CN114724644B - Method and equipment for predicting gasoline octane number based on intermolecular interaction - Google Patents
Method and equipment for predicting gasoline octane number based on intermolecular interaction Download PDFInfo
- Publication number
- CN114724644B CN114724644B CN202210300866.9A CN202210300866A CN114724644B CN 114724644 B CN114724644 B CN 114724644B CN 202210300866 A CN202210300866 A CN 202210300866A CN 114724644 B CN114724644 B CN 114724644B
- Authority
- CN
- China
- Prior art keywords
- gasoline
- octane number
- interaction
- families
- function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Production Of Liquid Hydrocarbon Mixture For Refining Petroleum (AREA)
Abstract
The application provides a method and equipment for predicting gasoline octane number based on intermolecular interaction, and relates to the field of gasoline octane number prediction. The method comprises the following steps: constructing an interaction relation equation of any two families based on the interaction of the six families between gasoline molecules; constructing a nonlinear molecular interaction function between gasoline molecules based on any two families of interaction relation equations; and obtaining a mixing equation for predicting the octane number of the gasoline based on the molecular interaction function. According to the method, model construction of the gasoline mixture is achieved from a molecular level, so that the phenomenon that interaction relations among different types of molecules of gasoline are neglected in a simulation process is avoided, the simulation process is closer to the real situation, the data error obtained after simulation is small, the result is more reliable, the formula blending process of different brands of commercial gasoline is guided theoretically, and waste of petroleum raw materials in the blending process is avoided.
Description
Technical Field
The application relates to gasoline octane number prediction, in particular to a gasoline octane number prediction method and equipment based on intermolecular interaction.
Background
Gasoline is one of the most important petrochemical products. The finished oil is blended from a variety of component oils, including catalytic cracking, hydrogenation, alkylation, reforming, straight run gasoline, and the like. The composition and properties of different kinds of gasoline streams vary widely. Optimization of refinery blend formulations requires accurate prediction of the macroscopic properties of each "blend component" and the finished oil. Of all the macroscopic properties involved, octane number prediction has long been the most difficult. The octane number is accurately predicted to solve two problems. On one hand, because octane number is highly sensitive to molecular structure, the change of methyl position also causes remarkable difference of octane number, the burning speed of the molecule depends on the thermal stability of the molecule, and the burning speed is highly related to the molecular structure, so that the octane number is closely related to the molecular composition of gasoline, and in order to accurately predict the octane number, a quantitative relation between the molecular structure and the octane number needs to be established, so as to know the contribution of different structures to the octane number. On the other hand, octane number can present strong nonlinearity in the mixing process, and has both synergistic effect and antagonistic effect, and due to the high nonlinearity of the octane number, the interaction relationship among different gasoline components needs to be researched and the mixing rule needs to be determined, so that the mixing rule can help people to fully utilize the synergistic effect of the components to improve the quality of gasoline.
Researchers have developed a variety of methods for predicting the octane number of gasoline blends, mainly classified as macroscopic property blending-based, spectroscopic technology-based, and gasoline molecule lumped-based methods. Since octane number is determined by comparing the behavior of the test fuel with the behavior of a mixture of n-heptane and isooctane defined by its liquid volume fraction. Researchers have replaced and simulated actual gasoline fuels with a combination of several compounds (n-heptane, isooctane, and toluene) while describing their blending behavior in detail.
However, the molecular composition and mixing effect of actual gasoline are extremely complex, and a octane number prediction model developed by a gasoline substitution mixture can generate large errors when applied to gasoline blending. Therefore, the above methods have common disadvantages: neglecting the information of gasoline molecule level, the interaction relation between different types of gasoline molecules is not explored.
Disclosure of Invention
The application provides a method and equipment for predicting gasoline octane number based on intermolecular interaction, which are used for solving the problems that an octane number prediction model developed by a gasoline substitution mixture ignores information of gasoline molecule levels and interaction relations among different types of gasoline molecules are not explored.
In a first aspect, the present application provides a method for predicting gasoline octane number based on intermolecular interaction, comprising:
constructing an interaction relationship equation between any two families based on six-family interactions between gasoline molecules, wherein the six-family interactions comprise interactions between paraffins and olefins, paraffins and aromatics, olefins and naphthenes, naphthenes and aromatics, olefins and aromatics, and oxygenates and hydrocarbons;
constructing a nonlinear molecular interaction function between gasoline molecules based on any two families of interaction relation equations;
and obtaining a mixing equation for predicting the octane number of the gasoline based on the molecular interaction function.
In one possible design, the constructing any two families of interaction equations based on six major families of interactions between gasoline molecules includes constructing any two families of interaction equations based on a rayleigh distribution function as a function of two parameter adjustment curves:
In one possible design, the non-linear molecular interaction function between gasoline molecules is constructed based on any two families of interaction relation equations:
ON NonLinear ＝∑ i ∑ j ΔON ij in which is ON NonLinear A molecular interaction function that is non-linear; delta ON ij Is an interaction function of the two families i and j.
In one possible design, before the obtaining the blending equation for predicting the octane number of the gasoline based on the molecular interaction function, the method further includes: and acquiring and constructing a linear mixing function based on the octane number of the ternary mixture, wherein the octane number of the ternary mixture is the ternary mixture of the known octane value data.
In one possible design, the linear mixing function is:
ON Linear ＝∑ i v i ON i wherein, ON Linear A molecular interaction function that is linear; v. of i Is the volume fraction of molecule i; ON i Is the octane number of molecule i.
In one possible design, before the obtaining the blending equation for predicting the octane number of the gasoline based on the molecular interaction function, the method further includes: adding the constructed linear mixing function and the molecular interaction function to obtain a mixing equation for predicting the octane number of the gasoline:
ON＝ON Linear +ON NonLinear wherein ON is a mixed molecular interaction function; ON Linear A molecular interaction function that is linear; ON NonLinear Is a non-linear molecular interaction function.
In one possible design, the binary interaction parameter is derived by: and fitting by adopting a genetic algorithm to obtain parameters, and inputting the parameters output by the genetic algorithm as initial values into a local optimization algorithm for optimization output.
In one possible design, the local optimization algorithm employs a sequential quadratic programming algorithm, and the upper limit and the lower limit of the sequential quadratic programming algorithm are set to 120% and 80% of the original parameters, respectively.
In a second aspect, the present application provides an electronic device comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement a gasoline octane number prediction method based on intermolecular interactions.
In a third aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing a method for gasoline octane prediction based on intermolecular interactions when executed by a processor.
According to the method and the device for predicting the gasoline octane number based on the intermolecular interaction, an interaction relation equation of any one group and two groups is constructed based on the interaction of six families among gasoline molecules, a nonlinear molecular interaction function among gasoline molecules is constructed based on the interaction relation equation of any one group and two groups, a mixing equation for predicting the gasoline octane number is obtained based on the molecular interaction function, and the model construction of a gasoline mixture from a molecular level is realized, so that the interaction relation among different types of gasoline molecules is prevented from being ignored in the simulation process, the simulation process is closer to the real condition, the data error obtained after the simulation is small, the result is more reliable, the formula blending process of commodity gasoline with different brands is favorably guided theoretically, and the waste of petroleum raw materials in the blending process is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart of a gasoline octane number prediction method based on intermolecular interactions according to the present application;
FIG. 2 is a schematic diagram of the positive and negative effects and linear mixing that occur during the fitting of gasoline blending according to the present application;
FIG. 3 is a schematic illustration of the octane blending rules of the present application;
FIG. 4 is a graph showing the distribution of octane value data and the comparison between the predicted value and the experimental value of the actual gasoline and the ternary mixture according to the research method of the present application;
FIG. 5 is a graph comparing the distribution of motor octane number experimental data and the predicted and experimental values of actual gasoline and ternary mixtures;
FIG. 6 is a graphical representation of the results of a comparison between experimental, linear and nonlinear blending values for the ternary blend of the present application;
FIG. 7 is a graphical representation of the octane number as a function of volume fraction for paraffins in admixture with different types of hydrocarbons according to the present application;
FIG. 8 is a graphical illustration comparing experimental and predicted values for different gasoline octane number and motor octane number values according to the present application;
fig. 9 is a schematic structural diagram of an electronic device according to the present application.
Specific embodiments of the present application have been shown by way of example in the drawings and will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
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, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims.
The terms referred to in this application are explained first:
ternary mixture: refers to the collective term for the gasoline replacement blend collected from the API Research Project 45, consisting of 48 volume percent 2, 4-trimethylpentane, 32% n-heptane and 20% other compounds, wherein the other compounds include 240 different types of hydrocarbon compounds.
API Research Project 45: refers to a data sheet containing molecular composition, mass fraction, and octane number.
Genetic algorithm: the method is a general algorithm in an octane number prediction method, and interaction parameters can be output after molecular composition, mass fraction and octane number are input.
A sequence quadratic programming algorithm: the method is used for solving the planning problem that an objective function or a constraint condition contains a nonlinear function, and is used for further optimizing interaction parameters output by a genetic algorithm.
The method is mainly applied to an octane number prediction model for simulating the blending process of the commercial gasoline with different brands. The compounds contained in the existing gasoline can be analyzed through a gas chromatography technology to obtain qualitative and quantitative information of molecules, but the accurate prediction of the octane number of the gasoline is still very difficult, mainly because linear and nonlinear behaviors exist simultaneously when the gasoline is mixed, the linear behavior is relatively easy to simulate through a plurality of main compounds, but the nonlinear behavior cannot be obtained through the conventional gasoline fuel analysis simulated through the combination of a plurality of compounds (n-heptane, isooctane and toluene), and a large error is generated.
The application provides a method for predicting the octane number of gasoline, and aims to solve the technical problems in the prior art.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a gasoline octane number prediction method based on intermolecular interaction according to the present application. As shown in fig. 1, the method includes:
s101, constructing an interaction relation equation of any two families based on six families of interactions among gasoline molecules, wherein the six families of interactions comprise the interactions between paraffin and olefin, paraffin and aromatic hydrocarbon, olefin and naphthenic hydrocarbon, naphthenic hydrocarbon and aromatic hydrocarbon, olefin and aromatic hydrocarbon and oxygen-containing compound and hydrocarbon.
The six-member family of interactions between gasoline molecules is a central part of the mixing regime. First, gasoline molecules are classified into the six major P/I/O/N/a/OXY family, and molecular interactions are composed of six pairs of interactions, including the interactions between paraffins and olefins, paraffins and aromatics, olefins and naphthenes, naphthenes and aromatics, olefins and aromatics, and oxygenates and hydrocarbons, and therefore two interacting family parameters are needed to manipulate the curvilinear variation of the molecular interactions, where the two interacting family parameters are one of the six pairs of interactions.
FIG. 2 is a schematic representation of the positive, negative effects and linear mixing that occur during the fitting gasoline blending process of the present application. And fitting positive and negative effects or linear mixing in the gasoline mixing process by using the binary interaction parameters as control quantities for controlling curve shape change. When the binary interaction parameter is positive, it indicates that the interaction has a favorable effect on the octane number of the mixture, and at this time, as the interaction function of the two families increases, the change curve gradually becomes prominent, as shown in fig. 2 b; conversely, when the binary interaction parameter is negative, it indicates that the interaction has an adverse effect on the octane number of the mixture, and the curve becomes gradually concave as the interaction function of the two families increases, as shown in fig. 2 c; in particular, when the binary interaction parameter is 0, it indicates that the interaction does not contribute to the octane number of the mixture, as shown in fig. 2 a.
S102, constructing a nonlinear molecular interaction function between gasoline molecules based on any two families of interaction relation equations.
And (4) summing all the interaction relation equations of any two families obtained in the step (S101), namely obtaining the nonlinear molecular interaction function among all gasoline molecules.
And S103, obtaining a mixing equation for predicting the octane number of the gasoline based on the molecular interaction function.
And adding the nonlinear molecular interaction function obtained in the step S102 and the linear molecular interaction function solved in advance to obtain the predicted gasoline octane number, wherein the linear molecular interaction function is determined by knowing the octane number of the gasoline fuel mixture, that is, adding and solving linear volume mixing values of all molecular octane numbers in the gasoline fuel.
In the embodiment, an interaction relation equation of any two families is constructed based on interaction of six families among gasoline molecules, a nonlinear molecular interaction function among gasoline molecules is constructed based on the interaction relation equation of any two families, a mixing equation for predicting the gasoline octane number is obtained based on the molecular interaction function, and model construction of a gasoline mixture from a molecular level is realized, so that the interaction relation among different types of gasoline molecules is prevented from being ignored in a simulation process, the simulation process is closer to a real condition, the data error obtained after simulation is small, the result is more reliable, guidance of the formula blending process of commodity gasoline of different brands is facilitated theoretically, and waste of petroleum raw materials in the blending process is avoided.
One possible example is further set forth below to illustrate how the method of gasoline octane number prediction based on intermolecular interactions can be specifically implemented.
Fig. 3 is a schematic diagram of the octane blending rule of the present application. As shown in fig. 3, the blending equation for predicting gasoline octane number includes two parts, a linear part and a non-linear part.
And (3) researching the interaction relation among different types of molecules by analyzing the octane number of the ternary mixture, thereby determining the interaction relation among the gasoline molecules. The specific process is as follows:
octane number experimental data were collected from API Research Project 45 for a ternary mixture of 48 volume percent 2, 4-trimethylpentane, 32% n-heptane and 20% other compounds. 2, 4-trimethylpentane and n-heptane were taken as a whole to investigate their interaction relationship with other compounds. Their linear mixing values with paraffins, olefins, naphthenes and aromatics were then calculated and compared with the experimental octane number. There was found to be little interaction between paraffins and paraffins, paraffins and naphthenes, with synergistic effects between paraffins and olefins, paraffins and aromatics. Since the database has no experimental data of octane numbers between olefins, naphthenes and aromatics, and the interaction relationship between the olefins, the naphthenes and the aromatics cannot be researched, it is necessary to assume that the olefins and the naphthenes, the olefins and the aromatics, and the naphthenes and the aromatics have interaction. Furthermore, the gasoline produced always contains small amounts of oxygenated additives, and there may be synergy or antagonism between the oxygenates and the hydrocarbons, thus increasing the interaction parameters between them.
Specifically, the linear portion is the linear volumetric blend of octane numbers of all molecules present in the gasoline fuel, which is calculated as:
ON Linear ＝∑ i v i ON i ，
wherein is ON Linear A molecular interaction function that is linear; v. of i Is the volume fraction of molecule i; ON i Is the octane number of molecule i. ON of molecules contained in the mixture i It may be an experimental value or a calculated value obtained by using a pre-developed structural property correlation model.
Gasoline molecules are classified into the P/I/O/N/A/OXY six-member family, and molecular interactions are composed of six pairs of interactions, including the interactions between paraffins and olefins, paraffins and aromatics, olefins and naphthenes, naphthenes and aromatics, olefins and aromatics, and oxygenates and hydrocarbons.
Specifically, the nonlinear part is a gasoline intermolecular nonlinear equation obtained by fitting the interaction functions of all families:
ON NonLinear ＝∑ i ∑ j ΔON ij ，
wherein is ON NonLinear Is a nonlinear molecular interaction function; delta ON ij Is an interaction function of the two families i and j.
Further, the interaction relationship between the two families is constructed by referring to the rayleigh distribution function, and the formula is as follows:
wherein k is ij_a And k ij_b Is a binary interaction parameter; v. of i And v j Corresponding to the volume fractions of the two families i and j, respectively.
Further, the value method of the binary interaction parameter is as follows: and fitting by adopting a genetic algorithm to obtain parameters, and inputting the parameters output by the genetic algorithm as initial values into a local optimization algorithm for optimized output.
In one possible design, the local optimization algorithm employs a sequential quadratic programming algorithm, the upper and lower limits of which are set to 120% and 80% of the original parameters, respectively. Although the genetic algorithm has good global search capability, the optimal solution is difficult to obtain in a huge search space, so that the parameters output by the genetic algorithm are input into a Sequence Quadratic Programming (SQP) algorithm as initial values. The SQP optimization algorithm needs a reasonable upper limit and lower limit, and repeated experiments determine that the upper limit and the lower limit are respectively the original parameter multiplied by 120% and 80%, and the binary interaction parameters are continuously optimized in a specific space to seek a better solution.
Binary interaction parameter k ij_a And k ij_b Controlling the shape of the curve variation. If k is ij_a Above 0, the interaction has a favorable effect on the octane number of the mixture. Also, with Δ ON ij The change curve gradually bulges. On the contrary, if k ij_a Less than 0, the interaction may adversely affect the octane number of the mixture. In a special case k ij_a Equal to 0, indicating that the interaction does not contribute to the octane number of the mixture.
Therefore, the blending equation that ultimately yields the predicted octane blending rule is:
ON＝ON Linear +ON NonLinear ＝∑ i v i ON i +∑ i ∑ j ΔON ij ，
where ON is the molecular interaction function after mixing.
And (5) verifying whether the constructed mixing equation meets the design requirement through a verification experiment. In a specific validation experiment, 231 known gasoline molecular composition data and corresponding octane number RON data (motor octane number MON of 170) were collected, wherein qualitative and quantitative data of gasoline molecules were determined by gas chromatography analysis. In addition, octane numbers RON of 248 ternary mixtures (motor octane number MON of 244) were collected from API Research Project 45, and data of 90 gasoline substitute mixtures (motor octane number MON of 50) were collected from the literature. Thus, the database built contains 569 RON and 464 MON experimental data. And substituting the established database into a model constructed by a mixing equation, calculating a predicted value of the octane number, comparing the predicted value with an experimental value, and verifying whether the prediction capability and the extrapolation performance of the mixing equation are reliable or not.
The mixing equation function needs to fit 12 parameters in total. Specific values for the 12 parameters for optimized RON and MON are listed in tables 1 and 2, respectively.
TABLE 1 research octane number binary interaction parameter matrix optimized by genetic algorithm and SQP algorithm
TABLE 2 optimized Motor octane number binary interaction parameter matrix
FIG. 4 is a graph comparing the distribution of research octane value data and the predicted value and experimental value of the actual gasoline and ternary mixture. Wherein, fig. 4a is a distribution diagram of octane number test data, fig. 4b is a comparison diagram of a predicted value and an experimental value of an actual gasoline octane number, and fig. 4c is a comparison diagram of a predicted value and an experimental value of a ternary mixture octane number. As shown in fig. 4b and 4c, it can be found that the mean absolute error of the real gasoline is less than 1 unit, which indicates that the model has good training effect, but the mean absolute error of the ternary mixture reaches 3.64 units, and the larger error is acceptable because the ternary mixture only trains the function of the binary interaction parameter.
Corresponding to fig. 4, fig. 5 is a graph comparing the distribution of experimental data of motor octane number and predicted values and experimental values of actual gasoline and ternary mixture. Fig. 5a is a distribution diagram of experimental data of motor octane number, fig. 5b is a comparison diagram of a predicted value and an experimental value of an actual gasoline motor octane number, and fig. 5c is a comparison diagram of a predicted value and an experimental value of a ternary mixture motor octane number. The error is close to the experimental data error of the octane number, and therefore the same conclusion is reached.
Furthermore, the predictive capability and the extrapolation performance of the model are verified in the following three different ways.
FIG. 6 is a graphical representation of the results of comparing experimental, linear and nonlinear mixing values of the ternary mixtures of the present application. As shown in fig. 6, the model fits well to the octane number of the ternary mixture, but some molecules have large errors. Because the model trains binary interaction parameters between the P/I/O/N/A/OXY families, rather than every molecule.
FIG. 7 is a graphical representation of the trend of octane number as a function of volume fraction for paraffins (2,2,4-trimethylpentane and n-heptane) of the present application when mixed with different types of hydrocarbons. And (4) verifying whether the model is overfitting or not by adopting a change curve of octane value increasing along with the volume integral number. As shown in fig. 7, the model still predicts the octane number of the mixture according to the volume fraction with only one experimental point for each hydrocarbon molecule, and the model is proved to have no overfitting and strong prediction capability.
Fig. 8 is a schematic diagram comparing experimental and predicted values of octane number by motor and octane number by gasoline stream. To validate the predictive ability of gasoline fuels not in the training set, different process streams were selected for validation, including straight run, alkylation, hydrogenation, catalytic cracking, reforming, and blended gasolines. As shown in fig. 8, the predicted octane number is substantially the same as the experimental value, the average absolute error of the research octane number is less than 0.6 unit, and the average absolute error of the motor octane number is less than 0.8 unit, which proves that the model is not over-fitted and meets the prediction requirement.
In the embodiment, interaction relations among different types of molecules are researched through octane number experimental data of a ternary mixture, then a mixing equation is constructed based on the relations, nonlinear behaviors among the molecules are described in detail through molecular interaction functions in the mixing equation, binary interaction parameters in the molecular interaction functions are fitted by combining a genetic algorithm and a sequence quadratic programming algorithm, and finally whether a model constructed by using the mixing equation meets the prediction capability and extrapolation performance of actual gasoline or not is verified through a plurality of groups of databases.
Fig. 9 is a schematic structural diagram of an electronic device according to the present application. As shown in fig. 9, the present embodiment provides an electronic device including a memory 902 and a processor 901, where the memory 902 is used for storing 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, with software programs and device drivers stored in the memory 902. The software program can execute various functions of the method provided by the embodiment of the invention; the device drivers may be network and interface drivers. The processor 901 is configured to execute a software program, and when the software program is executed, the method for predicting the octane number of gasoline based on the intermolecular interaction according to the embodiment of the present invention can be implemented as follows:
specifically, the processor 901 constructs any two families of interaction equations based on six families of interactions between gasoline molecules, including: constructing any two families of interaction relation equations changed by two parameter adjusting curves based on Rayleigh distribution functions:
Specifically, the value taking method of the binary interaction parameter comprises the following steps: the genetic algorithm is adopted for fitting to obtain parameters, then the parameters output by the genetic algorithm are input into the local optimization algorithm as initial values for optimized output, wherein the local optimization algorithm adopts a sequence quadratic programming algorithm, and the upper limit and the lower limit of the sequence quadratic programming algorithm are respectively set to be 120% and 80% of the original parameters, so that the model has better prediction and generalization performance.
The processor 901 constructs a non-linear molecular interaction function between gasoline molecules based on any two families of interaction relation equations:
ON NonLinear ＝∑ i ∑ j ΔON ij in which is ON NonLinear A molecular interaction function that is non-linear; delta ON ij Is an interaction function of both families i and j.
Before the processor 901 obtains the blending equation for predicting the octane number of gasoline based on the molecular interaction function, the method further includes: and acquiring and constructing a linear mixing function based on the octane number of the ternary mixture, wherein the octane number of the ternary mixture is the ternary mixture of known octane value data.
Specifically, the linear mixing function is:
ON Linear ＝∑ i v i ON i in which is ON Linear A molecular interaction function that is linear; v. of i Is the volume fraction of molecules i; ON i Is the octane number of molecule i.
Before the processor 901 obtains the blending equation for predicting the octane number of gasoline based on the molecular interaction function, the method further includes: adding the constructed linear mixing function and the molecular interaction function to obtain a mixing equation for predicting the octane number of the gasoline:
ON＝ON Linear +ON NonLinear wherein ON is a mixed molecular interaction function; ON Linear A molecular interaction function that is linear; ON NonLinear Is a non-linear molecular interaction function.
The electronic device provided in this embodiment may be used to execute the above method for predicting gasoline octane number based on intermolecular interaction, and the implementation principle and technical effect are similar, which are not described herein again.
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when being executed by a processor, the computer program realizes the method for predicting the octane number of the gasoline based on the intermolecular interaction.
The computer-readable storage medium provided in this embodiment may be used to execute the above method for predicting gasoline octane number based on intermolecular interaction, which has similar implementation principles and technical effects, and this embodiment is not described herein again.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Other embodiments of the present application will be 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 variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that 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 method for predicting the octane number of a gasoline based on intermolecular interactions, wherein the octane number includes a research octane number and a motor octane number, the method comprising:
constructing an interaction relationship equation between any two families based on six-family interactions between gasoline molecules, wherein the six-family interactions comprise interactions between paraffins and olefins, paraffins and aromatics, olefins and naphthenes, naphthenes and aromatics, olefins and aromatics, and oxygenates and hydrocarbons;
constructing a nonlinear molecular interaction function between gasoline molecules based on any two families of interaction relation equations;
obtaining a mixing equation for predicting the octane number of the gasoline based on a molecular interaction function;
the method for constructing the interaction relation equation of any two families based on the interaction of the six families between gasoline molecules comprises the following steps of constructing the interaction relation equation of any two families changed by two parameter adjusting curves based on a Rayleigh distribution function:
2. The method of claim 1, wherein the non-linear molecular interaction function between gasoline molecules is constructed based on any two families of interaction relation equations:
ON NonLinear ＝∑ i ∑ j ΔON ij in which is ON NonLinear Is a nonlinear molecular interaction function; delta ON ij Is an interaction function of both families i and j.
3. The method of claim 1, wherein prior to deriving the blending equation that predicts gasoline octane number based on the molecular interaction function, further comprising: and acquiring and constructing a linear mixing function based on the octane number of the ternary mixture, wherein the octane number of the ternary mixture is the ternary mixture of the known octane value data.
4. The method of claim 3, wherein the linear mixing function is:
ON Linear ＝∑ i v i ON i in which is ON Linear A molecular interaction function that is linear; v. of i Is the volume fraction of molecule i; ON i Is the octane number of molecule i.
5. The method of claim 3, wherein prior to deriving the blending equation that predicts gasoline octane number based on the molecular interaction function, further comprising: adding the constructed linear mixing function and the molecular interaction function to obtain a mixing equation for predicting the octane number of the gasoline:
ON＝ON Linear +ON NonLinear wherein ON is a mixed molecular interaction function; ON Linear A molecular interaction function that is linear; ON NonLinear Is a nonlinear molecular interaction function.
6. The method of claim 1, wherein the binary interaction parameter is derived by: and fitting by adopting a genetic algorithm to obtain parameters, and inputting the parameters output by the genetic algorithm as initial values into a local optimization algorithm for optimized output.
7. The method of claim 6, wherein the local optimization algorithm employs a sequential quadratic programming algorithm with upper and lower limits set to 120% and 80% of original parameters, respectively.
8. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1 to 7.
9. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210300866.9A CN114724644B (en) | 2022-03-25 | 2022-03-25 | Method and equipment for predicting gasoline octane number based on intermolecular interaction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210300866.9A CN114724644B (en) | 2022-03-25 | 2022-03-25 | Method and equipment for predicting gasoline octane number based on intermolecular interaction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114724644A CN114724644A (en) | 2022-07-08 |
| CN114724644B true CN114724644B (en) | 2023-04-07 |
Family
ID=82239651
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210300866.9A Active CN114724644B (en) | 2022-03-25 | 2022-03-25 | Method and equipment for predicting gasoline octane number based on intermolecular interaction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114724644B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1584555A (en) * | 2004-06-01 | 2005-02-23 | 浙江大学 | Petroleum products quality rapid measuring instrument based on low-resolution Raman spectra |
| CN113705021A (en) * | 2021-09-15 | 2021-11-26 | 西南石油大学 | Method for predicting gasoline octane number |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103955778B (en) * | 2014-05-15 | 2017-07-07 | 南京富岛信息工程有限公司 | A kind of high-performance gasoline concoction nonlinear optimization method |
| CN107085059B (en) * | 2017-05-14 | 2020-04-03 | 杭州辛孚能源科技有限公司 | Method and device for measuring gasoline octane number |
| CN108956814B (en) * | 2018-07-03 | 2020-07-03 | 中国石油大学(北京) | Method for directly constructing gasoline molecular composition model and property prediction method |
| US11119088B2 (en) * | 2019-03-15 | 2021-09-14 | Chevron U.S.A. Inc. | System and method for calculating the research octane number and the motor octane number for a liquid blended fuel |
-
2022
- 2022-03-25 CN CN202210300866.9A patent/CN114724644B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1584555A (en) * | 2004-06-01 | 2005-02-23 | 浙江大学 | Petroleum products quality rapid measuring instrument based on low-resolution Raman spectra |
| CN113705021A (en) * | 2021-09-15 | 2021-11-26 | 西南石油大学 | Method for predicting gasoline octane number |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114724644A (en) | 2022-07-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ghosh et al. | Development of a detailed gasoline composition-based octane model | |
| US10082807B2 (en) | Method of preparing a certified target product from a mixture of components by spectral analysis | |
| JP2010500452A (en) | Predict stream composition and properties in near real time | |
| US11119088B2 (en) | System and method for calculating the research octane number and the motor octane number for a liquid blended fuel | |
| US9939821B2 (en) | Computer implemented blend control system and method for preparation of a hydrocarbon blend | |
| Liu et al. | Petroleum refinery process modeling: integrated optimization tools and applications | |
| Castillo et al. | Inventory pinch algorithm for gasoline blend planning | |
| JP7312211B2 (en) | How to provide the blending ratio of the base material | |
| Siamizade | Global optimization of refinery-wide production planning with highly nonlinear unit models | |
| Gordillo et al. | Customizing CO 2 allocation using a new non-iterative method to reflect operational constraints in complex EU refineries | |
| Rusin et al. | A" transformation" method for calculating the research and motor octane numbers of gasoline blends | |
| Menezes et al. | Nonlinear production planning of oil-refinery units for the future fuel market in Brazil: process design scenario-based model | |
| CN114724644B (en) | Method and equipment for predicting gasoline octane number based on intermolecular interaction | |
| Liu | Molecular characterisation and modelling for refining processes | |
| Wu et al. | Molecular management for refining operations | |
| Clark et al. | Quantification of gasoline-ethanol blend emissions effects | |
| Carlson et al. | Economic analysis of the benefits to petroleum refiners for low carbon boosted spark ignition biofuels | |
| Sales et al. | An integrated optimization and simulation model for refinery planning including external loads and product evaluation | |
| Hu et al. | Combine molecular modeling with optimization to stretch refinery operation | |
| Oduola et al. | Development of model equations for predicting gasoline blending properties | |
| Grubinger et al. | Surrogate generation and evaluation of gasolines | |
| Stratiev et al. | Evaluation of accuracy of literature gasoline blending models to predict octane numbers of gasoline blends | |
| Monder | Real-time optimization of gasoline blending with uncertain parameters | |
| Twu et al. | A generalized interaction method for the prediction of octane numbers for gasoline blends | |
| WO2017062416A1 (en) | Hybrid analyzer for fluid processing processes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |