CN111899812B

CN111899812B - Product simulation method, system, equipment and storage medium of petroleum processing device

Info

Publication number: CN111899812B
Application number: CN202010533482.2A
Authority: CN
Inventors: 纪晔; 韩景宽; 王杭州; 杨诗棋; 孙宝文; 熊纯青; 李凤琪
Original assignee: Petrochina Co Ltd
Current assignee: Petrochina Co Ltd
Priority date: 2020-06-12
Filing date: 2020-06-12
Publication date: 2024-05-28
Anticipated expiration: 2040-06-12
Also published as: CN111899812A

Abstract

The invention discloses a product simulation method, a system, equipment and a storage medium of a petroleum processing device. The method comprises the following steps: determining the molecular composition of the raw materials; inputting the molecular composition of the raw materials into a pre-trained product prediction model; wherein the product prediction model corresponds to the type of petroleum processing device; and obtaining the output of the target product under different reaction conditions, which is output by the product prediction model. The invention correspondingly trains a product prediction model for each petroleum processing device in advance, and predicts the yield of target products of the corresponding petroleum processing device under different reaction conditions through the corresponding product prediction model. The method has the advantages of higher accuracy, shorter time consumption and lower cost.

Description

Product simulation method, system, equipment and storage medium of petroleum processing device

Technical Field

The invention relates to the technical field of computers, in particular to a product simulation method, a system, equipment and a storage medium of an oil processing device.

Background

The current petroleum resource shortage is increasingly remarkable, and along with the increase of the requirements of clean oil products and the improvement of environmental protection consciousness of various countries, the production of clean and environmental protection fuel and the reduction of environmental pollution hazard become urgent. Accordingly, researchers have focused their attention on improving existing gasoline production processes or developing new technologies to produce cleaner petroleum products and maximize the production of aromatics, ethylene and other important chemical products.

However, petroleum processing apparatuses are various, and when they are faced with complicated and variable raw materials, it is often difficult to determine the reaction process of the raw materials in each petroleum processing apparatus, and it is still impossible to determine the product information of each petroleum processing apparatus, so that the components of the product cannot be determined in advance, and it is difficult to estimate the value of the product. For example: catalytic reforming is an important naphtha processing technology in the oil refining industry, and is also an important process for improving the octane number of gasoline and realizing the oil refining/chemical engineering integration goal. The catalytic reforming device produces clean gasoline with low sulfur and low olefin, and can also produce high-purity hydrogen as a byproduct. The product produced by the catalytic reforming process can meet the requirements of the market for clean fuel and aromatic hydrocarbon to a great extent. Accordingly, efforts to develop catalytic reforming processes are a trend in the petrochemical industry. However, in the face of complex and changeable raw materials, it is difficult to accurately reflect the reaction mechanism of the catalytic reforming process, and the product after catalytic reforming cannot be determined.

Disclosure of Invention

The invention mainly aims to provide a product simulation method, a system, equipment and a storage medium of a petroleum processing device, so as to solve the problem that product information of the petroleum processing device cannot be determined in the prior art.

Aiming at the technical problems, the invention is solved by the following technical scheme:

The invention provides a product simulation method of a petroleum processing device, which comprises the following steps: determining the molecular composition of the raw materials; inputting the molecular composition of the raw materials into a pre-trained product prediction model; wherein the product prediction model corresponds to the type of petroleum processing device; and obtaining the output of the target product under different reaction conditions, which is output by the product prediction model.

Wherein after obtaining the output of the product prediction model, the method preferably further comprises: inquiring the yield of a target product under different reaction conditions; determining a reaction condition corresponding to the maximum yield value of the target product; and/or determining the reaction condition corresponding to the minimum yield value of the target product.

Wherein after obtaining the output of the product prediction model, the method preferably further comprises: and determining physical properties of the target product under different reaction conditions according to the molecular composition of the target product under different reaction conditions.

Wherein, obtaining the output of the product prediction model under different reaction conditions preferably comprises: obtaining the content of each product molecule under different reaction conditions output by the product prediction model; and obtaining the content of the target product molecules under different reaction conditions in the content of each product molecule under different reaction conditions.

Wherein determining the molecular composition of the feedstock preferably comprises: the molecular composition of the feedstock is determined by one or more of full two-dimensional gas chromatography, four-stage rod gas chromatography-mass spectrometer detection, gas chromatography/field ionization-time-of-flight mass spectrometry, gas chromatography, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, raman spectroscopy, fourier transform ion cyclotron resonance mass spectrometry, electrostatic field orbitrap mass spectrometry, and ion mobility mass spectrometry.

Wherein the step of training the product predictive model preferably comprises: establishing a product prediction model; wherein the product prediction model comprises: a set of reaction rules and a reaction rate algorithm; the reaction rule set comprises a plurality of reaction rules; acquiring sample raw material information of a sample raw material; training the reaction rule set by utilizing the sample raw material information, and fixing the reaction rule set after training; and training the reaction rate algorithm by using the sample raw material information, and fixing the reaction rate algorithm after training to obtain the product prediction model after training.

Wherein the sample raw material information of the sample raw material preferably includes: the molecular composition of the sample raw material, the molecular content of each molecule in the sample raw material, the molecular composition of the actual product corresponding to the sample raw material and the actual content of each molecule in the actual product.

Wherein training the set of reaction rules using the sample raw material information preferably comprises: processing the molecular composition of the sample raw material according to a preset reaction rule set to obtain a reaction path corresponding to each molecule in the molecular composition of the sample raw material; obtaining a first molecular composition of a device product according to a reaction path corresponding to each molecule in the molecular composition of the sample raw material; in the device product, comprising: the sample raw material, intermediate product and predicted product; calculating a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product; if the first relative deviation meets a preset condition, fixing the reaction rule set; if the first relative deviation does not meet the preset condition, the reaction rules in the reaction rule set are adjusted, and the first relative difference value is recalculated according to the adjusted reaction rule set until the first relative deviation meets the preset condition.

Wherein calculating a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product preferably comprises: obtaining the types of single molecules in the first molecular composition to form a first set; obtaining the types of single molecules in the second molecular composition to form a second set; determining whether the second set is a subset of the first set; if the second set is not a subset of the first set, acquiring a pre-stored relative deviation value which does not meet a preset condition as the first relative deviation value; if the second set is a subset of the first set, a first relative deviation is calculated by:

determining a first relative deviation from the ratio of the number of species of the portion of the molecular composition of the predicted product that is not in the second set to the total number of species of the single molecule of the molecular composition of the predicted product;

For example, the first relative deviation is calculated by the following formula:

Wherein x ₁ is the first relative deviation, M is the first set, M ₁ is a set of species composition of single molecule in the molecular composition of the sample raw material, M ₂ is a set of species composition of single molecule in the molecular composition of the intermediate product, M ₃ is the second set, and the card represents the number of elements in the set.

Wherein training the reaction rate algorithm using the sample raw material information preferably comprises: according to the reaction rate algorithm, respectively calculating the reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample raw material; obtaining the predicted content of each molecule in a predicted product corresponding to the sample raw material according to the molecular content of each molecule in the sample raw material and the reaction rate corresponding to the reaction path of the molecule; calculating a second relative deviation from the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product; if the second relative deviation meets a preset condition, fixing the reaction rate algorithm; and if the second relative deviation does not meet the preset condition, adjusting parameters in the reaction rate algorithm, and recalculating the second relative deviation according to the adjusted reaction rate algorithm until the second relative deviation meets the preset condition.

Wherein, according to the reaction rate algorithm, the reaction rate of the reaction path corresponding to each molecule in the molecular composition of the sample raw material is calculated, preferably including: calculating the reaction rate of each reaction path according to the reaction rate constant in the reaction rate algorithm; wherein the reaction rate constant is determined based on a transitional state theory calculation method;

For example, the reaction rate constant is determined according to the following calculation formula:

Wherein k is a reaction rate constant, k _B is a Boltzmann constant, h is a Planck constant, R is an ideal gas constant, E is a temperature value of an environment where a reaction path is located, exp is an exponential function based on a natural constant, deltaS is entropy change before and after a reaction corresponding to a reaction rule corresponding to the reaction path, deltaE is a reaction energy barrier corresponding to the reaction rule corresponding to the reaction path, The catalyst active factor, P is the pressure value of the environment where the reaction path is located, and alpha is the pressure influence factor corresponding to the reaction rule corresponding to the reaction path.

Wherein, the kinds of petroleum processing device preferably include: catalytic cracking units, delayed coking units, residuum hydrotreaters, hydrocrackers, diesel hydro-upgrading units, diesel hydrofining units, gasoline hydrofining units, catalytic reforming units and alkylation units; wherein each petroleum processing device corresponds to a set of reaction rules.

Wherein the reaction conditions preferably include: reaction temperature, reaction pressure and space velocity.

The present invention also provides a product simulation system for a petroleum processing plant, the system comprising:

An acquisition unit for determining the molecular composition of the raw material;

A first processing unit for inputting the molecular composition of the feedstock into a pre-trained product prediction model; and obtaining the output of the target product under different reaction conditions, which is output by the product prediction model, wherein the product prediction model corresponds to the type of the petroleum processing device.

Wherein the system further comprises: the second processing unit is used for inquiring the yield of the target product under different reaction conditions; determining a reaction condition corresponding to the maximum yield value of the target product; and/or determining the reaction condition corresponding to the minimum yield value of the target product.

Wherein the system further comprises: the model training unit is used for establishing a product prediction model; acquiring sample raw material information of a sample raw material; training a reaction rule set included in the product prediction model by utilizing the sample raw material information, and fixing the reaction rule set after training; training a reaction rate algorithm included in the product prediction model by using the sample raw material information, and fixing the reaction rate algorithm after training to obtain the product prediction model after training, wherein the product prediction model comprises the following components: a set of reaction rules and a reaction rate algorithm; the set of reaction rules includes a plurality of reaction rules.

The invention also provides a product simulation device of the petroleum processing device, which comprises a processor and a memory; the processor is used for executing the product simulation program of the petroleum processing device stored in the memory so as to realize the product simulation method of the petroleum processing device.

The present invention also provides a storage medium storing one or more programs executable by one or more processors to implement the product simulation method of the petroleum processing apparatus described above.

The invention has the following beneficial effects:

The invention correspondingly trains a product prediction model for each petroleum processing device in advance, and predicts the yield of target products of the corresponding petroleum processing device under different reaction conditions through the corresponding product prediction model. The method has the advantages of higher accuracy, shorter time consumption and lower cost.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

FIG. 1 is a flow chart of a method of simulating the production of a petroleum processing plant in accordance with an embodiment of the present invention;

FIG. 2 is a flowchart of the steps for training a product prediction model according to one embodiment of the invention;

FIG. 3 is a flowchart of training steps for a reaction rule set according to one embodiment of the present invention;

FIG. 4 is a flowchart of training steps of a reaction rate algorithm according to one embodiment of the present invention;

FIG. 5 is a flowchart of the steps for physical property determination according to one embodiment of the present invention;

FIG. 6 is a flowchart of the steps for calculating physical properties of a single molecule according to one embodiment of the present invention;

FIG. 7 is a flowchart of steps for training a physical property calculation model according to one embodiment of the invention;

FIG. 8 is a block diagram of a product simulation system of a petroleum processing plant in accordance with an embodiment of the present invention;

fig. 9 is a block diagram of a product simulation apparatus of a petroleum processing plant according to an embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and the embodiments, in order to make the objects, technical solutions and advantages of the present invention more apparent.

According to an embodiment of the present invention, a product simulation method of a petroleum processing plant is provided. Referring to fig. 1, a flow chart of a product simulation method of an oil processing apparatus according to an embodiment of the present invention is shown.

Step S110, determining the molecular composition of the raw material.

The molecular composition of the raw material is information of various molecules (single molecules) in the raw material. For example: the raw materials comprise single molecules, the types of the single molecules, the volume and the content of each single molecule and the like.

In this embodiment, the molecular composition of the feedstock may be determined by one or more of full two-dimensional gas chromatography, four-stage rod gas chromatography-mass spectrometer detection, gas chromatography/field ionization-time-of-flight mass spectrometry, gas chromatography, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, raman spectroscopy, fourier transform ion cyclotron resonance mass spectrometry, electrostatic field orbitrap mass spectrometry, and ion mobility mass spectrometry.

Of course, the molecular composition of the starting material may also be determined by other means. For example: the molecular composition of the feedstock was determined by ASTM D2425, SH/T0606, ASTM D8144-18.

In this example, individual single molecules (molecules) in the molecular composition were characterized using a SOL (Structure-oriented lumped) -based molecular characterization method. In the SOL-based molecular characterization method, 24 groups can be utilized to characterize the structure of hydrocarbon molecules. A group is a characteristic structure of a portion of a molecule, each group typically being chemically reacted as a whole.

Further, SOL belongs to the lumped on the molecular scale, so that the number of molecules in a practical system is reduced from millions to thousands, and the complexity of physical property detection is greatly reduced. The SOL-based molecular characterization method may represent not only alkanes, cycloalkanes, up to complex aromatic structures containing 50-60 carbon atoms, but also olefins or cycloalkenes as intermediate products or secondary reaction products, and in addition, sulfur-, nitrogen-, oxygen-and other heteroatom compounds are contemplated.

Step S120, inputting the molecular composition of the raw materials into a pre-trained product prediction model; wherein the product prediction model corresponds to a type of petroleum processing apparatus.

The types of petroleum processing equipment include, but are not limited to: catalytic cracking units, delayed coking units, residuum hydrotreaters, hydrocrackers, diesel hydro-upgrading units, diesel hydrofining units, gasoline hydrofining units, catalytic reforming units and alkylation units; wherein each petroleum processing device corresponds to a set of reaction rules.

A product prediction model for determining the yield of a product of a feedstock under different reaction conditions based on the molecular composition of the feedstock. Further, a product prediction model is used for determining the content of various product molecules of the raw material under different reaction conditions according to the molecular composition of the raw material.

The step of training the product prediction model will be described later, and will not be described in detail herein.

And step S130, obtaining the output of the target product under different reaction conditions, which is output by the product prediction model.

In this embodiment, the reaction conditions include: reaction temperature, reaction pressure and space velocity.

Further, the yield of the target product under different reaction conditions can be represented by a spatial coordinate system, for example: the horizontal axis and the vertical axis are reaction conditions, which may be two of reaction temperature, reaction pressure and space velocity, and the vertical axis may be the yield of the target product.

Specifically, the content of each product molecule under different reaction conditions output by the product prediction model can be obtained; and obtaining the content of the target product molecules under different reaction conditions in the content of each product molecule under different reaction conditions.

In this embodiment, after obtaining the yields of the target products under different reaction conditions output by the product prediction model, the yields of the target products under different reaction conditions may also be queried; determining a reaction condition corresponding to the maximum yield value of the target product; and/or determining the reaction condition corresponding to the minimum yield value of the target product.

In this embodiment, after obtaining the yields of the target products under different reaction conditions output by the product prediction model, the physical properties of the target products under different reaction conditions may also be determined according to the molecular compositions of the target products under different reaction conditions.

The physical properties of the target product under different reaction conditions will be described later, and will not be described here.

In this embodiment, a product prediction model is trained in advance for each petroleum processing device, and the yield of the target product of the corresponding petroleum processing device under different reaction conditions is predicted by the corresponding product prediction model. The method has the advantages of higher accuracy, shorter time consumption and lower cost.

The step of training the product predictive model is further described below. FIG. 2 is a flowchart illustrating the steps for training a product predictive model according to one embodiment of the invention.

Step S210, a product prediction model is built.

The product predictive model includes: a set of reaction rules and a reaction rate algorithm. Wherein the set of reaction rules includes a plurality of reaction rules.

And correspondingly establishing a product prediction model according to the type of the petroleum processing device. The set of reaction rules corresponding to different types of petroleum processing equipment may be different. The reaction rate algorithms corresponding to different types of petroleum processing units may also be different.

Further, the product prediction model corresponding to the type of petroleum processing apparatus includes: a set of reaction rules and a reaction rate algorithm corresponding to the type of petroleum processing plant. Wherein, the reaction rule set comprises: a plurality of reaction rules corresponding to the type of petroleum processing device.

In step S220, sample raw material information of the sample raw material is acquired.

Sample raw material information of the sample raw material, comprising: the molecular composition of the sample raw material, the molecular content of each molecule in the sample raw material, the molecular composition of the actual product corresponding to the sample raw material and the actual content of each molecule in the actual product. The actual product is the product obtained by treating the sample raw material by the petroleum processing device of the type.

Step S230, training the reaction rule set by using the sample raw material information, and fixing the reaction rule set after training.

One way to train the reaction rule set is given below. It should be understood by those skilled in the art that this manner is merely illustrative of the present embodiment and is not intended to be limiting.

As shown in FIG. 3, a flowchart of training steps for a reaction rule set according to an embodiment of the present invention is shown.

Step S310, processing the molecular composition of the sample raw material according to a reaction rule set to obtain a reaction path corresponding to each molecule in the molecular composition of the sample raw material.

The reaction path is used to represent the chemical reaction path of the intermediate product obtained by the reaction of each molecule or the final product obtained by the reaction.

And when the reaction path is calculated for the first time, processing the molecular composition of the sample raw material according to a preset reaction rule set to obtain a reaction path corresponding to each molecule in the molecular composition of the sample raw material.

And (3) reacting each molecule in the sample raw material according to a reaction rule in the reaction rule set to obtain a reaction path corresponding to each molecule. After each molecule reacts for the first time to generate an intermediate product, the molecular structure of the intermediate product may meet another reaction rule, and then the intermediate product continues to react according to the satisfied reaction rule until the molecular structure of a product generated by the molecule does not meet any reaction rule in the reaction rule set, so as to obtain a final product of the reaction of the molecule, wherein the summary of the reactions is the reaction path of the molecule.

Step S320, obtaining a first molecular composition of the device product according to the reaction path corresponding to each molecule in the molecular composition of the sample raw material.

In the device product, comprising: the sample raw material, intermediate product and predicted product.

Step S330, calculating a first relative deviation according to the first molecular composition of the device product and the second molecular composition of the actual product.

Specifically, obtaining the types of single molecules in the first molecular composition to form a first set; obtaining the types of single molecules in the second molecular composition to form a second set; determining whether the second set is a subset of the first set; if the second set is not a subset of the first set, acquiring a pre-stored relative deviation value which does not meet a preset condition as the first relative deviation value; if the second set is a subset of the first set, calculating a first relative deviation by the following calculation formula:

Wherein x ₁ is the first relative deviation, M is the first set, M ₁ is a set of species composition of single molecule in the molecular composition of the sample raw material, M ₂ is a set of species composition of single molecule in the molecular composition of the intermediate product, M ₃ is the second set, and the card represents the number of elements in the set. M, M ₁、M₂ and N are the same in number.

The preset conditions include: a range of relative deviations. The two end points of the relative deviation range are empirical values or experimentally obtained values. The pre-stored relative deviation value is not within the relative deviation range.

Step S340, judging whether the first relative deviation meets a preset condition; if yes, go to step S350; if not, step S360 is performed.

If the first relative deviation is within the relative deviation range, the first relative deviation is judged to be in accordance with the preset condition, otherwise, the first relative deviation is judged to be not in accordance with the preset condition.

Step S350, if the first relative deviation meets a preset condition, fixing the reaction rule set.

Step S360, if the first relative deviation does not meet the preset condition, the reaction rules in the reaction rule set are adjusted, and the step S310 is skipped, and the first relative difference is recalculated according to the adjusted reaction rule set until the first relative deviation meets the preset condition.

And step S240, training the reaction rate algorithm by using the sample raw material information, and fixing the trained reaction rate algorithm to obtain the trained product prediction model.

One way to train the reaction rate algorithm is given below. It should be understood by those skilled in the art that this manner is merely illustrative of the present embodiment and is not intended to be limiting.

FIG. 4 is a flowchart showing training steps of a reaction rate algorithm according to an embodiment of the present invention.

Step S410, respectively calculating the reaction rate corresponding to the reaction path corresponding to each molecule in the molecular composition of the sample raw material according to the reaction rate algorithm.

Specifically, according to the reaction rate constant in the reaction rate algorithm, calculating the reaction rate of each reaction path;

The reaction rate constant is determined according to the following calculation formula:

Specifically, the reaction rate of the reaction path is obtained according to the reaction rate constant and the reaction concentration corresponding to the reaction path. For example: in the case where the reaction rate constant has been determined, the larger the space velocity, the shorter the contact time of the raw material and the catalyst, the shorter the reaction time of the raw material, the higher the concentration of the reactant in the raw material, and the higher the reaction rate of the reaction path; conversely, the smaller the space velocity, the longer the contact time of the feedstock and catalyst, the longer the reaction time of the feedstock, the lower the concentration of reactants in the feedstock and the lower the reaction rate of the reaction path.

In this embodiment, k in the above formula is referred to as a reaction rate constant, and its physical meaning is that the value corresponds to the reaction rate when the substances participating in the reaction are all at a unit concentration (1 mol/L), so that it is also called a specific rate of the reaction, and different reactions have different rate constants, and the rate constants are related to the reaction temperature, the reaction medium (solvent), the catalyst, etc., and even vary with the shape and properties of the reactor. Irrespective of the concentration, but affected by factors such as temperature, catalyst, solid surface properties, etc. In this embodiment, after the reaction rate constant is calculated, the real-time reaction rate of the molecule can be obtained according to the concentration of the molecule, for example, if the reaction rate of the molecule per unit concentration is k, after the concentration V is confirmed, the reaction rate of the molecule corresponding to the reaction path is Vk, thereby calculating the reaction rate.

Step S420, obtaining the predicted content of each molecule in the predicted product corresponding to the sample raw material according to the molecular content of each molecule in the sample raw material and the reaction rate corresponding to the reaction path of the molecule.

In this embodiment, the reaction rate corresponding to each reaction path is calculated by a reaction rate calculation method in a product prediction model, and the predicted content of each single molecule in the predicted product can be calculated by combining the single molecule content of each single molecule in the raw material.

For example, if the single molecule a in the raw material has 3 reaction paths, the reaction rate corresponding to 3 reaction paths is known, the concentration of the single molecule a decreases as the reaction proceeds, and the reaction rate corresponding to 3 reaction paths decreases in proportion to the decrease in concentration, so that the single molecule a generates a product in proportion to the reaction rate of 3 reaction paths, a product obtained by reacting each molecule can be obtained by the above steps, and a predicted product can be obtained, and when the single molecule content of each single molecule in the catalytic reforming raw material is known, the content of each single molecule in the predicted product can be obtained.

Step S430, calculating a second relative deviation according to the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product.

The second relative deviation is calculated, for example:

second relative deviation= (actual content-predicted content)/actual content.

Step S440, judging whether the second relative deviation meets a preset condition; if yes, go to step S450; if not, step S460 is performed.

If the second relative deviation is within the relative deviation range, the second relative deviation is judged to be in accordance with the preset condition, otherwise, the second relative deviation is judged to be not in accordance with the preset condition.

Step S450, if the second relative deviation meets a preset condition, fixing the reaction rate algorithm.

Step S460, if the second relative deviation does not meet the preset condition, adjusting parameters in the reaction rate algorithm, and jumping to step S410, and recalculating the second relative deviation according to the adjusted reaction rate algorithm until the second relative deviation meets the preset condition.

In this embodiment, parameters in the reaction rate algorithm in the reaction rate calculation method corresponding to each reaction path in the product prediction model are adjusted. And the accuracy of a reaction rate algorithm in the product prediction model is ensured through feedback adjustment.

The physical properties of the target product under different reaction conditions are described below.

As shown in fig. 5, a flowchart of the steps for determining physical properties according to an embodiment of the present invention is shown.

Step S510, obtaining target products under each reaction condition, and determining various single molecules contained in the target products.

The various single molecules contained in the target product, i.e., the molecular composition of the target product, are determined.

And step S520, calculating physical properties of each single molecule in the target product according to the number of groups of each group constituting the single molecule and the contribution value of each group to the physical properties.

Physical properties of a single molecule include, but are not limited to: density, boiling point, density, octane number. For example: the physical properties of the single molecule may also include: viscosity, solubility parameters, cetane number, unsaturation, and the like.

The group is part of a molecule, which generally participates in a chemical reaction as a whole.

Determining the groups contained in each single molecule by a molecular characterization method based on SOL; the number of groups of each group of the single molecule and the contribution value of each group to the physical properties in the single molecule are determined for each single molecule. Since the number of physical properties of a single molecule is plural, it is necessary to determine the contribution value of each group in the single molecule to each physical property.

The manner in which the physical properties of a single molecule are specifically calculated will be described later, and will not be described in detail here.

And step S530, calculating the physical properties of the target product according to the physical properties of various single molecules in the target product and the content of each single molecule in the target product.

Physical properties of the target product include, but are not limited to: density, cloud point, pour point, aniline point, and octane number. Of course, physical properties of the target product may also include: cetane number, congealing point, cold filtration point, etc.

The following five ways of calculating physical properties of the target product are provided, but those skilled in the art will recognize that the following ways are merely illustrative of the present embodiment, and are not intended to limit the present embodiment.

In one mode, when the physical property of the target product is density, the density of the target product is calculated by the following calculation formula:

density＝Σ(D_i×x_i-volume)；

Wherein, density is the density of the target product, D _i is the density of the ith single molecule, and x _i-volume is the content of the ith single molecule.

In a second mode, when the physical property of the target product is the cloud point, the physical property of the target product is calculated, including:

Calculating the cloud point contribution value of each single molecule according to the density and the boiling point of the single molecule;

And calculating the cloud point of the target product according to the cloud point contribution value of each single molecule in the target product and the content of each single molecule in the target product.

In a third aspect, when the physical property of the target product is a pour point, calculating the physical property of the target product includes:

calculating the pour point contribution value of each single molecule according to the density and the molecular weight of the single molecule;

And calculating the pour point of the target product according to the pour point contribution value of each single molecule in the target product and the content of each single molecule in the target product.

In a fourth mode, when the physical property of the target product is an aniline point, calculating the physical property of the target product includes:

Calculating the aniline point contribution value of each single molecule according to the density and the boiling point of the single molecule;

and calculating the aniline point of the target product according to the aniline point contribution value of each single molecule in the target product and the content of each single molecule in the target product.

In a fifth mode, when the physical property of the target product is an octane number, the octane number of the target product is calculated by the following calculation formula:

Wherein, ON is the octane number of the target product, HISQFG is a molecular set, H is a molecular set of normal alkane, I is a molecular set of isoparaffin, S is a molecular set of naphthene, Q is a molecular set of olefin, F is a molecular set of aromatic hydrocarbon, G is a molecular set of oxygen-containing compound, v _i is the content of each molecule in the target product; v _H、υ_I、υ_S、υ_Q、υ_F、υ_G is the total content of normal paraffins, the total content of isoparaffins, the total content of naphthenes, the total content of olefins, the total content of aromatic hydrocarbons and the total content of compounds containing oxygen compounds in the target product respectively; beta _i is the regression parameter of each molecule in the target product; ON _i is the octane number of each molecule in the target product; c _H represents the interaction coefficient of normal alkane and other molecules; c _I represents the interaction coefficient of isoparaffin with other molecules; c _S represents the interaction coefficient of cycloalkanes with other molecules; c _Q represents the interaction coefficient of olefins with other molecules; c _F represents the interaction coefficient of aromatic hydrocarbons with other molecules; c _G represents the interaction coefficient of the oxygenate with other molecules; A first constant coefficient representing the relationship between normal alkane and isoparaffin, Representing the first constant coefficient between normal and cycloalkanes,/>Representing a first constant coefficient between normal paraffins and olefins,/>Representing the first constant coefficient between normal paraffins and aromatic hydrocarbons,/>Representing a first constant coefficient between normal paraffins and oxygenates,/>Representing the first constant coefficient between isoparaffin and naphthene,/>Representing the first constant coefficient between isoparaffin and olefin,/>Representing the first constant coefficient between isoparaffin and aromatic hydrocarbon,/>Representing the first constant coefficient between isoparaffin and oxygenate,/>Representing the first constant coefficient between cycloalkanes and olefins,/>Representing the first constant coefficient between naphthenes and aromatics,/>Representing a first constant coefficient between cycloalkane and oxygenate,/>Representing a first constant coefficient between olefins and aromatic hydrocarbons,/>Representing a first constant coefficient between olefin and oxygenate,/>Representing a first constant coefficient between aromatic hydrocarbon and oxygen-containing compound,/>Representing the two constant coefficients between normal and isoparaffins,/>Representing the second constant coefficient between normal and cycloalkanes,/>Representing a second constant coefficient between normal paraffins and olefins,/>Representing the second constant coefficient between normal paraffins and aromatic hydrocarbons,/>Representing a second constant coefficient between normal paraffins and oxygenates,/>Representing the second constant coefficient between isoparaffin and naphthene,/>Representing the second constant coefficient between isoparaffin and olefin,/>Representing the second constant coefficient between isoparaffin and aromatic hydrocarbon,/>Representing the second constant coefficient between isoparaffin and oxygenate,/>Representing the second constant coefficient between cycloalkanes and olefins,/>Representing the second constant coefficient between naphthenes and aromatics,/>Representing a second constant coefficient between cycloalkane and oxygenate,/>Representing the second constant coefficient between olefins and aromatic hydrocarbons,/>Representing a second constant coefficient between olefin and oxygenate,/>A second constant coefficient representing the relationship between the aromatic hydrocarbon and the oxygen-containing compound; wherein the octane number comprises: research octane number and motor octane number.

In this embodiment, before calculating the physical properties of the single molecule, the number of groups of each group constituting the single molecule may be compared with the molecular information of a template single molecule with known physical properties prestored in a database; wherein the molecular information includes: the number of groups of each group constituting the template single molecule; judging whether a template single molecule identical to the single molecule exists or not; outputting physical properties of the template single molecule as physical properties of the single molecule if the template single molecule identical to the single molecule exists; if the same template single molecule as the single molecule does not exist, the physical property of the single molecule is calculated. Further, if the kind of the group constituting the template single molecule is the same as the kind of the group constituting the single molecule, and the number of groups of each group of the template single molecule is the same as the number of groups of the corresponding group of the single molecule, it is determined that the template single molecule is the same as the single molecule, and conversely, it is determined that the template single molecule is different from the single molecule.

In this embodiment, after the number of groups of each group constituting a single molecule is obtained, by comparing the corresponding number of groups, it is confirmed whether the structure of the single molecule is stored in the database, and after confirming that a template single molecule consistent with the single molecule appears, the physical properties of the single molecule are directly output, so that the calculation efficiency of the physical properties of the single molecule is improved, and the calculation amount is reduced.

The physical properties of the single molecule are further described below.

FIG. 6 is a flowchart showing the steps for calculating the physical properties of a single molecule according to an embodiment of the present invention.

Step S610, for each single molecule in the target product, acquiring the number of groups of each group constituting the single molecule, and acquiring the contribution value of each group to the physical property.

Further, the types of the groups contained in the single molecule are determined, the number of the groups of each type is determined, and the contribution value of each type of the groups to each physical property of the target product is obtained.

And step S620, inputting the number of groups of each group constituting the single molecule and the contribution value of each group to physical properties into a physical property calculation model trained in advance, and obtaining the physical properties of the single molecule output by the physical property calculation model.

In this example, a physical property calculation model is used to calculate the physical properties of a single molecule based on the number of groups of each group included in the single molecule and the contribution value of each group to the physical properties.

Further, the number of groups of each type of groups of a single molecule is obtained, the contribution value of each type of groups to each physical property of a target product is obtained, a physical property calculation model trained in advance is input, and a plurality of physical properties of the single molecule output by the physical property calculation model are obtained.

The steps for training the physical property calculation model are further described below.

FIG. 7 is a flowchart showing the steps for training a physical property calculation model according to an embodiment of the present invention.

Step S710, constructing a physical property calculation model of single molecule.

The physical property calculation model includes: contribution value of each group to physical properties. The contribution value is an adjustable value, and the contribution value is an initial value when training for the first time. Further, the physical property calculation model includes: contribution value of each group to each physical property.

Two calculation models of physical properties that can be used for different physical properties are given below. It should be understood by those skilled in the art that the following two physical property calculation models are merely illustrative of the present embodiment, and are not intended to limit the present embodiment.

Model one: the physical property calculation model is established as follows:

Wherein f is the physical property of the single molecule, n _i is the number of groups of the ith group, Δf _i is the contribution value of the ith group to the physical property, and a is a correlation constant.

For the groups constituting a single molecule, it can be further divided into multiple groups. Further, a primary group and a multi-stage group are determined among all groups of the single molecule; wherein all groups constituting a single molecule are taken as primary groups; a plurality of groups which are simultaneously present and contribute to the same physical property together are used as a multi-stage group, and the number of the plurality of groups is used as a grade of the multi-stage group. Further, when the groups are present in different molecules alone, the physical properties are affected to some extent, and when the groups are present in one molecule at the same time, the contribution value to the physical properties fluctuates to some extent in addition to the original contribution value to the physical properties. The manner of dividing the above-mentioned multi-stage groups may also be divided by the bond force interval to which the chemical bond force between the groups belongs. The influence of different chemical bond forces on different physical properties can be specifically classified according to the influence of molecular stability on physical properties.

For example: for boiling point, 24 groups are all primary groups in SOL-based molecular characterization methods; the simultaneous presence of one or more of N6, N5, N4, N3, me, AA, NN, RN, NO, RO, KO among the 24 groups contributes to the boiling point. When dividing a group of a single molecule, taking all groups constituting the single molecule as primary groups, checking whether a plurality of groups which can commonly contribute to boiling point exist in all groups of the single molecule, and if so, taking the plurality of groups which can commonly contribute to boiling point as multi-stage groups, such as: if N6 and N4 are simultaneously present in the single molecule, the number of groups which simultaneously contribute to the boiling point is two, and the combination of N6 and N4 is taken as a secondary group.

Model two: based on the divided multi-stage groups, the following physical property calculation model can be established:

Wherein f is the physical property of the single molecule, m _1i is the number of groups of the ith group in the primary groups, Δf _1i is the contribution value of the ith group in the primary groups to the physical property, m _2j is the number of groups of the jth group in the secondary groups, and Δf _2j is the contribution value of the jth group in the secondary groups to the physical property; m _Nl is the number of the first group in the N-level groups, and Δf _Nl is the contribution value of the first group in the N-level groups to physical properties; a is a correlation constant; n is a positive integer greater than or equal to 2.

Step S720, obtaining the number of groups of each group constituting a single molecule of the sample; wherein the physical properties of the sample single molecule are known.

A training sample set is preset. A plurality of sample single molecule information is included in the training sample set. Sample single molecule information including, but not limited to: the number of groups of each group constituting a sample single molecule, and the physical properties of the sample single molecule.

In step S730, the number of groups of each group constituting a single molecule of the sample is input into a physical property calculation model.

Step S740, obtaining the predicted physical properties of the sample single molecule output by the physical property calculation model.

Step S750, judging whether the deviation value between the predicted physical property and the known physical property is smaller than a preset deviation threshold value; if yes, go to step S760; if not, step S770 is performed.

If the physical properties of the sample single molecule are plural, the predicted physical properties of the sample single molecule outputted by the physical property calculation model are plural, and at this time, the deviation value between each predicted physical property and the corresponding known physical property is calculated, and it is determined whether the deviation value between all the predicted physical properties and the corresponding known physical properties is smaller than the preset deviation value, if so, step S760 is executed; if not, step S770 is performed.

In this example, a relative deviation value or an absolute deviation value between the predicted physical property and the corresponding known physical property can be calculated.

And step S760, if the deviation value between the predicted physical property and the known physical property is smaller than a preset deviation threshold, determining that the physical property calculation model converges, acquiring a contribution value of each group to the physical property from the converged physical property calculation model, and storing the contribution value of the group to the physical property.

Since the physical properties of a single molecule may be plural, the contribution value of each group to each physical property can be obtained in the converged physical property calculation model.

The contribution values of the groups to different physical properties may be inconsistent for different physical properties, but the contribution values of the same group to the same physical property are consistent in different molecules, and in this embodiment, the physical property calculation model is constructed, and the physical property calculation model is converged by training the constructed physical property calculation model, that is, the contribution value of each group in the physical property calculation model to the physical property is trained, so as to obtain the contribution value of each group to the physical property.

The contribution value of each group to each physical property is stored for each group, so that when the physical property of a single molecule is calculated later, the contribution value of each group in the single molecule to the physical property to be obtained can be obtained, the number of groups of each group of the single molecule and the contribution value of each group to the physical property to be obtained are taken as the input of a physical property calculation model, the physical property calculation model takes the number of groups of each group of the single molecule as model variables, the contribution value of each group to the physical property to be obtained is taken as model parameters (the adjustable contribution value of each group in the alternative physical property calculation model to the physical property), and the physical property to be obtained is calculated.

Step S770, if the deviation value between the predicted physical property and the known physical property is equal to or greater than the deviation threshold, adjusting the contribution value of each group in the physical property calculation model to the physical property, and jumping to step S750 until the physical property calculation model converges.

In addition to the general-purpose physical property calculation model described above, a physical property calculation model may be constructed for each physical property, depending on the type of physical property.

For example: the boiling point of the single molecule was calculated according to the following physical property calculation model:

wherein T is the boiling point of the single molecule, SOL is a single molecule vector obtained by converting the number of GROUPs constituting each GROUP of the single molecule, GROUP ₁₁ is a first contribution value vector obtained by converting the contribution value of a primary GROUP to the boiling point, GROUP ₁₂ is a second contribution value vector obtained by converting the contribution value of a secondary GROUP to the boiling point, GROUP _1N is an nth contribution value vector obtained by converting the contribution value of an N-stage GROUP to the boiling point, numh is the number of atoms in the single molecule other than hydrogen atoms, d is a first preset constant, b is a second preset constant, and c is a third preset constant; n is a positive integer greater than or equal to 2.

B. c and d may be empirical values or values obtained experimentally.

A single molecule vector converted from the number of groups constituting each group of the single molecule, comprising: taking the number of species of the groups constituting the single molecule as the dimension of the single molecule vector; the number of groups of each group is taken as the element value of the corresponding dimension in the single-molecule vector.

According to a first contribution value vector obtained by converting the contribution value of each primary group of the single molecule to the boiling point, the method comprises the following steps: taking the number of kinds of primary groups as the dimension of the first contribution value vector; and taking the contribution value of each primary group as the element value of the corresponding dimension in the first contribution value vector. A second contribution value vector obtained by converting the contribution value of each secondary group of the single molecule to the boiling point respectively comprises the following components: taking the number of categories of the secondary groups as the dimension of the second contribution value vector; and taking the contribution value of each secondary group as the element value of the corresponding dimension in the second contribution value vector. And so on, according to the N-th contribution value vector obtained by converting the contribution value of each N-level group of the single molecule to the boiling point, the N-th contribution value vector comprises: taking the number of the N-level groups as the dimension of the N-th contribution value vector; and taking the contribution value of each N-level group as the element value of the corresponding dimension in the N-th contribution value vector.

Another example is: the density of single molecules was calculated according to the following physical properties calculation model:

Wherein D is the density of the single molecule, SOL is a single molecule vector converted from the number of GROUPs constituting each GROUP of the single molecule, GROUP ₂₁ is an n+1th contribution value vector converted from the contribution value of the primary GROUP to the density, GROUP ₂₂ is an n+2th contribution value vector converted from the contribution value of the secondary GROUP to the density, GROUP _2N is a 2nd contribution value vector converted from the contribution value of the N-stage GROUP to the density, and e is a fourth preset constant; n is a positive integer greater than or equal to 2.

E may be an empirical value or a value obtained by experiment.

According to the n+1th contribution value vector obtained by converting the contribution value of each primary group of the single molecule to the density, the method comprises the following steps: taking the number of kinds of primary groups as the dimension of the n+1 contribution value vector; and taking the contribution value of each primary group as the element value of the corresponding dimension in the N+1th contribution value vector. According to the n+2-th contribution value vector obtained by converting the contribution value of each secondary group of the single molecule to the density, the method comprises the following steps: taking the number of kinds of secondary groups as the dimension of the N+2 contribution value vector; and taking the contribution value of each secondary group as the element value of the corresponding dimension in the N+2-th contribution value vector. And so on, according to the 2N contribution value vector obtained by converting the contribution value of each N-level group of the single molecule to the density, the method comprises the following steps: taking the number of the N-level groups as the dimension of the 2N contribution value vector; and taking the contribution value of each N-level group as the element value of the corresponding dimension in the 2 nd N contribution value vector.

And the following steps: the octane number of the single molecule was calculated according to the following physical property calculation model:

X＝SOL×GROUP₃₁+SOL×GROUP₃₂+......+SOL×GROUP_3N+h；

Wherein X is the octane number of the single molecule, SOL is a single molecule vector obtained by conversion according to the number of GROUPs of each GROUP constituting the single molecule, GROUP ₃₁ is a 2N+1-th contribution vector obtained by conversion according to the contribution value of a primary GROUP to the octane number, GROUP ₃₂ is a 2N+2-th contribution vector obtained by conversion according to the contribution value of a secondary GROUP to the octane number, and GROUP _3N is a 3N-th contribution vector obtained by conversion according to the contribution value of an N-stage GROUP to the octane number; n is a positive integer greater than or equal to 2; h is a fifth preset constant. h is an empirical value or a value obtained by experiment.

And according to 2N+1 contribution value vectors obtained by converting the contribution value of each primary group of the single molecule to the octane value, the method comprises the following steps: taking the number of kinds of primary groups as the dimension of the 2N+1-th contribution value vector; and taking the contribution value of each primary group as the element value of the corresponding dimension in the 2N+1-th contribution value vector. And a 2N+2-th contribution value vector obtained by converting the contribution value of each secondary group of the single molecule to the octane value comprises the following components: taking the number of kinds of secondary groups as the dimension of the 2N+2 contribution value vector; and taking the contribution value of each secondary group as the element value of the corresponding dimension in the 2N+2-th contribution value vector. And by analogy, according to the 3N contribution value vector obtained by converting the contribution value of each N-level group of the single molecule to the octane value, the method comprises the following steps: taking the number of the N-level groups as the dimension of the 3N contribution value vector; and taking the contribution value of each N-level group as the element value of the corresponding dimension in the 3N-th contribution value vector.

The invention also provides a product simulation system of the petroleum processing device. Referring to fig. 8, a block diagram of a product simulation apparatus of a petroleum processing system according to an embodiment of the present invention is shown.

Referring to fig. 8, the simulation system includes: an acquisition unit 11 and a first processing unit 12.

In the present embodiment, an acquisition unit 11 for determining the molecular composition of the raw material;

in this embodiment, a first processing unit 12 for inputting the molecular composition of the feedstock into a pre-trained product prediction model; and obtaining the output of the target product under different reaction conditions, which is output by the product prediction model, wherein the product prediction model corresponds to the type of the petroleum processing device.

Wherein the system further comprises: and the third processing unit is used for determining the physical properties of the target product under different reaction conditions according to the molecular composition of the target product under different reaction conditions.

The first processing unit 12 is specifically configured to obtain the content of each product molecule under different reaction conditions output by the product prediction model; and obtaining the content of the target product molecules under different reaction conditions in the content of each product molecule under different reaction conditions.

The obtaining unit 11 is specifically configured to determine a molecular composition of the raw material by one or more of full two-dimensional gas chromatography, four-stage gas chromatography-mass spectrometer detection method, gas chromatography/field ionization-time-of-flight mass spectrometry detection method, gas chromatography, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, raman spectroscopy, fourier transform ion cyclotron resonance mass spectrometry, electrostatic field orbitrap mass spectrometry, and ion mobility mass spectrometry.

Wherein the sample raw material information of the sample raw material includes: the molecular composition of the sample material, the molecular content of each molecule in the sample material, the second molecular composition of the actual product corresponding to the sample material, and the actual content of each molecule in the actual product.

The model training unit is specifically used for processing the molecular composition of the sample raw material according to a preset reaction rule set to obtain a reaction path corresponding to each molecule in the molecular composition of the sample raw material; obtaining a first molecular composition of a device product according to a reaction path corresponding to each molecule in the molecular composition of the sample raw material; in the device product, comprising: the sample raw material, intermediate product and predicted product; calculating a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product; if the first relative deviation meets a preset condition, fixing the reaction rule set; if the first relative deviation does not meet the preset condition, the reaction rules in the reaction rule set are adjusted, and the first relative difference value is recalculated according to the adjusted reaction rule set until the first relative deviation meets the preset condition.

The model training unit is specifically used for acquiring the types of single molecules in the first molecular composition to form a first set; obtaining the types of single molecules in the second molecular composition to form a second set; determining whether the second set is a subset of the first set; if the second set is not a subset of the first set, acquiring a pre-stored relative deviation value which does not meet a preset condition as the first relative deviation value; if the second set is a subset of the first set, a first relative deviation is calculated by:

The model training unit is specifically used for respectively calculating the reaction rate of the reaction path corresponding to each molecule in the molecular composition of the sample raw material according to the reaction rate algorithm; obtaining the predicted content of each molecule in a predicted product corresponding to the sample raw material according to the molecular content of each molecule in the sample raw material and the reaction rate corresponding to the reaction path of the molecule; calculating a second relative deviation from the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product; if the second relative deviation meets a preset condition, fixing the reaction rate algorithm; and if the second relative deviation does not meet the preset condition, adjusting parameters in the reaction rate algorithm, and recalculating the second relative deviation according to the adjusted reaction rate algorithm until the second relative deviation meets the preset condition.

The model training unit is specifically used for calculating the reaction rate of each reaction path according to the reaction rate constant in the reaction rate algorithm; wherein the reaction rate constant is determined according to the following calculation formula:

Wherein the types of the petroleum processing device include: catalytic cracking units, delayed coking units, residuum hydrotreaters, hydrocrackers, diesel hydro-upgrading units, diesel hydrofining units, gasoline hydrofining units, catalytic reforming units and alkylation units; wherein each petroleum processing device corresponds to a set of reaction rules.

Wherein the reaction conditions include: reaction temperature, reaction pressure and space velocity.

The embodiment provides a product simulation device of an oil processing device. As shown in fig. 9, a structure diagram of a product simulation apparatus of an oil processing apparatus according to an embodiment of the present invention is shown.

In this embodiment, the product simulation equipment of the petroleum processing apparatus includes, but is not limited to: processor 810, memory 820.

The processor 810 is configured to execute a product simulation program of the petroleum processing apparatus stored in the memory 820 to implement the product simulation method of the petroleum processing apparatus described above.

Specifically, the processor 810 is configured to execute a product simulation program of the petroleum processing apparatus stored in the memory 820, to implement the steps of: determining the molecular composition of the raw materials; inputting the molecular composition of the raw materials into a pre-trained product prediction model; wherein the product prediction model corresponds to the type of petroleum processing device; and obtaining the output of the target product under different reaction conditions, which is output by the product prediction model.

Wherein after obtaining the output of the product prediction model, the method further comprises the following steps: inquiring the yield of a target product under different reaction conditions; determining a reaction condition corresponding to the maximum yield value of the target product; and/or determining the reaction condition corresponding to the minimum yield value of the target product.

Wherein after obtaining the output of the product prediction model, the method further comprises the following steps: and determining physical properties of the target product under different reaction conditions according to the molecular composition of the target product under different reaction conditions.

The method for obtaining the output of the target product under different reaction conditions by the product prediction model comprises the following steps: obtaining the content of each product molecule under different reaction conditions output by the product prediction model; and obtaining the content of the target product molecules under different reaction conditions in the content of each product molecule under different reaction conditions.

Wherein determining the molecular composition of the feedstock comprises: the molecular composition of the feedstock is determined by one or more of full two-dimensional gas chromatography, four-stage rod gas chromatography-mass spectrometer detection, gas chromatography/field ionization-time-of-flight mass spectrometry, gas chromatography, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, raman spectroscopy, fourier transform ion cyclotron resonance mass spectrometry, electrostatic field orbitrap mass spectrometry, and ion mobility mass spectrometry.

Wherein the step of training the product prediction model comprises: establishing a product prediction model; wherein the product prediction model comprises: a set of reaction rules and a reaction rate algorithm; the reaction rule set comprises a plurality of reaction rules; acquiring sample raw material information of a sample raw material; training the reaction rule set by utilizing the sample raw material information, and fixing the reaction rule set after training; and training the reaction rate algorithm by using the sample raw material information, and fixing the reaction rate algorithm after training to obtain the product prediction model after training.

Wherein the sample raw material information of the sample raw material includes: the molecular composition of the sample raw material, the molecular content of each molecule in the sample raw material, the molecular composition of the actual product corresponding to the sample raw material and the actual content of each molecule in the actual product.

Wherein training the set of reaction rules using the sample raw material information comprises: processing the molecular composition of the sample raw material according to a preset reaction rule set to obtain a reaction path corresponding to each molecule in the molecular composition of the sample raw material; obtaining a first molecular composition of a device product according to a reaction path corresponding to each molecule in the molecular composition of the sample raw material; in the device product, comprising: the sample raw material, intermediate product and predicted product; calculating a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product; if the first relative deviation meets a preset condition, fixing the reaction rule set; if the first relative deviation does not meet the preset condition, the reaction rules in the reaction rule set are adjusted, and the first relative difference value is recalculated according to the adjusted reaction rule set until the first relative deviation meets the preset condition.

Wherein calculating a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product comprises: obtaining the types of single molecules in the first molecular composition to form a first set; obtaining the types of single molecules in the second molecular composition to form a second set; determining whether the second set is a subset of the first set; if the second set is not a subset of the first set, acquiring a pre-stored relative deviation value which does not meet a preset condition as the first relative deviation value; if the second set is a subset of the first set, calculating a first relative deviation by the following calculation formula:

Wherein training the reaction rate algorithm using the sample raw material information comprises: according to the reaction rate algorithm, respectively calculating the reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample raw material; obtaining the predicted content of each molecule in a predicted product corresponding to the sample raw material according to the molecular content of each molecule in the sample raw material and the reaction rate corresponding to the reaction path of the molecule; calculating a second relative deviation from the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product; if the second relative deviation meets a preset condition, fixing the reaction rate algorithm; and if the second relative deviation does not meet the preset condition, adjusting parameters in the reaction rate algorithm, and recalculating the second relative deviation according to the adjusted reaction rate algorithm until the second relative deviation meets the preset condition.

According to the reaction rate algorithm, the reaction rate of the reaction path corresponding to each molecule in the molecular composition of the sample raw material is calculated respectively, and the method comprises the following steps: calculating the reaction rate of each reaction path according to the reaction rate constant in the reaction rate algorithm; wherein,

The embodiment of the invention also provides a storage medium. The storage medium here stores one or more programs. Wherein the storage medium may comprise volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid state disk; the memory may also comprise a combination of the above types of memories.

The one or more programs, when executed by the one or more processors, implement the product simulation method of the petroleum processing apparatus described above.

Specifically, the processor is configured to execute a product simulation program of the petroleum processing apparatus stored in the memory to implement the steps of: determining the molecular composition of the raw materials; inputting the molecular composition of the raw materials into a pre-trained product prediction model; wherein the product prediction model corresponds to the type of petroleum processing device; and obtaining the output of the target product under different reaction conditions, which is output by the product prediction model.

The above description is only an example of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for simulating a product of a petroleum processing plant, comprising:

Determining the molecular composition of the raw materials;

Inputting the molecular composition of the raw materials into a pre-trained product prediction model; wherein the product prediction model corresponds to the type of petroleum processing device;

obtaining the output of the target product under different reaction conditions, which is output by the product prediction model;

wherein the step of training the product prediction model comprises:

Establishing a product prediction model; wherein the product prediction model comprises: a set of reaction rules and a reaction rate algorithm; the reaction rule set comprises a plurality of reaction rules;

acquiring sample raw material information of a sample raw material;

training the reaction rule set by utilizing the sample raw material information, and fixing the reaction rule set after training;

training the reaction rate algorithm by using the sample raw material information, and fixing the reaction rate algorithm after training to obtain the product prediction model after training;

Wherein the sample raw material information of the sample raw material includes: the molecular composition of the sample raw material, the molecular content of each molecule in the sample raw material, the second molecular composition of the actual product corresponding to the sample raw material and the actual content of each molecule in the actual product;

wherein training the set of reaction rules using the sample raw material information comprises:

Processing the molecular composition of the sample raw material according to a preset reaction rule set to obtain a reaction path corresponding to each molecule in the molecular composition of the sample raw material;

obtaining a first molecular composition of a device product according to a reaction path corresponding to each molecule in the molecular composition of the sample raw material; in the device product, comprising: the sample raw material, intermediate product and predicted product;

calculating a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product;

if the first relative deviation meets a preset condition, fixing the reaction rule set;

If the first relative deviation does not meet the preset condition, adjusting the reaction rules in the reaction rule set, and recalculating the first relative difference value according to the adjusted reaction rule set until the first relative deviation meets the preset condition;

Wherein calculating a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product comprises:

obtaining the types of single molecules in the first molecular composition to form a first set;

Obtaining the types of single molecules in the second molecular composition to form a second set;

determining whether the second set is a subset of the first set;

if the second set is not a subset of the first set, acquiring a pre-stored relative deviation value which does not meet a preset condition as a first relative deviation value;

if the second set is a subset of the first set, a first relative deviation is calculated by:

2. The method of claim 1, further comprising, after obtaining the yield of the target product under the different reaction conditions output by the product prediction model:

inquiring the yield of a target product under different reaction conditions;

Determining a reaction condition corresponding to the maximum yield value of the target product; and/or the number of the groups of groups,

And determining the reaction condition corresponding to the minimum yield value of the target product.

3. The method of claim 2, further comprising, after obtaining the yield of the target product under the different reaction conditions output by the product prediction model:

And determining physical properties of the target product under different reaction conditions according to the molecular composition of the target product under different reaction conditions.

4. A method according to any one of claims 1-3, wherein obtaining the output of the product prediction model for the yield of the target product under different reaction conditions comprises:

obtaining the content of each product molecule under different reaction conditions output by the product prediction model;

And obtaining the content of the target product molecules under different reaction conditions in the content of each product molecule under different reaction conditions.

5. A method according to any one of claims 1-3, characterized in that determining the molecular composition of the feedstock comprises:

The molecular composition of the feedstock is determined by one or more of full two-dimensional gas chromatography, four-stage rod gas chromatography-mass spectrometer detection, gas chromatography/field ionization-time-of-flight mass spectrometry, gas chromatography, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, raman spectroscopy, fourier transform ion cyclotron resonance mass spectrometry, electrostatic field orbitrap mass spectrometry, and ion mobility mass spectrometry.

6. The method of claim 1, wherein training the reaction rate algorithm using the sample raw material information comprises:

according to the reaction rate algorithm, respectively calculating the reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample raw material;

Obtaining the predicted content of each molecule in a predicted product corresponding to the sample raw material according to the molecular content of each molecule in the sample raw material and the reaction rate corresponding to the reaction path of the molecule;

calculating a second relative deviation from the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product;

If the second relative deviation meets a preset condition, fixing the reaction rate algorithm;

and if the second relative deviation does not meet the preset condition, adjusting parameters in the reaction rate algorithm, and recalculating the second relative deviation according to the adjusted reaction rate algorithm until the second relative deviation meets the preset condition.

7. The method according to claim 6, wherein calculating the reaction rate of the reaction path corresponding to each molecule in the molecular composition of the sample raw material according to the reaction rate algorithm, respectively, comprises:

Calculating the reaction rate of each reaction path according to the reaction rate constant in the reaction rate algorithm; wherein the reaction rate constant is determined according to the following calculation formula:

8. The method of claim 1, wherein the types of petroleum processing equipment include:

Catalytic cracking units, delayed coking units, residuum hydrotreaters, hydrocrackers, diesel hydro-upgrading units, diesel hydrofining units, gasoline hydrofining units, catalytic reforming units and alkylation units; wherein each petroleum processing device corresponds to a set of reaction rules.

9. The method of claim 1, wherein the step of determining the position of the substrate comprises,

The reaction conditions include: reaction temperature, reaction pressure and space velocity.

10. A product simulation system for a petroleum processing plant, the system comprising:

a first processing unit for inputting the molecular composition of the feedstock into a pre-trained product prediction model; obtaining the output of target products under different reaction conditions output by the product prediction model, wherein the product prediction model corresponds to the type of the petroleum processing device;

The model training unit is used for establishing a product prediction model; acquiring sample raw material information of a sample raw material; training a reaction rule set included in the product prediction model by utilizing the sample raw material information, and fixing the reaction rule set after training; training a reaction rate algorithm included in the product prediction model by using the sample raw material information, and fixing the reaction rate algorithm after training to obtain the product prediction model after training, wherein the product prediction model comprises the following components: a set of reaction rules and a reaction rate algorithm; the reaction rule set comprises a plurality of reaction rules;

The model training unit is specifically used for processing the molecular composition of the sample raw material according to a preset reaction rule set to obtain a reaction path corresponding to each molecule in the molecular composition of the sample raw material; obtaining a first molecular composition of a device product according to a reaction path corresponding to each molecule in the molecular composition of the sample raw material; in the device product, comprising: the sample raw material, intermediate product and predicted product; calculating a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product; if the first relative deviation meets a preset condition, fixing the reaction rule set; if the first relative deviation does not meet the preset condition, adjusting the reaction rules in the reaction rule set, and recalculating the first relative difference value according to the adjusted reaction rule set until the first relative deviation meets the preset condition;

The model training unit is specifically used for acquiring the types of single molecules in the first molecular composition to form a first set; obtaining the types of single molecules in the second molecular composition to form a second set; determining whether the second set is a subset of the first set; if the second set is not a subset of the first set, acquiring a pre-stored relative deviation value which does not meet a preset condition as a first relative deviation value; if the second set is a subset of the first set, a first relative deviation is calculated by:

11. The system of claim 10, wherein the system further comprises:

The second processing unit is used for inquiring the yield of the target product under different reaction conditions; determining a reaction condition corresponding to the maximum yield value of the target product; and/or determining the reaction condition corresponding to the minimum yield value of the target product.

12. A product simulation device of a petroleum processing device, which is characterized by comprising a processor and a memory; the processor is configured to execute a product simulation program of the petroleum processing apparatus stored in the memory to implement the product simulation method of the petroleum processing apparatus according to any one of claims 1 to 9.

13. A storage medium storing one or more programs executable by one or more processors to implement the product simulation method of the petroleum processing apparatus of any of claims 1-9.