions between the six groups of gasoline molecules, construct any two groups of interaction equations, including constructing any one of the two parameter adjustment curves based on the Rayleigh distribution function. The interaction equation of the two families:

其中，ΔON_ij为i和j两个族类的相互作用函数；k_{ij_a}和k_{ij_b}是二元交互作用参数；v_i和v_j分别对应i和j两个族类的体积分数。

Among them, ΔON _ij is the interaction function of two groups i and j; _{kij_a} and _{kij_b} are binary interaction parameters; v _i and v _j correspond to the volume fractions of two groups i and j, respectively.

In a possible design, the non-linear molecular interaction function between gasoline molecules is constructed based on any two families of interaction equations:

ON _NonLinear = ∑ _i ∑ _j ΔON _ij , wherein, ON _NonLinear is a nonlinear molecular interaction function; ΔON _ij is an interaction function of two groups i and j.

In a possible design, before obtaining the mixing equation for predicting the gasoline octane number based on the molecular interaction function, it also includes: obtaining and constructing a linear mixing function based on the octane number of the ternary mixture, wherein the Ternary mixture octane number Ternary mixture for which octane data is known.

In one possible design, the linear mixing function is:

ON _Linear =∑ _i v _i ON _i , wherein, ON _Linear is a linear molecular interaction function; v _i is the volume fraction of molecule i; ON _i is the octane number of molecule i.

In a possible design, before obtaining the mixing equation for predicting the gasoline octane number based on the molecular interaction function, it also includes: adding the constructed linear mixing function and the molecular interaction function to obtain the predicted gasoline octane number The mixing equation for :

ON=ON _Linear +ON _NonLinear , wherein, ON is a mixed molecular interaction function; ON _Linear is a linear molecular interaction function; ON _NonLinear is a nonlinear molecular interaction function.

In a possible design, the value selection method of the binary interaction parameters is as follows: the genetic algorithm is used to perform fitting to obtain the parameters, and then the parameters output by the genetic algorithm are input as initial values into the local optimization algorithm for optimized output.

In a possible design, the local optimization algorithm adopts a sequential quadratic programming algorithm, and the upper limit and lower limit of the sequential quadratic programming algorithm are respectively set to 120% and 80% of the original parameters.

In a second aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

the memory stores computer-executable instructions;

The processor executes the computer-executed instructions stored in the memory, so as to realize the gasoline octane number prediction method based on intermolecular interactions.

In a third aspect, the present application provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium. Alkane Number Prediction Method.

The gasoline octane number prediction method and equipment based on intermolecular interactions provided by this application construct the interaction equations of any two groups based on the interactions between the six groups of gasoline molecules, and based on the interaction between any two groups Equation of action relationship, construct nonlinear molecular interaction function between gasoline molecules, and obtain the mixing equation for predicting gasoline octane number based on the molecular interaction function, realize the model construction of gasoline mixture from the molecular level, so as to avoid the Neglecting the interaction relationship between different types of gasoline molecules makes the simulation process closer to the real situation. The error of the data obtained after the simulation is small, and the results are more reliable. Raw material waste.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description serve to explain the principles of the application.

Fig. 1 is the flow chart of the gasoline octane number prediction method based on intermolecular interaction of the present application;

Fig. 2 is the positive and negative effect and the linear mixing schematic diagram that occur in the application fitting gasoline mixing process;

Fig. 3 is the schematic diagram of octane number mixing rule of the present application;

Fig. 4 is the distribution situation of the application's research method octane number data and the comparison chart of the predicted value and experimental value of actual gasoline and ternary mixture;

Fig. 5 is the distribution situation of the application motor method octane number experiment data and the contrast figure of the predicted value and the experimental value of actual gasoline and ternary mixture;

Fig. 6 is the comparative result schematic diagram between the experiment of ternary mixture of the present application, linear and nonlinear mixing value;

Figure 7 is a schematic diagram of the variation trend of the legal octane number with the volume fraction after the paraffins of the present application are mixed with different types of hydrocarbons;

Fig. 8 is the comparison schematic diagram of the experimental value and predicted value of different gasoline stream method octane number and motor method octane number of the present application;

FIG. 9 is a schematic structural diagram of an electronic device of the present application.

By means of the above drawings, specific embodiments of the present application have been shown, which will be described in more detail hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of the application in any way, but to illustrate the concept of the application for those skilled in the art by referring to specific embodiments.

Detailed ways

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present application as recited in the appended claims.

First, the nouns involved in this application are explained:

Ternary mixture: refers to the general term for the gasoline replacement mixture collected from API Research Project 45, consisting of 48% volume fraction of 2,2,4-trimethylpentane, 32% n-heptane and 20% other compounds Composition, among others, includes 240 different types of hydrocarbons.

API Research Project 45: refers to a data booklet that includes molecular composition, mass fraction, and octane number.

Genetic Algorithm: It refers to a general algorithm in octane number prediction method, which can output interaction parameters after inputting molecular composition, mass fraction and octane number.

Sequential quadratic programming algorithm: refers to solving the programming problem that contains nonlinear functions in the objective function or constraints, and is used in this application to further optimize the interaction parameters output by the genetic algorithm.

This application is mainly used in an octane number prediction model for simulating the blending process of different grades of commercial gasoline. The compounds contained in the existing gasoline can be analyzed by gas chromatography to obtain molecular qualitative and quantitative information, but it is still very difficult to accurately predict the gasoline octane number. The main reason is that there are both linear and nonlinear behaviors when gasoline is mixed. The linear behavior is relatively easy to simulate through several main compounds, but the nonlinear behavior cannot be simply obtained by simulating gasoline fuel analysis through the combination of several compounds (n-heptane, isooctane and toluene) in the past, which will cause large errors. Because the nonlinear nature is the result of different types of molecular interactions (synergism and antagonism) when gasoline is mixed, how to explore and determine the interaction relationship between different types of molecules in gasoline is very important, which requires the development of a An interaction function is used to accurately describe the nonlinear behavior between molecules, so that the model can have better prediction and generalization performance.

The method for predicting gasoline octane number provided by this application aims to solve the above technical problems in the prior art.

The technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments. The following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below in conjunction with the accompanying drawings.

Fig. 1 is a flow chart of the method for predicting gasoline octane number based on intermolecular interactions of the present application. As shown in Figure 1, the method includes:

S101. Based on the interaction of the six major groups of gasoline molecules, construct the interaction equation of any two groups, wherein the interaction of the six major groups includes paraffins and alkenes, paraffins and aromatics, alkenes and cycloalkanes, Interactions between naphthenes and aromatics, olefins and aromatics, and oxygenates and hydrocarbons.

The interaction of the six major families of gasoline molecules is the core part of the mixing rules. First of all, gasoline molecules are divided into six groups: P/I/O/N/A/OXY, and molecular interactions are composed of six pairs of interactions, including paraffins and alkenes, paraffins and aromatics, alkenes and cycloalkanes, The interactions between cycloalkanes and aromatic hydrocarbons, alkenes and aromatic hydrocarbons, and oxygen-containing compounds and hydrocarbons, therefore, two interaction family parameters are needed to regulate the curve change relationship of intermolecular interactions, here the two The family parameter of the interaction is one of the above six pairs of interaction relations.

Figure 2 is a schematic diagram of the positive and negative effects and linear mixing that occur during the fitting of gasoline mixing in the present application. The binary interaction parameter is used as the control quantity to control the shape change of the curve, and the positive and negative effects or linear mixing occurring in the gasoline mixing process are fitted. When the binary interaction parameter is a positive number, it indicates that the interaction has a favorable effect on the octane number of the mixture. At this time, as the interaction function of the two groups increases, the change curve will gradually become prominent, as shown in Figure 2b ; on the contrary, when the binary interaction parameter is negative, it indicates that the interaction has an adverse effect on the octane number of the mixture. At this time, as the interaction function of the two groups increases, the change curve will gradually sag , as shown in Figure 2c; in particular, when the binary interaction parameter is 0, it indicates that the interaction has no contribution to the octane number of the mixture, as shown in Figure 2a.

S102. Based on the interaction equations of any two families, construct a non-linear molecular interaction function between gasoline molecules.

The sum of all the interaction relation equations of any two families obtained in step S101 is the non-linear molecular interaction function between all gasoline molecules.

S103. Obtain a mixing equation for predicting the gasoline octane number based on the molecular interaction function.

Adding the nonlinear molecular interaction function obtained in step S102 to the linear molecular interaction function calculated in advance is the predicted gasoline octane number, wherein the linear molecular interaction function is obtained through the known gasoline fuel mixture The octane number is determined by summing and solving for a linear volumetric mixture of all molecular octane numbers in the gasoline fuel.

In this example, based on the interaction of the six major groups of gasoline molecules, the interaction equations of any two groups are constructed, and the nonlinear molecular interaction functions between gasoline molecules are constructed based on the interaction equations of any two groups. , based on the molecular interaction function, the mixture equation for predicting the octane number of gasoline is obtained, and the gasoline mixture is modeled at the molecular level, so as to avoid ignoring the interaction relationship between different types of gasoline molecules during the simulation process, making the simulation process closer to In the real situation, the error of the data obtained after the simulation is small, and the results are more reliable, which is conducive to theoretically guiding the blending process of different brands of commercial gasoline and avoiding the waste of petroleum raw materials in the blending process.

The following uses a possible embodiment to make a further statement to illustrate how to realize the gasoline octane number prediction method based on intermolecular interactions.

Fig. 3 is a schematic diagram of the octane number mixing rule of the present application. As shown in Fig. 3, the mixing equation for predicting gasoline octane number includes two parts, namely, a linear part and a nonlinear part.

By analyzing the octane number of the ternary mixture, the interaction relationship between different types of molecules is studied, so as to determine the interaction relationship between gasoline molecules. The specific process is:

The octane number experimental data of the ternary mixture was collected from API Research Project 45, wherein the ternary mixture was composed of 48% volume fraction of 2,2,4-trimethylpentane, 32% of n-heptane and 20% of other compounds. Take 2,2,4-trimethylpentane and n-heptane as a whole to study their interaction with other compounds. Their linear blend values with paraffins, olefins, naphthenes and aromatics are then calculated and compared to the experimental octane numbers. Few interactions were found between paraffins and paraffins, paraffins and naphthenes, and synergistic effects were found between paraffins and alkenes, and paraffins and aromatics. Since there is no octane experimental data between alkenes, cycloalkanes and aromatics in the database, it is impossible to study the interaction relationship between them. Therefore, it is necessary to assume here that alkenes and cycloalkanes, alkenes and aromatics, and cycloalkanes Interacts with aromatic hydrocarbons. Furthermore, gasoline is always produced with small amounts of oxygenated additives, and there may be synergistic or antagonistic effects between oxygenates and hydrocarbons, thus increasing the interaction parameters between them.

Specifically, the linear portion is the linear volumetric blend of the octane ratings of all molecules present in the gasoline fuel, calculated as:

ON _Linear =∑ _i v _i ON _i ,

Among them, ON _Linear is a linear molecular interaction function; v _i is the volume fraction of molecule i; ON _i is the octane number of molecule i. The ON _i of the molecules contained in the mixture can be either experimental or calculated using a pre-developed structure-property correlation model.

Gasoline molecules are divided into six categories: P/I/O/N/A/OXY, and molecular interactions are composed of six pairs of interactions, including alkanes and alkenes, alkanes and aromatics, alkenes and naphthenes, naphthenes Interactions between aromatics, olefins and aromatics, and oxygenates and hydrocarbons.

Specifically, the nonlinear part is the gasoline intermolecular nonlinear equation obtained by fitting the interaction functions of all families:

ON _NonLinear = ∑ _i ∑ _j ΔON _ij ,

Among them, ON _NonLinear is a nonlinear molecular interaction function; ΔON _ij is an interaction function of two classes i and j.

Further, referring to the Rayleigh distribution function to construct the interaction relationship between the two families, the formula is as follows:

Among them, _{kij_a} and _{kij_b} are binary interaction parameters; v _i and v _j correspond to the volume fractions of the two families i and j, respectively.

Further, the value method of binary interaction parameters is as follows: the genetic algorithm is used for fitting to obtain the parameters, and then the parameters output by the genetic algorithm are input into the local optimization algorithm as initial values to optimize the output.

In a possible design, the local optimization algorithm adopts the sequential quadratic programming algorithm, and the upper limit and lower limit of the sequential quadratic programming algorithm are set to 120% and 80% of the original parameters, respectively. Although the genetic algorithm has a good global search ability, it is difficult to obtain the optimal solution in a huge search space. Therefore, the parameters output by the genetic algorithm are input into the sequential quadratic programming algorithm (SQP) as initial values. The SQP optimization algorithm needs a reasonable upper limit and lower limit. After trial and error, the upper limit and lower limit are determined to be the original parameters multiplied by 120% and 80%, respectively, and the binary interaction parameters are continuously optimized in a specific space to seek a better solution.

The binary interaction parameters _{kij_a} and _{kij_b} control the shape of the curve change. If k _{ij_a} is greater than 0, the interaction has a favorable effect on the octane number of the mixture. Moreover, with the increase of ΔON _ij , the change curve will gradually protrude. Conversely, if _{kij_a} is less than 0, the interaction can adversely affect the octane rating of the mixture. The special case is that _{kij_a} is equal to 0, indicating that the interaction does not contribute to the octane number of the mixture.

Therefore, the final mixing equation for the predicted octane mixing rule is:

ON＝ON _Linear +ON _NonLinear ＝∑ _i v _i ON _i +∑ _i ∑ _j ΔON _ij ，

Among them, ON is the molecular interaction function after mixing.

The verification experiment is used to verify whether the constructed hybrid equation meets the design requirements. In a specific verification experiment, 231 groups of known gasoline molecular composition data and corresponding RON data (motor octane number MON is 170 groups) were collected, and the qualitative and quantitative data of gasoline molecules were obtained by gas phase detected by chromatography. In addition, the legal octane number RON of 248 ternary mixtures (motor octane number MON is 244) was collected from API Research Project 45, and the data of 90 sets of gasoline substitute mixtures (motor octane number MON was 244) were collected from the literature. is 50). Therefore, the established database contains 569 RON and 464 MON experimental data. Bring the established database into the model constructed by the mixing equation, calculate the predicted value of octane number, and compare the predicted value with the experimental value to verify whether the prediction ability and extrapolation performance of the above mixing equation are reliable.

The mixed equation function needs to fit a total of 12 parameters. Table 1 and Table 2 list the specific values of the optimized 12 parameters of RON and MON respectively.

Table 1. Octane number binary interactive parameter matrix of research method optimized by genetic algorithm and SQP algorithm

Table 2 Optimized binary interaction parameter matrix of motor octane number

Fig. 4 is the distribution of the octane number data of the research method of the present application and the comparison chart of the predicted value and the experimental value of the actual gasoline and the ternary mixture. Wherein, Fig. 4a is the distribution diagram of the experimental data of the octane number of the method, Fig. 4b is a comparison chart of the predicted value and the experimental value of the octane number of the actual gasoline method, and Fig. 4c is the predicted value and the experimental value of the octane number of the ternary mixture method Value comparison chart. As shown in Figures 4b and 4c, it can be found that the average absolute error of the actual gasoline is less than 1 unit, indicating that the model has a good training effect, but the average absolute error of the ternary mixture reaches 3.64 units, a larger error is acceptable because the ternary mixture only trains the effect of the binary interaction parameter.

Corresponding to Fig. 4, Fig. 5 is a comparison chart of the distribution of the experimental data of the octane number of the motor method of the present application and the predicted value and the experimental value of the actual gasoline and the ternary mixture. Among them, Figure 5a is the distribution diagram of the experimental data of the motor octane number, Figure 5b is a comparison chart of the predicted value and the experimental value of the actual gasoline motor octane number, and Figure 5c is the prediction of the motor octane number of the ternary mixture The comparison chart of the value and the experimental value. The error is close to the experimental data of the above-mentioned French octane number, so it has the same conclusion.

Furthermore, the prediction ability and extrapolation performance of the model are verified in the following three different ways.

Fig. 6 is a schematic diagram showing the comparison results between experiments and linear and nonlinear mixing values of the ternary mixtures of the present application. As shown in Figure 6, the model fits the octane numbers of the ternary mixtures well, but some molecules have large errors. Because the model is trained on binary interaction parameters between P/I/O/N/A/OXY families, not per molecule.

Fig. 7 is a schematic diagram of the change trend of the legal octane number with the volume fraction after the paraffins (2,2,4-trimethylpentane and n-heptane) of the present application are mixed with different types of hydrocarbons. The change curve of octane number with increasing volume fraction was used to verify whether the model was overfitting. As shown in Figure 7, when there is only one experimental point for each hydrocarbon molecule, the model can still predict the change trend of the octane number of the mixture with the volume fraction, which proves that the model has no overfitting and strong predictive ability.

Fig. 8 is a schematic diagram of comparison of experimental and predicted values of gasoline stream octane numbers and motor octane numbers of different gasoline streams in the present application. To verify the predictive ability for gasoline fuels not in the training set, different process streams were selected for validation, including straight-run, alkylation, hydrotreating, catalytic cracking, reforming and blending gasoline. As shown in Figure 8, the predicted octane number is basically consistent with the experimental value, the average absolute error of the octane number of the research method is less than 0.6 units, and the average absolute error of the octane number of the motor method is less than 0.8 units, which proves that the model is not overfitting Combined to meet the forecast demand.

In this example, first, the interaction relationship between different types of molecules is studied through the experimental data of the octane number of the ternary mixture, and then the mixing equation is constructed based on the above relationship, and the molecular interaction function in the mixing equation is used to describe the nonlinearity between the molecules in detail Behavior, combined with genetic algorithm and sequence quadratic programming algorithm to fit the binary interaction parameters in the molecular interaction function, and finally through multiple sets of databases to verify whether the model constructed with the above mixed equation meets the prediction ability and extrapolation performance of actual gasoline .

FIG. 9 is a schematic structural diagram of an electronic device of the present application. As shown in FIG. 9 , this embodiment provides an electronic device, including a memory 902 and a processor 901 , the memory 902 is used to store programs, and the memory 902 can be connected to the processor 901 through a bus 903 . The memory 902 may be a non-volatile memory, such as a hard disk drive and a flash memory, and software programs and device drivers are stored in the memory 902 . The software program can execute various functions of the above method provided by the embodiment of the present invention; the device driver can be a network and interface driver. The processor 901 is used to execute a software program. When the software program is executed, the gasoline octane number prediction method based on the intermolecular interaction of the embodiment of the present invention can be realized:

Specifically, the processor 901 constructs any two groups of interaction equations based on the interactions of the six groups of gasoline molecules, including: constructing any two groups of equations that are changed by two parameter adjustment curves based on the Rayleigh distribution function Interaction relationship equation:

其中，ΔON_ij为i和j两个族类的相互作用函数；k_{ij_a}和k_{ij_b}是二元交互作用参数；v_i和v_j分别对应i和j两个族类的体积分数。

Among them, ΔON _ij is the interaction function of two groups i and j; _{kij_a} and _{kij_b} are binary interaction parameters; v _i and v _j correspond to the volume fractions of two groups i and j, respectively.

Specifically, the value method of binary interaction parameters is as follows: use the genetic algorithm to perform fitting to obtain the parameters, and then input the parameters output by the genetic algorithm as initial values into the local optimization algorithm to optimize the output, wherein the local optimization algorithm uses the sequence The upper and lower limits of the quadratic programming algorithm and the sequence quadratic programming algorithm are set to 120% and 80% of the original parameters respectively, so that the model can have better prediction and generalization performance.

Processor 901 constructs a nonlinear molecular interaction function between gasoline molecules based on any two families of interaction equations:

ON _NonLinear = ∑ _i ∑ _j ΔON _ij , wherein, ON _NonLinear is a nonlinear molecular interaction function; ΔON _ij is an interaction function of two groups i and j.

Before the processor 901 obtains the mixing equation for predicting the gasoline octane number based on the molecular interaction function, it also includes: obtaining and constructing a linear mixing function based on the octane number of the ternary mixture, wherein the octane number of the ternary mixture has been Ternary mixtures with octane data.

Specifically, the linear mixing function is:

ON _Linear =∑ _i v _i ON _i , wherein, ON _Linear is a linear molecular interaction function; v _i is the volume fraction of molecule i; ON _i is the octane number of molecule i.

Before the processor 901 obtains the mixing equation for predicting the gasoline octane number based on the molecular interaction function, it also includes: adding the constructed linear mixing function and the molecular interaction function to obtain the mixing equation for predicting the gasoline octane number:

ON=ON _Linear +ON _NonLinear , wherein, ON is a mixed molecular interaction function; ON _Linear is a linear molecular interaction function; ON _NonLinear is a nonlinear molecular interaction function.

The electronic equipment provided in this embodiment can be used to implement the above-mentioned method for predicting gasoline octane number based on intermolecular interactions, and its implementation principle and technical effect are similar, so this embodiment will not repeat them here.

The embodiment of the present invention also provides a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the gasoline octane number based on the intermolecular interaction provided by the embodiment of the present invention is realized. method of prediction.

The computer-readable storage medium provided in this embodiment can be used to implement the above method for predicting gasoline octane number based on intermolecular interactions, and its implementation principle and technical effect are similar, so this embodiment will not repeat them here.

Professionals should further realize that the units and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the relationship between hardware and software Interchangeability. In the above description, the composition and steps of each example have been generally described according to their functions. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented by hardware, software modules executed by a processor, or a combination of both. Software modules can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other Any other known storage medium.

Other embodiments of the present application will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the application, these modifications, uses or adaptations follow the general principles of the application and include common knowledge or conventional technical means in the technical field not disclosed in the application . The specification and examples are to be considered exemplary only, with a true scope and spirit of the application indicated by the following claims.

It should be understood that the present application is not limited to the precise constructions which have been described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (9)

1. a gasoline octane number prediction method based on intermolecular interaction, it is characterized in that, described octane number comprises research method octane number and motor method octane number, and described method comprises: Based on the interactions of the six major groups of gasoline molecules, the interaction equations of any two groups are constructed, wherein the interactions of the six major groups include paraffins and alkenes, paraffins and aromatics, alkenes and cycloalkanes, Interactions between naphthenes and aromatics, olefins and aromatics, and oxygenates and hydrocarbons; Based on the interaction equations of any two families, a nonlinear molecular interaction function between gasoline molecules is constructed; Based on the molecular interaction function, a hybrid equation for predicting gasoline octane number is obtained; The construction of any two groups of interaction equations based on the interaction of the six major groups of gasoline molecules includes constructing any two groups of interaction equations that are changed by two parameter adjustment curves based on the Rayleigh distribution function :

Among them, ΔON _ij is the interaction function of two groups i and j; _{kij_a} and _{kij_b} are binary interaction parameters; v _i and v _j correspond to the volume fractions of two groups i and j, respectively. 2. method according to claim 1, is characterized in that, described based on the interaction relation equation of any two groups, constructs the nonlinear molecular interaction function between gasoline molecules: ON _NonLinear = ∑ _i ∑ _j ΔON _ij , wherein, ON _NonLinear is a nonlinear molecular interaction function; ΔON _ij is an interaction function of two groups i and j. 3. The method according to claim 1, characterized in that, before obtaining the mixing equation for predicting the gasoline octane number based on the molecular interaction function, it also includes: obtaining and constructing a linear method based on the octane number of the ternary mixture A mixing function, wherein the octane number of the ternary mixture is a ternary mixture with known octane number data. 4. method according to claim 3, is characterized in that, described linear mixing function is: ON _Linear =∑ _i v _i ON _i , wherein, ON _Linear is a linear molecular interaction function; v _i is the volume fraction of molecule i; ON _i is the octane number of molecule i. 5. The method according to claim 3, characterized in that, before obtaining the mixing equation for predicting gasoline octane number based on the molecular interaction function, it also includes: adding the linear mixing function and the molecular interaction function of construction , to get the mixing equation for predicting gasoline octane number: ON=ON _Linear +ON _NonLinear , wherein, ON is a mixed molecular interaction function; ON _Linear is a linear molecular interaction function; ON _NonLinear is a nonlinear molecular interaction function. 6. The method according to claim 1, characterized in that, the value-taking method of the binary interaction parameter is: adopt genetic algorithm to perform fitting to obtain parameters, and then input the parameters output by genetic algorithm as initial values to the local Optimize the output in the optimization algorithm. 7. The method according to claim 6, wherein the local optimization algorithm adopts a sequential quadratic programming algorithm, and the upper limit and lower limit of the sequential quadratic programming algorithm are respectively set to 120% and 80% of the original parameters. 8. An electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; The processor executes the computer-implemented instructions stored in the memory to implement the method of any one of claims 1-7. 9. A computer-readable storage medium, characterized in that, computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions are used to implement any one of claims 1 to 7 when executed by a processor the method described.

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