CN116013425A

CN116013425A - Molecular level refining process model construction method and device and storage medium

Info

Publication number: CN116013425A
Application number: CN202310133352.3A
Authority: CN
Inventors: 王杭州; 任军革; 徐文清; 秦军; 陈起; 张四清
Original assignee: Xinjiang Dushanzi Petrochemical Co ltd; Petrochina Co Ltd
Current assignee: Xinjiang Dushanzi Petrochemical Co ltd; Petrochina Co Ltd
Priority date: 2023-02-20
Filing date: 2023-02-20
Publication date: 2023-04-25
Anticipated expiration: 2043-02-20
Also published as: CN116013425B

Abstract

The disclosure relates to a method and a device for constructing a molecular level refining process model and a storage medium, wherein the method comprises the following steps: training a molecular composition analysis model according to the corresponding relation between the known physical property data of the crude oil and the molecular composition of the crude oil and the physical property data of each molecule of the crude oil; inputting raw material molecular compositions corresponding to a preset secondary processing device model into the secondary processing device model, predicting a molecular composition matrix of a product, and training the secondary processing device model according to a comparison result between the predicted product and an actual product; inputting the predicted product into a preset blending optimization model, training the blending optimization model based on a comparison result between physical property parameters of the blended product and preset target physical property parameters, and based on a molecular composition analysis model, a secondary processing device model and the blending optimization model, so that the proportion adjustment of new crude oil and different crude oil components can be realized, and the maximum utilization of the molecular values of the crude oil and the product thereof is facilitated.

Description

Molecular level refining process model construction method and device and storage medium

Technical Field

The disclosure relates to the technical field of petrochemical industry, in particular to a method and a device for constructing a molecular level refining process model, electronic equipment and a storage medium.

Background

At present, a lumped model method is mainly adopted for dynamic modeling of a reaction system in the petroleum processing process. The lumped model classifies similarity according to macroscopic physical properties, structural features and other dynamic properties of various components in the material, and builds a reaction network among the lumped components and calculates reaction parameters. Each lumped component actually contains a large number of pure molecular components, which are considered as virtual single components with uniform physical properties in the modeling process. Thus, the lumped model has obvious drawbacks in the extended adaptability of new feedstock and new catalysts.

The advanced refining enterprises at home and abroad begin to develop and apply the production device by using the molecular level process model, however, the molecular level model is still mainly concentrated on the model development of a single reaction system in the petroleum processing process, and the full-process molecular level modeling of distillation, secondary processing devices and blending systems in the production process is not yet reported, so that the full-process molecular level modeling of the processing device cannot be realized.

Therefore, a method and apparatus for modeling a molecular level refining process, an electronic device, and a storage medium are needed.

Disclosure of Invention

To solve or at least partially solve the above technical problems, embodiments of the present disclosure provide a method and apparatus for constructing a molecular level refining process model, an electronic device, and a storage medium.

In a first aspect, embodiments of the present disclosure provide a method for constructing a molecular level refining process model including a molecular composition analysis model, a secondary processing device model, and a reconciliation optimization model connected in sequence, the method comprising:

training a preset molecular composition analysis model according to the known physical property data of the crude oil, the crude oil molecular composition and the corresponding relation between the physical property data of each molecule of the crude oil to obtain parameters of the molecular composition analysis model, wherein the parameters of the molecular composition analysis model comprise the molecular type and the content;

selecting a raw material molecular composition corresponding to a preset secondary processing device model from known crude oil molecular compositions, converting the raw material molecular composition into a raw material molecular composition matrix, inputting the raw material molecular composition matrix into the preset secondary processing device model, predicting a molecular composition matrix of a product, comparing the predicted product with an actual reaction product, and adjusting dynamic parameters in the secondary processing device model according to a comparison result until the difference between the yield and physical parameters corresponding to the molecular composition of the predicted product and the yield and physical parameters of the actual reaction product is smaller than a first preset threshold value;

Inputting the molecular composition matrix of the predicted product into a preset reconciliation optimization model, outputting physical parameters of the reconciliation product based on the reconciliation proportion of the preset reconciliation optimization model, comparing the physical parameters of the reconciliation product with preset target physical parameters, and adjusting the reconciliation proportion according to the comparison result until the difference between the physical parameters of the reconciliation product and the preset target physical parameters is smaller than a second preset threshold.

In one possible embodiment, the molecular level refining process model further comprises a physical property database coupled to the molecular composition analytical model, the method comprising:

for known gasoline and diesel oil products, quantitatively analyzing the molecular composition of the oil products, converting the molecular composition of each gasoline and diesel oil product into the molecular composition of the structure-oriented lumped representation, and constructing a gasoline and diesel oil molecular composition database based on the molecular composition of the structure-oriented lumped representation of each gasoline and diesel oil product;

cutting known crude oil into light fraction and heavy fraction, quantitatively analyzing the light fraction of the crude oil, detecting physical parameters of the heavy fraction of the crude oil, determining molecular composition of the heavy fraction according to the physical parameters of the heavy fraction of the crude oil, converting the molecular composition of each light fraction and the heavy fraction into molecular composition of structure-oriented lumped representation, and constructing a crude oil molecular composition database based on the molecular composition of the structure-oriented lumped representation of each light fraction and the heavy fraction;

Respectively calculating physical properties of gasoline and diesel oil and crude oil based on data of a gasoline and diesel oil molecular composition database and a crude oil molecular composition database, and establishing a gasoline and diesel oil physical property database and a crude oil physical property database;

wherein, for each molecule in gasoline, diesel oil and crude oil, physical properties of each molecule expressed based on a structure-oriented lumped method are calculated by using a group contribution method;

for the gasoline and diesel oil products and crude oil products, the physical properties of the gasoline and diesel oil products and the crude oil products are calculated according to the molecular composition of the gasoline and diesel oil products and the crude oil products.

In one possible embodiment, the molecular composition analytical model analyzes crude oil by:

receiving physical property data of crude oil to be analyzed, and inquiring crude oil consistent with the physical property data of the crude oil in the crude oil physical property database;

for crude oil to be resolved existing in a crude oil physical property database, directly outputting the crude oil molecular composition inquired in the crude oil physical property database;

for crude oil to be analyzed which does not exist in a crude oil physical property database, inquiring crude oil closest to the physical property data in the crude oil physical property database according to the physical property data;

and fitting the crude oil molecular composition to be resolved which does not exist in the crude oil physical property database by taking the closest crude oil molecular composition and content as initial values.

In one possible implementation, the secondary processing corresponding to the secondary processing device model is one of residuum hydrogenation, catalytic cracking, delayed coking, hydrocracking, catalytic reforming, alkylation, gasoline hydrogenation, diesel hydrogenation, wax oil hydrogenation, gasoline and diesel hydrogenation, gas fractionation, aromatic hydrocarbon extraction and hydrogen production.

In one possible implementation manner, the converting the raw material molecular composition into the raw material molecular composition matrix is input into a preset secondary processing device model, predicting a molecular composition matrix of a product, comparing the predicted product with an actual reaction product, and adjusting a kinetic parameter in the secondary processing device model according to a comparison result until a difference between the molecular composition of the predicted product and the actual reaction product is smaller than a first preset threshold value, including:

obtaining a raw material molecule composition matrix of a secondary processing reaction, wherein the raw material molecule composition matrix comprises structure-oriented lumped representation and content of each raw material molecule;

generating a reaction path corresponding to each molecule according to the structure-oriented lumped representation of each raw material molecule, obtaining a product molecule of each reaction path, comparing the product molecule with a preset molecular set, only retaining the product molecules existing in the preset molecular set and the corresponding reaction paths thereof as effective product molecules and effective reaction paths, and constructing a reaction network based on the effective product molecules and the effective reaction paths, wherein the preset component is a collection represented by a structure-oriented lumped method based on the crude oil molecules and the product molecules of each secondary processing device in the oil refining process;

Predicting a product molecule composition matrix of the secondary processing reaction according to the content of each raw material molecule based on a reaction kinetic equation set and a reaction time length corresponding to a reaction network, wherein the product molecule composition matrix comprises structure-oriented lumped representation and content of each product molecule;

predicting the attribute parameters of the product according to the attribute parameters of the reserved product molecules, taking the difference between the attribute parameters of the predicted product and the actual reaction product as an optimization target, adjusting the reaction rate constant corresponding to the reaction rule, and taking the reaction rate constant meeting the optimization target condition as a secondary processing device model parameter.

In one possible implementation manner, the reaction paths corresponding to each molecule are generated according to the structure-oriented lumped representation of each raw material molecule, the product molecule of each reaction path is obtained and compared with a preset molecular set, only the product molecules existing in the preset molecular set and the corresponding reaction paths thereof are reserved as effective product molecules and effective reaction paths, and a reaction network is constructed based on the effective product molecules and the effective reaction paths, and the method comprises the following steps:

traversing the structure-oriented lumped representation of each raw material molecule according to a preset reaction rule to obtain a reaction path corresponding to each raw material molecule; the reaction rules comprise aromatic hydrocarbon reaction rules, cycloparaffin reaction rules, olefin reaction rules, alkane reaction rules and heteroatom-containing molecules;

A second step of comparing each product molecule of the reaction path with a preset molecular set;

a third step of reserving product molecules existing in a preset molecular set and corresponding reaction paths thereof;

a fourth step of returning the reserved product molecules as raw material molecules to the first step until all the product molecules do not accord with a preset reaction rule;

and fifth, summarizing all product molecules and reaction paths of the first step to the fourth step as effective product molecules and effective reaction paths, and constructing a reaction network by using the raw material molecules, the effective product molecules and the effective reaction paths.

In one possible embodiment, the system of reaction kinetics equations and the reaction duration corresponding to the reaction network are determined by:

determining a reaction rule corresponding to each effective reaction path in a reaction network, wherein each effective reaction rule has a corresponding reaction kinetic equation, and the reaction kinetic equations corresponding to all the effective reaction paths form a reaction kinetic equation set corresponding to the reaction network;

the length of the reaction is determined by the reactor volume and the feed flow rate.

In one possible embodiment, the predicting a product molecular composition matrix of the secondary processing reaction based on the corresponding reaction kinetic equation set of the reaction network and the reaction duration according to the content of each raw material molecule includes:

For each effective reaction path in the reaction network, determining the raw material molecules and the product molecules of the current effective reaction path;

substituting the reaction time length of the current effective reaction path and the content of the raw material molecules in the reaction network into a corresponding reaction kinetic equation to obtain the content of the raw material molecules and the product molecules of the current effective reaction path, wherein the corresponding reaction time length of the reaction network comprises the reaction time length of each effective reaction path in the reaction network;

summarizing the contents of raw material molecules and product molecules of all effective reaction paths, and determining the contents of all summarized product molecules of all effective reaction paths;

directing the structure of each summarized product molecule to a lumped representation and content as a complete vector;

and combining the complete vectors of all summarized product molecules of the secondary processing reaction into a product molecule composition matrix of the secondary processing reaction.

In one possible embodiment, the property parameter of the product molecule is at least one of a content and a physical property parameter, and the property parameter of the product is at least one of a yield and a physical property parameter.

In one possible embodiment, the predicting the property parameters of the product according to the property parameters of the reserved product molecules includes:

Determining the product molecules contained in each product;

and obtaining the yield and physical parameters of each product according to the content and physical parameters of each product molecule in each product.

In one possible embodiment, the reaction rate corresponding to the reaction rule is calculated by the following expression:

；

wherein ,r _i is the firstiThe reaction rate corresponding to the bar reaction rule,c _i is the firstiThe bar reaction rule corresponds to the molar concentration of the reactant,tis the reaction time.

In one possible embodiment, the method further comprises:

taking the constraint condition that the difference between the physical property parameter of the blended product and the preset target physical property parameter is smaller than a second preset threshold, taking at least one of the maximum profit setting and the minimum residual blending component setting as a target function, adjusting the blending proportion, and taking the blending proportion when the target function value is optimal as the fixed parameter of the blending optimization model.

In a second aspect, embodiments of the present disclosure provide a molecular level refining process model building apparatus, comprising:

the first training module is used for training a preset molecular composition analysis model according to the physical property data of known crude oil, the crude oil molecular composition and the corresponding relation between the physical property data of each molecule of the crude oil to obtain parameters of the molecular composition analysis model, wherein the parameters of the molecular composition analysis model comprise molecular types and contents;

The second training module is used for selecting a raw material molecular composition corresponding to a preset secondary processing device model from known crude oil molecular compositions, converting the raw material molecular composition into a raw material molecular composition matrix, inputting the raw material molecular composition matrix into the preset secondary processing device model, predicting a molecular composition matrix of a product, comparing the predicted product with an actual reaction product, and adjusting dynamic parameters in the secondary processing device model according to a comparison result until the difference between the yield and physical parameters corresponding to the molecular composition of the predicted product and the yield and physical parameters of the actual reaction product is smaller than a first preset threshold value;

the third training module is used for inputting the molecular composition matrix of the predicted product into a preset reconciliation optimization model, outputting physical parameters of the reconciliation product based on the reconciliation proportion of the preset reconciliation optimization model, comparing the physical parameters of the reconciliation product with preset target physical parameters, and adjusting the reconciliation proportion according to the comparison result until the difference between the physical parameters of the reconciliation product and the preset target physical parameters is smaller than a second preset threshold.

In a third aspect, embodiments of the present disclosure provide an electronic device including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus;

A memory for storing a computer program;

and the processor is used for realizing the molecular level refining process model construction method when executing the program stored in the memory.

In a fourth aspect, embodiments of the present disclosure provide a computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the above-described molecular level refining process model building method.

Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has at least part or all of the following advantages:

according to the molecular level refining process model construction method, based on the molecular composition analysis model, the secondary processing device model and the reconciliation optimization model, simulation model development is carried out on each secondary processing device of the whole process of a refinery from a molecular level, petrochemical industry molecular level whole process simulation software is developed, proportion adjustment of new crude oil and different crude oil components can be achieved, and maximum utilization of crude oil and product molecular values is facilitated.

Drawings

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

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the related art will be briefly described below, and it will be apparent to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 schematically illustrates a flow diagram of a molecular level refining process model building method according to an embodiment of the disclosure;

FIG. 2 schematically illustrates a structural schematic of a molecular level refining process model according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a block diagram of a molecular level refining process model building apparatus according to an embodiment of the disclosure;

fig. 4 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some, but not all, embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the disclosure, are within the scope of the disclosure.

Referring to fig. 1, an embodiment of the present disclosure provides a molecular level refining process model construction method including a molecular composition analysis model, a secondary processing device model, and a reconciliation optimization model connected in sequence, the method including:

s1, training a preset molecular composition analysis model according to the physical property data of known crude oil, the crude oil molecular composition and the corresponding relation between the physical property data of each molecule of the crude oil to obtain parameters of the molecular composition analysis model, wherein the parameters of the molecular composition analysis model comprise molecular types and contents;

s2, selecting a raw material molecular composition corresponding to a preset secondary processing device model from known crude oil molecular compositions, converting the raw material molecular composition into a raw material molecular composition matrix, inputting the raw material molecular composition matrix into the preset secondary processing device model, predicting a molecular composition matrix of a product, comparing the predicted product with an actual reaction product, and adjusting dynamic parameters in the secondary processing device model according to a comparison result until the difference between the yield and physical parameters corresponding to the molecular composition of the predicted product and the yield and physical parameters of the actual reaction product is smaller than a first preset threshold value, wherein secondary processing corresponding to the secondary processing device model is one of residual oil hydrogenation, catalytic cracking, delayed coking, hydrocracking, catalytic reforming, alkylation, gasoline hydrogenation, diesel hydrogenation, wax oil hydrogenation, gasoline and diesel hydrogenation, gas fractionation, aromatic hydrocarbon extraction and hydrogen production;

S3, inputting the molecular composition matrix of the predicted product into a preset blending optimization model, outputting physical property parameters of the blended product based on the blending proportion of the preset blending optimization model, comparing the physical property parameters of the blended product with preset target physical property parameters, and adjusting the blending proportion according to the comparison result until the difference between the physical property parameters of the blended product and the preset target physical property parameters is smaller than a second preset threshold, wherein the preset target physical property parameters are determined according to the requirements of national standards on the physical properties of the gasoline product.

In some embodiments, the molecular level refining process model further comprises a physical property database coupled to the molecular composition analytical model, the method further comprising:

for known gasoline and diesel oil products, quantitatively analyzing the molecular composition of the oil products, reserving physical property data of all single molecules in analysis results, converting the molecular composition of each gasoline and diesel oil product into a molecular composition expressed by structure-oriented lumped representation, and constructing a gasoline and diesel molecular composition database based on the molecular composition expressed by structure-oriented lumped representation of each gasoline and diesel oil product and the data of all single molecules;

cutting known crude oil into light fraction and heavy fraction, quantitatively analyzing the light fraction of the crude oil, obtaining single molecular physical property data of the light fraction, detecting physical property parameters of the heavy fraction of the crude oil, determining molecular composition of the heavy fraction according to the physical property parameters of the heavy fraction of the crude oil, converting molecular composition of each light fraction and the heavy fraction into molecular composition with structure-oriented lumped representation, and constructing a crude oil molecular composition database based on the molecular composition with structure-oriented lumped representation of each light fraction and the heavy fraction, wherein the physical property parameters of the heavy fraction of the crude oil are input into a trained molecular composition analysis model to obtain the molecular composition of the heavy fraction;

Calculating physical properties of the gasoline and the diesel oil respectively based on data of a gasoline and diesel oil molecular composition database and a crude oil molecular composition database by using a preset physical property calculation module, and establishing a gasoline and diesel oil physical property database and a crude oil physical property database;

the preset physical property calculation module calculates physical properties of each molecule expressed based on a structure-oriented lumped method by using a group contribution method for each molecule in gasoline, diesel oil and crude oil;

for the gasoline and diesel oil products and the crude oil products, the preset physical property calculation module calculates physical properties of the gasoline and diesel oil products and the crude oil products according to the molecular composition of the gasoline and diesel oil products and the crude oil products based on a preset mixing rule.

In some embodiments, in step S1, the molecular composition analytical model parses the crude oil by:

receiving physical property data of crude oil to be analyzed, and inquiring crude oil consistent with the physical property data in the crude oil physical property database;

In some embodiments, in step S2, the converting the raw material molecular composition into a raw material molecular composition matrix is input into a preset secondary processing device model, predicting a molecular composition matrix of a product, comparing the predicted product with an actual reaction product, and adjusting a kinetic parameter in the secondary processing device model according to a comparison result until a difference between the molecular composition of the predicted product and the actual reaction product is less than a first preset threshold value, including:

generating a reaction path corresponding to each molecule according to the structure-oriented lumped representation of each raw material molecule based on a preset secondary processing reaction rule, obtaining a product molecule of each reaction path, comparing the product molecule with a preset molecule set, only retaining the product molecules existing in the preset molecule set and the corresponding reaction paths thereof as effective product molecules and effective reaction paths, and constructing a reaction network based on the effective product molecules and the effective reaction paths, wherein the reaction rule comprises the change of the structure-oriented lumped representation of each raw material molecule in the corresponding reaction paths thereof, the preset subset is predefined according to the actual reaction of each device in the oil refining process and the structure-oriented lumped method based on the molecular products, and the preset subset is a set of the representation of the product molecules of each secondary processing device in the oil refining process based on the structure-oriented lumped method;

predicting attribute parameters of a product according to the attribute parameters of reserved product molecules, taking a difference value between the attribute parameters of the predicted product and an actual reaction product as an optimization target, adjusting a reaction rate constant corresponding to a reaction rule, and taking the reaction rate constant meeting the optimization target condition as a secondary processing device model parameter, wherein the attribute parameters are at least one of physical properties and content.

In some embodiments, predicting a molecular composition matrix of a product of the secondary processing reaction according to the content of each raw material molecule based on a corresponding reaction kinetic equation set of the reaction network and a reaction duration, including:

the effective reaction path in the reaction network is divided into the average reaction time lengthnThe reaction time of each micro-element is as follows

；

Taking the reaction temperature, pressure, enthalpy and specific heat capacity of an inlet of an actual secondary processing device as the initial temperature, initial pressure, initial enthalpy and initial specific heat capacity of an inlet of a first micro-segment;

For each micro-element segment, solving a reaction rate constant corresponding to each reaction rule by using a Dragon-Gregory tower algorithmk _i The following ordinary differential equation set obtains the reactant concentration and the change of the concentration along with time at the outlet of the micro-segment

Wherein the system of ordinary differential equations includes: zero order reaction (S)>

The method comprises the steps of carrying out a first treatment on the surface of the First order reaction, tiger>

The method comprises the steps of carrying out a first treatment on the surface of the The secondary reaction is carried out, the reaction is carried out,

；k _i is the firstiA reaction rate constant corresponding to the bar reaction rule;

according to

And the entry enthalpy value of the infinitesimal section, calculating the change of the enthalpy value along with time +.>

；

According to

And specific heat capacity, calculate temperature change over time +.>

Obtaining the temperature of the outlet of the micro-segment;

according to ideal gas constantRAnd temperature change with time

Calculate the pressure change over time +.>

To obtain the pressure at the outlet of the micro-segment;

and taking the temperature, pressure, enthalpy value and each product concentration of the outlet of the micro-segment as the inlet parameter of the next micro-segment, calculating the parameter of the outlet of the next micro-segment until the last micro-segment, and taking the product content of the outlet of the last micro-segment as the product content of the secondary processing device.

In some embodiments, calculating the difference between the property parameter of the predicted product and the actual reaction product by the following expression comprises:

Wherein Err is the difference between the property parameter of the predicted product and the actual reaction product,

is the firstjPredicted mass fraction of species product, +.>

Is the firstjThe actual mass fraction of the seed product,mis the total number of the product types.

In addition, the reaction rate corresponding to the reaction rule is calculated by the following expression:

；/>

In some embodiments, the generating a reaction path corresponding to each molecule according to the structure-oriented lumped representation of each raw material molecule based on the preset secondary processing reaction rule, obtaining a product molecule of each reaction path, comparing the product molecule with a preset molecular set, and only retaining the product molecules existing in the preset molecular set and the corresponding reaction paths thereof as effective product molecules and effective reaction paths, and constructing a reaction network based on the effective product molecules and the effective reaction paths, wherein the method comprises the steps of:

traversing the structure-oriented lumped representation of each raw material molecule according to a preset reaction rule to obtain a reaction path corresponding to each raw material molecule; wherein the reaction rules comprise aromatic hydrocarbon reaction rules, cycloparaffin reaction rules, olefin reaction rules, alkane reaction rules and reaction rules of heteroatom-containing molecules;

a fourth step of returning the reserved product molecules as raw material molecules to the first step until all the product molecules do not accord with any reaction rule in the preset secondary processing reaction rules;

It should be noted that the preset secondary reaction rules include aromatic hydrocarbon reaction rules, naphthene reaction rules, olefin reaction rules, alkane reaction rules and reaction rules containing heteroatom molecules, wherein the aromatic hydrocarbon reaction rules include aromatic hydrocarbon condensation reaction rules, aromatic hydrocarbon dehydrogenation reaction rules, aromatic hydrocarbon dealkylation reaction rules and aromatic hydrocarbon side chain breaking reaction rules; the cycloalkane reaction rules comprise a cycloalkane ring-opening reaction rule and a cycloalkane dehydrogenation aromatization reaction rule; the olefin reaction rules comprise olefin aromatization reaction rules, diene synthesis reaction rules, olefin cleavage reaction rules and olefin dehydrogenation reaction rules; the alkane reaction rules comprise alkane cracking reaction rules and alkane dehydrogenation reaction rules; the reaction rules of the heteroatom-containing molecules comprise an oxygenate carbon monoxide removal reaction rule, an oxygenate carbon dioxide removal reaction rule and a sulfur-containing compound desulfurization reaction rule.

Wherein, the reactant selection rule of the N4 naphthene dehydrogenation aromatization reaction rule is as follows: (N4)>0) Λ (n3+n2+n1+=0)/(IH. Gtoreq-1), wherein N4>0 represents that N4 exists, N3+N2+N1= 0 represents that N1, N2 and N3 do not exist at the same time, IH is more than or equal to-1 represents that cycloolefin exists, and Λ is an exclusive OR operator; the product generation rule is: n4 ₁ = N4-1,A4 ₁ =A4+1,IH ₂ =1; wherein N4, N3, N2, N1: additional increments of alicyclic structures containing four, three, two and one carbon, which must be attached in other alicyclic or aromatic ring structures, cannot be present alone; n4 is four carbons on the cycloalkane ring, N3 is three carbons on the cycloalkane ring, N2 is two carbons on the cycloalkane ring, N1 is one carbon on the cycloalkane ring, A4 is a four carbon aromatic ring, which is a structural increment to build a polymeric polycyclic structure that cannot exist alone; IH: the molecular saturation is described by introducing a hydrogen element-dependent structural increment. If there is no ring structure, ih=1 represents paraffin, and ih= -1 represents diolefin; if a ring is present, ih= -1 represents a cyclic olefin; subscript 1 indicates the first product and subscript 2 indicates the second product.

In some embodiments, the set of reaction kinetics equations and the reaction duration corresponding to the reaction network are determined by:

In some embodiments, the predicting a molecular composition matrix of a product of the secondary processing reaction based on the corresponding reaction kinetic equation set of the reaction network and the reaction duration according to the content of each raw material molecule includes:

In some embodiments, the property parameter of the product molecule is at least one of a content and a property parameter, the property parameter of the product is at least one of a yield and a property parameter, and predicting the property parameter of the product based on the property parameter of the retained product molecule comprises:

determining the product molecules contained in each product;

In some embodiments, the method further comprises:

In some embodiments, inputting the molecular composition matrix of the predicted product into a preset reconciliation optimization model, outputting the physical parameters of the reconciled product based on the reconciliation proportion of the preset reconciliation optimization model, comprising:

Taking a molecular composition matrix of the predicted product as input, and respectively calculating the octane number, the boiling point, the density and the cetane number of the blended product by adopting a gasoline blending octane number calculation method, a boiling point and density calculation method and a cetane number prediction method based on the blending proportion of a preset blending optimization model.

In some embodiments, the calculating the cetane number of the blended product using the cetane number prediction method comprises:

defining molecular lumped in a pre-trained cetane number prediction model according to a carbon number range, selecting 129 molecular lumped of 9 different hydrocarbons, wherein the 129 molecular lumped comprises 12 n-alkanes lumped, 21 alkyl cyclohexane lumped, 11 alkyl cyclohexane lumped, 5 decalin lumped, 14 decalin lumped, 15 alkylbenzene lumped, 16 tetralin lumped and 14 olefin lumped respectively;

and calculating the cetane number of the physical properties of the gasoline mixture in the blending proportion according to a pre-trained cetane number prediction model, judging whether the physical properties of the gasoline product meet the requirements of the national standard, and continuously adjusting the blending proportion until the requirements are met if the physical properties of the gasoline product are not met, so as to generate a gasoline blending formula.

In some embodiments, the constraint condition that the difference between the physical property parameter of the blended product and the preset target physical property parameter is smaller than a second preset threshold value, the adjustment of the blending proportion by setting at least one of the maximum profit and the minimum residual blending component as the target function, and the adjustment of the blending proportion when the target function value is optimal as the fixed parameter of the blending optimization model includes:

Collecting and counting market economic data of component oils under different price systems, and storing and producing component oil in refinery production;

the method for optimizing the profit and the method for optimizing the product benefit are utilized, and the solving algorithm adopts a global optimizing algorithm of multi-starting point random search.

The calculation model of the profit maximization method is shown in the following expression.

wherein ,P _a -units of

Price of the product, element;

X _a after harmonizing

The quality of the product, ton;

F _b -units ofbThe price of the components and the element;

X _b -participate in reconciliation before reconciliationbThe mass of the components, ton;

Tall other costs (labor costs, experimental costs, etc.).

The calculation model of the product benefit maximum method is shown in the following expression.

wherein ,P _a -units of

Price of the product, element;

X _a after harmonizing

The quality of the product is ton.

Constraints on the supply amount and the demand amount of the reconciling component are shown in the following expressions, respectively.

wherein ,

-participate in reconciliationbThe components participate in harmonizing->

Blend amount of product;

-participate in reconciliationbInventory or throughput of components;

u -the kind of blended product;

v-the kind of the harmonizing component.

wherein ,

、/>

-finished oilaMinimum and maximum demand.

Referring to fig. 2, the molecular level refining process model further includes a result file output module and an interface providing module, wherein, based on the molecular level refining process model, reaction networks of different processing processes are constructed through reaction rules, corresponding reaction network image files, a time-dependent change curve of concentration of reactants and calculation results are generated according to components and content of raw materials, the calculation results include concentration change curve data, reaction description, reactants, reaction rule labels, K values, reaction rule names and the like, and the interface providing module is used for providing interfaces for other software connection, on one hand, calling other software functions to expand correspondingly, and on the other hand, other software is convenient to call the molecular level refining process model.

The molecular level refining process model disclosed by the disclosure builds 176 reaction rules altogether, under the condition that the number of crude oil feed molecules is 2000, 12 sets of oil refining devices (atmospheric and vacuum distillation device, delayed coking device, catalytic cracking device, hydrocracking device, catalytic reforming device, gasoline hydrogenation device, diesel hydrogenation device, wax oil hydrogenation device, gasoline and diesel hydrogenation device, gas fractionation device, aromatic hydrocarbon extraction device and hydrogen production device) react 19982 in total, and 9991 reaction molecules are involved. Running on a computing server (CPU: 4 XIntel to intense platinum 8260, 24 cores; memory: 768G), the total run time of the full-flow model was 1488s, approximately 25 minutes. Compared with the existing flow simulation software, the molecular level refining process model takes petroleum molecules as an information carrier, the data scale is obviously improved, the reaction process and the material flow direction of the oil refining device can be more accurately described, meanwhile, the total calculated amount is controllable, the operation speed is high, and a large amount of crude oil molecular composition data can be processed.

Further, through the whole-flow reaction kinetic parameter regression of the molecular level refining process model, the yield calculation result of each oil refining device has smaller actual deviation, and the yield error of main products of each production device can be controlled within 2 percent and meets the index requirement, so that the molecular level refining process model has higher calculation precision when processing a large amount of molecular data, and can accurately predict the trend of the whole-flow stream of the oil refining on the molecular level.

In addition, in physical property, the main product physical property calculation result of the molecular level refining process model has high conformity with an actual production device, comprises the sulfur content, the 10% distillation temperature and the final distillation point of refined gasoline, the density, the cetane index, the sulfur content, the nitrogen content and the final distillation point of refined diesel, and physical properties such as the light naphtha octane number, the heavy naphtha naphthene mass fraction, the aviation kerosene aromatic hydrocarbon mass fraction, the hydrogenated tail oil aromatic hydrocarbon index and the aromatic hydrocarbon mass fraction of a hydrocracking device all conform to actual production requirements, meet product indexes, and fully show the calculation precision of the molecular level refining process model and the accuracy of a model physical property calculation method.

According to the molecular level refining process model construction method, the molecular level reaction dynamics calculation engine is developed, the model of the secondary processing device and the reconciliation optimization model are based on the crude oil molecular composition database, the simulation model development is carried out on each secondary processing device of the whole process of the refinery from a molecular level, the whole process simulation software of the petrochemical industry molecular level is independently developed, the proportion adjustment of new crude oil and different crude oil components can be realized, and the maximum utilization of the molecular values of crude oil and products thereof is facilitated.

The method for constructing the model of the molecular level refining process is different from the traditional lumped model, models are conducted on all secondary processing devices of the whole process of a refinery in a molecular level layer, the functions of crude oil fraction cutting simulation based on molecular level, secondary processing simulation based on molecular level, harmonic optimization simulation based on molecular level, multistage reactor simulation based on molecular level coupling temperature and pressure, whole process parameter optimization based on the molecular level of the refinery and the like are achieved, conversion paths of molecular composition among materials, intermediates and products are described in detail from a primitive reaction layer on the basis of representing molecular level components of process materials, and the built dynamic reaction model effectively avoids the dependence of the lumped model on raw materials, has better prediction expansibility, and achieves conversion tracking of molecular composition in the reaction process and prediction of product properties in all reaction devices in the whole process.

The molecular level refining process model construction method is based on a structure-oriented lumped method, models are conducted on all secondary processing devices in the whole process of a refinery in a molecular level layer, a molecular level model of the whole process of the refinery is formed through intermediate material linkage, firstly a crude oil molecular composition database provides molecular level information for a molecular management technology, secondly conversion paths formed by molecules among materials, intermediates and products are described in detail from a primitive reaction layer, a corresponding reaction network is generated according to reaction rule setting, better prediction expansibility is achieved, conversion tracking of the molecular composition and prediction calculation of product properties in the reaction process in all the reaction devices in the whole process are achieved, a report can be generated on a result file, a software interface is provided, firstly, other software functions can be conveniently called to be correspondingly expanded, and secondly other software can be conveniently called to corresponding functions of the simulation software.

Referring to fig. 3, an embodiment of the present disclosure provides a molecular level refining process model building apparatus, comprising:

a first training module 11, configured to train a preset molecular composition analysis model according to the physical property data of known crude oil, the crude oil molecular composition and the correspondence between the physical property data of each molecule of the crude oil, so as to obtain parameters of the molecular composition analysis model, where the parameters of the molecular composition analysis model include a molecular type and a content;

a second training module 12, configured to select a raw material molecular composition corresponding to a preset secondary processing device model from known raw oil molecular compositions, convert the raw material molecular composition into a raw material molecular composition matrix, input the raw material molecular composition matrix into the preset secondary processing device model, predict a molecular composition matrix of a product, compare the predicted product with an actual reaction product, and adjust kinetic parameters in the secondary processing device model according to a comparison result until a difference between a yield and physical parameters corresponding to the predicted molecular composition of the product and a yield and physical parameters of the actual reaction product is smaller than a first preset threshold;

the third training module 13 is configured to input the predicted molecular composition matrix of the product into a preset reconciliation optimization model, output physical parameters of the reconciled product based on a reconciliation proportion of the preset reconciliation optimization model, compare the physical parameters of the reconciled product with preset target physical parameters, and adjust the reconciliation proportion according to the comparison result until a difference between the physical parameters of the reconciled product and the preset target physical parameters is less than a second preset threshold.

The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.

For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

In the second embodiment described above, any of the first training module 11, the second training module 12, and the third training module 13 may be incorporated in one module to be implemented, or any of the modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. At least one of the first training module 11, the second training module 12 and the third training module 13 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging circuitry, or in any one of or a suitable combination of any of three implementations of software, hardware and firmware. Alternatively, at least one of the first training module 11, the second training module 12 and the third training module 13 may be at least partially implemented as computer program modules which, when executed, may perform the respective functions.

Referring to fig. 4, an electronic device provided by an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, where the processor 1110, the communication interface 1120, and the memory 1130 perform communication with each other through the communication bus 1140;

a memory 1130 for storing a computer program;

the processor 1110 is configured to implement a method for constructing a molecular level refining process model, where the molecular level refining process model includes a molecular composition analysis model, a secondary processing device model, and a reconciliation optimization model that are sequentially connected, when executing a program stored in the memory 1130, the method includes:

the molecular level refining process model comprises a molecular composition analysis model, a secondary processing device model and a reconciliation optimization model which are connected in sequence, and the method comprises the following steps:

The communication bus 1140 may be a peripheral component interconnect standard (Peripheral ComponentInterconnect, PCI) bus or an extended industry standard architecture (Extended Industry StandardArchitecture, EISA) bus, among others. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.

The communication interface 1120 is used for communication between the electronic device and other devices described above.

The memory 1130 may include random access memory (Random Access Memory, simply RAM) or may include non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. Optionally, the memory 1130 may also be at least one storage device located remotely from the processor 1110.

The processor 1110 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

Embodiments of the present disclosure also provide a computer-readable storage medium. The computer-readable storage medium stores thereon a computer program which, when executed by a processor, implements the molecular level refining process model construction method as described above.

The computer-readable storage medium may be embodied in the apparatus/means described in the above embodiments; or may exist alone without being assembled into the apparatus/device. The above-described computer-readable storage medium carries one or more programs which, when executed, implement a molecular level refining process model building method according to an embodiment of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The method for constructing the molecular level refining process model is characterized by comprising a molecular composition analysis model, a secondary processing device model and a reconciliation optimization model which are connected in sequence, wherein the method comprises the following steps:

2. The method of claim 1, wherein the molecular level refining process model further comprises a physical property database coupled to the molecular composition analytical model, the method comprising:

3. The method of claim 2, wherein the molecular composition analytical model analyzes crude oil by:

4. The method of claim 1, wherein the secondary process corresponding to the secondary process device model is one of residuum hydrogenation, catalytic cracking, delayed coking, hydrocracking, catalytic reforming, alkylation, gasoline hydrogenation, diesel hydrogenation, wax oil hydrogenation, gasoline and diesel hydrogenation, gas fractionation, aromatic hydrocarbon extraction, and hydrogen production.

5. The method of claim 4, wherein converting the feedstock molecular composition into a feedstock molecular composition matrix is performed in a predetermined secondary processing device model, predicting a molecular composition matrix of the product, comparing the predicted product with an actual reaction product, and adjusting kinetic parameters in the secondary processing device model based on the comparison until a difference between the predicted molecular composition of the product and the actual reaction product is less than a first predetermined threshold, comprising:

6. The method according to claim 5, wherein the generating of the reaction paths corresponding to each molecule according to the structure-oriented lumped representation of each raw material molecule, obtaining the product molecules of each reaction path, comparing the product molecules with the preset molecular set, retaining only the product molecules existing in the preset molecular set and the corresponding reaction paths thereof as effective product molecules and effective reaction paths, and constructing a reaction network based thereon, comprises:

7. The method of claim 6, wherein the set of reaction kinetics equations and the reaction duration corresponding to the reaction network are determined by:

8. The method according to claim 5, wherein predicting a secondary processing reaction product molecule composition matrix based on the reaction kinetics equation set and the reaction time period corresponding to the reaction network according to the content of each raw material molecule comprises:

9. The method of claim 5, wherein the property parameter of the product molecule is at least one of a content and a physical property parameter, and wherein the property parameter of the product is at least one of a yield and a physical property parameter.

10. The method of claim 9, wherein predicting the property parameters of the product based on the property parameters of the retained product molecules comprises:

Determining the product molecules contained in each product;

11. The method of claim 6, wherein the reaction rate corresponding to the reaction rule is calculated by the following expression:

；

12. The method according to claim 1, wherein the method further comprises:

13. A molecular level refining process model building device, comprising:

14. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;

A memory for storing a computer program;

a processor for implementing the molecular level refining process model building method of any of claims 1-12 when executing a program stored on a memory.

15. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the molecular level refining process model building method of any of claims 1-12.